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

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0.429177	992ac795541a493fa6626addd48d4039	Regenerated T0 plantlets from rooting stage to acclimatization in soil. (A) Plantlets growing on the rooting medium. Sundae cup is used as a cover to give vertical space to grow. (B) Regenerated plantlets ready to be moved to potting mix. Red arrows indicate plantlets with healthy roots. (C) Plantlet representing bushy phenotype or multiple shoot cluster. (D) Single-shoot plantlet ready to be transplanted to potting mix. (E) T0 regenerated plantlets transplanted to potting mix. (F) A plant propagation tray covered with a humi-dome to aid in acclimatization.	PMC9114882	fpls-13-860971-g004.jpg
0.430131	04f553fa4b8a4c8d8e51cfcee1725ba3	Transient and stable transformation. Observation of transient RFP expression 3 days post-infection on the middle of the scutellum (A) or the side of the embryos (B). (C) Somatic embryogenesis on embryo scutellum side, 8 days post-infection in bright field. Observation of stable transformation and callus formation of maize embryo under bright field (D) and RFP field overlayed (E). The arrows indicate callus with the RFP expression. (F) Tentacle-like structure on a callus during the maturation stage, 21 days post-infection. Maize callus on the maturation I medium, 18 days post-infection (G) and the maturation II medium, 31 days post-infection (H).	PMC9114882	fpls-13-860971-g005.jpg
0.432592	ef195936f88444bcb94b109c6de9aa05	Phenotypes of T0 transgenic plants. Observation of mature shoots with roots on the maturation II medium under an RFP channel (A) and a bright field (B). Black arrow indicates a shoot with RFP expression; white arrow indicates a shoot without RFP expression. Observation of the gl2 knock-out phenotype on a T0 regenerated plant (C) and a wild-type seedling after water spray (D). Observations of pollen grains collected from a T0 plant (E,F) and a wild-type B104 control (G,H). Images of (E,G), bright field; (F,H), RFP field overlay.	PMC9114882	fpls-13-860971-g006.jpg
0.388127	3bbb5b6ad2024537bd23776b32437683	Phenotypes and genotypes of sixteen T0 mutant events. Plant ID shows experiment ID, embryo ID, and plantlet ID (EXP-embryo-plantlet). Three gl2 phenotypes: gl2, glossy mutant phenotype; gl2/wt mixed, both glossy mutant and wild type phenotype on the same leaf; wt, wild-type phenotype. Red letters, target sequences in Gl2 exon2; Blue letters, PAM sequences; Black letter, insertion mutations; dashed lines, deletions. T0 gl2 phenotyping is considered a preliminary screening assay.	PMC9114882	fpls-13-860971-g007.jpg
0.49413	a93eaef2d1334742b533899d53372600	Sesquiterpenes obtained from germacrene D and bioactivity of (–) germacrene D. The known major sesquiterpenoids derived from germacrene D including γ-amorphene, amorphene, α-copaene, cubenene, γ-murolene and δ-cadinene. (–)-Germacrene D has repellent activity of anti-tick (1), anti-mosquito (2) and anti-aphid (3)	PMC9115970	12934_2022_1814_Fig1_HTML.jpg
0.428052	ba719d692bb044318d0084653b6c126c	Phylogenetic analysis of A. chrysogenum sesquiterpene synthases and germacrene D synthases. The TPS-a, TPS-b, TPS-c, TPS-d, TPS-e/f, TPS-g, TPS-h clades and the microbial branching are shown in different colors. A. chrysogenum sesquiterpene synthases and germacrene D synthases are shown in red font. Terpene synthases of Arabidopsis thaliana, Selaginella moellendorffii Abies grandis, Populus trichocarpa and Ginkgo biloba were used for phylogenetic analysis and are shown in Additional file 1: Table. S4. The branch lengths and bootstrap values are presented at the nodes in blue and black, respectively	PMC9115970	12934_2022_1814_Fig2_HTML.jpg
0.468839	4360803b50c845d093753c3685e33eed	GC/MS analysis of volatile organic compounds produced by yeast transformants expressing AcTPS1. A GC-FID detection of AcTPS1 products in the yeast strain. Peak 1 represents the major product of AcTPS1 and is shown with a blank arrow. JCR27, the starting yeast strain used in this study. JCR27 + AcTPS1, AcTPS1 expressed in JCR27. B Detected by GC–MS, the major product of AcTPS1 is shown by the 204 m/z ion in the chromatographic trace	PMC9115970	12934_2022_1814_Fig3_HTML.jpg
0.481139	7fcc5a86d9ce4b739c66046e38f58bd5	GC spectrum of the in vitro analysis of AcTPS1 enzyme function. The AcTPS1 enzymatic product is shown as a blank arrow. Blank, solvent hexane; Vector + FPP, pET-45b cell extracts reacting with the substrate FPP; AcTPS1 + FPP, AcTPS1 protein cell extracts reacting with the substrate FPP. AcTPS1 + FPP was concentrated tenfold for detection	PMC9115970	12934_2022_1814_Fig4_HTML.jpg
0.432621	6e56b42aa2ef496da9231d8dbecbf533	Comparing germacrene D synthases for high germacrene D production in S. cerevisiae. A Schematic diagram of comparing germacrene D production in S. cerevisiae. Transformation, flask fermentation and quantitative analysis were performed as described in “Methods” (B) Strategy for the construction of SC1-SC21 via homologous recombination. JCR27, the starting strain; SC1-SC21, yeast strains expressing different germacrene D synthases; ROX1, the insertion site for exogenous fragments; LB (ROX1), the upstream sequence of rox1; RB (ROX1), the downstream sequence of ROX1; tCYC1, the terminator of CYC1; X-Sc, the germacrene D gene optimized for S. cerevisiae; pGAL1pGAL10, the promoter of GAL1 and GAL10. B The production of different germacrene D producing strains. Germacrene D levels were determined after 72 h fermentation in YPDH medium as described in the “Methods”. Error bars represent standard deviations from three independent experiments. ND, not detected. SSLH2 et al., the names of germacrene D synthases from different organisms detailed in the text	PMC9115970	12934_2022_1814_Fig5_HTML.jpg
0.453344	f1ca023260934a34b21aec22734d59f5	Titer of (–)-germacrene D in engineered yeast strains in shake-flask fermentation. A Biosynthesis pathways for (–)-germacrene D in engineered yeast. ERG10 acetyl-CoA C-acetyltransferase, ERG13 3-hydroxy-3-methylglutaryl coenzyme A synthase, tHMG1 truncated 3-hydroxy-3-methylglutaryl-coenzyme A reductase 1, ERG8 phosphomevalonate kinase, ERG12 mevalonate kinase, MVD1 mevalonate diphosphate decarboxylase 1, IDI1 isopentenyl-diphosphate delta isomerase 1, ERG20 farnesyl pyrophosphate synthetase, AcTPS1 (–)-germacrene D synthase. Overexpressed enzymes are shown in blue. Red slash, gene knockout; Red downward arrow, downregulated gene expression. B The engineered yeast strains integrated with gene modules. These expression modules were integrated into the chromosomal sites of ROX1 (module 1, chromosome XIV), Exg1 (module 2, chromosome XII), DPP1 (module 3, chromosome IV), and ERG9 (module 4, chromosome VIII). The engineered strain LSc53 contained expression module 1 (abbreviated as ‘M1’). The engineered strain LSc54 contained the expression module 1 (‘M1’), module 2 (‘M2’) and module 3 (‘M3’). The engineered strain LSc81 contained the expression module 1 (‘M1’), module 2 (‘M2’), module 3 (‘M3’) and module 4 (‘M4’). (C) The production of different engineered stains. Germacrene D levels were determined after 72 h fermentation in YPDH medium as described in “Methods”. Error bars represent standard deviations from three independent experiments	PMC9115970	12934_2022_1814_Fig6_HTML.jpg
0.43856	2ea22297ce62487c8a9600a067a3ba1f	High-density fermentation of strain LSc81 for (–)-germacrene D production. A Results of fermentation in a 5-L bioreactor. (–)-Germacrene D production, cell growth, and residual concentrations of glucose and ethanol during fed-batch fermentation were measured. Error bars indicate the standard deviations of three replicates. B Fermentation broth in the 5-L bioreactor. The upper oil phase contained isopropyl myristate and (–)-germacrene D	PMC9115970	12934_2022_1814_Fig7_HTML.jpg
0.453527	5c94d7612d4243f591b89cc3546459cf	The convergence rates	PMC9116929	11269_2022_3169_Fig10_HTML.jpg
0.4226	a8b892bd25524bce92ae3f310ecbcc43	The sampling points in Xi’an moat	PMC9116929	11269_2022_3169_Fig1_HTML.jpg
0.421301	8237e99473b94895a650cd6b5d438bb5	The procedure for water quality assessment	PMC9116929	11269_2022_3169_Fig2_HTML.jpg
0.441114	6c4376f48cf24153aaf9d132cc829025	WQIi of different parameters	PMC9116929	11269_2022_3169_Fig3_HTML.jpg
0.466073	582ba1c184a3404685cc74ad531c87e4	The statistical WQIi	PMC9116929	11269_2022_3169_Fig4_HTML.jpg
0.438935	8459d657eb234f37abda5b422eeb152e	The comprehensive WQI	PMC9116929	11269_2022_3169_Fig5_HTML.jpg
0.529511	e3c836a092944fd898622e2a98c99fb2	The spatial distribution of the comprehensive WQI	PMC9116929	11269_2022_3169_Fig6_HTML.jpg
0.460228	5e88dcb234bf40f3ac5ce52688a25645	The temporal variation of the comprehensive WQI	PMC9116929	11269_2022_3169_Fig7_HTML.jpg
0.458761	23ea7f9d3c484df099ffdbe50b2558d6	The WQImin models	PMC9116929	11269_2022_3169_Fig8_HTML.jpg
0.450878	69c67343a4394d38a482acf094c9556a	The optimized weights	PMC9116929	11269_2022_3169_Fig9_HTML.jpg
0.463593	c0b871a3b80d47cd839a4f9def63495d	Altman Z score for the years between 2019–2015.	PMC9116989	pone.0264016.g001.jpg
0.457242	c9bd5fd6f7f8459bbe1237278c26dc32	Index 05 for the years between 2019–2015.	PMC9116989	pone.0264016.g002.jpg
0.4356	44f699f79d1e44d2b612fa57f8626324	Conceptual framework of the research model. PRO, perceived revenue opportunities; PES, perceived ecotourism satisfaction; PNE, perceived negative effects; PUS, perceived insufficient services.	PMC9117084	11356_2022_20882_Fig1_HTML.jpg
0.441856	316baf6c25bb481894e6cd519751d90d	Hypothesized model 1 (PRO-PES model).	PMC9117084	11356_2022_20882_Fig2_HTML.jpg
0.453117	0388616363c64ea99e61e55c41f8e363	Complete mediated model (PRO-PNE-PUS-PES model).	PMC9117084	11356_2022_20882_Fig3_HTML.jpg
0.399876	e4bd013186854164babe2ca215b9eff6	Developing visual barcodes for multiplexing live imaging applications.A Representative image of the A375 cell line with an iRFP-H2A nuclear marker. B Representative images of five CFP localizations in the A375 cell line: Whole Cell (WC), Nuclear Export Signal (NES), Nuclei, Peroxisome (Peroxi), and Endoplasmic Reticulum (ER). C Heatmap of false detection rate (FDR) for barcode calling for the five CFP localizations in the A375 cell line. D Representative images of all 12 visual barcodes used in A375 cell line. E Average miss rate of barcode calling for all 12 visual barcodes of the A375 cell line, treated with DMSO controls (n = 39). Numbers in the diagonal represent the sensitivity for each barcode. F Violin plots showing miss rate for all 12 visual barcodes of the A375 cell line, treated with DMSO controls (n = 39) or with drugs (n = 75). G–I Scatter plot showing the separation of nine A375 clones with visual barcodes by the ImageStream system according to their fluorescent color and localization. The separation by localization is only demonstrated for GFP-positive clones (I). J Representative images from ImageStream of all nine clones. Two cells from each clone are presented. Scale bar in (A and B) is 50 µm, in (D) 100 µm and in (J) 7 µm. Source data are provided with this paper.	PMC9117331	41467_2022_30008_Fig1_HTML.jpg
0.456044	f6eaaa10323540b98f7db86c32ea5217	Generating the A375 “Signalome” reporter cell line.A Illustration of the 12 clones that were used to generate the A375 Signalome cell line. A375 cells were first infected with iRFP-H2A to mark the cell nucleus. Then, 12 clones were generated with 12 visual barcodes. Lastly, a different live reporter was added to each of the clones. Transcription activating reporters are represented by gold while translocation reporters are represented in red. Binding partners in the nucleus are represented in purple. B Relative number of cells from each clone in a DMSO control wells over time. C Scatter plot showing the correlation between the reporter activity scores, for all 12 clones when grown separately or as part of the Signalome cell line, in response to 75 drugs. D–F Reporter activity plots of the A375 signalome cell line in response to DMSO, vemurafenib (1 µM) or trametinib (0.125 µM) over time. Blue and red backgrounds represent activation or inhibition scores above 0.2 or below −0.2, respectively. The average cell count per reporter in (D–F) is: 656, 545, and 530 cells, respectively. Source data are provided with this paper.	PMC9117331	41467_2022_30008_Fig2_HTML.jpg
0.435026	918ea4b381924db5be5c698527990270	Large-scale correlations in signaling suggest a generalized response that is compound independent.A Unsupervised hierarchical clustering of A375 signalome cells treated by 122 active drugs according to their activity scores. Due to technical error, Geminin reporter was not measured for all drugs and was thus discarded. Results from all 12 reporters can be seen in Supplementary Fig. 4I for the subset of drugs that for which Geminin data are available. B Scatter plot showing the correlation between the activities of the p38 and p53 reporters after 48 h of treatment with 122 active drugs. C Heatmap showing the pairwise Pearson correlations between the different A375 Signalome clones, after 48 h of treatment with 122 active drugs. D A model proposing how drugs with different mechanisms may converge into two major signaling states. While each of the drugs has different targets, many of the targets affect the same sensing mechanism that later governs the p53- vs p38-signaling states. Source data are provided with this paper.	PMC9117331	41467_2022_30008_Fig3_HTML.jpg
