dataesr/scientific-paragraphs-categorization huggingface dataset:
S5 ). The cardiomyocyte lineage marker Nkx2-5 was only enriched in neonatal and adult cardiomyocytes (138 cells; SI Appendix, Fig. S5 ), as shown before  (38, 39). We further found expression of the fetal cardiac progenitor marker Isl1 (40) in 87 cells belonging to the noncardiomyocyte lineages (cardiac fibroblasts, endothelial cells, and smooth muscle cells), but not in cardiomyocytes (SI Appendix, Fig. S5 ). These data imply that cell culture, transplantation, and lineage-tracing experiments relying on these putative CSC markers should be interpreted with great caution  (17). To determine whether unique cell clusters appeared following MI in our single-cell mRNA-sequencing dataset, we quantified the number of cells from each experimental condition present within a given cluster (SI Appendix, Fig.
S4  B and C and Dataset S1). Cardiomyocytes clustered into two main clusters (clusters 8 and 9) irrespective of the cell cycle stage (actively cycling vs. quiescent) (SI Appendix, Fig. S4  B and C and Dataset S1). Endothelial cells clustered into clusters 2 and 3 irrespective of the experimental condition of origin (Fig. 2F , SI Appendix, Fig. S4  B and C, and Dataset S1). The majority of hematopoietic cells in the dataset were purified from injured adult hearts (clusters 1 and 4); however, cluster 1 mainly contained hematopoietic cells present in homoeostatic (adult or neonatal) hearts (SI Appendix, Fig. S4  B and C and Dataset S1). Fibroblast cluster 7 was predominantly composed of cells purified from homoeostatic adult hearts (65%). Surprisingly, while the majority of cells assigned to fibroblast cluster 6 were purified from injured adult heart (48%), 17% of the cells originated from neonatal hearts (SI Appendix, pie chart Insets in SI Appendix, Fig.
S4C ). This suggests that in response to ischemic injury, some activated fibroblasts adopt a neonatal-like state. The fibroblasts in cluster 6 were predominantly Ki67-RFP + (Fig. 2F , SI Appendix, Fig. S4 B  and C , and Dataset S1) and had up-regulated levels of genes involved in extracellular matrix remodeling such as Cola1a2, Col3a1, Col5a1, and Sparc, as well as genes encoding secreted proteins such as Sfrp2 and Dkk3 (Dataset S1). To explore the cardiomyocyte fraction in more detail, we extracted all 437 cardiomyocytes out of the complete dataset and performed cell type-specific subclustering. We reran the extracted cells through the RaceID2 algorithm with a higher cutoff of 2,000 transcripts per cell to allow for refined cell clustering. The 276 cardiomyocytes retained after quality filtering grouped into six k-medoids clusters (Fig.
3D ). Neonatal cardiomyocytes were enriched in clusters 1, 2, and 3, while adult injury-associated cardiomyocytes were mainly found in cluster 5 and adult homoeostatic cardiomyocytes in clusters 4 and 6 (Fig. 3  D-F and Dataset S2). We found robust expression of Mki67 and other markers of proliferation (Anln, Aurka, Birc5, Pcna, and Plk1) used by Soonpaa et al. (2)  in neonatal cardiomyocytes (Fig. 3G ), in agreement with our bulk mRNA-sequencing data (Fig. 1F ). However, expression of these proliferation markers was largely negligible in adult cardiomyocytes (Fig. 1F ). We performed differential gene expression analysis by comparing neonatal and adult (homoeostatic and injury-associated) cardiomyocyte clusters to each other (Fig. 3H ). Among the 100 most up-regulated genes in neonatal cardiomyocytes were genes related to the mitochondrial electron transport chain (Cycs, Cox6a1) and cardiac development (Mef2a, Tnni1) (Fig.
3I  and Dataset S3). Genes upregulated in adult homoeostatic cardiomyocytes included sarcomere assembly (Tnni3) and classical myosin marker (Myh6) (Fig. 3J  and Dataset S3). Adult injury-associated cardiomyocytes upregulated genes involved in oxidative damage response (Gpx4) and damage-induced cell cycle arrest (Ccng1) (Fig. 3K  and Dataset S3). Taken together, these data provide no evidence for the existence of a distinct CSC population in neonatal or adult (homoeostatic or postdamage) murine hearts and demonstrate that immature neonatal cardiomyocytes are readily proliferative. However, the presence of proliferative cardiomyocytes in adult murine hearts remains low even after ischemic injury, as indicated by the rare Ki67-RFP + cardiomyocyte found in our single-cell dataset collected from adult hearts (SI Appendix, Fig.
