dedup-isc-ft-v107-score
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0.501913
30dacd6fe75b4083a86e70181e1a28fc
Resolution enhancement results for in vivo imaging of rat ear blood vessels. AR-PAM images: (a) raw PA image, (b) by R-L-10, (c) by FDUNet, (d) by EDSR, (e) by RRDBNet, and (f) by FFANet. (g,h) 1D profiles along the lines #6 (g) and #7 (h), respectively, in (a)–(f). The blue arrow indicates the representative area with small vessels. Scale bar: 1 mm.
PMC9095893
gr8.jpg
0.442976
82ddde5c39a844589291b5605e254b61
Prognostic value of alanine aminotransferase (ALT) in pediatric patients with DCM. ROC curves showing ALT levels in patients with DCM predicting the cardiac death. AUC, area under the curve; DCM, dilated cardiomyopathy; ROC, receiver operating characteristic.
PMC9096786
fped-10-833434-g001.jpg
0.533317
5ce469fef2b64acbbe06ec94e221f0ff
The comparison of mortality according the follow-up time after diagnosis of DCM.
PMC9096786
fped-10-833434-g002.jpg
0.487216
096839cdad1d447db881d7efc6bb2424
Survival rates from Kaplan–Meier estimates among the study population by sex (A) and age at diagnosis (B).
PMC9096786
fped-10-833434-g003.jpg
0.440858
fd46d22541eb479cb498cc947f914cf7
Representation of genes and loci in syndromic and non syndromic syndactyly analyzed in this review. These variants are further characterized according to involvement in documented syndactyly pathogenesis interactions. WNT Wingless‐type integration site family, BMP Bone Morphogenic Protein, FGF Fibroblast Growth Factor, RA Retinoic Acid
PMC9097448
13023_2022_2339_Fig1_HTML.jpg
0.499503
8ae0b186357c44dfa3dd30342f41e08b
Mean compliance scores of the conservative and the liberal participants in the disgust and the non-disgust conditions of study 1.(Note: Error bars show standard errors).
PMC9098091
pone.0267735.g001.jpg
0.400017
10bb5b8346e54fabadc1922d0951b2d3
Mean compliance scores of the unvaccinated conservative and the liberal participants in the disgust, non-disgust, and the vaccine-perks conditions in study 2.(Note: Error bars show standard errors).
PMC9098091
pone.0267735.g002.jpg
0.390957
ce0f991f05534d9eb462c619ae4748d5
Mean Likelihood ratings of receiving COVID-19 vaccine by the unvaccinated conservative and the liberal participants in the disgust, non-disgust, and the vaccine-perks conditions in study 2.(Note: Error bars show standard errors).
PMC9098091
pone.0267735.g003.jpg
0.400145
0d095a95666b4508bfeaff1d1a0c6048
Clinical presentations in the right eye (OD) and left eye (OS) of the patient. (A) Manifestation of Kayser-Fleischer (K-F) ring and sunflower-like cataract. (B) Fundus images of osteocyte-like pigmentation (white arrows) in bilateral retina. (C) Optical coherence tomography (OCT) showing outer retina atrophy and cystoid macular edema. (D) Vision detection featuring binocular tunnel vision.
PMC9098211
fmed-09-877752-g0001.jpg
0.466316
e8e146b6d9234c3290b7fda3201d6018
Pedigree of the patient's family. (A) CNGA1 variant family pedigree. Circles represent females and the square represents the male. The filled circle represents the patient with RP. The proband is indicated by a black arrow, while A represents a mutation. (B) Partial sequence of ATP7B gene locus of the proband (II-2) and the unaffected family members (I-1 and I-2). The columnar graphics indicate the site of the variant. (C) Partial sequence of the family's CNGA1 gene locus. (D) Partial sequence of the family's RP2 gene locus. (E) Partial sequence of the family's SNRNP200 gene locus.
PMC9098211
fmed-09-877752-g0002.jpg
0.460686
80d18ce0ba084bb0b3c94e47e0d5b7dd
Prediction of the protein structure of the ATP7B(R778L) mutant expression product.
PMC9098211
fmed-09-877752-g0003.jpg
0.494604
64a2bd058cad448e990a139e6edc77c4
Logical mapping illustrates the possible pathogenesis of Wilson disease and retinitis pigmentosa in this patient.
PMC9098211
fmed-09-877752-g0004.jpg
0.373636
78a79b4bfe6743d3b9eca05c21ff92b7
hPSC-derived neurons express markers of MSNs. (A) hPSCs are differentiated into hMSN-like cells following an Activin A induction protocol (Arber et al., 2015 [17]), (B) immunostained for MAP2+ (red), DARPP32 (green), and DAPI (blue) and quantified for DARPP32+ percentage. GFP-transfected hMSN-like cells were imaged to better highlight cell morphology. Scale bar = 50 µm. (C) QRT-PCR of RNA isolated from H1 (blue) and H9 (orange) hPSCs and hPSCs differentiated into hMSN-like cells at DIV 16 (D16) and DIV 45 (D45), for genes of interest. Values were normalized to HPRT1 mRNA levels in the same samples and expressed as normalized fold changes in hMSN-like versus hPSC cells. Values normalized to GUSB are provided in Figure S1. Gene categories are labeled in red. n = 3–4 independent replicates & 2 technical replicates. Error bars = Standard error.
PMC9100557
cells-11-01411-g001.jpg
0.444966
c946110141b44ab58ab20b09a4a5fcda
hMSN-like cells exhibit dose and time-dependent responses to dopamine. (A) QRT-PCR of RNA isolated from DIV45 H1 and H9 hMSN-like cells 1 h after exposure to different dopamine concentrations (1 µM to 10 mM) and analyzed for FOSB and FOS. (B) QRT-PCR of RNA isolated from DIV 45 H1 and H9 hMSN-like cells 0 to 120 min after exposure to 1 mM dopamine and analyzed for FOSB and FOS. (A,B) For QRT-PCR, values were normalized to GUSB mRNA levels in the same samples and expressed as a fold change in dopamine versus PBS control cultures. * = p < 0.05; One-way ANOVA. n = 3–4 independent replicates and 2 technical replicates. (C) Venn Diagram showing the number of shared differentially expressed genes (DEG) between DIV45 hMSN-like cells quantified by RNA-seq 1 hour after acute 1 μM and 1 mM dopamine. (D) Functional enrichment analysis of RNA-seq data for KEGG pathways of DEGs unique to DIV45 1 μM dopamine dosed (left) and 1 mM dosed (right) hMSN-like cells. Significance is represented by Log10-transformed p-values. Dotted red line indicates p-value of 0.05. (C,D) DEGs were identified by max group mean ≥ 0.75, FDR p-value < 0.05, and Log2(Fold Change) > |1|. Differential expression was performed against PBS control group using the Wald test. n = 3 independent replicates.
