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0.463098 | 1e20ef86a64545338b0df4cd00076f9c | Buffered metal concentration-dependent
rate of conversion of SHC
to CoII-SHC by CbiK via steady-state kinetics. (a) UV–visible
absorbance of SHC (4.3 μM) before (black line) and 4 h after
(red line) incubation with CoII (100 μM) and CbiK
(0.5 μM) in the presence of His (1 mM). (b) Formation of Co-SHC
when SHC (5 μM) was incubated with CoII (100 μM)
in the presence (red lines) or absence (black lines) of a histidine
metal buffer (1 mM) and CbiK (0.5 μM, as labeled). (c) CbiK-catalyzed
metalation of SHC (initial concentration 5 μM) when available
[CoaqII] was buffered using NTA or EGTA to achieve
sub-nanomolar concentrations (Table S3).
Initial rates (v0) were calculated from linear fits of
the data for the first 2 min of reaction at each condition (shown
by dashed lines). (d) Steady-state kinetics for CoII insertion
into SHC by CbiK. Initial rates of metalation (v0) relative
to enzyme concentration ([E]tot = 0.375 μM) were
determined at varying available [CoII] (see Table S3 for experimental conditions). Data are
the mean ± s.d. of three independent experiments. All reactions
were carried out in 50 mM Hepes buffer pH 7.0, 100 mM NaCl. | PMC10206600 | au3c00119_0005.jpg |
0.415223 | 1813a0848e404beabe26923530f5df62 | Intracellular CoII availability in Salmonella under anaerobic conditions. (a) Abundance of rcnA transcripts (regulated by CoII sensor RcnR)
in anaerobic Salmonella cultures grown in LB media,
measured by qPCR. Transcript abundances are relative to the control
condition (1 mM EDTA, assigned a value of 1). Data are the mean ±
s.d. of three biologically independent replicates. (b) Solid line
shows the calculated relationship between intracellular available
[CoII] and DNA occupancy of the CoII sensor
RcnR in Salmonella.(14) Fold
changes in rcnA gene expression (from panel (a))
were converted to DNA occupancies of RcnR to determine intracellular
CoII availabilities for each culture. Red crosses in panel
(a) indicate the minimum and maximum observed fold changes in gene
expression and defined the boundary conditions (θD of 0.01 and 0.99) for the dynamic range of the sensor response in
panel (b). | PMC10206600 | au3c00119_0006.jpg |
0.442988 | 47ef57b977084d098caa439d99db9029 | GTP-dependent CobW metallochaperone elevates
the available free
energy of CoII binding. (a) Free energies of CoII-complex formation in the CbiK versus CobW/CobNST pathways (free
energy calculations in Table S4). Bold
arrows denote CoII transfer. In the early cobalt insertion
pathway, binding of the substrate SHC provides sufficient thermodynamic
driving force for the chelatase CbiK to acquire CoII from
the intracellular milieu of idealized Salmonella without
the need of an additional chaperone (1). Conversely, in the late cobalt
insertion pathway, CoII transfer from the intracellular
milieu to the substrate bound chelatase, CobNST-HBAD, is thermodynamically
unfavorable (2, red). Binding of MgIIGTP provides the necessary
free energy gradient for the chaperone CobW to acquire CoII in a cell (3) and nucleotide hydrolysis to generate MgIIGDP-CobW (4, blue) elevates the free energy of CoII binding
sufficiently to enable CoII transfer to CobNST-HBAD (5).
Representations of CoII-SHC and CoII-HBAD are
placed below the scale reflective of undetermined free energy values.
(b) Magnitude of the standard free energy for hydrolysis of GTP (dashed
blue) is shown for comparison (note common scales but arbitrary placement
of y-axis in panel (b)). | PMC10206600 | au3c00119_0007.jpg |
0.415258 | f16f93c05e6d414c9f86004543fb330e | Number of therapeutic paracentesis per month post-transplant. | PMC10208690 | hc9-7-e0158-g002.jpg |
0.437481 | f3ce98fc9add4657a792f940105dfeb9 | (A) Number of therapeutic paracentesis per month in early post-transplant ascites. (B) Number of therapeutic paracentesis per month in late post-transplant ascites. | PMC10208690 | hc9-7-e0158-g003.jpg |
0.38906 | b90f856db4f9490ca2a18f4c3adf9d6f | (A) Number of therapeutic paracentesis per month in the patients with clinical resolution. (B) Number of therapeutic paracentesis per month in the patients without clinical resolution. | PMC10208690 | hc9-7-e0158-g004.jpg |
0.426372 | af22eeeeeed24b92a3ffbed007742908 | Number of therapeutic paracentesis per month before and after the splenic intervention. Each row is an individual patient. For 1 patient, “splenic intervention” was splenectomy. For 11 patients, “splenic intervention” was splenic artery embolization. Abbreviation: LVP, large volume paracentesis. | PMC10208690 | hc9-7-e0158-g005.jpg |
0.40231 | 80f7a4d91dca4894b3c5d27f90fd88e1 | Kaplan Meier analysis of recurrence free and overall survival curves for VFI tertiles and BMI categories. Recurrence free and overall survival curves were generated for 492 patients categorized based on VFI into bottom (red), middle (blue), and top (green) teritles and BMI into < 25 kg/m2 (red), 25–30 kg/m2 (blue), and > 30 kg/m2 (green) categories. Kaplan Meier survival curve analysis revealed that higher VFI had poorer RFS (log rank p = 0.01) and OS (p = 0.01), but not BMI. | PMC10209144 | 41598_2023_34690_Fig1_HTML.jpg |
0.424505 | a56007a7b9024b0e956a1810c1bf1790 | Univariate and multivariate cox proportional models of VFI tertiles for overall and recurrence free survival. Univariate and multivariate cox proportional model survival curves were generated for 492 patients based on the bottom (red), middle (blue), and top (green) VFI tertiles. Multivariate analysis was performed with patient age, sex, tumor stage, and VFI as covariates for RFS. Patient age, sex, race, and VFI were used as covariates in the multivariate model for OS. Univariate (Wald p = 0.01) and multivariate (p = 0.005) analysis showed that a bottom VFI tertile was associated with a significantly better recurrence free survival. Univariate analysis showed that the bottom VFI tertile (p = 0.01) was associated with a significantly better OS. However, VFI lost its significance and was not included in the final multivariate model for OS. | PMC10209144 | 41598_2023_34690_Fig2_HTML.jpg |
0.452182 | 495db322ba8e4fafac54fe7c1a0746dd | In vivo MRSA-infected wound healing effect of Ag/BMO nanozyme. 1064 nm laser: 1 Wcm−2 for 10 min, H2O2: 3 mM, Ag/BMO NPs: 200 μg mL−1. If not otherwise specified, all NPs were dissolved in DI water for detection. a The change of wound areas for 7 d. P-value indicates the significant difference. **P < 0.01, ***P < 0.001. b, c Photographs of MRSA-infected wounds in various groups and the corresponding plates after treatments. Scar bar: 1 mm. d The quantified data of survival MRSA in infected wounds treated with different groups. P-value indicates the significant difference. **P < 0.01, ***P < 0.001. e ROS level of infected wounds (more red fluorescence indicated more ROS content). f H&E and Masson-stained tissues slices of infected wounds. g The percentage number of neutrophils and collagen index. h Levels of IL-6 and TNF-α. i Blood biochemistry and physiological index analysis for control and Ag/BMO groups | PMC10209156 | 41392_2023_1476_Fig1_HTML.jpg |
0.463779 | 1db1cdb328074e6ea336b71fef5ba7be | Meteorological variation during maize growth periods from 2015 to 2021. (a) Daily average temperature. (b) Monthly effective rainfall. | PMC10209193 | 41598_2023_35611_Fig1_HTML.jpg |
0.396693 | bc6454ba15b842e691a790d88121ef6a | Diagram of drip irrigation and conventional border irrigation for maize cultivation. | PMC10209193 | 41598_2023_35611_Fig2_HTML.jpg |
0.361251 | a7f3cd040b154795bbc7bdf62fb89337 | Plant height between drip irrigation and conventional border irrigation at flowering and maturity stages of maize. (a) Plant height at flowering stage of maize. (b) Plant height at maturity of maize. | PMC10209193 | 41598_2023_35611_Fig3_HTML.jpg |