FSTL1 Expression Is Characteristic to a Population of Injury-Activated Cardiac Fibroblasts. To more carefully document the role of cardiac fibroblasts in postdamage repair, we went back to our singlecell transcriptome dataset and performed refined cell typespecific subclustering. We first extracted the cardiac fibroblast fraction from our initial analysis (257 cells) and reran these through the RaceID2 algorithm with a higher cutoff of 2,000 transcripts per cell to allow for refined cell clustering (as done for the cardiomyocyte subclustering). After filtering, the 243 remaining cardiac fibroblasts clustered into 11 subclusters (SI Appendix, Fig. S6A ). Cardiac fibroblasts purified from homoeostatic adult heart were mainly found in clusters 1, 3, 5, and 7, while neonatal fibroblasts and in the neonatal-like injury-associated fibroblasts were enriched in clusters 2, 4, and 8-11 (SI Appendix, Fig.
S6 A  and B ). We combined these clusters and performed differential gene expression analysis (Fig. 7A ). Lineage markers Pdgfra and Vim were robustly expressed in all fibroblasts (Fig. 7B )  (27, 43, 44) , while Gsn, a gene previously associated with cardiac remodeling after MI  (45) , was up-regulated in clusters enriched for adult homoeostatic fibroblasts (Fig. 7C  and Dataset S4). Fibroblasts also robustly expressed Fstl1, a marker that was recently described as a cardiomyogenic factor of epicardial origin expressed in the adult murine heart  (29). Expression of Fstl1 was significantly up-regulated in the clusters containing neonatal fibroblasts and neonatal-like injury-associated fibroblasts, but Fstl1 was also expressed by the fibroblasts in the healthy adult heart (Fig. 7C  and Dataset S4).
To further explore this observation, we utilized a mouse model carrying an eGFP-IRES-CreERT2 expression cassette inserted into the Fstl1 locus (46) (Fig. 7D ). As suggested by our single-cell RNA-sequencing dataset, eGFP + cells in the neonatal hearts of these mice were cardiac fibroblasts residing in the myocardium (SI Appendix, Fig. S6 C-I ). In contrast to a previous report  (29) , we did not find any eGFP expression in the epicardium (SI Appendix, Fig. S6H ); eGFP + cells in the adult heart were PDGFRα + cardiac fibroblasts (SI Appendix, Fig. S6J ). Next, we asked whether Fstl1-eGFP + fibroblasts residing in the myocardium were a source for the fibroblast populations that appear in the infarcted area following cardiac ischemic injury. We therefore generated Fstl1 eGFP-IRES-CreERT2 × LSL-tdTomato mice to enable genetic lineage tracing of FSTL1 + cells (Fig.
As expected, confocal microscopy revealed that most FSTL1 immunoreactivity was present in the infarcted region of the myocardium and the border zone (SI Appendix, Fig. S6 K-N ). Collagens such as Col1a1 and Col3a1 were robustly expressed by cardiac fibroblasts (Fig. 7J ) and were strongly up-regulated in response to cardiac injury (Dataset S4). Fibroblasts expressing these collagens made up a large fraction of the fibrotic scar (Fig. 7K ). To elucidate the functional role of FSTL1 in the formation of the fibrotic scar, we generated Col1a1-CreERT2 × Fstl1 flox/flox mice to enable conditional knockout (cKO) of Fstl1 in cardiac fibroblasts (Fig. 7L )  (46). We injected cKO and control mice with tamoxifen 2 wk before LAD occlusion to specifically remove Fstl1 from the Col1a1-expressing fibroblast population.
Discussion We have generated an exhaustive map of proliferative cardiac cells and their progeny during three critical conditions of postnatal life. Here, we assessed cardiac cell proliferation in an unbiased fashion. Most studies on cardiac regeneration focus on cell cycle reentry of cardiomyocytes or the existence of resident stem cells, thereby restricting the analysis by the use of generic stem cell markers or of DNA label retention as a surrogate stem cell marker  (4, 5, 11, 12, 18). Hence, we think that our dataset allows resolution of current controversies on cardiac regeneration  (6) (7) (8) (9) 17). While our data demonstrate that neonatal murine cardiomyocytes are actively cycling (Figs. 1  and 2 ), we did not find evidence for significant de novo generation of cardiomyocytes in the homoeostatic or regenerating adult murine heart through cell division (Figs.
3 ). Putative c-KIT + CSCs, for example, are often purified by removing CD45 + hematopoietic cells and CD31 + endothelial cells  (4, 11, 36). However, cardiac fibroblasts in our single-cell transcriptome dataset also express Kit transcripts. This demonstrates that Kit is expressed by a wide range of cardiac cell populations, which may explain some of the opposing results published on the role of c-KIT + cells for cardiac regeneration  (4, 8, 9, 11, 15, 36). In contrast to previous studies utilizing label incorporation (i. e. , BrdU) into the DNA of S-phase cells to assess proliferation of putative CSCs or cardiomyocytes  (4, 11, 42) , we utilize genetic lineage tracing of Ki67 + cardiac cells to investigate evidence for de novo generation of cardiomyocytes in response to myocardial damage.