PMC9100557
cells-11-01411-g002.jpg
0.467486
2436e5b0985c4da1abf13df7abeee1d1
Chronic administration of dopamine leads to desensitization of genes implicated in cocaine and dopamine responses. (A) Schematic for isolation of RNA from H9 hMSN-like cells dosed acutely (DIV45) and chronically (DIV50) with dopamine. (B) Total number of DEGs from RNA-seq of H9 hMSN-like cells dosed with 1 μM and 1 mM dopamine. (C) Venn diagrams showing shared number of DEGs 1 hour after dosage between hMSN-like cells dosed with DIV45 acute and DIV50 chronic dopamine. (D) Heatmaps of top 20 desensitized genes for hMSN-like cells exposed to acute and chronic dopamine. Desensitized genes are defined as the ratio of Acute 1 h Log2(Fold Change)/Chronic 1 h Log2(Fold Change) > |1.1|. Overlapping genes highlighted by tan-colored bars. (E) IPA upstream regulators and gProfiler transcription factor regulatory motifs of desensitized genes common between 1 μM and 1 mM dopamine conditions. (F) Gene ontology biological processes for highly desensitized genes after chronic 1 mM dopamine, defined when the ratio Acute 1 h Log2(Fold Change)/Chronic 1 h Log2(Fold Change) > |2|. (E,F) Significance is represented by Log10-transformed p-values with dotted red line indicating p-value of 0.05. In all cases, data were obtained from RNA-seq of H9 hMSN-like cells. DEGs were identified by max group mean ≥ 0.75, FDR p-value < 0.05, and Log2(Fold Change) > |1|. Differential expression was performed against PBS control group using the Wald test. n = 3 independent replicates.
PMC9100557
cells-11-01411-g003.jpg
0.473682
bfc19af5c92e44808854e5d7c92c2ac0
Time course of chronic dopamine administration reveals peak in DEGs at day 3 and desensitization at day 5. (A) Total number of DEGs from RNA-seq of DIV50 hMSN-like cells 1 h after 2, 3, 4, or 5 days of daily dosing of 1 mM dopamine. (B) Venn diagrams showing shared (white) and unique (red) numbers of DEGs between hMSN-like cells dosed with dopamine for 2–5 days. (C) Gene ontology biological processes and KEGG pathways for shared and unique genes from hMSN-like cells dosed with dopamine for 2–5 days. Significance is represented by Log10-transformed p-values with dotted red line indicating p-value of 0.05. In all cases, data were obtained from RNA-seq of H9 hMSN-like cells. DEGs were identified by FDR p-value < 0.05. Differential expression was performed against ascorbic acid vehicle control group using the Wald test. n = 2–3 independent replicates.
PMC9100557
cells-11-01411-g004.jpg
0.422874
a59d38675ab04faea3e47b034d35b588
hMSN-like cells capture some features of dopamine receptor cross-interactions. (A) Total number of DEGs from RNA-seq of DIV45 hMSN-like cells dosed with receptor agonists. (B) Gene ontology molecular functions and reactome pathways for DEGs from hMSN-like cells dosed with ADORA2A agonist CGS21680 and D2-like receptor agonist quinpirole. Dotted red line indicates p-value of 0.05. (C) Venn diagram showing shared and unique numbers of DEGs for DIV45 hMSN-like cells dosed with agonists. In all cases, data were obtained from RNA-seq of DIV45 H9 hMSN-like cells. DEGs were identified by FDR p-value < 0.05. Differential expression was performed against a water (vehicle) control group using the Wald test. n = 3 independent replicates.
PMC9100557
cells-11-01411-g005.jpg
0.507057
f9c4e0b8a1524b81b707235c17fef06b
Fibrous composite materials with metal matrices disposal methods.
PMC9100760
materials-15-03207-g001.jpg
0.430439
f057ca2eaef14d06951b7fd2d10eac30
Fragments of (B-W/fibrous composite materials with metal matrices): prepregs from W-B fibres with subsequent aluminium sputtering (a); tubes after hot pressing of prepregs (diameter 20–100 mm (b); also—in section (c); multilayer plate of B-W fibres in a matrix of aluminium alloy (c) thickness—4 mm (d).
PMC9100760
materials-15-03207-g002.jpg
0.468046
7f0cf16061f54588992b3dfb6606ec24
Al-W-B microstructure (a,b): 1—tungsten wire rod 10–12 µm, 2—boron coating with a diameter of 70–100 µm; 3—matrix material aluminium.
PMC9100760
materials-15-03207-g003.jpg
0.463634
a6ff4abd1def428f8b4dd68b64f86786
Stages of Al-W-B waste shredding: breaking (a) and cutting the plates (b); shredding and milling the material (c,d).
PMC9100760
materials-15-03207-g004.jpg
0.440937
2f401a5dd6aa4ac98021776437e78a64
Microstructure of Al-W-B composite after grinding in a vibrating mill (a–c).
PMC9100760
materials-15-03207-g005.jpg
0.445817
9db2ae13b40b4599ac9ae3cb75bda1e5
Grinding diagram Al-W-B waste into powder material at various stages.
PMC9100760
materials-15-03207-g006.jpg
0.399333
7540e6ccaf52407c891fd4b89612c078
Microstructure of Al-W-B composite powders after grinding in disintegrator (a,b).
PMC9100760
materials-15-03207-g007.jpg
0.421461
2b940fa640714ebc980b5e15ed22c70a
Elements for grinding and polishing devices: honing stones 6 × 10 × 120 mm (a,b); ring drill Ø 45 mm (c).
PMC9100760
materials-15-03207-g008.jpg
0.461658
64e9a291772e4b90b3993f204acfdd54
Coefficient of friction versus time, cycles, distance.
PMC9100760
materials-15-03207-g009.jpg
0.484427
fd2783a6e84f4a15a21730930dbaccf9
Wear images of the counterparty and the body under study (a) spherical counterpart made of steel 100 Cr 6; (b) the body being examined.
PMC9100760
materials-15-03207-g010.jpg
0.397899
5a50800e54434f0098ab570f22885560
Sample of concrete containing boron 40 × 40 × 160 mm3.
