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0.399027 | 2f16383ca8e74d1ebe6e91ce86c0f75b | Four types of model tests (left) and four types of evaluation metrics (right). | PMC9353319 | gr14_lrg.jpg |
0.421715 | a4dccfbb53c240b9b9a4f63c0e7bca6a | Accuracy verification methods with extreme values. | PMC9353319 | gr15_lrg.jpg |
0.388643 | 7b46f95e3eef435490cecc7ace85854b | The interplay between urban spatial risk and urban design. | PMC9353319 | gr16_lrg.jpg |
0.470929 | cf37c646ec5e4abbb64fc6952d0b5955 | Correlation superposition classification analysis of elements. | PMC9353319 | gr17_lrg.jpg |
0.451284 | 8f79cccc922b4b5f8ecf4b8a079da8b7 | Two-class element superposition mode. | PMC9353319 | gr18_lrg.jpg |
0.452246 | f4118b17a2d448b9aa01d6473276fe97 | Three-class element superposition mode. | PMC9353319 | gr19_lrg.jpg |
0.520935 | d25619d40fea4ceba9eb248aee6499cf | Statistics for daily new infections in Wuhan. | PMC9353319 | gr1_lrg.jpg |
0.411619 | e8bd1233b67c4524a7ddfce1c0ac9e7c | Five-class element superposition mode. | PMC9353319 | gr20_lrg.jpg |
0.432667 | 1259ebc51b1c48dca8e8a0e2370dff08 | Predicting POI facilities based on urban maps. | PMC9353319 | gr21_lrg.jpg |
0.429504 | efc0e3d554054854abc4255fd396a100 | Using generative models to predict the incidence of Shanghai City. | PMC9353319 | gr22_lrg.jpg |
0.454378 | 8d95320898f545feb4dc16035fcecf4e | Thermal and spatial risk factors of the epidemic in Wuhan. | PMC9353319 | gr2_lrg.jpg |
0.437628 | f1e435eae7254562a2f94891bdf4a47f | Top: Semi-variogram of kriging interpolation model. Bottom: Search neighborhood graph. | PMC9353319 | gr3_lrg.jpg |
0.450032 | 13d2e4c3ed7f4ead924969349fd4b279 | Population density. | PMC9353319 | gr4_lrg.jpg |
0.439506 | 3d3b4ad7e3674e9fb6b4f36f25d2c50a | Incidence rate. | PMC9353319 | gr5_lrg.jpg |
0.508688 | 61181d31c57f4b1d80467a5fb403c38b | Sample labels of incidence rate. | PMC9353319 | gr6_lrg.jpg |
0.450048 | 006223bbbed0485ab109352a6b98eb01 | Density distribution of COVID-19 spatial risk factors in Wuhan. | PMC9353319 | gr7_lrg.jpg |
0.397451 | d6c8c187fce542e9ab240bde7b286806 | Measurement of correlation coefficient. | PMC9353319 | gr8_lrg.jpg |
0.487316 | 5486b533005e46dea299f5901452a601 | Processing of features and labeled samples. | PMC9353319 | gr9_lrg.jpg |
0.386648 | 740f5530497b440f92777d6c172e80b5 | Frequency of antiviral medicine prescription by date for coronavirus disease 2019 among outpatient individuals that recently tested positive for severe acute respiratory syndrome coronavirus 2 from March 31, 2022 to July 8, 2022 (n = 1216) | PMC9353369 | JMV-9999-0-g001.jpg |
0.440841 | ac7f5a1cc7db4b17b758741a2c93a611 | a) Schematic of the opaque and semi‐transparent (ST) perovskite solar cell (PeSC) device structures. DMD represents the semi‐transparent dilelectric‐metal‐dielectric electrode used in ST‐PeSCs. b) Photograph of perovskite devices, demonstrating color tunability through control of the cation (MA, FA, and Cs) and halide (iodide and bromide) compositions in the perovskite crystal structure. c) V
oc as a function of the perovskite band gap for perovskite devices using CsFAMA (black), CsFA (red), and MA (blue) A‐site cations. Selected V
oc values from the literature are shown as empty circles and a linear fit to these is shown by the black line.[
20
,
22
] The solid red line corresponds to 90% of the V
oc in the Shockley–Queisser (S–Q) limit. | PMC9353478 | ADVS-9-2201487-g002.jpg |
0.402379 | a077b49a15a74ab2aa00009f507ef60a | SEM images of the surfaces of perovskite films of various cation composition (CsFAMA1‐4, CsFA1‐4, and MA1‐4) and their band gaps (1.63–1.92 eV). | PMC9353478 | ADVS-9-2201487-g003.jpg |
0.424167 | 9cfa9dd8087745be90e8e58aa282139a | Stability characteristics of encapsulated ST‐PeSCs with various perovskite cations under a) continuous one‐sun illumination and MPP tracking and b) during thermal stability testing at 85 °C in ambient air. c) Effect of solution aging on PCE of PeSCs based on various cations. 1H NMR spectra of the perovskite precursor solutions when freshly prepared or aged for 10 days: d) CsFAMA‐2, e) CsFA‐2, and f) MA‐2. | PMC9353478 | ADVS-9-2201487-g004.jpg |
0.508177 | f712feb0753e4a72b7cc7d01de9db4ab | a) J–V characteristics for hole‐only (ITO/PEDOT:PSS/perovskite/VNPB/Au) and b) electron‐only devices (ITO/SnO2/perovskite/PCBM/Ag) with various perovskite compositions. c) Nyquist plots of ST‐PeSCs with various perovskite compositions. The inset figure shows the equivalent circuit model for PeSCs. d) Transient absorption decay probed at the band edge for various perovskite compositions. | PMC9353478 | ADVS-9-2201487-g005.jpg |
