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<reponame>Jjschwartz/CyberAttackSimulator
"""Runs some general tests on environment"""
import pytest
import nasim
from nasim.scenarios.benchmark import \
AVAIL_GEN_BENCHMARKS, AVAIL_STATIC_BENCHMARKS
def test_render_error():
env = nasim.make_benchmark("tiny")
with pytest.raises(NotImplementedError):
env.render(mode="a bad mode str")
def test_render_readable():
env = nasim.make_benchmark("tiny")
env.render(mode="readable")
def test_render_state_error():
env = nasim.make_benchmark("tiny")
with pytest.raises(NotImplementedError):
env.render_state(mode="a bad mode str")
def test_render_state_readable():
env = nasim.make_benchmark("tiny")
env.render_state(mode="readable")
@pytest.mark.parametrize("flat_actions", [True, False])
def test_render_action(flat_actions):
env = nasim.make_benchmark("tiny", flat_actions=flat_actions)
env.render_action(env.action_space.sample())
@pytest.mark.parametrize(
("scenario", "expected_value"),
[("tiny", 0.0), ("small", 0.0)]
)
def test_get_total_discovery_value(scenario, expected_value):
env = nasim.make_benchmark(scenario)
actual_value = env.network.get_total_discovery_value()
assert actual_value == expected_value
@pytest.mark.parametrize(
("scenario", "expected_value"),
[("tiny", 200.0), ("small", 200.0)]
)
def test_get_total_sensitive_host_value(scenario, expected_value):
env = nasim.make_benchmark(scenario)
actual_value = env.network.get_total_sensitive_host_value()
assert actual_value == expected_value
|
package com.github.bradjacobs.logging.jdbc;
/**
* TODO: this class will (probably) get removed.
*/
public enum DatabaseType {
DB2,
DERBY,
H2,
HSQL,
INFORMIX,
MYSQL,
ORACLE,
POSTGRES,
SQLITE,
SQLSERVER,
SYBASE,
// many many more ...
UNKNOWN;
/**
* Attempt to identify db type via string
* @param dbName can be database product name, jdbc connection url, driver name, other, etc.
* @return DatabaseType based on input parameter
*/
public static DatabaseType identifyDatabaseType(String dbName) {
if (dbName == null) {
return UNKNOWN;
}
dbName = dbName.toUpperCase();
if (dbName.contains("DB2")) { return DB2; }
else if (dbName.contains("DERBY")) { return DERBY; }
else if (dbName.contains("H2")) { return H2; }
else if (dbName.contains("HSQL")) { return HSQL; }
else if (dbName.contains("INFORMIX")) { return INFORMIX; }
else if (dbName.contains("MYSQL")) { return MYSQL; }
else if (dbName.contains("ORACLE")) { return ORACLE; }
else if (dbName.contains("POSTGRES")) { return POSTGRES; }
else if (dbName.contains("SQLITE")) { return SQLITE; }
else if (dbName.contains("SQLSERVER") || dbName.contains("SQL SERVER")) { return SQLSERVER; }
else if (dbName.contains("SYBASE")) { return SYBASE; }
else { return UNKNOWN; }
}
}
|
Effect of omeprazole, lansoprazole, and ranitidine on the DNA synthesis of mononuclear cells. OBJECTIVE To examine and compare the effects of omeprazole, lansoprazole, and ranitidine on the DNA synthesis of peripheral blood mononuclear cells. DESIGN Ex vivo laboratory study. SETTING Clinical research laboratory of an academic medical center. SUBJECTS Healthy volunteers. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Venous blood was collected from normal subjects and peripheral blood mononuclear cells (PBMCs) were isolated using centrifugation techniques over a Ficoll-Hypaque density gradient. PBMCs were added to 12-well culture plates in four groups of media: a) control; b) control plus lansoprazole (25 microg/mL); c) control plus omeprazole (0.35 microg/mL); and d) control plus ranitidine (50 microg/mL). PBMCs were exposed to the drug for 96 hrs, with addition of phytohemagglutinin (2.5 microg/ mL) for the last 48 hrs, and 3H-thymidine (1 microCi) during the final 6 hrs. PBMCs were filtered onto glass-fiber filter paper and the radioactivity was determined by scintillation counting. Since radioactivity is measured only in those cells undergoing DNA synthesis or cell division, results are expressed as quantification of 3H-thymidine uptake. Median disintegrations per min (DPM)/number of PBMCs per well+/-SEM are reported: control 68.3+/-37.8; ranitidine 38.4 +/-94.2; lansoprazole 14.6+/-84.4; and omeprazole 15.1+/-48.9. There was a significant difference between lansoprazole vs. ranitidine (p<.01), and omeprazole vs. ranitidine (p<.05), and no significant difference between lansoprazole and omeprazole. CONCLUSIONS This is the first study to compare the potential immunomodulating effects of these commonly used agents. Ranitidine caused increased DNA synthesis in PBMCs when compared with lansoprazole and omeprazole. This phenomenon may be an important, often disregarded, effect of histamine-2-receptor antagonists when used in postsurgical or trauma patients who have T-lymphocyte-mediated immune suppression. |
Director Neil Marshall’s Hellboy reboot is a textbook example of the kind of film where, as you’re watching it in theaters, you can still pick up on the echoes of what it was or might have been earlier in the production process—perhaps when the studio was still considering who should helm the project, or which actors would be best to bring the characters to life. Sometimes, this can work in a movie’s favor. More often than not, though, it’s a major distraction that draws attention away from the story being told, and makes you begin to look for other cracks in the film’s narrative substance.
The new film opens with its titular demon hero (Stranger Things star David Harbour) well into his career working at the Bureau for Paranormal Research and Defense (B.P.R.D.) with his adoptive father Trevor Bruttenholm (American Gods’ Ian McShane). Along with others, they hunt and detain the kinds of magical creatures who pose threats to humanity’s existence. At this point in his life, Hellboy’s no stranger to taking out his demon brethren with ease for the sake of the world but, despite having the physicality of a hulking, grown man from the bowels of the underworld, he’s still relatively young—an immature teenager by demonic standards—and just as petulant as any other kid who’s forced to work alongside their parents.
The moment Harbour appears onscreen as Hellboy, he definitely sells himself as the character much in the same way Ron Perlman did, both because of his solid take on the character’s personality and the impressive amounts of makeup that went into crafting his devilish appearance. As much as we all enjoyed del Toro’s Hellboy films, you can’t deny that the Hellboy costume department stepped its game up and then some with its approach to creating a demon who looks, feels, and moves like a believable being with minimal CGI help.
Hellboy’s relationship with his human father is a key part of the character’s identity because Bruttenholm is the person responsible for giving Hellboy an initial introduction to humanity. Despite the fact that Hellboy is destined to bring about the apocalypse, Bruttenholm loves his son and wants only the best for his monstrous, misunderstood boy.
But because Hellboy’s a film that abhors the idea of showing us things and would rather have characters explain them in order to move on to the next, blood-soaked, lifeless scene, we don’t actually see much of Bruttenholm and Hellboy’s father/son dynamic. Instead, the film’s much more focused on beating you over the head with a selection of greatest hits moments from the Hellboy comics, hoping that you’ll get so swept up in the blood and gore that you mistakenly think you’re having a good time. There’s scarcely enough time to actually drink in what the movie’s serving up as it hops from scene to scene in a way that feels as if Hellboy’s trying to make sure you can’t get a solid lock on it.
As far as the film’s main drama goes, after a failed attempt at unleashing a magical plague upon the world, the Blood Queen witch Nimue (genre mainstay Milla Jovovich) returns to the world of the living with a vengeance and every intention of finishing her important work of transforming the planet into a new Eden for demonkind. As Nimue, Jovovich is at her very most Jovovich—grinding chewed-up scenery she’s long since spat out beneath the heel of her foot with the ease of someone who’s familiar with being dropped into the midst of spectacular, cinematic train wrecks. At times, Nimue is every bit as boring and silly a villain as Suicide Squad’s Enchantress, but whenever Jovovich actually has to deliver lines directly into the camera, she does her damnedest to make the character as intimidating as humanly possible.
When Nimue encounters Hellboy for the first time, she realizes she’s in the presence of demonic greatness and that together, they’d be able to achieve a kind of power neither of them could have ever imagined. But again, Hellboy never slows down enough for those kinds of revelations to mean much of anything, which is a disappointment given you can tell that the film’s actors are perfectly capable of embodying the roles they’ve been tasked with. It’s just that none of the characters are written in a particularly interesting or compelling way.
Hellboy’s joined in his quest to stop Nimue by Alice (Sasha Lane), an old friend of Big Red’s who was left with empathic powers after being kidnapped by fairies as a child, and Ben Daimio (Daniel Dae Kim), a gruff B.P.R.D. agent with a grudge against anything that isn’t of the human world. Together, the trio’s meant to be a ragtag group of misfits who are better off with one another than they know, and they are, but whatever chemistry Harbour, Lane, and Kim have with one another gets lost in the deafening cacophony of Hellboy’s overall messiness. And it has no bearing on the film’s plot, but Daimio’s role in the story is so exceedingly small that claims of the character originally being whitewashed in order to give him “an English background” are laughable.
While Guillermo del Toro wasn’t at all involved in the production of the movie, you can feel the ghosts of his Hellboy films haunting this one in the most unfortunate of ways. While Lionsgate initially pushed the idea of this Hellboy’s R-rating being a necessary part of its story being equal parts horror and fantasy, the end product isn’t scary as much as it is gross. Because the story doesn’t imagine that demons trying to wipe humanity out as scary enough, the film ladles on excessive amounts of gratuitous bloodshed that, after a while, fail to get your heart rate up. There are only so many times you can be horrified to see someone being torn in two before you get bored and start thinking about what all that fake blood might smell like.
Like two great tastes that somehow don’t go well together, Hellboy’s greatest sin is that it makes you long for the film that it might have been because there’s so much about the movie that works in a vacuum. David Harbour absolutely nails the charming, lunk-ish aspects of Hellboy’s personality, and a handful of the film’s action sequences are legitimately fun to watch before you get back to the slog of the rest of the film. Sadly, those few bright spots aren’t likely to convince anyone that this reboot deserves to be a jumping off point for a new series or doing the Hellboy franchise any favors.
Hellboy hits theaters April 12. |
FEZ1 phosphorylation regulates HSPA8 localization and interferon-stimulated gene expression Fasciculation and elongation protein zeta-1 (FEZ1) is a multifunctional kinesin adaptor involved in processes ranging from neurodegeneration to retrovirus and polyomavirus infection. Here, we show that, although modulating FEZ1 expression also impacts infection by large DNA viruses in human microglia, macrophages, and fibroblasts, this broad antiviral phenotype is associated with the pre-induction of interferon-stimulated genes (ISGs) in a STING-independent manner. We further reveal that S58, a key phosphorylation site in FEZ1s kinesin regulatory domain, controls both binding to, and the nuclear-cytoplasmic localization of, heat shock protein 8 (HSPA8), as well as ISG expression. FEZ1- and HSPA8-induced changes in ISG expression further involved changes in DNA-dependent protein kinase (DNA-PK) accumulation in the nucleus. Moreover, phosphorylation of endogenous FEZ1 at S58 was reduced and HSPA8 and DNA-PK translocated to the nucleus in cells stimulated with DNA, suggesting that FEZ1 is a regulatory component of the recently identified HSPA8/DNA-PK innate immune pathway. INTRODUCTION FEZ1 is an adaptor for the outward-directed microtubule (MT) motor, kinesin-1, and a hub protein that interacts with a range of other proteins (Bloom and Horvitz, 1997) (reviewed in ;). These combined functions mean that FEZ1 is involved in various biological processes, including kinesin-mediated transport of vesicles and organelles along MTs in neurons ;). Indeed, despite being expressed in many cell types, FEZ1 is highly expressed in neurons and both FEZ1 and its interacting partners have been linked to neurological diseases. FEZ1's interaction with disrupted-in-schizophrenia (DISC1), a candidate gene for schizophrenia, is crucial for neurite outgrowth (;), while its interaction with nuclear distribution element-like plays a role in regulating neurogenesis (). Disruption of FEZ1 activity can also cause cytoskeletal rearrangements resulting in interference with axonal outgrowth upon its interaction with necdin () and FEZ1 expression is reported to be reduced in patients with schizophrenia (;). Recent work from both our group and others has also implicated FEZ1 in regulating viral infection in both neuronal and non-neuronal cell types. FEZ1 was found to block intracellular trafficking of the human neurotropic polyomavirus JC virus (JCV), while FEZ1-mediated inhibition of infection could be overcome by JCV agnoprotein (). At the same time, our screens for host factors that regulate retroviral infection in non-neuronal cells found that FEZ1 regulates infection by retroviruses, such as murine leukemia virus and human immunodeficiency virus type 1 (HIV-1) (Gao and Goff, 1999;;). In the case of HIV-1, FEZ1 binds directly to the viral capsid () and, although it is a kinesin-1 adaptor, it regulates the balance of retrograde (inward) versus anterograde (outward) motility of HIV-1 particles to ensure net forward movement toward the nucleus for efficient infection (). This pro-viral function of FEZ1 is further regulated by MT-associated regulatory kinase 2, a host kinase that locally regulates FEZ1 phosphorylation on virus particles (;Malikov and Naghavi, 2017). Indeed, phosphorylation of FEZ1 at Serine 58 (S58), which regulates its interaction with the kinesin-1 heavy chain, is required for trafficking and disassembly of incoming HIV-1 capsid during early infection (;). Cumulatively, these findings link FEZ1 to the diverse processes of RNA and small DNA virus infection, as well as neuronal development and neurological disorders, yet the underlying mechanisms by which FEZ1 influences physiological and pathological processes and its broader role in pathology remains poorly understood. As a result of testing its effects on infection by two distinct large DNA viruses, here we reveal that FEZ1 regulates stimulator of interferon genes (STING)-independent induction of IFN and ISG expression. Furthermore, we reveal that S58 in the N-terminal kinesin regulatory domain of FEZ1 controls the localization and activity of heat shock protein A8 (HSPA8) and DNA-PK, thereby positioning FEZ1 as a regulatory component of the HSPA8/DNA-PK arm of host innate immune response pathways. Depletion of FEZ1 induces ISG responses and inhibits DNA virus infection Given that FEZ1 is known to regulate infection by RNA and small DNA viruses, we tested whether it could also influence infection by large DNA viruses. We did this by examining the effects of depleting FEZ1 in human immune cell lines and primary fibroblasts as these cells are central to infection by many types of viruses. We found that, compared with control non-targeting siRNAs, siRNA-mediated depletion of FEZ1 potently suppressed infection of either human microglia CHME3 cells or primary normal human dermal fibroblasts (NHDFs) with herpes simplex virus type 1 (HSV-1), as determined by reduced expression of early or intermediate infected cell proteins, ICP0, ICP4, or ICP5 in western blotting (WB) (Figures 1A and S1A, respectively). Similar to retroviruses or polyomaviruses, herpesviruses must reach the nucleus to establish infection. However, FEZ1 depletion also impaired infection of either CHME3 or NHDFs by vaccinia virus (VacV), a poxvirus that replicates entirely in the cytoplasm, as determined by reduced expression of a range of viral early, intermediate, and late proteins (I3, G8, and A25; Figures 1B and S1B, respectively). Given this broad inhibition of infection by viruses with very different modes of replication, we tested whether FEZ1 depletion perhaps pre-induced an antiviral state in these cells. WB analysis of either uninfected or infected cell lysates revealed that expression of several of ISGs, namely MxA, MxB, PKR, and ISG56, was increased in both CHME3 and NHDFs treated with FEZ1 siRNA compared with the control siRNA-treated cells ( Figures 1A, 1B, S1A, and S1B). This increase in ISG expression was also observed in uninfected cells, suggesting that loss of FEZ1 induces an antiviral state before infection. Increases in ISG levels in response to FEZ1 depletion were observed with multiple different FEZ1 siRNAs ( Figure S1C), ruling out off-target effects of the primary siRNA that we used. Intriguingly, FEZ1 levels were also decreased in control siRNA-treated CHME3 and NHDFs upon infection with HSV-1, and a moderate increase in MxA and ISG56 was detected (Figures 1A and S1A, respectively). It must be noted that viruses such as HSV-1 encode a wide range of proteins to modulate ISG expression, including targeting them for degradation, such that the robust induction of ISGs observed in uninfected cells in response to loss of FEZ1 is unlikely to be observed. However, compared with VacV wherein neither FEZ1 loss nor ISG induction were observed (Figures 1B and S1B), this raises the intriguing possibility that FEZ1 may be downregulated as part of a host response during infection with some viruses. These observations prompted us to explore the role of FEZ1 in regulating ISG expression in more detail, focusing on uninfected cells to avoid the broader complexity of ISG regulation by both host and virus during infection. FEZ1 S58 is required for its function as a regulator of ISG responses To further test the role of FEZ1 in ISG regulation we used two additional independent approaches, namely CRISPR-Cas9-mediated FEZ1 knockout (KO) and FEZ1 overexpression. To do this, we generated KO cells by electroporating CHME3 with CRISPR-Cas9 complexes loaded with either non-targeting (NT) or FEZ1-target single guide RNAs (sgRNAs). In line with our siRNA-based findings, compared with NT controls the loss of FEZ1 using either of four different sgRNAs (FEZ1 1-4) or with a pool of all four sgRNAs (FEZ1 pooled) resulted in a statistically significant increase in the ISGs (Figures 1C and 1D). Notably, while one NT sgRNA induced moderate expression of PKR and ISG56, which can occur in response to stress, little to no effect on MxA or MxB was observed compared with FEZ1 KO. This further suggested that FEZ1 functioned in innate immune and antiviral rather than stress response pathways. In line with depletion or KO of FEZ1 resulting in increased ISG expression, transduction of either CHME3 or NHDFs with Flag-tagged FEZ1 resulted in reproducible reductions in ISG expression ( Figures 1E, 1F, S1D, and S1E, respectively). Furthermore, expression of Flag-FEZ1 S58A, which blocks phosphorylation of FEZ1's kinesin regulatory domain (), resulted in reproducible increases in ISG expression in CHME3s (Figures 1E and 1F). While mean expression of ISGs were not all statistically significant across all three groups (one-way ANOVA, p > 0.05), overexpression of the FEZ1 wild-type (WT) versus S58A mutant resulted in significantly different expression levels in pairwise comparisons (Student's t test, p < 0.05). By contrast, Flag-FEZ1 S58A did not increase ISG expression over controls in NHDFs (Figures S1D and S1E), demonstrating that S58A impairs the ISG-regulatory function of FEZ1 but that its ability to act in a dominant negative manner likely depends on cell type and differences in their basal level of FEZ1 activity and ISG production. The ability of exogenously expressed forms of FEZ1 to regulate ISG expression was moderate compared with the effects of robust FEZ1 depletion or KO. While we cannot exclude the possibility that tagging affects this function of FEZ1 to some extent, this is in line with the notion that endogenous FEZ1 already functions efficiently to regulate basal levels of ISG expression and is therefore only moderately affected by supplying more FEZ1 to the system. Regardless, findings using these overexpression approaches were broadly in line with findings using either RNAi-or CRISPR-mediated depletion or KO, respectively. Complementing WB-based observations, ELISA analysis of IFN levels in supernatants from CHME3s confirmed that Flag-FEZ1 decreased, while Flag-FEZ1 S58A increased, the secretion of IFN- relative to controls ( Figures 1G and 1H). As expected, IFN- or IFN- were undetectable in microglia cells (data not shown). Together, these findings demonstrated that FEZ1 regulates ISG expression in a manner that is dependent upon S58. FEZ1 regulates IRF activation in a STING-independent manner To understand the innate immune pathway in which FEZ1 operates, we first tested if FEZ1 activity required STING. To do this, monocytic THP-1 cells that are genetically KO for STING and have been engineered to express distinct reporters for IRF and NF-B activity were differentiated to macrophages ( Figure S2A). Similar to our findings in CHME3 and NHDFs above, compared with control siRNA-treated samples FEZ1 depletion induced ISG expression in uninfected cells and blocked infection by either HSV-1 or VacV (Figures S2B and S2C,respectively). Furthermore, similar to other cell types, FEZ1 was downregulated in control siRNA-treated samples upon infection with HSV-1, and moderate increases in MxB and ISG56 were detected in these samples (Figures S2B). This both validated our core findings in other cell types and suggested that STING was not required for FEZ1-mediated ISG expression. To determine which downstream effector(s) was activated by loss of FEZ1, we used these same THP-1 reporter cell lines that contain two stably integrated inducible reporters for detection of IRF and NF-B activity. Comparing WT and STING KO THP-1-derived macrophages, reporter assays showed that, compared with control siRNA-treated samples, FEZ1 depletion resulted in an increase in IRF activity and, to a lesser extent, NF-B activity, in either the presence or absence of STING ( Figures S2D and S2E, respectively). As expected, loss of STING results in a significant drop in the basal level of IRF or NF-B activity in KO cells, but equivalent increases in the activity of both reporters above each baseline was evident for both WT and STING KOs. In line with these observations in reporter THP-1s, we also observed that IRF9 levels in the nucleus were reduced in CHME3s expressing exogenous FEZ1 WT, but not FEZ1 S58A ( Figures S2F and S2H), suggesting that FEZ1 regulates the basal IRF activity of these immune cells. Notably, effects on nuclear accumulation of NF-B were modest ( Figures S2I and S2J), in line with data in THP1 reporters above suggesting that IRF activity was predominant in this FEZ1-regulated STING-independent pathway. FEZ1 interacts with HSPA8, a factor in STING-independent innate immune signaling Given its function as a hub protein, we next investigated whether FEZ1-interacting proteins might provide clues to the specific innate response pathway in which it was operating. To identify FEZ1-interacting proteins, we generated CHME3s expressing GFP-tagged forms of FEZ1 WT, FEZ1 S58A, or GFP control alone. Soluble cell lysates were prepared and incubated with GFP-TRAP agarose, and isolated complexes were analyzed by mass spectrometry (MS). In line with our findings using STING KO cells, we failed to detect STING in our MS analyses but we detected peptides from HSPA8 as enriched in GFP-FEZ1 WT compared with empty or GFP-expressing controls, and even greater enrichment was observed for GFP-FEZ1 S58A (data not shown). This MS screen was only performed once to identify potential FEZ1-interacting proteins for further investigation, but HSPA8 was notable to us as it was recently identified as a phosphorylation target during DNA protein kinase (DNA-PK)-mediated DNA sensing pathways that operate independently of STING (SIDSP) (;). Validating this interaction, WB analysis of GFP-pull-down samples and densitometry confirmed that FEZ1 binds endogenous HSPA8, along with increased recovery of HSPA8 in association with GFP-FEZ1 S58A (Figures 2A and 2B). Performing reciprocal GFP pulldowns from CHME3 lysates expressing GFP-tagged HSPA8 in the presence of Flag-FEZ1 WT or S58A independently showed that GFP-HSPA8 specifically bound Flag-FEZ1WT and, to a greater degree, Flag-FEZ1 S58A ( Figures 2C and 2D). To confirm that endogenous forms of FEZ1 and HSPA8 interacted, anti-FEZ1 co-immunoprecipitation (coIP) was performed using lysates from control NT and FEZ1 KO CHME3s. In control cells, HSPA8 was detectable in FEZ1 coIP complexes, while recovery of HSPA8 was reduced proportionally to the level of residual FEZ1 expression and recovery in FEZ1 KO samples ( Figures 2E and 2F), demonstrating the FEZ1-dependent recovery of HSPA8 using this approach. It is important to note that the enrichment of either binding partner compared with input levels in each approach was low, but this is to be expected for proteins such as HSPA8 that are highly expressed and function in several processes, meaning that only a small fraction of HSPA8 is bound to FEZ1. Despite this, these reciprocal approaches confirmed that FEZ1 interacts with HSPA8 and further showed that this interaction was regulated by S58 in FEZ1, the site that our earlier data showed also regulates FEZ1-mediated ISG expression. While HSPA8 has been shown to be phosphorylated in a DNA-PK-dependent manner, its functional contribution to this pathway remains unknown (). In testing whether HSPA8 overexpression influenced expression of ISGs, we found that expression of either GFP-tagged or Flag-tagged HSPA8 reduced ISG levels compared with their respective GFP or Flag controls ( Figure 2G). Complementing overexpression approaches, we found that CRISPR-Cas9 KO of HSPA8 using either of three different sgRNAs, as well as depletion of HSPA8 using a pool of all three sgRNAs, resulted in an increase in ISGs compared with control cells treated with NT sgRNAs (Figures 3H and 3I). Whether ISG induction after FEZ1 depletion is HSPA8 dependent remains an outstanding question that we hope to address in future studies. FEZ1 regulates HSPA8 localization HSPA8 is a constitutively expressed protein that shuttles between the cytoplasm, where it is particularly abundant under normal conditions, and the nucleus, where it relocalizes during stresses, such as heat shock (). While signaling pathways involved in the nucleocytoplasmic trafficking of HSPA8 are not known, blocking its relocation into the cytoplasm impairs cell survival during stress recovery (), suggesting a prominent role in the cytoplasm under normal homeostasis and nuclear functions in pathological states. As a transport protein, FEZ1's binding to HSPA8 might affect its localization and/or activity. To test this, we first looked at HSPA8 localization in CHME3s. Immunofluorescence (IF) analysis in methanol-fixed CHME3 and HSPA8 KO cells showed the specificity of HSPA8 staining and that endogenous HSPA8 is distributed throughout the cytoplasm and in perinuclear regions under normal conditions, and translocates into the nucleus upon heat shock as expected ( Figures S3A and S3B). Imaging also showed that exogenously expressed Flag-tagged HSPA8 localized in the cytoplasm ( Figure 3A). Moreover, staining for HSPA8 demonstrated that overexpression of HSPA8 does not drive translocation of the endogenous protein into the nucleus. Given that exogenous expression of HSPA8 reduces basal ISG expression ( Figure 2G), this suggests that similar to its functions during stress recovery, HSPA8 ensures low basal ISG levels through its functions in the cytoplasm. As a kinesin adaptor, FEZ1 may be required to maintain HSPA8 in the cytoplasm and sustain low ISG expression levels under normal conditions. To test this, we first measured the effects of FEZ1 depletion on the levels of HSPA8 in the nucleus. Because of the high levels of HSPA8 expression, its dynamic nucleocytoplasmic shuttling, its diverse functions in various processes, and the irregularity of cell size and shape, measurements of nuclear accumulation of HSPA8 were the simplest and most reliable readout for changes in the cytoplasmic versus nuclear pool of HSPA8 that is controlled by FEZ1, as opposed to other FEZ1-independent HSPA8 pools performing other functions in the cell. Imaging revealed that more HSPA8 was present in the nucleus of CHME3s treated with FEZ1-specific siRNA compared with either of the two different NT siRNA-treated controls ( Figures 3B and 3C). Independently, levels of HSPA8 in the nucleus were also increased in three different FEZ1 KO CHME3 pools ( Figures 3D and 3E). This occurred in the absence of changes in the total abundance of HSPA8 ( Figure 1C). Complementing depletion approaches, expression of FEZ1-Flag in either CHME3 or NHDFs reduced HSPA8 levels in the nucleus of either cell type compared with control Flag-expressing cells, while Flag-FEZ1 S58A did not ( Figures 3F, 3G, S3C, and S3D, respectively). Again, this occurred in the absence of changes in HSPA8 levels ( Figure 1E), suggesting that these changes reflected differences in cytoplasmic versus nuclear localization of a subpopulation of HSPA8. Complementing imaging-based findings, nuclear fractionation of cell lysates showed more HSPA8 in the nuclear fractions recovered from FEZ1 KO cells compared with that of the control NT cells, while less HSPA8 was recovered in the nuclear fractions from CHME3s expressing FEZ1-Flag compared with that of the Flag-FEZ1 S58A ( Figures 3H and 3I, respectively). Together, these findings suggest that FEZ1 and its phosphorylation at S58A is required to retain a subpopulation of HSPA8 in the cytoplasm to maintain low basal levels of ISG expression. FEZ1 and HSPA8 regulate nuclear localization of DNA-PK As a chaperone, HSPA8 regulates folding and degradation of many cellular proteins, while it has also been implicated in regulating the localization of many others (;;;). Prompted by its recent connections with DNA-PK-mediated innate signaling pathways, we explored whether HSPA8 regulated DNA-PK nuclear localization. Probing our fractionated samples above we found that, similar to its effects on HSPA8, FEZ1 KO increased the levels of DNA-PK in nuclear fractions ( Figure 3H). Conversely, and in line with broader effects on HSPA8 localization and ISG expression, FEZ1 overexpression decreased the levels of DNA-PK in nuclear fractions and this required S58 ( Figure 3I). Complementing fractionation approaches, IF staining of cells revealed significantly more DNA-PK in the nuclei of FEZ1 KO CHME3 compared with that of the NT control ( Figures 4A and 4B). By contrast, nuclear levels of DNA-PK were reduced in either CHME3 or NHDFs expressing FEZ1-Flag compared with control Flag-expressing cells, while nuclear DNA-PK levels were increased in cells expressing FEZ1 S58A-Flag ( Figures 4C, 4D, S3E, and S3F, respectively). This suggests that FEZ1 prevents DNA-PK from entering the nucleus, potentially through retention of HSPA8 in the cytoplasm. Supporting this idea, staining of HSPA8 KO pools revealed a significant increase in DNA-PK levels in the nuclei of HSPA8 KO pools compared with that of the NT control ( Figures 4E and 4F). These findings suggest that FEZ1 and HSPA8 prevent DNA-PK from accumulating in the nucleus and inducing ISG expression. Indeed, beyond links to SIDSP, DNA-PK is reported to phosphorylate and regulate IRF3 and broadly influence host immune responses (). We therefore tested whether DNA-PK was required for ISG expression in either FEZ1 or HSPA8 KO CHME3s. In both cell types, the DNA-PK inhibitor NU7441 significantly reduced the expression of several ISGs tested ( Figures S4A and S4B). In line with inhibitor-based findings, CRISPR-based KO of DNA-PK similarly reduced ISG expression in either HSPA8 or FEZ1 KO cells ( Figures S4C-S4E). Notably, the effects of DNA-PK inhibition or KO on ISG expression were constituent in trend but somewhat variable in magnitude across experiments. This is perhaps unsurprising in a complex biological response pathway and affects statistical significance at times, but also suggests that DNA-PK is likely not the only effector of HSPA8's control over ISG expression. Indeed, HSPA8 has been shown to regulate the accumulation of several proteins in the nucleus (;) and ISG induction was found to be also highly dependent on IRFs ( Figures S4F-S4H), in line with earlier data using THP1 reporter cell lines above. FEZ1 phosphorylation is reduced during DNA-mediated IFN responses The broad induction of IFN and ISG expression in cells where FEZ1 activity was experimentally modulated prompted us to test whether many of these phenotypes were attributable to IFN itself. Compared with untreated or control antibody-treated samples, treatment of FEZ1 or HSPA8 KO CHME3s with IFN--neutralizing antibody significantly reduced the expression of ISGs in both FEZ1 and HSPA8 KO CHME3 (Figures S5A-S5D). More moderate effects on ISG56 are perhaps not unexpected due to its high basal expression and responsiveness to a broader range of stimuli than other ISGs tested that are more tightly linked to IFN regulation. IF imaging also suggested that IFN- neutralization blocked the translocation of IRF9, but not HSPA8 in CHME3s, although antibody cross-reactivity with internalized IFN--neutralizing antibody prevented us from reliably quantifying these effects ( Figure S5E). However, in a reciprocal experiment, treatment of CHME3s with IFN- revealed that IFN- was sufficient to increase nuclear accumulation of IRF9 but not HSPA8 (Figures S5F-S5H). Combined with the fact that we have not detected changes in FEZ1 phosphorylation in IFN--treated cells (unpublished observation), this suggests that the induction of IRF9 translocation and ISG expression in cells depleted of FEZ1 or expressing inactive S58A FEZ1 is attributable to IFN production, but that production of this IFN initiates due to specific effects of FEZ1 perturbations on HSPA8 localization. Finally, we tested whether endogenous FEZ1 phosphorylation is altered in cells responding to CT-DNA, which induces SIDSP. Treatment of CHME3s with CT-DNA induced ISG expression and this was accompanied by a significant decrease in endogenous FEZ1 phosphorylation at S58 and activation of IRF3, a DNA-PK target () (Figures 4G and 4H) as well as translocation of HSPA8 and DNA-PK to the nucleus ( Figures 4I, 4J, 4K, and 4L, respectively). Given that IFN alone is not sufficient to cause HSPA8 translocation to the nucleus ( Figure S5H), this suggests that regulating FEZ1 phosphorylation and HSPA8 localization is an important initiating event in broader IFN-based responses to stimuli such as CT-DNA. HSPA8 and DNA-PK are central to host immune responses yet paradoxically, they play complex multifunctional roles in infection outcomes. Indeed, while HSPA8 functions in SIDSP, translocation of HSPA8 to the nucleus has also been suggested to promote lytic HSV-1 infection (;Burch and Weller, 2004). Meanwhile, several viruses, including HSV-1, directly antagonize DNA-PK (;;;;;;;). Indeed, HSPA8 and DNA-PK play broad and extremely complex roles in infection by a variety of viruses (;;Lu and Zhang, 2020;). It is now apparent that FEZ1 plays similarly complex roles in both host responses and infection. Intriguingly, we observe that HSV-1 infection results in loss of FEZ1 expression, which may contribute to HSPA8 re-localization that is reported to occur in HSV-1 infected cells. In the case of HIV-1, FEZ1 directly binds to viral particles and regulates their kinesin-based motility, yet our findings here reveal that FEZ1 also plays a role in regulating host ISG expression. As such, control of ISG expression may represent a previously unrecognized mechanism by which FEZ1 affects HIV-1 infection (). A similar situation has been reported for the dynein adaptor, BICD2, which both mediates HIV-1 motility and host responses to infection (). As such, these combined data suggest that components of the SIDSP pathway, namely FEZ1, HSPA8, and DNA-PK play multiple and often opposing roles that can be challenging to disentangle in the complex context of infection. Avoiding this complexity by focusing on uninfected cells, our findings show that FEZ1 phosphorylation functions as part of SIDSP, or a variation of this pathway that involves common components, such as HSPA8 and DNA-PK. In an apparent contradiction that may provide clues to the underlying process, FEZ1 S58A binds more robustly to HSPA8 than to WT FEZ1, yet more HSPA8 enters the nucleus in FEZ1 S58A-expressing cells. From this observation, and based on the high abundance of HSPA8, we propose that FEZ1 is a limiting factor and that phosphorylation enables it to cycle through the cellular pool of HSPA8 to retain it in the cytoplasm. Dephosphorylation of FEZ1 increases HSPA8 binding but this actually sequesters FEZ1 away from other HSPA8 molecules, allowing them to escape FEZ1-mediated retention in the cytosol and enter the nucleus. There is precedence for such a mechanism across multiple processes, including nucleotide sensing; PKR phosphorylates and inactivates the translation factor, eIF2, in response to dsRNA (). Phosphorylation-based inactivation functions by causing eIF2 to bind more tightly to eIF2B, a lower abundance guanine nucleotide exchange factor that cycles across the larger eIF2 population to maintain the active eIF2-GTP state. Effectively, eIF2B's target also acts to sequester it from the broader eIF2 population when it becomes more tightly bound to phosphorylated eIF2. A similar mechanism is likely used by HSPA8 to sequester FEZ1 upon dephosphorylation and allow translocation of other HSPA8 molecules to the nucleus during host responses. While much of the underlying mechanism remains to be elucidated in future studies, our findings here show that FEZ1 plays a central role in regulating host innate immune signaling by regulating the localization of HSPA8 and DNA-PK, adding insights to our understanding of this newly emerging STING-independent pathway. Limitations of the study Beyond the specific limitations discussed above, it remains unknown and an avenue for future study to determine whether modulating FEZ1 activity affects infection by the DNA viruses tested here through its ability to regulate ISG expression or whether FEZ1 functions directly, or both, similar to the complexity of its functions during early HIV-1 infection. STAR★METHODS RESOURCE AVAILABILITY Lead contact-Please direct any requests for further information and reagents to the lead contact, Mojgan H. Naghavi ([email protected]). Materials availability-All plasmids and cell lines generated in this study will be made available upon request from the lead author. Data and code availability-All data reported in this study will be shared by the lead contact upon request. This study does not report original code. Any additional information required to reanalyze the data reported in this work is available from the lead contact upon request. Generation of stable cell pools-A plasmid encoding eGFP was generated by inserting amplified eGFP PCR product into pQCXIN vector at SbfI and Not-I restriction sites. Constructs encoding human FEZ1-Flag and FEZ1(S58A)-Flag (S58A-Flag) proteins tagged to eGFP at the N-terminus were generated through re-cloning from pQCXIN-FEZ1-Flag and pQCXIN-FEZ1-S58A-Flag () into pQCXIN-eGFP vector at Not-I and EcoRI restriction sites. HSPA8 was amplified by PCR reaction from pPM-C-HA-HSPA8 plasmid (Applied Biological Materials) and inserted into pQCXIN-eGFP to produce eGFPtagged protein or into pQCXIN to create pQCXIN-HSPA8-Flag construct using primers containing Not-I and EcoRI restriction sites. In addition, Flag sequences were included in front of EcoRI in the reverse primer for generation of C-terminus Flag-tagged HSPA8. Several E. coli clones for each plasmid were amplified, the plasmids were purified with QIAprep spin miniprep kits (Qiagen) and inserts were sequenced at ACGT, Inc. using universal primers provided by the company. Plasmids with verified sequences of inserts were amplified with QIAfilter plasmid maxi kits (Qiagen). Murine leukemia virus (MuLV)-based retroviruses pseudotyped with vesicular stomatitis virus G envelope protein (VSV-G) were produced using 293T cells. Confluent 10 cm dishes were split at 1:4 ratio and the next day were transfected with 2.85 g of each pCMV-intron and pVSV-G along with 4.3 g of a transducing vector. The plasmids were mixed with 22.5 L of 50 g/mL Polyethylenimine (PEI, Polysciences, #23966-1) in 1 mL of Opti-Mem medium, incubated for 10 min at room temperature (RT) and applied onto cells. Growth medium was changed the next morning and supernatants were collected 48 hrs post transfection, clarified through 0.45 m filters, aliquoted and stored at −80°C. CHME3 and NHDF cell pools stably expressing Flag, FEZ1-Flag and FEZ1(S58A)-Flag (S58A-Flag), eGFP, eGFP-FEZ1, eGFP-FEZ1S58A (GFP-S58A), eGFP-HSPA8 or HSPA8-Flag proteins were generated by transduction of the cell lines with the appropriate retroviruses described above. Low passage cells were split in 6-well plates at 10-20% confluency and the next day were transduced with the viral vector of interest in 1 mL regular growth medium containing 10 g/mL polybrene. 1 mL of medium was added to each well following overnight incubation. Selection of expressing cells was started 48 h post transduction by changing medium to growth medium containing 1 mg/mL G-418 and selection continued until all non-transduced control cells were dead. WB analysis was used to confirm expression of the protein of interest after selected cell lines were established. To generate knock-out cells, CHME3 cells were trypsinized, re-suspended in culture medium and counted. Immediately prior to electroporation, 250,000 cells were centrifuged at 400 g for 3 min, supernatant was removed by aspiration, and the pellet was resuspended in 20 L of room-temperature electroporation buffer prepared by combining 16 L of SF Nucleofector solution with 4 L of supplement (Lonza). Then, cell suspension was gently mixed with 4 L of each crRNP and aliquoted into a 96-well electroporation cuvette for nucleofection with the 4-D Nucleofector X-Unit (Lonza) using pulse code CM-158. Immediately after electroporation, 100 L of pre-warmed culture media was added to each well and cells were allowed to rest for 30 min in a 37°C cell culture incubator. Cells were subsequently moved to 12-well flat-bottomed culture plates pre-filled with 500 L pre-warmed media. After 48 hrs in culture, cells were split in 10 cm dishes and their aliquots were lysed with Laemmli buffer to validate KO efficiencies by Western blot (WB). HSPA8 and FEZ1 were targeted by 4 gRNA delivered either independently or as a single multiplexed pool. Non-targeting (NT) gRNA (Dharmacon, U-007504-20) was delivered in parallel as a non-cutting, negative control. To generate the double knock-out cell lines, the CHME3 cells previously treated with HSPA8-targeting gRNA #4 or FEZ1-targeting gRNA #3 were nucleofected for a second time with a multiplex pool of 5 gRNA targeting PRKDC (DNA-PKcs) or IRF3. To generate the triple knock-out cell lines, the FEZ1/IRF3 and HSPA8/IRF3 double knock-out cell lines from above were nucleofected for a third time with a multiplex pool of 5 gRNA targeting IRF7. All gRNA were derived from the Dharmacon pre-designed Edit-R library for gene knock-out (refer to Table S1 for sequences). METHOD DETAILS Generation of viruses and infections-VacV and HSV-1 virus stocks were grown and titrated using BSC40 cells. Briefly, cultures were infected at multiplicity of infection (MOI) 0.01 and once 90-100% CPE was observed, virus was harvested by three rounds of freeze-thaw. Cell debris was removed by centrifugation and virus titer was determined by serial dilution and plaque assay (). In experimental set-ups, VacV or HSV-1 infections were performed at MOI 5 for the indicated times. Detection of NF-B and IRF reporters- The secreted embryonic alkaline phosphatase (SEAP) and Lucia luciferase reporters were detected in cell culture media using QUANTI-Blue and QUANTI-Luc (InvivoGen) reagents according to the manufacturer instructions. Briefly, 20 L of cell supernatant or control samples were mixed with 180 L of QUANTI-Blue in 96-well plate, incubated for 1 hr at 37°C and SEAP activity were measured in a microplate reader at 655 nm. Lucia luciferase levels were determined in a luminometer set to end-point measurement with a 4 s start time, 0.1 s reading time and 50 L of injection. The instrument was primed with QUANTI-Luc solution and measurements were made in an opaque 96-well plate with pre-loaded 20 L of cell supernatant or control samples. WB analysis and measurement of protein quantities on membranes-Samples for WB were produced by lysing cells in wells with freshly made 1xLaemmli buffer (62.5 mM Tris-HCl at pH6.8, 2% SDS, 10% glycerol, 0.7 M -mercaptoethanol) followed by boiling for 5 min. Sample proteins were resolved in 10% or 15% SDS PAGE at 150 V followed by transfer to a nitrocellulose membrane at 70 V for 1 hr. Membranes were incubated in blocking buffer (3% non-fat milk in TBS-T) for 1 hr on a rotary shaker, rinsed and washed for 5 min in TBS-T. Proteins were bound by primary antibodies during overnight incubation at +4°C. Proteins bands were visualized with Pierce ECL or Femto High Sensitivity Western Blotting Substrates (Thermo Fisher Scientific) after 1 hr incubation at RT with appropriate HRP-conjugated secondary antibody (GE Healthcare UK) at 1:10,000 dilution in the blocking buffer. The following primary antibodies at 1:1,000 dilution in 3% BSA in TBS-T were used: anti-HSPA8 (1B5, #ADI-SPA-815-J) from Enzo, anti-GFP (ab13970), anti-pIRF3 (phospho S386, ab76493), anti-Tubulin (YL1/2, ab6160), anti-HSV-1 ICP0 (ab6513), and anti-ICP4 (ab6514) from Abcam, anti-FEZ1 (#42480), anti-PKR (#12297), anti-MxA (#37849), anti-MxB (#43924), anti-ISG56 (#14769), anti-IRF3 (#11904), anti-IRF7 (D8V1J, #72073), anti-DNA-PK (3H6, #12311), anti-Lamin B1 (D9V6H, #13435) from Cell Signaling, anti-GAPDH (sc-25778) from Santa Cruz.Antibodies. Anti-phospho-S58-FEZ1 antibody was custom made (Synaptic Systems). Antibodies against VacV proteins were a kind gift of Dr. Yan Xiang, University of Texas Health Sciences Center, San Antonio, Dr. Paula Traktman, Medical University of South Carolina and Dr. David Evans, University of Alberta. Relative intensities of WB bands were calculated in Fiji software. Grayscale WB film scans were inverted in the software, protein bands were demarcated with rectangular selection tool of same size for all bands and areas and integrated densities of each selection along with background were measured. Data were transferred into Microsoft Excel, where all values underwent background subtraction. Then, data points were directly plotted on the scattered graphs with dots representing protein level in WB bands. If controls values were significantly varied between repeats, the ratios of WB data to absolute values of the differences between controls and treatment groups were plotted. These ratios were presented as relative protein level in WB bands. GFP-pulldowns and mass-spectrometry-CHME3 stable pools expressing eGFP and eGFP-HSPA8 described above, were transiently transfected with pQCXIN-FEZ1-Flag or pQCXIN-FEZ1-S58A-Flag. The pools were plated into 10 cm dishes at 25% confluency and the next day they were transfected with 10 g plasmid using the PEI method as described above. 