PMC9100760
materials-15-03207-g011.jpg
0.40941
770a923be5c548f7b57e713e2fb0a10f
Boron-containing material: in an aluminium matrix (a); (W-B) fibres after chemical cleaning from aluminium matrix material: general view (b); W-B fibre powder (c).
PMC9100760
materials-15-03207-g012.jpg
0.409997
80bf4e56d436444d886d9c321dd6cc91
Elements filled with W-B (30–50%) based on copper (a,b) and iron (c) powder after pressing and sintering.
PMC9100760
materials-15-03207-g013.jpg
0.444345
82f75b20d9f64f79ae4499138627f4f9
Microstructure of elements based on copper with the inclusion of W-B fibers in a copper matrix (a,b).
PMC9100760
materials-15-03207-g014.jpg
0.451176
08c3759869984bf593c1718e5378d72e
Distribution of examined lymph nodes.
PMC9101477
fsurg-09-864593-g001.jpg
0.445954
0c50cc55df824e649567605d6a4c04d4
The fitting curves for the OR of nodal stage migration were smoothed using the LOWESS technique, and the corresponding cutoff point of the ELNs was calculated by the Chow test. ELN, examined lymph node. OR, odds ratio.
PMC9101477
fsurg-09-864593-g002.jpg
0.487048
409cb7c742bc4095bd03fee6f02337a4
Overall survival rates among patients with node-negative EC at the cutoff point of 16 ELNs. ELN, examined lymph node. EC, esophageal cancer.
PMC9101477
fsurg-09-864593-g003.jpg
0.497385
277db3864a5a4805a7492ad4de382865
Overall survival rates among patients with node-positive EC at the cutoff point of 16 ELNs. ELN, examined lymph node. EC, esophageal cancer.
PMC9101477
fsurg-09-864593-g004.jpg
0.474152
2c07d592d0924dbd97327bb2bfd13341
The fitting curves for the HR of each ELN count (>16 nodes) compared with 16 ELNs (as a reference) among patients with node-negative EC were smoothed using the LOWESS technique. ELN, examined lymph node. EC, esophageal cancer. OR, odds ratio.
PMC9101477
fsurg-09-864593-g005.jpg
0.413003
b323c14743c84e3c8dcedcc93f07ffa6
The fitting curves for the HR of each ELN count (>16 nodes) compared with 16 ELNs (as a reference) among patients with node-positive EC were smoothed using the LOWESS technique. ELN, examined lymph node. EC, esophageal cancer. OR, odds ratio.
PMC9101477
fsurg-09-864593-g006.jpg
0.446835
171171d28b294d6cbaf74fe89f563fde
Response surface plots of the interaction of alcohol insoluble residue from potato peel byproduct (AIR-PPB) and particle size (PS) on (A) total dietary fibers (TDF) (g/100 g FEP), (B) insoluble dietary fibers (IDF) (g/100 g FEP), and (C) soluble dietary fibers (SDF) (g/100 g FEP) of FEP samples as compared to control pasta (CP) (0 AIR-PPB, 0 PS). FEP, fiber-enriched pasta.
PMC9101751
molecules-27-02868-g001.jpg
0.497084
1dea681c4a674d35973b95ccd4a8b63f
Response surface plots of the interaction of alcohol insoluble residue from potato peel byproduct (AIR-PPB) and Particle size (PS) on (A) optimum cooking time (OCT) (min), (B) cooking loss (Cl) (%), (C) mass increase index (MII) (kg cooked pasta/kg uncooked pasta), (D) Firmness (N), and (E) Stickiness (N) of fiber-enriched pasta (FEP) samples.
PMC9101751
molecules-27-02868-g002.jpg
0.438685
a0af700ae7794ed08ea3bf454d617c39
Response surface plots of the interaction of alcohol insoluble residue from potato peel byproduct (AIR-PPB) and particle size (PS) on (A) onset temperature (To) (°C), (B) melting temperature (Tp) (°C), (C) conclusion temperature (Tc) (°C), and (D) ∆H (J/g) of fiber-enriched pasta (FEP) samples.
PMC9101751
molecules-27-02868-g003.jpg
0.460536
b613ad1d5d6c4d2ab764e9639d5c730a
Survival analyses. Kaplan–Meier analyses of cumulative liver graft survival (a) and overall survival (b) in patients with no intensive care unit discharge delay (ICUDD) and patients with ICUDD.
PMC9101850
jcm-11-02561-g001.jpg
0.453777
3f28191c7ae54d60a3abfcfde7ce101a
Survival analyses. Kaplan–Meier analyses of cumulative liver graft survival (a) and overall survival (b) in patients who did not become newly colonized with a multi-resistant organism infection and patients who did become colonized.
PMC9101850
jcm-11-02561-g002.jpg
0.47841
00bf3bd35e2949fdbfd6038dd43058ce
(A–C): 3D-Response surface plots demonstrating the influence of independent variables i.e., amount of span 60 (X1), amount of cholesterol (X2) and amount of drug (X3) on VZ (nm) of EZL-PNs.
PMC9101870
molecules-27-02748-g001.jpg
0.509143
a673ef650977428fa2dc795af5f58105
(A–C): 3D-Response surface plots demonstrating the influence of independent variables i.e., amount of span 60 (X1), amount of cholesterol (X2) and amount of drug (X3) on EE (%).
PMC9101870
molecules-27-02748-g002.jpg
0.460086
058b07829bea41f0bdb8b42af4ea7047
(A–C): 3D-Response surface plots demonstrating the influence of independent variables i.e., amount of span 60 (X1), amount of cholesterol (X2) and amount of drug (X3) on drug release.
PMC9101870
molecules-27-02748-g003.jpg
0.458147
422534c243e2406aa3397715fd9d1f25
(A) Vesicle size and (B) Zeta potential graph of EZL-PNsopt.
PMC9101870
molecules-27-02748-g004.jpg
0.469083
dc38ec8671454f9a86826bbe4887ebb2
SEM photograph of EZL-PNsopt.
PMC9101870
molecules-27-02748-g005.jpg
0.401023
a52824810c28482487d372ec6db2ac7a
Thermal transition analysis of (A) pure EZL, (B) maltodextrin and (C) EZL-PNsopt.
PMC9101870
molecules-27-02748-g006.jpg
0.461083
117516503a624225ae0135fd2151f227
X-ray diffraction spectra of (A) pure EZL, (B) maltodextrin and (C) EZL-PNsopt.