0.422028	16942a7680574b79bafc09e519bc4283	Drug treatments increase the correlations between the activity of pathways.A Scatter plots of the activity scores of p38 and p53 reporters in the A375 signalome cell line before and at multiple time points after treatment with 122 active drugs. Each dot represents a different drug. Pearson’s r is depicted for each of the time points. B Pearson correlation coefficients were calculated for each pair of pathways in the A375 signalome cell line before and at different time points after treatment with 122 active drugs. Each datapoint represents a correlation value for one given pair of pathways over all 122 active drugs. The correlation between the activity score of p38 and p53 pathways is marked by a red circle. C–H Locally weighted smoothing (Lowess) regression of all reporters in each of the two signaling states is shown for six representative drug treatments, each associated with different drug targets. While three of the drugs drive the p53-signaling state (C–E), the other three drugs drive the p38 signaling state (F–H). The bold (red and blue) lines and the gray sleeves represent mean values and + /− SEM respectively. Source data are provided with this paper.	PMC9117331	41467_2022_30008_Fig4_HTML.jpg
0.410583	98250e4e1d71418db805c7b1474e7579	PCA suggests that the p38 and p53-signaling states exist pretreatment and increase in weight over time.A PCA of the activity scores of 11 signaling pathways after 48 h of treatment with 122 drugs. The color of each drug is indicating its cluster in Fig. 3A. B, C Bar plots representing the PC1 loading of each pathway after 48 h of drug treatment (B) or pretreatment (C). D Variance of pathway activity, when projected on the principal components calculated from measurements on cells that were not exposed to drug treatment. Source data are provided with this paper.	PMC9117331	41467_2022_30008_Fig5_HTML.jpg
0.476994	dbb9daa0255e468da6e44fd92193fc28	Cell growth and proliferation are tightly regulated and correlate with p38- and p53-signaling states.A A model demonstrating how sensing of cell size can affect both cell growth and proliferation to keep homeostasis of cell size. B Scatter plot showing the initial and long-term effects of rapamycin or SNS-032 on the average cellular growth rate and division rate of Rpe1 cells. Data points indicate the average growth and division rates measured: (1) during the first 24 h of drug treatment and (2) during 24–60 h of drug treatment. C Average growth rate vs. division rate in five cell lines (Rpe1, HeLa, U2OS, SAOS2, 16HBE) treated with either growth inhibitors (red) or cdk1/2 inhibitors (blue). Measurements in each cell line were normalized by the values measured for untreated control samples (gray) of the same cell line. The growth inhibitors used were: Cycloheximide, Torin-2, and Rapamycin, at varying doses (de- tailed in “Methods”). The cdk1/2 inhibitors used were: SNS-032, PHA848125, Cdk2 Inhibitor III, and Dinaciclib, at varying doses (detailed in “Methods”). D, E The average cell size for a given drug correlated with its PC1 value calculated on the reporters’ activity for data from Kaufman et al. (D) and Liu at al. (E). F Average growth rate vs. division rate for A375 cells in Signalome screen. Each circle represents one screened condition (drug treatment). The circle’s color indicates the value of PC1 in that condition. Contour lines show the average value of PC1 as a function of growth rate and division rate. G The average level of p38 (top) and p53 (bottom) activity as a function of growth rate and cell cycle length. H A model proposing how drugs which affect cell division activate the p53-signaling state while drugs that affect cell growth activate the p38-signaling state. Each state in return actives a compensation mechanism resulting in a new equilibrium. Source data are provided with this paper.	PMC9117331	41467_2022_30008_Fig6_HTML.jpg
0.38336	42894a79630241ce94bf1d6f5da678d1	Chromosome location analysis of dsRNase gene of S. frugiperda. Tbtools was used to analyze the chromosome location of the S. frugiperda dsRNases gene.	PMC9117646	fphys-13-850022-g001.jpg
0.41348	8f78a443ce42421f9028e3954f703365	Phylogenetic tree of different species dsRNase in insects. The maximum likelihood method was used to construct phylogenetic trees of different species of dsRNase in insects. S. frugiperda is represented by a red star, and S. litura is represented by a blue star. Its four sldsRNases GenBank numbers of S. frugiperda dsRNase (OL960003, OL960002, OL960004, and OM001111) and S. litura dsRNaseare respectively (QJD55609.1), (QJD55610.1), (QJD55611.1), (QJD55612.1).	PMC9117646	fphys-13-850022-g002.jpg
0.440905	41ff9d9daa0c4d7a89e132da7d19531a	Expression analysis of S. frugiperda dsRNase in different tissues. Collect three different tissues of sixth instar larvae, head (HD), midgut (MG), and epidermis (CL) for RT-qPCR, and calculate the expression levels of sfdsRNases in different tissues (A–D). Using sfdsRNase1 as a control, analyze the relative expression differences of the four sfdsRNases in the midgut and hemolymph (E,F). Five larvae are a biological replicate, and this experiment was performed three times in total. The data shown are mean ± SE, n = 15, different letters indicate a significant difference among tissues [p < 0.05, one-way ANOVA followed by Duncan’s multiple range test for (A,B)].	PMC9117646	fphys-13-850022-g003.jpg
0.444766	86b57750e2714f5f9b935f49df2f316b	Expression analysis of S. frugiperda dsRNases at different ages. Using egg, first instar larvae (first), second instar larvae (second), third instar larvae (third), fourth instar larvae (fourth), fifth instar larvae (fifth), and sixth instar larvae (sixth) as templates for qPCR and using the eggs as a control to calculate the dsRNases in different instars. The relative expression level of mRNA (A–D). Five larvae are a biological replicate, and this experiment was performed three times in total. The data shown are mean ± SE, n = 21, different letters indicate a significant difference among ages [p < 0.05, one-way ANOVA followed by Duncan’s multiple range test for (A,B,C)].	PMC9117646	fphys-13-850022-g004.jpg
0.384562	eac8efcf327041a4834c476e4a1b6087	In vitro degradation of Lip-dsRNA in midgut juice and hemolymph. The relative content of dsRNA was negatively correlated with the ability to degrade dsRNA. Degradation of liposome-encapsulated dsRNA in midgut juice (A). Degradation of liposome-encapsulated dsRNA in hemolymph (B). Liposome-encapsulated dsRNA (LIP-dsEGFP), dsRNA not encapsulated with liposomes, replaced liposomes with the same amount of water as a control (W-dsEGFP). The two forms of dsRNA were incubated with midgut juice and hemolymph for 10 and 60 min at room temperature, respectively. The dsRNA content after incubation was detected by qPCR, and the relative content ratio was calculated. The data shown are mean ± SE, n = 12 for the dsRNA relative content ratio calculation (One-way ANOVA, the least significant difference (LSD) test, ns = No significant difference, p < 0.05, **p < 0.001).	PMC9117646	fphys-13-850022-g005.jpg
0.442351	9f50abc5b0c74d129347d24b3c4b881e	Lip-dsRNA down-regulates the expression of sfdsRNase. DssfdsRNase represents the injection of 4 dsRNA of sfdsRNases into the fourth instar larvae, and the control injection of the same amount of dsEGFP. After 24 h, the expression level of sfdsRNase is measured by RT-qPCR. The data shown are mean ± SE, n = 12 for the Relative mRNA expression measurement of sfdsRNases （One-way ANOVA, the least significant difference (LSD) test, p < 0.05, *p < 0.01）.	PMC9117646	fphys-13-850022-g006.jpg
0.458762	a7fcd0844d68485985686f3c969ea084	In vitro incubation of dsRNA and midgut juice and hemolymph. Relative content of dsEGFP in the control group = 1. The relative content of dsRNA was negatively correlated with the ability to degrade dsRNA. In vitro incubation of dsRNA and midgut fluid, dsRNases are the midgut extract of S. frugiperda larvae after successfully interfering with 4 sfdsRNase genes. At the same time, to avoid the influence of dsEGFP injection on the detection of dsEGFP content after incubation, the larval midgut fluid injected with water was used as a control. The midgut extract was incubated with dsEGFP for 10 and 60 min at room temperature, and the midgut juice was replaced by water as a control. The dsRNA content after incubation was detected by qPCR, and the relative content ratio was calculated [(A) represented relative content of dsEGFP after 10 min in midgut, (B) represented relative content of dsEGFP after 60 min in midgut, (C) represented relative content of dsEGFP after 10 min in hemolymph, (D) represented relative content of dsEGFP after 60 min in hemolymph]. The data shown are mean ± SE, n = 12 for the dsRNA relative content ratio calculation, different letters indicate a significant difference among treatments [p < 0.05, one-way ANOVA followed by Duncan’s multiple range test for (A,B)].	PMC9117646	fphys-13-850022-g007.jpg
0.3619	47f7ee9bda704ddc869a177a8b932966	PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) flow diagram of the literature selection process.RCT, randomized controlled trial.	PMC9117787	kjod-52-3-220-f1.jpg
0.47493	bef64ff4b1544951b728d0ab54067b7a	Microbial composition of the 3 rhizocompartments of the 4 wheat cultivars used in the study. The relative abundances of the main phyla (relative abundance > 1%) as identified by 16S amplicon sequencing are shown.	PMC9119058	mra.00222-22-f001.jpg
0.464986	bfe13ec22955473188ed4382bfc14d84	Model schematic and studied regions.(a) Schematic depicting the major interactions between the coral host and the symbiotic algae captured by the model; the coral host grows logistically as a function of symbiotic feedback and environmental temperature; corals invest energy in symbiosis (for example the energy necessary for providing CO2 to the algae [34–36]) and receives a symbiotic feedback (for example photosynthate); corals sustain symbiotic costs, which are associated to the maintenance of microenvironments for housing the algae [34] and to stress associated to photosynthesis (like the presence of reactive oxygen species [35]), and are subject to mortality; algae grow logistically as a function of environmental temperature and coral abundance; algae are expelled during bleaching events, which occur when environmental temperature exceeds the optimal regional temperature (i.e., Topt, panel b) for coral growth by a variable range of temperature increase reflecting the severity of bleaching (see subsection Bleaching). (b) Temperature-limited coral growth curves (Eq 7); dashed lines mark the regional temperature optima. (c) Studied regions: Great Barrier Reef (GBR), South East Asia (SEA), and Caribbean (CAR). The map is produced with the Python’s Matplotlib Basemap Toolkit available from https://matplotlib.org/basemap/api/basemap_api.html (© 2011, Jeffrey Whitaker).	PMC9119535	pcbi.1010099.g001.jpg
0.439749	590602f84fcc4c7dbe3fc6d34d61d683	Model simulations at different speeds of acclimation.Model simulations (purple lines) are qualitatively compared to observations [61, 62], expressed as yearly median of coral abundances (green dots). The selected speed of acclimation, in each region (panels a, b, and c, respectively), is the one that produces results (thick purple line) consistent with observations. The arrows indicate the direction of increase in speed of acclimation and the thin grey lines indicate, for each region, environmental temperatures relative to corresponding optimal growth temperatures Topt.	PMC9119535	pcbi.1010099.g002.jpg
0.386275	1a6e97d8e5574576a7c6dc6fddb32bda	Simulated trait and abundance dynamics in the three regions and under different RCPs.(a-c) Cumulative number of bleaching assuming that these events occur whenever environmental temperature exceeds by 2°C the optimal temperature for coral growth. (d-f) Coral energy investment trait. (g-i) Coral abundance and (j-l) symbiont abundance, relative to the period 1986–2005. Note that in South East Asia and in the Caribbean, the dynamics of coral trait and coral abundance under RCP 4.5 overlap with those under RCP 8.5. In the year 2010, the coloured lines and the black lines are at different levels because, in the runs with acclimation (coloured lines), corals are able to reach higher abundances during the spin-up phase and between 1955 and 2010, thus increasing the overall performance of the coral-algae complex.	PMC9119535	pcbi.1010099.g003.jpg
0.42625	852b300e6aeb411d91122bc2ac36b244	Monthly temperature forcing scenarios and thermal-limited coral growth curves.Low emissions (RCP 2.6) produce temperature trends (grey lines) that fall within the thermal-limited coral growth curves (coloured lines in the right side of each panel) in all regions (a-c). As emissions increase (RCP 4.5), the trends in environmental temperatures move away from the temperature optima Topt (marked by dashed lines), especially in the South East Asia and in the Caribbean (d-f) and even fall outside the thermal tolerance curves under RCP 8.5 (g-i). The dotted lines mark the limits of the coral thermal tolerances.	PMC9119535	pcbi.1010099.g004.jpg