48 hrs post transfection each dish was washed with 10 mL cold PBS and lysed in a 1 mL NP-40 lysis buffer (50 mM HEPES pH 7.4, 150 mM NaCl, 0.5 mM MgCl 2, 2 mM EDTA, 2 mM Na 3 VO 4, 25 mM glycerophosphate, 1.5% NP-40) completed with mini EDTA-free protease inhibitor cocktail (Roche). To compensate for differences in proteins quantities due to low expression, lysates from eight dishes of eGFP-HSPA8 pool were combined at this stage and treated further as a lysate from one dish. Lysates were rocked for 40 min followed by centrifugation at 10,000 g for 10 min. Supernatants were separated from pellets and 30 L were retained as input samples. Remaining supernatants were incubated with 3.5 L (dry volume) GFP-Trap agarose (ChromoTek; cat. #gta-100) pre-equilibrated with NP-40 lysis buffer and rocked for 4 hrs. Agarose beads with bound proteins were spun down at 2,000 rpm for 1 min and washed 3 times for 5 min in 0.5 mL NP-40 lysis buffer. Each wash was followed by a brief centrifugation at 2,000 rpm for 1 min. Final pellets were re-suspended and boiled for 3 min in 30 L of 2x Laemmli buffer. All procedures were carried out at 4°C. CHME3 stable pools expressing eGFP, eGFP-FEZ1 oreGFP-S58A were processed equally. GFP-pulldown samples for mass-spectrometry identification were re-suspended and boiled in 30 L 2x Laemmli buffer omitting Bromophenol Blue dye and centrifuged at 2,000 rpm for 1 min. Supernatants were transferred to fresh epi-tubes and sample proteins were precipitated with Trichloroacetic acid (TCA) as described (Link and LaBaer, 2011). Briefly, 7.5 L of TCA was added to each sample, incubated for 10 min at +4°C and precipitate was spun down at 14,000 rpm for 5 min. Pellet was washed twice with 200 L of ice-cold acetone and dried up in +95°C heat block for 5 min. The precipitated proteins were incubated in 50 L of 8 M urea in 50 mm ammonium bicarbonate for 1 hr and then in 50 L of 0.2% ProteaseMAX (Promega; Cat# V207A) for 1 hr. Protein extracts were reduced and alkylated with 1 L of 500 mM TCEP for 1 hr and after that in 2 L of 500 mM iodoacetamide for 20 min in the dark. The reaction was stopped with 5 l of 500 mM TCEP. After the addition of 215 L 50 mM ammonium bicarbonate, 2.5 L 1% ProteaseMAX and 1.0 g Trypsin Gold (Promega; Cat# V528A), samples were digested overnight at 37°C. The digestion reaction was quenched with formic acid and purified using C18 resin in Pierce Spin Columns (Thermo Fisher Scientific, cat. #89879). The peptides were quantified using a microBCA protein assay kit (Thermo Fisher Scientific; cat. #23235), separated on nanoViper trap column (Thermo Fisher Scientific; cat. #164535) coupled to nanoViper analytical column (Thermo Fisher Scientific; cat. #164942) and analyzed on an Orbitrap Fusion Tribrid mass spectrometer set with the parameters described previously (). Spectrum raw files were obtained with the in-house program RawConverter (http://fields.scripps.edu/downloads.php), and the tandem mass spectra were searched against UniProt human database (downloaded on 25 March 2014). The parameters used for the search were previously described (). Co-immunoprecipitation (co-IP) analysis-Co-IP's were performed as described previously (Walsh and Mohr, 2006). Briefly, lysates of CHME3 NT KO and FEZ1 KO pool #3 were prepared as for GFP-pulldowns described above. Following centrifugation at 10,000 g for 10 min, supernatants were pre-cleared by incubating with 25 L of 10% G-sepharose from GE Healthcare (#17-0618-01) rocking for 1hr at 4°C. Sepharose was then sedimented by centrifugation, input samples were taken and the remaining pre-cleared lysates were rocked with 10 L anti-FEZ1 antibody from Abnova (#H00009638-B02P) for 2 hrs at 4°C followed by overnight incubation with 40 L 50% G-sepharose. Beads were then washed 3 times with 700 L of NP-40 buffer for 5 min and boiled in 30 L of Laemmli buffer. Inhibitor, antibody neutralization and CT-DNA treatment-CHME3 cells were plated in 12-well plates at 80% confluency and transfected with a mixture of 4 l Lipofectamine 2000 and 4 g CT DNA purchased from Sigma (#D4764). Cells were lysed 16 h post transfection and analyzed by WB. For immunofluorescence analysis cells were plated on glass coverslips in 24-well plates and transfected with 2 g CT-DNA using 2 l Lipofectamine 2000 for 16 h. Cells were fixed with ice-cold methanol and stained and quantified as described below. For inhibition of DNA-PK, CHME3 FEZ1 KO pool #3 and HSPA8 KO pool #4 were treated overnight with 0.25 M Nu-7441 DNA-PK inhibitor (SelleckChem, #S2638). Neutralization of IFN was performed by adding anti-IFN antibody (Millipore-Sigma; ab1431) at a concentration of 7.5 l/200 ml to the cell culture medium overnight. Knockdowns-Transient siRNAs transfections were carried out as described (). CHME3 and NHDF cells were plated in 12-well plates in normal growth medium omitting antibiotics at 80,000 cells/well or at 1/25 cell number of a confluent 10 cm dish, respectively. The next day growth media were substituted for 0.5 mL pre-warmed Opti-Mem medium (Gibco) and cells were transfected with 50 L of transfection mixture per a well of 12-well plate for 4 hrs. At the end of incubation 0.5 mL of normal growth medium omitting antibiotics were added to each well. Transfection mixture was prepared by combining 17 L Opti-Mem medium with 1.34 L of RNAiMax reagent and 33 L of Opti-Mem with 1 L of 100 pmol/L siRNA stock followed by 20 min incubation before adding onto the cells. 24 hrs post transfection media was replaced with normal growth medium with antibiotics and in-well whole cell lysates were prepared for a WB analysis 48 hrs after transfections. For immunofluorescence staining cells were split onto 3-4 gelatin-treated coverslips placed in 24-well plates. Remnants of cells were plated back in 12-well plates to further check for knockdown efficiency. The following pre-designed siRNAs from Ambion (Thermo Fisher Scientific) were used: ID# AM4635 (Negative Control #1 siRNA), ID# AM4637 (Negative Control #2 siRNA), ID# 15759 (FEZ1-A), ID# 45012 (FEZ1-B), ID# 45101 (FEZ1-C). Immunofluorescence (IF) and analysis of proteins quantities-Cells were plated at 70-80% confluency on gelatin coated coverslips a day before they were fixed. Fixation was performed with 3.7% paraformaldehyde (PFA) for 15 min or with chilled down to −20°C methanol for 5 min. PFA-fixed cells were permeabilized with 0.1% Triton X-100 in PBS for 30 min, rinsed twice and washed for 5 min with PBS before blocking. Both methanol-and PFA-fixed cells were blocked with 10% donkey normal serum in PBS containing 0.25% saponin for 1 hr as described (). Coverslips were incubated with primary antibodies at 4 °C overnight. Anti-HSPA8 (1B5, #ADI-SPA-815-J) from Enzo, anti-ICP4 (ab6514) from Abcam, anti-A27 (J97Q) from Life Technologies, anti-DNA-PK (3H6, #12311), anti-IRF9 (D2T8M, #76684) or anti-Flag (D6W5B, #86861) from Cell Signaling were diluted at 1:400 in antibody buffer (PBS supplemented with 10% donkey normal serum and 0.025% saponin). Coverslips were washed 3 times for 5 min with wash buffer (PBS supplemented with 0.025% saponin) and incubated for 1 hr at RT with the appropriate Alexa 647-conjugated secondary antibody diluted at 1:400 in antibody buffer. After a rinse and 5 min wash, nuclei were stained with Hoechst 33342 for 20 min. 10 mg/mL Hoechst 33342 stock solution in water was diluted at 1:500 in wash buffer. Coverslips were washed 3 times in wash buffer for 5 min, rinsed with water, partially dried up at RT and mounted onto glass slides with FluorSave reagent (Calbiochem). Widefield images were acquired in Metamorph imaging software using Leica DMI 6000B motorized microscope equipped with Photometrics Prime 95B camera. All acquisitions settings were kept strictly equal for the images used in quantifications. Calculations of protein quantities in nuclei were performed in Metamorph imaging software. Cell nuclei on Hoechst 33342 images were selected with "auto threshold for light objects" function and subsequently converted into regions. The regions were transferred onto HSPA8 images and their areas and integrated fluorescence intensities were measured by region measurement tool. All images were calibrated using pre-determined calibrate distances function of the software before measurements. Enzyme-linked immunosorbent assay (ELISA)-To measure concentration of interferons (IFN) alpha, beta and gamma, culture media of CHME3 pools stably expressing Flag, FEZ1-Flag or S58A-Flag were collected, filtered through 0.45 m filter and stored at −80°C. On the day of procedure, culture media were thawed and concentrated 10-fold using Amicon Ultra-15 Ultracel-3K centrifugal filters (Millipore). Culture media of CHME3 Flag pool supplemented with 1,000 U/mL of IFN, IFN or 100 ng/mL of IFN were collected in parallel as positive controls. Fresh culture medium was measured to determine baselines and subtracted from values in samples. ELISA was performed on 150 L samples according to the instructions by the manufacturers. VeriKine Human IFN alpha (#411001) and Human IFN beta (#41100-1) ELISA kits were purchased form PBL Assay Sciences, and interferon gamma Human ELISA kit (KHC4021) was obtained from Invitrogen. O.D. values were measured using a microplate reader (Bio-Rad, iMARK plate reader), actual concentrations of interferons were calculated from a linear plot of standards provided by the kit manufacturer. Fractionation assay-Fractionation of cytoplasmic and nuclear fractions was performed with NE-PER nuclear and cytoplasmic extraction reagents from Thermo Scientific (#78833) in accordance with the manufacturer's instructions. Briefly, 10 6 cells were washed with PBS, centrifuged at 500 g for 5 min, and then re-suspended in 100 l of buffer CER I. After 15 sec vortexing followed by 10 minutes incubation on ice and addition of 5.5 l of buffer CER II, the cytoplasmic fraction was isolated by centrifugation at 21,130 g for 5 min. The pelleted nuclear fraction was prepared by incubation on ice for 40 minutes with 50 l buffer NER. The fractions were mixed with equal volumes of Laemmli buffer, boiled, and analyzed by WB. Anti-Tubulin and anti-Lamin B1 antibodies were used to evaluate cross-contamination of the fractions. QUANTIFICATION AND STATISTICAL ANALYSIS Statistical significance was calculated in GraphPad Prizm software version 8.4.3 using either a Student's t-test for two groups or one-way analysis of variance (ANOVA) with Tukey's honestly significant difference (HSD), Dunnett or Sidak's post hoc tests for groups of three or more groups. Results are expressed as means ± standard deviations (SD). Statistical significance is represented by ns, *, ** and *** over the bars representing a p-value >0.05, ≤0.05, ≤0.01 and ≤0.001, respectively. Supplementary Material Refer to Web version on PubMed Central for supplementary material. FEZ1 is a regulator of STING-independent induction of interferon and ISG expression FEZ1's function as a regulator of ISG responses requires Serine 58 phosphorylation FEZ1 Serine 58 controls both binding to, and localization of, HSPA8 FEZ1 and HSPA8 regulate nuclear accumulation of DNA-PK and ISG responses (F) Quantification of ISG levels relative to HSPA8 in control, FEZ1-Flag, or FEZ1-S58A-Flag expressing CHME3s from (E). Statistical significance is presented as "A" when it was calculated for all groups using one-way ANOVA followed by Dunnett post-hoc test or as "t" if Student's t test was applied to compare pairwisely Flag or FEZ1-S58A-Flag with FEZ1-Flag. (G and H) Measurement of IFN- levels in culture medium of CHME3 expressing Flag, FEZ1-Flag, and S58A-Flag by ELISA (G). Culture medium of CHME3s spiked with IFN- was included as positive control. Note that samples (except for the spiked control) were concentrated in order to obtain readings within the range of the standard curve (H) and are divided accordingly to present the actual values shown. One-way ANOVA followed by Tukey post-hoc test was used to calculate statistical significance in (D and G). (D, F and G) n = 3; red line, mean; bars, SD. |
It doesn't matter if you like coffee or tea. Everyone can use all of these expressions!
Coffee or Tea? We Have Expressions For Both!
Today we will talk about two of the world’s most popular drinks -- coffee and tea.
Now, many people are loyal coffee drinkers. They cannot imagine starting their morning without -- what some call -- a hot “Cup of Joe.” Some people hate coffee and prefer to drink tea. Others enjoy drinking both!
Whichever you prefer, know that English has some useful expressions using tea and coffee.
Let’s start with things around the home.
The table sitting in the middle of the living room (and usually in front of a sofa) is called a coffee table. We never call it a “tea table.” There is no such thing. Even if you hate the taste of coffee and never set a coffee cup on that table, you would still call it a "coffee table."
Naturally, a coffee table is the perfect place for a few coffee-table books. These large books usually have beautiful pictures and are meant to entertain people or make an impression on them. They are among the first things visitors see when sitting down in the living room. Many people use them as conversation pieces -- you know, items that start people talking about something.
Coffee, as you know, can help you feel more energized. In fact, many people use coffee to wake up in the morning.
We say this to people who are not accepting a situation as it really is. They are not being realistic. They need to face facts. You can say it to someone or about someone.
These two are more direct. And while "wake up and smell the coffee" can be used in a lighthearted, funny way, "get real" and "face facts" both sound more serious.
Let's hear how these expressions can be used.
A: Um, who was at the door?
A: Ugh. Did she ask to move back in with us … again?!
A: That’s the third time this week!
B: She apologized again for destroying some of the furniture during that party she threw. And she made us something to eat -- this cake. She really wants to move back in with us.
A: That woman is unbelievable! Does she have any idea how much damage she did? Our neighbors are still unhappy about that party. Next time she asks to move back in, let me deal with it.
B: What are you going to say to her?
A: I'm going to tell her to wake up and smell the coffee!
B: Uh, I don't think she drinks coffee.
A: You know what I mean. She needs to face facts. She is never, ever, EVER moving back in with us!
B: But she makes really good cake.
A: Ugh. Get real, Meredith. If you think for one minute that I would live with her just because she makes good cake, then you need to wake up and smell the coffee, too!
B: Mm. Coffee and cake are great together. I'll start making a pot of coffee now.
A: (sighs) Well … she does make good cake.
Even if you love tea, we just don't say, "Wake up and smell the tea." But don't worry. For all of you tea drinkers, we have a great expression for you!
If you really like something or are really good at something, it is your cup of tea.
But not so fast. That’s not really the way we use today. Yes, many years ago when the expression came into the language it was used in a positive way. But these days we almost always use this expression in a negative way.
So, you don’t say that something is your cup of tea -- even if you are talking about your beloved cup of tea.
If something is not your cup of tea, you simply don’t like it or are not good at it. The cup of tea in the expression can mean anything. If you don’t like to go camping, you could say camping is not your cup of tea. If you’re not good at dancing, it is not your cup of tea.
In fact, if you don’t like coffee, you could say it's not your cup of tea. And actually, that would be a funny way to use this expression.
Now, some places are known for their coffee and others for their tea. For example, Japan and China are famous for their tea. Their tea culture has a long history and tradition. And that is where we get our final expression for today.
Some famous Chinese teas are very pricey. If you gathered all the tea in China, it would be worth a lot of money.
So, if you want to say that you are unwilling to do something ... at any cost, you could say, "I wouldn't do it for all the tea in China." For example, if you are deathly afraid of heights, you could say "I would not skydive for all the tea in China."
This expression is simply an exaggerated, or larger than life way of saying, "No way! I won't do it! I don't care what you offer me!"
Okay, we don't want anyone to accuse us of playing favorites. We already heard a conversation using a popular coffee expression. So, here is a short conversation with the tea expressions we just heard.
A: Tomorrow night I'm going out to a spoken word event. Want to join me?
B: Spoken word? You mean like a poetry reading?
A: Yeah! It’s really awesome.
B: Um … no thanks. Spoken word really isn't my cup of tea.
A: Oh. So, you probably won't be interesting in going to a three-day Spoken Word Festival next month.
B: Not for all the tea in China.
A: So … that’s a no?
B: Yes. That’s a big N-O.
And that brings us to the end of this Words and Their Stories.
Choose one of the expressions heard here and try using it. Or share with us a coffee or tea idiom from your language. You can do both in the Comments Section! |
Scott Jensen (Minnesota politician)
Early life, education, and career
Jensen was born on November 19, 1954, and graduated valedictorian from Sleepy Eye High School in 1973. He attended Luther Northwestern Theological Seminary from 1977 to 1978 and the University of Minnesota, graduating with a Bachelor of Arts in physiology in 1978 and a Doctor of Medicine in 1981. He was a Bush Fellow of leadership and policy studies at the University of Minnesota in 1999.
Jensen was a member of the Waconia School Board from 1993 to 2002 and was its chair from 1995 to 1996 and in 1999. He was also a member of the Citizens Alliance Bank board, of which he was the audit committee chair. Jensen is the founder and president of Catalyst Medical Clinic in Watertown where he is a family physician. He is also a clinical associate professor at the University of Minnesota Medical School in the Family Practices department.
Minnesota Senate
Jensen was elected to the Minnesota Senate in 2016.
Personal life
Jensen and his wife, Mary, have three children and reside in Chaska. |
<reponame>tiwaria1/oneAPI-samples
//==============================================================
// Copyright © 2020 Intel Corporation
//
// SPDX-License-Identifier: MIT
// =============================================================
/**
* Matrix_mul multiplies two large matrices both the CPU and the offload device,
* then compares results. If the code executes on both CPU and the offload
* device, the name of the offload device and a success message are displayed.
*
* For comprehensive instructions regarding DPC++ Programming, go to
* https://software.intel.com/en-us/oneapi-programming-guide and search based on
* relevant terms noted in the comments.
*/
#include <CL/sycl.hpp>
#include <iostream>
#include <limits>
// dpc_common.hpp can be found in the dev-utilities include folder.
// e.g., $ONEAPI_ROOT/dev-utilities/<version>/include/dpc_common.hpp
#include "dpc_common.hpp"
using namespace std;
using namespace sycl;
/**
* Each element of the product matrix c[i][j] is computed from a unique row and
* column of the factor matrices, a[i][k] and b[k][j]
*/
// Matrix size constants.
constexpr int m_size = 150 * 8; // Must be a multiple of 8.
constexpr int M = m_size / 8;
constexpr int N = m_size / 4;
constexpr int P = m_size / 2;
/**
* Perform matrix multiplication on host to verify results from device.
*/
int VerifyResult(float (*c_back)[P]);
int main() {
// Host memory buffer that device will write data back before destruction.
float(*c_back)[P] = new float[M][P];
// Intialize c_back
for (int i = 0; i < M; i++)
for (int j = 0; j < P; j++) c_back[i][j] = 0.0f;
// Initialize the device queue with the default selector. The device queue is
// used to enqueue kernels. It encapsulates all states needed for execution.
try {
queue q(default_selector{}, dpc_common::exception_handler);
cout << "Device: " << q.get_device().get_info<info::device::name>() << "\n";
// Create 2D buffers for matrices, buffer c is bound with host memory c_back
buffer<float, 2> a_buf(range(M, N));
buffer<float, 2> b_buf(range(N, P));
buffer c_buf(reinterpret_cast<float *>(c_back), range(M, P));
cout << "Problem size: c(" << M << "," << P << ") = a(" << M << "," << N
<< ") * b(" << N << "," << P << ")\n";
// Using three command groups to illustrate execution order. The use of
// first two command groups for initializing matrices is not the most
// efficient way. It just demonstrates the implicit multiple command group
// execution ordering.
// Submit command group to queue to initialize matrix a
q.submit([&](auto &h) {
// Get write only access to the buffer on a device.
accessor a(a_buf, h, write_only);
// Execute kernel.
h.parallel_for(range(M, N), [=](auto index) {
// Each element of matrix a is 1.
a[index] = 1.0f;
});
});
// Submit command group to queue to initialize matrix b
q.submit([&](auto &h) {
// Get write only access to the buffer on a device
accessor b(b_buf, h, write_only);
// Execute kernel.
h.parallel_for(range(N, P), [=](auto index) {
// Each column of b is the sequence 1,2,...,N
b[index] = index[0] + 1.0f;
});
});
// Submit command group to queue to multiply matrices: c = a * b
q.submit([&](auto &h) {
// Read from a and b, write to c
accessor a(a_buf, h, read_only);
accessor b(b_buf, h, read_only);
accessor c(c_buf, h, write_only);
int width_a = a_buf.get_range()[1];
// Execute kernel.
h.parallel_for(range(M, P), [=](auto index) {
// Get global position in Y direction.
int row = index[0];
// Get global position in X direction.
int col = index[1];
float sum = 0.0f;
// Compute the result of one element of c
for (int i = 0; i < width_a; i++) {
sum += a[row][i] * b[i][col];
}
c[index] = sum;
});
});
} catch (sycl::exception const &e) {
cout << "An exception is caught while multiplying matrices.\n";
terminate();
}
int result;
cout << "Result of matrix multiplication using DPC++: ";
result = VerifyResult(c_back);
delete[] c_back;
return result;
}
bool ValueSame(float a, float b) {
return fabs(a - b) < numeric_limits<float>::epsilon();
}
int VerifyResult(float (*c_back)[P]) {
// Check that the results are correct by comparing with host computing.
int i, j, k;
// 2D arrays on host side.
float(*a_host)[N] = new float[M][N];
float(*b_host)[P] = new float[N][P];
float(*c_host)[P] = new float[M][P];
// Each element of matrix a is 1.
for (i = 0; i < M; i++)
for (j = 0; j < N; j++) a_host[i][j] = 1.0f;
// Each column of b_host is the sequence 1,2,...,N
for (i = 0; i < N; i++)
for (j = 0; j < P; j++) b_host[i][j] = i + 1.0f;
// c_host is initialized to zero.
for (i = 0; i < M; i++)
for (j = 0; j < P; j++) c_host[i][j] = 0.0f;
for (i = 0; i < M; i++) {
for (k = 0; k < N; k++) {
// Each element of the product is just the sum 1+2+...+n
for (j = 0; j < P; j++) {
c_host[i][j] += a_host[i][k] * b_host[k][j];
}
}
}
bool mismatch_found = false;
// Compare host side results with the result buffer from device side: print
// mismatched data 5 times only.
int print_count = 0;
for (i = 0; i < M; i++) {
for (j = 0; j < P; j++) {
if (!ValueSame(c_back[i][j], c_host[i][j])) {
cout << "Fail - The result is incorrect for element: [" << i << ", "
<< j << "], expected: " << c_host[i][j]
<< ", but found: " << c_back[i][j] << "\n";
mismatch_found = true;
print_count++;
if (print_count == 5) break;
}
}
if (print_count == 5) break;
}
delete[] a_host;
delete[] b_host;
delete[] c_host;
if (!mismatch_found) {
cout << "Success - The results are correct!\n";
return 0;
} else {
cout << "Fail - The results mismatch!\n";
return -1;
}
}
|
Gag3p, an outer membrane protein required for fission of mitochondrial tubules. Mitochondrial morphology and function depend on MGM1, a Saccharomyces cerevisiae gene encoding a dynamin-like protein of the mitochondrial outer membrane. Here, we show that mitochondrial fragmentation and mitochondrial genome loss caused by lesions in MGM1 are suppressed by three novel mutations, gag1, gag2, and gag3 (for glycerol-adapted growth). Cells with any of the gag mutations displayed aberrant mitochondrial morphology characterized by elongated, unbranched tubes and highly fenestrated structures. Additionally, each of the gag mutations prevented mitochondrial fragmentation caused by loss of the mitochondrial fusion factor, Fzo1p, or by treatment of cells with sodium azide. The gag1 mutation mapped to DNM1 that encodes a dynamin-related protein required for mitochondrial fission. GAG3 encodes a novel WD40-repeat protein previously found to interact with Dnm1p in a two-hybrid assay. Gag3p was localized to mitochondria where it was found to associate as a peripheral protein on the cytosolic face of the outer membrane. This association requires neither the DNM1 nor GAG2 gene products. However, the localization of Dnm1p to the mitochondrial outer membrane is substantially reduced by the gag2 mutation, but unaffected by loss of Gag3p. These results indicate that Gag3p plays a distinct role on the mitochondrial surface to mediate the fission of mitochondrial tubules. Introduction Mitochondria are complex organelles that perform essential metabolic functions in nearly all eukaryotic cells. These functions depend on a highly conserved composition and internal structure, yet the shape and distribution of mitochondria vary enormously depending on cellular type, function, and nutritional status (Yaffe, 1999a). In many proliferating cells, mitochondria comprise dynamic reticular networks whose tubules undergo frequent division, fusion, and redistribution in the cytoplasm (Bereiter-Hahn and Voth, 1994). Recent studies have uncovered a few of the components that facilitate these changes in mitochondrial shape and distribution Yaffe, 1999b), but many mechanisms mediating mitochondrial dynamics have yet to be described. Among key factors regulating mitochondrial behavior are three GTP-binding proteins that act at the mitochondrial surface. One of these, the fuzzy onions protein in Drosophila or its yeast homologue, Fzo1p, mediates mitochondrial fusion (Hales and Fuller, 1997;). Another component, the dynamin-related yeast protein Dnm1p or its animal cell ho-mologue, Drp1, facilitates the fission of mitochondrial tubules (;;;;Sesaki and Jensen, 1999). A third factor, Mgm1p, another dynamin-like protein, is essential for the maintenance of mitochondrial tubules and also plays a role in mitochondrial inheritance in Saccharomyces cerevisiae (;Shepard and Yaffe, 1999). This latter protein is also essential for the maintenance of mitochondrial DNA (Jones and Fangman, 1992;), and cells depleted of Mgm1p rapidly become respiration-deficient. To further investigate the role of Mgm1p, genetic suppressors that prevent mitochondrial DNA loss in mgm1 mutant cells were isolated. The analysis of these suppressors has led to the identification of a novel protein that mediates the fission of mitochondrial tubules. Strains and Genetic Techniques Yeast strains used in this study are listed in Table I. Strain JSY1361 was a gift from J. Shaw (University of Utah, Salt Lake City, UT). All other strains are isogenic to MYY290 (Smith and Yaffe, 1991). Strain MYY971 was described previously (Shepard and Yaffe, 1999). Strains MYY993, MYY994, and MYY995 were isolated as described below. Strain MYY1200 was constructed as described below, and strain MYY1201 was derived as a haploid segregant 334 from a cross of MYY1200 to MYY291. Strains MYY977, MYY981, and MYY986 were isolated as haploid segregants from crosses of MYY993, MYY994, and MYY995, respectively, to MYY291. Strains MYY2033, MYY2029, MYY2030, and MYY2031 were derived from crosses of MYY977, MYY981, and MYY986 to MYY1201. Strain MYY2001 was created as described below. MYY2000 was a haploid segregant from a cross of strain MYY2001 to MYY290. Strain MYY2005 was derived from a cross of MYY2000 to MYY1201. MYY2007, MYY2009, and MYY2011 were haploid segregants derived from crosses of strain MYY2001 to strains MYY2013, MYY2029, and MYY2031, respectively. Strain MYY2013 and MYY2014 were derived from a cross of strain JSY1391 to MYY1201. Epitope-tagged strains MYY2016 and MYY1202 were created as described below. Strains MYY2017 and MYY2019 were derived from a cross of yeast disruption strain 11311 (Research Genetics, Inc.) to strain MYY1201. Culture conditions and genetic analysis of yeast followed standard procedures (). Plasmid DNA was prepared from Escherichia coli strain DH5 ␣. A version of green fluorescent protein (GFP) 1 fused in frame with the mitochondrial targeting sequence of cytochrome oxidase subunit 4 (COX4) was created as follows. Sequences corresponding to GFP were amplified by PCR from plasmid pS65T-C1 (Clontech) using primer C4-GFP, 5 -CGGGATCCGTCGACATGCTTTCACTACGTCAATC-TATAAGATTTTTCAAGCCAGCCACAAGAACTTTGTGTAGCT-CTAGAGTGGGTAAAGGAGAAGAACT-3, which encoded the mitochondrial targeting sequence of COX4, and primer GFP-3P, 5 -TGC-CCGGGATCCCTAGTATAGTTCATCCATGCC-3. The resulting PCR product was digested with BamHI, made blunt-ended with Klenow, and ligated into the blunted EcoRI site downstream of the ADH1 promoter sequence of plasmid pAC1 (). The resulting plasmid was named pAC1-c4GFP. Plasmid YIp5-LEU2 was created by inserting the LEU2 -containing XhoI-SalI fragment from plasmid YEp13 () into the SalI site of plasmid YIp5 (). Plasmid YIp5-LEU2-c4GFP was constructed by isolating the EcoRV fragment encoding ADH1-c4GFP from plasmid pAC1-c4GFP, and ligating this fragment into the SmaI site of YIp5-LEU2. Then, strain MYY1200 was created by transformation of MYY290 with YIp5-LEU2-c4GFP that had been linearized with StuI. Colonies that were auxotrophic for leucine were verified for the presence of green mitochondria by fluorescence microscopy. The GFP-tagged DNM1 vector, pHS20, was a gift from H. Sesaki and R. Jensen (Johns Hopkins University Medical School, Baltimore, MD). Subcellular Fractionation and Analysis Cells were grown in semisynthetic lactate medium (), converted to spheroplasts, homogenized, and subcellular fractions were isolated by differential centrifugation as previously described (;Yaffe, 1991). Mitochondria were treated with trypsin, sodium chloride, sodium carbonate, and urea as described previously (Sogo and Yaffe, 1994), except that the extraction time was reduced to 10 min. Quantification of Dnm1p Binding to Mitochondria To quantify the presence of GFP-labeled Dnm1p on mitochondria, cells freshly transformed with plasmid pHS20 were stained with 5 g/ml DASPMI and examined by fluorescence microscopy. Mitochondria appeared yellow and GFP-Dnm1p appeared green when viewed on the fluorescein channel. For each sample, 300 cells were examined by two independent observers, and the fractions of GFP-Dnm1p dots that were associated with the mitochondria were determined. To visualize the mitochondria separately from GFP-Dnm1p, cells were labeled with Mi-toTracker red CMXRos (Molecular Probes). Results gag Mutations Suppress mgm1 Defects in Mitochondrial Morphology and Function MGM1 was shown previously to encode a mitochondrial outer membrane protein required for mitochondrial genome maintenance, mitochondrial inheritance, and the determination of normal mitochondrial morphology (Shepard and Yaffe, 1999). To identify components that act together with Mgm1p to mediate these functions, second-site suppressors that restored growth of mgm1 mutant cells on glycerol-containing media were isolated. These suppressors were isolated in a strain harboring mdm17, a temperature-sensitive allele of MGM1. From 10 9 cells, 30 colonies able to grow at 37 C on glycerol-medium were identified. Fluorescence microscopy revealed that cells harboring the suppressing mutations possessed tubular mitochondria at 37 C, in contrast to unsuppressed mdm17 cells that contained fragmented and spherical mitochondria ( Fig. 1). However, suppression of the mitochondrial morphology defects was not complete, as mitochondrial tubules appeared bundled, aggregated, and restricted in their lateral distribution (Fig. 1). To determine whether the "glycerol-adapted" strains harbored second-site suppressor mutations, each candidate was crossed to a wild-type strain, and the meiotic progeny were analyzed for defects in mitochondrial morphology. The suppressors were found to be genetically unlinked to MGM1 and were designated gag (glycerol-adapted growth) mutations. The gag lesions defined three distinct complementation groups, gag1, gag2, and gag3. Interestingly, each of the gag mutations was able to rescue the respiration deficiency associated with a null allele of MGM1, suggesting that the mechanism of suppression involved a bypass of normal MGM1 function. Additionally, crossing the suppressed mdm17 cells to an mgm1 -null strain resulted in diploids with an mdm17 -like phenotype, indicating that gag suppression is recessive (data not shown). Haploid cells harboring any of the gag mutations, together with wild-type MGM1, displayed aberrant mitochondrial morphology characterized by large reticular bundles and net-like sheets of highly branched tubules (Fig 2). Despite their unusual appearance, these abnormal mitochondrial structures caused no apparent defect in growth under a variety of conditions (data not shown). These phenotypes were remarkably similar to those described for cells mutant for DNM1/MDM29, another gene known to control mitochondrial dynamics (). To determine whether any of the gag mutants mapped to the dnm1 locus, gag1, gag2, and gag3 cells were crossed to a strain bearing a dnm1 -null allele, and their meiotic progeny were analyzed. No recombination was detected between gag1 and dnm1, indicating that gag1 is a mutation in DNM1. The double mutant recombinants dnm1 gag2, gag2 gag3, and dnm1 gag3, and the triple mutant, dnm1 gag2 gag3, were recovered and displayed the same mitochondrial phenotype as the single mutants (data not shown), suggesting that the mutations are located in genes that participate in the same cellular process. gag Mutants Are Defective for Mitochondrial Fission Recently, two groups have demonstrated that Dnm1p is an essential component of the mitochondrial fission machinery (;Sesaki and Jensen, 1999). The similarity of the gag2 and gag3 mutant phenotypes to that of dnm1 suggested that they also participate in mitochondrial fission. To test this possibility, the gag mutants were crossed to a yeast strain deleted for the mitochondrial fusion factor gene, FZO1. Cells lacking Fzo1p display frag-mented mitochondria that readily lose mitochondrial DNA ), and the development of these phenotypes depends on Dnm1p (i.e., a dnm1 mutation prevents mitochondrial fragmentation and genome loss in an fzo1 mutant; ;Sesaki and Jensen, 1999). Analysis of gag2 fzo1 and gag3 fzo1 double mutants revealed that these new gag lesions similarly prevented the fragmentation of mitochondrial tubules (Fig. 3 A) and allowed growth on glycerol-containing medium (Fig. 3 B), indicating that mitochondrial genome loss was prevented. Further evidence that GAG2 and GAG3 gene products participate in mitochondrial fission was revealed by examination of mitochondria in cells treated with sodium azide. Treatment of wild-type cells with this energy poison leads to fragmentation of mitochondrial tubules (Fig. 3 C). However, such fragmentation does not occur in cells harboring dnm1 (gag1), gag2, or gag3 mutations (Fig. 3 C). The absence of fragmentation in dnm1 cells indicates that the mitochondrial fission apparatus mediates the alterations in mitochondrial morphology induced by sodium azide. Furthermore, the absence of fragmentation in gag2 or gag3 cells support the role of these novel GAG genes in the fission process. GAG3 Encodes a WD40 Domain Protein A recent global screen for protein interactions in S. cerevisiae by two-hybrid analysis revealed an interaction between Dnm1p and the product of an uncharacterized open reading frame (ORF), YJL112w (). The predicted product of this ORF is an 80-kD (714 aa) polypeptide possessing six WD40 repeats in the COOHterminal half of the protein and a region in the NH 2 -terminal half predicted to form a coiled-coil. Since proteins that interact with Dnm1p might comprise other components of the mitochondrial fission machinery, the function of YJL112w and its relationship to gag2 and gag3 were examined. Cells bearing a null-mutation in YJL112w displayed mitochondrial morphology and distribution defects identical to those found in gag mutant cells (data not shown). Additionally, genetic analysis involving crosses of strains deleted for YJL112w to gag2 or gag3 strains, revealed that gag3 maps to YJL112w. This identity was further supported by DNA sequence analysis of YJL112w isolated from the gag3 mutant, which revealed a deletion of nucle-otides 1453 and 1454. This lesion results in a frameshift mutation at amino acid residue 485 and a truncation of the protein after five additional residues. These results demonstrate that GAG3 corresponds to YJL112w. Gag3p Is a Peripheral Protein of the Mitochondrial Outer Membrane To determine the site of Gag3p activity, a yeast strain was created in which the chromosomal copy of GAG3 was replaced by a version of the gene encoding a form of Gag3p with an HA-epitope tag at the COOH terminus. This tagged protein was fully functional, as cells expressing only this variant gene displayed mitochondrial morphology in- distinguishable from that of wild-type cells (data not shown). Cells expressing the tagged protein were homogenized and subcellular fractions were isolated by differential centrifugation. Immunoblot analysis revealed that Gag3p was enriched in the mitochondrial fraction (Fig. 4 A). This pattern was similar to that observed for the mitochondrial outer membrane protein, OM45, but contrasted with that found for glucose-6-phosphate dehydrogenase, a cytosolic protein. An identical pattern of Gag3p fractionation with mitochondria was observed in ⌬dnm1, gag2, and double mutant ⌬dnm1 gag2 cells, indicating that the mitochondrial association of Gag3p is independent of functional Dnm1p or Gag2p (data not shown). In contrast to Gag3p, Dnm1p was essentially absent from the mitochondrial fraction and largely recovered in the cytosolic fraction (Fig. 4 A). This distribution agrees with that previously reported for Dnm1p in subcellular fractions (), although indirect immunofluorescence microscopy revealed a mitochondrial localization for Dnm1p (;Sesaki and Jensen, 1999). This discrepancy between microscopic localization and recovery in isolated subcellular fractions may reflect a weak association or transient interaction of Dnm1p with mitochondria. Although we have been unable to detect Gag3p by indirect immunofluorescence microscopy (data not shown), its association with mitochondria is strong enough to persist through purification of the organelle. To determine the submitochondrial location of Gag3p, the protein's accessibility to the protease trypsin was assessed (Fig. 4 B). Mild trypsin treatment of isolated mitochondria readily digested Gag3p. The same treatment also cleaved Tom70p, a mitochondrial outer membrane protein with a large cytosolic domain. Proteins of the mitochondrial matrix and intermembrane space, Mas2p and cytochrome b 2, respectively, were protected from the protease treatment unless membranes were disrupted with the detergent deoxycholate (Fig. 4 B). These results suggest that Gag3p is located on the mitochondrial outer membrane. The association of Gag3p with the mitochondrial outer membrane was investigated by chemical treatment of isolated mitochondria (Fig. 4 C). Gag3p remained associated with mitochondria in the presence of 1 M NaCl, was partially extracted by washing with 0.1 M Na 2 CO 3, and was mostly removed by treatment with 8 M urea (Fig. 4 C). Although both Na 2 CO 3 and urea treatment released Gag3p from the mitochondria, a substantial fraction remained associated under these conditions, suggesting a strong interaction between Gag3p and mitochondria. All together, these data demonstrate that Gag3p is a peripheral protein bound to the cytosolic surface of the mitochondrial outer membrane. Localization of Dnm1p Depends on GAG2, but Not on GAG3 To examine the role of the GAG2 and GAG3 gene products in localization of Dnm1p to the mitochondrial surface, the distribution of GFP-tagged Dnm1p was determined in gag2 and gag3 mutant cells by fluorescence microscopy (Fig. 5). Mitochondria in these same cells were labeled with MitoTracker red dye. As described previously (;Sesaki and Jensen, 1999), in wild-type cells, Dnm1p was localized to punctate structures largely associated with mitochondrial tubules (Fig 5). This pattern Figure 4. Gag3p is a peripheral protein of the mitochondrial outer membrane. A, Cells expressing HA-tagged Gag3p (MYY2016) or myc-tagged Dnm1p (MYY1202) were grown on semisynthetic lactate medium (). Cells were homogenized and subcellular fractions were isolated by differential centrifugation. The homogenate (hom), low-speed pellet (LSP), mitochondrial (mito), intermediate-speed pellet (ISP), highspeed pellet (HSP), and the cytosolic fractions (cyto) were analyzed by SDS-PAGE and immunoblotting to detect Gag3p, Dnm1p, the mitochondrial outer membrane protein OM45, and the cytosolic protein glucose-6-phosphate dehydrogenase (G6PDH). Each lane contained 10 g total protein. B, Trypsin treatment of mitochondria. Isolated mitochondria containing HA-tagged Gag3p were treated with trypsin in the presence ( ) or absence () of deoxycholate detergent and analyzed by SDS-PAGE and immunoblotting. Samples were tested for the presence of Gag3p, the outer membrane protein Tom70p, the intermembrane space protein cytochrome b 2, and the mitochondrial matrix protein Mas2p. C, Gag3p is peripherally associated with mitochondrial membranes. Isolated mitochondria containing HA-tagged Gag3p were treated with 1 M NaCl, 0.1 M Na 2 CO 3, or 8 M urea. Mitochondria were centrifuged, and the resulting pellet (p) and supernatant (s) fractions were analyzed by SDS-PAGE and immunoblotting for Gag3p and OM45. was also observed in gag3 cells, despite their abnormal mitochondrial morphology (Fig. 5). In gag2 cells, however, a substantial fraction of Dnm1p appeared to be displaced from the mitochondrial network and randomly distributed through the cytoplasm. Interestingly, Dnm1p appeared to be associated in punctate structures, even when not localized to the mitochondria (Fig. 5). Quantitative analysis of this distribution indicated that 94% ( 4%, n 319) of fluorescent Dnm1p spots were associated with mitochondria in wild-type cells, and 89% ( 5%, n 384) in gag3null cells. In contrast, only 46% ( 14%, n 642) of Dnm1p structures were localized to mitochondria in gag2 cells. These results indicate that the GAG2 gene product plays a role in promoting the interaction of Dnm1p with the mitochondrial surface, but that Gag3p does not mediate this interaction. Discussion Division of mitochondrial tubules contributes to the overall morphology of the mitochondrial reticulum (Yaffe, 1999a). Our findings reveal that the gag mutations define components that mediate mitochondrial fission. gag1 mapped to DNM1 that previously was shown to encode a dynamin-related protein essential for mitochondrial division (;;Sesaki and Jensen, 1999). Three observations indicate that GAG2 and GAG3 also encode fission factors. First, the gag2 and gag3 mutants display defects in mitochondrial morphology that are essentially identical to those observed in dnm1 mutant cells (Fig. 2). Second, as previously shown for DNM1, mutations in both GAG2 and GAG3 prevent mitochondrial fragmentation caused by loss of the fusion factor, Fzo1p (Fig. 3 A). Finally, mitochondria in gag2, gag3, and dnm1 mutants remain tubular upon addition of sodium azide, an agent that stimulates the fragmentation of mitochondrial tubules in wild-type cells (Fig. 3 C). Both Gag3p and Dnm1p function on the outer membrane to mediate mitochondrial division, yet they display distinct characteristics in their association with the mitochondrial surface. Gag3p is tightly bound to the mitochondrial outer membrane, whereas the association of Dnm1p appears weaker, since the protein is recovered with the cytosolic fraction during subcellular fractionation (Fig. 4 A; ). Additionally, the gag2 mutation causes displacement of a major fraction of Dnm1p to the cytosol in intact cells (Fig. 5), yet Gag3p remains tightly bound to mitochondria isolated from gag2 mutant cells (data not shown). Although two-hybrid analysis and localization studies are consistent with an interaction of Dnm1p and Gag3p, such an association is not required for the localization of the two proteins to the outer membrane, since neither protein is displaced in the absence of the other. These differences suggest that Gag3p and Dnm1p play distinct roles in the fission process. The specific molecular function of Gag3p in mitochondrial division is unknown. Unlike Dnm1p, whose homologue dynamin mediates the scission step at the neck of coated pits in animal cells (, van der Bliek, 1999, no proteins related to Gag3p have been implicated in other membrane fission events. One clue to the protein's function might lie in its two distinct structural domains, a coiled-coil region in the NH 2 -terminal half of the protein and six WD40-repeats in the COOH-terminal half. These features are likely to comprise protein interaction domains () and suggest a role for Gag3p in bringing together or organizing multiple components on the mitochondrial surface. One interacting partner is likely to be Dnm1p, although the dissimilar behavior of these components during subcellular fractionation and our inability to detect binding with coimmunoprecipitation analysis (data not shown) suggest a transient or unstable interaction of these proteins. Gag3p is likely to bind other outer membrane proteins and, in particular, one or more integral membrane proteins. The identification of these binding partners and an analysis of the dynamic interactions of fission components will lead to the elucidation of molecular mechanisms that mediate mitochondrial division. |
// DefaultUser returns a default MockUser that is set in
// `authorization_endpoint` if the UserQueue is empty.