PMC9101870
molecules-27-02748-g007.jpg
0.45568
01c3777c34dc40efb880a828cc59c19a
In vitro drug release profile of EZL-PNopt and pure EZL.
PMC9101870
molecules-27-02748-g008.jpg
0.438568
aac3da2a16bc4abfabcdd8c05856bb02
In vitro release kinetic model for EZL-PNsopt.
PMC9101870
molecules-27-02748-g009.jpg
0.40736
4d27f9a01d1d41e78088ec5df1059ea4
Ex-vivo gut EZL permeation profile from pure EZL and EZL-PNsopt.
PMC9101870
molecules-27-02748-g010.jpg
0.412486
d4a3b95272e948bbaf8f7c7aff8dd137
EZL plasma concentration vesicle size time profile of pure EZL and EZL-PNsopt.
PMC9101870
molecules-27-02748-g011.jpg
0.485495
0d34867d81304972a8c3724f89e8c9ba
Photographs of (A) non-ulcer induce stomach (Group-I), (B) indomethacin induced (Group-II), (C) Pure EZL (Group III) (D) EZL-PNopt (Group IV).
PMC9101870
molecules-27-02748-g012.jpg
0.431049
d799be6b96e347d9973b3af4e81e7454
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram of study selection process.
PMC9102382
fpsyg-13-882389-g001.jpg
0.444263
4406380d82c54311ad8208d1d6e4db4e
Diagram representing included studies on the three levels of evidence for dyadic interdependence.
PMC9102382
fpsyg-13-882389-g002.jpg
0.429929
7853aa8a8efa4a4ba31448a6af306228
Schematic illustrating the 2 × 2 crossover experimental design used in this study. Participants followed a home-based balance and coordination training program for 12 weeks split into two six-week blocks. Following an initial laboratory-based performance assessment (A1), participants were split into two groups. Group 1 performed home-based training with vibrotactile SA for the first six weeks then trained without vibrotactile SA for the following six weeks. Group 2 performed home-based training without vibrotactile SA for the first six weeks then trained with vibrotactile SA for the following six weeks. After each block of six weeks, participants’ balance and coordination were assessed halfway through (A2) and at the end (A3) of the 12-week intervention.
PMC9103288
sensors-22-03512-g001.jpg
0.429088
fbe988df9c5048aeb6a62a535a5eaf4d
The smartphone-based balance trainer included a user interface unit (Apple iPod) and sensing unit (elastic band with a sensing Apple iPod and four tactors to provide vibrotactile SA). Participants were instructed to wear the user interface unit on a lanyard while performing exercises. The user interface unit allowed participants to select exercises and acted as a timer that instructed participants to start and stop exercises. The sensing unit used (1) the tri-axial gyroscopes embedded in the Apple iPod to measure ML and AP angular velocities, (2) both the tri-axial accelerometers and gyroscopes embedded in the Apple iPod to estimate tilt with respect to gravitational acceleration, and (3) an audio signal to trigger the four tactors to provide vibrotactile feedback. Participants were instructed to make a postural correction in the opposite direction (“move away from the vibration”) when they perceived a vibration.
PMC9103288
sensors-22-03512-g002.jpg
0.491758
658397d342ea4dd1940e2fb4f2c2e951
Schematic demonstrating the architecture of the vibrotactile feedback algorithm used in the smartphone-based balance trainer. For each exercise, tilt angles and angular velocities extracted from the Core Motion SDK were used to determine when the vibrating actuators (tactors) should be activated. A control signal (trunk tilt plus one half of the angular rate of tilt [42]) was used for static standing, compliant standing, and arm-raise exercises. However, the control signal only considered trunk tilt for the weight shifting exercises. When a participant’s control signal exceeded a pre-defined threshold in a particular direction (Table 2), the tactor bud in the direction of movement was activated via an audio signal to provide a vibrotactile cue to the participant.
PMC9103288
sensors-22-03512-g003.jpg
0.416552
0d2db4c5684545d5a64406cba1484f6c
Screenshots showcasing the prompts participants responded to during home-based balance and coordination training sessions. After each repetition of a home-based exercise, participants were prompted (a) to report whether they stepped out during the exercise, and (b) to indicate a self-rating on the VAS 1–5 scale.
PMC9103288
sensors-22-03512-g004.jpg
0.449196
219778470d5d4c9abac4090db50f5c34
SARAposture&gait scores measured during assessments performed in the laboratory immediately before (initial assessment A1), halfway through (intermediate assessment A2), and immediately after (final assessment A3) home-based balance and coordination training for each group. No statistically significant changes in the SARAposture&gait scores were observed after 12 weeks of training (between A1 and A3), but all participants’ scores improved (Participants 3, 6, 9, and 10) or remained the same as baseline at assessment A1 (Participants 1, 2, and 8). Group 1 received vibrotactile SA during the first six weeks of training (between A1 and A2). Group 2 received vibrotactile SA during the second six weeks of training (between A2 and A3).
PMC9103288
sensors-22-03512-g005.jpg
0.476571
6ee49e1118ab42fca9d17d21cc5e141b
DGI scores measured during assessments performed in the laboratory before (pre−SA) and after training at home without vibrotactile SA (post−SA). Participants scored significantly higher on the DGI assessment after training with vibrotactile SA. The main plot shows individual trends for the participants’ DGI scores, and the inset shows the average DGI scores. Error bars on the bar plot indicate standard error (SEM) values. DGI scores from Participant 2 are not included due to missing data. (*) indicates statistically significant changes (p < 0.05).
PMC9103288
sensors-22-03512-g006.jpg
0.442514
4636c6fdb32b4eb28117281a68b8711d
SARAposture&gait scores measured during assessments performed in the laboratory before (pre+SA) and after (post+SA) training at home with vibrotactile SA. Participants scored significantly lower on the SARAposture&gait after training with vibrotactile SA. The main plot shows individual trends for the participants’ SARAposture&gait scores and the inset shows the average SARAposture&gait scores. Error bars on the bar plot indicate SEM values. (*) indicates statistically significant changes (p < 0.05).
PMC9103288
sensors-22-03512-g007.jpg
0.460442
bd29c47e9cb342169af9bb8e2cda936c
RMS ML Sway measured while participants were standing on foam with eyes open before pre+SA,pre−SA and after post+SA,post−SA each of the six-week training blocks. No statistically significant differences in postural sway measures were captured through IMU-based kinematic features (p > 0.05). The main plots show individual trends for the participants’ RMS ML Sway values, and the inset shows average RMS ML Sway values before and after training for each training block. Error bars on the bar plot indicate SEM values. Participants 1 and 6 were excluded due to missing data.