0.392142 | add8c7e420fe49f093360f2cefc9e82e | Leaf area index between drip irrigation and conventional border irrigation at flowering and mature stages of maize. (a) Leaf area index at flowering stage of maize. (b) Leaf area index at maturity of maize. | PMC10209193 | 41598_2023_35611_Fig4_HTML.jpg |
0.428659 | 4920705596324b268bd1f80b6a05ea02 | SPAD between drip irrigation and conventional border irrigation at flowering and maturity stages of maize. (a) SPAD at flowering stage of maize. (b) SPAD at maturity of maize. | PMC10209193 | 41598_2023_35611_Fig5_HTML.jpg |
0.449576 | e842ba0d1fe84af1be7863a69a8b94ba | Dry matter accumulation at flowering stage of maize between drip irrigation and conventional border irrigation. | PMC10209193 | 41598_2023_35611_Fig6_HTML.jpg |
0.431516 | afd8ea3c94094d6a8f400c2a48a9c735 | Dry matter accumulation at maturity stage of maize between drip irrigation and conventional border irrigation. | PMC10209193 | 41598_2023_35611_Fig7_HTML.jpg |
0.456669 | f29e856938494c469e72ec7dbfb2fa1f | Flowchart. | PMC10209532 | gr1.jpg |
0.444727 | 6277b7bf1e304c63afa15a13ba1d239f | Main analysis. | PMC10209532 | gr2.jpg |
0.439431 | f80e5828016a4b06be171a4f2dd7e320 | Sex distribution of the studied patients | PMC10209818 | ijo-35-157-g002.jpg |
0.430335 | 4f4dd7c427114c8bbc6874b712c12eaf | Clinical manifestations of toxoplasmic lymphadenitis among the included cases (n-12) | PMC10209818 | ijo-35-157-g003.jpg |
0.431262 | 82f40844390c416f9f48cf4bd48fd9bc | Phylogram of Phyllosticta genus resulting from a maximum likelihood analysis based on a combined matrix of ITS, LSU, tef1, act and gapdh loci. The tree is artificially rooted to B.obtusa (CMW 8232) and B.stevensii (CBS 112553). ML bootstrap values (left, ML-BS ≥ 50%) and Bayesian posterior probabilities (right, BYPP ≥ 0.9) are given at the nodes. Ex-type strains are indicated in bold. Strains from the present study are marked in blue. | PMC10210045 | mycokeys-95-189-g001.jpg |
0.476153 | 086d0f27ada84a9fb6b3941bb01efdcf | Morphology of Phyllostictaanhuiensis (CFCC 54840) A diseased leaf of QuercusalienaB conidiomata C conidiogenous cells D, E conidia F–H colonies on PDA, MEA and SNA after two weeks at 25 °C. Scale bars: 500 μm (B); 10 μm (C–E). | PMC10210045 | mycokeys-95-189-g002.jpg |
0.39801 | 75f42d26bc3f4059a0851198baf02dd9 | Morphology of Phyllostictaguangdongensis (CFCC 58144) A diseased leaf of ViburnumodoratissimumB conidiomata C conidiogenous cells D, E conidia F–H colonies on PDA, MEA and SNA after two weeks at 25 °C. Scale bars: 500 μm (B); 10 μm (C–E). | PMC10210045 | mycokeys-95-189-g003.jpg |
0.42713 | 3626677ffa0547499a10a5d1d3b7a13d | Representative chromatogram of major compounds in DZF. 1. Naringin; 2. Hesperidin; 3. Neohesperidin; 4. Jatrorrhizine; 5. Palmatine; 6. Berberine. [The Figure is quoted from (Zhu et al., 2018)]. | PMC10211343 | fphar-14-1176443-g001.jpg |
0.513148 | b0750c35b2504fedba16fe797795ed0b | DZF improves HDF-induced obesity in C57BL/6J mice. (A) Body weight changes in diet-induced obese (DIO) C57BL/6J mice. Note: NCD is normal control diet group, n = 12; HFD is high-fat diets group, n = 105. Data are presented as the mean ± standard deviation (SD). ###p < 0.001 vs NCD. (B) NCD C57BL/6J mice (left) and HFD C57BL/6J mice at the 10th week of modeling. (C) Rate of model success in the DIO C57BL/6J mouse model. Note: The criterion for modeling was body weight >32.17 g. The number of mice in the HFD group that met the modeling criteria was 80/105. (D) FBG and body weight at 0 week. Changes in body weight (E) and daily food intake (F) of mice in each group within 6 weeks of the intervention. Body length(G), abdominal circumference (H) and Lee’s index (I) after 6-week intervention. Note: NCD is normal control diet group, n = 12; HFD + Veh is vehicle control group, n = 12; HFD + Met is metformin control group, n = 12; HFD + DZF-L is DZF low dose group, n = 12; HFD + DZF-H is DZF high dose group, n = 12; Data are presented as the mean ± SD. ###p < 0.001 vs NCD; *p < 0.05, **p < 0.01, ***p < 0.001 vs HFD + Veh. | PMC10211343 | fphar-14-1176443-g002.jpg |
0.447834 | 8fd4b0d866274d1783fc89f4baad2405 | DZF improves abnormal glucose and lipid metabolism in DIO mice. (A) Fasting blood glucose (FBG), (B) Serum total cholesterol (TC), (C) Serum triglycerides (TG), (D) Serum high-density lipoprotein cholesterol (HDL-C), (E) Serum low-density lipoprotein cholesterol (LDL-C) at the 6 week of intervention. Note: (A–E)
n = 12. Data are presented as the mean ± SD. ###p < 0.001 vs NCD; *p < 0.05, **p < 0.01, ***p < 0.001 vs HFD + Veh. | PMC10211343 | fphar-14-1176443-g003.jpg |
0.421163 | d2152d4521564099bc667ddc43eccac8 | DZF promotes browning and activates PKA pathway in iWAT of DIO mice. (A) Weight of epididymal white adipose tissue (eWAT), inguinal white adipose tissue (iWAT), perirenal white adipose tissue (pWAT) and brown adipose tissue (BAT). (B) WAT weight/body weight and BAT weight/body weight of each group. (C) HE-staining of adipose tissue in NCD group (×200). BAT at top left, iWAT at top right, pWAT at bottom left, eWAT at bottom right. (D) HE-staining of iWAT; NCD (left), HFD + Veh (middle), HFD + DZF-H (right) (×200). (E) Mitochondrial transmission electron microscopy results of iWAT; NCD (left), HFD + Veh (middle), HFD + DZF-H (right) (×7000). The gene expression of UCP1 (F), PGC-1α (G), and PKA (H) of iWAT. Note: (A and B)
n = 12, (F–H)
n = 6. Data are presented as the mean ± SD. ###p < 0.001 vs NCD; *p < 0.05, **p < 0.01, ***p < 0.001 vs HFD + Veh. | PMC10211343 | fphar-14-1176443-g004.jpg |
0.434056 | 0968ad404d06413bbe35e580fcde42b8 | DZF inhibits lipid accumulation and adipogenesis in 3T3-L1 cells. (A) Cell Counting Kit-8 (CCK8) was used to detect the effect of DZF in different concentrations on the activity of 3T3-L1 adipocytes. (B) BODIPY493/503 staining images of lipid droplet in 3T3-L1 adipocytes (green for lipid droplet, blue for nucleus, ×100). (C) Intracellular TG content. (D and E) Western blotting of SREBP-1 protein expression in 3T3-L1 cells. Note: Veh is vehicle control group, DZF-L is low-dose DZF group, DZF-H is high-dose DZF group, (A,C and E)
n = 3. Data are presented as the mean ± SD. ###p < 0.001 vs NCD; *p < 0.05, **p < 0.01 vs Veh. | PMC10211343 | fphar-14-1176443-g005.jpg |
0.364349 | a40472856b5c4bfc8eeb4c53fb0b0545 | DZF promotes browning of 3T3-L1 adipocytes. (A) Dark-field (left) and bright-field (right) images of 3T3-L1 adipocytes were stained with mito-tracker Green to observe the number of mitochondria. Expression of UCP1, PGC-1α protein (B) and mRNA (C). Note: (B)
n = 3, (C)
n = 4, data are presented as the mean ± SD.*p < 0.05, **p < 0.01 vs Veh. | PMC10211343 | fphar-14-1176443-g006.jpg |
0.416855 | 3c2d9b099a7948d493255876b5e0320d | DZF promotes browning of 3T3-L1 adipocytes through activation of the PKA pathway. (A–D) Expression of PKA, CREB and pCREB proteins. Note: n = 3, data are presented as the mean ± SD, *p < 0.05 and**p < 0.01, vs Veh group. (E–I) Expression of PGC-1α, UCP1, CREB and pCREB protein after PKA inhibition. Note: Veh is vehicle control group (10% FBS-DMEM, Veh), DZF is DZF group (0.8 mg/mL), H-89 is PKA inhibitor group (30 μM H-89 dihydrochloride), DZF + H-89 is DZF + PKA inhibitor group (0.8 mg/mL DZF+30 μM H-89 dihydrochloride); n = 3, data are presented as the mean ± SD, *p < 0.05, **p < 0.01 vs Veh group; #p < 0.05, ##p < 0.01 vs DZF group. | PMC10211343 | fphar-14-1176443-g007.jpg |