func DefaultUser() *MockUser {
return &MockUser{
Subject: "1234567890",
Email: "[email protected]",
PreferredUsername: "jane.doe",
Phone: "555-987-6543",
Address: "123 Main Street",
Groups: []string{"engineering", "design"},
EmailVerified: true,
}
} |
<gh_stars>1-10
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from os import listdir, remove, makedirs
from os.path import isfile, join, isdir, exists
from shutil import rmtree
from lib.fileflags import FileFlags as fflags
from lib.imdbfilmextension import IMDbExtension
from lib.tvdbshowextension import TVDbShowExtension
from lib.malanimeextension import MalAnimeExtension
class DescriptionDownloader():
def __init__(self):
self.name = self.__class__.__name__
self.location = './cache/description_downloads/'
if not exists(self.location):
makedirs(self.location)
def get_info(self, fflag, title):
try:
info = ''
if fflag == fflags.SHOW_DIRECTORY_FLAG:
clean_title = title.replace(':', '')
if self.check_chache(clean_title) is not True:
show_extension = TVDbShowExtension()
info = show_extension.get_show_info(title)
self.save_in_cache(clean_title, info)
return info
else:
return self.get_info_from_cache(clean_title)
elif fflag == fflags.FILM_DIRECTORY_FLAG:
clean_title = title.replace(':','')
if self.check_chache(clean_title) is not True:
film_extension = IMDbExtension()
info = film_extension.get_movie_info(title)
self.save_in_cache(clean_title, info)
return info
else:
return self.get_info_from_cache(clean_title)
elif fflag == fflags.ANIME_DIRECTORY_FLAG:
clean_title = title.replace(':', '')
if self.check_chache(clean_title) is not True:
anime_extension = MalAnimeExtension()
info = anime_extension.get_anime_info(title)
self.save_in_cache(clean_title, info)
return info
else:
return self.get_info_from_cache(clean_title)
except Exception as err:
print('Description Downloader: Error - ', err)
def check_chache(self, title):
file_path = self.location + '{0}-description.txt'.format(title)
try:
if isfile(file_path):
print(self.name, 'Skipping This Step: File Already in Cache')
return True
else:
return False
except Exception as err:
print('checkcache: ',err)
def save_in_cache(self, title, info):
try:
file_path = self.location + '{0}-description.txt'.format(title)
with open(file_path, "w") as file:
file.write(info)
except Exception as err:
print(self.name, 'Save Cache Error: ', err)
def get_info_from_cache(self, title):
info = ''
try:
file_path = self.location + '{0}-description.txt'.format(title)
with open(file_path, "r+") as file:
info = file.read()
return info
except Exception as err:
print(self.name, 'Get Cache Error: ', err)
def clear_cache(self):
try:
if isdir(self.location):
rmtree(self.location)
except Exception as err:
print(self.name, 'Clear Cache Error: ', err) |
//
// Generated by class-dump 3.5 (64 bit).
//
// class-dump is Copyright (C) 1997-1998, 2000-2001, 2004-2013 by <NAME>.
//
#import "QzoneModel.h"
@class NSString;
@interface QZFmInfo : QzoneModel
{
}
+ (id)fromJceObject:(id)arg1;
// Remaining properties
@property(retain, nonatomic) NSString *showAudioUrl; // @dynamic showAudioUrl;
@property(nonatomic) long long showDuration; // @dynamic showDuration;
@property(retain, nonatomic) NSString *showID; // @dynamic showID;
@property(retain, nonatomic) NSString *showName; // @dynamic showName;
@property(retain, nonatomic) NSString *showPicUrl; // @dynamic showPicUrl;
@end
|
def talk():
try:
while True:
text = input()
post(text)
except KeyboardInterrupt:
pass |
Effect of weather and environmental factors on the clinical course of psoriasis Dear Editor, Psoriasis is a chronic disease, the prevalence of which shows geographic variations,1 suggesting that it might be influenced by climatic factors such as sun exposure and humidity.2,3 In order to assess the effect of weather and both outdoor and indoor environmental factors on the clinical course of psoriasis, we analysed the answers given to a specific questionnaire administered to 300 consecutive patients attending the psoriasis outpatient clinic of our department. The role of exposure to rainy, windy, muggy, hot, cold and sunny climates, as well as of seasonality in relation to the outdoor environment and the effect of domestic heating and ventilation systems |
The MEG X399 CREATION is an excellent motherboard, it offers a monster 16 phase VRM with 70A power stages, solid USB implementation and much more.
Talking about top of the line X399 motherboards designed for the ThreadRipper WX series isn't something we have been doing much as of late, but today we are going to take a look at MSI's MEG X399 CREATION.
The motherboard is designed to support the latest and greatest AMD ThreadRipper CPUs, so let's see what it has in store for us.
The MEG X399 CREATION features dual Intel NICs, USB 3.1, USB 3.0, SATA6Gb/s, three M.2 slots, support for up to 3-way SLI/CrossFireX, and even multiple RGB LED headers.
United Kingdom: The MSI Gaming AMD Ryzen ThreadRipper DDR4 VR Ready HDMI USB 3 SLI CFX Extended-ATX Motherboard (MEG X399 CREATION) retails for £505.30 at Amazon UK. |
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.drill.exec.planner.index;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.Set;
import org.apache.drill.shaded.guava.com.google.common.collect.ImmutableList;
import org.apache.drill.shaded.guava.com.google.common.collect.Lists;
import org.apache.drill.shaded.guava.com.google.common.collect.Maps;
import org.apache.drill.shaded.guava.com.google.common.collect.Sets;
import org.apache.calcite.plan.RelTraitSet;
import org.apache.calcite.plan.volcano.RelSubset;
import org.apache.calcite.rel.RelCollation;
import org.apache.calcite.rel.RelCollationTraitDef;
import org.apache.calcite.rel.RelCollations;
import org.apache.calcite.rel.RelFieldCollation;
import org.apache.calcite.rel.RelNode;
import org.apache.calcite.rel.core.Sort;
import org.apache.calcite.rex.RexBuilder;
import org.apache.calcite.rex.RexUtil;
import org.apache.calcite.rex.RexLiteral;
import org.apache.calcite.sql.SqlKind;
import org.apache.drill.common.expression.FieldReference;
import org.apache.drill.common.expression.LogicalExpression;
import org.apache.drill.common.expression.SchemaPath;
import org.apache.drill.exec.physical.base.DbGroupScan;
import org.apache.drill.exec.physical.base.GroupScan;
import org.apache.drill.exec.physical.base.IndexGroupScan;
import org.apache.drill.exec.planner.common.DrillProjectRelBase;
import org.apache.drill.exec.planner.common.DrillScanRelBase;
import org.apache.drill.exec.planner.fragment.DistributionAffinity;
import org.apache.drill.exec.planner.logical.DrillOptiq;
import org.apache.drill.exec.planner.logical.DrillParseContext;
import org.apache.drill.exec.planner.logical.DrillScanRel;
import org.apache.drill.exec.planner.physical.DrillDistributionTrait;
import org.apache.drill.exec.planner.physical.Prel;
import org.apache.drill.exec.planner.physical.PrelUtil;
import org.apache.drill.exec.planner.physical.ScanPrel;
import org.apache.drill.exec.planner.physical.ProjectPrel;
import org.apache.drill.exec.planner.common.OrderedRel;
import org.apache.calcite.rel.type.RelDataType;
import org.apache.calcite.rel.type.RelDataTypeField;
import org.apache.calcite.rex.RexInputRef;
import org.apache.calcite.rex.RexNode;
public class IndexPlanUtils {
public enum ConditionIndexed {
NONE,
PARTIAL,
FULL}
/**
* Check if any of the fields of the index are present in a list of LogicalExpressions supplied
* as part of IndexableExprMarker
* @param exprMarker, the marker that has analyzed original index condition on top of original scan
* @param indexDesc
* @return ConditionIndexed.FULL, PARTIAL or NONE depending on whether all, some or no columns
* of the indexDesc are present in the list of LogicalExpressions supplied as part of exprMarker
*
*/
static public ConditionIndexed conditionIndexed(IndexableExprMarker exprMarker, IndexDescriptor indexDesc) {
Map<RexNode, LogicalExpression> mapRexExpr = exprMarker.getIndexableExpression();
List<LogicalExpression> infoCols = Lists.newArrayList();
infoCols.addAll(mapRexExpr.values());
if (indexDesc.allColumnsIndexed(infoCols)) {
return ConditionIndexed.FULL;
} else if (indexDesc.someColumnsIndexed(infoCols)) {
return ConditionIndexed.PARTIAL;
} else {
return ConditionIndexed.NONE;
}
}
/**
* check if we want to apply index rules on this scan,
* if group scan is not instance of DbGroupScan, or this DbGroupScan instance does not support secondary index, or
* this scan is already an index scan or Restricted Scan, do not apply index plan rules on it.
* @param scanRel
* @return
*/
static public boolean checkScan(DrillScanRel scanRel) {
GroupScan groupScan = scanRel.getGroupScan();
if (groupScan instanceof DbGroupScan) {
DbGroupScan dbscan = ((DbGroupScan) groupScan);
// if we already applied index convert rule, and this scan is indexScan or restricted scan already,
// no more trying index convert rule
return dbscan.supportsSecondaryIndex() && (!dbscan.isIndexScan()) && (!dbscan.isRestrictedScan());
}
return false;
}
/**
* For a particular table scan for table T1 and an index on that table, find out if it is a covering index
* @return
*/
static public boolean isCoveringIndex(IndexCallContext indexContext, FunctionalIndexInfo functionInfo) {
if (functionInfo.hasFunctional()) {
// need info from full query
return queryCoveredByIndex(indexContext, functionInfo);
}
DbGroupScan groupScan = (DbGroupScan) getGroupScan(indexContext.getScan());
List<LogicalExpression> tableCols = Lists.newArrayList();
tableCols.addAll(groupScan.getColumns());
return functionInfo.getIndexDesc().isCoveringIndex(tableCols);
}
/**
* This method is called only when the index has at least one functional indexed field. If there is no function field,
* we don't need to worry whether there could be paths not found in Scan.
* In functional case, we have to check all available (if needed) operators to find out if the query is covered or not.
* E.g. cast(a.b as INT) in project, a.b in Scan's rowType or columns, and cast(a.b as INT)
* is an indexed field named '$0'. In this case, by looking at Scan, we see only 'a.b' which is not in index. We have to
* look into Project, and if we see 'a.b' is only used in functional index expression cast(a.b as INT), then we know
* this Project+Scan is covered.
* @param indexContext
* @param functionInfo
* @return false if the query could not be covered by the index (should not create covering index plan)
*/
static private boolean queryCoveredByIndex(IndexCallContext indexContext,
FunctionalIndexInfo functionInfo) {
// for indexed functions, if relevant schemapaths are included in index(in indexed fields or non-indexed fields),
// check covering based on the local information we have:
// if references to schema paths in functional indexes disappear beyond capProject
if (indexContext.getFilter() != null && indexContext.getUpperProject() == null) {
if (!isFullQuery(indexContext)) {
return false;
}
}
DrillParseContext parserContext =
new DrillParseContext(PrelUtil.getPlannerSettings(indexContext.getCall().rel(0).getCluster()));
Set<LogicalExpression> exprs = Sets.newHashSet();
if (indexContext.getUpperProject() != null) {
if (indexContext.getLowerProject() == null) {
for (RexNode rex : indexContext.getUpperProject().getProjects()) {
LogicalExpression expr = RexToExpression.toDrill(parserContext, null, indexContext.getScan(), rex);
exprs.add(expr);
}
// now collect paths in filter since upperProject may drop some paths in filter
IndexableExprMarker filterMarker = new IndexableExprMarker(indexContext.getScan());
indexContext.getFilterCondition().accept(filterMarker);
for (RexNode rex : filterMarker.getIndexableExpression().keySet()) {
LogicalExpression expr = RexToExpression.toDrill(parserContext, null, indexContext.getScan(), rex);
exprs.add(expr);
}
} else {
// we have underneath project, so we have to do more to convert expressions
for (RexNode rex : indexContext.getUpperProject().getProjects()) {
LogicalExpression expr = RexToExpression.toDrill(parserContext, indexContext.getLowerProject(), indexContext.getScan(), rex);
exprs.add(expr);
}
// Now collect paths in filter since upperProject may drop some paths in filter.
// Since this is (upper)Proj+Filter+(lower)Proj+Scan case, and IndexableExprMarker works
// only with expressions that referencing directly to Scan, it has to use indexContext.origPushedCondition
IndexableExprMarker filterMarker = new IndexableExprMarker(indexContext.getScan());
indexContext.getOrigCondition().accept(filterMarker);
for (RexNode rex : filterMarker.getIndexableExpression().keySet()) {
// Since rex represents the filter expression directly referencing the scan row type,
// (the condition has been pushed down of lowerProject), set the lowerProject as null.
LogicalExpression expr = RexToExpression.toDrill(parserContext, null, indexContext.getScan(), rex);
exprs.add(expr);
}
}
}
else if (indexContext.getLowerProject() != null) {
for (RexNode rex : indexContext.getLowerProject().getProjects()) {
LogicalExpression expr = DrillOptiq.toDrill(parserContext, indexContext.getScan(), rex);
exprs.add(expr);
}
}
else { // upperProject and lowerProject both are null, the only place to find columns being used in query is scan
exprs.addAll(indexContext.getScanColumns());
}
Map<LogicalExpression, Set<SchemaPath>> exprPathMap = functionInfo.getPathsInFunctionExpr();
PathInExpr exprSearch = new PathInExpr(exprPathMap);
for (LogicalExpression expr: exprs) {
if (expr.accept(exprSearch, null) == false) {
return false;
}
}
// if we come to here, paths in indexed function expressions are covered in capProject.
// now we check other paths.
// check the leftout paths (appear in capProject other than functional index expression) are covered by other index fields or not
List<LogicalExpression> leftPaths = Lists.newArrayList(exprSearch.getRemainderPaths());
indexContext.setLeftOutPathsInFunctions(exprSearch.getRemainderPathsInFunctions());
return functionInfo.getIndexDesc().isCoveringIndex(leftPaths);
}
static private boolean isFullQuery(IndexCallContext indexContext) {
RelNode rootInCall = indexContext.getCall().rel(0);
// check if the tip of the operator stack we have is also the top of the whole query, if yes, return true
if (indexContext.getCall().getPlanner().getRoot() instanceof RelSubset) {
final RelSubset rootSet = (RelSubset) indexContext.getCall().getPlanner().getRoot();
if (rootSet.getRelList().contains(rootInCall)) {
return true;
}
} else {
if (indexContext.getCall().getPlanner().getRoot().equals(rootInCall)) {
return true;
}
}
return false;
}
/**
* Build collation property for the 'lower' project, the one closer to the Scan
* @param projectRexs
* @param input
* @param indexInfo
* @return the output RelCollation
*/
public static RelCollation buildCollationLowerProject(List<RexNode> projectRexs, RelNode input, FunctionalIndexInfo indexInfo) {
// if leading fields of index are here, add them to RelCollation
List<RelFieldCollation> newFields = Lists.newArrayList();
if (!indexInfo.hasFunctional()) {
Map<LogicalExpression, Integer> projectExprs = Maps.newLinkedHashMap();
DrillParseContext parserContext = new DrillParseContext(PrelUtil.getPlannerSettings(input.getCluster()));
int idx=0;
for (RexNode rex : projectRexs) {
projectExprs.put(DrillOptiq.toDrill(parserContext, input, rex), idx);
idx++;
}
int idxFieldCount = 0;
for (LogicalExpression expr : indexInfo.getIndexDesc().getIndexColumns()) {
if (!projectExprs.containsKey(expr)) {
break;
}
RelFieldCollation.Direction dir = indexInfo.getIndexDesc().getCollation().getFieldCollations().get(idxFieldCount).direction;
if ( dir == null) {
break;
}
newFields.add(new RelFieldCollation(projectExprs.get(expr), dir,
RelFieldCollation.NullDirection.UNSPECIFIED));
}
idxFieldCount++;
} else {
// TODO: handle functional index
}
return RelCollations.of(newFields);
}
/**
* Build collation property for the 'upper' project, the one above the filter
* @param projectRexs
* @param inputCollation
* @param indexInfo
* @param collationFilterMap
* @return the output RelCollation
*/
public static RelCollation buildCollationUpperProject(List<RexNode> projectRexs,
RelCollation inputCollation, FunctionalIndexInfo indexInfo,
Map<Integer, List<RexNode>> collationFilterMap) {
List<RelFieldCollation> outputFieldCollations = Lists.newArrayList();
if (inputCollation != null) {
List<RelFieldCollation> inputFieldCollations = inputCollation.getFieldCollations();
if (!indexInfo.hasFunctional()) {
for (int projectExprIdx = 0; projectExprIdx < projectRexs.size(); projectExprIdx++) {
RexNode n = projectRexs.get(projectExprIdx);
if (n instanceof RexInputRef) {
RexInputRef ref = (RexInputRef)n;
boolean eligibleForCollation = true;
int maxIndex = getIndexFromCollation(ref.getIndex(), inputFieldCollations);
if (maxIndex < 0) {
eligibleForCollation = false;
continue;
}
// check if the prefix has equality conditions
for (int i = 0; i < maxIndex; i++) {
int fieldIdx = inputFieldCollations.get(i).getFieldIndex();
List<RexNode> conditions = collationFilterMap != null ? collationFilterMap.get(fieldIdx) : null;
if ((conditions == null || conditions.size() == 0) &&
i < maxIndex-1) {
// if an intermediate column has no filter condition, it would select all values
// of that column, so a subsequent column cannot be eligible for collation
eligibleForCollation = false;
break;
} else {
for (RexNode r : conditions) {
if (!(r.getKind() == SqlKind.EQUALS)) {
eligibleForCollation = false;
break;
}
}
}
}
// for every projected expr, if it is eligible for collation, get the
// corresponding field collation from the input
if (eligibleForCollation) {
for (RelFieldCollation c : inputFieldCollations) {
if (ref.getIndex() == c.getFieldIndex()) {
RelFieldCollation outFieldCollation = new RelFieldCollation(projectExprIdx, c.getDirection(), c.nullDirection);
outputFieldCollations.add(outFieldCollation);
}
}
}
}
}
} else {
// TODO: handle functional index
}
}
return RelCollations.of(outputFieldCollations);
}
public static int getIndexFromCollation(int refIndex, List<RelFieldCollation> inputFieldCollations) {
for (int i = 0; i < inputFieldCollations.size(); i++) {
if (refIndex == inputFieldCollations.get(i).getFieldIndex()) {
return i;
}
}
return -1;
}
public static List<RexNode> getProjects(DrillProjectRelBase proj) {
return proj.getProjects();
}
public static boolean generateLimit(OrderedRel sort) {
RexNode fetchNode = sort.getFetch();
int fetchValue = (fetchNode == null) ? -1 : RexLiteral.intValue(fetchNode);
return fetchValue >=0;
}
public static RexNode getOffset(OrderedRel sort) {
return sort.getOffset();
}
public static RexNode getFetch(OrderedRel sort) {
return sort.getFetch();
}
/**
* generate logical expressions for sort rexNodes in SortRel, the result is store to IndexPlanCallContext
* @param indexContext
*/
public static void updateSortExpression(IndexCallContext indexContext, List<RelFieldCollation> coll) {
if (coll == null) {
return;
}
DrillParseContext parserContext =
new DrillParseContext(PrelUtil.getPlannerSettings(indexContext.getCall().rel(0).getCluster()));
indexContext.createSortExprs();
for (RelFieldCollation collation : coll) {
int idx = collation.getFieldIndex();
DrillProjectRelBase oneProject;
if (indexContext.getUpperProject() != null && indexContext.getLowerProject() != null) {
LogicalExpression expr = RexToExpression.toDrill(parserContext, indexContext.getLowerProject(), indexContext.getScan(),
indexContext.getUpperProject().getProjects().get(idx));
indexContext.getSortExprs().add(expr);
}
else { // one project is null now
oneProject = (indexContext.getUpperProject() != null)? indexContext.getUpperProject() : indexContext.getLowerProject();
if (oneProject != null) {
LogicalExpression expr = RexToExpression.toDrill(parserContext, null, indexContext.getScan(),
getProjects(oneProject).get(idx));
indexContext.getSortExprs().add(expr);
}
else { // two projects are null
SchemaPath path;
RelDataTypeField f = indexContext.getScan().getRowType().getFieldList().get(idx);
String pathSeg = f.getName().replaceAll("`", "");
final String[] segs = pathSeg.split("\\.");
path = SchemaPath.getCompoundPath(segs);
indexContext.getSortExprs().add(path);
}
}
}
}
/**
* generate logical expressions for sort rexNodes in SortRel, the result is store to IndexPlanCallContext
* @param indexContext
*/
public static void updateSortExpression(IndexPhysicalPlanCallContext indexContext, List<RelFieldCollation> coll) {
if (coll == null) {
return;
}
DrillParseContext parserContext =
new DrillParseContext(PrelUtil.getPlannerSettings(indexContext.call.rel(0).getCluster()));
indexContext.sortExprs = Lists.newArrayList();
for (RelFieldCollation collation : coll) {
int idx = collation.getFieldIndex();
ProjectPrel oneProject;
if (indexContext.upperProject != null && indexContext.lowerProject != null) {
LogicalExpression expr = RexToExpression.toDrill(parserContext, indexContext.lowerProject, indexContext.scan,
indexContext.upperProject.getProjects().get(idx));
indexContext.sortExprs.add(expr);
}
else { // one project is null now
oneProject = (indexContext.upperProject != null)? indexContext.upperProject : indexContext.lowerProject;
if (oneProject != null) {
LogicalExpression expr = RexToExpression.toDrill(parserContext, null, indexContext.scan,
oneProject.getProjects().get(idx));
indexContext.sortExprs.add(expr);
}
else { // two projects are null
SchemaPath path;
RelDataTypeField f = indexContext.scan.getRowType().getFieldList().get(idx);
String pathSeg = f.getName().replaceAll("`", "");
final String[] segs = pathSeg.split("\\.");
path = SchemaPath.getCompoundPath(segs);
indexContext.sortExprs.add(path);
}
}
}
}
/**
*
* @param expr
* @param context
* @return if there is filter and expr is only in equality condition of the filter, return true
*/
private static boolean exprOnlyInEquality(LogicalExpression expr, IndexCallContext context) {
// if there is no filter, expr wont be in equality
if (context.getFilter() == null) {
return false;
}
final Set<LogicalExpression> onlyInEquality = context.getOrigMarker().getExpressionsOnlyInEquality();
return onlyInEquality.contains(expr);
}
/**
* Build collation property for project, the one closer to the Scan
* @param projectRexs the expressions to project
* @param project the project between projectRexs and input, it could be null if no such intermediate project(lower project)
* @param input the input RelNode to the project, usually it is the scan operator.
* @param indexInfo the index for which we are building index plan
* @param context the context of this index planning process
* @return the output RelCollation
*/
public static RelCollation buildCollationProject(List<RexNode> projectRexs,
DrillProjectRelBase project,
RelNode input,
FunctionalIndexInfo indexInfo,
IndexCallContext context) {
Map<LogicalExpression, Integer> projectExprs = getProjectExprs(projectRexs, project, input);
return buildCollationForExpressions(projectExprs, indexInfo.getIndexDesc(), context);
}
/**
* Build the collation property for index scan
* @param indexDesc the index for which we are building index plan
* @param context the context of this index planning process
* @return the output RelCollation for the scan on index
*/
public static RelCollation buildCollationCoveringIndexScan(IndexDescriptor indexDesc,
IndexCallContext context) {
Map<LogicalExpression, Integer> rowTypeExprs = getExprsFromRowType(context.getScan().getRowType());
return buildCollationForExpressions(rowTypeExprs, indexDesc, context);
}
public static Map<LogicalExpression, Integer> getProjectExprs(List<RexNode> projectRexs,
DrillProjectRelBase project,
RelNode input) {
Map<LogicalExpression, Integer> projectExprs = Maps.newLinkedHashMap();
DrillParseContext parserContext = new DrillParseContext(PrelUtil.getPlannerSettings(input.getCluster()));
int idx=0;
for (RexNode rex : projectRexs) {
LogicalExpression expr;
expr = RexToExpression.toDrill(parserContext, project, input, rex);
projectExprs.put(expr, idx);
idx++;
}
return projectExprs;
}
public static Map<LogicalExpression, Integer> getExprsFromRowType( RelDataType indexScanRowType) {
Map<LogicalExpression, Integer> rowTypeExprs = Maps.newLinkedHashMap();
int idx = 0;
for (RelDataTypeField field : indexScanRowType.getFieldList()) {
rowTypeExprs.put(FieldReference.getWithQuotedRef(field.getName()), idx++);
}
return rowTypeExprs;
}
/**
* Given index, compute the collations for a list of projected expressions(from Scan's rowType or Project's )
* in the context
* @param projectExprs the output expression list of a RelNode
* @param indexDesc the index for which we are building index plan
* @param context the context of this index planning process
* @return the collation provided by index that will be exposed by the expression list
*/
public static RelCollation buildCollationForExpressions(Map<LogicalExpression, Integer> projectExprs,
IndexDescriptor indexDesc,
IndexCallContext context) {
assert projectExprs != null;
final List<LogicalExpression> sortExpressions = context.getSortExprs();
// if leading fields of index are here, add them to RelCollation
List<RelFieldCollation> newFields = Lists.newArrayList();
if (indexDesc.getCollation() == null) {
return RelCollations.of(newFields);
}
// go through indexed fields to build collation
// break out of the loop when found first indexed field [not projected && not _only_ in equality condition of filter]
// or the leading field is not projected
List<LogicalExpression> indexedCols = indexDesc.getIndexColumns();
for (int idxFieldCount=0; idxFieldCount<indexedCols.size(); ++idxFieldCount) {
LogicalExpression expr = indexedCols.get(idxFieldCount);
if (!projectExprs.containsKey(expr)) {
// leading indexed field is not projected
// but it is only-in-equality field, -- we continue to next indexed field, but we don't generate collation for this field
if (exprOnlyInEquality(expr, context)) {
continue;
}
// else no more collation is needed to be generated, since we now have one leading field which is not in equality condition
break;
}
// leading indexed field is projected,
// if this field is not in sort expression && only-in-equality, we don't need to generate collation for this field
// and we are okay to continue: generate collation for next indexed field.
if (sortExpressions != null &&
!sortExpressions.contains(expr) && exprOnlyInEquality(expr, context) ) {
continue;
}
RelCollation idxCollation = indexDesc.getCollation();
RelFieldCollation.NullDirection nullsDir = idxCollation == null ? RelFieldCollation.NullDirection.UNSPECIFIED :
idxCollation.getFieldCollations().get(idxFieldCount).nullDirection;
RelFieldCollation.Direction dir = (idxCollation == null)?
null : idxCollation.getFieldCollations().get(idxFieldCount).direction;
if (dir == null) {
break;
}
newFields.add(new RelFieldCollation(projectExprs.get(expr), dir, nullsDir));
}
return RelCollations.of(newFields);
}
// TODO: proper implementation
public static boolean pathOnlyInIndexedFunction(SchemaPath path) {
return true;
}
public static RelCollation buildCollationNonCoveringIndexScan(IndexDescriptor indexDesc,
RelDataType indexScanRowType,
RelDataType restrictedScanRowType, IndexCallContext context) {
if (context.getSortExprs() == null) {
return RelCollations.of(RelCollations.EMPTY.getFieldCollations());
}
final List<RelDataTypeField> indexFields = indexScanRowType.getFieldList();
final List<RelDataTypeField> rsFields = restrictedScanRowType.getFieldList();
final Map<LogicalExpression, RelFieldCollation> collationMap = indexDesc.getCollationMap();
assert collationMap != null : "Invalid collation map for index";
List<RelFieldCollation> fieldCollations = Lists.newArrayList();
Map<Integer, RelFieldCollation> rsScanCollationMap = Maps.newTreeMap();
// for each index field that is projected from the indexScan, find the corresponding
// field in the restricted scan's row type and keep track of the ordinal # in the
// restricted scan's row type.
for (int i = 0; i < indexScanRowType.getFieldCount(); i++) {
RelDataTypeField f1 = indexFields.get(i);
for (int j = 0; j < rsFields.size(); j++) {
RelDataTypeField f2 = rsFields.get(j);
if (f1.getName().equals(f2.getName())) {
FieldReference ref = FieldReference.getWithQuotedRef(f1.getName());
RelFieldCollation origCollation = collationMap.get(ref);
if (origCollation != null) {
RelFieldCollation fc = new RelFieldCollation(j,
origCollation.direction, origCollation.nullDirection);
rsScanCollationMap.put(origCollation.getFieldIndex(), fc);
}
}
}
}
// should sort by the order of these fields in indexDesc
for (Map.Entry<Integer, RelFieldCollation> entry : rsScanCollationMap.entrySet()) {
RelFieldCollation fc = entry.getValue();
if (fc != null) {
fieldCollations.add(fc);
}
}
final RelCollation collation = RelCollations.of(fieldCollations);
return collation;
}
public static boolean scanIsPartition(GroupScan scan) {
return (scan.isDistributed() || scan.getDistributionAffinity() == DistributionAffinity.HARD);
}
public static GroupScan getGroupScan(DrillScanRelBase relNode) {
return relNode.getGroupScan();
}
public static RelCollation getCollation(Sort sort) {
return sort.getCollation();
}
public static List<DrillDistributionTrait.DistributionField> getDistributionField(Sort rel) {
List<DrillDistributionTrait.DistributionField> distFields = Lists.newArrayList();
for (RelFieldCollation relField : getCollation(rel).getFieldCollations()) {
DrillDistributionTrait.DistributionField field = new DrillDistributionTrait.DistributionField(relField.getFieldIndex());
distFields.add(field);
}
return distFields;
}
public static ScanPrel buildCoveringIndexScan(DrillScanRelBase origScan,
IndexGroupScan indexGroupScan,
IndexCallContext indexContext,
IndexDescriptor indexDesc) {
FunctionalIndexInfo functionInfo = indexDesc.getFunctionalInfo();
//to record the new (renamed)paths added
List<SchemaPath> rewrittenPaths = Lists.newArrayList();
DbGroupScan dbGroupScan = (DbGroupScan) getGroupScan(origScan);
indexGroupScan.setColumns(
rewriteFunctionColumn(dbGroupScan.getColumns(),
functionInfo, rewrittenPaths));
DrillDistributionTrait partition = scanIsPartition(getGroupScan(origScan))?
DrillDistributionTrait.RANDOM_DISTRIBUTED : DrillDistributionTrait.SINGLETON;
RelDataType newRowType = FunctionalIndexHelper.rewriteFunctionalRowType(origScan, indexContext, functionInfo, rewrittenPaths);
// add a default collation trait otherwise Calcite runs into a ClassCastException, which at first glance
// seems like a Calcite bug
RelTraitSet indexScanTraitSet = origScan.getTraitSet().plus(Prel.DRILL_PHYSICAL).
plus(RelCollationTraitDef.INSTANCE.getDefault()).plus(partition);
// Create the collation traits for index scan based on the index columns under the
// condition that the index actually has collation property (e.g hash indexes don't)
if (indexDesc.getCollation() != null) {
RelCollation collationTrait = buildCollationCoveringIndexScan(indexDesc, indexContext);
indexScanTraitSet = indexScanTraitSet.plus(collationTrait);
}
ScanPrel indexScanPrel = new ScanPrel(origScan.getCluster(),
indexScanTraitSet, indexGroupScan,
newRowType, origScan.getTable());
return indexScanPrel;
}
/**
* For IndexGroupScan, if a column is only appeared in the should-be-renamed function,
* this column is to-be-replaced column, we replace that column(schemaPath) from 'a.b'
* to '$1' in the list of SchemaPath.
* @param paths
* @param functionInfo functional index information that may impact rewrite
* @return
*/
public static List<SchemaPath> rewriteFunctionColumn(List<SchemaPath> paths,
FunctionalIndexInfo functionInfo,
List<SchemaPath> addedPaths) {
if (!functionInfo.hasFunctional()) {
return paths;
}
List<SchemaPath> newPaths = Lists.newArrayList(paths);
for (int i = 0; i < paths.size(); ++i) {
SchemaPath newPath = functionInfo.getNewPath(paths.get(i));
if (newPath == null) {
continue;
}
addedPaths.add(newPath);
// if this path only in indexed function, we are safe to replace it
if (pathOnlyInIndexedFunction(paths.get(i))) {
newPaths.set(i, newPath);
}
else { // we should not replace this column, instead we add a new "$N" field.
newPaths.add(newPath);
}
}
return newPaths;
}
/**
*A RexNode forest with three RexNodes for expressions "cast(a.q as int) * 2, b+c, concat(a.q, " world")"
* on Scan RowType('a', 'b', 'c') will be like this:
*
* (0)Call:"*" Call:"concat"
* / \ / \
* (1)Call:CAST 2 Call:"+" (5)Call:ITEM ' world'
* / \ / \ / \
* (2)Call:ITEM TYPE:INT (3)$1 (4)$2 $0 'q'
* / \
* $0 'q'
*
* So for above expressions, when visiting the RexNode trees using PathInExpr, we could mark indexed expressions in the trees,
* as shown in the diagram above are the node (1),
* then collect the schema paths in the indexed expression but found out of the indexed expression -- node (5),
* and other regular schema paths (3) (4)
*
* @param parseContext
* @param project
* @param scan
* @param toRewriteRex the RexNode to be converted if it contain a functional index expression.