0.435036 | 8f5c84ed1cdf4d23b8e4edcd779759d1 | a) Dependence of LUE (PCE × AVT) values of ST‐PeSCs on perovskite film thickness. The solid lines show the predicted values based on 90% of the V
oc in the Shockley–Queisser (S–Q) limit. The inset shows photographs of the CsFA‐2 and ‐3‐based ST‐PeSCs with 100 nm thickness (with the National Gallery of Victoria (NGV) and Flinders Street Station in Melbourne in the background). b,c) LUEs and PCEs against AVTs of our ST‐PeSCs compared to our previous work and with literature (see Table S5, Supporting Information for details).[
4
,
5
,
28
] All AVT values presented in this graph were determined over the 400–800 nm range. Solid lines show the maximum predicted device performance for devices operating at 90% of the performance given by the Shockley–Queisser (S–Q) limit across all band gaps. Meanwhile, the dashed line shows the predicted performance characteristics for 1.73 eV (red) and 1.81 eV (black) band gaps. d) Color coordinates on the CIE 1931 chromaticity diagram of the ST‐PeSCs with different perovskite thicknesses. | PMC9353478 | ADVS-9-2201487-g006.jpg |
0.437315 | de113ca2e82c41088fc222d7c663d70d | Maximum theoretical efficiency of ST‐PeSCs based on LUE (PCE × AVT(%)) with PCE values calculated using V
oc values of a) 90% of the V
oc in the S–Q limit and b) literature limits of the band gap‐dependent V
oc. | PMC9353478 | ADVS-9-2201487-g007.jpg |
0.488477 | 4b1977b8e1464507a3982a60fb67f83a | Research workflow. The input data for the study were a set of BAM files with the results of mapping DNA reads to the reference genome, a BED file containing the coordinates of the sequencing regions in WES, and the DNA sequence of the reference genome. The depth of coverage was counted using 7 strategies implemented in CANOES, CODEX, exomeCopy, ExomeDepth, CLAMMS, CNVkit, and CNVind. As a result of the depth of coverage counting, we obtained 7 other tables containing the depth of coverage in a given sequencing region for each sample. The resulting tables were used as input to the appropriately modified CANOES, CODEX, exomeCopy, ExomeDepth, CLAMMS, CNVkit, and CNVind tools to detect CNVs. As a result of the 7 CNVs callers’ operation, we obtained 49 result sets of CNVs—7 CNVs calling tools were run 7 times, each time using a different table with the depth of coverage. CNV indicates copy number variation; WES, whole-exome sequencing. | PMC9354125 | 10.1177_11779322221115534-fig1.jpg |
0.433746 | 7f63550319a742c5ab7d686b5b7d2ff8 | Comparison of the results of counting the depth of coverage with different tools. The figure presents the box plot (A) and course changes in the depth of coverage values (B) for chromosome 11 of the NA06984 sample. First, statistics of the counted depth of coverage values for each tool are different (A). Second, an interesting observation is that the other behavior of different counting depth of coverage strategies in the following sequencing regions. CNV indicates copy number variation. | PMC9354125 | 10.1177_11779322221115534-fig2.jpg |
0.382902 | 1896587105d4410c9391907397761467 | Comparison of the results obtained by the CANOES, CODEX, exomeCopy, ExomeDepth, CLAMMS, CNVkit, and CNVind applications for chromosome 1 data set. The diagram shows the evaluation of the resulting CNVs sets for the CANOES (A), CODEX (B), exomeCopy (C), ExomeDepth (D), CLAMMS (E), CNVkit (F), and CNVind (G) tools. Each of the tools was run 7 times with different input depth of coverage tables counted by other methods (the 7 strategies for counting the depth of coverage tested in the experiment were shown in different colors). Each of the result sets of detected CNVs was divided based on frequency (common and rare) and length (short and long). The number of FP calls is presented on the vertical axis, and the number of TP calls on the horizontal axis. It is worth noting that for all applications, the results obtained using the coverage table from the CODEX and CNVind tools are identical—the CODEX and CNVind tools calculate the coverage depth in the same way. CNV indicates copy number variation; FP, false positive; TP, true positive. | PMC9354125 | 10.1177_11779322221115534-fig3.jpg |
0.400805 | 82b45206310c48f59ad609d5d3e4ae12 | Comparison of the results obtained by the CANOES, CODEX, exomeCopy, ExomeDepth, CLAMMS, CNVkit, and CNVind applications for chromosome 11 data set. The diagram presents a comparison of the different results of CNVs detected by another tool and with different input depth of coverage tables. The following panels show the results for the CANOES (A), CODEX (B), exomeCopy (C), ExomeDepth (D), CLAMMS (E), CNVkit (F), and CNVind (G) tools, and different algorithms for counting the depth of coverage are marked with other colors. CNV indicates copy number variation; FP, false positive; TP, true positive. | PMC9354125 | 10.1177_11779322221115534-fig4.jpg |