0.432234 | 7c4baddc772a4dc28549d866979531a4 | Mechanism of action (MOA) of DZF. The orange line represents the MOA of DZF confirmed in this study, the green line represents the MOA of DZF verified by our team in previous studies, and the black line represents the possible MOA of DZF related to the PKA pathway. In this study, DZF was used to intervene DIO mice and 3T3-L3 adipose cells, and it was found that DZF could improve the excessive lipid deposition in adipose cells, promote the expression of PKA, phosphorylate CREB, activate PGC-1α and thus promote the expression of UCP1, causing WAT browning. Previous studies have confirmed that DZF inhibits the expression of adipogenesis-related enzymes ACC, and FAS and promotes the expression of key lipolysis enzyme HSL, which may also be related to the activation of PKA pathway. Note: DIO, diet-induced obese; DZF, Dai-Zong-Fang; ACC, acetyl-CoA carboxylase; FAS, fatty acid synthetase; HSL, hormone-sensitive lipase. (Pictrue by Figdraw). | PMC10211343 | fphar-14-1176443-g008.jpg |
0.390745 | a86af2a5bdda40c2b069b317c2eb61db | Inclusion criteria for index cases and household contacts in South Korea, June 25, 2021 to January 22, 2022. a)Alpha: 15 index cases, 43 household contacts; Beta: 2 index cases, 5 household contacts. b)Viral-vector: 831 index cases, 1,574 household contacts; mixed: 294 index cases, 579 household contacts.VS, vaccination status. | PMC10211438 | j-phrp-2022-0243f1.jpg |
0.38139 | 7a22ae7571fa404592edce40e5ac05f2 | Adjusted relative risks (aRR) by sex, age and vaccination status of index cases in South Korea, from June 25, 2021 to January 22, 2022. (A) Total, (B) Delta variant, (C) Omicron variant.SAR, secondary attack rate; cRR, crude attack rate; CI, confidence interval. | PMC10211438 | j-phrp-2022-0243f2.jpg |
0.507602 | bba239b8142d4d368fc1599b6404efc2 | Seb1 is recruited to the rDNA and physically associates with the RNAPI transcription machinery.a Normalized ChIP-seq signal of Seb1-HTP and of the indicated proteins involved in mRNA processing (Rna14, Pcf11, Ysh1, and Cbp80), transcription termination (Dhp1), and ribosome biogenesis (Cbf5 and Nop58) on one representative rDNA repeat. The coverage is expressed in thousands reads mapped and averaged over two biological replicates. b ChIP-qPCR analysis of Seb1-TAP at the rDNA locus relative to an untagged control strain. a.u.: arbitrary units. Data and error bars represent the mean and standard deviation of N = 3 independent experiments. c Schematic of the rRNA gene. The bars under the rDNA indicate the positions of the PCR products used in the ChIP-qPCR analysis. d Venn diagram showing 268 Seb1-associated proteins identified independently in both AP-MS of Seb1-HTP and proximity-dependent biotinylation of Seb1-TurboID analyses. e Top-10 most enriched gene ontology (GO) terms by biological process (fold enrichment calculated using amigo2) among the 268 Seb1-associated proteins. Terms related to RNAPI and RNAPII transcription are indicated by purple and green circles, respectively. FDR = false discovery rate. f 51 high-confidence Seb1-associated proteins that possess functions related to RNAPI transcription and ribosome biogenesis (from the 268 identified interactions). g Distribution of Seb1 CRAC reads19 across the indicated categories of genes. h Seb1 CRAC read distribution on one representative rDNA repeat in a wild-type strain averaged over two biological replicates. | PMC10212976 | 41467_2023_38826_Fig1_HTML.jpg |
0.484227 | dec0bb30221e43d3b0b35abbb7744d72 | Seb1 deficiency results in reduced RNAPI density at the rDNA.a Schematic of the rRNA gene. The bars under the rDNA indicate the positions of the PCR products used in the ChIP-qPCR analysis. b ChIP-qPCR analysis of RNAPI subunit (Rpa2-myc) in wild-type and Pnmt1-seb1 strains on the rDNA repeats after the addition of thiamine for 10–12 h. Data and error bars represent the mean and standard deviation of N = 3 independent experiments. *P-value < 0.05; **P-value < 0.01, as determined by unpaired two-tailed Student’s t-test corrected for multiple comparisons using the Holm-Sidak method. c Western blot analysis of Rpa2-myc in wild-type (lane 3) and Pnmt1-seb1 (lane 2) strains following the addition of thiamine. The data in lanes 1–3 were from the same blot, with the vertical line indicating some intervening lanes that were cropped out. The experiment was done N = 2 from independent biological replicates. d ChIP-qPCR analysis of Rrn3-myc in wild-type and Pnmt1-seb1 strains on the rDNA repeats after the addition of thiamine for 10–12 h. Data and error bars represent the mean and standard deviation of N = 3 independent experiments. ns, P-value > 0.05; **P-value < 0.01, as determined by unpaired two-tailed Student’s t-test corrected for multiple comparisons using the Holm-Sidak method. e Western blot analysis of Rrn3-myc in wild-type (lane 2) and Pnmt1-seb1 (lane3) strains following the addition of thiamine. The experiment was done N = 2 from independent biological replicates. | PMC10212976 | 41467_2023_38826_Fig2_HTML.jpg |
0.447164 | 5864a65071ea4aeaba1172efc2a7256c | Seb1 is required for efficient pre-rRNA processing.a Northern Blot analysis of total RNA prepared from wild-type (lane 1) and Pnmt1-seb1 (lane 2) strains following addition of thiamine for 10–12 h. Top, the membrane was analyzed using a probe complementary to ITS1-specific sequences (see Supplementary Fig. 3a). Middle and bottom, mature rRNAs were analyzed using probes complementary to 25S- and 18S-specific sequences, respectively. Pre-rRNAs and mature rRNAs are indicated on the left. The experiment was done N = 6 from independent biological replicates. b Quantification of 35 S/27 S pre-rRNA ratio in the Pnmt1-seb1 strain relative to the wild-type strain. Data and error bars represent the mean and standard deviation of N = 6 independent experiments. ****P-value < 0.0001, as determined by unpaired two-tailed Student’s t-test. c Pulse-Chase analysis of total RNA prepared from wild-type (lanes 1–5) and Pnmt1-seb1 (lanes 6–10) strains following the addition of thiamine for 10–12 h. The cells were then pulse-labeled with [5,6H]-uridine for 4 min and chased with an excess of unlabeled uridine. Total RNA was extracted from cells samples harvested at the indicated time points and resolved on a 0.8% agarose–formaldehyde gel. The position of the rRNA species is indicated on the right. The experiment was done N = 2 from independent biological replicates. d Levels of 35 S pre-rRNA in wild-type and Pnmt1-seb1 cells relative to t = 0 min at the indicated time points. Datapoints from two biologically independent experiments are shown. ns, P-value > 0.05; *P-value < 0.05; **P-value < 0.01; as determined by Student’s t-test. | PMC10212976 | 41467_2023_38826_Fig3_HTML.jpg |
0.426016 | 81994a24bc034131935d32b8011fe200 | Seb1-dependent pre-rRNA processing requires its RNA-binding function.a Schematic of Seb1 complementation assay. See Result section for details. b Northern Blot analysis of pre-rRNA from the indicated strains after the addition of thiamine for 10–12 h. Top, the membrane was analyzed using a probe complementary to ITS1-specific sequences. Middle and bottom, mature rRNAs were analyzed using probes complementary to 25S- and 18S-specific sequences, respectively. Pre-rRNAs and mature rRNAs are indicated on the right. c Quantification of 35 S/27 S pre-rRNA ratio in the indicated strains expressed relative to Seb1-depleted cells complemented with wild-type Seb1-Flag. EV, empty vector. Data and error bars represent the mean and standard deviation of N = 3 independent experiments. ns, P-value > 0.05; *P-value < 0.05; **P-value < 0.01; as determined by unpaired two-tailed Student’s t-test corrected for multiple comparisons using the Holm-Sidak method. | PMC10212976 | 41467_2023_38826_Fig4_HTML.jpg |