PMC9103288
sensors-22-03512-g008.jpg
0.402873
389cfeab861b4c39a58dccd7fb955fda
Flow chart of the study participants describing their participation and allocation.
PMC9103307
nutrients-14-01759-g001.jpg
0.427789
751c275d6cd74225bd1d8e88b81f197b
SEM micrographs of MC powder (a) ×1500 and (b) ×4000.
PMC9103540
polymers-14-01794-g001.jpg
0.413231
c2d7bc6c771f43e69047bbd3c967a82b
OM images of (a) LDPE/MC-0, (b) LDPE/MC-0.5, (c) LDPE/MC-1, (d) LDPE/MC-3, and (e) LDPE/MC-5.
PMC9103540
polymers-14-01794-g002.jpg
0.455753
b651196ed86a43b2a6c84b8626b60c91
SEM micrographs of (a) LDPE/MC-0, (b) LDPE/MC-0.5, (c) LDPE/MC-1, (d) LDPE/MC-3, and (e) LDPE/MC-5.
PMC9103540
polymers-14-01794-g003.jpg
0.532888
5353c6d4ef5f4c6ea8d479c17f9675ea
TGA thermograms of LDPE/MC composites films at various MC concentrations.
PMC9103540
polymers-14-01794-g004.jpg
0.486673
378fa512bf464680a782338c9290f8ef
Tensile strength and elongation at break of the LDPE/MC composite films at various MC powder concentrations.
PMC9103540
polymers-14-01794-g005.jpg
0.452854
15610830801a4c65ada85b67d0c39d42
FIR emission spectra of the LDPE/MC composite films at various MC powder concentrations.
PMC9103540
polymers-14-01794-g006.jpg
0.456998
0bc89389021447529ade2015d615bc77
The oxygen transmission rate (OTR) and water vapor transmission rate (WVTR) as the barrier properties of various LDPE/MC composite films.
PMC9103540
polymers-14-01794-g007.jpg
0.54872
a10b1ce3192b4bf2a04c869aa1ae0541
Light transmission and opacity of various LDPE/MC composite films.
PMC9103540
polymers-14-01794-g008.jpg
0.446781
1c68f36618074e9ab96de2c7a9e2d60d
Photographs of lettuces packed with various LDPE/MC composite films for 3/5/7 days at 28 °C.
PMC9103540
polymers-14-01794-g009.jpg
0.546139
ce624bdbb8dc4f46add73e62c22fcd39
Photographs of strawberries packed with various LDPE/MC composite films for 3/5/7 days at 28 °C.
PMC9103540
polymers-14-01794-g010.jpg
0.423332
cdfc36ee2bd54ca69fd5dc1d26246ffb
FTIR spectra for Ex, ExAh, and ExHp. Absorption bands at 3401, 2941, 1621, 1421, 1230, 1055, 837, 620, and 580 cm−1 are indicated.
PMC9103907
polymers-14-01812-g001.jpg
0.52674
bc19349c60c54147a21bc3e5efde9488
NMR spectra for Ex, ExAh, and ExHp. (A) 1H-NMR of Ex, ExAh, and ExHp and (B) 13C-NMR of Ex, ExAh, and ExHp. The characteristic peaks are indicated in each graph.
PMC9103907
polymers-14-01812-g002a.jpg
0.612687
3d58428bd21f4eddb62a0e4119f0fa21
Effects of various concentrations of Ex, ExAh, and ExHp on cell viability, NO production, TNF-α production, IL-1β production, IL-6 production, and IL-10 production in RAW 264.7 macrophages. (A) cell viability, (B) NO production, (C) TNF-α production, (D) IL-1β production, (E) IL-6 production, and (F) IL-10 production. Values are mean ± SD (n = 3); values in the same graph with different letters (in a, b, c, d, e, f, g, h, and i) are significantly different (p < 0.05).
PMC9103907
polymers-14-01812-g003a.jpg
0.431919
ccc5e49df9354826bf6b84c5aec87d94
Uniformly distributed-slit array coded spectral imaging system.
PMC9103919
sensors-22-03206-g001.jpg
0.419769
f97a2c94ce694aab994bbeef81093707
Data flow chart of the uniformly distributed-slit array coded spectral imaging system. (a) L times of a whole detector data acquisition during the movement of the uniformly distributed-slit array. (b) The L positions of the uniformly distributed-slit array corresponding to one period of detection. (c) The dispersion of the ith slit located in the aperture diaphragm. (d) A single slit moving within one sample period, taking the row distribution to obtain the full rank unit matrix M′. (e) The dispersion of the ith slit at the kth exposure measurement.
PMC9103919
sensors-22-03206-g002.jpg
0.464075
3cde7c428ba244a6ba9910cc7954283c
Optical system of uniformly distributed-slit array coded spectral imaging system.
PMC9103919
sensors-22-03206-g003.jpg
0.424943
64737437426b4ed59c02a0bd67e5148f
System design model.
PMC9103919
sensors-22-03206-g004.jpg
0.388397
39bf1f8caa1a470f9fa9ceac279528c9
Prototype system.
PMC9103919
sensors-22-03206-g005.jpg
0.458702
2e9105e6387546deab59578f8c4e7d2d
System spectral calibration schematic (a) and physical diagram (b).
PMC9103919
sensors-22-03206-g006.jpg
0.385205
409e965aa1f849a5b52b43231fba539c
Spectral resolution test results of the hyperspectral imager. (a) For spectral channels 1–38 and (b) for spectral channels 39–60.
PMC9103919
sensors-22-03206-g007.jpg
0.394232
22489cd2c8f045e482ddcccc428df997
Spectral restoration image and spectral reconstruction curve.
PMC9103919
sensors-22-03206-g008.jpg
0.428363
138c1a7e0d064e2298885f4ff67af8cd
The 10 Hz frame rate for the spectral data recovery results of moving targets. (a–z) are the spectral data cubes acquired over a continuous period of 2.6 s. Reconstructed spectral data cubes are displayed by ENVI (The Environment for Visualizing Images).
PMC9103919
sensors-22-03206-g009.jpg
0.455668
667d680062354edb9dc69389e50865ed
The optical flow method identifies the motion information between adjacent frames with a time difference of 0.1 s.