* @param newRowType
* @param functionInfo
* @return
*/
public static RexNode rewriteFunctionalRex(IndexCallContext indexContext,
DrillParseContext parseContext,
DrillProjectRelBase project,
RelNode scan,
RexNode toRewriteRex,
RelDataType newRowType,
FunctionalIndexInfo functionInfo) {
if (!functionInfo.hasFunctional()) {
return toRewriteRex;
}
RexToExpression.RexToDrillExt rexToDrill = new RexToExpression.RexToDrillExt(parseContext, project, scan);
LogicalExpression expr = toRewriteRex.accept(rexToDrill);
final Map<LogicalExpression, Set<SchemaPath>> exprPathMap = functionInfo.getPathsInFunctionExpr();
PathInExpr exprSearch = new PathInExpr(exprPathMap);
expr.accept(exprSearch, null);
Set<LogicalExpression> remainderPaths = exprSearch.getRemainderPaths();
// now build the rex->logical expression map for SimpleRexRemap
// left out schema paths
Map<LogicalExpression, Set<RexNode>> exprToRex = rexToDrill.getMapExprToRex();
final Map<RexNode, LogicalExpression> mapRexExpr = Maps.newHashMap();
for (LogicalExpression leftExpr: remainderPaths) {
if (exprToRex.containsKey(leftExpr)) {
Set<RexNode> rexs = exprToRex.get(leftExpr);
for (RexNode rex: rexs) {
mapRexExpr.put(rex, leftExpr);
}
}
}
// functional expressions e.g. cast(a.b as int)
for (LogicalExpression functionExpr: functionInfo.getExprMap().keySet()) {
if (exprToRex.containsKey(functionExpr)) {
Set<RexNode> rexs = exprToRex.get(functionExpr);
for (RexNode rex: rexs) {
mapRexExpr.put(rex, functionExpr);
}
}
}
SimpleRexRemap remap = new SimpleRexRemap(indexContext.getScan(), newRowType, indexContext.getScan().getCluster().getRexBuilder());
remap.setExpressionMap(functionInfo.getExprMap());
return remap.rewriteWithMap(toRewriteRex, mapRexExpr);
}
public static RexNode getLeadingPrefixMap(Map<LogicalExpression, RexNode> leadingPrefixMap,
List<LogicalExpression> indexCols,
IndexConditionInfo.Builder builder, RexNode condition) {
boolean prefix = true;
int i = 0;
RexNode initCondition = condition.isAlwaysTrue() ? null : condition;
while (prefix && i < indexCols.size()) {
LogicalExpression p = indexCols.get(i++);
List<LogicalExpression> prefixCol = ImmutableList.of(p);
IndexConditionInfo info = builder.indexConditionRelatedToFields(prefixCol, initCondition);
if (info != null && info.hasIndexCol) {
// the col had a match with one of the conditions; save the information about
// indexcol --> condition mapping
leadingPrefixMap.put(p, info.indexCondition);
initCondition = info.remainderCondition;
if (initCondition.isAlwaysTrue()) {
// all filter conditions are accounted for, so if the remainder is TRUE, set it to NULL because
// we don't need to keep track of it for rest of the index selection
initCondition = null;
break;
}
} else {
prefix = false;
}
}
return initCondition;
}
public static List<RexNode> getLeadingFilters (Map<LogicalExpression, RexNode> leadingPrefixMap, List<LogicalExpression> indexCols) {
List<RexNode> leadingFilters = Lists.newArrayList();
if (leadingPrefixMap.size() > 0) {
for (LogicalExpression p : indexCols) {
RexNode n;
if ((n = leadingPrefixMap.get(p)) != null) {
leadingFilters.add(n);
} else {
break; // break since the prefix property will not be preserved
}
}
}
return leadingFilters;
}
public static RexNode getLeadingColumnsFilter(List<RexNode> leadingFilters, RexBuilder rexBuilder) {
if (leadingFilters.size() > 0) {
RexNode leadingColumnsFilter = RexUtil.composeConjunction(rexBuilder, leadingFilters, false);
return leadingColumnsFilter;
}
return null;
}
public static RexNode getTotalRemainderFilter(RexNode indexColsRemFilter, RexNode incColsRemFilter, RexBuilder rexBuilder) {
if (indexColsRemFilter != null && incColsRemFilter != null) {
List<RexNode> operands = Lists.newArrayList();
operands.add(indexColsRemFilter);
operands.add(incColsRemFilter);
RexNode totalRemainder = RexUtil.composeConjunction(rexBuilder, operands, false);
return totalRemainder;
} else if (indexColsRemFilter != null) {
return indexColsRemFilter;
} else {
return incColsRemFilter;
}
}
public static RexNode getTotalFilter(RexNode leadColsFilter, RexNode totRemColsFilter, RexBuilder rexBuilder) {
RexNode condition = leadColsFilter;
if (leadColsFilter != null && totRemColsFilter != null && !totRemColsFilter.isAlwaysTrue()) {
List<RexNode> conditions = new ArrayList<RexNode>();
conditions.add(leadColsFilter);
conditions.add(totRemColsFilter);
return RexUtil.composeConjunction(rexBuilder, conditions, true);
}
return condition;
}
}
|
<filename>ipcam_list.c
#include <stdlib.h>
#include <string.h>
#include <errno.h>
#include "ipcam_list.h"
#include "debug_print.h"
ipcam_link create_empty_ipcam_link(void)
{
ipcam_link link0 = malloc(sizeof(struct ipcam_node));
if (link0)
link0->next = NULL;
else {
debug_print("out of space!");
exit(errno);
}
return link0;
}
ipcam_link insert_ipcam_node(ipcam_link link, const pipcam_node insert_node)
{
pipcam_node q = malloc(sizeof(struct ipcam_node));
memcpy(q, insert_node, sizeof(struct ipcam_node));
q->next = link->next;
link->next = q;
return link;
}
int delete_ipcam_all_node(ipcam_link link)
{
int ret = 0;
pipcam_node *curr = &link;
pipcam_node entry;
while ((entry = (*curr)->next) != NULL) {
(*curr)->next = entry->next;
free(entry);
ret++;
}
return ret;
}
int delete_ipcam_node_by_mac(ipcam_link link, const char *mac)
{
int ret = 0;
pipcam_node *curr;
pipcam_node nextnode;
for (curr = &link; *curr; ) {
nextnode = (*curr)->next;
if (nextnode == NULL)
break;
if (!strncmp((const char *)nextnode->node_info.mac, mac,
sizeof(nextnode->node_info.mac))) {
(*curr)->next = nextnode->next;
free(nextnode);
ret++;
} else
curr = &nextnode;
}
return ret;
}
ipcam_link delete_this_ipcam_node(ipcam_link link, const pipcam_node this_node)
{
pipcam_node *curr;
pipcam_node entry;
for (curr = &link; (*curr)->next; ) {
entry = (*curr)->next;
if (entry == this_node) {
*curr = entry->next;
free(entry);
} else
curr = &entry;
}
return link;
}
int strvalncmp(const uint8_t *s1, const uint8_t *s2, size_t n)
{
size_t i;
int ret = 0;
for (i = 0; i < n; i++) {
ret = s1[i] - s2[i];
if (ret)
return ret;
}
return 0;
}
pipcam_node search_ipcam_node_by_mac(ipcam_link link, const uint8_t *mac)
{
pipcam_node q = link->next;
while (q) {
if (!strvalncmp(q->node_info.mac, mac,
sizeof(q->node_info.mac)))
return q;
q = q->next;
}
return NULL;
}
int num_ipcam_node(const ipcam_link link)
{
int n = 0;
ipcam_link *curr;
for (curr = (ipcam_link *)&link; (*curr)->next; curr = &((*curr)->next))
n++;
return n;
}
/*
* return 0: fail
* return 1: succeed
*/
int insert_nodulp_ipcam_node(ipcam_link link, const pipcam_node insert_node)
{
int ret = 0;
if (!search_ipcam_node_by_mac(link,
insert_node->node_info.mac)) {
insert_ipcam_node(link, insert_node);
ret = 1;
}
return ret;
}
void free_ipcam_link(ipcam_link link)
{
pipcam_node q = link->next;
if (!link->next)
return;
else
q = link->next->next;
do {
free(link->next);
if (q) {
link->next = q;
q = q->next;
} else
break;
} while (1);
return;
}
|
<gh_stars>0
/*****************************************************************************
*
* Copyright (c) 2000 - 2014, Lawrence Livermore National Security, LLC
* Produced at the Lawrence Livermore National Laboratory
* LLNL-CODE-442911
* All rights reserved.
*
* This file is part of VisIt. For details, see https://visit.llnl.gov/. The
* full copyright notice is contained in the file COPYRIGHT located at the root
* of the VisIt distribution or at http://www.llnl.gov/visit/copyright.html.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* - Redistributions of source code must retain the above copyright notice,
* this list of conditions and the disclaimer below.
* - Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the disclaimer (as noted below) in the
* documentation and/or other materials provided with the distribution.
* - Neither the name of the LLNS/LLNL nor the names of its contributors may
* be used to endorse or promote products derived from this software without
* specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
* ARE DISCLAIMED. IN NO EVENT SHALL LAWRENCE LIVERMORE NATIONAL SECURITY,
* LLC, THE U.S. DEPARTMENT OF ENERGY OR CONTRIBUTORS BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
* OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
* DAMAGE.
*
*****************************************************************************/
#ifndef PROGRAMMABLEOPATTRIBUTES_H
#define PROGRAMMABLEOPATTRIBUTES_H
#include <string>
#include <AttributeSubject.h>
#include "JSONNode.h"
#include <algorithm>
#include <cctype>
#include <locale>
#include <functional>
// ****************************************************************************
// Class: ProgrammableOpAttributes
//
// Purpose:
// ProgrammableOpAttributes
//
// Notes: Autogenerated by xml2atts.
//
// Programmer: xml2atts
// Creation: omitted
//
// Modifications:
//
// ****************************************************************************
class ProgrammableOpAttributes : public AttributeSubject
{
public:
int index;
JSONNode script;
// These constructors are for objects of this class
ProgrammableOpAttributes();
ProgrammableOpAttributes(const ProgrammableOpAttributes &obj);
protected:
// These constructors are for objects derived from this class
ProgrammableOpAttributes(private_tmfs_t tmfs);
ProgrammableOpAttributes(const ProgrammableOpAttributes &obj, private_tmfs_t tmfs);
public:
virtual ~ProgrammableOpAttributes();
virtual ProgrammableOpAttributes& operator = (const ProgrammableOpAttributes &obj);
virtual bool operator == (const ProgrammableOpAttributes &obj) const;
virtual bool operator != (const ProgrammableOpAttributes &obj) const;
private:
void Init();
void Copy(const ProgrammableOpAttributes &obj);
public:
virtual const std::string TypeName() const;
virtual bool CopyAttributes(const AttributeGroup *);
virtual AttributeSubject *CreateCompatible(const std::string &) const;
virtual AttributeSubject *NewInstance(bool) const;
// Property selection methods
virtual void SelectAll();
void SelectScriptMap();
// Property setting methods
void SetScriptMap(const std::string &scriptMap_);
// Property getting methods
const std::string &GetScriptMap() const;
std::string &GetScriptMap();
// Persistence methods
virtual bool CreateNode(DataNode *node, bool completeSave, bool forceAdd);
virtual void SetFromNode(DataNode *node);
// Keyframing methods
virtual std::string GetFieldName(int index) const;
virtual AttributeGroup::FieldType GetFieldType(int index) const;
virtual std::string GetFieldTypeName(int index) const;
virtual bool FieldsEqual(int index, const AttributeGroup *rhs) const;
// User-defined methods
void AddConstant(const std::string& name, const std::string& constant);
void AddFunction(const std::string& name, const stringVector& atts);
void AddRScript(const std::string& name, const stringVector& atts, const std::string& code);
void AddPythonScript(const std::string& name, const stringVector& atts, const std::string& code);
void AddNode(const std::string& name, const std::string& type);
void AddConnection(const std::string& from, const std::string& to, const std::string& portName);
void AddFinalOutputConnection(const std::string &from);
bool SetupPipeline(const JSONNode& atts, stringVector& args, const std::string &parent);
void LoadRKernel(const std::string& name, const JSONNode& atts, const std::string& code);
void LoadPythonKernel(const std::string& name, const JSONNode& atts, const std::string& code);
std::string trim(const std::string &s);
std::string rtrim(const std::string &ts);
std::string ltrim(const std::string &ts);
std::string getNextName();
void replace(std::string& str, const std::string& oldStr, const std::string& newStr);
// IDs that can be used to identify fields in case statements
enum {
ID_scriptMap = 0,
ID__LAST
};
private:
std::string scriptMap;
// Static class format string for type map.
static const char *TypeMapFormatString;
static const private_tmfs_t TmfsStruct;
};
#define PROGRAMMABLEOPATTRIBUTES_TMFS "s"
#endif
|
/**
* Used to filter out passwords from command logging.
*/
@Component(immediate = true)
@Service({ CommandLoggingFilter.class })
public class ContainerCreateCloudCommandLoggingFilter extends RegexCommandLoggingFilter {
public ContainerCreateCloudCommandLoggingFilter() {
addCommandOption("--password", "container-create-cloud");
addCommandOption("--new-user-password", "container-create-cloud");
addCommandOption("--zookeeper-password", "container-create-cloud");
setGroup(2);
}
} |
Evolving Optical Properties of Annealing Silicate Grains: From Amorphous Condensate to Crystalline Mineral Laboratory studies of the evolution of a magnesium silicate smoke from an amorphous condensate to a crystalline mineral by annealing in vacuum provide a foundation for the development of a silicate evolution index (SEI). The SEI can be used to predict the emergent IR spectrum of silicate dust in a circumstellar shell based on the time-temperature history of the silicate grains in the outflow. Optical constants for the magnesium silicate smoke samples compatible with those of Draine & Lee are derived over the range 0.001-1000 m. |
/**
* Metoda koja se poziva na pocetku izvodenja programa. Trazi od korisnika da
* joj preda cijelobrojne vrijednosti za dodavanje u binarno stablo. Samo
* dodavanje obavlja pomocna metoda addNode. Rad programa se prekida kada
* korisnik unese "kraj", te se tada ispisuju rastuce i padajuce poredani
* elementi novonastalog binarnog stabla.
*
* @param args
* Argumenti naredbenog retka.
* @throws IOException
*/
public static void main(String[] args) throws IOException {
TreeNode glava = null;
while (true) {
System.out.print("Unesite broj > ");
BufferedReader reader = new BufferedReader(new InputStreamReader(System.in));
String line = reader.readLine();
if (line.equals("kraj")) {
System.out.printf("Ispis od najmanjeg: ");
ascendingConsoleLog(glava);
System.out.printf("\nIspis od najvećeg: ");
descendingConsoleLog(glava);
break;
}
try {
int number = Integer.parseInt(line);
glava = addNode(glava, number);
} catch (NumberFormatException ex) {
System.out.printf("'%s' nije cijeli broj.\n", line);
}
}
} |
export interface Contacts{
name:string;
contactno:number;
} |
While Thailand has declared war on poverty, its efforts have been focused on systemic factors that keep a significant portion of the population, especially those who participate in the informal economic sector, trapped in poverty. Breaking out of this trap which is built on unemployment, low wages, poor community infrastructure, income disparity and lack of access to financial assets and services, requires a change of approach.
Thailand Says – Finance for the People
Government policymakers in Thailand have come to realize that the single most important key to solving the population’s financial issues requires improved financial services for all and increased opportunities to own financial assets and take part in the formal financial sector. Hence, the strategy has been named – Finance for the People.
Let’s break it down: when looking at higher wage earners in Thailand, researchers found that 30% of them held a savings account as opposed to lower-income households where only 8% had any financial asset at all, totaling less than $1,500. While less income plays a role in a diminished ability to save, it is not the whole story.
According to a 2011 study, in the northeast area of Thailand, where the informal market accounts for roughly 65% of the economy, there isn’t much real-wage growth nor is there an improvement in poverty rates. This comes to show that participation in the formal national financial system is of essence, and the lack of microfinance products available to low income residents only serves to exacerbate the situation.
Inclusive Finance
Following the lead of other nations, especially neighbors in the Southeast Asia region, Thailand has begun focusing on inclusive finance as a cornerstone of their strategy to reduce poverty rates. Inclusive finance means overall access to the full package of financial assets as well as a knowledge-base for how to use financial services. In other words, improving both financial asset infrastructure and financial literacy in order to improve financial participation. It also means providing access to a wide range of financial products and services to an entire population, including those who are most impoverished, as well as the struggling middle class. The full package of financial services would consist of traditional and electronic banking, mobile financial services, micro savings, long-term housing loans, insurance and money transfer services and more.
Overcoming Institutional Obstacles
An examination of the existing infrastructure revealed the following obstacles which had to be addressed if Thailand was to have a successful financial inclusion strategy. (1) Regulations make it difficult for non-bank financial institutions to participate in the microfinance sector; (2) sustainable growth of the microfinance sector is restricted by limitations in licensing procedures; (3) access to multiple lenders for a single non-income producing project has put many borrowers in a debt cycle; (4) absence of a credit history reporting system means that microfinance providers may make financial assets available to a borrower who does not have the means to repay the obligation, thus hurting both the lending system and the borrower; (5) the institutional capacity of the microfinance system is impaired by lack of management stability, restricting the range of product that is offered and the quality of customer service; and (6) non-traditional microfinance providers, such as NGOs do not have the same access to capital as do banks.
To address these deficiencies the Finance Ministry’s strategy includes a number of action items including: (1) launching a financial literacy program for impoverished household across the entire country; (2) establishing a credit reporting bureau for the microfinance industry; (3) removing the barriers inherent in the microfinance sector that limit private sector participation and phase out the 15% interest rate ceiling; (4) creating a supporting environment for mobile banking; and (5) promoting the growth of a healthy micro-insurance industry. |
def update_all(self):
self._update_close_button()
self._update_title()
self._update_title_bar_background()
self.update_background()
self._update_drag_zone()
self._update_focus_box() |
Central kappa opioid receptor-evoked changes in renal function in conscious rats: participation of renal nerves. The present investigations examined the cardiovascular and renal responses produced by central nervous system stimulation of kappa opioid receptors by the selective kappa opioid receptor agonist, U-50488H, in conscious Sprague-Dawley rats. Administration of U-50488H (1 microgram total) into the lateral cerebroventricle produced a profound diuretic and antinatriuretic response. In addition, concurrent with the decrease in urinary sodium excretion, i.c.v. U-50488H elicited an increase in renal sympathetic nerve activity. The increases in urine flow rate and renal sympathetic nerve activity and the decrease in urinary sodium excretion produced by U-50488H were completely prevented in rats that had undergone pretreatment with the selective kappa opioid receptor antagonist, nor-binaltorphimine. In contrast, in animals that had undergone irreversible mu opioid receptor blockade with the selective mu opioid receptor antagonist, beta-funaltrexamine, central U-50488H administration elicited similar diuretic and antinatriuretic responses as observed in intact naive animals. In further studies, the antinatriuretic response produced by i.c.v. U-50488H was completely abolished in rats that had undergone chronic bilateral renal denervation, a technique used to remove the influence of the renal sympathetic nerves. Glomerular filtration rates and effective renal plasma flows were not altered by i.c.v. administration of U-50488H in intact or renal denervated animals. Together, these studies provide evidence for the role of central kappa opioid receptor mechanisms in the regulation of urinary sodium and water excretion. Moreover, these studies indicate that the changes in renal sodium handling produced by central kappa opioid agonists result from an action of these compounds to modulate sympathetic neural outflow to the kidneys. |
/**
* Builds an MBean interface for the Cache State Manager {@link modelInterface} object given a {@link Cache} Specification as input;
*
* Model construction properties:
* <ul>
* <li>generateJavadoc</li>
* </ul>
*
* @author tfisher
*/
public class CacheUnitManagerMBeanBuilder extends AbstractDataUnitManagerMBeanBuilder {
public CacheUnitManagerMBeanBuilder(GenerationContext context) {
super(context);
}
public List<ModelInterface> buildInterfaces(Process process) throws Exception {
return buildInterfaces(process.getNamespace(), process.getCacheUnits());
}
public List<ModelInterface> buildInterfaces(String namespace, List<Cache> cacheUnits) throws Exception {
List<ModelInterface> modelInterfacees = new ArrayList<ModelInterface>();
Iterator<Cache> iterator = cacheUnits.iterator();
while (iterator.hasNext()) {
Cache cacheUnit = iterator.next();
context.setCache(cacheUnit);
modelInterfacees.add(buildInterface(namespace, cacheUnit));
}
return modelInterfacees;
}
public ModelInterface buildInterface(String namespace, Cache cache) throws Exception {
String packageName = DataLayerHelper.getCacheUnitPackageName(namespace, cache);
String managerClassName = DataLayerHelper.getCacheUnitInterfaceName(cache) + "ManagerMBean";
String beanName = DataLayerHelper.getCacheUnitNameUncapped(cache) + "ManagerMBean";
String beanType = org.aries.util.TypeUtil.getTypeFromNamespaceAndLocalPart(namespace, beanName);
setBeanName(beanName);
setPackageName(packageName);
setClassName(managerClassName);
ModelInterface modelInterface = new ModelInterface();
modelInterface.setType(beanType);
modelInterface.setPackageName(packageName);
modelInterface.setClassName(managerClassName);
modelInterface.setName(beanName);
modelInterface.addImportedClass(packageName+"."+managerClassName);
initializeInterface(modelInterface, cache);
return modelInterface;
}
public void initializeInterface(ModelInterface modelInterface, Cache cache) throws Exception {
this.modelUnit = modelInterface;
initializeImportedClasses(modelInterface);
initializeInterfaceAnnotations(modelInterface);
initializeInstanceFields(modelInterface, "cache");
initializeInstanceMethods(modelInterface, cache);
}
protected void initializeInstanceMethods(ModelInterface modelInterface, Cache cache) throws Exception {
//createMethod_CreateState(modelInterface, cache);
//createMethod_ResetState(modelInterface, cache);
//createMethod_UpdateState(modelInterface, cache);
//createMethod_SaveState(modelInterface, cache);
//createMethod_CommitState(modelInterface, cache);
createMethods_DataAccess(modelInterface, cache, SourceType.SharedCache);
//createMethods_DataAccess(modelInterface, cache, SourceType.CurrentState);
//createMethods_DataAccess(modelInterface, cache, SourceType.PendingState);
//createMethods_DataAccess(modelInterface, cache, SourceType.PreparedState);
}
protected void createMethod_UpdateState(ModelInterface modelInterface, Cache cache) throws Exception {
ModelOperation modelOperation = new ModelOperation();
modelOperation.setName("updateState");
modelOperation.setModifiers(Modifier.PUBLIC);
modelOperation.setResultType("void");
modelInterface.addInstanceOperation(modelOperation);
}
protected void createMethod_SaveState(ModelInterface modelInterface, Cache cache) throws Exception {
String className = CacheUtil.getClassName(cache) + "State";
String beanName = "state";
ModelOperation modelOperation = new ModelOperation();
modelOperation.setName("saveState");
modelOperation.setModifiers(Modifier.PUBLIC);
modelOperation.setResultType("boolean");
modelOperation.addParameter(CodeUtil.createParameter(className, beanName));
modelInterface.addInstanceOperation(modelOperation);
}
protected void createMethod_CommitState(ModelInterface modelInterface, Cache cache) throws Exception {
ModelOperation modelOperation = new ModelOperation();
modelOperation.setName("commitState");
modelOperation.setModifiers(Modifier.PUBLIC);
modelOperation.setResultType("void");
modelInterface.addInstanceOperation(modelOperation);
}
protected void createMethods_DataAccess(ModelUnit modelUnit, Cache cache, SourceType sourceType) throws Exception {
if (cache != null) {
List<Field> fields = ElementUtil.getFields(cache);
if (fields == null || fields.size() == 0) {
log.warn("No items found in cache: "+cache.getName());
} else {
createMethods_DataAccess(modelUnit, fields, sourceType);
}
}
}
} |
#include <avr/io.h>
#include <avr/interrupt.h>
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include <util/delay.h>
#include "que.h"
#include "uart.h"
// ----------------------------------------------------------------------------
static QUE que[2];
static uint8_t rx_byte0;
//-----------------------------------------------------------------------------
void uart_init(int ch, uint16_t baud)
{
unsigned char RXData;
if(ch==0) {
UBRR0H = (unsigned char) (((F_CPU/(16L*baud))-1) >> 8);
UBRR0L = (unsigned char) ((F_CPU/(16L*baud))-1);
UCSR0C = 0x06;
UCSR0B = (1<<RXEN0)|(1<<TXEN0)|(1<<RXCIE0); // Rx/Tx enable, 8 data, RX interrupt
UCSR0A = 0;
RXData = UDR0;
QInit(&que[ch]);
}
int uart_getc(int ch)
{
return QGet(&que[ch]);
}
int uart_putc(int ch, char c)
{
if(ch==0){
while(!(UCSR0A & (1<<UDRE0)));
UDR0 = c;
return c;
}
void uart_puts(int ch, char *buf, uint8_t len)
{
for(uint8_t i=0; i<len; i++) uart_putc(ch, buf[i]);
}
int uart_available(int ch)
{
return QCount(&que[ch]);
}
void uart_flush(int ch)
{
QFlush(&que[ch]);
}
void uart_pkt_send(uint8_t ch, uint8_t cmd, uint16_t kv, uint8_t pmode, uint8_t run)
{
char buf[UART_PKT_LEN+1], tmp[8];
uint8_t cs;
buf[0] = STX;
sprintf(&buf[1], "%1X%02X%1X%1X0000", UART_CMD_SET, kv, pmode, run);
cs = 0;
for(uint8_t i=0; i<UART_PKT_LEN-2; i++) cs += buf[i];
cs &= 0x0F; // use low byte only
itoa(cs ,tmp, 16);
buf[10] = tmp[0];
buf[11] = ETX;
uart_puts(ch, buf, UART_PKT_LEN);
#ifdef UART_DEBUG
printf("UART_TX[len]=");
for(uint8_t i=0, ; i<UART_PKT_LEN; i++) printf("[%02x] ", buf[i]);
printf("\n\r");
#endif
}
// 0: OK 1: not received fully 2:CS err 3:ETX err
int uart_pkt_recv(uint8_t ch, char *buf)
{
int length;
char tmp[8];
uint8_t i, cs;
length = uart_available(ch);
if(length < UART_PKT_LEN) return 0;
while (length >= UART_PKT_LEN) {
buf[0] = uart_getc(ch); //read first byte
if(buf[0] != STX) {
length--;
continue;
}
cs = buf[0];
for(i=0; i<UART_PKT_LEN-1; i++) {
buf[i+1] = uart_getc(ch);
if(i < UART_PKT_LEN-2) cs += buf[i+1];
}
// do err check
itoa((cs & 0x0F), tmp, 16); // use low byte only
if(buf[UART_PKT_LEN-2] != tmp[0]) return 0;
if(buf[UART_PKT_LEN-1] != ETX) return 0;
return 1; // OK
}
return 0;
}
//-----------------------------------------------------------------------------
/*
ISR (USART_RX_vect )
{
rx_byte0 = UDR0; // Read a character from UART
QPut(&que[0], rx_byte0);
}
*/
|
/**
* Test taskana configuration without roles.
*
* @author bbr
*/
public class TaskanaConfigAccTest extends TaskanaEngineImpl {
public TaskanaConfigAccTest() throws SQLException {
super(new TaskanaEngineConfiguration(TaskanaEngineConfigurationTest.getDataSource(), true));
}
@Test
public void testDomains() {
assertEquals(2, getConfiguration().getDomains().size());
assertTrue(getConfiguration().getDomains().contains("DOMAIN_A"));
assertTrue(getConfiguration().getDomains().contains("DOMAIN_B"));
assertFalse(getConfiguration().getDomains().contains("Domain_A"));
}
@Test
public void testClassificationTypes() {
assertEquals(2, getConfiguration().getClassificationTypes().size());
assertTrue(getConfiguration().getClassificationTypes().contains("TASK"));
assertTrue(getConfiguration().getClassificationTypes().contains("DOCUMENT"));
assertFalse(getConfiguration().getClassificationTypes().contains("document"));
}
@Test
public void testClassificationCategories() {
assertEquals(4, getConfiguration().getClassificationCategories().size());
assertTrue(getConfiguration().getClassificationCategories().contains("EXTERNAL"));
assertTrue(getConfiguration().getClassificationCategories().contains("MANUAL"));
assertTrue(getConfiguration().getClassificationCategories().contains("AUTOMATIC"));
assertTrue(getConfiguration().getClassificationCategories().contains("PROCESS"));
assertFalse(getConfiguration().getClassificationCategories().contains("manual"));
}
} |
Universal convolutional neural network for histology-independent analysis of collagen fiber organization in scar tissue Histological examination of collagen fiber organization is essential for pathologists to observe the wound healing process. A convolutional neural network (CNN) can be utilized to visually analyze collagen fibers during tissue remodeling in histology images. In this study, a universal CNN (UCNN) independent of the histological staining process is proposed to classify the histology images of burn-induced scar tissues and characterize collagen fiber organization. Normal and scar tissues obtained from an in vivo rodent model are stained using Massons Trichrome (MT) and Hematoxylin & Eosin (H&E). The proposed universal model is trained using both MT- and H&E-stained histological image datasets over multiple scales with color augmentation, and classification accuracies of up to 98% and 97% are achieved for the MT- and H&E-stained image datasets, respectively. Regardless of the histological staining process used, the collagen characteristics are visualized by determining the density and directional variance of the normal and scar tissues by using the features extracted with the proposed universal model. Statistical analysis results demonstrated clear differences between scar and normal tissues in terms of collagen fiber organization. The proposed UCNN model can contribute to the development of an intelligent and efficient method that pathologists can use to rapidly evaluate wound healing and tissue remodeling. I. INTRODUCTION Dermal wound healing is a dynamic process that can be triggered by thermal tissue injury. It involves complex interactions between dermal cells and the extracellular matrix (ECM) -. The wound healing process essentially comprises three overlapping phases: inflammatory, proliferative, and remodeling,. The characteristics of collagen constitute an index that can be used to quantify the wound healing process. Collagen is initially synthesized in the proliferation phase by fibroblasts and myofibroblasts. Collagen accumulation provides strength to healing tissues, and the shape, quantity, and organization of collagen fibers change gradually during tissue remodeling. Scar tissue is the connective tissue that forms over a wound during the wound healing process. The most prominent differences between scar tissue and normal tissue are the appearance of covalent crosslinking and the amount of collagen fiber in the tissue. Dense distribution and aligned orientation of collagen fibers are considered as the main characteristics of scar tissue. It is critical to evaluate the changes in the morphology and organization of collagen fibers to assess the wound healing process and establish methodologies for various medical approaches and therapeutic interventions. Histology is the study of tissues through staining and microscopic examination,. The method can reveal remarkable bioinformatics, such as microstructural features. For this reason, several histological studies have been conducted to derive biomarkers for prognosis and diagnosis,. Histological staining methods enhance the hue contrast of various tissue constituents under a microscope without distorting the structure of a tissue specimen. Histological images have been utilized to semantically segment biological components (e.g., stroma, nuclei, and cytoplasm) that are the most relevant for cancer diagnosis,. Several histological stains are available, for instance, Hematoxylin & Eosin (H&E) and Masson's Trichrome (MT) -. The H&E staining method is the most widely used tissue staining method owing to its simplicity and low cost. In H&E-stained histological images, nuclei are stained dark blue owing to their hematoxyphilia, and the cytoplasm and ECM are stained varying shades of pink owing to their eosinophilia,. Because collagen is one of the substances deposited in the extracellular compartment, collagen fibers and matrices can be stained different shades of pink in H&E-stained histology images. In MT-stained histology images, collagen is stained blue or green, and other skin appendages including hair follicles, sebaceous glands, and granules are stained shades of red and purple. In histology images, color is an important discriminator of specific stained structures in tissues. Although the color of collagen fibers is not clearly differentiated with H&E staining, it is easily discriminated from other tissue structures with MT staining. Quinn et al. introduced an image processing method to quantify the density and directional variance of collagen fibers during the wound healing process of cutaneous burns by using MTstained histology images. To quantify and characterize the organization of collagen in tissue, various microscopy techniques, such as conventional light microscopy, confocal microscopy, second harmonic generation microscopy, and multiphoton fluorescence microscopy, have been used. Macros-Garces et al. measured collagen bundle orientations in samples processed using different staining and microscopy techniques, including H&E staining with confocal microscopy. Fereidouni et al. investigated brightfield (BF) and fluorescence images of H&E-stained tissue samples to highlight the collagen distribution in them by conducting spectral phasor analysis. Deep learning (DL) techniques have contributed greatly to the current biomedicine revolution. DL can be used to extract complex patterns from annotated clinical datasets for solving numerous diagnostic tasks, such as disease diagnosis, treatment selection, and patient monitoring. In medical image analysis, convolutional neural networks (CNNs) have been widely used and have yielded promising results in terms of computer-aided diagnostics, segmentation, and object detection. Computational pathology has been utilized to analyze tissues corresponding to distinct biological features such as tumors or stroma. Keikhosravi et al. utilized an autoencoder to synthesize collagen-specific images from BF images of H&E-stained tissue samples. To avoid variations in the process of histology imaging, such as the imaging system and staining manipulation, color augmentation and normalization were introduced -. Tellez et al. developed a CNN model based on color augmentation by varying the brightness, contrast, and huesaturation-value (HSV) transformation to classify H&Estained slides acquired from multiple sites. In a previous study, we proposed a CNN model trained using MT-stained histology images to classify normal tissue and scar tissue and to characterize collagen fiber organization (i.e., density and directional variance). Although various staining methods can be used for histology imaging, the biological structures in tissue are consistent. Thus, it is expected that the corresponding histology images can be differentiated, regardless of the colors of the structures in a tissue sample. In this study, we propose a universal CNN (UCNN) model that can be applied to both MT-stained and H&E-stained histology images. This model can distinguish between normal tissue and scar tissue, and it can visually characterize the microstructure of collagen fibers (density and directional variance). Because the main difference between the two staining methods is the color feature, we perform color augmentation and hue saturation specificity analysis. A burn injury is initially induced by applying laser irradiation to in vivo rodent models. Both MT-and H&E-stained histology images of the resulting scar tissue on the skin are captured after four weeks of wound healing. The classification and characterization performances of the proposed UCNN model are evaluated using the MT-and H&E-stained histology images, including individual normal and scar tissue images and the whole histology images. A. ANIMAL SCAR MODEL AND STAINED HISTOLOGY IMAGES In the experiments, we used eight male Sprague Dawley rats (age = 7 weeks, weight = 200-250 g) to generate an in vivo scar model. The Institutional Animal Care and Use Committee at Pukyong National University approved all the animal tests conducted in this study (Number PKNUIACUC-2019-31). Each animal was anesthetized with 3% isoflurane (Terrell isoflurane, Piramal Critical Care, Bethlehem, PA, USA) by using a respiratory anesthesia system (Classic T3, SurgiVet, USA) in a chamber. The scar model was created by irradiating the back of each rat with a high-power laser light to induce a thermal burn on the skin through photothermal interactions. Before laser irradiation, the hair on the back of each rat was removed using an electric hair clipper and waxing cream (Nair Sensitive Hair Removal Cream, Nair, Australia) to maximize light absorption by the skin tissue. As a light source, a 1470nm laser system (FC-W-1470, CNI Optoelectronics Tech. Co., China) was used to generate burn-induced scars in the in vivo models. Because of strong light absorption by water (absorption coefficient = 28.4 cm -1 ), the selected wavelength led to a short optical penetration depth in the skin, which limited the thermal burn to within the dermal layer. A 600-m end-firing optical fiber was placed 25 mm vertically above the skin surface (beam size = 0.3 cm 2 ) to deliver the laser light. Perpendicular irradiation created a circular-shaped thermal burn on the skin with a diameter of 10 mm. To reliably establish the burn wound without carbonization, we applied a laser power of 5 W for 30 s on the skin surface (i.e., corresponding irradiance = 16.7 W/cm 2 ). Four weeks after irradiation, because the wound healing process was complete, a mature hypertrophic scar was fully developed in each animal. Burn-induced scar tissue samples were harvested from all of the animals after complete tissue re-epithelialization by the ECM. Initially, all samples were fixed in 10% formalin for 48 hours. Then, paraffin blocks were prepared and sliced to a thickness of 5 m to prepare histology slides (N = 10 slides per block). All histology slides were stained with two different histochemical dyes: MT and H&E (American MasterTech, California, USA). A Motic Digital Slide Assistant System was used to acquire high-resolution microscopy images of the histology slides (MoDSA, Richmond, British Columbia, Canada; 40X and 0.26 m/pixel resolution). The acquired MT-and H&E-stained histology images (14269 6637 and 15059 4735 pixels, respectively) of the wounded skin are shown in Figs. 1(a) and (b), respectively. The histology images display the regions of interest (ROIs), namely the normal region (ROI; orange dashed box on the left and right sides) and the scar region (ROI; orange dashed box in the middle of the image). The normal region consists of coarse collagen fibers, whereas the scar region consists of fine collagen fibers. In the MT-stained histology image ( Fig. 1(a)), the collagen fibers are stained blue, which distinguishes them from the other structures (e.g., sweat glands, sebaceous glands, and hair follicles) stained red or purple. Meanwhile, in Fig. 1(b), the collagen fibers are mostly stained pink or purple, which makes it challenging to differentiate them from appendages owing to their similarity on the color spectrum. Fig. 2 presents a block diagram of the proposed UCNN model for classifying normal and scar tissues and characterizing collagen fibers. The architecture of the proposed UCNN model is identical to that of the model proposed in our previous study. In this study, we trained the model by using MT-and H&E-stained histology images. Because the proposed model is effective when applied to histology images acquired using both staining methods, we call it the universal CNN (UCNN) model. The histology images ( Fig. 2(a)) were resized to the input size of the model (224 224 pixels) by means of bilinear interpolation and normalized to with respect to each channel for preprocessing ( Fig. 2(b)). The model comprises three blocks of stacked convolution layers. The first two blocks consist of two convolution layers and a max-pooling layer each. The last block consists of a convolution layer, global average pooling (GAP) layer, dropout layer, and sigmoid classifier. The GAP layer acts as a structural regularizer, and the dropout layer is used to prevent overfitting. The architecture of the proposed model is illustrated in Fig. 2(c). A sigmoid classifier is employed in the last layer to classify normal tissue and scar tissue by ensuring that the output score lies in the interval ( Fig. 2 (d)). 2) DATA GENERATION As summarized in Table 1, the MT-and H&E-stained histology image datasets of the two labeled groups (normal and scar) with image sizes of 250 250 pixels (560 images in each group) and 500 500 pixels (120 images in each group) were prepared to train the proposed model. For augmentation, the images were rotated to 18 different angles (from 5° to 180° in increments of 10°) to change the angle of the collagen bundles. Moreover, they were flipped along the horizontal and/or vertical directions. The basic augmented data of 10,400 images (5,200 images each of normal and scar tissues) were generated. Then, to mimic the color and illumination variations due to the staining process and image acquisition, random color variation, including changes to brightness and contrast in the ranges of and , were applied to the MT-and H&E-stained images, respectively. Then, the images were further augmented in the HSV color space by shifting hue and value in the range of and saturation in the range of for MT images and by shifting the value channel in the range of for H&E images. Then, the augmented images were reconverted to the RGB color space for training and validation purposes. Fig. 3 shows the augmentation process used to generate the training data for the proposed UCNN model. The training and validation data were utilized in the ratio of 7:3. To avoid overfitting and optimize the training hyperparameters (e.g., learning rate and number of epochs), loss of validation data during the training process was monitored. To test the proposed model, three sets of images of different sizes (500 500, 1000 500, and 1017 1920 pixels) were prepared, and these sets contained 1380, 690, and 230 images, respectively. In addition to the individual normal and scar histology images, 42 whole histology images containing both normal and scar regions were utilized (21 for each staining method). 3) MODEL TRAINING AND TEST The parameters of first two blocks of the proposed UCNN model were initialized using the pre-trained weights of VGG-16 (VGG: Visual Geometry Group, 16: number of learnable parameter layers) by using the ImageNET dataset. The parameters of the last convolution layers and the sigmoid layer of the model were initialized by means of Xavier random initialization. The parameters of the proposed UCNN model were trained using both MT-and H&E-stained histology images (Table 1). Table 2 lists the hyperparameters used to train the UCNN model. An Adam optimizer with a learning rate of 0.0001 was used. A binary cross-entropy loss function was applied for classification. The regularizer L2 imposed penalties on the last convolution layer and the sigmoid layer during optimization. The number of epochs was 75, and the process was stopped early when no improvement in validation loss was observed over the last 10 epochs. The batch size of the model was 10. The model was trained and tested on a computer equipped with an Intel® Core™ i7-8700 CPU @ 3.2 GHz, NVIDIA GeForce GTX 1050 Ti graphical processing unit (GPU), Python 3.7.9, and the Keras module within TensorFlow 1.14.0. 4) PERFORMANCE EVALUATION The proposed UCNN model was compared to EfficientNetB0, EfficientNetB2, and support vector machine (SVM). EfficientNetB0 and EfficientNetB2 were initialized with the weights pre-trained using the ImageNET dataset, and the last sigmoid layer was trained using both MT-and H&E-stained histology images (Table 1). In the case of SVM, features were extracted and quantized using the scale-invariant feature transform (SIFT) algorithm and K-means clustering algorithm, respectively. Then, a bag of features was used to reconstruct the main features for SVM to distinguish between normal tissue and burn tissue in histology images. The SVM was configured as follows: regularization parameter 40 and Gaussian kernel with scale gamma 0.002. To evaluate the classification performance of the proposed model, its accuracy, precision, recall, receiver operating characteristics (ROC), area under the curve (AUC), and confusion matrix were obtained using the test data presented in Table 1. For EfficientNetB0 and SVM, accuracy, precision, and recall were measured to compare their classification performances with that of the proposed method.. One thousand monochrome images were generated by varying the hue between 0 and 1 in increments of 0.02 and the saturation between 0 and 1 in increments of 0.05, as well as fixing the value to 1 by following the monochrome representation in Fig. 4(e). Then, the generated monochrome images were used as input images for the trained UCNN model to realize monochrome activation in the hue-saturation specific feature extraction process. 