0.437063 | f527a869a67942879c75b2a09a6e40d2 | Seb1 delays RNAPI progression along the rDNA.a RNAPI CRAC distribution over the rRNA gene. Top, schematic representation of the pre-rRNA transcription unit, including 18 S, 5.8 S, and 25 S rRNA as well as ETS and ITS regions. Bottom, Rpa2 CRAC profiles, presented as fractions of reads, for wild-type (green) and Pnmt1-seb1 (blue) cells after the addition of thiamine for 10–12 h. The solid dark lines mark the median for two biological replicates, while the pale profiles indicate the range between second and third quartile. b RNAPI CRAC profiles across the first 1300 nt of the transcription unit corresponding to the 5'ETS. c
Top, RNAPI CRAC profiles across the ITS1, 5.8 S, and ITS2 regions of the transcription unit; Bottom, Log2-transformed ratios (∆log2) between Seb1-depleted and wild-type control are displayed. Regions showing decrease and increase RNAPI occupancy in Seb1-depleted cells are marked with blue and red, respectively. d Comparison of the distribution of CRAC signal across the ITS1, 5.8 S, and ITS2 regions of the rRNA transcription unit for independent replicates (rep) of wild-type (wt) and Seb1-deficient (Seb1) cells. e RNAPI CRAC profiles across the 3'ETS region of the transcription unit. | PMC10212976 | 41467_2023_38826_Fig5_HTML.jpg |
0.459164 | c1bb411a2b894ca1842c722ada234a39 | Model for how Seb1 promotes cotranscriptional pre-rRNA processing.a NET-seq and CRAC analysis in wild-type budding and fission yeasts show that RNAPI frequently pauses when transcribing the long rRNA gene35,36. We propose that the cooperative binding of Seb1 to the RNAPI complex and to nascent pre-rRNA contributes to RNAPI pausing, thereby facilitating recognition of processing sites by the SSU-processome (gray pacman), which will cotranscriptionally (C) cleave the nascent pre-rRNA. A minority of pre-rRNAs that are not processed cotranscriptionally will be cleaved posttranscriptionally (P) after transcription termination27. b In the absence of Seb1, RNAPI progresses faster along the rRNA gene, impairing the efficiency of cotranscriptional pre-rRNA processing. | PMC10212976 | 41467_2023_38826_Fig6_HTML.jpg |
0.395861 | 25c7a145e1094deba934b72690ae0e71 | Microstructures of the cold-rolled alloy, annealed alloy, and SCCPs alloy.a–c Schematic drawings the evolution of stepwise controllable coherent nanoprecipitations. d, e EBSD maps of alloys subject to cold-rolling and annealing, respectively. f–h Bright-field (BF) TEM images and corresponding SAED patters reveal the specific structure evolution. i Dark-field (DF) TEM image and SAED pattern of the selected area in (f) show the distribution of \documentclass[12pt]{minimal}
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\begin{document}$${{{{{{\rm{\gamma }}}}}}}^{\prime\prime}$$\end{document}γ″-particles along the zone axis <011> of the fcc matrix. The scale bars in (d), (e), (f–h), and (i) are 10 μm, 300 nm, and 5 nm, respectively. The scale bars of SAED patterns are 10 nm−1. | PMC10213035 | 41467_2023_38531_Fig1_HTML.jpg |
0.404048 | 7b68d59066f745268a73700c78163cdc | Atomic structures of the SCCPs alloy.a, b Atomic-resolution HAADF-STEM images and corresponding FFT patterns of δ, \documentclass[12pt]{minimal}
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\begin{document}$${{{{{{\rm{\gamma }}}}}}}^{\prime\prime}$$\end{document}γ″ and the fcc phase along different zone axes show the coherent relationship and specific coherent interfaces of the two different precipitations. I, II, III, and IV are the fast Fourier transforms of the corresponding regions in (a) and (b), respectively. An accurate atomic placeholder map is also provided in the correlative illustrations (red balls represent heavy elements and green ones represent light elements). c, d 3D-APT atom maps show the elemental distribution of δ-lamellae and \documentclass[12pt]{minimal}
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\begin{document}$${{{{{{\rm{\gamma }}}}}}}^{\prime\prime}$$\end{document}γ″-particles together with the 65 at.% Ni iso-concentration surfaces. e, f Proximity histograms show the elemental partitioning between the matrix and δ-lamellae and between the matrix and \documentclass[12pt]{minimal}
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\begin{document}$${{{{{{\rm{\gamma }}}}}}}^{\prime\prime}$$\end{document}γ″-particles, respectively. The scale bars in (a) and (b) are 1 and 2 nm, respectively. | PMC10213035 | 41467_2023_38531_Fig2_HTML.jpg |
0.450426 | b67f9f19f8694cfa97fdee6fc86c326f | Uniaxial tensile properties of samples at different processing conditions at ambient temperature.a Engineering tensile stress-strain curves of the as-cast alloy, annealed alloy, and the SCCPs alloy. The inset presents the strain-hardening rate (\documentclass[12pt]{minimal}
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\begin{document}$${{{{{\rm{d}}}}}}{{{{{\rm{\sigma }}}}}}/{{{{{\rm{d}}}}}}{{{{{\rm{\varepsilon }}}}}}$$\end{document}dσ/dε) of the SCCPs alloy. b Maps of yield strength vs. uniform elongation of W-based heavy alloys, Ni-based superalloys, and Refractory HEAs1–6. | PMC10213035 | 41467_2023_38531_Fig3_HTML.jpg |
0.426635 | e4eddf3db60e425c9d79371b310fbbaf | Micro-mechanisms of plastic deformation in the W-based HEA.a–c TEM and HAADF-STEM images show plastically deformed δ-lamellae at various tensile strains at room temperature. a Deformation microstructure at the 4% tensile strain reveals that the pronounced dislocation activity in steadily cutting through the δ-lamellae. b Images of dislocation slip bands (DSBs) during tensile deformation at the 10% strain. c Misalignments of the δ-lamellae within a grain at 14% strain and the corresponding HRTEM. d Schematic diagram showing the interaction of dislocations with δ-lamellae. e Dislocation motion is pinned by \documentclass[12pt]{minimal}
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\begin{document}$${{{{{{\rm{\gamma }}}}}}}^{\prime\prime}$$\end{document}γ″-particles at 4% strain. f Corresponding HRTEM and FFT images of \documentclass[12pt]{minimal}
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\begin{document}$${{{{{{\rm{\gamma }}}}}}}^{\prime\prime}$$\end{document}γ″ in (e). g Schematic diagram of the interaction between a dislocation and particle, consistent with the shearing of coherent (\documentclass[12pt]{minimal}
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\begin{document}$${\mathop{{{{{{\bf{b}}}}}}}\limits^{ \rightharpoonup }}_{{{{{{\boldsymbol{\gamma }}}}}}}\approx {\mathop{{{{{{\bf{b}}}}}}}\limits^{ \rightharpoonup }}_{{{{{{{\boldsymbol{\gamma }}}}}}}^{\prime\prime}}$$\end{document}b⇀γ≈b⇀γ″) particles. Unless otherwise indicated, the scale bars are all 50 nm. | PMC10213035 | 41467_2023_38531_Fig4_HTML.jpg |
0.453624 | 18789ee7cd8b457886173dc6dec2b645 | CircDLG1 is highly expressed in HCC and the prognosis of patients with high levels of expression have a poor prognosis. A Expression of the circDLG1 in HCC tissues and normal tissues in GES97332. Tumor tissue is shown in red, and normal tissue is shown in gray. B, C Expression of the circDLG1 in 120 cases of HCC tissues and normal tissues. D The circDLG1 expression in hepatocytes and five hepatocellular carcinoma cells (BEL7402, Huh7, HepG2, HepG3B, SK-hep-1, and SNU449). E Prognosis of patients with different circDLG1 expression levels. Data represent the mean ± SD of three independent experiments. All of the above experiments were performed with three biological replicates (*p < 0.05, **p < 0.01, and ***p < 0.001) | PMC10213070 | 10142_2023_1096_Fig1_HTML.jpg |