PMC9103919
sensors-22-03206-g010.jpg
0.426666
ef781140f30440f89c66d3f742f1198a
Changes to bacterial family communities.Changes to bacterial communities with different liquid supplements. The relative abundance (family) present in caecal contents collected from individual rats (n = 50) are shown. Two control groups received water with either casein diet (Casein water) or amino acid diet (AA water). Treatment groups received AA diet with either bovine milk (AA milk), soy beverage (AA soy), or almond beverage (AA almond). Diets and treatments were supplied over 4 weeks from weaning to 7 weeks old. The colours represent different families as indicated in the figure legend.
PMC9104089
peerj-10-13415-g001.jpg
0.504434
af39b073930d46efb004efe20f216a64
Relative abundance (%) of Actinobacteria for the five groups.Two control groups received water with either casein diet (Casein water) or amino acid diet (AA water). Treatment groups received AA diet with either bovine milk (AA milk), soy beverage (AA soy), or almond beverage (AA almond). Differences between individual taxa among the different treatments were assessed for significance using permutation analysis of variance. Dissimilar letters denote significant differences (P < 0.05). Midline shows median, the upper and lower limits of the box showing the third and first quartile (i.e., 75th and 25th percentile), respectively and the whiskers represent 1.5 times the interquartile range. Open circles represent outliers (i.e., >1.5 × IQR). n = 10 per group.
PMC9104089
peerj-10-13415-g002.jpg
0.456409
25d840222238473793a0b548a1ce94e4
Relative abundance (%) of Bacteriodetes in the five groups.Two control groups received water with either casein diet (Casein water) or amino acid diet (AA water). Treatment groups received AA diet with either bovine milk (AA milk), soy beverage (AA soy), or almond beverage (AA almond). Differences between individual taxa among the different treatments were assessed for significance using permutation analysis of variance. Dissimilar letters denote significant differences (P < 0.05). Midline shows median, the upper and lower limits of the box showing the third and first quartile (i.e., 75th and 25th percentile), respectively and the whiskers represent 1.5 times the interquartile range. Open circles represent outliers (i.e., >1.5 × IQR) n = 10 per group.
PMC9104089
peerj-10-13415-g003.jpg
0.526487
fc1af63598524b5bad645fb002bc5df5
Relative abundance (%) of Firmicutes in the five groups.Two control groups received water with either casein diet (Casein water) or amino acid diet (AA water). Treatment groups received AA diet with either bovine milk (AA milk), soy beverage (AA soy), or almond beverage (AA almond). Differences between individual taxa among the different treatments were assessed for significance using permutation analysis of variance. Dissimilar letters denote significant differences (P < 0.05). Midline shows median, the upper and lower limits of the box showing the third and first quartile (i.e., 75th and 25th percentile), respectively and the whiskers represent 1.5 times the interquartile range. Open circles represent outliers (i.e., >1.5 × IQR) n = 10 per group.
PMC9104089
peerj-10-13415-g004.jpg
0.431263
c62e1b28f8384864977988bcbca8f727
Relative abundance (%) of Proteobacteria in the five groups.Two control groups received water with either casein diet (Casein water) or amino acid diet (AA water). Treatment groups received AA diet with either bovine milk (AA milk), soy beverage (AA soy), or almond beverage (AA almond). Differences between individual taxa among the different treatments were assessed for significance using permutation analysis of variance. Dissimilar letters denote significant differences (P < 0.05). Midline shows median, the upper and lower limits of the box showing the third and first quartile (i.e., 75th and 25th percentile), respectively and the whiskers represent 1.5 times the interquartile range. Open circles represent outliers (i.e., >1.5 × IQR). n = 10 per group.
PMC9104089
peerj-10-13415-g005.jpg
0.419248
7ba632fd26204f60b1afb5ef705ef695
Overview of RNA crosslink-ligation experiments and analysis pipeline. (A) Outline of a typical crosslink-ligation experiment leading to FASTQ output files. The proximity ligation of crosslinked duplexes can produce both forward and backward arrangements. Circularized RNAs are rare and lost during library preparation because they cannot be ligated to adaptors. Similarly, concurrent crosslinking at multiple locations and subsequent ligation of them produce multigapped reads (gapm in panel B). (B) Several different types of crosslinking methods, such as psoralen, UV, and formaldehyde, together with proximity ligation produces noncontinuous reads that can be used to determine RNA structures. Newly developed computational tools and optimized parameters are listed on the right in nine steps (steps 1–9). Sequencing data that include both continuous and noncontinuous reads are demultiplexed, and the adapter/primer sequences are removed using published tools, for example, FASTX and Trimmomatic (step 1). The processed reads are mapped to genome references using optimized STAR parameters (permissive parameters, step 2). After the first round of STAR mapping, continuous alignments with softclips (indicating unmapped segments) are rearranged for a second round of STAR mapping (step 3). All alignments from the two rounds of STAR mapping are combined and filtered based on the gap penalty and a database of gapped alignments with longer segments, and then classified into six alignment types, including continuous (cont.sam in SAM format), one-gap (gap1), multigap (gapm), trans interactions (trans), homotypic interactions (homodimers, or homo), and miscellaneous bad alignments (bad) (step 4, using the gaptypes.py script) (see details in Fig. 3A–D; Supplemental Fig. S4). Data quality is checked using seglendist.py and gaplendist.py scripts, which calculate segment and gap length distributions (step 5). After removal of splicing events and reverse transcription artifacts, for example, short 1- to 2-nt gaps (step 6, using gapfilter.py), each of these alignment types is further processed to extract information for duplexes (step 7) (see Fig. 4 for details), high-level structures (step 8) (see Fig. 5 for details), and RNA homodimers (step 9, homo.sam) (see Fig. 6 for details). In step 7, two types of alignments, gap1filter.sam and trans.sam, are used to generate duplex groups and non-overlapping groups (DGs and NGs). In step 8, gapmfilter.sam alignments and the precomputed DGs and NGs are used to build trisegment groups (TGs). In step 9, overlapping chimeras are used to build potential homodimers. Detailed descriptions of these steps are in the Methods section and Supplemental Material.