0.501115 | 2fa358bf94e947a9a6afc057e804296b | The serum levels of IL-18 in patients with depression and healthy people in the Peripheral blood. MDD: major depressive disorder, Control: healthy control; **: < 0.01 | PMC9354267 | 12888_2022_4176_Fig1_HTML.jpg |
0.517609 | 4ae56360ca9f404f89cb6d8cba42227e | Correlation between IL-18 levels and DC in patients with depression and healthy people in the whole brain. Blue clusters are the brain area with negative correlation. MDD: major depressive disorder, HC: healthy control; L: left hemisphere; R: right hemisphere | PMC9354267 | 12888_2022_4176_Fig2_HTML.jpg |
0.535915 | 74206ab4602b43fe81698cbea2289266 | IL-18 and DC in brain imaging of patients with depression | PMC9354267 | 12888_2022_4176_Fig3_HTML.jpg |
0.476774 | 35a5dbd565db4ff19b351050977d08d3 | Detailed workflow of this study. | PMC9354608 | fgene-13-906496-g001.jpg |
0.44423 | ba2af5b79b6b4c25923d428e70a869ec | Identification of m7G-related lncRNAs in LIHC patients. (A) Heatmap showed the differences in the expression of m7G regulators between LIHC and normal groups. (B) Sankey diagram displayed the relationship between 22 m7G genes and 992 m7G-related lncRNAs. | PMC9354608 | fgene-13-906496-g002.jpg |
0.438767 | 9b76b918b705432987ff16fc33045d48 | Construction of a m7G-related lncRNA risk model for LIHC patients. (A) Forest plot showed 41 prognostic lncRNAs screened via univariate regression analysis. (B) LASSO regression of 20 m7G-related lncRNAs. (C) Cross-validation in LASSO regression. (D) Forest plot displayed 9 m7G-related lncRNAs selected by multivariate regression analysis. (E) PCA based on entire LIHC gene expression profiles in the two groups. (F) PCA based on 22 m7G regulator gene expressions in the two groups. (G) PCA based on 992 m7G-related lncRNA expressions in the two groups. (H) PCA based on 9 prognostic m7G-related lncRNA expressions in the two groups. | PMC9354608 | fgene-13-906496-g003.jpg |
0.474307 | ea6d07292bfb4507ab33ff976d974659 | Prognostic value of the 9-m7G-related-lncRNA risk model between the two groups in the training set. (A) Distribution of the risk score (the x-axis represented the LIHC patients arranged according to the risk score, and the y-axis represented values of the risk score for each patient). (B) Scatter plot of survival status and risk score (the x-axis represented the LIHC patients arranged according to the risk score, and the y-axis represented the survival time of each patient). (C) Heatmap of the expression profile of the 9 m7G-related lncRNAs. (D) KM curves displayed the OS of LIHC patients between high- and low-risk groups. (E) ROC curves of the risk model of 1, 3, and 5 years for OS. | PMC9354608 | fgene-13-906496-g004.jpg |
0.424394 | 46cda5b63e074b93afa16443be83d964 | Prognostic value of the 9-m7G-related-lncRNA risk model between the two groups in the testing set. (A) Distribution of the risk score (the x-axis represented the LIHC patients arranged according to the risk score, and the y-axis represented values of the risk score for each patient). (B) Scatter plot of survival status and risk score (the x-axis represented the LIHC patients arranged according to the risk score, and the y-axis represented the survival time of each patient). (C) Heatmap of the expression profile of the 9 m7G-related lncRNAs. (D) KM curves displayed the OS of LIHC patients between high- and low-risk groups. (E) ROC curves of the risk model of 1, 3, and 5 years for OS. | PMC9354608 | fgene-13-906496-g005.jpg |
0.463534 | e4319626be124793a15ae2f543957e0b | Kaplan–Meier survival analysis stratified by age, gender, tumor grade, and stage between the high- and low-risk groups in the entire set. (A) Patients with age <65. (B) Patients with male gender. (C) Patients with tumor grade 1-2. (D) Patients with tumor stage Ⅰ–Ⅱ. (E) Patients with age ≥65. (F) Patients with female gender. (G) Patients with tumor grade 3-4. (H) Patients with tumor stage Ⅲ–Ⅳ. | PMC9354608 | fgene-13-906496-g006.jpg |
0.425997 | 07dd4b47abb144c9943b9465f38edf03 | Assessment of the independent prognostic factors and construction of a prognostic nomogram in the entire LIHC set. (A) Univariate Cox regression analysis of the clinical characteristics and risk score with the OS. (B) Multivariate Cox regression analysis of the clinical characteristics and risk score with the OS. (D) Nomogram predicting the probability of 1-, 2-, and 3-year OS. (C) ROC curves of the clinical characteristics and risk score. (E) Calibration plot of the nomogram predicting the probability of the 1-, 2-, and 3-year OS. | PMC9354608 | fgene-13-906496-g007.jpg |
