nopperl/pmc-image-text · Datasets at Hugging Face

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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

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