PMC9104705
968f01.jpg
0.454591
6878879fced746628f38d708e4727ec5
Optimization of short-read mapping from crosslink-ligation experiments. (A,B) RNA stems were extracted from the human cytoplasmic and mitochondrial ribosome and spliceosome crystal or cryo-EM structures. The following RNAs are included: 12S, 16S, 5S, 5.8S, 18S, 28S, U1, U2, U4, U6, U5, U11, U12, U4atac, and U6atac. (C) List of critical STAR parameters that are optimized to map noncontinuous reads. The default value for chimSegmentMin is unset, whereas setting this value to any positive integer triggers chimeric alignments. The recommended value of 15 is used here as the “default.” (D) Strategy for the two-round STAR mapping. After the first round of optimized STAR mapping, continuous alignments with softclips (“S” in CIGAR) are rearranged and then mapped again using the optimized STAR parameters. (E,F) Strategies for filtering alignments after STAR mapping. (E) Confident alignments: all segments or arms are uniquely mapped to the genome. Alignments with shorter segments that cannot be mapped uniquely are to be tested against confident ones. (F) Filtering method for the less confident alignments: all arms of the confident alignments are built into a database of connections between segments, in five nucleotide intervals (dots shown at the bottom). The connection database consists of reference name (RNAME), strand (STRAND), and coordinates between start and end (START, END). Then, the less confident alignments are tested against this database. (G–J) Benchmarking four mapping strategies on simulated reads for the human ACTB gene. Alignments are quantified on the following four aspects: (G) % mapped reads, that is, reads that are mappable to hg38 primary genome; (H) % correct alignments, that is, alignments with the same mapped positions and gap lengths as the simulated values, allowing 10-nt differences in positions or lengths due to ambiguities at the ends of reads; (I) Suboptimal alignments per read, defined as alignments that are not mapped to the correct locations; (J) % forward or backward chimera. In theory, both forward and backward chimera should be ∼50% (randomly assigned during simulation, so they are not precisely 50%). Here, only STAR alignments are calculated. (K) Gap1 (one gap, i.e., two segments) alignments in PARIS and hiCLIP data were recovered by various mapping methods and segment-length selections. Fractions for the highest-performing method (STAR_optimized) are set to 1. For STAR analysis, sequencing reads were mapped to the genome (hg38 primary); then alignments were filtered and classified into six categories using gaptypes.py. The gap1 alignments were filtered to remove short gaps and splicing alignments (gapfilter.py). Primary alignments were extracted from all alignments and used for analysis. For Bowtie 2 mapping, previously reported parameters (hyb and Aligater) were used. Unique alignments with deletions (D in SAM CIGAR string) were extracted and alignments were converted to join the multiple segments (bowtie2chim.py). Then, the alignments were classified using gaptypes.py. The gap1 alignments were filtered to remove short gaps and splicing alignments (gapfilter.py). The selection of alignments with both arms > 15 nt or 20 nt mimics the mapping and chaining strategy in previous studies that employ Bowtie 2 (hyb and Aligater). (L,M) Alignments in the ACTB mRNA from PARIS data in HEK cells were separated into ones where both arms (or segments) are at least 20 nt (L), or at least one arm is shorter than 20 nt (M). The inset boxes show DGs that support the same duplex regardless of segment length.
PMC9104705
968f02.jpg
0.423095
357ef89331fc4027af7eaae753e67914
Classification and processing of alignments from crosslink-ligation experiments. (A) Types of alignments and classification after processing. This diagram presents a unified model for data from all types of crosslink-ligation experiments, and the terms are defined as follows. A read: one piece of sequence from the sequencing machine, and it may have one or multiple alignments to the reference; segment or arm: part of an alignment with no “N” in the CIGAR substring; continuous alignments: type 1, with only one segment or arm, either from non-crosslinked or crosslinked but not ligated RNA; gapped: forward arrangement, with one or more gaps, including gap1 and gapm (types 2 and some of type 8); chimeric: noncontinuous alignments similar to the definition from the STAR method, including types 3–7 and some of type 8; noncontinuous: including both gapped and chimeric alignments; homotypic: chimeric alignments where the arms overlap, suggesting RNA homodimers; trans: segments mapped to different chromosomes or strands (types 6–7 and some of type 8). In SAM files, each record describes one alignment, and it is represented by one CIGAR string. For example, a CIGAR string of “20M25N21M” (M for match, N for gap) has two segments or arms, 20 nt and 21 nt, separated by a 25-nt gap. In type 1, these two segments are from two different reads, and therefore represented by two records in SAM files (two CIGAR strings, e.g., “20M” and “21M”). Type 1 alignments are output to cont.sam. In type 2, these two segments are from the same read and therefore represented by one record in SAM files (one CIGAR string, e.g., “20M25N21M”). This alignment is either output to gap1.sam, or cont.sam if it does not pass the filtering (e.g., the gap corresponds to a splice junction). In type 3, the two segments are from the same read but still represented by two records in SAM files because they are mapped beyond the alignment window in STAR (two CIGAR strings, e.g., “20M” and “21M”). Type 3 alignments are rearranged and output to gap1.sam, or cont.sam if it does not pass the filtering. In type 4, the two segments are from the same read but mapped in reverse order and cannot be represented by one record because reverse order is not allowed in the CIGAR string (therefore represented by two records). Type 4 alignments are rearranged and output to gap1.sam, or cont.sam if it does not pass the filtering. In type 5, the two segments are from one read but overlap each other, which cannot be represented by one CIGAR string and therefore must be represented by two records in SAM files. Type 5 alignments are rearranged and output to homo.sam. In types 6 and 7, the two segments are from the same read but mapped to opposite strands of the same chromosome (type 6) or different chromosomes regardless of strand (type 7), and therefore must be represented by two records in SAM files. Type 6 and 7 alignments are output to trans.sam, or cont.sam if they do not pass filtering. In type 8, the multiple segments are from the same read but are mapped either to the same strand or to different strands or chromosomes. These arrangements are represented either by one record or multiple records in SAM files. Type 8 alignments are rearranged and output to gapm.sam, gap1.sam, or trans.sam, depending on their relative mapping locations. (B) Diagram for joining collinear distant segments into gapped alignments. The two segments are connected so that the two arms are represented by one record in SAM format, where xM and zM are the two arms, and yN is the gap. (C) Diagram for rearranging backward chimeric alignments to normal gapped alignments. The 5′ and 3′ arms are switched so that the two segments can be represented by one record in SAM format, where xM and zM are the two arms, and yN is the gap. (D) Diagram for rearranging overlapped chimera. The two arms are converted to three segments: left overhang, overlap, and right overhang. The new alignment can be represented by one record in SAM format, where y(2I1D) represents the overlapped region. (E) Classification of alignments from previously published crosslink-ligation experiments, in which the low abundance categories are magnified on the right.