0.436801 | d130eda1af894d4692757c4607b0e6be | Evaluation of the tumor immune landscape and immunotherapy response based on the m7G-related lncRNA model in the entire LIHC set. (A,B) Waterfall plot displayed top 20 mutation genes’ information in the two risk groups. (C) KM survival analysis of OS stratified by tumor mutation burden and the m7G-related lncRNA model. (G) TIDE prediction difference between two risk score subgroups. (D) Significant KEGG pathways enriched in high-risk patients. (E) Difference in tumor infiltration immune cells based on ssGSEA scores between two risk groups. (F) Difference in immune-related functions based on ssGSEA scores between two risk score subgroups. (H) Expression of immune checkpoint blockade-related genes between the two risk groups. | PMC9354608 | fgene-13-906496-g008.jpg |
0.44692 | 509b3c7ae9df43358fc54e9ffce3c306 | Ten years of spherical equivalent values on the pseudophakic and fellow phakic eyes in the four groups divided by age.(A) Group I had the steepest yearly change in spherical equivalent in the pseudophakic eyes among the four age groups at -1.11 D (R2 = 0.954), followed by group II at -0.91 D (R2 = 0.979), group III at -0.42 D (R2 = 0.975), and group IV at -0.20 D (R2 = 0.674) (P < 0.001). (B) No significant difference was shown in yearly SE changes in the phakic eyes among the age groups (-0.39 D in group I; -0.52 D in group II; -0.29 D in group III; and -0.14 D in group IV, P = 0.502). | PMC9355217 | pone.0272369.g001.jpg |
0.459794 | 3260a99bf02e42569f7819200005ef79 | Approved anticancer drugs in China and the USA from 2012 to 2021. Note: The vertical ordinate is the number of approved anticancer drugs. The green line represents the quantitative changes of listed drugs in China while the blue one represents the changes in the USA. | PMC9355250 | fonc-12-930846-g001.jpg |
0.478499 | 2279d67a9bf9412a9ac05e1d9374f96c | The Venn diagram and the latest progress of listed anticancer drugs in China and the USA. Note: In the upper Venn diagram, the orange circle indicates listed drugs in the USA while the blue one indicates marketed products in China. The overlapping region indicates the products listed in both countries. The horizontal bars underneath described the research progress of drugs that have not been approved in the two countries from 2012 to 2021. Out of 45 anticancer drugs only approved in China in this period, 15 were approved in the United States before 2012. | PMC9355250 | fonc-12-930846-g002.jpg |
0.508458 | c77290ee9d6649f0b128752426cf067c | Listing time lag of anticancer drugs between China and the USA from 2012 to 2021. Note: The horizontal axis indicates the approval time of anticancer drugs in the USA, and the vertical axis indicates the approved time lag between China and the USA. Each red star represents one product approved in the two countries. The blue line shows a change tendency of the median time lag from 2012 to 2021. | PMC9355250 | fonc-12-930846-g003.jpg |
0.454518 | 76a23cc747b949b68ea84a5c01eab705 | Target distribution of new cancer drugs in China and the USA from 2012 to 2021. | PMC9355250 | fonc-12-930846-g004.jpg |
0.466112 | 12a4320b2d7742f7a4114368581ee8a5 | Cancer site distribution of approved new cancer drugs in China and the USA from 2012 to 2021. Note: MCC, Merkel cell carcinoma. BPDCN, blastic plasmacytoid dendritic cell neoplasm. TGCT, tenosynovial giant cell tumor. | PMC9355250 | fonc-12-930846-g005.jpg |
0.40425 | e717b4c42536488294719fcaf8464f36 | Dose-volume histograms comparison for BM structures From left to right, SOC (blue) and LS (red) dose volume histograms for the Wp-BM, Ls-BM, Il-BM and Lp-BM respectively, in both prostate and prostate bed plans. The solid line represents the median and the colored spread is the interquartile range. Abbreviations: SOC = standard-of-care, LS = lymphocyte-sparing, BM = bone marrow, Ls = lumbosacral spine, Il = ilium, Lp = low pelvis, Wp = whole pelvis. | PMC9356260 | gr1.jpg |
0.49732 | 5314eda7a99b4de387b63289828809e0 | Dosimetric parameters comparisons for bone marrow volumesAbbreviations: SOC = standard-of-care, LS = lymphocyte-sparing, BM = bone marrow, Ls = lumbosacral spine, Il = ilium, Lp = low pelvis, Wp = whole pelvis. | PMC9356260 | gr2.jpg |
0.440584 | afa536f106c1432f819740f834b353b1 | The effect of nitrogen fertilizer rate on total plant dry weight in plants that were not inoculated with Fusarium oxysporum f.sp. cubense