6) CHARACTERIZATION OF COLLAGEN FIBERS a) Collagen density Figs. 2 (e), (f), (h), and (j) illustrate the process of extracting collagen characteristics by using the features obtained from the pooling layer in block 1 (first yellow box in Fig. 2(c)) in the proposed model. Feature numbers were assigned according to the filter training order, which represents the order in the third dimension of the output of each layer (Fig. 2 (c)). For the MT-and H&E-stained images, features 8, 28, and 53 and features 19, 46, and 53, respectively, were utilized to extract collagen-dense regions in the images based on the results of hue-saturation specific analysis. Because appendage structures exhibited higher levels of activation in the H&E-stained histology images, an additional mask was generated following Otsu's method by using features 3 and 52 for appendage removal. After averaging the features, the collagen-positive map ( ) was generated as follows: where is the average activation of the selected feature maps, and is calculated by means of multiple linear regression between the saturation of the original image and the averaged feature map, as follows: where and denote the mean and standard deviation extracted from the saturation (Fig. 2(e)), respectively; is the standard deviation of the averaged feature map; k1 and k2 are slope coefficients, and k3 is a bias, which are calculated by fitting a plane to the means of average activation ( ), statistical representations of saturation ( and ), and standard deviations of the average activation ( ). The values of (,, ) are (0.15, 0.95, 0.29) and (0.59, 0.95, 0.17) for the MT-and H&E-stained images, respectively. To calculate the local collagen density, the collagen-positive map was convolved with the disk kernel. The disk kernel size was adjusted according to the size of the input image. Finally, the density map (, ) was obtained by up-sampling to the original image size by means of bicubic interpolation. b) Directional variance Six of the most strongly activated features of the last convolution layer in the proposed UCNN model (block3_conv in Fig. 2(g)) were utilized owing to their directional filter patterns and high levels of contribution to the classification decision. Figs. 2(g), (i), and (j) represent the process of calculating the directional variance. The magnitude map (, ) was calculated as follows: c) Statistical analysis The differences in density and directional variance between normal tissue and scar tissue were examined using Wilcoxon signed rank statistics, where p < 0.05 was considered statistically significant,. The results of our previous work involving MT-stained histology images and those of the present work involving MT-and H&E-stained histology images were compared. To evaluate the differences in density and directional variance between the previous and current studies, we conducted a two-tailed hypothesis test. The null hypothesis (H0) was that there is no significant difference between the mean values of density and directional variance extracted in the previous study and those extracted using the approach proposed in the present study. To investigate whether there was an improvement in the characterization performance of the approach proposed in the current study, a one-tailed hypothesis test was conducted to compare the mean values of normal and scar tissues in terms of density and directional variance. In the one-tailed test, an alternative hypothesis (H1) was employed to demonstrate that density or directional variation in the normal tissue extracted using the previous approach was significantly greater than or less than that extracted using the proposed approach. Moreover, a onetailed test was conducted to indicate any significant decrease or increase in density and directional variance between normal and scar tissues when applying the proposed approach to MTand H&E-stained histology images. Fig. 5 shows the flowchart of the statistical analysis performed herein to evaluate the performance of the proposed approach in characterizing the density and variance of normal and scar tissues. (accuracies 97.8% and 99.5%, respectively), but not for MTstained images (accuracies 94.1% and 92.8%, respectively). The proposed UCNN model outperformed SVM by 9.9% and 11.3% in terms of classification accuracy when applied to MTand H&E-stained images, respectively. Overall, the proposed model achieved superior precision and recall for both MT-and H&E-stained images. Thus, the features extracted using the proposed model with strong classification performance can expectedly be used to characterize the collagen properties of both MT-and H&E-stained histology images. B. HUE-SATURATION SPECIFICITY ANALYSIS The upper rows of Figs. 7(a-c) show the monochrome activation images obtained as a result of the hue-saturation specificity analysis performed using the features of the block1pool layer, while varying the color hues (i.e., x-axis) and saturation (i.e., y-axis) between 0 and 1. The color bar shows the range of monochrome activation. The lower rows of Figs. 7(a-c) show the corresponding feature maps of the sample histology images in Fig. 4 (Figs. 4 (a), (c): normal and (b), (d): scar). Fig. 5(a) shows the feature maps 8, 28, and 53 selected for the MT-stained images based on the results of a hue specificity analysis. These features, the high activation spectrum of which was in the range of green to blue color (corresponding hue: 0.2-0.8), illustrate the collagen regions at higher activation levels (brighter pixels) and the background and other structures at lower activation levels (darker pixels). For the H&E-stained images, Fig. 7(b) shows the feature maps 19, 46, and 53 from the top three activations and the corresponding activated spectrum in the H&E-staining color range (blue (nucleic)-pink (collagen)-red (blood cells), corresponding hues: 0.6-1 and 0-0.2). Features 3 and 52 ( Fig. 7(c)) with activation in blue and dark purple (corresponding hue: 0.6-0.8) were selected to exclude the region with other structures, as illustrated in Fig. 7(c). -a8), as determined using the proposed method. The mean of directional variance of the normal tissue is significantly higher (42%) than that of the scar tissue in the cases of the MT-and H&E-stained images. C. CHARACTERIZATION OF COLLAGEN FIBERS The whole histology image of the MT-and H&E-stained tissues (Figs. 10 (a1, a2)) containing both normal and scar regions was used to visualize the collagen density and directional variance. The scar region (middle) contained denser and more well-oriented collagen than the normal regions (left and right sides in Figs. 10 (a1, a2)). The collagen density was higher in the scar tissue region (Figs. 10 (b1, b2)). The scar tissue in the middle of the images exhibited significantly low directional variance, meaning that the collagen fibers were more aligned with each other (Figs. 9 (c1, c2)).. Comparison between H&E-stained normal tissue (left column) and scar tissue (right column) in differently sized patches (500 500 pixels, 1000 500 pixels, and 1017 1920 pixels) of the H&E-stained histology image: (a1-a4) for normal tissue and (a5-a8) for scar tissue. (b1-b8) collagen density map, and (c1-c8) directional variance map. Fig. 11 presents the results of a statistical analysis of the collagen density and directional variances extracted from the histology images obtained using two different staining methods (left: MT-stained, right: H&E-stained). The bar graphs presented in Figs. 11(a) and (b) show the means of density and directional variance, respectively. The mean collagen density of the normal tissue (0.47 ± 0.086) is 38% lower than that of the scar tissue (0.76 ± 0.067) owing to an increase in the amount of denser collagen fibers during tissue re-epithelization. The normal tissue with a basket weave-like collagen fiber pattern has randomly distributed collagen bundles. Thus, the mean of directional variance of fibers in the normal tissue, as extracted using the proposed method, is 0.67 ± 0.139. By contrast, the collagen fibers in the scar tissue are aligned; therefore, the directional variance of collagen fibers in the scar tissue is noticeably lower (0.44 ± 0.083) than that in the normal tissue. The directional variance of the scar tissue is significantly lower (34%) than that of the normal tissue. All comparisons of density and directional variance of the histology images of normal and scar tissues have a significant p-value (p<0.001) in terms of the Wilcoxon signed rank statistics. D. STATISTICAL ANALYSIS In a statistical comparison between the results of the present and previous studies, the scar tissues stained using the two staining methods were not significantly different (p > 0.05) in terms of density and directional variance. In case of the normal tissue, the mean density determined using the previous approach was significantly higher than that determined using the proposed approach (p < 0.001). By contrast, the directional variance determined using the previous approach was significantly lower than that determined using the proposed approach (p < 0.001). These results demonstrated that the proposed UCNN model enhances the discrimination between scar tissue and normal tissue, with a greater difference in characterization (i.e., density and directional variance). IV. DISCUSSION In the present study, we demonstrated the ability of the proposed UCNN model in terms of both classification and characterization of MT-and H&E-stained histology images. The results of our previous study proved that collagen organization can be clearly differentiated and characterized using MT-stained histology images. However, distinguishing collagen organization using H&E-stained histology images remained challenging owing to the narrow derivation of the staining color. Although the color components of various staining methods are different, the tissue structures remain the same. Thus, we proposed a UCNN model and a method for visually characterizing histology images, regardless of the staining method (i.e., H&E and MT staining in this study). In addition, the classification results indicated that the proposed UCNN model could extract the primary features of tissues to differentiate between normal and scar tissues (accuracy > 97%), regardless of the staining method used. To prove the multi-scalability of the proposed model, test and training image data of different sizes were used, as summarized in Table 1. Because it was not possible to split the data into several groups under the same condition (i.e., image size), cross validation, such as k-fold or Monte Carlo, was inapplicable. Given that we had a sufficient amount of data, a large test dataset was utilized instead to validate the network. The performance of the proposed network confirmed that it was trained well with a small amount of training data. The EfficientNetB0 and EfficientNetB2 models yielded adequate classification performance. Also, advanced models with higher image resolutions, such as EfficientNetB7, can also be trained to achieve improved classification performance. However, the architectures of the advanced models are rather complicated from the viewpoint of extracting features for characterization purposes. It will be further investigated to utilize the advanced models for the characterization. The performance of SVM was worse than those of EfficientNetB0, EfficientNetB2, and the proposed model. SVM and EfficientNet can be further optimized by tuning its hyperparameters by using the grid search tool. However, this is beyond the scope of the present study, which focuses on classification and feature extraction for characterization from the DL model. The results of the present study indicate that the color-and texture-based features obtained using the proposed model can be utilized to analyze the organization of collagen bundles. The collagen densities of normal and scar tissues, as extracted using the proposed method, differ significantly (38%). In addition, directional variance, which is the strongest differentiator of collagen fiber organization post tissue remodeling after a burn injury, decreased notably (34%) for the scar tissue than it did for the normal tissue. The results of this study indicate that the proposed approach that utilizes the features extracted using the proposed UCNN model can possibly replace expensive tissue characterization methods. While we used only RGB color features in the previous study, herein, we conducted a hue-saturation specificity analysis. Hue-saturation specificity analysis helps one to extract color features. Thus, the main color components of the histology images examined in the 2D histogram analysis (Fig. 4) could be selected from the extracted color features (Fig. 7). In this light, we developed a universal model for two different staining methods that represent various tissue structures with different colors. The model utilized the first two blocks pretrained using ImageNet because they were observed to be highly sensitive to the color of the input images, which is a prominent feature for extracting a collagen mask. To extract the specific color features of the histology images, we used three steps. First, hue-saturation specificity analysis was performed to visualize the color features (hue and saturation) from the network (Fig. 7). Next, the main color components (hue and saturation) of the collagen area in the MT-and H&Estained histology images were determined from 2D histogram analysis (Fig. 4). Finally, the matching color features between the hue-saturation specificity analysis and the 2D histogram analysis were selected for MT-(features 7, 28, and 53) and H&E-(features 19, 46, and 53) stained images, respectively. Although the histogram of the example MT-stained images used in this study (Figs. 4 (a, b)) contained collagen areas stained with a strong shade of blue (i.e., hue > 0.5), the collagen areas in the MT-stained images ranged from green to blue in color (i.e., hue: 0.2-0.8) owing to variations in the staining process. Thus, hue features ranging from 0.2 to 0.8 were included in the feature map (Fig. 7). In addition, it was observed that the characterization process deteriorated when the image saturation was low (< 0.4) because the color representing collagen was extremely close to white color, and the hue colors were diminished (Fig. 4(e)). Thus, the sigmoid function (Eq. ) was applied to compensate for the decrease in saturation based on a linear regression between saturation and activation of the extracted features. According to the slope ( in Eq. ), the MT-stained histology images were less affected by saturation than the H&E-stained histology images. The results obtained using the proposed UCNN model demonstrated that variations in the histology staining process (e.g., color variation) did not compromise the classification and characterization performance of the proposed model. Further investigations with broader variations in the staining and scanning processes are necessary. Given that the training images were resized to the input size of 224 224 pixels to facilitate utilization of the pre-trained network weights, the performance of the proposed UCNN model can be degraded when it is applied to images with resolutions higher (i.e., 1000 500, 1017 1920, and 500 500) than that of the input images. However, the results of this study proved that the trained model performed adequately well in terms of classification and characterization when applied to multi-scale images. As demonstrated by the confusion matrices (Figs. 6 (d, e)), the proposed UCNN model mislabeled a few test instances. Fig. 12 showed examples of the misclassified cases, including MTstained normal and scar tissues and H&E-stained normal tissues. It is observed that these histology images contain smaller proportions of collagen than the other structures, such as glandular cells and follicles. Although misclassified, the characterization results (density and directional variance) obtained in these cases are acceptable. This confirms that the proposed model learns and differentiates the features properly, regardless of the staining method. Further investigation is necessary to understand whether the misclassified cases affect the characterization performance of the proposed approach. While the color-based features of the collagenous regions in the MT-and H&E-stained images could be selected using the results of the hue-saturation specificity analysis (Figs. 4 and 7), the texture-based features representing the directional variance could be extracted from the last convolution layer. Our observations in this study were consistent with our expectation that as the tissue undergoes remodeling, the collagen fibers are organized in a denser and more aligned manner in terms of their distribution and orientation. Moreover, the proposed approach can be applied to whole histology images for characterization, as illustrated in Fig. 10. For the proposed UCNN model, it was observed that the colorbased features constituted the key factor in both classification and characterization. The proposed model utilized RGB color images that were augmented in the HSV color space. In future studies, DL models using input images in color spaces other than the RGB color space (e.g., HSV, CIELAB) can be investigated,. For color augmentation, color normalization methods, including color deconvolution and various DL models, can be utilized,,. Furthermore, an extended universal model for the classification and characterization of histology images obtained using other staining methods (e.g., picrosirius red, Movat's pentachrome) will be investigated,. Although the current study was limited to differentiation and characterization between two categories (i.e., normal tissue and scar tissue), it can be extended to various types of scars such as normal, keloid, hypertrophic, and depressed scars that are formed after a burn injury. In the future, we intend to investigate collagen quantification for distinguishing other types of scars. For clinical and prognostic applications, it is important to exploit the characteristics of collagen fiber organization to intergrade information related to various pathological studies, including cancer, aging, wound healing, and diabetes -,. In this regard, the promising results obtained herein demonstrate the competence of the proposed model in assisting pathologists to achieve prompt and accurate diagnoses. V. CONCLUSION The proposed UCNN can classify and characterize collagen organization after tissue remodeling by using MT-and H&Estained histology images. Regardless of the staining method in use, we were able to utilize the proposed model to extract significant features for characterization by employing the results of hue-saturation specificity analysis. In the future, we will extend the proposed UCNN model to various staining methods and ensure that it can stage scars for quantitative assessments in clinical scar treatment. |
package edu.byu.edge.coreIdentity.client.impl;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import edu.byu.edge.coreIdentity.client.MemberOfClient;
import edu.byu.edge.coreIdentity.client.exceptions.RestHttpException;
import edu.byu.edge.coreIdentity.client.rest.HttpRestBuilder;
import edu.byu.wso2.core.provider.TokenHeaderProvider;
import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import java.io.IOException;
import java.net.URLEncoder;
/**
* Created by eric on 2/3/16.
*/
public class MemberOfClientImpl implements MemberOfClient {
private static final Logger LOG = LogManager.getLogger(MemberOfClientImpl.class);
private static final ObjectMapper MAPPER = new ObjectMapper();
private TokenHeaderProvider tokenHeaderProvider;
private final String baseUrl;
public MemberOfClientImpl(TokenHeaderProvider tokenHeaderProvider) {
this.tokenHeaderProvider = tokenHeaderProvider;
this.baseUrl = "https://api.byu.edu:443/domains/legacy/identity/access/ismember/v1/";
}
public MemberOfClientImpl(TokenHeaderProvider tokenHeaderProvider, String baseUrl) {
this.tokenHeaderProvider = tokenHeaderProvider;
this.baseUrl = baseUrl;
}
@Override
public boolean isPersonMemberOfGroup(String personId, String group) throws RestHttpException, IOException {
final String url = baseUrl + URLEncoder.encode(group, "UTF-8").replace("+", "%20") + "/" + personId;
final String result = new HttpRestBuilder(url)
.accept("application/json")
.contentType("application/json")
.authorization(tokenHeaderProvider.getTokenHeaderValue())
.get();
final JsonNode root = MAPPER.readTree(result);
final JsonNode response = root.findPath("response");
if (!response.isMissingNode()){
final JsonNode isMemberNode = response.path("isMember");
if (isMemberNode.isBoolean()){
final boolean isMember = isMemberNode.asBoolean();
LOG.trace("isPersonMemberOfGroup " + personId + " " + group + " = " + isMember);
return isMember;
}
}
LOG.trace("isPersonMemberOfGroup " + personId + " " + group + " = " + false);
return false;
}
}
|
import sys, gzip
PatientCancerType = sys.argv[1]
outFilePath = sys.argv[2]
#Read in Patient Cancer Types (Sample ID's and Values)
patientIDToCancerDict = {}
with open(PatientCancerType, 'r') as f:
for line in f:
lineList= line.strip('\n').split('\t')
patientIDToCancerDict[lineList[0]] = lineList[1]
# Create output file
with open(outFilePath, 'w') as outFile:
outText = "\t".join(["Sample","Variable","Value"]) + "\n"
outFile.write(outText)
for cancerType in patientIDToCancerDict:
mutation = patientIDToCancerDict[cancerType]
outFile.write(cancerType[:15] + "\tSomatic mutation\t" + mutation + "\n")
|
Civilian Health and Medical Program of the Uniformed Services (CHAMPUS); clarification of the CHAMPUS exclusion of unproven drugs, devices and medical treatments and procedures--DoD. Final rule. This final rule clarifies the CHAMPUS exclusion of unproven drugs, devices and medical treatments and procedures and describes the process that the Office of CHAMPUS follows in determining when such drugs, devices, treatments and procedures have moved from the status of unproven to the position of proven medical effectiveness. This clarification is necessary to ensure the CHAMPUS beneficiary and provider population understand the process the Office of CHAMPUS (OCHAMPUS) follows prior to endorsement by CHAMPUS of a new emerging medical technology, drug, or device for which the safety and efficacy have been proven. |
<reponame>karanmagdani1/ARO-RP
package e2e
// Copyright (c) Microsoft Corporation.
// Licensed under the Apache License 2.0.
import (
"context"
. "github.com/onsi/ginkgo"
. "github.com/onsi/gomega"
mgmtcompute "github.com/Azure/azure-sdk-for-go/services/compute/mgmt/2020-06-01/compute"
metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
"github.com/Azure/ARO-RP/pkg/util/stringutils"
)
var _ = Describe("Encryption at host should be enabled", func() {
Specify("each VM should have encryption at host enabled", func() {
ctx := context.Background()
By("getting the resource group where the VM instances live in")
oc, err := clients.OpenshiftClustersv20200430.Get(ctx, vnetResourceGroup, clusterName)
Expect(err).NotTo(HaveOccurred())
clusterResourceGroup := stringutils.LastTokenByte(*oc.OpenShiftClusterProperties.ClusterProfile.ResourceGroupID, '/')
By("listing all VMs")
vms, err := clients.VirtualMachines.List(ctx, clusterResourceGroup)
Expect(err).NotTo(HaveOccurred())
Expect(vms).NotTo(HaveLen(0))
By("checking the encryption property on each VM")
for _, vm := range vms {
Expect(vm.SecurityProfile).To(Not(BeNil()))
Expect(vm.SecurityProfile.EncryptionAtHost).To(Not(BeNil()))
Expect(*vm.SecurityProfile.EncryptionAtHost).To(Equal(true))
}
})
})
var _ = Describe("Disk encryption at rest should be enabled with customer managed key", func() {
Specify("each disk should have encryption at rest with customer managed key enabled", func() {
ctx := context.Background()
By("getting the resource group where the VM instances live in")
oc, err := clients.OpenshiftClustersv20200430.Get(ctx, vnetResourceGroup, clusterName)
Expect(err).NotTo(HaveOccurred())
clusterResourceGroup := stringutils.LastTokenByte(*oc.OpenShiftClusterProperties.ClusterProfile.ResourceGroupID, '/')
By("listing all VMs")
vms, err := clients.VirtualMachines.List(ctx, clusterResourceGroup)
Expect(err).NotTo(HaveOccurred())
Expect(vms).NotTo(HaveLen(0))
// We have to get the disks by VM, because when getting all disks by resource group,
// we do not get recently created disks, see https://github.com/Azure/azure-cli/issues/17123
By("checking the encryption property on each OS disk of each VM")
for _, vm := range vms {
osDisk, err := clients.Disks.Get(ctx, clusterResourceGroup, *vm.StorageProfile.OsDisk.Name)
Expect(err).NotTo(HaveOccurred())
Expect(osDisk.Encryption.Type).To(Equal(mgmtcompute.EncryptionAtRestWithCustomerKey))
}
By("checking if the encrypted storage class is default")
sc, err := clients.Kubernetes.StorageV1().StorageClasses().Get(ctx, "managed-premium-encrypted-cmk", metav1.GetOptions{})
Expect(err).NotTo(HaveOccurred())
Expect(sc).NotTo(BeNil())
Expect(sc.Annotations).NotTo(BeNil())
Expect(sc.Annotations["storageclass.kubernetes.io/is-default-class"]).To(Equal("true"))
Expect(sc.Parameters).NotTo(BeNil())
Expect(sc.Parameters["diskEncryptionSetID"]).NotTo(BeEmpty())
})
})
|
class Solution {
private :
int solve(TreeNode* root, int& res){
if(root==nullptr){
return 0;
}
int l=solve(root->left,res);
int r=solve(root->right,res);
int temp=max(max(l,r)+root->val,root->val);
int ans=max(temp, l+r+root->val);
res=max(res,ans);
return temp;
}
public:
int maxPathSum(TreeNode* root ){
int res=INT_MIN;
solve(root,res);
return res;
}
};
|
package com.wlcb.jpower.dbs.dao;
import com.wlcb.jpower.dbs.dao.mapper.LogMonitorResultMapper;
import com.wlcb.jpower.dbs.entity.TbLogMonitorResult;
import com.wlcb.jpower.module.dbs.dao.JpowerServiceImpl;
import org.springframework.stereotype.Repository;
/**
* @author mr.g
* @date 2021-04-07 16:10
*/
@Repository
public class LogMonitorResultDao extends JpowerServiceImpl<LogMonitorResultMapper, TbLogMonitorResult> {
}
|
import React from 'react'
export default (props: { children: React.ReactNode }) => <div style={{ fontStyle: 'italic' }}>
{props.children}
</div> |
Community research collaboration to develop a promotores-based hereditary breast cancer education program for Spanish-speaking Latinas. Breast cancer (BC) is the most common cancer in Latinas and the leading cause of cancer death. Latinas tend to be diagnosed at later stages, receive poorer quality care and have a higher risk of mortality than non-Latina White (NLW) women. Among women with a genetic predisposition to hereditary BC, genetic counseling can be beneficial. Latinas participate in genetic counseling at lower rates than NLW women. The goal of this study was to develop comprehensive, culturally appropriate materials for community health educators (promotores)-led hereditary BC education program for Spanish-speaking Latinas. We developed the curriculum through feedback from 7 focus groups, with a total of 68 participants (35 promotores and 33 community members). We used a mixed-methods approach that relied on quantitative analysis of survey questions and qualitative content analysis of the focus groups transcripts. Pre and post promotores' training survey responses suggested improvement in the promotores' cancer-related knowledge. Themes that emerged from the qualitative analyses were (i) barriers to health education and/or care; (ii) importance of educating the Latino community about BC and genetics and (iii) role of the promotores. Future research will further evaluate the impact of the program in promotores' knowledge and community members' screening behaviors. |
// Why it is OK to round up number of rows internally:
// All of the buffers: hashes, out_match_bitvector, out_group_ids, out_next_slot_ids
// are temporary buffers of group id mapping.
// Temporary buffers are buffers that live only within the boundaries of a single
// minibatch. Temporary buffers add 64B at the end, so that SIMD code does not have to
// worry about reading and writing outside of the end of the buffer up to 64B. If the
// hashes array contains garbage after the last element, it cannot cause computation to
// fail, since any random data is a valid hash for the purpose of lookup.
//
// This is more or less translation of equivalent scalar code, adjusted for a different
// instruction set (e.g. missing leading zero count instruction).
//
void SwissTable::early_filter_imp_avx2_x8(const int num_hashes, const uint32_t* hashes,
uint8_t* out_match_bitvector,
uint8_t* out_local_slots) const {
constexpr int unroll = 8;
const int num_group_id_bits = num_groupid_bits_from_log_blocks(log_blocks_);
const __m256i* vhash_ptr = reinterpret_cast<const __m256i*>(hashes);
const __m256i vstamp_mask = _mm256_set1_epi32((1 << bits_stamp_) - 1);
for (int i = 0; i < ((num_hashes + unroll - 1) / unroll); ++i) {
constexpr uint64_t kEachByteIs8 = 0x0808080808080808ULL;
constexpr uint64_t kByteSequenceOfPowersOf2 = 0x8040201008040201ULL;
__m256i vhash = _mm256_loadu_si256(vhash_ptr + i);
__m256i vblock_id = _mm256_srlv_epi32(
vhash, _mm256_set1_epi32(bits_hash_ - bits_stamp_ - log_blocks_));
__m256i vstamp = _mm256_and_si256(vblock_id, vstamp_mask);
vblock_id = _mm256_srli_epi32(vblock_id, bits_stamp_);
We now split inputs and process 4 at a time,
in order to process 64-bit blocks
__m256i vblock_offset =
_mm256_mullo_epi32(vblock_id, _mm256_set1_epi32(num_group_id_bits + 8));
__m256i voffset_A = _mm256_and_si256(vblock_offset, _mm256_set1_epi64x(0xffffffff));
__m256i vstamp_A = _mm256_and_si256(vstamp, _mm256_set1_epi64x(0xffffffff));
__m256i voffset_B = _mm256_srli_epi64(vblock_offset, 32);
__m256i vstamp_B = _mm256_srli_epi64(vstamp, 32);
auto blocks_i64 = reinterpret_cast<arrow::util::int64_for_gather_t*>(blocks_);
auto vblock_A = _mm256_i64gather_epi64(blocks_i64, voffset_A, 1);
auto vblock_B = _mm256_i64gather_epi64(blocks_i64, voffset_B, 1);
__m256i vblock_highbits_A =
_mm256_cmpeq_epi8(vblock_A, _mm256_set1_epi8(static_cast<unsigned char>(0x80)));
__m256i vblock_highbits_B =
_mm256_cmpeq_epi8(vblock_B, _mm256_set1_epi8(static_cast<unsigned char>(0x80)));
__m256i vbyte_repeat_pattern =
_mm256_setr_epi64x(0ULL, kEachByteIs8, 0ULL, kEachByteIs8);
vstamp_A = _mm256_shuffle_epi8(
vstamp_A, _mm256_or_si256(vbyte_repeat_pattern, vblock_highbits_A));
vstamp_B = _mm256_shuffle_epi8(
vstamp_B, _mm256_or_si256(vbyte_repeat_pattern, vblock_highbits_B));
__m256i vmatches_A = _mm256_cmpeq_epi8(vblock_A, vstamp_A);
__m256i vmatches_B = _mm256_cmpeq_epi8(vblock_B, vstamp_B);
In case when there are no matches in slots and the block is full (no empty slots),
pretend that there is a match in the last slot.
vmatches_A = _mm256_or_si256(
vmatches_A, _mm256_andnot_si256(vblock_highbits_A, _mm256_set1_epi64x(0xff)));
vmatches_B = _mm256_or_si256(
vmatches_B, _mm256_andnot_si256(vblock_highbits_B, _mm256_set1_epi64x(0xff)));
__m256i vmatch_found = _mm256_andnot_si256(
_mm256_blend_epi32(_mm256_cmpeq_epi64(vmatches_A, _mm256_setzero_si256()),
_mm256_cmpeq_epi64(vmatches_B, _mm256_setzero_si256()),
0xaa), 0b10101010
_mm256_set1_epi8(static_cast<unsigned char>(0xff)));
vmatches_A =
_mm256_sad_epu8(_mm256_and_si256(_mm256_or_si256(vmatches_A, vblock_highbits_A),
_mm256_set1_epi64x(kByteSequenceOfPowersOf2)),
_mm256_setzero_si256());
vmatches_B =
_mm256_sad_epu8(_mm256_and_si256(_mm256_or_si256(vmatches_B, vblock_highbits_B),
_mm256_set1_epi64x(kByteSequenceOfPowersOf2)),
_mm256_setzero_si256());
__m256i vmatches = _mm256_or_si256(vmatches_A, _mm256_slli_epi64(vmatches_B, 32));
We are now back to processing 8 at a time.
Each lane contains 8-bit bit vector marking slots that are matches.
We need to find leading zeroes count for all slots.
Emulating lzcnt in lowest bytes of 32-bit elements
__m256i vgt = _mm256_cmpgt_epi32(_mm256_set1_epi32(16), vmatches);
__m256i vlocal_slot =
_mm256_blendv_epi8(_mm256_srli_epi32(vmatches, 4),
_mm256_and_si256(vmatches, _mm256_set1_epi32(0x0f)), vgt);
vlocal_slot = _mm256_shuffle_epi8(
_mm256_setr_epi8(4, 3, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 4, 3, 2, 2, 1, 1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0),
vlocal_slot);
vlocal_slot = _mm256_add_epi32(_mm256_and_si256(vlocal_slot, _mm256_set1_epi32(0xff)),
_mm256_and_si256(vgt, _mm256_set1_epi32(4)));
Convert slot id relative to the block to slot id relative to the beginnning of the
table
uint64_t local_slot = _mm256_extract_epi64(
_mm256_permutevar8x32_epi32(
_mm256_shuffle_epi8(
vlocal_slot, _mm256_setr_epi32(0x0c080400, 0, 0, 0, 0x0c080400, 0, 0, 0)),
_mm256_setr_epi32(0, 4, 0, 0, 0, 0, 0, 0)),
0);
(reinterpret_cast<uint64_t*>(out_local_slots))[i] = local_slot;
Convert match found vector from 32-bit elements to bit vector
out_match_bitvector[i] = _pext_u32(_mm256_movemask_epi8(vmatch_found),
0x11111111); 0b00010001 repeated 4x
}
} |
package com.unoapp.uno.ui.screens;
import java.awt.BorderLayout;
import java.awt.Dimension;
import java.awt.FlowLayout;
import java.awt.event.MouseEvent;
import javax.swing.Box;
import javax.swing.BoxLayout;
import com.unoapp.uno.UNO;
import com.unoapp.uno.UNO.ScreenObject;
import com.unoapp.uno.abstracts.MouseClickListener;
import com.unoapp.uno.abstracts.onClickListener;
import com.unoapp.uno.ui.components.GenericMenuScreen;
import com.unoapp.uno.ui.components.RoundedRectangle;
import com.unoapp.uno.ui.components.ScaledBackground;
import com.unoapp.uno.ui.components.SmoothText;
import com.unoapp.uno.ui.components.TransparentPanel;
import com.unoapp.uno.utils.Assets;
import com.unoapp.uno.utils.Colors;
import com.unoapp.uno.utils.Constants;
import com.unoapp.uno.utils.Constants.Screens;
import com.unoapp.uno.utils.Fonts;
/**
* Title screen
* Opens on start of program
*/
public class TitleScreen extends GenericMenuScreen {
public TitleScreen() {
init();
}
private void init() {
ScaledBackground background = new ScaledBackground(Assets.getAsset("mainBg.png"), xSize, ySize);
getContentPane().add(background);
TransparentPanel mainPanel = new TransparentPanel();
mainPanel.setLayout(new BoxLayout(mainPanel, BoxLayout.PAGE_AXIS));
mainPanel.setPreferredSize(new Dimension(MAX_COMPONENT_X, MAX_COMPONENT_Y));
TransparentPanel buttonPanel = new TransparentPanel();
// Play button
buttonPanel.add(createPanel("Play", Assets.getAsset("playIcon.png"), 178, 124,
() -> UNO.changeScreen(new ScreenObject(this, Screens.PLAYER_SELECT))));
buttonPanel.add(Box.createHorizontalStrut(60));
// Settings button
buttonPanel.add(createPanel("Achievements", Assets.getAsset("trophy.png"), 123, 123,
() -> UNO.changeScreen(new ScreenObject(this, Screens.ACHIEVEMENT))));
buttonPanel.add(Box.createHorizontalStrut(60));
// Quit button
buttonPanel.add(createPanel("Quit", Assets.getAsset("power.png"), 144, 144,
() -> UNO.changeScreen(new ScreenObject(this, Screens.EXIT))));
mainPanel.add(Box.createVerticalGlue());
mainPanel.add(Box.createVerticalGlue());
mainPanel.add(Box.createVerticalGlue());
mainPanel.add(buttonPanel);
mainPanel.add(Box.createVerticalGlue());
background.add(mainPanel);
pack();
}
/**
* Creates panel with button and text for respective operation
* @param labelText label of button
* @param iconSrc src of icon
* @param iconWidth width of icon
* @param iconHeight height of icon
* @return panel with label and icon
*/
private TransparentPanel createPanel(String labelText, String iconSrc, int iconWidth, int iconHeight,
onClickListener listener) {
TransparentPanel mainPanel = new TransparentPanel();
mainPanel.setLayout(new BoxLayout(mainPanel, BoxLayout.PAGE_AXIS));
mainPanel.addMouseListener(new MouseClickListener() {
@Override
public void mouseClicked(MouseEvent e) {
listener.onClick();
}
});
int rectSize = (MAX_COMPONENT_X / 4) - (60);
TransparentPanel textPanel = new TransparentPanel();
SmoothText text = new SmoothText(labelText, Colors.getColor(Constants.Color.RED, false),
Fonts.getProximaInstance(26, true));
textPanel.add(text);
textPanel.add(Box.createVerticalStrut(rectSize / 3));
RoundedRectangle playButton = new RoundedRectangle(rectSize, rectSize, 80, new BorderLayout());
TransparentPanel label = new TransparentPanel();
label.add(new ScaledBackground(iconSrc, iconWidth, iconHeight, new FlowLayout()));
playButton.add(Box.createVerticalStrut(rectSize / 4), BorderLayout.NORTH);
playButton.add(label, BorderLayout.CENTER);
playButton.add(textPanel, BorderLayout.SOUTH);
mainPanel.add(playButton);
return mainPanel;
}
} |
Heterogeneity of highgrade cervical intraepithelial neoplasia related to HPV16: Implications for natural history and management Factors associated with progression from cervical intraepithelial neoplasia (CIN) grades 2 and 3 to invasive cancer are not well understood; most CIN2 and CIN3 do not progress to cancer. Among carcinogenic human papillomavirus (HPV) types, infections with HPV16 have the highest risk of progressing to cancer. We evaluated the heterogeneity of risk factors, lesion size, colposcopic impression and colposcopic biopsy results in relation to HPV16 status among 627 women with CIN2 or CIN3 in women referred to colposcopy at the University of Oklahoma. Loop excision specimens were evaluated in 12 radial segments to estimate lesion size. The mean age at CIN3 was 27.7 years for HPV16positive women (n = 225) and 33.6 years for HPV16negative women (n = 104). The average lesion size did not differ by HPV16 status (p = 0.83). Among HPV16positive women with CIN3, lesions were significantly larger in women 30 years and older (p = 0.03). Colposcopic impression was worse in women with HPV16 infections (p = 0.009), but the detection of CIN3 at the preceding biopsy was not improved in HPV16positive women. CIN3 is detected at the same lesion size, but at much younger age in women with HPV16 infections, suggesting faster growth. CIN2 lesion size in women without HPV16 peaks below 30 years and then decreases, suggesting frequent regression, whereas HPV16related CIN2 is more likely to persist. Lesion size seems to be an important determinant of colposcopy and biopsy performance. Genotyping for HPV16 in cervical cancer screening can improve risk stratification but may pose challenges to finding small lesions in colposcopy. |
package com.ymy.xxb.migrat.job;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.boot.autoconfigure.jdbc.DataSourceAutoConfiguration;
import org.springframework.cloud.client.discovery.EnableDiscoveryClient;
import org.springframework.cloud.openfeign.EnableFeignClients;
import org.springframework.context.annotation.ComponentScan;
import org.springframework.context.annotation.EnableAspectJAutoProxy;
import com.alibaba.druid.spring.boot.autoconfigure.DruidDataSourceAutoConfigure;
import com.ymy.xxb.migrat.job.constant.Const;
@EnableDiscoveryClient
@EnableAspectJAutoProxy
@ComponentScan(Const.APPLICATION_COMPONENT_SCAN)
@SpringBootApplication(exclude = {DataSourceAutoConfiguration.class, DruidDataSourceAutoConfigure.class})
@EnableFeignClients(basePackages = com.ymy.xxb.migrat.job.constant.Const.FEIGN_CLIENTS_INFACE_SCAN)
public class XxlJobApplication {
public static void main(String[] args) {
SpringApplication.run(XxlJobApplication.class, args);
}
}
|
package biz.golek.whattodofordinner.business.contract.interactors;
/**
* Created by <NAME> on 2016-02-05.
*/
public interface AddNewDinner {
void Run();
}
|
Richard Dawkins likened memes to genes, but a new study by Facebook shows just how accurate that analogy is. Memes adapt to their surroundings in order to survive, just like organisms. Post a liberal meme saying no one should die for lack of healthcare, and conservatives will mutate it to say no one should die because Obamacare rations their healthcare. And nerds will make it about Star Wars.
Facebook’s data scientists used anonymized data to determine that “Just as certain genetic mutations can be advantageous in specific environments, meme mutations can be propagated differentially if the variant matches the subpopulation’s beliefs or culture.”
Take this meme:
“No one should die because they cannot afford health care, and no one should go broke because they get sick. If you agree, post this as your status for the rest of the day”.
In September 2009, 470,000 Facebook users posted this exact phrase as a status update. But a total of 1.14 million status updates containing 121,605 variants of the meme were spawned, such as “No one should be frozen in carbonite because they can’t pay Jabba The Hut”. Why? Because humans help bend memes to better fit their audience.
In the chart below you can see how people of different political leanings adapted the meme to fit their own views, and likely the views of people they’re friends with. As Facebook’s data scientists explain, “the original variant in support of Affordable Care Act (aka Obamacare) was propagated primarily by liberals, while those mentioning government and taxes slanted conservative. Sci-fi variants were slightly liberal, alcohol-related ones slightly conservative”. That matches theories by Dawkins and Malcom Gladwell.
As I wrote in my Stanford Cybersociology Master’s program research paper, memes are more shareable if they’re easy to remix. When a meme has a clear template with substitutable variables, people recognize how to put their own spin on it. They’re then more likely to share their own modified creations, which drives awareness of the original. When I recognized this back in 2009, I based my research on Lolcats and Soulja Boy, but more recently The Harlem Shake meme proved me right.
Facebook’s findings and my own have signficant implications for marketers or anyone looking to make a message go viral. Once you know memes are naturally inclined to mutate, and that these mutations increase sharing, you can try to purposefully structure your message in a remixable way. By creating and seeding a few variants of your own, you can crystallize how the template works and encourage your audience to make their own remixes.
As you can see in this graph from my research paper, usage of the word “haz” as in the Lolcat phrase “I can haz cheezburger” grew increasingly popular for several years. Meanwhile, less remixable memes often only create a spike in mentions for a few days. I posit that high remixability — or adaptability — keeps memes popular for a much longer period of time.
For social networks like Facebook, understanding how memes evolve could make sure we continue to see fresh content. Rather than showing us the exact copies of a meme over and over again in the News Feed, Facebook’s algorithms could purposefully search for and promote mutated variations.
That way instead of hearing about healthcare over and over, you might see that “No one should twerk just because they can’t avoid hearing Miley Cyrus on the radio. If you agree, sit perfectly still with your tongue safely inside your mouth for the rest of the day.”
For more of my Cybersociology work, check out “The Science Behind Why The Harlem Shake Was So Popular“ |
Few things can be as frustrating as locking yourself out of your car, with no spare key in sight. Since many of us don’t know how to pick locks without completely ruining the lock or door, the only recourse available is seeking the services of a professional car lock specialist.
A professional lock specialist has the training and skill needed to pick just about any car lock without much difficulty. At the same, finding an affordable auto lock specialist at short notice may not be easy, reason being many auto lock specialists don’t provide reliable 24-hour services. The key to quickly finding the best services is using the right approach and preparing beforehand.
Online searches – Online search engines are an effective way of coming up with a preliminary list of potential auto lock specialists. In addition to being convenient since you can access them straight from your phone, they also cast a wide net. Be sure to check out the potential lock specialists’ reviews from previous customers to learn more about them.
Recommendations – Another quick way of getting contacts to a good auto lock specialist is getting recommendations from trusted family and friends that have been in your situation and found a great locksmith. It is, however, important to supplement such recommendations with your personal research.
Online directories – A number of qualified and experienced auto lock specialists have their services listed on major directories online. You can easily find them online and visit their websites to learn more about what services they offer, as well as how and what time they offer the services.
Understandably, you may want to find a good auto lock specialist as soon as possible, especially if you have just locked yourself out of your car and are nowhere close to home or your office. Nevertheless, it would not be a good idea to simply go for the first auto lock specialist service you come across after a quick Google search. There are a number of things you need to consider before hiring one.
Trustworthiness and a good reputation are important. This is where online reviews from previous customers come in. Don’t limit yourself to testimonials. Check out other sources of customer feedback, including the lock specialist service’s social media accounts.
You also want to an auto lock specialist that has experience with your car model. The car models that the lock specialist handles should be listed on their website. Also important is how long the auto lock specialist service has been in operation. A company that has been around long enough has probably gained enough experience to provide you with high-quality services.
Needless to say, you should also consider the rates that the auto lock specialist services you are considering charge for their services. When considering cost, keep in mind that this is determined by various factors, including the urgency of the service, the level of expertise required, etc. You can get a quick estimate by calling the company and giving the details of your situation. |
Internet-based remote control using a microcontroller and an embedded Ethernet We describe a recently developed DC motor position control experimental setup that can be accessed via the Internet. This setup consists of two primary elements communicating with each other: i) a server consisting of a low-cost microcontroller, Parallax's 40-pin Basic Stamp 2, interfaced with an embedded Ethernet IC, Cirrus Logic's Crystal CS8900A, and ii) a client computer. The client computer sends/receives data to/from the microcontroller using the user datagram protocol packets. The client computer connects to the server using Java applets that allow the user to command the position of the motor via a graphical user interface. The interface includes a slider for commanding the motor position from 0/spl deg/-360/spl deg/ and text input boxes for tuning the parameters of a position control algorithm "on-the-fly." A plot provides a visual display of the current position of the motor using real-time sensor data sent by the microcontroller. Our microcontroller-based remote control methodology can be readily applied to monitor and control other experimental hardware over the Internet. |
Reproductive age-associated fibrosis in the stroma of the mammalian ovary. Under normal physiological conditions, tissue remodeling in response to injury leads to tissue regeneration without permanent damage. However, if homeostasis between synthesis and degradation of extracellular matrix (ECM) components is altered, fibrosis - or the excess accumulation of ECM - can disrupt tissue architecture and function. Several organs, including the heart, lung and kidney, exhibit age-associated fibrosis. Here we investigated whether fibrosis underlies aging in the ovary - an organ that ages chronologically before other organs. We used Picrosirius Red (PSR), a connective tissue stain specific for collagen I and III fibers, to evaluate ovarian fibrosis. Using bright-field, epifluorescence, confocal and polarized light microscopy, we validated the specific staining of highly ordered PSR-stained fibers in the ovary. We next examined ovarian PSR staining in two mouse strains (CD1 and CB6F1) across an aging continuum and found that PSR staining was minimal in ovaries from reproductively young adult animals, increased in distinct foci in animals of mid-to-advanced reproductive age, and was prominent throughout the stroma of the oldest animals. Consistent with fibrosis, there was a reproductive age-associated increase in ovarian hydroxyproline content. We also observed a unique population of multinucleated macrophage giant cells, which are associated with chronic inflammation, within the ovarian stroma exclusively in reproductively old mice. In fact, several genes central to inflammation had significantly higher levels of expression in ovaries from reproductively old mice relative to young mice. These results establish fibrosis as an early hallmark of the aging ovarian stroma, and this altered microenvironment may contribute to the age-associated decline in gamete quality. |
//
// NO57.c
// NO57
//
// Created by wanyakun on 2020/11/4.