0.417292 | 71afae7ac74f4dad84514c92a5144a7d | CircDLG1 is a circular structure in HCC cells. A, D The RNA levels of circDLG1 and DLG1 in HCC cells were detected by qRT-PCR. B, E qRT-PCR analysis of the circDLG1 and DLG1 expression in the HCC cells under the treatment with actinomycin D. C, F qPCR analysis of circDLG1 in the cytoplasm and nuclear of which was separated by kit. G cDNA and gDNA of HCC cells were used as the templates to amplify circDLG1, DLG1, and GAPDH with divergent primers and convergent primers, respectively. H The FISH experiment was used to detect the subcellular localization of circDLG1 in HCC cells. All of the above experiments were performed with three biological replicates (**p < 0.01 and ***p < 0.001) | PMC10213070 | 10142_2023_1096_Fig2_HTML.jpg |
0.390593 | 86bdfc3ab911439e85573e5fd87aff15 | CircDLG1 promoted the proliferation of HCC cells and inhibited apoptosis. A qRT-PCR analysis of the transfection efficacy of shRNA in HCC cells after 48-h transfection. B, C The proliferation status of HCC cells was determined by CCK-8 assay after circDLG1 knockdown. D, E Colony formation ability of HCC cells after circDLG1 knockdown. F The protein levels of GLUT1, PKM2, and HK2 were examined by western blot after transfected with shRNA. G The mRNA levels of GLUT1, PKM2, and HK2 were examined by RT-qPCR after transfected with shRNA. H Immunofluorescence was used to detect the expression changes of GLUT1, PKM2, and HK2 after silencing circDLG1. I, K, L Detection of ECAR (I), glucose consumption (K), and lactate production (L) in transfected cells. J The proliferation status of HCC cells was determined by CCK-8 assay after circDLG1 knockdown with or without sorafenib. All of the above experiments were performed with three biological replicates (**p < 0.01 and ***p < 0.001) | PMC10213070 | 10142_2023_1096_Fig3_HTML.jpg |
0.46319 | 8f7af3edf8fc4d7289ca18448f5002ca | CircDLG1 promotes proliferation of HCC cells in vivo. A Images of nude mice with xenograft tumors made from transfected cells. B The line chart represented a summary of tumor volume curves. The mean standard deviation of five mice was used to represent the typical tumor volume. C The tumor mass was measured across various groups. D IHC was used to display the expression level of Ki67 and PCNA in various groups. All of the above experiments were performed with three biological replicates (**p < 0.01) | PMC10213070 | 10142_2023_1096_Fig4_HTML.jpg |
0.469751 | 8ac2699749a5416c83bac7f3630d2b59 | CircDLG1 binds to miR-141-3p in HCC cells. A The binding between circDLG1 and miR-141-3p was predicted by using Circinteractome software and StarBase, Venn graph of the intersection of two software results. B The expression levels of has-mir-224-5p, has-miR-141-3p, has-mir-300, has-mir-449a, has-mir-34a-5p, and has-mir-145-5p in the tumor and adjacent normal tissues were examined by qRT-PCR. C The interaction between circDLG1 and has-miR-141-3p, has-miR-449a, and has-miR-300 was verified by dual-luciferase reporter assay. D The expression relationship between circDLG1 and has-miR-141-3p. E CircDLG1 contained the binding sites of has-miR-141-3p. F, G The interaction between circDLG1 and miR-141-3p was verified by dual-luciferase reporter assay and RIP assay. All of the above experiments were performed with three biological replicates (*p < 0.05, **p < 0.01, and ***p < 0.001) | PMC10213070 | 10142_2023_1096_Fig5_HTML.jpg |
0.493706 | a1e06b2f2f8b441287374ae3752c44ca | WTAP expression was specifically regulated by miR-141-3p in HCC cells. A GEPIA was used to verify WTAP expression in 369 HCC samples and 160 normal tissues. Normal tissue is depicted in gray, whereas tumor tissue is indicated in red. B The association between miR-141-3p levels and WTAP mRNA in HCC tissues was assessed. C A qRT-PCR experiment was used to determine the degree of WTAP mRNA expression in HCC cells. D Western blot analysis was used to determine the degree of WTAP protein expression in HCC cells. E The sequences that WTAP and miR-141-3p have in common. F Using a dual-luciferase reporter experiment, it was shown that WTAP and miR-141-3p interact. All of the above experiments were performed with three biological replicates (*p < 0.05, **p < 0.01, and ***p < 0.001) | PMC10213070 | 10142_2023_1096_Fig6_HTML.jpg |
0.43866 | d314464682314913ac254f594948f8d5 | CircDLG1 promoted progression of HCC via the miR-141-3p/WTAP axis. A, B The proliferative status of HCC cells was determined by CCK-8 assay after circDLG1 knockdown with miR-141-3p inhibitor or WTAP overexpression. C, D Colony formation ability of HCC cells after circDLG1 knockdown with miR-141-3p inhibitor or WTAP overexpression. E The proliferation status of HCC cells was determined by CCK-8 assay after circDLG1 knockdown with miR-141-3p inhibitor or WTAP overexpression adding or not adding sorafenib. F–H Detection of ECAR (F), glucose consumption (G), and lactate production (H) in HCC cells after circDLG1 knockdown with miR-141-3p inhibitor or WTAP overexpression. All of the above experiments were performed with three biological replicates (*p < 0.05, **p < 0.01, and ***p < 0.001) | PMC10213070 | 10142_2023_1096_Fig7_HTML.jpg |
0.361066 | c4f360ef21024aee90a9d9d769920f5f | Research model. | PMC10213194 | gr1.jpg |
0.399691 | 30de06248bba44b793c20021f518206a | ANN model A. | PMC10213194 | gr2.jpg |
0.457032 | a1e19760138d4e349b6e8f34b4816fab | ANN model B. | PMC10213194 | gr3.jpg |
0.466955 | b86a29e7aa4044e499df727843567000 | ANN model C. | PMC10213194 | gr4.jpg |
0.463166 | 902568605e114d71b547cd1c97424c79 | ANN model D. | PMC10213194 | gr5.jpg |
0.494099 | eeef3f76727f419590827405ee129d2d | Design and workflow of this study. *p < 0.05, **p < 0.01, ***p < 0.001. | PMC10213725 | fimmu-14-1117585-g001.jpg |
0.411933 | 2fe416c205324c489cf783c5b70e021b | SNAI2 expression analysis in pan-cancer analysis. (A) The expression level of the SNAI2 in normal tissues. (B) The expression level of the SNAI2 in tumor cell lines. (C) The expression level of the SNAI2 between different tumors and normal tissues was analyzed based on TCGA and GTEx databases. The box plot data were supplied, and Log2 (TPM+1) was applied for the log scale. (D) Representative images of SNAI2 immunohistochemical staining analysis in the PAAD tissue and adjacent normal tissue in the HPA database. (E) Western blotting analysis of SNAI2 protein expression in the paired PAAD tissues and adjacent normal tissues. (F)The SNAI2 mRNA expression in 9 pairs of PAAD tissues and adjacent normal tissues was evaluated using qRT-PCR. (G) The immunofluorescence images showed the distribution of SNAI2 in the HEK293T, Hela, HUVEC, and HCCLM3 cell lines. All data are presented as the mean ± SD of three independent experiments. *p < 0.05, **p < 0.01, ***p < 0.001, ns, no significance. | PMC10213725 | fimmu-14-1117585-g002.jpg |
0.424499 | 5e8048f40b434f1eae7615e502d46b27 | Analysis of mutation feature of SNAI2 in different tumors using TCGA database. (A) The alteration frequency with mutation type in the SNAI2 gene. (B) The mutation count of the SNA2 gene in various cancers. (C) The specific alteration site of the SNAI2 gene. (D) The 3D structure of SNAI2 in the mutation site with the highest alteration frequency (X209_splice) was displayed. | PMC10213725 | fimmu-14-1117585-g003.jpg |
0.420072 | 0f844af627ea4867af145e45e7b2ad9a | Prognostic Value of SNAI2. (A) Summary of the correlation between expression of SNAI2 with overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) based on the univariate Cox regression and Kaplan-Meier models. Red indicated that SNAI2 was a risk factor affecting the prognosis of cancer patients, and blue represents a protective factor. Only p values < 0.05 were shown. (B) Univariate Cox regression analysis of SNAI2 in pan-cancer (OS). (C) Kaplan-Meier overall survival curves of SNAI2 in ACC, BLCA, STAD, PAAD, LGG, KIRP, MESO, and LUAD. | PMC10213725 | fimmu-14-1117585-g004.jpg |