(A), disease severity in inoculated plants (B) and dry weight in inoculated plants (C). For regression model statistics see Table 1. Points have been jittered in the x dimension to avoid overplotting. | PMC9356348 | fpls-13-907819-g0001.jpg |
0.487778 | 0542a136aba64d84aa42e204ebc4a413 | The effect of ammonium and nitrate fertilizer addition on the concentration of soil ammonium (A), nitrate (B), and pH (C) at the time of harvest. | PMC9356348 | fpls-13-907819-g0002.jpg |
0.515974 | 81f89f99003e4cd6a1d61d0317e4ef64 | (A) The effect of nitrate and ammonium fertilizer addition on the δ15N of aboveground plant tissue, showing values for the original soil (dotted line) and fertilizer (large colored points for each fertilizer type and rate, red = ammonium, blue = nitrate). (B) δ13C of aboveground plant tissue. Points have been jittered in the x dimension to avoid overplotting. | PMC9356348 | fpls-13-907819-g0003.jpg |
0.469753 | 817e60f90d3540e59fdcd96facbf51b4 | Fusarium oxysporum f.sp. cubense DNA concentration in banana rhizosphere soil in relation to nitrogen fertilizer form and (A) Internal disease severity of banana plants, (B) Nitrogen fertilizer rate, and (C) Soil pH. | PMC9356348 | fpls-13-907819-g0004.jpg |
0.442402 | 9a42a4634f5c467a9a90bafd872aff12 | Bacterial alpha diversity (Shannon index) as affected by the rate and form of nitrogen fertiliser addition (A) and soil pH (B). Points in the left panel have been jittered in the x dimension to minimize overplotting. | PMC9356348 | fpls-13-907819-g0005.jpg |
0.442913 | 201748a309484c04bb15dd1b86078218 | A redundancy plot of the fungal community with inoculation of Fusarium oxysporum f.sp. cubense (Y) or not (N) constrained by inoculation. Circles represent samples and crosses OTUs. The far-left cross represents the genus Fusarium, which exerts considerable influence on the significance of the treatment effect. | PMC9356348 | fpls-13-907819-g0006.jpg |
0.496611 | 868bf4d654a941ee98fe69d682a02345 | Redundancy analysis of the proteomic output constrained by factors of inoculation, nitrogen form and nitrogen rate. Point colours indicates nitrogen rate and ellipses with text labels indicate combinations of inoculation (Y or N) with nitrogen form (Ammonium or Nitrate). | PMC9356348 | fpls-13-907819-g0007.jpg |
0.414498 | 8ce70a2ba60d450db2da1469ff2057bb | Log2 transformed expression rates of measured forms of pathogenesis related protein 1 (PR1), shown with gene locations, with treatments of inoculation with Fusarium oxysporum f.sp. cubense, nitrogen rate and form. | PMC9356348 | fpls-13-907819-g0008.jpg |
0.458846 | e422fa433a394ecd9b5503854c5955d2 | Risk factors for cerebral palsy [2, 7, 10]. | PMC9356840 | OMCL2022-2622310.001.jpg |
0.379185 | 13e5de013929479089b197b46c781a15 | Causes of cerebral palsy [2, 8, 11–13, 15]. | PMC9356840 | OMCL2022-2622310.002.jpg |
0.452334 | f1c18bdaca954ebc838b148c55addca0 | Events leading to cerebral palsy [2, 8, 11–13, 15]. | PMC9356840 | OMCL2022-2622310.003.jpg |
0.454676 | 5734514c566549ad967592d8542abb60 | Different types of cerebral palsy [2, 14]. | PMC9356840 | OMCL2022-2622310.004.jpg |
0.446273 | 2546601886094467869235b470846613 | Comorbidities associated with cerebral palsy [11, 17–20]. | PMC9356840 | OMCL2022-2622310.005.jpg |
0.490769 | 9e064ecbaaed4c43930928ef2e276f5b | Diagnostic criteria for detecting cerebral palsy [2, 13]. | PMC9356840 | OMCL2022-2622310.006.jpg |
0.455419 | b8ae103ee157401ab12fce62dcd6b906 | Prevention and management of cerebral palsy [28, 30, 31]. | PMC9356840 | OMCL2022-2622310.007.jpg |
0.406158 | cf2a85d04d3445d0aad12d9bd4c6b5ef | Time-dependent AUC values of pN stage and LNR for the prediction of OS in the training set (a), the internal validation set (b) and the external validation set (c). | PMC9356966 | 10434_2022_11911_Fig1_HTML.jpg |
0.463576 | 9c31c340e9324132a9227196673bc40e | LASSO Cox regression model construction. a LASSO coefficients of fourteen features; b Selection of tuning parameter (λ) for the LASSO model. c Nomogram for predicting 3- and 5-year OS in YBC patients. | PMC9356966 | 10434_2022_11911_Fig2_HTML.jpg |
0.44227 | 2681cbc706d3422585d6d6886194f5d7 | The calibration curves to predict 3- and 5-year OS in the training set (a), the internal validation set (b) and the external validation set (c). Time-dependent ROC curves comparing the use of the nomogram and AJCC TNM staging system to predict the 3- and 5- year OS for YBC patients in the training set (d,e), the internal validation set (f,g) and the external validation set (h,i), respectively. | PMC9356966 | 10434_2022_11911_Fig3_HTML.jpg |