PMC9104705
968f03.jpg
0.440473
0510902287e145f59dfeb56d4548cd93
Network/graph-based method for automatic assembly of duplex groups underlying RNA structures and interactions (CRSSANT). (A) Overlap and span calculation for a pair of alignments. Two alignments r1 and r2 each comprising a left and right arm (solid blue bars), share left and right overlaps ol, or, respectively, and left and right spans sl, sr, respectively. The arm start and stop positions of read/alignment i are represented by the 4-tuple (ai,l,0, ai,l,1, ai,r,0, ai,r,1). The two arms can be on the same chromosome and strand (gap1.sam), or different ones (trans.sam). (B) Diagram for network/graph-based clustering. All alignments with a single gap (gap1 and trans) are represented as a graph where each alignment is a vertex and the relative overlap ratio between the arms is the edge. Highly connected vertices cluster together forming subgraphs, corresponding to individual DGs. (C) Diagram for the DG tag information. The string after DG:Z includes the names of the two genes that the DG connects (gene1 and gene2). gene1 and gene2 are identical when the DG describes intramolecular structures or homodimers. DGID is a number based on assembly order. covfrac (coverage fraction) is defined as the number of alignments in this DG divided by the geometric mean of the coverages at the two arms. (D) Diagram for NG assembly. Non-overlapping DGs (e.g., DG1 and DG3, DG2 and DG4) are combined into NGs for visualization in genome browers like IGV. (E,F) Benchmarking CRSSANT clustering on 100 simulated DGs. All alignments map to Chr 1: 1–1000 and consist of cores 5, 10, or 15 nt (corelen = 5, 10 or 15), and random extensions on each side between 5 and 15 nt. Gaps between the two cores are at least 50 nt and at most the length of the Chr 1: 1–1000. Each DG contains between 10 and 100 alignments. The alignments were clustered using cliques or spectral algorithms. For cliques, overlap threshold to was varied between 0.1 and 0.9. For spectral clustering, to was varied between 0.1 and 0.9 when the eigenratio threshold was set at teig = 5. Alternatively, for spectral clustering, teig was varied between 1 and 10 when to was set at 0.5. The fraction of assigned alignments (out of 5335 input) was plotted in panel E. The fraction of assembled DGs (against 100 input) was plotted in panel F. (G) For each simulated DG data set and clustering parameter combination, the sensitivity and specificity of DG assembly was calculated for each of the top 100 DGs. The sensitivity of DG assembly is defined as the fraction of remaining alignments in each DG after CRSSANT assembly. The specificity is defined as the fraction of alignments from the dominant simulated DG. (H) Human U2 snRNA structure model based on previous studies. (I,J) Human HEK and mouse ES PARIS data were clustered using CRSSANT. The DGs were labeled corresponding to the secondary structure models in panel H. Alignments are grouped in IGV using the NG tag. “?” is a new duplex not in the known structure model. (K) Human HeLa SHARC data were clustered using CRSSANT, and the DGs were labeled as above. (L) The duplex SLIId is conserved from human down to yeast based on multiple sequence alignment of 208 seed sequences (Rfam: RF00004, in WebLogo format). (M) SLIId model; top strand is the 5′ arm, and the bottom is the 3′. Black letters, GUAUGA, indicate the BPRS masked by SLIId. (N) The alternative SLIII + SLIV structure models.
PMC9104705
968f04.jpg
0.452786
93b6be665c9746e3b09fecb893ce5f03
Multisegment alignments support higher level structures and interactions. (A) Distributions of the numbers of arms/segments in gapm alignments. (B) Numbers of RNAs involved in each gapm alignment. Gapm alignments with three arms are shown. R1, R2, and R3 represent three different RNAs. (C) Gapm alignments with three arms indicate the coexistence of two helical regions. Sequential helices joined by gapm alignments indicate two separate stem–loops (left). Interlocked helices joined by gapm alignments indicate pseudoknots (middle). Overlapping helices joined by gapm alignments indicate triplexes. (D) Strategy to cluster gapm alignments, assuming that all TGs should be combinations of DGs. Alignments with more than two gaps are ignored for now. The DGs were produced by CRSSANT using gap1.sam and trans.sam alignments. The boundaries for each arm are the medians for the DGs. For the TGs, the merged middle arm is the redefined as boundaries of both DGs. Alignments from gapm.sam are then matched to the TGs so that each arm is overlapped. (E) Gapm alignment number distribution for TGs on the human 28S rRNA. PARIS2 HEK293 gapm alignments were assembled directly on the DGs (blue) or shuffled randomly across the 28S rRNA before assembly (red). The shuffling preserves the distances among the segments in each gapm read (i.e., the same CIGAR string). The crossing point (242, 14) indicates that the first 242 TGs each contain at least 14 gapm alignments. (F) Coverage of reads along the 28S rRNA for (1) all DGs, (2) only DGs that support the TGs, (3) TGs from original PARIS2 gapm alignments, and (4) TGs from shuffled gapm alignments. Coverage depth is indicated in the brackets. (G) For the top ranked 242 TGs either from the original gapm alignments (left) or the shuffled gapm alignments (right), the numbers of alignments (x-axis) were plotted against the geometric means of the numbers of alignments in the two DGs that support each TG (y-axis). Alignment numbers are log10-transformed before plotting and calculation of Pearson's correlation. (H) gapm alignments mapped to the human 5.8S rRNA. Top track: base-pairing secondary structure model in arc format. (I) Mapping the three segments to the secondary structure model. The three segments are color-coded in panels H,I.
PMC9104705
968f05.jpg
0.385783
e110cdbbc07648de88a5a081be92ded9
Identification of potential RNA homodimers using homotypic alignments. (A) The same base-pairing interactions can mediate intramolecular stem–loops (top) and homotypic interactions between two (middle) or more (bottom) copies of the same molecule. (B) Diagram showing alignments with gapped or overlapped arms, suggesting RNA stem–loops or homodimers. (C) Coverage of five different types of alignments on U1. The overlapped part of homo alignments is shown individually at the bottom. (D) Heat map of U1 snRNA homo alignments in three data sets. (E) PARIS2 data showing overlapped regions and corresponding local stem–loop (SLII). DGs were assembled from 1000 total alignments. (F) Secondary structure of U1homo interaction, with the SLII in bold letters. (G) Secondary structure model for the SLII homodimer.
PMC9104705
968f06.jpg
0.436462
862241222f754a2e984d4e6be2e80ca4
Progression-free survival in both groups. Kaplan–Meier estimates. log Rank: 0.011.
PMC9105143
cancers-14-02108-g001.jpg