//
#include "NO57.h"
#include <stdlib.h>
#include <stdbool.h>
int min(int a, int b) {
return a > b ? b : a;
}
int max(int a, int b) {
return a > b ? a : b;
}
void add(int** res, int* returnColumnSizes, int* returnSize, int left, int right) {
int n = ++(*returnSize);
returnColumnSizes[n-1] = 2;
res[n-1] = malloc(2*sizeof(int));
res[n-1][0] = left;
res[n-1][1] = right;
}
int** insert(int** intervals, int intervalsSize, int* intervalsColSize, int* newInterval, int newIntervalSize, int* returnSize, int** returnColumnSizes){
// base case
if (intervals == NULL && newInterval == NULL) {
*returnSize = 0;
*returnColumnSizes = NULL;
return NULL;
}
// 存储返回结果
int** res = malloc((intervalsSize+1)*sizeof(int*));
*returnSize = 0;
*returnColumnSizes = malloc((intervalsSize+1)*sizeof(int));
bool isAdd = false;
for(int i = 0; i < intervalsSize; i++) {
int left = intervals[i][0];
int right = intervals[i][1];
if(right < newInterval[0]) {
// 区间在新区间左侧，直接加入到结果
add(res, *returnColumnSizes, returnSize, left, right);
} else if (left > newInterval[1]) {
// 区间在新区间右侧，插入新区间，然后插入
if(isAdd == false) {
add(res, *returnColumnSizes, returnSize, newInterval[0], newInterval[1]);
isAdd = true;
}
add(res, *returnColumnSizes, returnSize, left, right);
} else {
// 区间与新区间有交集，合并为新区间
newInterval[0] = min(left, newInterval[0]);
newInterval[1] = max(right, newInterval[1]);
}
}
// 循环完也没添加，最后添加新区间
if (!isAdd) {
add(res, *returnColumnSizes, returnSize, newInterval[0], newInterval[1]);
}
return res;
}
|
Features of isolation of the anthrax pathogen depending on the type of nutrient medium The purpose of the work is to develop a nutrient medium for differentiation of bacillus from soil aerobic bacilli. In order to achieve the set goal, we used the method of introduction into the environment of cultivation of microorganisms separated from animals and objects of external environment (water, soil, feed, air, scrapes from different surfaces suspected of contamination by their bacillus ) of nutrient substrate sucrose, used by bacteria of Bacillus genus for synthesis of product of their metabolism, a sign absent in bacillus. This feature is essential for identification and differentiation of bacillus from closely related saprophytes. To identify and differentiate the bacillus, the microbes isolated from the external environment were cultivated in a nutrient medium consisting of agar (MPA) and () synthesis of sucrose in the amount of 10% to 100 ml of melted agar. The proposed nutrient media was prepared as follows. agar (500 ml) was melted at 1000C, 10 g of sucrose was added per 100 ml of medium and after the complete dissolution of sucrose, the nutrient medium was poured into dishes and used for sowing the studied material for identification and differentiation of grown crops. The efficiency of the method has been tested in production experiments with positive evaluation. For this purpose, soil samples taken from the territory of old cattle cemeteries were fractionally sown on MPA and for 16-18 hours at 370C and examined crops for the presence of matte and rough (R-form) colonies. |
package dao.comptes;
import java.util.List;
import javax.persistence.EntityManager;
import javax.persistence.EntityManagerFactory;
import javax.persistence.EntityTransaction;
import javax.persistence.Query;
import factory.Client;
import factory.Compte;
import factory.CompteCourant;
import factory.CompteEpargne;
public class CompteDAOJPA implements ComptesDAO{
public EntityManagerFactory emf ;
public CompteDAOJPA(EntityManagerFactory emf) {
super();
this.emf = emf;
}
@Override
public List<Compte> FindAll() {
EntityManager em = emf.createEntityManager();
Query query = em.createQuery("select h from Compte h", Compte.class);
List<Compte> resultList = query.getResultList();
em.close();
return resultList;
}
@Override
public void deleteAllCompteCourant() {
// TODO Auto-generated method stub
}
@Override
public void deleteAllCompteEpargne() {
// TODO Auto-generated method stub
}
@Override
public void saveCompte(Compte compte) {
EntityManager em = emf.createEntityManager();
EntityTransaction et = em.getTransaction();
et.begin();
em.persist(compte);
et.commit();// et.rollback()
em.close();
}
}
|
A Quantitative Research on Carbon Emissions in the Residential Area of China Based on LCP Theory As the residential area is the basic functional unit in the city, its number is large and its construction project is huge; accordingly, the CO2 emission is also huge in the process of construction and use. On the basis of the LCP theory and method, the researchers in this paper propose a new approach used to calculate the carbon emissions in the urban residential area through comprehensively considering the CO2 emission and CO2 absorption of carbon sink-green space in the total life cycle of the urban community. In addition, taking the typical multi-story residential areas in Shenyang City as the sample, the researchers calculate the carbon emission and discuss the features of emission in the residential area as well as the method and potential of reducing the carbon emission. The calculation results show that, the carbon emissions in the process of operation an use account for the largest proportion for the total life cycle, up to 83.8%; the carbon emissions in the process of preparing the materials for construction account for 7.69%; the carbon emissions at the stage of building demolition account for 5.32%. The carbon emissions at the stage of construction are the smallest in amount, which can be basically negligible. According to the existing energy structure, construction specifications and technical level, 4.8% of the CO2 emissions in the residential area can be absorbed through the green space and that the carbon emissions in the residential area can be reduced through taking the energy conservation measures, using the renewable energy and increasing the area of carbon sinks. |
SuperMicro is a huge name in the server motherboard business, and there was a time when they were a go-to manufacturer of consumer and enthusiast motherboards. While the company technically still has a "gaming" lineup that stretches to the Z390 generation, SuperMicro's boards are relatively uncommon here in the US. But the company invited KitGuru to their headquarters in California, and said they intend to change that. Among other things, SuperMicro wantss to be on the bleeding edge of the transition to DDR4 and PCI-e Gen 4, and already have plans to bring on more staff as they enter more markets . While the company's consumer-facing lineup is currently Intel only, the company was quick to point out they were first to market with AMD's EPYC platform, though they stopped short of confirming future AM4 based offerings. Check out the interview below.
Asked if SuperMicro could reclaim its former glories in the gaming/desktop space, Vik explained that they had created market leading products for High Performance Computing (HPC), OEM solutions and InfraStructure as a Service - so he was extremely confident about the consumer market. He told us that in 2016 SuperMicro was thinking about the market and planning. In 2017 they started to bring new products to market and in 2018 they managed to set a new world record for performance. "In 2019, we will put the peddle to the metal," said Vik. "Setting the record itself showed that 'we are here,' but it doesn't mean anything unless we bring the right product to the customer. We're here to make an impact." |
export function uintToImgUrl(img: Uint8Array) {
const blob = new Blob([img], { type: "image/png" });
return URL.createObjectURL(blob);
}
|
<gh_stars>1-10
import { HttpError, RuntimeError } from "../errors";
import { ClientRequest, IncomingMessage, request as requestHttp } from "http";
import { request as requestHttps, RequestOptions } from "https";
import { Writable } from "stream";
import { request, requestStream } from "./http";
jest.mock("http");
const mockRequestHttp = requestHttp as jest.MockedFunction<typeof requestHttp>;
jest.mock("https");
const mockRequestHttps = requestHttps as jest.MockedFunction<typeof requestHttps>;
const getMockClientRequest = (
error?: Error,
mockIncomingMessage?: Partial<IncomingMessage>,
): Partial<ClientRequest> => {
return {
on: jest.fn().mockImplementation((event, listener) => {
if (error && event === "error") {
return listener(error);
}
if (mockIncomingMessage && event === "response") {
return listener(mockIncomingMessage);
}
}),
end: jest.fn(),
};
};
const getMockIncomingMessage = (
statusCode: number | null,
error?: Error,
mockData?: Buffer,
): Partial<IncomingMessage> => {
return {
statusCode,
on: jest.fn().mockImplementation((event, listener) => {
if (error && event === "error") {
return listener(error);
}
if (mockData && event === "data") {
return listener(mockData);
}
if (mockData && event === "end") {
return listener();
}
}),
pipe: jest.fn().mockImplementation((stream) => stream),
};
};
describe("http", () => {
beforeEach(() => {
mockRequestHttp.mockClear();
mockRequestHttps.mockClear();
});
describe("request", () => {
it("should make http(s) requests", async () => {
const test = async (url: string, options?: RequestOptions) => {
mockRequestHttp.mockClear();
mockRequestHttps.mockClear();
const incomingMessageData = Buffer.from("data");
const mockIncomingMessage = getMockIncomingMessage(200, null, incomingMessageData);
const mockClientRequest = getMockClientRequest(null, mockIncomingMessage);
mockRequestHttp.mockImplementation(
(_url, _options) => mockClientRequest as ClientRequest,
);
mockRequestHttps.mockImplementation(
(_url, _options) => mockClientRequest as ClientRequest,
);
const ret = await request(url, options);
expect(ret).toEqual(String(incomingMessageData));
if (url.toLowerCase().startsWith("https")) {
expect(mockRequestHttps).toBeCalledTimes(1);
expect(mockRequestHttps).toBeCalledWith(url, options);
} else {
expect(mockRequestHttp).toBeCalledTimes(1);
expect(mockRequestHttp).toBeCalledWith(url, options);
}
expect(mockClientRequest.on).toBeCalledWith("response", expect.any(Function));
expect(mockClientRequest.end).toBeCalledTimes(1);
expect(mockIncomingMessage.on).toBeCalledWith("data", expect.any(Function));
expect(mockIncomingMessage.on).toBeCalledWith("end", expect.any(Function));
};
await test("http://foo.bar");
await test("https://foo.bar");
await test("HTTPS://FOO.BAR");
await test("https://foo.bar", { method: "POST" });
});
it("should handle redirections", async () => {
const url = "https://foo.bar";
const redirection = "https://bar.foo";
const headers = {
location: redirection,
};
const incomingMessageData = Buffer.from("data");
const mockIncomingMessageSecond = getMockIncomingMessage(
200,
null,
incomingMessageData,
);
const mockClientRequestSecond = getMockClientRequest(null, mockIncomingMessageSecond);
mockRequestHttps.mockImplementation(
(_url, _options) => mockClientRequestSecond as ClientRequest,
);
const test = async (
statusCode: number,
options?: RequestOptions,
strict: boolean = false,
) => {
mockRequestHttps.mockClear();
const mockIncomingMessageFirst = getMockIncomingMessage(statusCode);
mockIncomingMessageFirst.headers = headers;
const mockClientRequestFirst = getMockClientRequest(null, mockIncomingMessageFirst);
mockRequestHttps.mockImplementationOnce(
(_url, _options) => mockClientRequestFirst as ClientRequest,
);
const ret = await request(url, options);
expect(ret).toEqual(String(incomingMessageData));
expect(mockRequestHttps).toBeCalledTimes(2);
expect(mockRequestHttps).toBeCalledWith(url, options);
expect(mockRequestHttps).toBeCalledWith(
redirection,
strict ? { method: "GET" } : options || { method: "GET" },
);
};
await test(301, undefined, true);
await test(302, { method: "POST" }, true);
await test(303, { method: "GET" }, true);
await test(307, { method: "GET" });
await test(307, { method: "POST" });
await test(308);
});
it("should throw errors", async () => {
const req = () => request("https://foo.bar");
let mockIncomingMessage = getMockIncomingMessage(null);
let mockClientRequest = getMockClientRequest(null, mockIncomingMessage);
mockRequestHttps.mockImplementation(
(_url, _options) => mockClientRequest as ClientRequest,
);
await expect(req).rejects.toThrow(RuntimeError);
await expect(req).rejects.toThrow("missing status code");
mockIncomingMessage = getMockIncomingMessage(0);
mockClientRequest = getMockClientRequest(null, mockIncomingMessage);
await expect(req).rejects.toThrow(RuntimeError);
await expect(req).rejects.toThrow("missing status code");
let error = new Error("test");
mockIncomingMessage = getMockIncomingMessage(200, error);
mockClientRequest = getMockClientRequest(null, mockIncomingMessage);
await expect(req).rejects.toThrow(error);
const statusMessage = "message";
mockIncomingMessage = getMockIncomingMessage(500);
mockIncomingMessage.statusMessage = statusMessage;
mockClientRequest = getMockClientRequest(null, mockIncomingMessage);
await expect(req).rejects.toThrow(HttpError);
await expect(req).rejects.toThrow(statusMessage);
const code = 500;
mockIncomingMessage = getMockIncomingMessage(code);
mockClientRequest = getMockClientRequest(null, mockIncomingMessage);
await expect(req).rejects.toThrow(HttpError);
await expect(req).rejects.toThrow(String(code));
});
});
describe("requestStream", () => {
it("should make http(s) requests", async () => {
const test = async (options?: RequestOptions) => {
mockRequestHttps.mockClear();
const url = "https://foo.bar";
const mockIncomingMessage = getMockIncomingMessage(200);
const mockClientRequest = getMockClientRequest(null, mockIncomingMessage);
mockRequestHttps.mockImplementation(
(_url, _options) => mockClientRequest as ClientRequest,
);
const mockWritable: Partial<Writable> = {
on: jest.fn().mockImplementation((event, listener) => {
if (event === "close") {
return listener();
}
}),
};
await requestStream(mockWritable as Writable, url, options);
expect(mockRequestHttps).toBeCalledTimes(1);
expect(mockRequestHttps).toBeCalledWith(url, options);
expect(mockClientRequest.on).toBeCalledWith("response", expect.any(Function));
expect(mockClientRequest.end).toBeCalledTimes(1);
expect(mockIncomingMessage.pipe).toBeCalledTimes(1);
expect(mockIncomingMessage.pipe).toBeCalledWith(mockWritable);
};
await test({ method: "POST" });
await test();
});
it("should throw errors", async () => {
const url = "https://foo.bar";
const error = new Error("test");
let mockClientRequest = getMockClientRequest(error);
mockRequestHttps.mockImplementation(
(_url, _options) => mockClientRequest as ClientRequest,
);
const mockWritable: Partial<Writable> = {
on: jest.fn(),
};
const reqStream = () => requestStream(mockWritable as Writable, url);
await expect(reqStream).rejects.toThrow(error);
let mockIncomingMessage = getMockIncomingMessage(200);
mockClientRequest = getMockClientRequest(null, mockIncomingMessage);
mockWritable.on = jest.fn().mockImplementation((event, listener) => {
if (event === "error") {
return listener(error);
}
});
await expect(reqStream).rejects.toThrow(error);
});
});
});
|
Kinetic analysis for catalytic co-pyrolysis of palm kernel shell and plastic waste mixtures with bifunctional HZSM-5 and mussel shell catalyst The present study is dedicated to investigate the kinetic analysis for catalytic co-pyrolysis of palm kernel shell (PKS) and polyethylene waste (HDPE) mixtures with bifunctional HZSM-5 and mussel shell (MS) catalyst. Artificial neural network (ANN) modeling through 17 models based on the functions of reaction mechanism denoted as chemical reactions, diffusion reactions, nucleation and growth reactions, interfacial phase reactions and power law reactions was used in this study to achieve a suitable order of reaction promoting higher rate of accuracy based on the data achieved. It was found that the 2nd order, 3rd order, anti jander, jander and ginsling were selected as the suitable models out of 17 reaction mechanism kinetic models due to the giving a positive values produced. And also, it was observed that 3rd order reaction mechanism model provided higher activation energy (E A) and A values for all feedstock used. Comparison between experimental data and predicted data using Logsig-Tansig (LT) and Tansig-Tansig (TT) were carried out and it was observed that the predicted results from ANN showed similar trend as experimental data with minimal error of 0.67%, 6.61%, and 2.46% for PKS, HDPE, and mixture of PKS and HDPE with the presence of MS and HZSM-5 catalyst, respectively. From the kinetic analysis, E A and A value of PKS, HDPE, and of PKS and HDPE with the presence of bifunctional of HZSM-5/MS catalyst are 196.93 kJ/mol, 388.00 kJ/mol, 147.12 kJ/mol, 3.241013 s-1, 6.001026 s-1, and 1.7210-1 s-1, respectively. |
def duplicate_article_remover(list_of_dictionaries: list[dict]) -> list[dict]:
titles_seen = []
non_duplicates = []
for ele in list_of_dictionaries:
if ele['title'].lower() in titles_seen:
logging.info("Duplicate headline found (and dealt with)")
continue
else:
titles_seen.append(ele['title'].lower())
non_duplicates.append(ele)
return non_duplicates |
It’s not enough that Alex Jones has accused the parents of Noah Pozner, a 6-year-old killed at Sandy Hook Elementary School in 2012, of being actors. It’s not enough that he’s exposed them to death threats and harassment that has led them to move homes seven times. And it’s not enough that he’s made tons of money in the process.
Now the founder of Infowars is asking a Texas judge to throw out a defamation suit filed by Pozner’s parents and trying to get the family to pay more than $100,000 for his court costs.
In 2016 the danger of Jones’s words became more tangible when one of his acolytes was arrested for threatening the lives of the Pozner parents. Lucy Richards of Florida had become so convinced that the shooting was a “false flag” that she sent threats to Lenny Pozner that said “you gonna die, death is coming to you real soon” and “LOOK BEHIND YOU IT IS DEATH.” According to court documents, as a condition of her parole, Richards was ordered to never again watch, read, or listen to content from Infowars. |
package jUnitTest;
public class BasePath {
protected final static String chromePath ="C:\\\\\\\\Users\\\\\\\\Foucz\\\\\\\\Desktop\\\\\\\\SeleniumTesty\\\\\\\\files\\\\\\\\chromedriver.exe";
}
|
Distal radial fractures outcome in relation to instability markers Background Fractures of the distal radius are common, especially in elderly patients. Treatment consists of closed reduction and immobilization. Long-term effects of malunion indicate surgery for potentially unstable fractures. Patients and methods A prospective study was conducted in 60 patients with a distal radius fracture between February 2012 and January 2013 and matched with criteria included in this study. The strategy of management was subdivided into plaster cast immobilization with subsequent rehabilitation, manipulation with subsequent cast immobilization, and surgery (volar plating). Outcomes were graded as good or poor based on the complications and the function achieved at the end of follow-up. Results A total of 60 patients were included in the study. Thirty five patients had less than or equal to 3 instability markers (group A) and 25 had more than or equal to 4 (group B). Overall, 42 were female and 18 were male. The average age was 58 years, with an age range of 2065 years, in group A, whereas the average age was 57 years, with an age range of 2163 years, in group B. Conclusion Presence of four or more instability markers is globally associated with a poorer outcome. Patients with four or more markers who underwent surgery did uniformly better than those with manipulation alone. In patients with three or fewer markers, nonoperative management achieved good outcomes. |
Drill holes made on exposed scalp bone promotes secondary intention healing of extended scalp laceration wounds: A Mbabane Government Hospital approach in the Kingdom of Eswatini Granulation tissue growth over exposed bone may be facilitated by creating fenestrations on the exposed bone. We report the case of a 6-year-old girl with a large-sized scalp laceration (10 cm 6 cm) with bone exposure (4 cm 3 cm) due to a road traffic accident in the Kingdom of Eswatini. We treated the wound with saline-soaked gauze dressing and drilled several holes on the exposed bone to enhance secondary healing. The aim of this case report was to provide a successful application of Drill holes procedure or trephination of exposed calvarium for the treatment of exposed bone and large-sized scalp lacerations in resource-poor areas, where flaps or more complicated procedures are not feasible. |
Mirror, mirror on the wall, which physiology journal is the fairest of all? Would beauty come with impact factors, the queen's vain enquiry would probably have remained unanswered. Nonetheless, when it comes to physiology journals, we cannot resist asking the mirror, which of our periodicals is best. Yet, all attempts remain futile. Misguided generosity may lead to choosing the parameter best suited for one's favorite journal. Bennet et al. summed up important confounding factors. For instance, review journals and highly specialized journals cannot be compared with those covering the entire field of physiology. |
Characterization of recombinant bet vI, the major pollen allergen of Betula verrucosa (white birch), produced by fed-batch fermentation. The gene encoding the major allergen, Bet v I, from Betula verrucosa (white birch) pollen was cloned by application of the polymerase chain reaction with double-stranded cDNA as template and specific primers based on the published nucleotide sequence of the gene. The gene was inserted into plasmid pKK223-3 and expressed in Escherichia coli K-12 strain JM105 grown to high cell density in a fermenter using a fed-batch procedure. Ample material was provided for a thorough characterization of the epitope structure of recombinant Bet v I contained in an unpurified soluble lysate. The antibody-binding characteristics of recombinant Bet v I was compared with that of the natural allergen using polyclonal rabbit antibodies raised against Bet v I in crossed immunoelectrophoresis, tandem crossed immunoelectrophoresis, and Western blotting. IgE from a pool of 16 allergic patients' serum was applied in crossed radioimmunoelectrophoresis, Western blotting, and a quantitative luminescence inhibition immunoassay. Well-defined epitopes were assayed by the application of a panel of six murine monoclonal antibodies in a quantitative radio inhibition immunoassay. In all assays the activity of recombinant Bet v I was comparable to that of natural Bet v I, and it is concluded that the epitope structure of recombinant Bet v I closely resembles that of natural Bet v I. This result has important implications for the future use of recombinant tree pollen allergens as a model system for the study of allergenic B and T cell epitopes aiming at improvements in reagents used for the management of allergic disease. |
/**
* Find Hellos by name.
*
* @param name The name to search for
* @param myParam My option parameter
* @return The Hellos that we found.
*/
@Roles(AppRoles.ADMIN)
@Get("/findbyname/{name}")
List<HelloDto> findByName(String name, @QueryParam("my-param") @Default("one") String myParam) {
return new ArrayList<>();
} |
//////////////////////////////////////////////////////////////////////////////
// Draws just the Digits that have been modified
//////////////////////////////////////////////////////////////////////////////
void CFreqCtrl::drawDigits(QPainter &Painter)
{
Painter.setFont(m_DigitFont);
m_FirstEditableDigit = m_DigStart;
for (int i = m_DigStart; i < m_NumDigits; i++)
{
if (m_DigitInfo[i].incval == 0)
m_FirstEditableDigit++;
if (m_DigitInfo[i].modified || m_DigitInfo[i].editmode)
{
if (m_DigitInfo[i].editmode && m_DigitInfo[i].incval != 0)
Painter.fillRect(m_DigitInfo[i].dQRect, m_HighlightColor);
else
Painter.fillRect(m_DigitInfo[i].dQRect, m_BkColor);
if (i >= m_LeadZeroPos)
Painter.setPen(m_BkColor);
else
Painter.setPen(m_DigitColor);
Painter.drawText(m_DigitInfo[i].dQRect, Qt::AlignHCenter|Qt::AlignVCenter,
QString().number(m_DigitInfo[i].val));
m_DigitInfo[i].modified = false;
}
}
} |
<reponame>MMORPG-Prototype/MMORPG_Prototype
package pl.mmorpg.prototype.server.objects.monsters.spells;
import pl.mmorpg.prototype.clientservercommon.packets.SpellIdentifiers;
import pl.mmorpg.prototype.data.entities.CharacterSpell;
import pl.mmorpg.prototype.server.exceptions.UnknownSpellException;
public class SpellFactory
{
public static Spell create(CharacterSpell spellModel)
{
SpellIdentifiers spellType = spellModel.getIdentifier();
if (spellType.equals(SpellIdentifiers.FIREBALL))
return new FireballSpell();
if (spellType.equals(SpellIdentifiers.HEAL))
return new HealSpell();
throw new UnknownSpellException(spellType);
}
}
|
<reponame>netgrif/components<gh_stars>1-10
import {Observable, of, Subject} from 'rxjs';
import {SideMenuEvent} from './side-menu-event';
import {SideMenuInjectionData} from './side-menu-injection-data';
import {tap} from 'rxjs/operators';
import {MatDrawerToggleResult} from '@angular/material/sidenav';
export class SideMenuControl {
private _event$: Subject<SideMenuEvent>;
constructor(bindingsFunction: (event$: Subject<SideMenuEvent>) => void = () => {},
sideMenuOpenedStateChange: Observable<boolean> = of(true),
private sideMenuCloseFunction: () => Observable<MatDrawerToggleResult>,
private readonly _injectionData?: SideMenuInjectionData,
public isVersionVisible?: boolean,
public allVersionEnabled?: boolean) {
this._event$ = new Subject<SideMenuEvent>();
bindingsFunction(this._event$);
sideMenuOpenedStateChange.subscribe((opened) => {
if (!opened) {
this._event$.next({opened, message: 'Side menu closed unexpectedly'});
this._event$.complete();
}
});
}
get data(): SideMenuInjectionData {
return this._injectionData;
}
public publish(event: SideMenuEvent): void {
this._event$.next(event);
}
public close(event: SideMenuEvent): Observable<MatDrawerToggleResult> {
if (!event.message) {
event.message = 'Side menu is closing';
}
this._event$.next({...event, opened: false});
return this.sideMenuCloseFunction().pipe(
tap((closed) => {
if (closed === 'close') {
this._event$.complete();
}
})
);
}
}
|
Long-Term Outcomes of Successful Recanalization Compared With Optimal Medical Therapy for Coronary Chronic Total Occlusions in Patients With and Without Left Ventricular Systolic Dysfunction Background: The number of coronary chronic total occlusion (CTO) patients with left ventricular (LV) systolic dysfunction is significant, but the clinical outcomes of these patients are rarely reported. The present retrospective cohort study aimed to investigate the long-term outcomes of successful recanalization vs. optimal medical therapy (MT) for CTOs in patients with preserved and impaired LV systolic function. Methods: A total of 1,895 patients with CTOs were stratified according to LV function. Of these, 1,420 patients (74.9%) with LV ejection fraction (LVEF) >45% and 475 patients (25.1%) with LVEF ≤45% were treated with optimal MT or successful CTO percutaneous coronary intervention (PCI). A 1:1 propensity score matching (PSM) was conducted to reduce the impact of potential confounding on the outcomes. The primary outcome was the frequency of major adverse cardiac events (MACEs). Results: Throughout a 2.6-year follow-up and after adjusting for confounders, among patients with preserved LV function, successful CTO PCI was associated with reduced incidence of MACE (14.2 vs. 23.9%, adjusted HR 0.63, 95% CI 0.480.83, p = 0.001) compared to MT. There was no significant difference in MACE occurrence (29.6 vs. 28.9%, adjusted HR 1.05, 95% CI: 0.711.56, p = 0.792) between successful recanalization and MT in patients with LV systolic dysfunction. The primary outcome among patients with impaired and preserved LV systolic function after PSM was similar to that from earlier findings before PSM was conducted. A significant interaction between LV function and therapeutic strategy for MACE was observed (interaction p = 0.038). Conclusions: Compared to MT alone for management of patients with CTOs, successful CTO PCI may reduce the risk of MACE in patients with preserved LV systolic function, but not in patients with LV dysfunction. INTRODUCTION Coronary chronic total occlusions (CTOs) are observed in 10-15% of all patients undergoing coronary angiography and remain one of the most challenging obstacles in coronary intervention. The presence of a CTO was found to be the strongest independent predictor of incomplete revascularization in patients with complex coronary artery disease (CAD) undergoing percutaneous coronary interventions (PCIs). It was also associated with higher rates of 4-year mortality and major adverse cardiac and cerebrovascular events (MACCEs) in the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) trial. Some observational studies have reported that CTO-PCI is associated with angina symptom relief, and that it improves long-term survival and left ventricular ejection fraction (LVEF). However, CTO-PCI is performed infrequently, due to lesional complexity, procedural complications, and controversial findings, and nearly half of CTO patients are managed by conservative medical therapy (MT) (1,. The number of patients with LV systolic dysfunction is significant. Previous studies have reported that approximately 40-53% of patients with CTOs had LV systolic dysfunction. However, the clinical outcomes of impaired and preserved LV systolic function in CTO patients have been rarely reported, and there are no current guidelines or consensus related to the optimal treatment strategy for CTO lesions in patients with LV systolic dysfunction. Accordingly, we aimed to assess the long-term outcomes of optimal MT vs. successful CTO recanalization in CTO patients with impaired and preserved LV systolic function. Study Design and Population A total of 27,231 coronary angiography procedures were performed at our center in the period between January 2007 and December 2018. Patients with a confirmed CTO diagnosis were selected. Exclusion criteria were as follows: history of coronary artery bypass grafting (CABG), because CTO lesions in patients with history of CABG surgery show more extensive calcification, negative remodeling, and accelerated progression of atherosclerosis, which are associated with lower success rates, higher rates of complications, and worse outcomes ; CABG or failed CTO-PCI; acute myocardial infarction (MI); cardiogenic shock; and cancer. A total of 1,895 patients were enrolled in the study. Patients were assigned to revascularization or optimal MT groups according to the initial treatment strategy. Decisions for patients referred for revascularization were based on at least one of the following conditions: presence of symptomatic angina, inducible myocardial ischemia evaluated by echocardiography or myocardial perfusion scan, and myocardial viability on cardiac magnetic resonance imaging. MT was strongly preferred in asymptomatic patients who did not have available viability data or in subjects with proven absence of viability. PCI was preferred in symptomatic patients even without information on viability or in asymptomatic patients with viability. The decision to perform PCI for CTO patients was also dependent on several factors, including co-morbidity, the extent of other coronary artery disease, CTO location, technical difficulty, and doctors' and patients' preference. Patients' LV systolic function was evaluated using a two-dimensional echocardiogram. Baseline characteristics, echocardiographic data, medications, and procedural data were collected upon study enrollment. Follow-up data were obtained by reviewing medical records and performing telephone interviews with the patients. The study was approved by local institutional review boards in accordance with the principles of the Declaration of Helsinki. Definitions and Clinical Endpoints A CTO is defined as a native coronary artery occlusion with typical appearance (Thrombolysis in Myocardial Infarction (TIMI) grade 0 flow through the lesion with no thrombus, no staining at the proximal cap, and presence of mature collaterals) and definitive corroborating evidence of occlusion duration ≥3 months according to the CTO Academic Research Consortium (CTO-ARC) Consensus Recommendations. CTOs in the present study were identified based on medical history or previous coronary angiography. LVEF >45% was defined as preserved LV systolic function, and LVEF ≤45% was defined as LV systolic dysfunction. Successful CTO-PCI was defined as residual stenosis of <20% and restoration of TIMI grade 3 flow after drug-eluting stent implantation. The primary endpoint was the frequency of MACE at follow up. Other endpoints included cardiac death, MI, and target vessel revascularization (TVR). MACEs included cardiac death, MI, and need for TVR. Cardiac death included sudden cardiac death and death due to MI or heart failure. MI was defined as creatine kinase-MB enzyme elevation three times the upper limit of the normal value with ischemic symptoms or electrocardiographic expression of ischemia. TVR was defined as any attempted PCI or surgical revascularization of the CTO target vessel. Perioperative MI was defined as a cTn elevation >5 times the 99th percentile of the upper reference limit within 48 h after the procedure in patients with normal baseline values plus the presence of additional supportive electrocardiographic, angiographic, or imaging evidence of ischemia. PCI Procedures and Medical Treatment All patients were pre-treated with aspirin and clopidogrel before the procedure. The angiographic characteristics of the CTO lesions were evaluated by dedicated CTO operators before the procedure. Contemporary techniques, such as bilateral injections, microcatheters, novel guidewires, and retrograde approaches, were used in the PCI procedure. The crossing equipment and procedure techniques were used according to the operator's discretion. During the procedure, patients received intravenous unfractionated heparin (100 IU/kg), and activated clotting time (ACT) was repeatedly checked to maintain an ACT of ≥300. Glycoprotein IIb or IIIa inhibitor was administered depending on the operator's choice. After PCI, all patients received 100 mg of aspirin per day indefinitely and 75 mg of clopidogrel per day for at least 12 months. MT included antiplatelet medication, statins, renin-angiotensin system blockade, -blockers, and nitrate. Optimal MT was defined as the use of at least two or more antianginal classes of therapies according to the appropriate use criteria. The medication dosages were maximized as allowed by heart rate, blood pressure, and side effects in the absence of justifiable relative contraindications. The MT was recorded at discharge after the index hospitalization. Statistical Analysis Continuous variables are presented as the mean ± SD and were evaluated using Student's t-test. Categorical variables were presented as frequencies and percentage and were compared using the chi-square test. Kaplan-Meier survival curves were plotted to assess long-term outcomes during the follow-up, and the log-rank test was used for comparison between groups. Cox proportional hazards model was used to adjust the potential influence factors. All univariate variables with p < 0.1 or clinically relevant variables were included in a statistical model to adjust the hazard ratio (HR). Univariate analysis was performed and the variables included age, sex, smoking, hypertension, diabetes, dyslipidemia, familial history of CAD, history of MI, chronic kidney disease (CKD), baseline medication use, number of CTO lesions, CTO location, multivessel disease, proximal or mid CTO, CTO length, calcification Japanese-chronic total occlusion (J-CTO) score, and SYNTAX score. To reduce the impact of potential confounding on MACEs of the observational study, a 1:1 propensity score matching (PSM) was conducted using all available variables measured based on a multivariable logistic regression to choose patients with comparable baseline data. The nearest-neighbor matching algorithm was used for PSM. Twosided p < 0.05 were considered statistically significant and all analyses were processed using Stata Version 15.1 (StataCorp LLC, TX, USA). Patient Characteristics Of the 1,895 patients enrolled in this study, 1,420 patients (74.9%) had LVEF >45% and 475 patients (25.1%) had LV systolic dysfunction. A total of 66 patients underwent repeated CTO-PCI procedures, and 45 patients received a successful CTO-PCI. Subsequently, 476 patients underwent a failed PCI (360 patients were with LVEF >45% and 116 patients were with LV systolic dysfunction). Tables 1, 2 show the baseline information for the study patients. Patients with LV systolic dysfunction tended to be male and smokers and had a higher prevalence of previous MI and CKD compared to patients with preserved LV systolic function. The use of -blocker and angiotensin-converting enzyme inhibitor (ACEI) or angiotensin-receptor blocker (ARB) was more common among patients with LV systolic dysfunction. Angiographic findings revealed that patients in the LV systolic dysfunction group had a higher prevalence of two CTO lesions, multivessel disease, occlusive length ≥20 mm, and higher J-CTO score. No significant differences were found in the prevalence of procedural complications and in-hospital death. Among the patients with preserved LV systolic function, 863 patients received MT and 557 patients underwent a successful CTO procedure. Compared to patients in the successful recanalization group, those with MT were older and had more cases of diabetes mellitus prior to MI. In addition, they had more involvement of the LCX coronary artery and complex lesions (multivessel disease, lesion calcification, and high J-CTO and SYNTAX scores). Among the patients with LV systolic dysfunction, 350 patients were treated by MT and 125 patients received successful PCI procedures. Baseline clinical characteristics, including age, gender, hypertension, diabetes, dyslipidemia, familial history of CAD, prior MI, and CKD, were not significantly different in the two study groups. The usage of baseline medication was also similar. Compared to patients in the MT group, those who underwent successful procedures more often had two CTO lesions, CTO location in the left anterior descending artery, and low J-CTO score. A total of 321 pairs of patients were matched among the patients with preserved LV systolic function after PSM. No considerable differences were found in the baseline clinical and lesion characteristics. Eighty-one pairs of patients were matched among the patients with LV systolic dysfunction. Similarly, the clinical baseline characteristics were not different between the successful PCI and medical therapy groups ( Table 3). Clinical Follow-Up The median follow-up period was 2.6 years (interquartile range: 1.2-4.7 years). Among patients with preserved LV systolic function, the occurrence of MACE (successful CTO-PCI vs. MT: 14.2 vs. 23.9%, adjusted HR 0.63, 95% CI 0.48-0.83, p = 0.001) in the successful CTO-PCI group was significantly lower than that in the MT group. For cardiac mortality, the univariate analysis showed that patients in the MT group had a higher risk than the successful PCI patients, although multivariate analysis failed to demonstrate a significant difference ( Figure 1). Analysis of MT compared to initial CTO-PCI (including successful CTO PCI and failed CTO PCI) in CTO patients with and without LV systolic dysfunction was also performed ( Table 7). Multivariable Cox regression analysis showed that when compared to MT, initial CTO-PCI was associated with fewer MACEs in both patients with preserved (adjusted HR 0.60, 95% CI 0.48-0.75, p < 0.001) or reduced (adjusted HR 0.65, 95% CI 0.46-0.93, p = 0.021) LV systolic function. A significant interaction was observed between LV function and therapeutic strategy for MACE (p = 0.038). Survival free from MACEs in successful CTO-PCI patients was significant among patients with preserved LV systolic function, but not in patients with LV systolic dysfunction (Figure 3). DISCUSSION The principal clinical findings in this large cohort study can be summarized as follows: CTO patients with LV systolic dysfunction had more comorbidities and complex lesions compared to patients with preserved LV systolic function; successful CTO PCI reduced MACE occurrence compared to MT alone in patients with preserved LV systolic function; and successful CTO recanalization was not associated with reduced MACE incidence or cardiovascular mortality compared to MT alone in CTO patients with LV systolic dysfunction. In terms of the baseline demographic characteristics in the present study, patients with LV systolic dysfunction had more comorbidities, such as previous MI and CKD, and presented with more complex lesions, including multivessel coronary artery disease and long occlusion. This was consistent with the findings from Tajstra et al. and Toma et al., which revealed that CTO patients with low LVEF had a higher prevalence of previous MI, CKD, multivessel disease, and severely calcified lesions. Of note, these patients are more likely to encounter contrastinduced nephropathy (CIN). Liu et al. performed preprocedural risk scoring to predict CIN after CTO intervention based on three factors, and LV dysfunction was included. These features were related to decreased success rates and increased periprocedural complications from CTO PCI, which have been considered by cardiac interventionist, and they were more often reluctant to perform more complex CTO procedures in high-risk subjects compared to those with preserved LVEF. Indeed, the rate of CTO PCI was only 3.5% in the COMMIT-HF registry, which enrolled 675 patients with systolic heart failure. In addition, the use of -blocker and ACEI or ARB was more common among patients with LV dysfunction. A recent random trial and meta-analysis demonstrated that these medications can improve LVEF and reduce adverse outcomes by reducing heart rate, altering vascular function, modifying neuro-endocrine response to heart failure, and reversing myocardial remodeling. CAD with severe LV dysfunction is associated with high morbidity and adverse outcomes, increased risk of ventricular arrhythmias, cardiogenic death, decreased quality of life, and high medical costs. As a subset of CAD, the presence of coronary CTO in patients with LV systolic dysfunction was related to a significant increase in all-cause death and cardiovascular mortality. In patients with CTO undergoing PCI, those with LV dysfunction encountered higher MACE occurrence and 3-fold risk of cardiac mortality (10.1 vs. 3.0%, p < 0.001). Recent registry studies also underscored that reduced LV systolic function is an independent predictor for mortality among patients undergoing CTO PCI. Improvement in LVEF after CTO recanalization in patients with LV dysfunction is still controversial. Recently, a weighted meta-analysis was performed by merging 34 studies and including a total of 2,243 patients to address the impact of CTO revascularization. It revealed that the absolute LVEF points increased by 4.44% after successful CTO recanalization. This was consistent with the findings in the latest meta-analysis performed by Megaly et al. in 2018. Nevertheless, the EXPLORE trial enrolled 304 patients with acute ST-segment elevation MI who underwent primary PCI and had concurrent CTO. Patients were randomized and assigned to early CTO-PCI or conservative treatment within 7 days from the infarction groups. After a follow-up of 4 months, there was no statistically significant difference in mean LVEF between the two groups. More recently, CTO patients in the REVASC trial were randomly assigned to revascularization or no revascularization of CTO groups. No benefit for CTO revascularization was observed with regard to segmental wall thickening or regional and global LV function after 6 months. The choice of optimal treatment strategy for CTO patients with LV dysfunction is often challenging. Of note, current guidelines for myocardial revascularization do not provide any evidence-based recommendation in terms of the most appropriate treatment strategy in such high-risk patients. A recent study based on 436 CTO patients with reduced LVEF showed that there were differences in clinical outcomes, such as death, MI, and stroke, between revascularized and not revascularized CTO (31. Even in the DECISION-CTO and the Euro-CTO trials, which compared revascularization to optimal MT for the treatment of CTO, the incidence of MACE, death, and repeated revascularization was comparable between the two groups. The number of CTO patients with LV systolic dysfunction is significant, and large contemporary CTO registries have reported that 40-53% of patients had LV systolic dysfunction. However, a comparison of successful CTO PCI vs. optimal MT effects on long-term clinical outcomes in patients with LV systolic dysfunction has not been performed until now, and the optimal treatment strategy for these high-risk subjects is unknown. In the present study, a good long-term outcome was achieved among patients with preserved LV function, but not with LV dysfunction, when they were referred for revascularization. These results were verified by both multivariable Cox regression analysis and PSM. Therefore, these findings are more convincing when compared to previous studies. Subgroup analysis showed that there was a significant interaction between LV function and therapeutic strategy for MACE, suggesting that superiority of revascularization over conservative therapy may be dependent on LV function. Notably, a failed CTO procedure is known to be related to a higher incidence of procedural complications and adverse events, leading to poor prognosis, especially in patients with high-risk factors. Therefore, in our study, patients with failed CTO-PCI were excluded. Clinical outcomes between successful CTO PCI and MT (CTO-PCI not attempted) groups were investigated to better reflect the overall risk of patients with coronary CTO. Our study differs from previous studies and is more reflective of the "real world" of clinical practice. To the best of our knowledge, this is the first large cohort study reporting on the long-term outcomes of successful recanalization compared to MT in unselected CTO patients with and without LV dysfunction. Galassi et al. confirmed that patients with LV dysfunction had a higher tamponade-induced coronary perforation (2.3 vs. 1.4%) and non-Q-wave MI (1.9 vs. 0.5%) incidence compared to those with preserved LV systolic function when they were undergoing CTO-PCI. These results indicate that CTO-PCI has to be carefully considered, taking into account multiple comorbidities, complex coronary lesions, operative complications, and clinical outcomes in this high-risk patient population. We noticed that the frequency of MACE is actually lower in failed CTO-PCI cases (12/116: 10.3%) than in successful CTO-PCI cases (37/125: 29.6%) with reduced LV systolic function. As we can see the Kaplan-Meier survival curve of MACE in Figure 1 and outcomes in successful recanalization group in Table 4, we found that TVR and MI were the predominant determinant of MACE. In our study, rate of multivessel disease was 80%, which was associated with high prevalence of MI, and some patients underwent CABG during follow-up. In addition, 70.9% patients were with proximal or mid CTO lesions, and more than half of them were with mid CTO lesions. The left (29.1%) patients were with distant CTO lesions. TVR was defined as repeat revascularization in the target vessel and included any emergency or elective CABG or repeat PCI according to Standard Definitions. Many patients were with prior coronary stenosis, new lesion or lesion progression before CTO location and they received following CABG or repeat PCI for these stenotic lesions when they had new or persistent angina, or even recent repeated MI after failed PCI and optimal medical therapy. Stent thrombosis and in-stent restenosis, which caused by what stent undersizing, presence of residual dissection, impaired TIMI flow and residual disease proximal or distal to the stent lesion, were main reasons for TVR, and often appeared in the first few months. The rate of TVR observed in our study was consistent with previously reported data, and TVR was the predominant determinant of MACE. In a recent study, Pinto et al. reported that, in patients with LV systolic dysfunction, the TVR rate was 29.8% in revascularized CTO group and 15.5% in not revascularized CTO group. In addition, Galassi et al. also reported, in CTO patients with low LVEF, the rate of TVR was 6.1% in successful CTO PCI group but 0% in failed CTO PCI group, which was consistent with our study. Some limitations of the study merit consideration. First, its non-randomized nature may cause a selection bias, which may influence the results due to confounding factors, though the multivariable Cox regression analysis and PSM were performed. Second, the percentage of patients with preserved LVEF experiencing diastolic dysfunction was not reported, and it may influence clinical outcomes. Third, the data on LVEF improvement and reverse remodeling during follow-up in patients with and without LV systolic dysfunction were not reported in the study. Fourth, since the study's time period was long (12 years), the changes in PCI materials and techniques or MT (such as anti-thrombotic medication or heart failure medication) may have an impact on clinical outcomes, though we have considered these factors in our previous studies. CONCLUSIONS Compared to MT alone in patients with native coronary CTOs, successful CTO-PCI may reduce the risk of MACE in patients with preserved LV systolic function, but not in patients with LV dysfunction. Well-designed and powered randomized investigations are needed to verify these findings from our large registry and further define the management strategy in this high-risk population. DATA AVAILABILITY STATEMENT The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ETHICS STATEMENT The studies involving human participants were reviewed and approved by First Affiliated Hospital of Dalian Medical University. The patients/participants provided their written informed consent to participate in this study. AUTHOR CONTRIBUTIONS LG, SM, HL, and RH prepared the manuscript. All authors contributed to the data collection, analyses, edited the draft manuscript, and approved the final manuscript. |
<gh_stars>10-100
package com.symphony.simpleserver.httpClient;
import org.apache.http.impl.client.HttpClientBuilder;
import org.apache.http.impl.client.HttpClients;
import org.apache.http.impl.conn.PoolingHttpClientConnectionManager;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Component;
@Component
public class HttpClientFactory {
private final PoolingHttpClientConnectionManager connectionManager;
@Autowired
public HttpClientFactory() {
this.connectionManager = new PoolingHttpClientConnectionManager();
}
public HttpClient createClient() {
final HttpClientBuilder httpClientBuilder = HttpClients.custom();
httpClientBuilder.setConnectionManager(connectionManager);
httpClientBuilder.setConnectionManagerShared(true);
return new HttpClient(httpClientBuilder.build());
}
}
|
package sa.gov.moe.etraining.module.notification;
import android.text.TextUtils;
import com.google.gson.annotations.SerializedName;
/**
{
"action": "course.announcement",
"course-name": "edX Demonstration Course",
"course-id": "edX/DemoX/Demo_Course",
"push_hash": "d41d8cd98f00b204e9800998ecf8427e",
"aps": {
"content-available": 1,
"alert": ""
},
"notification-id": "742e9881-e224-4ba9-b4d0-8a1f8b62b276"
}
**/
public class CourseUpdateNotificationPayload extends BaseNotificationPayload {
private @SerializedName("course-name") String courseName;
private @SerializedName("course-id") String courseId;
public String getCourseName() {
return courseName;
}
public void setCourseName(String courseName) {
this.courseName = courseName;
}
public String getCourseId() {
return courseId;
}
public void setCourseId(String courseId) {
this.courseId = courseId;
}
public boolean isValid(){
return !TextUtils.isEmpty(courseId);
}
}
|
Last month was a good one for Facebook in Africa, as growth went up in five of the seven countries that Facebook tracks. The group added almost half a million new users during the month of March, according to the latest stats from the Global Monitor data within Inside Facebook Gold.