0.424325	fc718e916bfa4a5c8b7c56185ab23776	Sensitivity to speed of acclimation N for +25% change in each parameter.Simulations with bleaching. Vertical lines mark the speed of acclimation we estimated from coral cover data (see S2 Appendix).	PMC9119535	pcbi.1010099.g005.jpg
0.409077	aa064a1895e849f2a9c7b0301f5c1619	Sensitivity to speed of acclimation N for −25% change in each parameter.Simulations with bleaching. Vertical lines mark the speed of acclimation we estimated from coral cover data (see S2 Appendix).	PMC9119535	pcbi.1010099.g006.jpg
0.50042	986f0a5391ed4ba8a0fb5e52de5ee3f0	ACR-20 response and change in HAQ-DI score over time. Supporting values are shown in online supplemental table S4. (A) Time course of ACR-20 response through week 16. Response rates are reported in the intention-to-treat population (ie, all randomised patients) with non-responder imputation; patients who discontinued the trial early, started a prohibited treatment, were lost to follow-up or had no ACR assessments had outcomes imputed as non-responses. (B) Adjusted mean change from baseline in HAQ-DI score through week 16. Placebo, n=66; deucravacitinib 6 mg once a day, n=70; deucravacitinib 12 mg once a day, n=67. P values indicate a difference from placebo: p<0.05, p≤0.01, **p≤0.001, adjusted for multiplicity at week 16 only. ACR, American College of Rheumatology; HAQ-DI, Health Assessment Questionnaire-Disability Index; QD, once a day.	PMC9120409	annrheumdis-2021-221664f01.jpg
0.411972	76daaa6b1f5e443db6dc7cd05439d113	Laboratory parameters over 16 weeks (mean±SD): (A) lymphocytes, (B) neutrophils, (C) platelets, (D) haemoglobin, (E) total cholesterol and (F) triglycerides. Supporting values are shown in online supplemental table S5. QD, once a day.	PMC9120409	annrheumdis-2021-221664f02.jpg
0.387769	1f5bfac720004cb696eafc43148fec97	Schematic presentation of the study design. DER, defective endometrial receptivity.	PMC9120433	fimmu-13-842607-g001.jpg
0.359283	c88a6d6785314e4cbef295969afa1cfd	Immune infiltration analysis and Mϕ1/Mϕ2 balance. (A) Bar plots of 22 immunocytes in endometrial mixed tissue samples, including four datasets with 218 subjects. (B) Violin plots for immune cells in the DER and control groups. Purple color represents the control group while yellow represents the DER group. (C) Meta-analysis of four datasets for Mϕ1/Mϕ2. DER, defective endometrial receptivity; CON, control.	PMC9120433	fimmu-13-842607-g002.jpg
0.396888	9b661219793447f7bab867659af34742	Mϕ1/Mϕ2-related WGCNA and module enrichment analyses. (A) Heatmap showing the average genetic significance of each particular module across clinical traits. (B) Percentage of GSE19834 DEGs within the modules. Percentages within modules are, from left to right, 40.9%, 47.0%, and 14.0% (red), 42.9%, 44.3%, and 13.7% (blue), 41.7%, 49.1%, 11.1% (turquoise), respectively. Ho-Ma-En=Endometrial cells in estradiol progesterone macrophage medium, Ma-En=Endometrial cells in macrophage conditioned medium, En-Ma=Macrophages in endometrial cell medium. (C) REVIGO plot of the red, blue, and turquoise modules (left to right). The scatterplot shows the cluster representatives in a two-dimensional space obtained by applying multidimensional scaling to the semantic similarities matrix of GO terms. (D) Protein-protein interaction (PPI) network of representative genes in each module. Each module has 30 representative genes selected according to their “degree”.	PMC9120433	fimmu-13-842607-g003.jpg
0.422974	982815609ce046ab9b48db34878b45ff	Representatives of the modules, and expression and functions of hub genes. (A) Heatmap of modules genes between DER vs. CON. In the classification column, red represents infertilities, whereas blue represents controls. (B) Cumulative risk curves of pregnant status analysis for hub genes. Log-rank p = 0.005 (RPS9), 0.044 (DUT), and 0.044 (MARF1) denotes the significance of hub genes. (C) Main effects and two-way interactions of hub genes. (D) GSEA plot of upregulated and downregulated KEGG pathways related to changes in hub gene expression levels. CON, control; DER, defective endometrial receptivity.	PMC9120433	fimmu-13-842607-g004.jpg
0.429907	b6c1fd4724be40118962193408319f47	Construction of machine learning model for clinical pregnancy prediction. (A) SHAP values of each feature for random forests in modules. Each point represents a sample, and the figure shows the impact of features on the model’s outcome. (B) The weight matrix of hub genes in the xgboost model. Each column represents the weight of each feature to the model outcome in one permutation. (C) The predictive values of DUT, RPS9, MARF1, and the risk model were established based on logistic regression and visualized by a nomogram. (D) ROCs for machine learning models.	PMC9120433	fimmu-13-842607-g005.jpg
0.40388	10599d2d1c3845c4ae369d0c3878ff14	The clinical pregnancy prediction ability of the reference and machine learning models for in the external validation dataset. (A) mRNA expression of hub genes in GSE165004. (B) Receiver-operating-characteristics (ROC) curves. The corresponding value of the area under the receiver-operating-characteristics curve (AUC) for each model is shown in the table below. (C) Decision curve analysis. The X-axis represents the threshold probability for positive pressure ventilation outcome; Y-axis represents the net benefit. The net benefit of all machine learning models was larger over the range of clinical threshold compared to the reference model. (D) Sensitivity, specificity, Youden index, positive predictive value (PPV), and negative predictive value (NPV) of models.	PMC9120433	fimmu-13-842607-g006.jpg
0.447572	3c3b7a0a220643efa24a4094b5364254	Hub genes and clinical validation and benefit analysis of the model. (A) Validation of mRNA expression of hub genes in DER (n=25) and control (n=15) groups. (B) Western blotting result of MARF1, DUT, and RPS9, normalized by β-actin (triplicates in each group). Bar graphs represent the ratio of densities of the respective protein bands and β-actin. Densitometric quantification graphs of blots are available in Supplementary Figure 2 . (C) ROC of xgboost and ultrasound results of endometrial thickness. The corresponding value of the AUC for each method are presented in the table. (D) Clinical impact curve (CIC) of the xgboost model and endometrial thickness. The red curve (number of high-risk individuals) denotes the number of people classified as positive (high risk) by the model at each threshold probability; the blue curve (number of high-risk individuals with outcomes) denotes the number of true positives at each threshold probability.	PMC9120433	fimmu-13-842607-g007.jpg
0.424362	39bf7f580c7e4e9988056482c5f0ccc2	(A) A hypothesis on the mechanism of macrophage-endometrium interaction modules regulation on endometrial receptivity and (B) a scenario for clinical application of the defective endometrial receptivity prediction model. ART, Assisted reproductive technology.	PMC9120433	fimmu-13-842607-g008.jpg
0.417698	94b280017f294e948ad8c238d24c04e8	This picture shows the orifice of the recto- extrophied bladder neck fistula with meconium stain.	PMC9121239	gr1.jpg
0.446465	bc23e7d166824e6db6bff56d75c7252c	This picture demonstrate the omphalocel and bladder exstrophy and both ureteric stents that were inserted. In addition to the above it clearly shows hemiphalus and hemiscrotum.	PMC9121239	gr2.jpg
0.409404	0f63d00428134154ac6819d969130a95	Segmented regression model new deaths of COVID-19 in Iran since February 19, 2020 to February 5, 2021 using the Newey-West standard errors.	PMC9121681	jpmh-2022-01-e125-g001.jpg
0.486066	abd2ce0d875c4a958496522072d95599	Segmented regression model new deaths of COVID-19 in the world since February 19, 2020 to February 5, 2021 with Newey-West standard errors.	PMC9121681	jpmh-2022-01-e125-g002.jpg
0.502157	c9b266572cfb4176835eae0f57743c7c	Correlation between the I the number of new COVID-19 happened in the world and Iran from February 19, 2020, to November 24 2020.	PMC9121681	jpmh-2022-01-e125-g003.jpg
0.54304	7616db9e2d5249238ba93d2130f0c337	Correlation between the number of new deaths of COVID-19 occurred in the world and iran from November 24 2020 to February 5, 2021.	PMC9121681	jpmh-2022-01-e125-g004.jpg
0.431458	7a537f106c06431095d66c14204cad1a	Initial condition, fall dynamics, and impact surface based on fall type and head impact (n = 174).	PMC9124183	41598_2022_12489_Fig1_HTML.jpg
0.488041	535c98a688b647049ad3cf73b6652f63	Distribution of fall heights defined as change in head COM and change in support surface height (n = 174). 25th and 75th percentiles are represented by the lower and upper bounds of box, respectively. Line within the box represents median value, and the whiskers represent 1.5 times the interquartile range (IQR; IQR = 75th percentile value minus 25th percentile value). Note: median and 25th percentile values are coincident (0.0) for change in support surface height.	PMC9124183	41598_2022_12489_Fig2_HTML.jpg
0.423816	5df063a10d5f4eeb8e59a64bceb133bf	Box plots of peak linear head acceleration, change in linear head velocity, rotational head acceleration and rotational head velocity by fall type (left column) and head impact (yes/no) (right column). 25th and 75th percentiles are represented by the lower and upper bounds of the box, respectively. Horizontal line within the box represents median value, and the whiskers illustrate the minimum and maximum values unless there are outliers. When outliers are present, whiskers represent 1.5 times the interquartile range (IQR; IQR = 75th percentile value minus 25th percentile value). (Note: SIM G sensor did not record rotational acceleration/velocity for 5 falls.)	PMC9124183	41598_2022_12489_Fig3_HTML.jpg
0.440937	798732f4e5234185aba21810fd21e7be	Linear head acceleration (n = 174) and rotational head acceleration (n = 169) vs. fall height (change in head COM).	PMC9124183	41598_2022_12489_Fig4_HTML.jpg
0.402174	fec55e0aa284487aafd9f52f1f833bb3	Relationships between biomechanical measures based on fall type (n = 174, except for rotational acceleration and velocity where n = 169).	PMC9124183	41598_2022_12489_Fig5_HTML.jpg
0.369135	bcf4dcfd12514de6b3363f007f266d81	Relationships between biomechanical measures based on head impact (n = 174, except for rotational acceleration and velocity where n = 169).	PMC9124183	41598_2022_12489_Fig6_HTML.jpg
0.473623	fad2ebd4c6b54f13b9d1670064f8c4bc	Heatmap showing the clinical characteristics in each cluster. CADM, clinically amyopathic dermatomyositis; CK, creatine kinase; CRP, C-reactive protein; KL-6, Krebs von den Lungen-6; SP-D, surfactant protein-D; GGA, ground-glass attenuation; anti-ARS, anti-aminoacyl transfer RNA synthetase; anti-MDA5, anti-melanoma differentiation-associated gene 5.	PMC9124901	fmed-09-883699-g0001.jpg
0.454165	b38a2ae1cebc450fa8e4e90a43c5049e	Main characteristics of the 6 clusters (clusters #1–#6) of patients with myositis-associated ILD treated with initial triple-combo therapy. (A) Proportions of each cluster with the main clinical characteristics of clinically amyopathic dermatomyositis (CADM), anti-aminoacyl transfer RNA synthetase (ARS) antibody, anti-melanoma differentiation-associated gene 5 (MDA5) antibody, and requirement of supplemental oxygen. (B) Age at disease onset and serum levels of C-reactive protein (CRP) and Krebs von den Lungen-6 (KL-6) at diagnosis in each cluster. (C) Mortality rates during the observation period in patients treated with initial triple-combo therapy and those treated with dual-combo therapy or monotherapy in each cluster.	PMC9124901	fmed-09-883699-g0002.jpg
0.386931	b7b4bad4b4404248a9c0e102b806aabd	Cumulative survival rates in each cluster in patients treated with initial triple-combo therapy (A) or those treated with initial dual-combo therapy or monotherapy (B). Cumulative survival rates were compared using Kaplan–Meier analysis, and the log-rank test was used to test for significant differences between two groups.	PMC9124901	fmed-09-883699-g0003.jpg
0.423294	b61aa9f9b8e348b9b0dde6ed88c957e1	Cumulative survival rates of patients treated with initial triple-combo therapy and those treated with initial dual-combo therapy or monotherapy in each cluster (clusters #1–#6). Cumulative survival rates were compared using Kaplan–Meier analysis, and the log-rank test was used to test for significant differences between two groups.	PMC9124901	fmed-09-883699-g0004.jpg
0.477421	0fa4f85ef32f4f2bb8f461b9b82cf29d	Histological examination of the robotic-assisted laparoscopic radical prostatectomy specimen indicated prostatic mucinous carcinoma. A: Hematoxylin and eosin staining of the residual prostate tissue indicated lakes of extracellular mucin. B-F: Immunohistochemistry staining of the residual prostate for (B) prostate specific antigen, (C) prostatic serum acid phosphatase, (D) cell keratin 7, (E) cell keratin 20, and (F) caudal-type homeobox transcription factor 2. Magnification × 200.	PMC9125274	WJCC-10-4654-g001.jpg