This phenomenon has previously been described for adult murine skin epidermis, where dermal fibroblasts are reprogrammed to a neonatal-like state in response to sustained activation of epidermal Wnt/β-catenin signaling  (50). A subsequent study showed that adult fibroblast remodeling significantly improved epidermal regeneration upon wounding of adult murine skin  (51). Our single-cell transcriptome dataset further showed that the proposed epicardial-specific cardiomyogenic factor FSTL1 (  29 ) is prominently expressed by the cardiac fibroblast lineage. We do not find evidence for significant epicardial expression of FSTL1 under any of the conditions analyzed. We confirmed our singlecell data using a Fstl1-eGFP reporter mouse as well as by histological staining (Fig. 7  and SI Appendix, Fig.
In Situ Hybridization. In situ hybridization (ISH) was performed on 8-μm paraffin sections of mouse hearts, as described  (46). Detailed ISH procedures are described in SI Appendix, SI Materials and Methods. Imaging. Images of cryosections were acquired on an inverted confocal laser scanning microscope (Leica SP8X and SP8). Paraffin sections were imaged using a Leica DM4000 or DM6000 optical microscope. ISH sections were imaged using a Leica DM5000 microscope. Bioinformatics Analysis. DNA library sequencing, mapping to the mouse reference genome, and quantification of transcript abundance were performed as described elsewhere  (30). Bulk sequencing libraries were analyzed using the DESeq2 package  (26). Cell clusters and gene expression levels across clusters were generated using the RaceID2 algorithm (31) from singlecell libraries.
Statistics. No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to the sample allocation during experiments and outcome assessment. No animals were excluded from analysis. All data are presented as mean ± SD, unless stated otherwise. Statistically significant differences between the mean of two conditions or experimental groups were determined using an unpaired two-tailed Student's t test. A χ 2 test (χ 2 ) was performed to determine statistical significance for the data presented in Fig. 2F. A log-rank (Mantel-Cox) test was performed to determine statistical significance for the data presented in Fig. 7M. Data were obtained from at least two biological replicates per condition and from at least two independent experiments to ensure reproducibility.
Kaplan-Meier survival curves were generated, and all statistical analyses were performed using GraphPad Prism, version 7. 0, software. Data Resources. Differentially expressed genes of the initial cardiac cell clustering, of the cardiomyocyte subclustering, of the main cardiomyocyte clusters, and of the cardiac fibroblast subclustering are provided in Datasets S1-S4, respectively. RNA-sequencing data were deposited in the Gene Expression Omnibus (GEO) database (accession number GSE102048), except for some single-cell transcriptome data of cardiac cells isolated from wild-type (Mki67 wt/wt ) mice, which were published elsewhere  (28).
ACKNOWLEDGMENTS. We thank  Benedetta Artegiani  and  Talya Dayton  for discussions and critical reading of the manuscript;  Reinier van den Linden  for help with flow cytometry;  Judith Vivié  for help with SORT-seq; the  Utrecht Sequencing Facility  for sequencing;  Mauro Muraro  and  Anna Alemany Arias  for help with bioinformatics analysis; and  Anko de Graaff  and the  Hubrecht Imaging Centre  for assistance with microscopy. This work was supported by a  Cardiovasculair Onderzoek Nederland (CVON)-Human Stem Cells for Cardiac Repair (HUSTCARE)  project grant (to H. C. ) and a  CVON-HUSTCARE Young Talent Grant  (to K. K. ). K. K. is a long-term fellow of the  Human Frontier Science Program Organisation  ( LT771/2015 ) and was the recipient of a VENI grant from the  Netherlands Organisation for Scientific Research (NWO-ZonMW)  ( 016.
Under these conditions all suspended cells contributed to the formation of a single spheroid. After 72 h of culture, the spheroids were collected, transferred onto a collagen gel (4 mg/mL), and stimulated with either VEGF alone (50 ng/mL), or in combination with mRCC therapies. RCC and ECs migration from the spheroid was imaged with a Zeiss AxioObserver Z1. After 24 h of culture, the position of each EC (n = 500) was manually recorded using the ImageJ Cell _ Counter plugin (http://rsbweb. nih. gov/ij/plugins/cellcounter. html) (http://imagej. nih. gov/ij/). X and Y-values were then imported in a customised Python (https://www. python. org) script, which we created to calculate the Euclidean distance of each cell to the centre of the spheroid and the mean distance of the cells to the centre (Supplementary Figure  S1 ).