0.40934 | 1be2f327c48c4c8e9a2e0ef69edc9f44 | Gene Set Enrichment Analysis of Hallmark gene sets. The size of the circle represented the FDR value of the enriching term in each cancer, and the color indicated the normalized enrichment score (NES) of each term. | PMC10213725 | fimmu-14-1117585-g005.jpg |
0.419483 | d538c5062a1b42f384e7c9423ba1d656 | Single-Cell Analysis of SNAI2 in Cancers (A) Summary of SNAI2 expression of various cell types in single-cell datasets. (B) The Scatter plot showed the distributions of 4 different cell types of the GSE130001 BLCA dataset and the SNAI2 expression levels of cells in the GSE130001 dataset. (C) The Scatter plot showed the distributions of 11 different cell types of the GSE103322 HNSC dataset and the SNAI2 expression levels of cells in the GSE103322 dataset. (D) The Scatter plot showed the distributions of 12 different cell types of the CRA001160 PAAD dataset and the SNAI2 expression levels of cells in the CRA001160 dataset. | PMC10213725 | fimmu-14-1117585-g006.jpg |
0.41727 | eb59a905e0fe43e1b6d269299ee4706d | Immune Cell Infiltration Analyses. The relationship between SNAI2 expression and the levels of infiltration of CD4+ T cells, CAF, progenitor cells, Endo, Eos, HSC, Tfh, gdT, NKT, regulatory T cells (Tregs), B cells, neutrophils, monocytes, macrophages, dendritic cells, NK cells, Mast cells, and CD8+ T cells in cancer. A positive correlation was shown in red, while a negative correlation was shown in blue. | PMC10213725 | fimmu-14-1117585-g007.jpg |
0.465892 | 453460976eb549d9a638be5631947ffe | Immunotherapy prediction analysis of SNAI2 in the pan-cancer. (A) The correlation between SNAI2 expression and immune checkpoint gene expression in cancers. (B) The radar chart displayed the correlation between SNAI2 expression and TMB. (C) The radar chart displayed the correlation between SNAI2 expression and MSI. (D) Kaplan Meier curves for low-SNAI2 and high-SNAI2 expression from the GSE91061 clinical cohort (anti-PD-1 immunotherapy), and (E) the fraction of melanoma patients with response to the blockade in the two groups. (F) Kaplan Meier curves for low-SNAI2 and high-SNAI2 expression from the Gide2019 clinical cohort (anti-PD-1 and anti-CTLA4 immunotherapy), and (G) the fraction of melanoma patients with response to the blockade. (H) Kaplan Meier curves for low-SNAI2 and high-SNAI2 expression from IMvigor210 clinical cohort (anti-PD-L1 immunotherapy), and (I) the fraction of urological tumors with therapeutic response to the blockade. *p <0.05, **p <0.01, ***p < 0.001. | PMC10213725 | fimmu-14-1117585-g008.jpg |
0.423076 | cff376db5bcd4818b3394f9e6092c060 | SNAI2-related gene enrichment analysis. (A) The protein-protein interaction (PPI) network presented the proteins interacting with SNAI2. (B) We also retrieved the top 100 SNAI2-linked genes in TCGA projects and evaluated the expression correlation between SNAI2 and chosen targeting genes, such as BMP1, DUSP7, EXT1, FAM126A, FRMD6, FSCN1, MARVELD1, MCC, MICALL1, MMP14, MYH9, PLXNA1, SERPINH1, SERPINH1, and TUBB6 using the GEPIA2 approach. (C) The corresponding heatmap data is provided in the detailed cancer kinds. | PMC10213725 | fimmu-14-1117585-g009.jpg |
0.444339 | 6877c3cb23c04711b5b491d7ee31cd4d | Knockdown of SNAI2 inhibits cell proliferation and promotes cell apoptosis of pancreatic cancer cells. (A) The knockdown efficiency of shSNAI2 was examined in PANC-1 cells with western blotting. (B) CCK-8 assays evaluated cellular growth curves in PANC-1 cells. (C) Representative images and quantification of colony formation assays of pancreatic cancer cells transfected with shSNAI2. (D) Representative images and quantification of EdU assays to evaluate cell proliferation ability after transfecting shSNAI2, magnification, ×200; scale bars, 50 µm. (E). Representative images and quantification of transwell assay to examine the invasion ability, magnification, ×200; scale bars, 50 µm. (F). Western blotting showed the changes of EMT proteins in PANC-1 cells transfected with shSNAI2 plasmids. All data are presented as the mean ± SD of three independent experiments. *p < 0.05, **p < 0.01, ns, no significance. | PMC10213725 | fimmu-14-1117585-g010.jpg |
0.389041 | 88973b5dc4454ba8a9c72e17e3b11527 | Generalizability of the Gr‐assisted SWNT handedness determination method and the handedness distributions for two types of SWNTs. a) Plot of the stacking angle (α) versus the SWNT diameter (d). The red‐boxed region highlights the diameter distribution of those SWNTs that are aligned with Gr with stacking angles less than 5°. b) Plot of the stacking angle (α) versus the SWNT helical angle (θ). The red‐boxed region highlights the helical angle distribution of those SWNTs that are aligned with Gr with stacking angles less than 5°. c) Plot showing the detailed chiral indices and handedness of 27 SWNTs, which adopt two types of morphologies due to different growth methods. R‐H, L‐H, and Z represent right‐handed, left‐handed, and zigzag SWNTs, respectively. SWNTs that are perfectly aligned with Gr (α = 0°) are represented by red, blue, and green markers without transparency, while SWNTs that are quasi‐parallelly stacked on Gr (α < 5°) are represented by red, blue, and green markers with 50% transparency. d) Histogram showing the handedness distribution of randomly aligned SWNTs grown on ST‐cut quartz, revealing similar proportions of left‐ and right‐handed nanotubes. Columns in nontransparent colors represent SWNTs that are perfectly aligned with Gr, while columns in semi‐transparent colors represent SWNTs that are quasi‐parallel with Gr (α < 5°). e) Histogram showing the handedness distribution of horizontal SWNT arrays grown on a‐sapphire, implying potential left‐handedness enrichment. | PMC10214254 | ADVS-10-2206403-g001.jpg |
0.407562 | 6210c50372cc47e8b4ee01aa5bdfd30c | SWNT handedness determination by the construction of the SWNT/Gr heterostructure with the aligned interface. a) Perspective views along the nanotube axes showing the atomic models of a pair of SWNT enantiomers, (11,4)‐R and (4,11)‐L, respectively. The mirror plane between them is represented by a black dashed line with a label of σ. The electron beam injection direction is labeled as y, along which the TEM image is captured. b) Projective views of the upper and lower walls of the (11,4)‐R and (4,11)‐L SWNTs, respectively. Representative zigzag directions are highlighted by the blue and red lines. c) Projective view displaying the atomic model of either a (11,4)‐R or a (4,11)‐L SWNT. Their top views look the same. d) Simulated TEM image based on the model in (a). e,f) Schematic illustration depicting the SWNT/Gr heterostructure construction process. g‐i) 3D perspective view (g), 2D projective view (h), and the corresponding simulated diffraction pattern (i) for the atomic model of the SWNT/Gr van der Waals heterostructure with an arbitrary interface configuration. The representative zigzag orientations in both Gr and the SWNT lower wall are highlighted in blue in (g) and (h), indicating the generation of an unaligned interface. j‐l) 3D perspective view (j), 2D projective view (k), and the corresponding simulated diffraction pattern (l) for the atomic model of the SWNT/Gr van der Waals heterostructure with an aligned interface. m) SEM image displaying the SWNTs/Gr van der Waals heterostructures transferred on a holey SiNx TEM grid. | PMC10214254 | ADVS-10-2206403-g003.jpg |