0.465021	829e908701ae42d1b5681a184b4fe051	Histological examination of the transurethral resections of bladder tumors specimens showed the extracellular mucin and signet ring cells. Hematoxylin and eosin staining of the first transurethral resection of bladder tumors specimen (A and B) vs the second transurethral resection of bladder tumors specimen (C and D). Magnification × 100 and × 200, respectively.	PMC9125274	WJCC-10-4654-g002.jpg
0.551872	49819e064bc24202b47791a0b0211a15	Timeline of the patient’s medical care. RARP: Robotic-assisted laparoscopic radical prostatectomy; TURBT: Transurethral resection of bladder tumors.	PMC9125274	WJCC-10-4654-g003.jpg
0.467811	07da408e4b334615ab291394075ad7f4	Col1a1-expressing perivascular fibroblasts coexist with mural cells on penetrating arterioles, arteriole-capillary transition zones, and large ascending venules. (a) Schematic demonstrating how PVF-mural cell reporter mice were generated (PdgfrbCre/+; Ai14fl/+; Col1a1GFP/+). We first crossed PdgfrbCre/+ with Ai14fl/fl mice (F0). We then took resulting PdgfrbCre/+; Ai14fl/+ mice and crossed themContinued.with Ai14fl/fl mice (F1) to create PdgfrbCre/+; Ai14fl/fl mice. Finally, the resulting PdgfrbCre/+; Ai14fl/fl mice were crossed with Col1a1GFP/+ (F2) mice to generate PdgfrbCre/+; Ai14fl/+; Col1a1GFP/+2 mice. (b) Top down, maximum projected image of the cortical vasculature from in vivo two-photon imaging of Col1a1-GFP; PdgfrβCre-tdTomato mice showing that tdTomato+ mural cells (red) and tdTomato+/GFP+ perivascular fibroblasts (PVFs; red and green) coexist along the vasculature. Blood vessels labeled with i.v. administration of Alexa-680 (2 MDa) (blue). (c) Side projection of in vivo image stack with (c’) schematic demonstrating the topological organization of PVFs (green) along the penetrating arteriole (0th order) as identified by smooth muscle cells (SMCs; red) and arteriole-capillary transition zone (1st-4th branch order). PVF somata and termination of territory end before the capillary zone (≥5th order). (d) Side projection of in vivo image stack with (d’) schematic demonstrating the topological organization of PVFs (green) along the ascending venules (0th order) as identified by VSMCs (red) and 1st order post-capillary connections. PVF somata only occupy the main trunk (0th order) of some ascending venules. (e) Scatter plot of penetrating arteriole (red) and ascending venule (blue) diameters versus PVF termination depth along 0th order vessels. Spearman’s rank correlation shows a positive correlation along ascending venules (****p < 0.0001, r = 0.7314, n = 32 ascending venules). PVFs were observed up to maximum imaging depths along penetrating arterioles regardless of vessel diameter (n = 15 penetrating arterioles). Data was compiled from 4 mural cell-PVF and PVF reporter (Col1a1-GFP) mice. (f) Histogram depicting the proportion of PVF soma occupancy from 0th to 6th branch order along the arteriole (red) or venule (blue) zones. Each point represents the average proportion found in each mouse (n = 4 mice). (g) Histogram depicting the proportion of PVF territorial termination from 0th to 6th branch order along the arteriole (red) or venule (blue) zones. Each point represents the average proportion found in each mouse (n = 4 mice).	PMC9125487	10.1177_0271678X211068528-fig1.jpg
0.411176	a65826816c69452b8ee445936efebd72	Topological organization of PVFs is similar across CNS regions. Representative confocal image of the (a) cortex, (b) corpus callosum, (c) hippocampus and (d) spinal cord from Col1a1-GFP; PdgfrβCre-tdTomato mice. Inset images show that perivascular fibroblasts (PVFs; green) are found along arterioles, arteriole-capillary transition zones, and venules in the (a′) cortex, (b′) corpus callosum, (c′) hippocampus (d′) and spinal cord. However, PVFs are not present on the capillary zone, which is occupied by thin-strand and mesh pericytes (red) in the (a″) cortex, (b″) corpus callosum, (c″) hippocampus (d″) and spinal cord. Mural cells are shown in red and DAPI in blue.	PMC9125487	10.1177_0271678X211068528-fig2.jpg
0.407914	0c508ca7f6284ff9b6a7bcf594e0dab2	Morphological features of PVFs are distinct from mural cells. (a-c) Representative two-photon images from PdgfrβCre-tdTomato mice of (a) smooth muscle cells (SMCs) on a penetrating arteriole, (b) ensheathing pericyte and a putative PVF in the arteriole-capillary transition zone and (c) VSMCs on an ascending venule with respective red channel separated to better show mural cell morphology (white). Vasculature labeled with i.v. administration of FITC-dextran (70kDa; green). (d) Schematic demonstrating in vivo experimental timeline on Col1a2CreER-tdTomato mice. Cranial windows were placed in Col1a2CreER-tdTomato (Col1a2Cre/+; Ai14fl/+) mice. On days 7 and 8, mice were given tamoxifen (80mg/kg). In vivo imaging began 21 days after window placement. (e–g) Representative two-photon images of PVFs (red) from Col1a2CreER-tdTomato mice on the (e) penetrating arteriole, (f) arteriole-capillary transition zone, and (G) ascending venule with respective red channel separated below to appreciate PVF morphology in white. Vasculature labeled with i.v. administration of FITC-dextran (70kDa; green). (h) Example of somata roundness analysis. Cell somata in PdgfrβCre-tdTomato mice were outlined (yellow dashed line) using the freehand tool in ImageJ and the Roundness Index was obtained by using Shape descriptor analysis. (i) Histogram of somata roundness of tdTomato+ perivascular cells along the arteriole-capillary transition zone in PdgfrβCre-tdTomato mice (black) and tdTomato+ PVFs along penetrating arterioles, arteriole-capillary transition zone, and ascending venules in Col1a2CreER-tdTomato mice (gray). A roundness index of 1 would be considered a perfect circle. Data was compiled from 122 perivascular cells from 8 PdgfrβCre-tdTomato mice and 77 PVFs from 4 Col1a2CreER-tdTomato mice. (j) Representative high-resolution confocal image of ruffled PVF membrane (white) from Col1a2CreER-mEGFP mice.	PMC9125487	10.1177_0271678X211068528-fig3.jpg
0.408619	1198d1c934b346319de75b0b467d293a	Perivascular fibroblasts are a dynamic perivascular cell population. (a–c) Representative in vivo two-photon images of perivascular fibroblasts (PVF; red) over 28 days from Col1a2CreER-tdTomato mice. (a, b) Non-junctional PVFs display more mobility than (c) PVFs found at vascular junctions. Dashed line indicates initial soma position on Day 0. Vasculature labeled withContinued.i.v. administration of FITC-dextran (70kDa; green). (d) Two representative examples of ensheathing pericytes from PdgfrβCre-tdTomato mice following 28 days of in vivo two-photon imaging. Dashed line indicates initial soma position on Day 0. Vasculature labeled with i.v. administration of FITC-dextran (70kDa; green). (e) Schematic demonstrating how displacement of PVFs and ensheathing pericytes from Day 0 was measured. The distance (d0) between the soma center and nearest vascular branch point was measured in the 2D projected image. Then the z-distance (z0) between these two structures was determined and used to calculate the Euclidean distance (D0). This was repeated at each imaging time point (Day T; DT). Displacement from Day 0 was determined by subtracting DT from D0. (f) Graph demonstrating soma displacement of PVFs over 28 days from initial position on day 0 (n = 102 PVFs from 4 Col1a2CreER-tdTomato mice). (g) Graph comparing maximum displacement of PVFs with non-junctional (n = 68 PVFs) and junctional (n = 34) vascular positions. PVFs with non-junctional positions were significantly more dynamic than PVFs at junctions (Mann-Whitney test U = 841, p = 0.0251. Median: Non-junctional = 3.627µm, junctional = 2.814 µm). (h) Graph demonstrating soma displacement of ensheathing pericytes (EP) over 28 days from initial position on day 0 (n = 32 EPs from 5 PdgfrβCre-tdTomato mice). (i) Graph comparing maximum displacement of PVFs (n = 102 PVFs) and EPs (n = 32). PVFs were significantly more dynamic than EPs (Mann-Whitney test U = 431, ***p < 0.0001. Median: PVFs = 3.081µm, EP = 1.142 µm).	PMC9125487	10.1177_0271678X211068528-fig4.jpg
0.469694	7702018853ea4dababf06b52fd0650be	Flow chart for screening patients.	PMC9126887	41598_2022_12518_Fig1_HTML.jpg
0.531146	867d8ac1b3a744b89eafa6bd439d2884	Comparison of the infammatory index and TBS in predicting recurrence.	PMC9126887	41598_2022_12518_Fig2_HTML.jpg
0.445327	f73f2abfa9314cc6a9a2859949346e11	Comparison of TBS, TTV, tumor diameter and tumor number in predicting recurrence.	PMC9126887	41598_2022_12518_Fig3_HTML.jpg
0.50442	3e2a4ab86ab749949b04bf99179bbb53	Comparison of NLR–TBS group, TBS group and NLR group in predicting recurrence.	PMC9126887	41598_2022_12518_Fig4_HTML.jpg
0.479783	407564078e844484884ab1c67592df6a	Comparison of NLR–TBS group, TNM stage and BCLC stage in predicting recurrence.	PMC9126887	41598_2022_12518_Fig5_HTML.jpg
0.419631	e4be52618aa04a8d81c06bea8895119e	(A) Recurrence-free survival curve of patients with high TBS (n = 53) and low TBS (n = 164).The median RFS times in the high TBS group and the low TBS group were 11.61 months and 30.98 months (P = 0.001). (B) Recurrence-free survival curves of patients with high NLR (n = 74) and low NLR (n = 143).The median RFS times in the high NLR group and the low NLR group were 18.12 months and 30.63 months (P = 0.001). (C) Recurrence-free survival curves of low-risk (n = 121), middle-risk (n = 66) and high-risk group (n = 30) patients. The median RFS times in the low-risk group, middle-risk group and high-risk group patients were 33.03 months, 22.16 months and 8.07 months (P = 0.001).	PMC9126887	41598_2022_12518_Fig6_HTML.jpg
0.396132	6296e127a54f42d3af4447d48de32d94	Subgroup analysis of the main efficacy indicators (per‐protocol set)	PMC9128564	CDT3-8-59-g001.jpg
0.460204	50c92922c6f043aeac36b5405e8a1de2	Target Hb maintenance ratios from Week 0 to Week 28 (per‐protocol set)	PMC9128564	CDT3-8-59-g002.jpg
0.495788	606aca1372af45829b6aa7e26408df89	Target Hb achievement cumulative rate (%) and time (week) of the two groups (per‐protocol set)	PMC9128564	CDT3-8-59-g003.jpg
0.426365	4a1c8b3eca8546758ccda2504324b70e	The weekly dose level of darbepoetin alfa and epoetin alfa group (full analysis set)	PMC9128564	CDT3-8-59-g004.jpg
0.481106	6294a620e026482e96acd92a606c049c	Patient disposition	PMC9128564	CDT3-8-59-g005.jpg
0.440869	b3055d2f7a934bc58e988cd7a61d14bf	Changes of the mean Hb level from baseline to the end of the study	PMC9128564	CDT3-8-59-g006.jpg
0.41534	8e0945d12e064d89a6a42c72109b2aa4	Subgroup analysis of incidence of adverse events (safety set). BMI, body mass index; CI, confidence interval; NESP, novel erythropoiesis stimulating protein	PMC9128564	CDT3-8-59-g007.jpg
0.399774	75297839c337458aaca4d0d81afddfed	Timeline of patient enrolment and schedule of data collection.Adapted from the SPIRIT statement [54]. Abbreviations: d, day; RAM, risk assessment model; VTE, venous thromboembolism.	PMC9128957	pone.0268833.g001.jpg
0.432717	9c331d621260488aa5c58f651bd8df91	Study organization and follow-up.	PMC9128957	pone.0268833.g002.jpg
0.495658	0012462fd0c54fe98301ce1ba76c6bb9	The mungbean, lentil, and Indian mustard genotypes used in the study.Where (a) mungbean genotypes are 1. Pusa Baisakhi, 2. Pusa Ratna, 3. Pusa Vishal, 4. Pusa105, 5. Pusa0672, 6. Pusa9072, 7. Pusa9531, 8. MH96-1, 9. MH318, 10. MH421, 11. MH521, 12. MH810, 13. ML512, 14. ML818, 15. PS16, 16. TM 96–2, 17. IPM02-3, 18. IPM02-14, 19. IPM409-4, 20. PMR1; (b) lentil genotypes are 1. L4076, 2. L4147, 3. L4594, 4. L7903, 5. HM1, 6. BM4, 7. JL1, 8. Sehore74-3, 9. NDL1, 10. IPL81, 11. IPL321, 12. K75, 13. KLS218, 14. DPL58, 15. DPL62, 16. PL1, 17. PL2, 18. PL6, 19. L830, 20. L4602; while (c) Indian mustard genotypes were 1. PM28 and 2. PDZM31.	PMC9128967	pone.0268085.g001.jpg
0.450861	9e824b6eb47740ec9cdb828014e5a27b	Microgreens yield of two Indian mustard genotypes (PM28 & PDZM31) (a) at a seeding density of 6, 8, and 10 seed/cm2 on 8th day after sowing and (b) at 6th, 8th, and 10th day of sowing.Where ‘D’ is days after sowing and values are expressed as mean±SD (n = 3).	PMC9128967	pone.0268085.g002.jpg
0.403531	356b8b529ceb4a36bf96d4157af47c32	Changes in electrical conductivity of washed (W) and unwashed (U) microgreens samples (mungbean, lentil, and Indian mustard) when packed in LLDPE films during the 2nd, 4th, and 6th day of storage at 4°C.Where D2-U, D4-U, and D6-U are ‘unwashed’; while D2-W, D4-W, and D6-W are ‘washed’ microgreens samples at 2nd, 4th, and 6th day of storage, respectively. Values are expressed as mean±SD (n = 3) and different letters indicate a significant difference (P≤.05).	PMC9128967	pone.0268085.g003.jpg
0.492854	32590b5734ed4d4e99e82f586105cdb2	Biosynthesis of AgNP: colour change into dark brown confirms AgNP formation.	PMC9129982	OMCL2022-1646687.001.jpg
0.479508	078482b5cb794cf2963edbd47f73bf39	UV spectral analysis of biosynthesized AgNPs showed absorption peak at 410 nm while no peak revealed for the bacterial extract.	PMC9129982	OMCL2022-1646687.002.jpg