0.49948 | a97af635483644d0b087940dc75d16e2 | DCA curves of the nomogram and AJCC TNM staging system for predicting 3- and 5-year OS in the training set (a,b), the internal validation set (c,d) and the external validation set (e,f). | PMC9356966 | 10434_2022_11911_Fig4_HTML.jpg |
0.453705 | bf1a9925106641a09cad6c092357ce71 | Kaplan-Meier curves of OS for risk stratification and defferent AJCC stages in the training set (a,b), the internal validation set (c,b) and the external validation set (e,f). | PMC9356966 | 10434_2022_11911_Fig5_HTML.jpg |
0.422587 | 37b1ee054f854d74b23ecce7db158034 | Resveratrol chemical structures (trans and cis forms) and its biological activities and toxic side effects. (A) Trans-resveratrol. (B) Cis-resveratrol. (C) Biological activities and toxic side effects of resveratrol. | PMC9357872 | fphar-13-921003-g001.jpg |
0.442006 | 0a7c8d0575024d59be8fccf00726d74e | Potential mechanisms of resveratrol in relieving knee osteoarthritis. There are many molecular mechanisms involved in the occurrence and development of knee osteoarthritis. Resveratrol plays multiple positive roles in knee osteoarthritis, reducing inflammatory activation, cell apoptosis, maintaining cartilage homeostasis and promoting autophagy through several signal pathways. (A) Anti-inflammatory effects. (B) Anti-apoptotic/proliferous effects. (C) Anti-catabolic/Pro-anabolic effects. (D) Autophagy-promoting effects. ↑: up-regulation. ↓: down-regulation. | PMC9357872 | fphar-13-921003-g002.jpg |
0.449354 | 642762ed3a2a432e868b7fca8b43a69a | The new application of resveratrol in knee osteoarthritis. The schematic diagram illustrates the current clinical method of combining resveratrol with cartilage tissue engineering to treat knee osteoarthritis. | PMC9357872 | fphar-13-921003-g003.jpg |
0.509981 | a16d4ddafe2348148d6e3a5ad39548f6 | Trait greed and the difference between mixed and gain only pumping behavior for not exploded balloons (A) or all balloons (B). Note: Y-axis intercept = difference in average number of inflations of unexploded balloons (mixed - gain only BART) as criterion; X-axis intercept = trait greed as predictor. The shaded areas depict SEM | PMC9358376 | 12144_2022_3553_Fig1_HTML.jpg |
0.456749 | 70ba7ab02cc74c31950b449c3c9c785a | Trait greed and the inflation behavior on unexploded balloons (gain only & mixed BART) (A & B) and all balloons (gain only & mixed BART) (C & D) as criterion. Note: The shaded areas depict SEM | PMC9358376 | 12144_2022_3553_Fig2_HTML.jpg |
0.444595 | 2c75c9f0645b439ab50c1d6c41dca924 | Cross-kingdom networks and bacteria-only networks in wild and domesticated rice seed microbiomes. (A,B) Bacteria-only co-occurrence network of wild and domesticated rice seed microbiomes. (C,D) Cross-kingdom (bacterial-fungal) co-occurrence network of wild and domesticated rice-seed microbiomes. Orange nodes are bacterial nodes. Purple nodes are fungal nodes. The size of the node is proportionate to betweenness centrality. Blue edges are co-occurrence network edges with positive correlation coefficients. Red edges are co-occurrence network edges with negative correlation coefficients. (E) Change of betweenness centrality of the same bacterial nodes in the bacteria-only network (x-axis) and bacterial-fungal network (y-axis). (F) Change in eigenvector centrality of the same bacterial nodes from a bacteria-only network (x-axis) to a bacterial-fungal network (y-axis). The dashed line is y = x. Solid lines (red and blue) are regression curves using locally weighted smoothing (loess) to wild and domesticated nodes, respectively. The grey area next to the regression curves indicates a 95% CI. | PMC9358436 | fmicb-13-953300-g001.jpg |
0.476354 | 2520b000203c43598f221d3d02e0cdeb | Robustness analysis and connectance, transitivity, modularity, and nestedness of cross-kingdom and bacteria-only networks in wild and domesticated rice seed microbiomes. (A) Robustness analysis by in silico extinction experiments. Nodes were deleted from the network in order of degree, betweenness centrality, and eigenvector centrality, and randomly. The size of the largest component (y-axis) was recorded after every extinction event until all vertices were removed. For robustness curves, the fraction of nodes extinct and the fraction of the largest component size (largest component size after the attack ÷ largest component size before any extinction) were plotted on the x- and y-axes, respectively. (B) Area under the robustness curves for each attack order and network. (C)