0.401024 | 643a67f09f434b88b13f561679351b19 | Interfacial configuration of SWNT/Gr van der Waals heterostructures. a,b) 3D perspective atomic models representing a “line contact” mode and a “surface contact” mode, respectively, between Gr and the SWNT lower wall. The contact areas at the interface are highlighted in red. c) TEM image showing prominent lattice distortion of Gr when it approaches a SWNT. d) Plot revealing the projective interatomic distance shrinkage ratio of the graphene lattice as a function of the distance from the SWNT left wall (red triangles). The interatomic distance represented in the inset is measured in the red‐boxed region in (b) along the purple line direction from left to right. The intrinsic interatomic distance in a benzene ring of Gr is represented as d, while the deformed interatomic distance due to out‐of‐plane distortion is denoted as D. The inclination angles to the horizontal of the corresponding benzene rings are plotted as the blue circles. e) Formation energy of the (12,12) SWNT/Gr heterostructure as a function of the compressive strain of Gr, which determines the out‐of‐plane distortion of the Gr at the interface. f) Formation energy of the SWNT/Gr heterostructure as a function of the stacking angle (α) between the SWNT lower wall and Gr. The compression ratio of Gr in this case is 6%. g) Parallel projection showing the interface configuration of a DFT‐relaxed (13,11)‐R SWNT/Gr heterostructure with a “surface contact” mode, in which a large area at the interface displays an AA stacking configuration (yellow‐shaded region). h) Central projection of the (13,11)‐R SWNT/Gr heterostructure interface, where most areas adopt staggered stacking (close to AB stacking, blue‐shaded region) with only a small region having AA stack (yellow‐shaded region). i) DFT‐calculated density of states of an intrinsic metallic (12,12) SWNT (black line) and a (12, 12) SWNT/Gr heterostructure in a “surface contact” mode (red line), respectively. j) DFT‐calculated density of states of an intrinsic semiconducting (20, 0) SWNT (black line) and the (20, 0) SWNT/Gr heterostructure in a “surface contact” mode (red line), respectively. | PMC10214254 | ADVS-10-2206403-g004.jpg |
0.400523 | 937046560c394932a0a833d92ecd1f09 | A typical example showing the identification of the SWNT handedness by one TEM image. a) TEM image of an aligned SWNT on Gr. b) FFT image of (a). Three principal layer line reflections contributed from the SWNT are labeled by L1
, L2
, and L3
, respectively. The spacings of L1
and L2
with respect to the equatorial layer line are marked as D1
and D2
, respectively. The white hexagon labels the reflexes contributed from the SWNT lower wall and Gr, which are superimposed. The blue hexagon marks the reflexes from the SWNT upper wall. A pair of L1
principal layer lines contributed by the upper SWNT is labeled by a blue dashed line, while the other pair of L1
reflexes contributed by the lower SWNT wall is marked by a white dashed line. They correspond to the helix along a zigzag direction in a SWNT that adopts the minimum screw pitch, as represented by the blue spiral in (c). Inset on the bottom left corner is the reflexes from Gr in the vicinity of the SWNT. The intensity line profile on the bottom right corner is taken along the white arrow in (b). c) Projective view of the SWNT/Gr atomic model based on (a). Inset displays the DFT relaxed side view of the model with the blue lines representing two zigzag lattice directions in the SWNT lower wall and Gr, respectively, which are parallel with each other. d) TEM image simulation based on the model in (c). e) FFT image of (a) with reflexes from Gr and the SWNT lower wall removed, as labeled by the short white lines. f) Reconstructed TEM image based on the FFT image in e), in which the lattice of the SWNT lower wall and Gr is filtered, as verified by the red box. The lattice configuration of the SWNT upper wall is represented by the pink model. g) FFT image of panel a with reflection spots from the SWNT upper wall removed, as labeled by the short blue lines. h) Reconstructed TEM image based on the FFT image in (g), in which the lattice of the SWNT upper wall is filtered. Blue and orange atomic models are superimposed on the reconstructed TEM image to represent the lattice structures of the SWNT lower wall and Gr, respectively. i) Histogram showing the distribution of the stacking angles (α) between SWNTs and Gr. | PMC10214254 | ADVS-10-2206403-g005.jpg |
0.39124 | 63c595f21eee4aeea3a5478f511ed6f5 | Four types of popularly studied PPI stabilizers.
(A) General PPI stabilizer which stabilizes receptor–ligand
binding directly or allosterically. (B) Molecular glue which induces
or enhances the binding of E3 ligase and the target protein. (C) Proteolysis
targeting chimera (PROTAC) which induces or enhances the binding of
E3 ligase and the protein of interest (target protein). (D) What is
referred to here as protein enhancement targeting chimera (PENTAC)
is a PPI stabilizer that stabilizes the binding of deubiquitinase
and the target protein. (B), (C), and (D) are subsets of (A). | PMC10214535 | oc3c00545_0001.jpg |
0.444625 | 2496691ab95c4b8cae00eb8bc2c7a104 | Beam’s eye view of beam arrangements and delineated structures for miVMAT-CSI. | PMC10214992 | rrad026f1.jpg |
0.467172 | fe79a18d7aea41148248286db44424f5 | The clinical workflow for treatment planning. A:MUPs; B:CAPs; C:FAPs. | PMC10214992 | rrad026f2.jpg |
0.434071 | 6d323a88b5d94870929173bf386a3f00 | Sagittal and transversal dose distributions. A1–A4 denote MUPs, B1–B4 denote CAPs and C1–C4 denote FAPs. | PMC10214992 | rrad026f3.jpg |
0.445332 | b67c73eb0f8f4e56a47693f7119bb67c | Comparison of DVHs for the PTV and OARs in MUPs, CAPs and FAPs. | PMC10214992 | rrad026f4.jpg |
0.500039 | 2fe7c4aaba644741b89465c9b4a327d9 | Genomic comparison of P. aeruginosa strains. The pan-genome analysis by Roary predicted the number of genes. The figure was created using Calculate and Draw Custom Venn Diagrams. The Venn diagram shows the number of unique and shared genes among the genomes of five strains: PAO1, PE21, PE52, PE52, PE63, and PE83 from the Mexican hospital. | PMC10215170 | antibiotics-12-00866-g001.jpg |
0.406952 | fcbbb0713fe54cdeaa80c6619097ff45 | Graphical representation of the pangenome analysis of the 65 strains. The phylogenetic tree was constructed based on the accessory genomes of the sixty-five strains. Strain names were colored according to the isolation source. Yellow: urinary genomes; orange: sputum genomes; green: environmental genomes; red: Mexican genomes; reference genomes are in black. | PMC10215170 | antibiotics-12-00866-g002.jpg |
0.432922 | 370779f4fa1b46d4b56f76128d851c61 | Presence and absence of resistance genes in urinary, sputum, and environmental genomes. Representation of resistance genes present and absent in the 65 genomes from urinary, sputum, and environmental. P. aeruginosa PAO1 and PA14 were used for comparison. ResFinder detected a total of 47 genes. The heatmap was constructed with the Bioconductor package in Rstudio. MH means Mexican Hospital. | PMC10215170 | antibiotics-12-00866-g003.jpg |
0.485241 | 9ee931439aea4c53ad583a8f339a9a3f | Mutational resistance of the 65 urinary, sputum, and environmental genomes. Graphical representation of mutations associated with antibiotic resistance (navy color), mutations with an unknown effect on antibiotic resistance (blue light), and mutations in oprD associated with susceptibility to meropenem (green) and wild-type genes (gray). P. aeruginosa PAO1 and PA14 were used as reference genomes. The heatmap was constructed with the Bioconductor package in Rstudio. MH means Mexican Hospital. | PMC10215170 | antibiotics-12-00866-g004.jpg |
0.39996 | 7a8bf4f5d5e74204af410ef9fb3d48dd | Mobile Genetic Elements (MGEs) are distributed in the 65 genomes of urinary (A), sputum (B), and environmental (C) strains. Each pie graphic represents the distribution of mobile genetic elements in each group of strains, and the numbers within the pie graphic represent the number of MGEs found. Tn (transposons), CTn (composite transposons), IS (insertion sequence), IMEs (integrative mobilizable elements), ICEs (integrative conjugative elements), and plasmids. | PMC10215170 | antibiotics-12-00866-g005.jpg |