0.429177

992ac795541a493fa6626addd48d4039

Regenerated T0 plantlets from rooting stage to acclimatization in soil. (A) Plantlets growing on the rooting medium. Sundae cup is used as a cover to give vertical space to grow. (B) Regenerated plantlets ready to be moved to potting mix. Red arrows indicate plantlets with healthy roots. (C) Plantlet representing bushy phenotype or multiple shoot cluster. (D) Single-shoot plantlet ready to be transplanted to potting mix. (E) T0 regenerated plantlets transplanted to potting mix. (F) A plant propagation tray covered with a humi-dome to aid in acclimatization.

PMC9114882

fpls-13-860971-g004.jpg

0.430131

04f553fa4b8a4c8d8e51cfcee1725ba3

Transient and stable transformation. Observation of transient RFP expression 3 days post-infection on the middle of the scutellum (A) or the side of the embryos (B). (C) Somatic embryogenesis on embryo scutellum side, 8 days post-infection in bright field. Observation of stable transformation and callus formation of maize embryo under bright field (D) and RFP field overlayed (E). The arrows indicate callus with the RFP expression. (F) Tentacle-like structure on a callus during the maturation stage, 21 days post-infection. Maize callus on the maturation I medium, 18 days post-infection (G) and the maturation II medium, 31 days post-infection (H).

PMC9114882

fpls-13-860971-g005.jpg

0.432592

ef195936f88444bcb94b109c6de9aa05

Phenotypes of T0 transgenic plants. Observation of mature shoots with roots on the maturation II medium under an RFP channel (A) and a bright field (B). Black arrow indicates a shoot with RFP expression; white arrow indicates a shoot without RFP expression. Observation of the gl2 knock-out phenotype on a T0 regenerated plant (C) and a wild-type seedling after water spray (D). Observations of pollen grains collected from a T0 plant (E,F) and a wild-type B104 control (G,H). Images of (E,G), bright field; (F,H), RFP field overlay.

PMC9114882

fpls-13-860971-g006.jpg

0.388127

3bbb5b6ad2024537bd23776b32437683

Phenotypes and genotypes of sixteen T0 mutant events. Plant ID shows experiment ID, embryo ID, and plantlet ID (EXP-embryo-plantlet). Three gl2 phenotypes: gl2, glossy mutant phenotype; gl2/wt mixed, both glossy mutant and wild type phenotype on the same leaf; wt, wild-type phenotype. Red letters, target sequences in Gl2 exon2; Blue letters, PAM sequences; Black letter, insertion mutations; dashed lines, deletions. T0 gl2 phenotyping is considered a preliminary screening assay.

PMC9114882

fpls-13-860971-g007.jpg

0.49413

a93eaef2d1334742b533899d53372600

Sesquiterpenes obtained from germacrene D and bioactivity of (–) germacrene D. The known major sesquiterpenoids derived from germacrene D including γ-amorphene, amorphene, α-copaene, cubenene, γ-murolene and δ-cadinene. (–)-Germacrene D has repellent activity of anti-tick (1), anti-mosquito (2) and anti-aphid (3)

PMC9115970

12934_2022_1814_Fig1_HTML.jpg

0.428052

ba719d692bb044318d0084653b6c126c

Phylogenetic analysis of A. chrysogenum sesquiterpene synthases and germacrene D synthases. The TPS-a, TPS-b, TPS-c, TPS-d, TPS-e/f, TPS-g, TPS-h clades and the microbial branching are shown in different colors. A. chrysogenum sesquiterpene synthases and germacrene D synthases are shown in red font. Terpene synthases of Arabidopsis thaliana, Selaginella moellendorffii Abies grandis, Populus trichocarpa and Ginkgo biloba were used for phylogenetic analysis and are shown in Additional file 1: Table. S4. The branch lengths and bootstrap values are presented at the nodes in blue and black, respectively

PMC9115970

12934_2022_1814_Fig2_HTML.jpg

0.468839

4360803b50c845d093753c3685e33eed

GC/MS analysis of volatile organic compounds produced by yeast transformants expressing AcTPS1. A GC-FID detection of AcTPS1 products in the yeast strain. Peak 1 represents the major product of AcTPS1 and is shown with a blank arrow. JCR27, the starting yeast strain used in this study. JCR27 + AcTPS1, AcTPS1 expressed in JCR27. B Detected by GC–MS, the major product of AcTPS1 is shown by the 204 m/z ion in the chromatographic trace

PMC9115970

12934_2022_1814_Fig3_HTML.jpg

0.481139

7fcc5a86d9ce4b739c66046e38f58bd5

GC spectrum of the in vitro analysis of AcTPS1 enzyme function. The AcTPS1 enzymatic product is shown as a blank arrow. Blank, solvent hexane; Vector + FPP, pET-45b cell extracts reacting with the substrate FPP; AcTPS1 + FPP, AcTPS1 protein cell extracts reacting with the substrate FPP. AcTPS1 + FPP was concentrated tenfold for detection

PMC9115970

12934_2022_1814_Fig4_HTML.jpg

0.432621

6e56b42aa2ef496da9231d8dbecbf533

Comparing germacrene D synthases for high germacrene D production in S. cerevisiae. A Schematic diagram of comparing germacrene D production in S. cerevisiae. Transformation, flask fermentation and quantitative analysis were performed as described in “Methods” (B) Strategy for the construction of SC1-SC21 via homologous recombination. JCR27, the starting strain; SC1-SC21, yeast strains expressing different germacrene D synthases; ROX1, the insertion site for exogenous fragments; LB (ROX1), the upstream sequence of rox1; RB (ROX1), the downstream sequence of ROX1; tCYC1, the terminator of CYC1; X-Sc, the germacrene D gene optimized for S. cerevisiae; pGAL1pGAL10, the promoter of GAL1 and GAL10. B The production of different germacrene D producing strains. Germacrene D levels were determined after 72 h fermentation in YPDH medium as described in the “Methods”. Error bars represent standard deviations from three independent experiments. ND, not detected. SSLH2 et al., the names of germacrene D synthases from different organisms detailed in the text

PMC9115970

12934_2022_1814_Fig5_HTML.jpg

0.453344

f1ca023260934a34b21aec22734d59f5

Titer of (–)-germacrene D in engineered yeast strains in shake-flask fermentation. A Biosynthesis pathways for (–)-germacrene D in engineered yeast. ERG10 acetyl-CoA C-acetyltransferase, ERG13 3-hydroxy-3-methylglutaryl coenzyme A synthase, tHMG1 truncated 3-hydroxy-3-methylglutaryl-coenzyme A reductase 1, ERG8 phosphomevalonate kinase, ERG12 mevalonate kinase, MVD1 mevalonate diphosphate decarboxylase 1, IDI1 isopentenyl-diphosphate delta isomerase 1, ERG20 farnesyl pyrophosphate synthetase, AcTPS1 (–)-germacrene D synthase. Overexpressed enzymes are shown in blue. Red slash, gene knockout; Red downward arrow, downregulated gene expression. B The engineered yeast strains integrated with gene modules. These expression modules were integrated into the chromosomal sites of ROX1 (module 1, chromosome XIV), Exg1 (module 2, chromosome XII), DPP1 (module 3, chromosome IV), and ERG9 (module 4, chromosome VIII). The engineered strain LSc53 contained expression module 1 (abbreviated as ‘M1’). The engineered strain LSc54 contained the expression module 1 (‘M1’), module 2 (‘M2’) and module 3 (‘M3’). The engineered strain LSc81 contained the expression module 1 (‘M1’), module 2 (‘M2’), module 3 (‘M3’) and module 4 (‘M4’). (C) The production of different engineered stains. Germacrene D levels were determined after 72 h fermentation in YPDH medium as described in “Methods”. Error bars represent standard deviations from three independent experiments

PMC9115970

12934_2022_1814_Fig6_HTML.jpg

0.43856

2ea22297ce62487c8a9600a067a3ba1f

High-density fermentation of strain LSc81 for (–)-germacrene D production. A Results of fermentation in a 5-L bioreactor. (–)-Germacrene D production, cell growth, and residual concentrations of glucose and ethanol during fed-batch fermentation were measured. Error bars indicate the standard deviations of three replicates. B Fermentation broth in the 5-L bioreactor. The upper oil phase contained isopropyl myristate and (–)-germacrene D

PMC9115970

12934_2022_1814_Fig7_HTML.jpg

0.453527

5c94d7612d4243f591b89cc3546459cf

The convergence rates

PMC9116929

11269_2022_3169_Fig10_HTML.jpg

0.4226

a8b892bd25524bce92ae3f310ecbcc43

The sampling points in Xi’an moat

PMC9116929

11269_2022_3169_Fig1_HTML.jpg

0.421301

8237e99473b94895a650cd6b5d438bb5

The procedure for water quality assessment

PMC9116929

11269_2022_3169_Fig2_HTML.jpg

0.441114

6c4376f48cf24153aaf9d132cc829025

WQIi of different parameters

PMC9116929

11269_2022_3169_Fig3_HTML.jpg

0.466073

582ba1c184a3404685cc74ad531c87e4

The statistical WQIi

PMC9116929

11269_2022_3169_Fig4_HTML.jpg

0.438935

8459d657eb234f37abda5b422eeb152e

The comprehensive WQI

PMC9116929

11269_2022_3169_Fig5_HTML.jpg

0.529511

e3c836a092944fd898622e2a98c99fb2

The spatial distribution of the comprehensive WQI

PMC9116929

11269_2022_3169_Fig6_HTML.jpg

0.460228

5e88dcb234bf40f3ac5ce52688a25645

The temporal variation of the comprehensive WQI

PMC9116929

11269_2022_3169_Fig7_HTML.jpg

0.458761

23ea7f9d3c484df099ffdbe50b2558d6

The WQImin models

PMC9116929

11269_2022_3169_Fig8_HTML.jpg

0.450878

69c67343a4394d38a482acf094c9556a

The optimized weights

PMC9116929

11269_2022_3169_Fig9_HTML.jpg

0.463593

c0b871a3b80d47cd839a4f9def63495d

Altman Z score for the years between 2019–2015.

PMC9116989

pone.0264016.g001.jpg

0.457242

c9bd5fd6f7f8459bbe1237278c26dc32

Index 05 for the years between 2019–2015.

PMC9116989

pone.0264016.g002.jpg

0.4356

44f699f79d1e44d2b612fa57f8626324

Conceptual framework of the research model. PRO, perceived revenue opportunities; PES, perceived ecotourism satisfaction; PNE, perceived negative effects; PUS, perceived insufficient services.

PMC9117084

11356_2022_20882_Fig1_HTML.jpg

0.441856

316baf6c25bb481894e6cd519751d90d

Hypothesized model 1 (PRO-PES model).

PMC9117084

11356_2022_20882_Fig2_HTML.jpg

0.453117

0388616363c64ea99e61e55c41f8e363

Complete mediated model (PRO-PNE-PUS-PES model).

PMC9117084

11356_2022_20882_Fig3_HTML.jpg

0.399876

e4bd013186854164babe2ca215b9eff6

Developing visual barcodes for multiplexing live imaging applications.A Representative image of the A375 cell line with an iRFP-H2A nuclear marker. B Representative images of five CFP localizations in the A375 cell line: Whole Cell (WC), Nuclear Export Signal (NES), Nuclei, Peroxisome (Peroxi), and Endoplasmic Reticulum (ER). C Heatmap of false detection rate (FDR) for barcode calling for the five CFP localizations in the A375 cell line. D Representative images of all 12 visual barcodes used in A375 cell line. E Average miss rate of barcode calling for all 12 visual barcodes of the A375 cell line, treated with DMSO controls (n = 39). Numbers in the diagonal represent the sensitivity for each barcode. F Violin plots showing miss rate for all 12 visual barcodes of the A375 cell line, treated with DMSO controls (n = 39) or with drugs (n = 75). G–I Scatter plot showing the separation of nine A375 clones with visual barcodes by the ImageStream system according to their fluorescent color and localization. The separation by localization is only demonstrated for GFP-positive clones (I). J Representative images from ImageStream of all nine clones. Two cells from each clone are presented. Scale bar in (A and B) is 50 µm, in (D) 100 µm and in (J) 7 µm. Source data are provided with this paper.

PMC9117331

41467_2022_30008_Fig1_HTML.jpg

0.456044

f6eaaa10323540b98f7db86c32ea5217

Generating the A375 “Signalome” reporter cell line.A Illustration of the 12 clones that were used to generate the A375 Signalome cell line. A375 cells were first infected with iRFP-H2A to mark the cell nucleus. Then, 12 clones were generated with 12 visual barcodes. Lastly, a different live reporter was added to each of the clones. Transcription activating reporters are represented by gold while translocation reporters are represented in red. Binding partners in the nucleus are represented in purple. B Relative number of cells from each clone in a DMSO control wells over time. C Scatter plot showing the correlation between the reporter activity scores, for all 12 clones when grown separately or as part of the Signalome cell line, in response to 75 drugs. D–F Reporter activity plots of the A375 signalome cell line in response to DMSO, vemurafenib (1 µM) or trametinib (0.125 µM) over time. Blue and red backgrounds represent activation or inhibition scores above 0.2 or below −0.2, respectively. The average cell count per reporter in (D–F) is: 656, 545, and 530 cells, respectively. Source data are provided with this paper.