Z-score normalized connectance, transitivity, and modularity. Z-score normalization of summary statistics was carried out using the mean and SD of random configuration models (with the curveball method). Because the connectance of the random model had a standard deviation of 0, Z-score normalization was done by dividing by the mean of the random model. (D,E) Visualization of nestedness in wild and domesticated cross-kingdom co-occurrence networks. Rows are bacterial species and columns are fungal species. A red pixel denotes a link between bacterial and fungal species. Only edges connecting bacterial and fungal species were used to create the bipartite network. | PMC9358436 | fmicb-13-953300-g002.jpg |
0.476853 | 3a1cd0ed754d4b5fbc2f90ca8a2d687a | Ecological interpretations of cross-kingdom co-occurrence networks. (Top) Cartoon illustrating ways to interpret individual negative and positive fungal-bacterial edges. Negative interactions include competition and predator–prey relationships, whereas positive interactions are cooperative, such as coexistence, facilitation, and mutualism. All positive and negative correlations are not cooperation and competition, respectively. (Middle) Dynamical modeling of co-occurrence networks using generalized Lotka-Volterra (gLV) and consumer-resource models. The cartoon depicts species dynamics related to the gLV and consumer-resource model, respectively (only for illustration purposes). These models can be used to supplement the network to elucidate the mechanism of the interactions. (Bottom) Effects of abiotic and biotic factors on cross-kingdom networks. To investigate cross-kingdom interactions in natural microbial communities, it is important to treat microbial interactions as variables dependent on the magnitude of stress, space, time (abiotic factors), and host (biotic factor). (Left) Stress gradient hypothesis—the relative importance of competitive vs. facilitative interactions varies along the environmental harshness gradient. Another consideration is the host effect (biotic factor). (Right) The host immune system mediates the interaction between the bacterial and fungal communities. The biotic factors influencing the microbial community can be affected by host plant pathogen susceptibility or resistance. | PMC9358436 | fmicb-13-953300-g003.jpg |
0.456987 | 70ab02ecc26d4a52bb70e5099a87d615 | Incidence of CAPA in low, median, and high groups, designated by tertiles of mean daily dose of dexamethasone, which were measured from admission to CAPA diagnosis in CAPA group, and measured from admission to ICU discharge or death in non-CAPA group. (low: 0–3.48 mg/day, median: 3.48–7.21 mg/day, high: >7.21 mg/day) | PMC9359693 | figs1_lrg.jpg |
0.489332 | 04a0c7d894d24ef8a87f83fd1be9de73 | Study flowchart. CAPA, COVID-19-associated pulmonary aspergillosis; ICU, intensive care unit; qPCR, quantitative polymerase chain reaction; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2. | PMC9359693 | gr1_lrg.jpg |
0.394152 | dbc48c6ae1e0468486a2060bc24aefa0 | Kaplan–Meier curves of (a) all patients (n = 72) (log-rank p = 0.024) and (b) the mechanically ventilated patients (log-rank p = 0.065) with and without CAPA. CAPA, COVID-19-associated pulmonary aspergillosis; ICU, intensive care unit. | PMC9359693 | gr2_lrg.jpg |
0.406812 | 12f35649e5414007910f0a9040109d6f | SARS-CoV-2 viral shedding time between the patients with and without CAPA. (a) The distribution of the SARS-CoV-2 viral shedding time is presented in dot plots (Mann–Whitney U test, p = 0.037). (b) Kaplan–Meier curves (log-rank p = 0.022). CAPA, COVID-19-associated pulmonary aspergillosis; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2. | PMC9359693 | gr3_lrg.jpg |
0.436477 | 3ef7080979fc4156b607ae0adea23507 | Results for performance measures against the third set of scenarios (Scenarios 23 to 33). | PMC9359798 | gr10_lrg.jpg |
0.513335 | 4868e6b8c2d348b99ed347c48da027c4 | EKMIH framework for NEPT. | PMC9359798 | gr1_lrg.jpg |
0.425678 | 7c572c4bf35b4af6bc652018e199f0ca | Pick-up locations and hospitals/clinics for patients. | PMC9359798 | gr2_lrg.jpg |
0.407431 | 612b74188bd74659a7f48707dbc1c8ca | WC and ERC for the SKMH framework. | PMC9359798 | gr3_lrg.jpg |
0.396589 | 690b3cf4c4c3479580098493274d199b | WC and ERC for the EKMIH framework. | PMC9359798 | gr4_lrg.jpg |
0.452151 | 8eb9acafe91448839b87c5c658883bb3 | Impact of increase in maximum cluster size on the cost for instance B4. | PMC9359798 | gr5_lrg.jpg |
0.473843 | 577c1cdc4b6f4f0b8628a9fffb9aac5e | Impact of increase in maximum cluster size on the cost for instance C4. | PMC9359798 | gr6_lrg.jpg |