Nigeria remains the third-fastest growing country, with 79,020 new users in March. With a population of 151 million people, it’s almost as big as all of north Africa; however, the country is still working to reach one percent penetration. That leaves it with the smallest relative pool of Facebook users in the African group, a situation likely to persist for a few months.
It’s followed by South Africa, which has a significantly higher penetration but is one of the two countries that didn’t add more users in March than in February, although the difference is only a few thousand MAU. The other country to lose some momentum was Ghana.
Between them is sandwiched Kenya with 32,820 new users and just 1.6 percent penetration — although a user commented last month that polls in the country show that 79 percent of internet users are on Facebook. That may actually be the case, as we count penetration against total population. Kenya could also have many more people connecting on mobile devices, like many of the other African countries.
At 10,474,660 MAU, Africa remains the smallest world region by number of Facebook users, running about even with the Arabian Peninsula sub-region. However, its penetration, at 2.8 percent, is slightly higher than Asia’s 2.3 percent. The above graph comes from the Global Monitor report, which provides in-depth info on 98 countries, but its original source is Facebook itself, which delays its data by several weeks. |
Employment Status, Duration of Residence and Mental Health Among Skilled Migrants To New Zealand: Results of a Longitudinal Study Aims: To report findings on employment, duration of residence and mental health from a longitudinal study of 107 skilled immigrants to New Zealand from the People's Republic of China, India and South Africa. Methods: Demographic and employment data were collected by face-to-face interviews using a structured questionnaire that included (as the mental health instrument) the General Health Questionnaire 12 (GHQ-12). The initial interview took place after the immigrants had been resident in New Zealand for an average of five months. Four subsequent interviews were conducted annually (19992002) on or about the anniversary of the first interview. Results and Conclusion: Rather than an initial euphoric period followed by a mental health crisis, the results indicated poor mental health status in the first two years irrespective of employment status. Thereafter, mental health slightly improved as did employment rates. A surprising result was that although the South Africans had the highest employment rate, there were neither substantial mental health differences among the three groups nor was there a significant improvement during the course of the longitudinal study. |
<gh_stars>0
/*
* Copyright 2021 Swisscom (Schweiz) AG
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.swisscom.ais.itext7.client.rest.model.signresp;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.annotation.JsonPropertyOrder;
@JsonInclude(JsonInclude.Include.NON_NULL)
@JsonPropertyOrder({
"async.ResponseID",
"sc.APTransID",
"sc.StepUpAuthorisationInfo",
"sc.RevocationInformation"
})
public class OptionalOutputs {
@JsonProperty("async.ResponseID")
private String asyncResponseID;
@JsonProperty("sc.APTransID")
private String scAPTransID;
@JsonProperty("sc.StepUpAuthorisationInfo")
private ScStepUpAuthorisationInfo scStepUpAuthorisationInfo;
@JsonProperty("sc.RevocationInformation")
private ScRevocationInformation scRevocationInformation;
@JsonProperty("async.ResponseID")
public String getAsyncResponseID() {
return asyncResponseID;
}
@JsonProperty("async.ResponseID")
public void setAsyncResponseID(String asyncResponseID) {
this.asyncResponseID = asyncResponseID;
}
public OptionalOutputs withAsyncResponseID(String asyncResponseID) {
this.asyncResponseID = asyncResponseID;
return this;
}
@JsonProperty("sc.APTransID")
public String getScAPTransID() {
return scAPTransID;
}
@JsonProperty("sc.APTransID")
public void setScAPTransID(String scAPTransID) {
this.scAPTransID = scAPTransID;
}
public OptionalOutputs withScAPTransID(String scAPTransID) {
this.scAPTransID = scAPTransID;
return this;
}
@JsonProperty("sc.StepUpAuthorisationInfo")
public ScStepUpAuthorisationInfo getScStepUpAuthorisationInfo() {
return scStepUpAuthorisationInfo;
}
@JsonProperty("sc.StepUpAuthorisationInfo")
public void setScStepUpAuthorisationInfo(ScStepUpAuthorisationInfo scStepUpAuthorisationInfo) {
this.scStepUpAuthorisationInfo = scStepUpAuthorisationInfo;
}
public OptionalOutputs withScStepUpAuthorisationInfo(ScStepUpAuthorisationInfo scStepUpAuthorisationInfo) {
this.scStepUpAuthorisationInfo = scStepUpAuthorisationInfo;
return this;
}
@JsonProperty("sc.RevocationInformation")
public ScRevocationInformation getScRevocationInformation() {
return scRevocationInformation;
}
@JsonProperty("sc.RevocationInformation")
public void setScRevocationInformation(ScRevocationInformation scRevocationInformation) {
this.scRevocationInformation = scRevocationInformation;
}
public OptionalOutputs withScRevocationInformation(ScRevocationInformation scRevocationInformation) {
this.scRevocationInformation = scRevocationInformation;
return this;
}
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append(OptionalOutputs.class.getName()).append('@').append(Integer.toHexString(System.identityHashCode(this))).append('[');
sb.append("asyncResponseID");
sb.append('=');
sb.append(((this.asyncResponseID == null) ? "<null>" : this.asyncResponseID));
sb.append(',');
sb.append("scAPTransID");
sb.append('=');
sb.append(((this.scAPTransID == null) ? "<null>" : this.scAPTransID));
sb.append(',');
sb.append("scStepUpAuthorisationInfo");
sb.append('=');
sb.append(((this.scStepUpAuthorisationInfo == null) ? "<null>" : this.scStepUpAuthorisationInfo));
sb.append(',');
sb.append("scRevocationInformation");
sb.append('=');
sb.append(((this.scRevocationInformation == null) ? "<null>" : this.scRevocationInformation));
sb.append(',');
if (sb.charAt((sb.length() - 1)) == ',') {
sb.setCharAt((sb.length() - 1), ']');
} else {
sb.append(']');
}
return sb.toString();
}
}
|
def _get_user_agent_for_current_platform (self):
import platform
self.log(msg='Building User Agent for platform: ' + str(platform.system()) + ' - ' + str(platform.machine()))
if platform.system() == 'Darwin':
return 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36'
if platform.system() == 'Windows':
return 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36'
if platform.machine().startswith('arm'):
return 'Mozilla/5.0 (X11; CrOS armv7l 7647.78.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.109 Safari/537.36'
return 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36' |
The events of Christmas Day that transpired at the San Francisco Zoo's big cat grotto are a tragedy for all involved - from the death of our beloved Siberian tiger, Tatiana, and the mauling death of 17-year-old Carlos Sousa Jr. and the injuries to his two companions. On behalf of the zoo board of directors and our many thousands of zoo members, I want to express our sincere and deep sympathies to the family of Carlos, the surviving individuals and their families.
We are helping the San Francisco Police Department conduct a top-to-bottom investigation concerning this incident. I am personally committed to finding all of the relevant facts. For the near future, the big cat grotto will remain closed while we complete security enhancements, including tempered glass walls that will raise the height of the big cat enclosures to 19 feet and the installation of electrified "hotwire," to ensure that this type of incident can be prevented in the future. In the meantime, the San Francisco Zoo will reopen today to provide public access to our many wonderful exhibits.
In the days to come, we will be consulting with a panel of zoo directors recommended by the accrediting institution for facilities like ours, the Association of Zoos and Aquariums, along with other experts, to review our policies and procedures. Our primary focus at the zoo has been and will continue to be a positive experience between zoo patrons and our animals. In short, we are dedicated to providing a safe and secure environment for our visitors and our animals.
Public scrutiny has rightly focused on the zoo's reaction to the events that unfolded on Christmas Day. We want to thank the San Francisco Police and Fire departments for their assistance. Equally important, I also want to acknowledge that it was zoo staff that guided first responders to areas where they were needed. It was a member of our zoo shooting team that first located Sousa and stayed with him. Another armed staff member joined San Francisco police in searching for the escaped Tatiana and was with officers when the San Francisco Police Department made the decision to shoot her.
I also want to express the zoo board's gratitude to zoo director Manuel Mollinedo and his staff, not only for their actions during a fast-moving series of events on Christmas Day but for their excellent performance in recent years. Since Manuel joined us in 2004, the zoo is in better physical and financial shape than it has ever been. Numerous facility upgrades have made the zoo a more attractive experience for our visitors. Through his leadership, the zoo has also improved and added numerous animal habitats, including the recently opened Hearst Grizzly Gulch, and broadened the diversity of zoo visitors. This has happened while the zoo continues to face the challenges of revamping historic buildings and managing these sites according to modern, professional zoo-keeping standards.
Today, the San Francisco Zoo has an annual attendance of more than 1 million visitors. We offer a low-cost, safe environment for the public to view and learn about wild animals, the environment and conservation.
Finally, I want to remind everyone of the proud history of the San Francisco Zoo, its contributions to our community and its vital functions as a civic institution and leader for wildlife preservation. Every exhibit we manage, every animal we house, is designed for the protection of those species and to further educate and inspire the public about the need for wildlife conservation. The opportunity, particularly for children, to see the rare and endangered wildlife of the world and witness these animals' awesome majesty is a unique experience that zoos provide. By promoting the preservation and conservation of wildlife, we help motivate future generations to become conservationists, veterinarians and biologists, who one day might help restore wildlife habitat and repopulate depleted species. The fundamental truth is that man has destroyed natural habitat for many animal species. As a result, there are more Siberian tigers living in captivity than those remaining in the wild. Zoos like ours play a critical role in maintaining the genetic diversity of species that would otherwise vanish from the face of the Earth within a generation.
I invite our members and the general public to visit the zoo with a renewed appreciation of how precious our animals are. I hope you will join me in supporting our mission to preserve and protect these animals for future generations. |
# Lichtenberg
from lichtenberg.util import draw_blur
import lichtenberg as lb
# Standard Libraries
from typing import List, Tuple, Callable
from random import randint
import tkinter as tk
from tkinter.filedialog import asksaveasfilename
import sys
# External Libraries
from PIL import Image, ImageTk, ImageMath
class Window:
"""
Widgets and Drawing
"""
def __init__(self, **kwargs):
self.image = None
self.root = tk.Tk()
title = kwargs.get("title", "tk App")
self._init_components(title)
def _init_components(self, title):
self.root.title(title)
self.root.resizable(False, False)
# self.root.bind("<Escape>", self.key_event)
self._init_left_frame()
self._init_right_frame()
self._init_menu()
def _init_left_frame(self):
frame = tk.Frame(self.root, bd=2)
# Upper - List Box
box = tk.Listbox(frame, selectmode=tk.SINGLE, width=30, bd=2, font=("", 12))
box.pack(fill=tk.BOTH, expand=1, side=tk.TOP)
box.bind("<<ListboxSelect>>", self.listbox_event)
self.listbox = box
# Lower - Frame
self._init_left_info_frame(frame)
frame.pack(side=tk.LEFT, fill=tk.Y, expand=1)
def _init_left_info_frame(self, parent):
frame = tk.Frame(parent, bd=2)
def callback(name):
return lambda v: self.scale_event(name, v)
def add_scale(label, id_, from_, to):
f = tk.Frame(frame)
tk.Label(f, text=label).grid(column=0, row=0)
scale = tk.Scale(f, from_=from_, to=to, length=150, resolution=0.01, orient=tk.HORIZONTAL, command=callback(id_))
scale.grid(column=1, row=0)
f.pack()
return scale
self.weight = add_scale("Weight:", "weight", 0.01, 2.00)
self.col_r = add_scale("Color-R:", "col-r", 0.01, 2.00)
self.col_g = add_scale("Color-G:", "col-g", 0.01, 2.00)
self.col_b = add_scale("Color-B:", "col-b", 0.01, 2.00)
tk.Label(frame, text="Update manually with Ctrl+R after changes").pack()
frame.pack(side=tk.BOTTOM, fill=tk.Y)
def _init_right_frame(self):
# Right - Image Label
label = tk.Label(self.root)
label.bind("<Button-1>", self.mouse_event) # Mouse-Left
label.bind("<Button-3>", self.mouse_event) # Mouse-Right
label.pack(side=tk.RIGHT)
self.label = label
def _init_menu(self):
# Menu
def callback(name):
return lambda: self.menu_event(name)
# File Menu
menubar = tk.Menu(self.root)
file_menu = tk.Menu(menubar, tearoff=0)
file_menu.add_command(label='Export as PNG', command=callback("file.export"))
file_menu.add_separator()
file_menu.add_command(label='Exit', command=callback("file.exit"))
menubar.add_cascade(label='File', menu=file_menu)
# Edit
edit_menu = tk.Menu(menubar, tearoff=0)
edit_menu.add_command(label='New Line', command=callback("edit.new"), accelerator="Right Button")
edit_menu.add_command(label='Remove Line', command=callback("edit.remove"), accelerator="Del")
edit_menu.add_command(label='Redraw with another seed', command=callback("edit.redraw"), accelerator="Ctrl+R")
edit_menu.add_separator()
edit_menu.add_command(label='Remove Last Point', command=callback("edit.back"), accelerator="Backspace")
menubar.add_cascade(label='Edit', menu=edit_menu)
self.root.config(menu=menubar)
self.root.bind_all("<Delete>", self.key_event)
self.root.bind_all("<BackSpace>", self.key_event)
self.root.bind_all("<Control-r>", self.key_event)
def change_image(self, new_image: Image):
self.image = ImageTk.PhotoImage(new_image)
self.label.configure(image=self.image)
def update_scales(self, weight, r, g, b):
self.weight.set(weight)
self.col_r.set(r)
self.col_g.set(g)
self.col_b.set(b)
# Key Events
def key_event(self, event):
pass
# Mouse Events
def mouse_event(self, event):
pass
# ListBox Events
def listbox_event(self, event):
pass
# Menu
def menu_event(self, name):
pass
# Scale
def scale_event(self, name, value):
pass
def run(self):
self.root.mainloop()
class Line:
"""
Data and Rendering
"""
def __init__(self, size: Tuple[int, int],
callback: Callable[["Image", "Image"], None] = None):
self.points: List[Tuple[int, int]] = []
self.callback = callback
self.weight = 0.50
self.col_r = 1.00
self.col_g = 1.00
self.col_b = 1.00
self.seed = randint(0, 2**16)
self.image_size = size
self.image = None
self.draw()
def add(self, point):
self.points.append(point)
self.draw()
def back(self):
try:
self.points.pop()
self.draw()
except IndexError:
pass
@property
def num_points(self):
return len(self.points)
@property
def color(self):
return self.col_r, self.col_g, self.col_b
def update_seed(self):
self.seed = randint(0, 2**16)
self.draw()
def __str__(self):
n = len(self.points)
w = self.weight
f = "" if n == 0 else str(self.points[0])
return f"points:{n} weight={w} {f}"
def draw(self):
last_image = self.image
image = Image.new("RGB", self.image_size)
lb.set_random_seed(self.seed)
if len(self.points) >= 2:
blur_params = [(0, 1), (1, 4), (1, 8), (2, 8)]
draw_blur(image, self.points, blur_params, self.weight, self.color, self.seed)
self.image = image
if self.callback:
self.callback(last_image, image) # notify update
class LineListManager:
"""
1. Synchronization between Line-data and ListBox
2. Image composition
sum = sigma(line.image)
But repetitive summation is costly. So apply differential update.
sum' = sum - last + current
"""
def __init__(self, listbox, callback_on_select: Callable[[Line], None], size: Tuple[int, int]):
self.listbox = listbox
self.data: List[Line] = []
self.current_index = -1
self.callback_on_select = callback_on_select
self.image_size = size
self.image = Image.new("RGB", size)
@property
def current_line(self):
if self.current_index < 0:
return None
else:
return self.data[self.current_index]
@property
def line_count(self):
return len(self.data)
def new_line(self):
# add new line
new_line = Line(self.image_size, self._update_image)
self.data.append(new_line)
self.current_index = self.line_count - 1
self.callback_on_select(self.current_line)
# sync with listbox
self._clear_all_selection()
self.listbox.insert(tk.END, str(self.current_line))
self.listbox.selection_set(first=self.current_index)
return False
def remove_line(self):
if self.current_line:
self._remove_line(self.current_index)
self._select_line(self.current_index)
return True
else:
return False
def add_point(self, point):
if self.current_line is None:
self.new_line()
self.current_line.add(point)
self._update_listbox()
update_require = self.current_line.num_points >= 2
return update_require
def remove_point(self):
if self.current_line:
self.current_line.back()
self._update_listbox()
return True
else:
return False
def update_seed(self):
if self.current_line:
self.current_line.update_seed()
return True
else:
return False
def _clear_all_selection(self):
for i in self.listbox.curselection():
self.listbox.selection_clear(first=i)
def _update_listbox(self):
self._clear_all_selection()
self.listbox.delete(self.current_index)
self.listbox.insert(self.current_index, str(self.current_line))
self.listbox.select_set(first=self.current_index)
def _remove_line(self, index):
self.data.pop(index)
self.listbox.delete(index)
def _select_line(self, index):
self._clear_all_selection()
n = self.line_count
if 0 <= index < n:
self.current_index = index
self.listbox.selection_set(first=self.current_index)
self.callback_on_select(self.current_line)
elif n > 0:
self.current_index = n - 1
self.listbox.selection_set(first=self.current_index)
self.callback_on_select(self.current_line)
else:
self.current_index = -1
@staticmethod
def _process_image(dest: Image, source: Image, op: str):
# Split
ch_list = ["R", "G", "B"]
dest_channels = [dest.getchannel(ch) for ch in ch_list]
source_channels = [source.getchannel(ch) for ch in ch_list]
# Operate
dest_channels = [ImageMath.eval(f"d{op}s", d=d, s=s).convert("L")
for d, s in zip(dest_channels, source_channels)]
# Merge
return Image.merge(dest.mode, dest_channels)
def _update_image(self, last: Image, current: Image):
# Update sum
# sum' = sum - last + current
if last:
self.image = self._process_image(self.image, last, "-")
if current:
self.image = self._process_image(self.image, current, "+")
class App(Window):
"""
Event Dispatching
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.base_image = self._init_image(**kwargs)
initial_image = self.base_image.copy()
self.change_image(initial_image)
self.result_image = initial_image
self.lines = LineListManager(self.listbox, self._callback_on_select, initial_image.size)
def _init_image(self, **kwargs):
filepath = kwargs.get("filepath", None)
if filepath:
image = Image.open(filepath)
else:
width = kwargs.get("width", 640)
height = kwargs.get("height", 480)
image = Image.new("RGB", (width, height))
return image
def _update(self):
new_image = self.lines.image
self.change_image(new_image)
self.result_image = new_image
def key_event(self, event):
update = False
if event.keycode == 0x1b: # Escape
self.menu_event("file.exit")
elif event.keycode == 0x2e: # Delete
self.menu_event("edit.remove")
elif event.keycode == 0x08: # Backspace
self.menu_event("edit.back")
elif event.keycode == 0x52: # Ctrl+R
self.menu_event("edit.redraw")
if update:
self._update()
def mouse_event(self, event):
update = False
if event.num == 1:
update = self.lines.add_point((event.x, event.y))
elif event.num == 3:
update = self.lines.new_line()
if update:
self._update()
def listbox_event(self, event):
selections = self.listbox.curselection()
if len(selections) > 0:
self.lines.current_index = selections[0]
self._callback_on_select(self.lines.current_line)
def menu_event(self, name):
update = False
if name == "file.export":
self._export_png()
elif name == "file.exit":
self.root.quit()
elif name == "edit.new":
update = self.lines.new_line()
elif name == "edit.remove":
update = self.lines.remove_line()
elif name == "edit.redraw":
update = self.lines.update_seed()
elif name == "edit.back":
update = self.lines.remove_point()
if update:
self._update()
def scale_event(self, name, value):
if self.lines.current_line:
value = float(value)
if name == "weight":
self.lines.current_line.weight = value
elif name == "col-r":
self.lines.current_line.col_r = value
elif name == "col-g":
self.lines.current_line.col_g = value
elif name == "col-b":
self.lines.current_line.col_b = value
def _callback_on_select(self, line: Line):
# The selected line is changed. Renew values of scales
self.weight.set(line.weight)
self.col_r.set(line.col_r)
self.col_g.set(line.col_g)
self.col_b.set(line.col_b)
def _export_png(self):
filepath = asksaveasfilename(initialdir=".", title="Export as PNG",
filetypes=[("Portable Network Graphics(PNG) file", "*.png")],
defaultextension=".png")
if filepath == "":
return
else:
self.result_image.save(filepath)
def main():
kwargs = {"title": "Thunder Painter"}
if len(sys.argv) == 2:
# Open with Base Image
base_image_path = sys.argv[1]
kwargs["filepath"] = base_image_path
elif len(sys.argv) == 3:
# Open with Image Size
kwargs["width"] = int(sys.argv[1])
kwargs["height"] = int(sys.argv[2])
app = App(**kwargs)
app.run()
if __name__ == "__main__":
main()
|
class MonzoData:
"""Class to query Monzo API."""
__slots__ = [
'_accounts',
'_transactions'
]
def __init__(self):
"""Initialize MonzoData."""
self._accounts: List[Account] = []
self._transactions: Dict[str, List[Transaction]] = {}
def get_accounts(self, auth: Authentication) -> List[Account]:
"""
Fetch Monzo accounts.
Args:
auth: Monzo Authentication object
Returns:
List of Account objects
"""
if not self._accounts:
self._accounts = Account.fetch(auth=auth)
return self._accounts
def get_transactions(self, auth: Authentication, account_id: str) -> List[Transaction]:
"""
Fetch account transactions.
Args:
auth: Monzo Authentication object
account_id: ID of the account to fetch transactions for
Returns:
List of transaction objects
"""
today = datetime.date.today()
since_date = today - datetime.timedelta(days=7)
since = datetime.datetime(
year=since_date.year,
month=since_date.month,
day=since_date.day,
)
if not self._transactions or account_id not in self._transactions:
try:
transactions = Transaction.fetch(auth=auth, account_id=account_id, since=since, expand=['merchant'])
except MonzoPermissionsError:
transactions = []
self._transactions[account_id] = transactions
return self._transactions[account_id]
@staticmethod
def get_raw_request(
auth: Authentication,
path: str,
authenticated: bool = True,
request_type: str = 'get',
headers: Optional[Dict[str, Any]] = None,
parameters: Optional[Dict[str, Any]] = None,
):
"""
Perform a raw request.
Args:
auth: Monzo Authentication object
path: API path for request
authenticated: True if call should be authenticated
request_type: HTTP method to use (DELETE, GET, POST, PUT)
headers: Dictionary of headers for the request
parameters: Dictionary of parameters for the request
Returns:
List of Account objects
"""
try:
res = auth.make_request(
path=path,
authenticated=authenticated,
method=request_type,
data=parameters,
headers=headers,
)
records = res['data']
except MonzoPermissionsError:
records = {'error': 'Permissions error'}
return records |
Catalysis using gold nanoparticles decorated on nanocrystalline cellulose. A novel nanocomposite was prepared by deposition of carbonate-stabilized Au nanoparticles (AuNPs) onto the surface of poly(diallyldimethyl ammonium chloride) (PDDA)-coated carboxylated nanocrystalline cellulose (NCC). The hybrid material possessed AuNPs (1.45% by weight) with an average diameter of 2.95 ± 0.06 nm. The catalytic activity of AuNP/PDDA/NCC for reducing 4-nitrophenol to 4-aminophenol was compared to other Au-supported composites. An activation energy of 69.2 kJ mol(-1) was obtained for the reaction. Indeed, the reaction rate constant k of (5.1 ± 0.2) 10(-3) s(-1) was comparable to the benchmark literature value obtained using AuNPs (<5 nm in diameter) decorated on a network of crystalline cellulose fibers. Our strategy promotes the use of natural resources to prepare reusable hybrid inorganic-organic materials for important reactions with facilitated product isolation/purification. |
REASONS FOR DESTRUCTION OF CONTINUOUS WELDED RAIL TRACKS The main reasons for occurrence of operational defects in continuous welded rail tracks, the main types of their destruction and causes of their occurrence are considered. The technologies for manufacture of the rails are analyzed, including heat treatment along the entire length of the rail, processing the ends of the rail, surface hardening and anti-flake treatment were analyzed. |
def retag_self(self) -> None:
assert self.index_mapper is not None, 'cannot retag when index mapper is None'
self_to_main_index_map = self.index_mapper.get_reverse_main_index(self.name)
new_self = self.__class__(self)
for atom in new_self:
atom.tag = self_to_main_index_map[atom.index]
return new_self |
/* Annot8 (annot8.io) - Licensed under Apache-2.0. */
package io.annot8.common.implementations.registries;
import java.util.Optional;
import io.annot8.common.implementations.factories.ContentBuilderFactory;
import io.annot8.core.data.Content;
/** A registry of content builder factories. */
public interface ContentBuilderFactoryRegistry {
/**
* Get the (best) content builder factory for the content class requested, if available.
*
* @param contentClass the content type required
* @return builder (if available for that content class)
*/
<D, C extends Content<D>> Optional<ContentBuilderFactory<D, C>> get(Class<C> contentClass);
<D, C extends Content<D>, I extends C> void register(
Class<C> contentClass, ContentBuilderFactory<D, I> contentBuilderFactory);
}
|
1. Field of the Invention
This invention relates generally to radio-controlled miniature vehicles, and more particularly to a radio-controlled vehicle having four-wheel steering capabilities.
2. Description of Related Art
Radio-controlled land vehicles have become quite developed and sophisticated and, in many cases, mirror the functional and structural capabilities of their full-sized drivable counterparts. One aspect of full-sized off road and land buggy type vehicles which has been imported into the radio controlled models is that of four-wheel drive characteristics. These off road and land buggy type vehicles utilize extreme four-wheel drive characteristics to achieve very unique steering performance characteristics.
A number of prior art systems and devices are known to afford various four-wheel steering characteristics. Mullaney et al. teaches a radio controlled toy vehicle with movable front end in U.S. Pat. No. 5,882,241. U.S. Patent Application Publication US 2006/0289218 to Allen discloses a vehicle capable of turning 360° and a four-wheel steering assembly is disclosed in U.S. Pat. No. 7,347,434 to Lewis, et al. United Kingdom Patent 2,278,064 to Kang teaches a radio-controlled car with rotatable driver having two independent motor-gear boxes to turn front and wheels in the same or opposite directions.
U.S. Pat. No. 3,305,041 to Schramm discloses a four-wheel steering system for tractors having front and rear steerable pairs of wheels. Ishii et al. teaches a steering control system for vehicular four-wheel steering mechanisms in U.S. Pat. No. 4,105,086. A multiple wheel steering mechanism is disclosed in U.S. Pat. No. 4,589,510 to Diierwald et al.
Chikuma et al. teaches a four wheel steering apparatus in U.S. Pat. No. 5,048,852 which permits increase of the steering angle of the rear wheels to be effected only in a relation to the steering angle of the front wheels and which also permits a reduction of the steering angle of the rear wheels independently of the steering of the front wheels
U.S. Pat. No. 5,503,586 to Suto discloses a gear system for use as a steering system in a toy vehicle for controlling the vehicle's wheels so as to have the vehicle move ahead or turn on the spot. A remotely operated model vehicle height adjustment device is disclosed in U.S. Pat. No. 5,527,059 to Lee, Jr.
Piston or gas engine powered R/C land vehicles are known to achieve very high speeds in the range of 40 to 60 mph. Utilized conventional two-wheel or four-wheel steering technology for such vehicles taxes the capabilities of current R/C servo or actuator and linkage technology with respect to both strength and stability.
The present invention provides an improved four-wheel steering system for R/C vehicles of the high performance and high speed capability category which enhances both strength and performance capability of the four-wheel drive steering system by providing two separate servo or actuator mechanisms simultaneously delivering power to the steering system in parallel to provide extreme performance and stability in a four-wheel drive steering system.
The foregoing examples of the related art and limitations related therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those skilled in the art upon a reading of the specification and a study of the drawings. |
///Publish `message` to `channel`. Returns how many clients received the message.
pub async fn publish<C, M>(&mut self, channel: C, message: M) -> Result<isize>
where
C: AsRef<[u8]>,
M: AsRef<[u8]>,
{
let command = Command::new("PUBLISH").arg(&channel).arg(&message);
self.run_command(command).await.map(|i| i.unwrap_integer())
} |
Iceland on Monday (15 April) became the first European country to sign a free trade agreement with China after six years of negotiations.
The agreement will remove tariffs on most goods.
Experts say China is interested in gaining a foothold in the northern territory and hopes to become a permanent observer at the eight-nation Arctic Council next month.
The giant has labelled itself a “near Arctic nation” though its closest geographical point to the ice-capped region is some 1,600 kilometres away.
Receding ice caps are opening up trade routes that could reportedly cut a third of the shipping time between Shangai and Hambourg. Iceland's foreign minister told The Wall Street Journal that the two nations are also in talks on exploring the vast reserves of oil buried beneath the north-eastern coastal waters.
At €10.7 billion, the tiny Nordic country’s GDP is dwarfed when compared to China’s €5.3 trillion.
Trade between the two is relatively small.
Iceland shipped mostly fish last year worth some €47,6 million to the Chinese mainland and imported some €264 million in goods and services.
China is now Iceland’s fourth biggest importing country and the biggest trading partner in Asia. The deal could see an additional boost to the trade figures as Iceland continues to recover from a banking sector that collapsed in 2008 and saw its economy in ruins.
The country paid back over half the loans received from the IMF and Nordic countries ahead of time.
In 2010, it became a candidate country to join the European Union but suspended talks in January following domestic opposition.
The island-nation currently enjoys bilateral free trade agreements with the bloc through its membership in the European Free Trade Association and the European Economic Area. Though the trade deal will not enable China to gain access to the EU market, reports the New York Times.
Meanwhile, the Union remains Iceland’s largest trading partner.
In 2011, Iceland exports to the Union totalled more than €933m which is equivalent to 5 percent of all EU fish imports. Manufactured exports such as aluminium and medical and pharmaceutical products are also on the rise.
Icelandic taxpayers are not liable to finance compensation of an estimated 350,000 British and Dutch citizens who lost their savings in the wake of the collapse of the country's banking sector in 2008, following a landmark court ruling.
US Secretary of State John Kerry will be in Brussels at the beginning of the week while the end of the week will see EU-ambivalent Icelanders go to the polls.