PMC9117331

41467_2022_30008_Fig2_HTML.jpg

0.435026

918ea4b381924db5be5c698527990270

Large-scale correlations in signaling suggest a generalized response that is compound independent.A Unsupervised hierarchical clustering of A375 signalome cells treated by 122 active drugs according to their activity scores. Due to technical error, Geminin reporter was not measured for all drugs and was thus discarded. Results from all 12 reporters can be seen in Supplementary Fig. 4I for the subset of drugs that for which Geminin data are available. B Scatter plot showing the correlation between the activities of the p38 and p53 reporters after 48 h of treatment with 122 active drugs. C Heatmap showing the pairwise Pearson correlations between the different A375 Signalome clones, after 48 h of treatment with 122 active drugs. D A model proposing how drugs with different mechanisms may converge into two major signaling states. While each of the drugs has different targets, many of the targets affect the same sensing mechanism that later governs the p53- vs p38-signaling states. Source data are provided with this paper.

PMC9117331

41467_2022_30008_Fig3_HTML.jpg

0.422028

16942a7680574b79bafc09e519bc4283

Drug treatments increase the correlations between the activity of pathways.A Scatter plots of the activity scores of p38 and p53 reporters in the A375 signalome cell line before and at multiple time points after treatment with 122 active drugs. Each dot represents a different drug. Pearson’s r is depicted for each of the time points. B Pearson correlation coefficients were calculated for each pair of pathways in the A375 signalome cell line before and at different time points after treatment with 122 active drugs. Each datapoint represents a correlation value for one given pair of pathways over all 122 active drugs. The correlation between the activity score of p38 and p53 pathways is marked by a red circle. C–H Locally weighted smoothing (Lowess) regression of all reporters in each of the two signaling states is shown for six representative drug treatments, each associated with different drug targets. While three of the drugs drive the p53-signaling state (C–E), the other three drugs drive the p38 signaling state (F–H). The bold (red and blue) lines and the gray sleeves represent mean values and + /− SEM respectively. Source data are provided with this paper.

PMC9117331

41467_2022_30008_Fig4_HTML.jpg

0.410583

98250e4e1d71418db805c7b1474e7579

PCA suggests that the p38 and p53-signaling states exist pretreatment and increase in weight over time.A PCA of the activity scores of 11 signaling pathways after 48 h of treatment with 122 drugs. The color of each drug is indicating its cluster in Fig. 3A. B, C Bar plots representing the PC1 loading of each pathway after 48 h of drug treatment (B) or pretreatment (C). D Variance of pathway activity, when projected on the principal components calculated from measurements on cells that were not exposed to drug treatment. Source data are provided with this paper.

PMC9117331

41467_2022_30008_Fig5_HTML.jpg

0.476994

dbb9daa0255e468da6e44fd92193fc28

Cell growth and proliferation are tightly regulated and correlate with p38- and p53-signaling states.A A model demonstrating how sensing of cell size can affect both cell growth and proliferation to keep homeostasis of cell size. B Scatter plot showing the initial and long-term effects of rapamycin or SNS-032 on the average cellular growth rate and division rate of Rpe1 cells. Data points indicate the average growth and division rates measured: (1) during the first 24 h of drug treatment and (2) during 24–60 h of drug treatment. C Average growth rate vs. division rate in five cell lines (Rpe1, HeLa, U2OS, SAOS2, 16HBE) treated with either growth inhibitors (red) or cdk1/2 inhibitors (blue). Measurements in each cell line were normalized by the values measured for untreated control samples (gray) of the same cell line. The growth inhibitors used were: Cycloheximide, Torin-2, and Rapamycin, at varying doses (de- tailed in “Methods”). The cdk1/2 inhibitors used were: SNS-032, PHA848125, Cdk2 Inhibitor III, and Dinaciclib, at varying doses (detailed in “Methods”). D, E The average cell size for a given drug correlated with its PC1 value calculated on the reporters’ activity for data from Kaufman et al. (D) and Liu at al. (E). F Average growth rate vs. division rate for A375 cells in Signalome screen. Each circle represents one screened condition (drug treatment). The circle’s color indicates the value of PC1 in that condition. Contour lines show the average value of PC1 as a function of growth rate and division rate. G The average level of p38 (top) and p53 (bottom) activity as a function of growth rate and cell cycle length. H A model proposing how drugs which affect cell division activate the p53-signaling state while drugs that affect cell growth activate the p38-signaling state. Each state in return actives a compensation mechanism resulting in a new equilibrium. Source data are provided with this paper.

PMC9117331

41467_2022_30008_Fig6_HTML.jpg

0.38336

42894a79630241ce94bf1d6f5da678d1

Chromosome location analysis of dsRNase gene of S. frugiperda. Tbtools was used to analyze the chromosome location of the S. frugiperda dsRNases gene.

PMC9117646

fphys-13-850022-g001.jpg

0.41348

8f78a443ce42421f9028e3954f703365

Phylogenetic tree of different species dsRNase in insects. The maximum likelihood method was used to construct phylogenetic trees of different species of dsRNase in insects. S. frugiperda is represented by a red star, and S. litura is represented by a blue star. Its four sldsRNases GenBank numbers of S. frugiperda dsRNase (OL960003, OL960002, OL960004, and OM001111) and S. litura dsRNaseare respectively (QJD55609.1), (QJD55610.1), (QJD55611.1), (QJD55612.1).

PMC9117646

fphys-13-850022-g002.jpg

0.440905

41ff9d9daa0c4d7a89e132da7d19531a

Expression analysis of S. frugiperda dsRNase in different tissues. Collect three different tissues of sixth instar larvae, head (HD), midgut (MG), and epidermis (CL) for RT-qPCR, and calculate the expression levels of sfdsRNases in different tissues (A–D). Using sfdsRNase1 as a control, analyze the relative expression differences of the four sfdsRNases in the midgut and hemolymph (E,F). Five larvae are a biological replicate, and this experiment was performed three times in total. The data shown are mean ± SE, n = 15, different letters indicate a significant difference among tissues [p < 0.05, one-way ANOVA followed by Duncan’s multiple range test for (A,B)].

PMC9117646

fphys-13-850022-g003.jpg

0.444766

86b57750e2714f5f9b935f49df2f316b

Expression analysis of S. frugiperda dsRNases at different ages. Using egg, first instar larvae (first), second instar larvae (second), third instar larvae (third), fourth instar larvae (fourth), fifth instar larvae (fifth), and sixth instar larvae (sixth) as templates for qPCR and using the eggs as a control to calculate the dsRNases in different instars. The relative expression level of mRNA (A–D). Five larvae are a biological replicate, and this experiment was performed three times in total. The data shown are mean ± SE, n = 21, different letters indicate a significant difference among ages [p < 0.05, one-way ANOVA followed by Duncan’s multiple range test for (A,B,C)].

PMC9117646

fphys-13-850022-g004.jpg

0.384562

eac8efcf327041a4834c476e4a1b6087

In vitro degradation of Lip-dsRNA in midgut juice and hemolymph. The relative content of dsRNA was negatively correlated with the ability to degrade dsRNA. Degradation of liposome-encapsulated dsRNA in midgut juice (A). Degradation of liposome-encapsulated dsRNA in hemolymph (B). Liposome-encapsulated dsRNA (LIP-dsEGFP), dsRNA not encapsulated with liposomes, replaced liposomes with the same amount of water as a control (W-dsEGFP). The two forms of dsRNA were incubated with midgut juice and hemolymph for 10 and 60 min at room temperature, respectively. The dsRNA content after incubation was detected by qPCR, and the relative content ratio was calculated. The data shown are mean ± SE, n = 12 for the dsRNA relative content ratio calculation (One-way ANOVA, the least significant difference (LSD) test, ns = No significant difference, *p < 0.05, ***p < 0.001).

PMC9117646

fphys-13-850022-g005.jpg

0.442351

9f50abc5b0c74d129347d24b3c4b881e

Lip-dsRNA down-regulates the expression of sfdsRNase. DssfdsRNase represents the injection of 4 dsRNA of sfdsRNases into the fourth instar larvae, and the control injection of the same amount of dsEGFP. After 24 h, the expression level of sfdsRNase is measured by RT-qPCR. The data shown are mean ± SE, n = 12 for the Relative mRNA expression measurement of sfdsRNases （One-way ANOVA, the least significant difference (LSD) test, *p < 0.05, **p < 0.01）.

PMC9117646

fphys-13-850022-g006.jpg

0.458762

a7fcd0844d68485985686f3c969ea084

In vitro incubation of dsRNA and midgut juice and hemolymph. Relative content of dsEGFP in the control group = 1. The relative content of dsRNA was negatively correlated with the ability to degrade dsRNA. In vitro incubation of dsRNA and midgut fluid, dsRNases are the midgut extract of S. frugiperda larvae after successfully interfering with 4 sfdsRNase genes. At the same time, to avoid the influence of dsEGFP injection on the detection of dsEGFP content after incubation, the larval midgut fluid injected with water was used as a control. The midgut extract was incubated with dsEGFP for 10 and 60 min at room temperature, and the midgut juice was replaced by water as a control. The dsRNA content after incubation was detected by qPCR, and the relative content ratio was calculated [(A) represented relative content of dsEGFP after 10 min in midgut, (B) represented relative content of dsEGFP after 60 min in midgut, (C) represented relative content of dsEGFP after 10 min in hemolymph, (D) represented relative content of dsEGFP after 60 min in hemolymph]. The data shown are mean ± SE, n = 12 for the dsRNA relative content ratio calculation, different letters indicate a significant difference among treatments [p < 0.05, one-way ANOVA followed by Duncan’s multiple range test for (A,B)].

PMC9117646

fphys-13-850022-g007.jpg

0.3619

47f7ee9bda704ddc869a177a8b932966

PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) flow diagram of the literature selection process.RCT, randomized controlled trial.

PMC9117787

kjod-52-3-220-f1.jpg

0.47493

bef64ff4b1544951b728d0ab54067b7a

Microbial composition of the 3 rhizocompartments of the 4 wheat cultivars used in the study. The relative abundances of the main phyla (relative abundance > 1%) as identified by 16S amplicon sequencing are shown.

PMC9119058

mra.00222-22-f001.jpg

0.464986

bfe13ec22955473188ed4382bfc14d84

Model schematic and studied regions.(a) Schematic depicting the major interactions between the coral host and the symbiotic algae captured by the model; the coral host grows logistically as a function of symbiotic feedback and environmental temperature; corals invest energy in symbiosis (for example the energy necessary for providing CO2 to the algae [34–36]) and receives a symbiotic feedback (for example photosynthate); corals sustain symbiotic costs, which are associated to the maintenance of microenvironments for housing the algae [34] and to stress associated to photosynthesis (like the presence of reactive oxygen species [35]), and are subject to mortality; algae grow logistically as a function of environmental temperature and coral abundance; algae are expelled during bleaching events, which occur when environmental temperature exceeds the optimal regional temperature (i.e., Topt, panel b) for coral growth by a variable range of temperature increase reflecting the severity of bleaching (see subsection Bleaching). (b) Temperature-limited coral growth curves (Eq 7); dashed lines mark the regional temperature optima. (c) Studied regions: Great Barrier Reef (GBR), South East Asia (SEA), and Caribbean (CAR). The map is produced with the Python’s Matplotlib Basemap Toolkit available from https://matplotlib.org/basemap/api/basemap_api.html (© 2011, Jeffrey Whitaker).

PMC9119535

pcbi.1010099.g001.jpg

0.439749

590602f84fcc4c7dbe3fc6d34d61d683

Model simulations at different speeds of acclimation.Model simulations (purple lines) are qualitatively compared to observations [61, 62], expressed as yearly median of coral abundances (green dots). The selected speed of acclimation, in each region (panels a, b, and c, respectively), is the one that produces results (thick purple line) consistent with observations. The arrows indicate the direction of increase in speed of acclimation and the thin grey lines indicate, for each region, environmental temperatures relative to corresponding optimal growth temperatures Topt.

PMC9119535

pcbi.1010099.g002.jpg

0.386275

1a6e97d8e5574576a7c6dc6fddb32bda

Simulated trait and abundance dynamics in the three regions and under different RCPs.(a-c) Cumulative number of bleaching assuming that these events occur whenever environmental temperature exceeds by 2°C the optimal temperature for coral growth. (d-f) Coral energy investment trait. (g-i) Coral abundance and (j-l) symbiont abundance, relative to the period 1986–2005. Note that in South East Asia and in the Caribbean, the dynamics of coral trait and coral abundance under RCP 4.5 overlap with those under RCP 8.5. In the year 2010, the coloured lines and the black lines are at different levels because, in the runs with acclimation (coloured lines), corals are able to reach higher abundances during the spin-up phase and between 1955 and 2010, thus increasing the overall performance of the coral-algae complex.

PMC9119535

pcbi.1010099.g003.jpg

0.42625

852b300e6aeb411d91122bc2ac36b244

Monthly temperature forcing scenarios and thermal-limited coral growth curves.Low emissions (RCP 2.6) produce temperature trends (grey lines) that fall within the thermal-limited coral growth curves (coloured lines in the right side of each panel) in all regions (a-c). As emissions increase (RCP 4.5), the trends in environmental temperatures move away from the temperature optima Topt (marked by dashed lines), especially in the South East Asia and in the Caribbean (d-f) and even fall outside the thermal tolerance curves under RCP 8.5 (g-i). The dotted lines mark the limits of the coral thermal tolerances.

PMC9119535

pcbi.1010099.g004.jpg

0.424325

fc718e916bfa4a5c8b7c56185ab23776

Sensitivity to speed of acclimation N for +25% change in each parameter.Simulations with bleaching. Vertical lines mark the speed of acclimation we estimated from coral cover data (see S2 Appendix).

PMC9119535

pcbi.1010099.g005.jpg

0.409077

aa064a1895e849f2a9c7b0301f5c1619

Sensitivity to speed of acclimation N for −25% change in each parameter.Simulations with bleaching. Vertical lines mark the speed of acclimation we estimated from coral cover data (see S2 Appendix).