0.388278 | 1097b327a21e488cbfa04a2c780208a5 | Impact of value of K on the cost. | PMC9359798 | gr7_lrg.jpg |
0.477105 | 1011b7df838445e3b5d7323c6a119665 | Results for performance measures against the first set of scenarios (Scenarios 1 to 11). | PMC9359798 | gr8_lrg.jpg |
0.445079 | a7918d8d2dae4f028253ce080d29cebc | Results for performance measures against the second set of scenarios (Scenarios 12 to 22). | PMC9359798 | gr9_lrg.jpg |
0.528809 | 8f9ff2720e8544b68a858f96d0204a7d | Evolution of the PFC I-PASS SCORE Program. Abbreviations: PFC, patient and family-centered; PFCRs, patient and family-centered rounds; SCORE, safer communication on rounds every time. | PMC9360201 | mep_2374-8265.11267-g001.jpg |
0.46783 | c3601dd7832444bbbb98b274a1b86541 | Patient and Family-Centered I-PASS Rounds: Do Every Time Process. Abbreviation: ACP, advanced care provider. | PMC9360201 | mep_2374-8265.11267-g002.jpg |
0.386009 | 1ffcdec8a08948ff8267b846b76f7cd9 | 9 year-old girl with right subtrochanteric fracture treated with triple ESINs. (A) AP view of femur before surgery. (B) AP view of femur after surgery. (C) Lateral view of femur after surgery. (D) AP view of femur at 6th month follow-up. (E) Lateral view of femur at 6th month follow-up. (F) AP view of femur after implant removal. | PMC9360405 | fped-10-894262-g001.jpg |
0.448615 | 7689f9426def41eabab7b7ed41eeb45c | 10 year-old girl with right subtrochanteric fracture treated with locking plate. (A) AP view of femur before surgery. (B) AP view of femur after surgery. (C) Lateral view of femur after surgery. (D) AP view of femur at 6th month follow-up. (E) AP view of femur after implant removal. (F) Lateral view of femur after implant removal. | PMC9360405 | fped-10-894262-g002.jpg |
0.412695 | 457bc5440eb646789b7b05b63568774f | MRI T2/FLAIR images showing periventricular white matter hyperintensity along with hyperintensities in basal ganglia and thalamus | PMC9363804 | ijccm-26-961-g001.jpg |
0.413112 | 174004f9f26d45339fadee07e32ec4a6 | Distribution ranges of five Takydromus lizard species and the locations of the populations sampled for behavioural, physiological and life-history responses to climate warming. Different colours in outlines indicate different species in the map and species photographs. (Online version in colour.) | PMC9363995 | rspb20221074f01.jpg |
0.444565 | c933a5221dbc4f6794f3aae1d6ee005a | TSM for five grass lizard (Takydromus) species across latitudes. Red, blue and green spots indicate the TSM under current, 2050 and 2070 situations. The species are listed from temperate to tropical areas on the x-axis. Error bars represent s.d. Because of the same trends of future climate, we only show the results of the RCP 4.5 emission scenario in the text. (Online version in colour.) | PMC9363995 | rspb20221074f02.jpg |
0.416513 | 57fa1584ec4f4ca2a84b0c5116ae0dde | Fitness-related traits for five grass lizard (Takydromus) species across latitudes in the current climate (red dots), and predictions for 2050 (blue dots) and 2070 (green dots). (a) Activity time, (b) metabolic rate, (c) water loss, (d) incubation period and (e) time window for successful embryonic development. Error bars represent s.d. Because of the same trends of future climates, we only show the RCP 4.5 emission scenario results in the text. (Online version in colour.) | PMC9363995 | rspb20221074f03.jpg |
0.476277 | 2544e0f4da9e417c826378001caa7ba3 | Spatial distribution and habitat suitability of five grass lizard (Takydromus) species in the current climate, and predictions for 2050 and 2070. The first two columns indicate the species distribution in China derived from spatial distribution maps (indicated by [70]). The third and fourth columns indicate the habitat suitability in 2050 and 2070 under climate warming from Hybrid-SDMs for each species. The colour indicates the suitability. The list of species on the y-axis indicates the latitudinal areas of the species. Because of the same trends of future climate, we only show the RCP 4.5 emission scenario results in text. (Online version in colour.) | PMC9363995 | rspb20221074f04.jpg |
0.423261 | 8a9ff4c682644733ba2f35567a15dd17 | Change of habitat suitability (a) and per cent area (b) for five grass lizard (Takydromus) species across latitudes under current and 2050 and 2070 climate warming. Blue and green spots (a) and bars (b) indicate the traits of 2050 and 2070, respectively. Error bars in (a) represent s.d. Because of the same trends of future climate, we only show the RCP 4.5 emission scenario results in the text. (Online version in colour.) | PMC9363995 | rspb20221074f05.jpg |
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