0.511141 | 24358f4c31b843618e309b73664d4c2c | Presence and absence of virulence genes. Heatmap representation of virulence genes associated with urinary and lung infections. Purple represents presence, and gray color represents absence. The genes were identified with VFDB, and the heatmap was constructed with the Bioconductor package in Rstudio. MH means Mexican Hospital. | PMC10215170 | antibiotics-12-00866-g006.jpg |
0.446948 | 3c30f4f379094dec9fd1069c63b06a86 | Schematic of a fungal cell describing the main characteristics of the membrane and cell wall and the sites of action of the main antifungal drugs. | PMC10215229 | antibiotics-12-00884-g001.jpg |
0.412446 | 680ce9826ddb4c92be92b8a6ca7467c6 | Timeline of antifungal drugs and main clinical indications. | PMC10215229 | antibiotics-12-00884-g002.jpg |
0.478573 | 6c8f1ce01b76415e8ada23b25dd40965 | Sample area in Northern Germany (top left); location of river estuaries (top right); and sampling locations of invertebrate species, sediment cores, and Platichthys flesus (bottom) along the German Wadden Sea coastline of Lower Saxony, with representation of the mean tidal level. | PMC10215328 | animals-13-01698-g001.jpg |
0.487049 | 5f8f129b456342f0895f1e84b884d6a0 | Microplastic concentrations (n per g weight of analyzed tissue) by invertebrate species and Platichthys flesus gastrointestinal tract per year and sampling location. (Outliers are excluded, please note different y-scale dimensions). No individuals could be sampled of Arenicola marina at Jadebusen, Littorina littorea at Tettens in 2019 and 2020, nor Platichthys flesus at Außenjade in 2020. This was due to habitat preferences of Arenicola marina, with the location Jadebusen being more sandy than the other locations. Littorina littorea does not occur along this shoreline section at all. Platichthys flesus could not be sampled in one season, due to the weather conditions. | PMC10215328 | animals-13-01698-g002.jpg |
0.416511 | d954332c0cb04c07bb4c582022a6e97e | Percentage distribution of polymer types based on identification with μRaman spectroscopy per invertebrate species and Platichthys flesus. (Arenicola marina (n = 35), Littorina littorea (n = 32), Mytilus edulis (n = 46), and Platichthys flesus (n = 27). | PMC10215328 | animals-13-01698-g003.jpg |
0.380696 | 4d59ee0846ec4667a5002a20b728d999 | Microplastic concentrations (per kg dry sediment) divided by morphology and their distribution within the different sediment depth profiles for the years 2019 and 2020. | PMC10215328 | animals-13-01698-g004.jpg |
0.430258 | 8c4557565885483aad4966d94cb9a060 | Percentage distribution of polymer types (n = 68) based on identification with μRaman-spectroscopy of sediment cores per depth level (cm). In total, n = 20 were identified for the depth 0–5 cm, n = 12 for 5–10 cm, n = 12 for 10–15 cm, n = 12 for 15–20 cm, n = 6 for 20–25 cm, and n = 1 for 25–30 cm. | PMC10215328 | animals-13-01698-g005.jpg |
0.4581 | 425a824f57334811bd589a7cb5bebc82 | Isatis tinctoria in vitro cultures: stationary shoot culture (MS variant BAP/NAA 1.0/1.0 mg/L) (A), agitated shoot culture (MS variant BAP/NAA 1.0/0 mg/L) (B). | PMC10215496 | antioxidants-12-01111-g001.jpg |
0.409523 | b2a335ddb01e47248791468621f00198 | Isatis tinctoria in vitro cultures (MS variant BAP/NAA 1.0/1.0 mg/L) after 168 h elicitation by CaCl2 20 mM (B), 50 mM (C), 100 mM (D), and control culture (A). | PMC10215496 | antioxidants-12-01111-g002.jpg |
0.399561 | 002c78d473d147b2857339ff057476cd | Isatis tinctoria in vitro cultures (MS variant BAP/NAA 1.0/1.0 mg/L) 120 h after the addition of Phenylalanine (left) and Tyrosine (right) at a concentration of 1 g/L. | PMC10215496 | antioxidants-12-01111-g003.jpg |
0.420008 | 19340efdc12a41db8833574221636c28 | Content of phenolic acids and flavonoids, determined by RP-HPLC analysis, in Isatis tinctoria in vitro culture extracts elicited by Methyl Jasmonate—MeJa (A,C) and Calcium chloride—CaCl2 (B,D). Values are expressed as the mean ± SD (n = 3). | PMC10215496 | antioxidants-12-01111-g004a.jpg |
0.389666 | 7ab7813afbdd48c09a56f660fc576058 | Content of phenolic acids and flavonoids, determined by RP-HPLC analysis, in Isatis tinctoria in vitro culture extracts supplemented with Phenylalanine-Phe (A,C) and Tyrosine—Tyr (B,D). Values are expressed as the mean ± SD (n = 3). | PMC10215496 | antioxidants-12-01111-g005a.jpg |
0.456165 | eed2bdf4ae4842e69113f2eec3d9258a | Conceptual model. | PMC10215727 | behavsci-13-00425-g001.jpg |
0.40669 | 1fe0eeeb478f42af93cbd0a68105bf41 | Direct effect of proactive personality and retirement anxiety domains. | PMC10215727 | behavsci-13-00425-g002.jpg |
0.45687 | 4b875699947a4483a54f7a9f005acfe7 | Mediating role of social comparison (ability and opinion) in the relationship between proactive personality and retirement anxiety domains. * p < 0.05. | PMC10215727 | behavsci-13-00425-g003.jpg |
0.495238 | 7a6f3032f7b4461f801f0ad1b3d38d73 | Motor and non-motor symptoms associated with Parkinson’s disease. Diagnosis of Parkinson’s disease occurs with the onset of motor symptoms (early-stage Parkinson’s disease) but can be preceded by a prodromal phase of several years, which is characterised by specific non-motor symptoms (prodromal Parkinson’s disease). Created with Biorender. | PMC10215921 | biomedicines-11-01349-g001.jpg |
0.447765 | 7deffb5b857f46a1a91b762027df2339 | Dysregulation of the microbiota–gut–brain axis in the pathogenesis of Parkinson’s disease. Gut dysbiosis increases intestinal barrier permeability and leakage of bacterial products from the lumen to the lamina propria, activating inflammatory responses and leading to gut inflammation. These inflammatory processes induce misfolding of α-synuclein, which results in its aberrant aggregation and accumulation in the gut and enteric neurons. α-synuclein aggregates are then transported from the gut to the brain through the vagus nerve, eventually triggering an inflammatory response of the microglia. Alternatively, pro-inflammatory cytokines released during gut inflammation might infiltrate the bloodstream, leading to a systemic inflammation that further increases the permeability of the blood–brain barrier and allows the infiltration of immune cells into the brain. These processes result in neuroinflammation and consequent neurodegeneration. Created with Biorender. | PMC10215921 | biomedicines-11-01349-g002.jpg |
0.524888 | b57a04be36944da79d002ab800d22922 | NLRP3 inflammasome activation. The activation of the NLRP3 inflammasome is triggered by a number of pathogen-associated molecular patterns (PAMPs) or damage-associated molecular patterns (DAMPs). PAMPs bind to Toll-like receptors (TLR) present on the cell membrane and upregulate the transcription of NLRP3 inflammasome components. DAMPs, such as ROS, mtDNA, or the externalization of cardiolipin to the outer mitochondrial membrane, also activate NLRP3. Assembly of the inflammasome activates caspase 1, which in turn cleaves pro-IL-1β and pro-IL-18 into IL-1β and IL-18, respectively. Created with Biorender. | PMC10215921 | biomedicines-11-01349-g003.jpg |
0.412573 | d679129b7bfd44fba46d61df665e0af2 | Reciprocal regulation of miRNAs and gut microbiota. Host’s intestinal epithelial cells release miRNAs that have the ability to regulate bacterial gene transcripts, affecting bacteria growth and replication. On the other hand, microbiota regulates host’s miRNA expression. Created with Biorender. | PMC10215921 | biomedicines-11-01349-g004.jpg |
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