PMC9119535

pcbi.1010099.g006.jpg

0.50042

986f0a5391ed4ba8a0fb5e52de5ee3f0

ACR-20 response and change in HAQ-DI score over time. Supporting values are shown in online supplemental table S4. (A) Time course of ACR-20 response through week 16. Response rates are reported in the intention-to-treat population (ie, all randomised patients) with non-responder imputation; patients who discontinued the trial early, started a prohibited treatment, were lost to follow-up or had no ACR assessments had outcomes imputed as non-responses. (B) Adjusted mean change from baseline in HAQ-DI score through week 16. Placebo, n=66; deucravacitinib 6 mg once a day, n=70; deucravacitinib 12 mg once a day, n=67. P values indicate a difference from placebo: *p<0.05, **p≤0.01, ***p≤0.001, adjusted for multiplicity at week 16 only. ACR, American College of Rheumatology; HAQ-DI, Health Assessment Questionnaire-Disability Index; QD, once a day.

PMC9120409

annrheumdis-2021-221664f01.jpg

0.411972

76daaa6b1f5e443db6dc7cd05439d113

Laboratory parameters over 16 weeks (mean±SD): (A) lymphocytes, (B) neutrophils, (C) platelets, (D) haemoglobin, (E) total cholesterol and (F) triglycerides. Supporting values are shown in online supplemental table S5. QD, once a day.

PMC9120409

annrheumdis-2021-221664f02.jpg

0.387769

1f5bfac720004cb696eafc43148fec97

Schematic presentation of the study design. DER, defective endometrial receptivity.

PMC9120433

fimmu-13-842607-g001.jpg

0.359283

c88a6d6785314e4cbef295969afa1cfd

Immune infiltration analysis and Mϕ1/Mϕ2 balance. (A) Bar plots of 22 immunocytes in endometrial mixed tissue samples, including four datasets with 218 subjects. (B) Violin plots for immune cells in the DER and control groups. Purple color represents the control group while yellow represents the DER group. (C) Meta-analysis of four datasets for Mϕ1/Mϕ2. DER, defective endometrial receptivity; CON, control.

PMC9120433

fimmu-13-842607-g002.jpg

0.396888

9b661219793447f7bab867659af34742

Mϕ1/Mϕ2-related WGCNA and module enrichment analyses. (A) Heatmap showing the average genetic significance of each particular module across clinical traits. (B) Percentage of GSE19834 DEGs within the modules. Percentages within modules are, from left to right, 40.9%, 47.0%, and 14.0% (red), 42.9%, 44.3%, and 13.7% (blue), 41.7%, 49.1%, 11.1% (turquoise), respectively. Ho-Ma-En=Endometrial cells in estradiol progesterone macrophage medium, Ma-En=Endometrial cells in macrophage conditioned medium, En-Ma=Macrophages in endometrial cell medium. (C) REVIGO plot of the red, blue, and turquoise modules (left to right). The scatterplot shows the cluster representatives in a two-dimensional space obtained by applying multidimensional scaling to the semantic similarities matrix of GO terms. (D) Protein-protein interaction (PPI) network of representative genes in each module. Each module has 30 representative genes selected according to their “degree”.

PMC9120433

fimmu-13-842607-g003.jpg

0.422974

982815609ce046ab9b48db34878b45ff

Representatives of the modules, and expression and functions of hub genes. (A) Heatmap of modules genes between DER vs. CON. In the classification column, red represents infertilities, whereas blue represents controls. (B) Cumulative risk curves of pregnant status analysis for hub genes. Log-rank p = 0.005 (RPS9), 0.044 (DUT), and 0.044 (MARF1) denotes the significance of hub genes. (C) Main effects and two-way interactions of hub genes. (D) GSEA plot of upregulated and downregulated KEGG pathways related to changes in hub gene expression levels. CON, control; DER, defective endometrial receptivity.

PMC9120433

fimmu-13-842607-g004.jpg

0.429907

b6c1fd4724be40118962193408319f47

Construction of machine learning model for clinical pregnancy prediction. (A) SHAP values of each feature for random forests in modules. Each point represents a sample, and the figure shows the impact of features on the model’s outcome. (B) The weight matrix of hub genes in the xgboost model. Each column represents the weight of each feature to the model outcome in one permutation. (C) The predictive values of DUT, RPS9, MARF1, and the risk model were established based on logistic regression and visualized by a nomogram. (D) ROCs for machine learning models.

PMC9120433

fimmu-13-842607-g005.jpg

0.40388

10599d2d1c3845c4ae369d0c3878ff14

The clinical pregnancy prediction ability of the reference and machine learning models for in the external validation dataset. (A) mRNA expression of hub genes in GSE165004. (B) Receiver-operating-characteristics (ROC) curves. The corresponding value of the area under the receiver-operating-characteristics curve (AUC) for each model is shown in the table below. (C) Decision curve analysis. The X-axis represents the threshold probability for positive pressure ventilation outcome; Y-axis represents the net benefit. The net benefit of all machine learning models was larger over the range of clinical threshold compared to the reference model. (D) Sensitivity, specificity, Youden index, positive predictive value (PPV), and negative predictive value (NPV) of models.

PMC9120433

fimmu-13-842607-g006.jpg

0.447572

3c3b7a0a220643efa24a4094b5364254

Hub genes and clinical validation and benefit analysis of the model. (A) Validation of mRNA expression of hub genes in DER (n=25) and control (n=15) groups. (B) Western blotting result of MARF1, DUT, and RPS9, normalized by β-actin (triplicates in each group). Bar graphs represent the ratio of densities of the respective protein bands and β-actin. Densitometric quantification graphs of blots are available in Supplementary Figure 2 . (C) ROC of xgboost and ultrasound results of endometrial thickness. The corresponding value of the AUC for each method are presented in the table. (D) Clinical impact curve (CIC) of the xgboost model and endometrial thickness. The red curve (number of high-risk individuals) denotes the number of people classified as positive (high risk) by the model at each threshold probability; the blue curve (number of high-risk individuals with outcomes) denotes the number of true positives at each threshold probability.

PMC9120433

fimmu-13-842607-g007.jpg

0.424362

39bf7f580c7e4e9988056482c5f0ccc2

(A) A hypothesis on the mechanism of macrophage-endometrium interaction modules regulation on endometrial receptivity and (B) a scenario for clinical application of the defective endometrial receptivity prediction model. ART, Assisted reproductive technology.

PMC9120433

fimmu-13-842607-g008.jpg

0.417698

94b280017f294e948ad8c238d24c04e8

This picture shows the orifice of the recto- extrophied bladder neck fistula with meconium stain.

PMC9121239

gr1.jpg

0.446465

bc23e7d166824e6db6bff56d75c7252c

This picture demonstrate the omphalocel and bladder exstrophy and both ureteric stents that were inserted. In addition to the above it clearly shows hemiphalus and hemiscrotum.

PMC9121239

gr2.jpg

0.409404

0f63d00428134154ac6819d969130a95

Segmented regression model new deaths of COVID-19 in Iran since February 19, 2020 to February 5, 2021 using the Newey-West standard errors.

PMC9121681

jpmh-2022-01-e125-g001.jpg

0.486066

abd2ce0d875c4a958496522072d95599

Segmented regression model new deaths of COVID-19 in the world since February 19, 2020 to February 5, 2021 with Newey-West standard errors.

PMC9121681

jpmh-2022-01-e125-g002.jpg

0.502157

c9b266572cfb4176835eae0f57743c7c

Correlation between the I the number of new COVID-19 happened in the world and Iran from February 19, 2020, to November 24 2020.

PMC9121681

jpmh-2022-01-e125-g003.jpg

0.54304

7616db9e2d5249238ba93d2130f0c337

Correlation between the number of new deaths of COVID-19 occurred in the world and iran from November 24 2020 to February 5, 2021.

PMC9121681

jpmh-2022-01-e125-g004.jpg

0.431458

7a537f106c06431095d66c14204cad1a

Initial condition, fall dynamics, and impact surface based on fall type and head impact (n = 174).

PMC9124183

41598_2022_12489_Fig1_HTML.jpg

0.488041

535c98a688b647049ad3cf73b6652f63

Distribution of fall heights defined as change in head COM and change in support surface height (n = 174). 25th and 75th percentiles are represented by the lower and upper bounds of box, respectively. Line within the box represents median value, and the whiskers represent 1.5 times the interquartile range (IQR; IQR = 75th percentile value minus 25th percentile value). Note: median and 25th percentile values are coincident (0.0) for change in support surface height.

PMC9124183

41598_2022_12489_Fig2_HTML.jpg

0.423816

5df063a10d5f4eeb8e59a64bceb133bf

Box plots of peak linear head acceleration, change in linear head velocity, rotational head acceleration and rotational head velocity by fall type (left column) and head impact (yes/no) (right column). 25th and 75th percentiles are represented by the lower and upper bounds of the box, respectively. Horizontal line within the box represents median value, and the whiskers illustrate the minimum and maximum values unless there are outliers. When outliers are present, whiskers represent 1.5 times the interquartile range (IQR; IQR = 75th percentile value minus 25th percentile value). (Note: SIM G sensor did not record rotational acceleration/velocity for 5 falls.)

PMC9124183

41598_2022_12489_Fig3_HTML.jpg

0.440937

798732f4e5234185aba21810fd21e7be

Linear head acceleration (n = 174) and rotational head acceleration (n = 169) vs. fall height (change in head COM).

PMC9124183

41598_2022_12489_Fig4_HTML.jpg

0.402174

fec55e0aa284487aafd9f52f1f833bb3

Relationships between biomechanical measures based on fall type (n = 174, except for rotational acceleration and velocity where n = 169).

PMC9124183

41598_2022_12489_Fig5_HTML.jpg

0.369135

bcf4dcfd12514de6b3363f007f266d81

Relationships between biomechanical measures based on head impact (n = 174, except for rotational acceleration and velocity where n = 169).

PMC9124183

41598_2022_12489_Fig6_HTML.jpg

0.473623

fad2ebd4c6b54f13b9d1670064f8c4bc

Heatmap showing the clinical characteristics in each cluster. CADM, clinically amyopathic dermatomyositis; CK, creatine kinase; CRP, C-reactive protein; KL-6, Krebs von den Lungen-6; SP-D, surfactant protein-D; GGA, ground-glass attenuation; anti-ARS, anti-aminoacyl transfer RNA synthetase; anti-MDA5, anti-melanoma differentiation-associated gene 5.

PMC9124901

fmed-09-883699-g0001.jpg

0.454165

b38a2ae1cebc450fa8e4e90a43c5049e

Main characteristics of the 6 clusters (clusters #1–#6) of patients with myositis-associated ILD treated with initial triple-combo therapy. (A) Proportions of each cluster with the main clinical characteristics of clinically amyopathic dermatomyositis (CADM), anti-aminoacyl transfer RNA synthetase (ARS) antibody, anti-melanoma differentiation-associated gene 5 (MDA5) antibody, and requirement of supplemental oxygen. (B) Age at disease onset and serum levels of C-reactive protein (CRP) and Krebs von den Lungen-6 (KL-6) at diagnosis in each cluster. (C) Mortality rates during the observation period in patients treated with initial triple-combo therapy and those treated with dual-combo therapy or monotherapy in each cluster.

PMC9124901

fmed-09-883699-g0002.jpg

0.386931

b7b4bad4b4404248a9c0e102b806aabd

Cumulative survival rates in each cluster in patients treated with initial triple-combo therapy (A) or those treated with initial dual-combo therapy or monotherapy (B). Cumulative survival rates were compared using Kaplan–Meier analysis, and the log-rank test was used to test for significant differences between two groups.

PMC9124901

fmed-09-883699-g0003.jpg

0.423294

b61aa9f9b8e348b9b0dde6ed88c957e1

Cumulative survival rates of patients treated with initial triple-combo therapy and those treated with initial dual-combo therapy or monotherapy in each cluster (clusters #1–#6). Cumulative survival rates were compared using Kaplan–Meier analysis, and the log-rank test was used to test for significant differences between two groups.

PMC9124901

fmed-09-883699-g0004.jpg

0.477421

0fa4f85ef32f4f2bb8f461b9b82cf29d

Histological examination of the robotic-assisted laparoscopic radical prostatectomy specimen indicated prostatic mucinous carcinoma. A: Hematoxylin and eosin staining of the residual prostate tissue indicated lakes of extracellular mucin. B-F: Immunohistochemistry staining of the residual prostate for (B) prostate specific antigen, (C) prostatic serum acid phosphatase, (D) cell keratin 7, (E) cell keratin 20, and (F) caudal-type homeobox transcription factor 2. Magnification × 200.

PMC9125274

WJCC-10-4654-g001.jpg

0.465021

829e908701ae42d1b5681a184b4fe051

Histological examination of the transurethral resections of bladder tumors specimens showed the extracellular mucin and signet ring cells. Hematoxylin and eosin staining of the first transurethral resection of bladder tumors specimen (A and B) vs the second transurethral resection of bladder tumors specimen (C and D). Magnification × 100 and × 200, respectively.

PMC9125274

WJCC-10-4654-g002.jpg

0.551872

49819e064bc24202b47791a0b0211a15

Timeline of the patient’s medical care. RARP: Robotic-assisted laparoscopic radical prostatectomy; TURBT: Transurethral resection of bladder tumors.