Merkel has pledged her support to China in a trade row with the European Commission over solar panels and wireless equipment. |
import { Option } from "../..";
export declare const getName: (first: Option<string>, last: Option<string>) => Option<string>;
export interface FullName {
firstName: string;
lastName: string;
}
export declare const getFullName: (first: Option<string>, last: Option<string>) => Option<FullName>;
|
#include <stdio.h>
void letter(FILE *);
int LetterNumber(char ch);
int main(void)
{
char ch;
FILE * fp;
int num[1000];
fp = fopen("Exercise9_7.c", "r");
letter(fp);
return 0;
}
void letter(FILE * fp)
{
char ch;
int factor = 1;
while ((ch = getc(fp)) != EOF)
{
if (LetterNumber(ch) == -1)
{
printf("Not all the factor were letters. \n");
factor = 0;
break;
}
}
if (factor == 1)
{
while ((ch = getc(fp)) != EOF)
printf("%d\t", LetterNumber(ch));
}
}
int LetterNumber(char ch)
{
int num = 0;
if (ch >= 65 && ch <= 90)
{
num = ch - 65 + 1;
return num;
}
if (ch >= 97 && ch <= 122)
{
num = ch - 97 + 1;
return num;
}
else
{
num = -1;
return num;
}
} |
<reponame>18296154813/eam-transform<gh_stars>0
package com.jtj.web.dto;
import com.jtj.web.common.BaseDto;
public class PointDto extends BaseDto {
private String name;
private Long pid;
private Integer order;
private Integer roleId;
public String getName() {
return name;
}
public void setName(String name) {
this.name = name;
}
public Long getPid() {
return pid;
}
public void setPid(Long pid) {
this.pid = pid;
}
public Integer getOrder() {
return order;
}
public void setOrder(Integer order) {
this.order = order;
}
public Integer getRoleId() {
return roleId;
}
public void setRoleId(Integer roleId) {
this.roleId = roleId;
}
}
|
def create_item_dict(df, id_col, name_col,author_col):
item_dict = {}
for i in range(df.shape[0]):
item_dict[(df.loc[i, id_col])] = df.loc[i, name_col] +' : '+df.loc[i, author_col]
return item_dict |
Host Plant Reaction to Rice Yellow Mottle Virus and Allelic Diversity of RYMV1 Gene in Rice Cultivars in Uganda Rice Yellow Mottle Virus (RYMV) disease is endemic to Africa where it affects rice production. Host plant resistance would form a cost effective and sustainable option for managing the disease. However, there is still lack of knowledge on the reaction of rice germplasm and the genetic basis of their resistance/susceptibility to RYMV coupled with lack of molecular markers to facilitate the development of RYMV resistant varieties. We screened 56 rice accessions drawn from breeding lines and varieties commercially cultivated in Uganda for their resistance/susceptibility to RYMV. We also sought to develop and validate allele specific markers for RYMV1 alleles. The rice accessions showed variation in their reaction to RYMV; 14, 12, 17 and 13 accessions were categorized as resistant, moderately resistant, moderately susceptible and susceptible respectively. Among the resistant accessions, five possessed a single SNP (G/A) corresponding to the rymv1-2 allele. The new accessions can be deployed as resistant cultivars and/or used to introgress rymv1-2 allele into susceptible adapted cultivars throughout Africa. We developed two functional allele specific markers, which co-segregated with the rymv1-2 resistance allele in an F2 population and clearly differentiated between the susceptible and resistant individuals in the F2 population. The functional allele specific marker developed in this study can be used in MAS programs for introgression of rymv1-2 resistance allele. Introduction In Uganda, rice is important both as a food security crop and a source of income for many smallholder farmers. The total rice produced in Uganda has steadily increased from 177,857 tonnes in 2008 to 200,000 tonnes in 2020. Rice production is still below the projected quantity of 680,000 Mt required to make Uganda self-sufficient in rice (). This target has not been achieved to date due to many abiotic and biotic stresses affecting rice production in Uganda. Rice Yellow Mottle Virus (RYMV) disease caused by a sobemovirus is a major disease limiting rice production in lowland and irrigated ecologies in Africa (). The virus is not transmissible via seed () but can be transmitted by insects () and mechanically by rubbing inoculum onto the leaves by hand (Pinel-Galzi et al., factor, eIF(iso)4G1 () and four independent RYMV1 resistance alleles (rymv1-2, rymv1-3, rymv1-4 and rymv1-5) have been reported in diverse Oryza sativa and Oryza glaberrima accessions. Preliminary breeding for RYMV resistance has focused on introgressing the rymv1-2 allele from O. sativa background as it produces fertile F1 hybrids. This has made the RYMV1 locus a major focus of analysis in the past decade (;;;). RYMV1 resistance gene and its alleles have been tagged by genetically linked DNA markers or other PCR based markers ((Albar et al.,, 2006). As such, indel markers corresponding to 10-100 base pair insertion/deletions and cleaved amplified polymorphic sequences (CAPS) markers corresponding to single nucleotide polymorphisms (SNPs) within the RYMV1 gene were developed (;). However, indel markers and CAPS require digestion of PCR amplicons for genotyping, making them costly and time consuming; they, thus, cannot be used for routine germplasm screening for RYMV resistance. In addition, owing to genetic recombination, the genetically linked markers may give rise to false positives (). Allele specific markers that target polymorphism within a gene of interest provide more efficient selection of desired genotypes compared to DNA markers at a nearby but functionally irrelevant site (Andersen & Lbberstedt, 2003). There is, therefore, need to develop alternative, rapid, accurate and affordable SNP-based tools for screening RYMV1 alleles. Attempts have been made to screen some of the rice accessions used in this study for resistance to RYMV using visual assessment of symptoms progress (;). However, there is lack of knowledge on the genetic basis of RYMV resistance of most of the accessions with reference to the RYMV1 gene allele diversity. In addition, there is a limited number of molecular makers to facilitate/accelerate routine screening for RYMV resistant accessions and breeding improved varieties that are resistant to RYMV. The objectives of this study were to: 1) to screen rice accessions for their reaction to RYMV; 2) determine the allele diversity of the RYMV1 resistance gene in selected rice accessions that have shown resistance or tolerance to RYMV in Uganda, and 3) develop and validate SNP markers that are useful for high throughput genotypic selection for RYMV resistance. Source and Description of Materials Fifty-six rice accessions were selected from a collection of rice germplasm that is currently held at the National Crops Resources Research Institute (NaCRRI). The germplasm collection was assembled from rice breeding centers around the world to support rice breeding efforts in Uganda () and were selected based on aroma and resistance to RYMV (Table 1). The checks included IR64 (susceptible to RYMV) and Gigante (resistant to RYMV) for phenotypic evaluation, and Gigante, Tog5681, Tog5672, and Tog5674 for genotypic evaluation (Table 1). Gigante, Tog5681, Tog5672, and Tog5674 were chosen because they carry the rymv1-2, rymv1-3, rymv1-4 and rymv1-5 recessive alleles, respectively. An F2 population was developed from the cross between accession ARS126-3-B-1-2 and SUPA5 for the purpose of validating new markers. ARS126-3-B-1-2 is resistant to RYMV and carries the rymv1-2 allele while SUPA5 is susceptible to RYMV and lacks the rymv1-2 allele but is also highly aromatic, a trait that is highly sought after by consumers. Source of RYMV Isolates Isolates of RYMV were obtained from rice fields in RYMV hotspots: Iganga in the eastern region of Uganda at coordinates 00 o 37960N; 33 o 32996E (Iganga isolate) and from NaCRRI, Namulonge in Central Uganda at coordinates 00 o 31834N; 32 o 37443E (Namulonge isolate). The isolates were multiplied and maintained separately on the standard susceptible cv. IR64. Phenotypic Evaluation Phenotyping for Rice Yellow Mottle Virus was done at NaCRRI, Namulonge, Wakiso district. NaCRRI is about 30 km northeast of Kampala in the central region of Uganda at the coordinates 0 o 3130 N 32 o 3654 E (Latitude: 0.5250 and Longitude: 32.6150). The area has a tropical climate with a bimodal rainfall regime; the first rainy season begins in March and ends in May and the second from August to December. The rest of the months are relatively dry and hot. The average annual rainfall and temperature are 1242 mm and 21.7 o C, respectively. The elevation is 1160 m above sea level, with undulating topography. The soils are mainly oxisols (ferralsols) in the plains and hills, and vertisols in the swamps and valleys (). Genotypic Evaluation Genotyping for RYMV1 alleles was done at the Biotechnology Laboratory at Makerere University Agricultural Research Institute, Kabanyolo (MUARIK) in Kampala, Uganda. Multiplication and Maintenance of Isolates The isolates were multiplied using rice cv IR64 which was planted in six 15-litre rectangular plastic buckets filled with black forest soil. Two weeks post germination, rice plants in three buckets were inoculated with RYMV isolate from Iganga and the plants in the other three buckets were inoculated with isolates from Namulonge and kept in separate screenhouses. Symptomatic plants were used as sources of inoculum. Experimental Set up Individual rice accessions were planted in 15-litre rectangular plastic buckets filled with black forest soil. Nine seeds were planted per bucket in two separate screen houses following a randomized complete design with two replications. The plants were watered every other day to keep the soil moist. Weeding was done by hand picking. Fertilizer application followed two regimes; Urea (10 g per bucket) applied at 21 days after planting (DAP) and NPK (10 g per bucket) applied 42 DAP to coincide with booting. Inoculation of Test Materials Two weeks post germination; all nine plants per bucket were inoculated with RYMV virus isolates following the procedure of Pinel-Galzi et al.. Infected leaves of cv IR64 on which the isolates were maintained were picked and cut in 0.5 to 1 cm pieces and placed in a mortar and finely ground. Distilled water was then added at a ratio of 10 ml of water to 1 g of leaves. Using a piece of paper towel dipped into the inoculum, each leaf was rubbed twice from the base to the tip. Phenotyping for Resistance to Rice Yellow Mottle Virus Disease Symptom intensity on leaves was monitored weekly from 14 days post inoculation (dpi) until anthesis of the earliest maturing accession (35 dpi). The standard evaluation system (SES) for rice was used to phenotype for RYMV disease using a scale of 1-9 where; 1 = No symptom observed; 3 = Leaves green but with sparse dots or streaks; 5 = Leaves green or pale green with mottling; 7 = Leaves pale yellow or yellow and 9 = Leaves turn yellow or orange, no flowering or some plants dead. DNA Extraction and Analysis Two weeks post germination just before inoculation, one leaf was picked from each of the nine plants per accession, wrapped in aluminum foil and stored at-80 °C. Leaf tissues of equal sizes were pooled from all nine plants to make a composite sample per accession. Genomic DNA was extracted from these leaves using the cetyl trimethylammonium bromide lysis buffer. Briefly, ground leaf powder was suspended in 1 ml of buffer solution kept at 65 °C for 1 hr. Upon cooling for 5 minutes a 24:1 chloroform:isoamyl alcohol mixture was added and mixed thoroughly, then span for 20 minutes at 10,000 rpm. The aqueous layer was transferred to a fresh tube and an equal volume of ice-cold isopropanol was added and inverted 10 times and then span for 30 minutes to precipitate the DNA. The supernatant was discarded, and the pellet was washed with 70% ethanol and air dried. The dry pellet was then dissolved in 250 L of 1 TE buffer solution. The quality of DNA was assessed by running an aliquot of 5 L of each extracted DNA sample in a 1% agarose gel electrophoresis stained with EZ TM (0.8 g -1 mL) (AMRESCO, Ohio, USA). The concentration and purity of DNA was determined using a NanoDrop 2000 spectrophotometer (ThermoFisher Scientific, Waltham, MA USA) at wavelengths 230, 260 and 280 nm. Primers and Primer Design Primers that amplify the regions of rymv1-2, rymv1-3, rymv1-4, and rymv1-5 alleles of the RYMV1 resistance gene were designed from nucleotide sequences retrieved from the NCBI website corresponding to the RYMV1 locus in cvs. Gigante (carrying the 1-2 allele), Tog5681 (carrying the 1-3 allele), Tog5672 (carrying the 1-4 allele) and Tog5674 (carrying the 1-5 allele). Primers Rymv1A and Rymv1B were designed using Primer1, a web primer design program (Collins & Ke, 2012). Additional primers obtained from Thiml et al. and kindly provided by Professor Laurence Albar (Institute of Research for Development (IRD), Marseille, France) were also used to study the RYMV1 allele diversity (Table 2). Note. * F1R1 is an internal control primer for the entire RYMV1 gene. Polymerase Chain Reaction Using Allele Specific Markers Genomic DNA from fifty-six accessions was subjected to Polymerase Chain Reaction (PCR) amplification each performed in a final volume of 10 L containing 1 L of 50 ng template DNA, 1 L of each primer, 5L of AccuPower PCR Premix (Bioneer Corporation, Daejeon, South Korea) and topped up using sterile distilled water. Primer Rymv1D was excluded in this stage because it was not producing clear bands separation after digestion and electrophoresis. The PCR cycle was programmed for an initial 5-min at 95 °C; the annealing temperatures were progressively reduced from 64 to 56 °C for 8 cycles and then maintained at 56 °C for the remaining 27 cycles and a final extension of 5 min at 72 °C. The amplification products were separated using a 1.5% agarose gel stained with EZ TM (0.8 ug -1 mL) (AMRESCO, Ohio, USA) and viewed under UV light using BioDoc-It TM Imaging System (Applied BiosystemsWaltham, Massachusettes, USA). A 100 bp ladder (Bioneer Corporation, Daejeon, Korea) was used to estimate the PCR fragment sizes. Polymerase Chain Reaction Using primer Rymv1D Based on the RYMV resistance phenotyping and genotyping, 38 accessions that were resistant or tolerant to either RYMV isolates (disease score: 1 to 5) were selected for further molecular screening to confirm the allelic status of their RYMV1 gene and to assess the frequency of RYMV1 alleles. Four susceptible accessions (IR64, SUPA5, Supa Local and Basmati370) were added as controls. Genomic DNA from the 42 accessions were further subjected to PCR using primer Rymv1D following the same PCR conditions as the allele specific markers. jas.ccsenet.org Journal of Agricultural Science Vol. 14, No. 6; 2022 2.8.5 DNA Sequencing and Sequence Analysis PCR products of primer Rymv1D were shipped to Macrogen Europe B.V (Amsterdam, the Netherlands) for sequencing. The PCR products were sequenced in the reverse direction using primer Rymv1D (R) (AGTAGCTCACCAATTAGACGGA) to obtain partial sequences of the RYMV1 gene. Each fragment was sequenced at least four times and high-quality consensus sequences were used for data analysis. 2.8.6 Analysis of Sequences for Presence of RYMV1 Alleles Sequences were manually edited by trimming off the trace data and reverse complements obtained using MEGA version X () before aligning using Clustal W (). Multiple sequence alignments were done to identify the presence of insertions/deletions and SNPs in the accessions. The allele sequence obtained from each accession was compared with that of the gene sequences from Gigante, Tog5672, Tog5674, Tog5681 and IR64 (possessing the susceptibility allele). Allelic and nucleotide diversity were then analyzed for percentage sequence identity between alleles by comparing the percentage sequence identity between allele and reference sequence by pairwise alignment using NCBI BLAST (). Development and Application of Functional Markers in Segregating F 2 Population Sequence divergences distinguishing the rymv1-2 allele from other alleles were used to develop markers by designing flanking primers, which were then used to genotype the F2 population. The genome sequence portion of ~50 bp flanking the target SNP on either side was sent to LGC genomics (Hoddesdon, UK) to design the Kompetitive Allele-Specific PCR (KASP) primer Rymv1-2kbd. The KASP assay was used to interrogate the SNP (G/A) corresponding to the rymv1-2 allele of the RYMV1 gene in 40 individuals of an F 2 segregating population generated from a cross between ARS126-3-B-1-2 and SUPA5. Primer Rymv1A was also converted to a marker as it gave rise to unambiguous polymorphic PCR products and used to genotype 71 individuals of the same F2 population. KASP assay genotyping was performed using the LGC SNPline system following standard KASP protocols (LGC Genomics) at the Makerere University Agricultural Research Institute Kabanyolo (MUARIK) Biotechnology Laboratory using the Real Time PCR machine 7500 (Applied Biosystems, Waltham, Massachusettes, USA). The KASP genotyping mix was prepared in a 96 well plate containing 5uL of DNA, 5uL of 2x-KASP master mix, 0.14 KASP assay mix and topped up to 10 L using sterile water. The following cycling conditions were used: Stage 1: 30 C 60s (pre-read); Stage 2: 94 C for 15 min hot start Taq An endpoint multiplexed PCR amplification was performed using the functional marker Rymv1A in a final volume of 10 L containing 1 L of DNA template (50 ng), 1 L of each primer and 5 L of AccuPower PCR Premix (Bioneer Corporation, Daejeon, South Korea). The final reaction volume was completed to 10 L using sterile distilled water. The PCR cycle was programmed for an initial 5 min at 95 °C; the annealing temperatures were progressively reduced from 68 °C to 60 °C for 8 cycles and then maintained at 60 °C for the remaining 27 cycles and a final extension of 5 min at 72 °C. Data Analyses Analysis of variance (ANOVA) was performed to determine the effect of accession, time of symptom assessment and isolate on the severity of RYMV using R version 3.6.3 software (R Core Team, 2017). Severity data were also used to compute the area under symptom progress curve (AUSPC) using the method of Thiml et al. as: where, S i corresponds to the severity score at the date t i, in days, S i+1 corresponds to severity score at date two and t (i+1) corresponds to date two. The AUSPC values were then arranged in descending order and the mean and standard deviation calculated. The accessions were then categorized into various groups based on the standard deviation values. Accessions with SD values falling to the right of the mean (positive) on the mean distribution curve were considered either as moderately susceptible (0 < SD < 1), susceptible (1 < SD < 2) or highly susceptible (SD > 2) while accessions falling to the left (negative) of the mean were considered either as moderately resistant (0 < |SD| < 1), resistant (1 < |SD| < 2) or highly resistant (|SD| > 2) (;). Accessions displayed significantly different (P-value < 2 10 -16 ) reaction patterns over the time of disease assessment ( Figure 1). The resistant accessions did not develop RYMV symptoms even after 35 dpi while the most susceptible accessions; IR64, SUPA1, Jaribu and Komboka developed initial symptoms of RYMV before 14 dpi (as early as 10 dpi). By 14 dpi some accessions from IRRI Supa series (2, 3, 4, 5, and 6) including IR64 and Komboka had already registered a score of 7 on the severity scale of 1-9 (Figure 1). At 35 dpi, most of the susceptible accessions had a score of 5 and above with varying symptoms ranging from delayed flowering or incomplete emergence of the panicles to no flowering, plant height reduction and in extreme cases, death of plants. The isolates and their interaction with accessions did not influence the severity of RYMV disease and AUSPC (P > 0.05). The AUSPC was significantly affected by accessions (P-value < 2 10 -16 ) but was not affected by isolate (P-value = 0.916) and the interaction between accession and isolate (P-value = 1.000). The AUSPC values of the different accessions ranged from 0 to 168 with a mean of 73.437 and standard deviation of 55.342 (Table 3). There were 14, 12, 17 and 13 accessions categorized as resistant, moderately resistant, moderately susceptible and susceptible respectively (Table 3). Accessions with positive SD between zero and one (0 < SD < 1) were considered as moderately susceptible, those with positive SD between 1 and 2 (1 < SD < 2) were considered susceptible while accessions with negative jas.ccsenet.org Vol. 14, No. 6; SD falling between zero and negative one (0 < |SD| < 1), were considered as moderately resistant and those with negative SD falling between 1 and 2 (1 < |SD| < 2) were considered as resistant. Occurence of RYMV1 Resistance Alleles Using Sequence Analysis of PCR Products Eight out of the 42 accessions had poor quality sequences and were excluded from further analysis. For the remaining 34 accessions, sequence lengths ranged between 612 and 622 nucleotides after trimming. Percentage identity of the nucleotide sequences of the alleles ranged between 98.32 to 100% in comparison to alleles sequences from Tog5672, Tog5674, Tog5681 and Gigante. Alignment of sequences of PCR products obtained from primer Rymv1D found five accessions (ARC36-2-1-2, ARC36-4-EP-2, ARC39-145-P-3, ARC39-145-P-2 and ARS126-3-B-1-2) possessing a single SNP (G/A) corresponding to the rymv1-2 allele profile of Gigante. The results also revealed the presence of a 9 bp deletion in the genotype Tog5681 and Tog5674 corresponding to the rymv1-3 allele and rymv1-5 allele, respectively ( Figure 2). The SNP associated with genetic basis of RYMV resistance in Tog5672 previously identified by Thiml et al. could not be determined. None of the other accessions had the profiles of Tog5681, Tog5672 and Tog5674. Supa Local and MET12 did not contain the rymv1-5 allele as earlier indicated by primer F3R4 and F4R3. Although all mutations characteristic of the resistant accessions occurred in a very small interval of 45 nucleotides, a section of the sequence alignment between 490 and 610 nucleotides is depicted to include polymorphisms that are not associated with RYMV resistance but rather have been reported () to distinguish between the different rice species and subspecies (Figure 3). Application of Functional Markers in the Segregating F 2 Population End point multiplexed PCR analysis detected a 204-bp fragment in either a homozygous or heterozygous pattern in all resistant accessions. In contrast, PCR amplification yielded a 177-bp fragment in all susceptible plants (Figure 4). Of the 71 F2 individuals genotyped, 20 were confirmed to be genotypically homozygous at the RYMV1 gene locus (rymv1-2 allele), and the remaining 33 individuals were heterozygous at the RYMV1 gene locus (containing alleles from both parents). The segregation ratio, 1:2:1, perfectly matched that of co-dominant monogenic inheritance. Using the KASP primer Rymv1-2kbd, 12 individuals were homozygous for allele A, six individuals were homozygous for allele G and 18 individuals were heterozygous for both alleles ( Figure 5). The SNP identity of four individuals was undetermined. Discussion We combined phenotype-based methods with molecular-based methods to identify new sources of resistance to RYMV. RYMV disease severity and AUSPC were significantly affected by accessions and the interaction between accession and time of disease scoring. Isolates and time of disease scoring had no significant effect on both RYMV severity and AUSPC. rymv1-2 allele was detected in five accessions and two new alleles specific markers clearly identified individuals carrying the rymv1-2 allele in a segregation F2 population. We developed two functional allele specific markers, which co-segregated with the rymv1-2 resistance allele in an F2 population and clearly differentiated between the susceptible and resistant individuals in the F2 population. The significant effects of accession and interaction between time of disease score and accession (i.e., differences in the disease progression in the accessions over time) can be explained by the genetic differences between the accessions. Some accessions (ARC36-2-1-2, ARC36-4-EP-2, ARC39-145-P-3, ARC39-145-P-2, ARS126-3-B-1-2) did not develop symptoms even after 35 dpi due to the presence of rymv1-2 allele of the RYMV1 gene. The RYMV1 gene acts by restricting cell to cell movement of the virus from the point of inoculation thus preventing symptom development (). Four accessions, NamChe1, NamChe2, MET14 and MET60 were resistant and did not either develop any symptoms or had very mild symptoms of RYMV and yet they had no RYMV1 gene. Their resistance could possibly be due to the presence of other resistance genes or loci. Thiml et al. and Pidon et al. reported the existence of two other resistance genes; RYMV2 and RYMV3, respectively associated with RYMV resistance in rice. In our study, however, we limited ourselves to RYMV1 gene. Subsequent studies will be required to identify the alleles of other RYMV resistance genes existing in these accessions. The significant difference between the AUSPC of the different accessions indicated that accessions displayed different symptom intensities. Accessions IR64, Supa1, Jaribu and Komboka were the most susceptible accessions recording the highest value of AUSPC irrespective of the isolate used while ARC36-2-1-2, ARC36-4-EP-2, ARC39-145-P-3, ARC39-145-P-2, ARS126-3-B-1-2 and NamChe1 were resistant to RYMV with lowest AUSPC values (zero) recorded. With exception of NamChe1 which has already been released as a commercial variety in Uganda, these accessions can be evaluated further in multilocation trials and released as new varieties or be used as parents for breeding for resistance to RYMV. While lack of symptoms on leaves after virus inoculation has been associated with high resistance, delayed symptom onset and lower symptom severity has been associated with partial resistance (moderately resistant). Partial resistance has previously been associated with upland rice O. sativa, japonica subspecies (). Indeed, with the exception of Kafaci326104, 11 out of the 12 moderately resistant accessions that showed partial resistance (moderately resistant) were upland O. sativa, japonica subspecies. Partial resistance to plant viruses has been attributed to incomplete penetrance of resistance () or possible resistance breakdown. Oryza glaberrima shows more frequent high resistance and less frequent partial resistance () as all three glaberrima accessions in this study were highly resistant to the RYMV isolates. Resistance in these accessions was confirmed to be due to the presence of the RYMV1 gene also reported in other literature (). The non-significant differences in both RYMV severity and AUSPC between the two isolates and the consistent reactions of the accessions when subjected to different isolates suggest that the two isolates may be similar in terms of virulence. Recently, Ugandan RYMV isolates were serotyped and grouped in Ser 4 based on polymorphism in amino acid sequences of the coat protein (CP) gene (). The polymorphism of the R domain of the CP and near the conserved position 151-154 of the S domain is reported to determine the differences and aggressiveness among isolates. In this study we used RYMV isolates that were obtained from symptomatic rice plants from RYMV hotspots in Uganda and thus were not characterized into serotypes. The utilization of wild relatives of a crop is indispensable in crop improvement as they host a lot of diversity that is not available in cultivated species. For instance, Oryza barthii hosts a lot of diversity that is not available in O. glaberrima. O. glaberrima was domesticated from its wild progenitor O. barthii (), therefore the majority of resistance genes in this species originated from O. barthii. In this study, accessions from the MET series were derived from introgression with O. barthii, therefore, the resistance or partial resistance observed in MET accessions could be due to the O barthii gene pool leading to different gene and allele interactions each contributing to variations in phenotype resistance. To date, rymv1-2 allele has only been found in O. sativa species. This allele was previously identified in rice cultivars Gigante and Bekarosaka (;). In this study, five more sources jas.ccsenet.org Journal of Agricultural Science Vol. 14, No. 6; of resistance carrying the rymv1-2 allele were identified. RYMV1 has only been very recently deployed in the field; specifically, resistance allele rymv1-2 was introgressed into four elite cultivars from AfricaRice NARS partners through Marker Assisted Selection (MAS) (;. Four of the accessions in the current study that possessed the rymv1-2 allele originated from the AfricaRice center and thus could be the elite cultivars. The accession ARS 126-3-B-1-2 that also carried the rymv1-2 allele was developed through shuttle breeding. This makes rymv1-2 very important for use in crop improvement because: 1) O. sativa is the most widely cultivated species worldwide while O. glaberrima is restricted to West Africa. 2) The existence of reproductive barriers between these two rice species limits utilization of other RYMV1 alleles (rymv1-3, rymv1-4 and rymv1-5) found in glaberrima species. The reproductive barriers can be overcome by combining backcross and MAS. Being co-dominant, the newly developed markers FMRymv1A and Rymv1-2kbd can distinguish between homozygotes and heterozygotes, thus, eliminating the extra laborious step and reduces the potential of error. FMRymv1A marker and Rymv1-2kbd KASP primer were designed to target the rymv1-2 resistance allele indicated by G/A SNP within the RYMV1 gene. This makes these allele-specific markers more efficient in selection of desired genotypes compared to DNA makers. FMRymv1A and Rymv1-2kbd are, thus, advantageous for MAS breeding programs targeting the rymv1-2 resistance allele especially in large scale population screening. The two primers were validated in an F 2 segregating population, which showed that the rymv1-2 allele genotype co-segregates with the RYMV resistance phenotype. Conclusion This study revealed resistance sources with resistance alleles at the RYMV1 locus in a collection of cultivated rice in Uganda. We report five RYMV resistant accessions possessing the rymv1-2 allele that can be deployed as resistant cultivars and/or used to introgress rymv1-2 allele into susceptible adapted cultivars throughout Africa. The two allele specific markers do not require digestion of PCR product and thus will be useful especially in large scale screening of materials and in MAS programs for introgression of rymv1-2 resistance allele. |
Endotheliumdependent relaxation and hyperpolarization evoked by bradykinin in canine coronary arteries: enhancement by exercisetraining 1 Kinins, which are produced locally in arterial walls, stimulate the release of endotheliumderived vasodilator substances. Therefore, they may participate in the metabolic adaptation to chronic exercise that occurs in the coronary circulation. Experiments were designed to compare the reactivity to bradykinin in coronary arteries isolated from sedentary and exercisedtrained dogs (for 810 weeks). 2 The organ chambers used in this study were designed for measurement of isometric tension and cell membrane potential with glass microelectrodes. Rings of canine isolated coronary arteries with endothelium were suspended in the organ chambers filled with modified KrebsRinger bicarbonate solution (37°C, gassed with 5% CO2 in 95 O2), and were all treated with indomethacin to prevent interference from prostaglandins. 3 Bradykinin evoked concentrationdependent relaxations of the coronary arteries. However, the kinin was significantly less potent in relaxing coronary arteries from the sedentary dogs than those from the trained ones. 4 In the presence of NGnitroLarginine (an inhibitor of nitric oxide synthases), concentrationrelaxation curves to bradykinin were shifted to the right in both types of preparations. Nonetheless, the peptide was still significantly more potent in arteries from exercisetrained animals. 5 In the electrophysiological experiments, concentrationhyperpolarization curves to bradykinin obtained in arteries from sedentary dogs were also significantly to the right of those in vessels from exercisetrained animals. Thus, in arteries from exercised animals, bradykinin more potently evoked the release of both nitric oxide (NO) and endotheliumderived hyperpolarizing factor (EDHF). 7 The angiotensin converting enzyme (ACE)inhibitor, perindoprilat, shifted to the left the concentrationrelaxation curves to bradykinin obtained under control conditions and in the presence of NGnitroLarginine. The concentrationhyperpolarization curves to bradykinin were also shifted to the left by perindoprilat. The shift induced by the ACEinhibitor in either type of preparation was not significantly different. 8 These findings demonstrate that exercisetraining augments the sensitivity of the coronary artery of the dog to the endotheliumdependent effects of bradykinin. This sensitization to bradykinin may reflect an increased role of both NO and EDHF, and is not the consequence of differences in ACE activity in the receptor compartment. |
from pydantic import BaseModel, Field, UUID4
from typing import Optional
from uuid import uuid4
from api.models import type_str, validators
class AlertTypeBase(BaseModel):
"""Represents a type of alert."""
description: Optional[type_str] = Field(description="An optional human-readable description of the alert type")
value: type_str = Field(description="The value of the alert type")
class AlertTypeCreate(AlertTypeBase):
uuid: UUID4 = Field(default_factory=uuid4, description="The UUID of the alert type")
class AlertTypeRead(AlertTypeBase):
uuid: UUID4 = Field(description="The UUID of the alert type")
class Config:
orm_mode = True
class AlertTypeUpdate(AlertTypeBase):
value: Optional[type_str] = Field(description="The value of the alert type")
_prevent_none: classmethod = validators.prevent_none("value")
|
package cn.com.heaton.blelibrary.ble.callback;
/**
* description $desc$
* created by jerry on 2019/7/22.
*/
public interface BleStatusCallback {
void onBluetoothStatusChanged(boolean isOn);
}
|
<filename>src/test/java/com/johnsproject/jpge/utils/ColorUtilsTest.java
/**
* MIT License
*
* Copyright (c) 2018 <NAME> - John´s Project
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
package com.johnsproject.jpge.utils;
import java.awt.Color;
import org.junit.Test;
/**
* Test class for {@link ColorUtils}.
*
* @author John's Project - <NAME>
*
*/
public class ColorUtilsTest {
@Test
public void convertRgbaTest() throws Exception {
int c = new Color(50,50,50,50).getRGB();
int cc = ColorUtils.convert(50,50,50,50);
assert(c == cc);
}
@Test
public void convertRgbTest() throws Exception {
int c = new Color(50,50,50).getRGB();
int cc = ColorUtils.convert(50,50,50);
assert(c == cc);
}
@Test
public void getRedTest() throws Exception {
Color c = new Color(50,50,50,50);
int cc = ColorUtils.getRed(c.getRGB());
assert(c.getRed() == cc);
}
@Test
public void getGreenTest() throws Exception {
Color c = new Color(50,50,50,50);
int cc = ColorUtils.getGreen(c.getRGB());
assert(c.getGreen() == cc);
}
@Test
public void getBlueTest() throws Exception {
Color c = new Color(50,50,50,50);
int cc = ColorUtils.getBlue(c.getRGB());
assert(c.getBlue() == cc);
}
@Test
public void getAlphaTest() throws Exception {
Color c = new Color(50,50,50,50);
int cc = ColorUtils.getAlpha(c.getRGB());
assert(c.getAlpha() == cc);
}
@Test
public void setRedTest() throws Exception {
int cc = ColorUtils.convert(50, 50, 50, 50);
cc = ColorUtils.setRed(cc, 20);
cc = ColorUtils.getRed(cc);
assert(20 == cc);
cc = ColorUtils.getBlue(cc);
//assert(50 == cc);
}
@Test
public void setGreenTest() throws Exception {
int cc = ColorUtils.convert(50, 50, 50, 50);
cc = ColorUtils.setGreen(cc, 20);
cc = ColorUtils.getGreen(cc);
assert(20 == cc);
}
@Test
public void setBlueTest() throws Exception {
int cc = ColorUtils.convert(50, 50, 50, 50);
cc = ColorUtils.setBlue(cc, 20);
cc = ColorUtils.getBlue(cc);
assert(20 == cc);
}
@Test
public void setAlphaTest() throws Exception {
int cc = ColorUtils.convert(50, 50, 50, 50);
cc = ColorUtils.setAlpha(cc, 20);
cc = ColorUtils.getAlpha(cc);
assert(20 == cc);
}
@Test
public void addRedTest() throws Exception {
int cc = ColorUtils.convert(50, 50, 50, 50);
cc = ColorUtils.addRed(cc, +20);
assert(70 == ColorUtils.getRed(cc));
cc = ColorUtils.addRed(cc, -20);
assert(50 == ColorUtils.getRed(cc));
}
@Test
public void addGreenTest() throws Exception {
int cc = ColorUtils.convert(50, 50, 50, 50);
cc = ColorUtils.addGreen(cc, +20);
assert(70 == ColorUtils.getGreen(cc));
cc = ColorUtils.addGreen(cc, -20);
assert(50 == ColorUtils.getGreen(cc));
}
@Test
public void addBlueTest() throws Exception {
int cc = ColorUtils.convert(50, 50, 50, 50);
cc = ColorUtils.addBlue(cc, +20);
assert(70 == ColorUtils.getBlue(cc));
cc = ColorUtils.addBlue(cc, -20);
assert(50 == ColorUtils.getBlue(cc));
}
@Test
public void addAlphaTest() throws Exception {
int cc = ColorUtils.convert(50, 50, 50, 50);
cc = ColorUtils.addAlpha(cc, +20);
assert(70 == ColorUtils.getAlpha(cc));
cc = ColorUtils.addAlpha(cc, -20);
assert(50 == ColorUtils.getAlpha(cc));
}
@Test
public void darkerTest() throws Exception {
int c = ColorUtils.convert(50, 50, 50, 50);
int cc = ColorUtils.darker(c, 5);
//System.out.println(ColorUtils.getBlue(cc));
assert (ColorUtils.getBlue(cc) < ColorUtils.getBlue(c));
assert (ColorUtils.getGreen(cc) < ColorUtils.getGreen(c));
assert (ColorUtils.getRed(cc) < ColorUtils.getRed(c));
}
@Test
public void brighterTest() throws Exception {
int c = ColorUtils.convert(50, 50, 50, 50);
int cc = ColorUtils.brighter(c, 2);
assert (ColorUtils.getBlue(cc) > ColorUtils.getBlue(c));
assert (ColorUtils.getGreen(cc) > ColorUtils.getGreen(c));
assert (ColorUtils.getRed(cc) > ColorUtils.getRed(c));
}
// @Test
// public void lerpRBGTest() throws Exception {
// int c1 = ColorUtils.convert(50, 50, 50, 50);
// int c2 = ColorUtils.convert(100, 100, 100, 100);
// int cc = ColorUtils.lerpRBG(c2, c1, 100);
// System.out.println(ColorUtils.getRed(cc));
// assert (ColorUtils.getBlue(cc) > 50);
// assert (ColorUtils.getBlue(cc) < 100);
// assert (ColorUtils.getGreen(cc) > 50);
// assert (ColorUtils.getGreen(cc) < 100);
// assert (ColorUtils.getRed(cc) > 50);
// assert (ColorUtils.getRed(cc) < 100);
// assert (ColorUtils.getAlpha(cc) == 100);
// }
//
// @Test
// public void lerpTest() throws Exception {
// int c1 = ColorUtils.convert(50, 50, 50, 50);
// int c2 = ColorUtils.convert(100, 100, 100, 100);
// int cc = ColorUtils.lerp(c1, c2, 122);
// assert (ColorUtils.getBlue(cc) > 50);
// assert (ColorUtils.getBlue(cc) < 100);
// assert (ColorUtils.getGreen(cc) > 50);
// assert (ColorUtils.getGreen(cc) < 100);
// assert (ColorUtils.getRed(cc) > 50);
// assert (ColorUtils.getRed(cc) < 100);
// assert (ColorUtils.getAlpha(cc) > 50);
// assert (ColorUtils.getAlpha(cc) < 100);
// }
// @Test
// public void blendAlphaTest() throws Exception {
// int c1 = ColorUtils.convert(50, 50, 50, 100);
// int c2 = ColorUtils.convert(255, 255, 255, 255);
// int cc = ColorUtils.blendAlpha(c1, c2);
// assert (ColorUtils.getBlue(cc) == 205);
// assert (ColorUtils.getGreen(cc) == 205);
// assert (ColorUtils.getRed(cc) == 205);
// }
}
|
<filename>CollectionsEnum/src/EnumSetExample2.java<gh_stars>0
import java.util.EnumSet;
import java.util.Set;
//enum days {
// SUNDAY, MONDAY, TUESDAY, WEDNESDAY, THURSDAY, FRIDAY, SATURDAY
//}
public class EnumSetExample2 {
public static void main(String[] args) {
Set<days> set = EnumSet.allOf(days.class);
System.out.println("Week Days: " + set);
Set<days> set2 = EnumSet.noneOf(days.class);
System.out.println("Week Days: " + set2);
}
}
|
Burroughs High School boys and girls varsity basketball are heading to CIF this week.
The boys team will play on Friday at Costa Mesa. The Burros ended up third in the Mojave River League (6-4), while the Mustangs finished first in the Orange Coast League (9-1).
“They are going to be tough. But I think we can play with anyone. We just need to get out there and play our game,” head coach Scott Hansen said.
Hansen said that because they do not play in the same league as Costa Mesa that there is not much known about how they play, but he does have some insight on the team that he hopes can help the Burros win.
The boys are also heading into the playoff game after a big win over Oak Hills.
The girls team will play the winner of Thursday’s matchup on Saturday night at home. The girls team finished tied for first in the MRL with Oak Hills with a 9-1 record, which allowed the team to have a bye on Thursday. They have the possibility of playing either Twentynine Palms or Chaparral High School.
Twentynine Palms finished second in the Desert Valley League (11-3) while Chaparral finished fourth in the Southwestern League (5-5). |
PRIMARY CARE PEDIATRICIANS' PERCEPTIONS OF VACCINE REFUSAL IN EUROPE An electronic survey assessing primary care pediatricians' estimations and practices regarding parents' vaccination refusal was sent to 395 members of the European Academy of Pediatrics Research in Ambulatory Setting network, with a response rate of 87%. Of respondents who vaccinate in the clinic, 93% estimated the total vaccine refusal rate as <1%. Of all respondents, 69% prefer a shared decision-making approach to handle refusing parents. |
// Show and hide empty list UI as needed with appropriate text based on view specifics
private void updateEmptyListUi(final boolean isEmpty) {
if (isEmpty) {
mEmptyListMessageView.setTextHint(R.string.conversation_list_empty_text);
mEmptyListMessageView.setVisibility(View.VISIBLE);
} else {
mEmptyListMessageView.setVisibility(View.GONE);
}
} |
#include <iostream>
#include <iomanip>
#include <stdexcept>
#include <math.h>
#include <set>
#include <boost/multiprecision/gmp.hpp>
#include <boost/multiprecision/number.hpp>
using namespace std;
using namespace boost::multiprecision;
int target = 100;
int main(int argc, char** argv) {
set<mpz_int> visited;
for (int a = 2; a <= target; a++) {
for (int b = 2; b <= target; b++) {
mpz_int val = 1;
for (int i = 0; i < b; i++) {
val *= a;
}
visited.insert(val);
cout << val << endl;
}
}
cout << "Total unique elements: " << visited.size() << endl;
return 0;
}
|
<filename>tests/test_feature_extraction.py
import pandas as pd
import torch
from project.feature_extraction import ImageFeatureExtractor
from pytorch_lightning import seed_everything
from torch import nn
seed_everything(42)
if torch.cuda.is_available:
device = "cpu"
else:
device = "cpu"
df = pd.DataFrame(
{
"paths": ["path1", "path2", "path3"],
0: [
"the quick brown fox jumps over the lazy dog",
"now is the time for all good folks",
"everything is fine",
],
1: [
"what do you mean",
"im sorry dave im afraid i cant do that",
"yes this is a test",
],
2: [
"on the internet no one knows youre a dog",
"i cant think of any more memes",
"this one describes a picture",
],
}
)
image_batch = torch.randn((16, 3, 224, 224), device=device)
caption_batch = torch.randint(1000, (16, 5, 30), device=device)
def test_image_extractor():
imgs = image_batch.clone()
ife_mobilenet = ImageFeatureExtractor().to(device)
for param in ife_mobilenet.encoder.parameters():
assert not param.requires_grad
imgs = ife_mobilenet.encoder(imgs)
assert imgs.shape == (16, 1280, 7, 7)
assert isinstance(ife_mobilenet.pooling, nn.AdaptiveAvgPool2d)
imgs = ife_mobilenet.pooling(imgs)
assert imgs.shape == (16, 1280, 1, 1)
assert isinstance(ife_mobilenet.projector, nn.Sequential)
assert isinstance(list(ife_mobilenet.projector.children())[0], nn.Linear)
imgs = imgs.view(16, -1)
imgs = ife_mobilenet.projector(imgs)
assert imgs.shape == (16, 128)
assert not ife_mobilenet.convolution
def test_image_extractor_conv():
imgs = image_batch.clone()
ife_mobilenet = ImageFeatureExtractor(
pooling=False, convolution_in="infer", projection_in=False,
)
for param in ife_mobilenet.encoder.parameters():
assert not param.requires_grad
imgs = ife_mobilenet.encoder(imgs)
assert imgs.shape == (16, 1280, 7, 7)
assert isinstance(ife_mobilenet.convolution, nn.Conv2d)
imgs = ife_mobilenet.convolution(imgs)
assert imgs.shape == (16, 128, 7, 7)
assert not ife_mobilenet.pooling
assert not ife_mobilenet.projector
|
A fast-moving blaze in Arizona, fueled by extreme heat and high winds, has killed 19 members of an elite firefighting team trained to survive the worst conditions. Judy Woodruff offers an update on the inferno, which quadrupled since the day before.
Officials in Arizona say a wildfire that killed 19 firefighters yesterday has now destroyed more than 8,300 acres and engulfed 13 square miles. It was the biggest loss of firefighters in a wildfire since 1933. They ranged in age from 21 to 43 years old.
The fire is still burning unchecked tonight, leaving crews to carry on their work with heavy hearts for their fallen colleagues.
Views from above showed orange flames consuming the Arizona skyline, as more than 400 firefighters tried to contain the Yarnell Hill fire, which has more than quadrupled in size since yesterday. Sparked by a lightning strike on Friday, it's located 85 miles northwest of Phoenix and about an hour southwest of Prescott, which is home to the Granite Mountain Hot Shot firefighting crew seen here in a training video from 2012.
The city's fire department confirmed 19 of the elite team's 20 members died yesterday while battling the blaze. The surviving member was moving equipment at a different location. The bodies of the team were retrieved from the site today, a day after fire chief Dan Fraijo mourned those lost.
DAN FRAIJO, fire chief, Prescott, Ariz.: Fire departments are like families. And so the entire fire department, the entire area, the entire state is being devastated by the magnitude of this incident. These are the guys that will go out there with 40, 50 pounds of equipment and walk five miles. They will sleep out there as they try to develop fire lines and put protection between homes, natural resources and still try to remain safe. These are quality people.
Officials said in battling the flames the firefighters were forced to wrap themselves in tent-like shelters made of fire-resistant material like these seen in the training video, a last-ditch method used in hopes that the fire would burn over them; 19 roses were among the items left at a makeshift memorial outside Fire Station 7 in Prescott, where the Granite Mountain Hot Shot team is based.
This morning, Arizona State Forestry Division spokesman Mike Reichling said weather conditions continue to be erratic.
This weather has really caused havoc on this fire with the types of fuels. As we said yesterday, the area has not been touched by fire for over 40 years. We have been in over a 10-year drought throughout the state.
Hundreds have been forced to leave their homes. One man described escaping with his wife as the fire moved in on their property.
We had to drive through the flames to get out of our gate. It was already that bad.
Within two minutes — I would say if we waited another two or three minutes, we wouldn't have got out of there. It was that fast coming in.
An investigation is under way into the deaths of the firefighter.
President Obama issued a statement, calling the firefighters heroes and said his administration would help investigate how the deaths happened. |
A Slotted Waveguide Sparse Array with Four-Corner-Feed for Near-Field Focusing This paper presents a double-layer four-corner-fed slotted waveguide sparse array antenna designed for the applications of near-field focusing. At the design frequency of 10 GHz, we achieve a good focusing effect at the focal point of 45 mm. Generally, it is difficult to realize the ideal phase distribution in the array antenna especially for short focal distance. The four-corner-feed structure associated with an appropriate waveguide dimension is adopted to realize approximately ideal phase distribution for a focus at 45mm. The residue phase error leads to the distinct degradation in the sidelobe level of the focal plane. The particle swarm optimization algorithm is introduced to further optimize the sidelobes and the focal spot in the focal plane for ideal focusing effect. |
// filterPrices filters a slice of prices based on given start and end
// timestamps.
func filterPrices(prices []*frdrpc.BitcoinPrice, startTime, endTime int64) (
[]*frdrpc.BitcoinPrice, error) {
if err := utils.ValidateTimeRange(
time.Unix(startTime, 0), time.Unix(endTime, 0),
utils.DisallowFutureRange,
); err != nil {
return nil, err
}
sort.SliceStable(prices, func(i, j int) bool {
return prices[i].PriceTimestamp < prices[j].PriceTimestamp
})
nolint: prealloc
var (
filteredPrices []*frdrpc.BitcoinPrice
earliestTimeStamp *frdrpc.BitcoinPrice
)
for _, p := range prices {
if p.PriceTimestamp <= uint64(startTime) {
if earliestTimeStamp == nil ||
earliestTimeStamp.PriceTimestamp <
p.PriceTimestamp {
earliestTimeStamp = p
}
continue
}
if p.PriceTimestamp >= uint64(endTime) {
continue
}
filteredPrices = append(filteredPrices, p)
}
if earliestTimeStamp == nil {
return nil, errors.New("a price point with a timestamp " +
"earlier than the given start timestamp is required")
}
return append([]*frdrpc.BitcoinPrice{earliestTimeStamp},
filteredPrices...), nil
} |
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