PMC9125274

WJCC-10-4654-g003.jpg

0.467811

07da408e4b334615ab291394075ad7f4

Col1a1-expressing perivascular fibroblasts coexist with mural cells on penetrating arterioles, arteriole-capillary transition zones, and large ascending venules. (a) Schematic demonstrating how PVF-mural cell reporter mice were generated (PdgfrbCre/+; Ai14fl/+; Col1a1GFP/+). We first crossed PdgfrbCre/+ with Ai14fl/fl mice (F0). We then took resulting PdgfrbCre/+; Ai14fl/+ mice and crossed themContinued.with Ai14fl/fl mice (F1) to create PdgfrbCre/+; Ai14fl/fl mice. Finally, the resulting PdgfrbCre/+; Ai14fl/fl mice were crossed with Col1a1GFP/+ (F2) mice to generate PdgfrbCre/+; Ai14fl/+; Col1a1GFP/+2 mice. (b) Top down, maximum projected image of the cortical vasculature from in vivo two-photon imaging of Col1a1-GFP; PdgfrβCre-tdTomato mice showing that tdTomato+ mural cells (red) and tdTomato+/GFP+ perivascular fibroblasts (PVFs; red and green) coexist along the vasculature. Blood vessels labeled with i.v. administration of Alexa-680 (2 MDa) (blue). (c) Side projection of in vivo image stack with (c’) schematic demonstrating the topological organization of PVFs (green) along the penetrating arteriole (0th order) as identified by smooth muscle cells (SMCs; red) and arteriole-capillary transition zone (1st-4th branch order). PVF somata and termination of territory end before the capillary zone (≥5th order). (d) Side projection of in vivo image stack with (d’) schematic demonstrating the topological organization of PVFs (green) along the ascending venules (0th order) as identified by VSMCs (red) and 1st order post-capillary connections. PVF somata only occupy the main trunk (0th order) of some ascending venules. (e) Scatter plot of penetrating arteriole (red) and ascending venule (blue) diameters versus PVF termination depth along 0th order vessels. Spearman’s rank correlation shows a positive correlation along ascending venules (****p < 0.0001, r = 0.7314, n = 32 ascending venules). PVFs were observed up to maximum imaging depths along penetrating arterioles regardless of vessel diameter (n = 15 penetrating arterioles). Data was compiled from 4 mural cell-PVF and PVF reporter (Col1a1-GFP) mice. (f) Histogram depicting the proportion of PVF soma occupancy from 0th to 6th branch order along the arteriole (red) or venule (blue) zones. Each point represents the average proportion found in each mouse (n = 4 mice). (g) Histogram depicting the proportion of PVF territorial termination from 0th to 6th branch order along the arteriole (red) or venule (blue) zones. Each point represents the average proportion found in each mouse (n = 4 mice).

PMC9125487

10.1177_0271678X211068528-fig1.jpg

0.411176

a65826816c69452b8ee445936efebd72

Topological organization of PVFs is similar across CNS regions. Representative confocal image of the (a) cortex, (b) corpus callosum, (c) hippocampus and (d) spinal cord from Col1a1-GFP; PdgfrβCre-tdTomato mice. Inset images show that perivascular fibroblasts (PVFs; green) are found along arterioles, arteriole-capillary transition zones, and venules in the (a′) cortex, (b′) corpus callosum, (c′) hippocampus (d′) and spinal cord. However, PVFs are not present on the capillary zone, which is occupied by thin-strand and mesh pericytes (red) in the (a″) cortex, (b″) corpus callosum, (c″) hippocampus (d″) and spinal cord. Mural cells are shown in red and DAPI in blue.

PMC9125487

10.1177_0271678X211068528-fig2.jpg

0.407914

0c508ca7f6284ff9b6a7bcf594e0dab2

Morphological features of PVFs are distinct from mural cells. (a-c) Representative two-photon images from PdgfrβCre-tdTomato mice of (a) smooth muscle cells (SMCs) on a penetrating arteriole, (b) ensheathing pericyte and a putative PVF in the arteriole-capillary transition zone and (c) VSMCs on an ascending venule with respective red channel separated to better show mural cell morphology (white). Vasculature labeled with i.v. administration of FITC-dextran (70kDa; green). (d) Schematic demonstrating in vivo experimental timeline on Col1a2CreER-tdTomato mice. Cranial windows were placed in Col1a2CreER-tdTomato (Col1a2Cre/+; Ai14fl/+) mice. On days 7 and 8, mice were given tamoxifen (80mg/kg). In vivo imaging began 21 days after window placement. (e–g) Representative two-photon images of PVFs (red) from Col1a2CreER-tdTomato mice on the (e) penetrating arteriole, (f) arteriole-capillary transition zone, and (G) ascending venule with respective red channel separated below to appreciate PVF morphology in white. Vasculature labeled with i.v. administration of FITC-dextran (70kDa; green). (h) Example of somata roundness analysis. Cell somata in PdgfrβCre-tdTomato mice were outlined (yellow dashed line) using the freehand tool in ImageJ and the Roundness Index was obtained by using Shape descriptor analysis. (i) Histogram of somata roundness of tdTomato+ perivascular cells along the arteriole-capillary transition zone in PdgfrβCre-tdTomato mice (black) and tdTomato+ PVFs along penetrating arterioles, arteriole-capillary transition zone, and ascending venules in Col1a2CreER-tdTomato mice (gray). A roundness index of 1 would be considered a perfect circle. Data was compiled from 122 perivascular cells from 8 PdgfrβCre-tdTomato mice and 77 PVFs from 4 Col1a2CreER-tdTomato mice. (j) Representative high-resolution confocal image of ruffled PVF membrane (white) from Col1a2CreER-mEGFP mice.

PMC9125487

10.1177_0271678X211068528-fig3.jpg

0.408619

1198d1c934b346319de75b0b467d293a

Perivascular fibroblasts are a dynamic perivascular cell population. (a–c) Representative in vivo two-photon images of perivascular fibroblasts (PVF; red) over 28 days from Col1a2CreER-tdTomato mice. (a, b) Non-junctional PVFs display more mobility than (c) PVFs found at vascular junctions. Dashed line indicates initial soma position on Day 0. Vasculature labeled withContinued.i.v. administration of FITC-dextran (70kDa; green). (d) Two representative examples of ensheathing pericytes from PdgfrβCre-tdTomato mice following 28 days of in vivo two-photon imaging. Dashed line indicates initial soma position on Day 0. Vasculature labeled with i.v. administration of FITC-dextran (70kDa; green). (e) Schematic demonstrating how displacement of PVFs and ensheathing pericytes from Day 0 was measured. The distance (d0) between the soma center and nearest vascular branch point was measured in the 2D projected image. Then the z-distance (z0) between these two structures was determined and used to calculate the Euclidean distance (D0). This was repeated at each imaging time point (Day T; DT). Displacement from Day 0 was determined by subtracting DT from D0. (f) Graph demonstrating soma displacement of PVFs over 28 days from initial position on day 0 (n = 102 PVFs from 4 Col1a2CreER-tdTomato mice). (g) Graph comparing maximum displacement of PVFs with non-junctional (n = 68 PVFs) and junctional (n = 34) vascular positions. PVFs with non-junctional positions were significantly more dynamic than PVFs at junctions (Mann-Whitney test U = 841, *p = 0.0251. Median: Non-junctional = 3.627µm, junctional = 2.814 µm). (h) Graph demonstrating soma displacement of ensheathing pericytes (EP) over 28 days from initial position on day 0 (n = 32 EPs from 5 PdgfrβCre-tdTomato mice). (i) Graph comparing maximum displacement of PVFs (n = 102 PVFs) and EPs (n = 32). PVFs were significantly more dynamic than EPs (Mann-Whitney test U = 431, ****p < 0.0001. Median: PVFs = 3.081µm, EP = 1.142 µm).

PMC9125487

10.1177_0271678X211068528-fig4.jpg

0.469694

7702018853ea4dababf06b52fd0650be

Flow chart for screening patients.

PMC9126887

41598_2022_12518_Fig1_HTML.jpg

0.531146

867d8ac1b3a744b89eafa6bd439d2884

Comparison of the infammatory index and TBS in predicting recurrence.

PMC9126887

41598_2022_12518_Fig2_HTML.jpg

0.445327

f73f2abfa9314cc6a9a2859949346e11

Comparison of TBS, TTV, tumor diameter and tumor number in predicting recurrence.

PMC9126887

41598_2022_12518_Fig3_HTML.jpg

0.50442

3e2a4ab86ab749949b04bf99179bbb53

Comparison of NLR–TBS group, TBS group and NLR group in predicting recurrence.

PMC9126887

41598_2022_12518_Fig4_HTML.jpg

0.479783

407564078e844484884ab1c67592df6a

Comparison of NLR–TBS group, TNM stage and BCLC stage in predicting recurrence.

PMC9126887

41598_2022_12518_Fig5_HTML.jpg

0.419631

e4be52618aa04a8d81c06bea8895119e

(A) Recurrence-free survival curve of patients with high TBS (n = 53) and low TBS (n = 164).The median RFS times in the high TBS group and the low TBS group were 11.61 months and 30.98 months (P = 0.001). (B) Recurrence-free survival curves of patients with high NLR (n = 74) and low NLR (n = 143).The median RFS times in the high NLR group and the low NLR group were 18.12 months and 30.63 months (P = 0.001). (C) Recurrence-free survival curves of low-risk (n = 121), middle-risk (n = 66) and high-risk group (n = 30) patients. The median RFS times in the low-risk group, middle-risk group and high-risk group patients were 33.03 months, 22.16 months and 8.07 months (P = 0.001).

PMC9126887

41598_2022_12518_Fig6_HTML.jpg

0.396132

6296e127a54f42d3af4447d48de32d94

Subgroup analysis of the main efficacy indicators (per‐protocol set)

PMC9128564

CDT3-8-59-g001.jpg

0.460204

50c92922c6f043aeac36b5405e8a1de2

Target Hb maintenance ratios from Week 0 to Week 28 (per‐protocol set)

PMC9128564

CDT3-8-59-g002.jpg

0.495788

606aca1372af45829b6aa7e26408df89

Target Hb achievement cumulative rate (%) and time (week) of the two groups (per‐protocol set)

PMC9128564

CDT3-8-59-g003.jpg

0.426365

4a1c8b3eca8546758ccda2504324b70e

The weekly dose level of darbepoetin alfa and epoetin alfa group (full analysis set)

PMC9128564

CDT3-8-59-g004.jpg

0.481106

6294a620e026482e96acd92a606c049c

Patient disposition

PMC9128564

CDT3-8-59-g005.jpg

0.440869

b3055d2f7a934bc58e988cd7a61d14bf

Changes of the mean Hb level from baseline to the end of the study

PMC9128564

CDT3-8-59-g006.jpg

0.41534

8e0945d12e064d89a6a42c72109b2aa4

Subgroup analysis of incidence of adverse events (safety set). BMI, body mass index; CI, confidence interval; NESP, novel erythropoiesis stimulating protein

PMC9128564

CDT3-8-59-g007.jpg

0.399774

75297839c337458aaca4d0d81afddfed

Timeline of patient enrolment and schedule of data collection.Adapted from the SPIRIT statement [54]. Abbreviations: d, day; RAM, risk assessment model; VTE, venous thromboembolism.

PMC9128957

pone.0268833.g001.jpg

0.432717

9c331d621260488aa5c58f651bd8df91

Study organization and follow-up.

PMC9128957

pone.0268833.g002.jpg

0.495658

0012462fd0c54fe98301ce1ba76c6bb9

The mungbean, lentil, and Indian mustard genotypes used in the study.Where (a) mungbean genotypes are 1. Pusa Baisakhi, 2. Pusa Ratna, 3. Pusa Vishal, 4. Pusa105, 5. Pusa0672, 6. Pusa9072, 7. Pusa9531, 8. MH96-1, 9. MH318, 10. MH421, 11. MH521, 12. MH810, 13. ML512, 14. ML818, 15. PS16, 16. TM 96–2, 17. IPM02-3, 18. IPM02-14, 19. IPM409-4, 20. PMR1; (b) lentil genotypes are 1. L4076, 2. L4147, 3. L4594, 4. L7903, 5. HM1, 6. BM4, 7. JL1, 8. Sehore74-3, 9. NDL1, 10. IPL81, 11. IPL321, 12. K75, 13. KLS218, 14. DPL58, 15. DPL62, 16. PL1, 17. PL2, 18. PL6, 19. L830, 20. L4602; while (c) Indian mustard genotypes were 1. PM28 and 2. PDZM31.

PMC9128967

pone.0268085.g001.jpg

0.450861

9e824b6eb47740ec9cdb828014e5a27b

Microgreens yield of two Indian mustard genotypes (PM28 & PDZM31) (a) at a seeding density of 6, 8, and 10 seed/cm2 on 8th day after sowing and (b) at 6th, 8th, and 10th day of sowing.Where ‘D’ is days after sowing and values are expressed as mean±SD (n = 3).

PMC9128967

pone.0268085.g002.jpg

0.403531

356b8b529ceb4a36bf96d4157af47c32

Changes in electrical conductivity of washed (W) and unwashed (U) microgreens samples (mungbean, lentil, and Indian mustard) when packed in LLDPE films during the 2nd, 4th, and 6th day of storage at 4°C.Where D2-U, D4-U, and D6-U are ‘unwashed’; while D2-W, D4-W, and D6-W are ‘washed’ microgreens samples at 2nd, 4th, and 6th day of storage, respectively. Values are expressed as mean±SD (n = 3) and different letters indicate a significant difference (P≤.05).

PMC9128967

pone.0268085.g003.jpg

0.492854

32590b5734ed4d4e99e82f586105cdb2

Biosynthesis of AgNP: colour change into dark brown confirms AgNP formation.

PMC9129982

OMCL2022-1646687.001.jpg

0.479508

078482b5cb794cf2963edbd47f73bf39

UV spectral analysis of biosynthesized AgNPs showed absorption peak at 410 nm while no peak revealed for the bacterial extract.

PMC9129982

OMCL2022-1646687.002.jpg