TORAVA. (clinicaltrials. gov NCT00619268-sponsor: Centre Léon Bérard, Lyon, France). Patients'recruitment and data-collection methods of the TORAVA trial were described in (Negrier et al. 44  ). Blood samples were collected at three different time points: at baseline prior to treatment (Day 0), 2 weeks (Day 15), and 5-6 weeks (Day 40) after the beginning of the treatment. Samples were stored at -196 °C in liquid nitrogen after collection in the Biological resource centre BB-0033-00050 of the Centre Léon Bérard, Lyon, France. SUVEGIL. (clinicaltrials. gov NCT00943839-sponsor: Centre Antoine Lacassagne, Nice, France). Each cycle lasted 6 weeks in the absence of disease progression or unacceptable toxicity and was organised in the protocol of four consecutive weeks of oral administration of sunitinib malate (50 mg/day) followed by 2 weeks of rest.
Statistical analysis For cell analysis. All of the experiments were repeated at least three times. Western blot bands have been analysed to densitometry using Image J software and data are expressed in arbitrary units as the mean ± SD of at least three identical experiments (same number of cells in each dishes). Student's ttest was used to compare the means of data from two experimental groups. One-way ANOVA was used when three or more experimental groups were compared, and analysis of significance was performed using Tukey's range test (*P < 0. 05; **P < 0. 01; ***P < 0. 001). For all tests, P-values ≤0. 05 were considered significant. For sVE analysis in blood sample. The non-parametric Mann-Whitney U-test was used to compare sVE levels between mRCC patients and the HD group. Wilcoxon and Fisher Exact tests were used to evaluate the relationship between sVE levels and clinical response to treatment.
ACKNOWLEDGEMENTS We are indebted to  Laurie Godart ,  Marie Courcon  for technical assistance. We thank  Pr Donald MARTIN  ( UGA-TIMC-IMAG-SyNABi team ) for revising the English writing in the last version of this manuscript. We thank the investigators and the patients and their families who made this study possible. This work was supported by the  French National Institute of Health and Medical Research INSERM  ( UMRS 1036 ), the  French Atomic Energy and Alternative Energies Commission (CEA) ,  Institut de Biosciences et Biotechnologies de Grenoble (BIG) ,  Grenoble University Hospital, Institut National du Cancer  ( INCA 07/3D1616/PL-96-031/NG-NC ) (I. V. ),  French Association against cancer (ARC Foundation ),  Federation Nationale des Centres de Lutte contre le Cancer (GEFLUC Grenoble-Dauphiné-Savoie ) (IV),  INCA Translationnel  ( 2016-026 ,  GP) Fondation ARC (GP) Fondation de France (GP) ,  Fondation Mora (GP).
Helena Polena  and  Abir Khalil-Mgharbel  received grants from  ARC Foundation (IV). Adama Sidibé  received a grant from  Courtin Arthritis Foundation (IV). The protocols TORAVA and SUVEGIL were approved by the ethics review boards of each participating institution and all patients provided informed consent for enrolment in the trial. These studies were sponsored by  Centre Léon Berard and Centre Antoine Lacassagne , respectively, and registered in ClinicalTrials. gov(NCT00619268 for TORAVA, and  NCT00943839  for SUVEGIL).
5 LightCycler Real Time instrument using LightCycler software, Version 3. 5 (Roche). The PCR reaction with a 20 μl final volume included 1x LightCycler Fast Start DNA Master HybProbe mix with 5 mM MgCl 2 (Roche), 5 μl purified DNA template or controls. The amplification reaction was performed as follows: 10 min initial denaturation step at 95˚C, followed by 50 cycles of 5 seconds at 95˚C, 10 seconds at 60˚C, and 15 seconds at 72˚C. T. gondii genomic DNA serially 10-fold diluted ranging from 10 6 to 10 1 parasites/μl was used positive control and distilled water was used as negative control. In addition, melting curve analysis was performed as follows: 20 s denaturation step at 95˚C (temperature transition rate 20˚C/s) 20 s annealing step at 40˚C (temperature transition rate 20˚C/s) and an extension step that gradually increases the temperature to 85˚C with a temperature transition rate of 0.
Then, 1 μl of diluted PCR product in deionized formamide was mixed with 0. 5 μl DNA standard ROX 500 (Applied Biosystems) and 23. 5 μl deionized formamide. Thereafter, the reaction mix was denatured at 95˚C for 5 min and electrophoresed using an automatic sequencer (ABI PRISM 3130xl; Applied Biosystems). Microsatellite sizes were assessed using GeneMapper analysis software (Version 4. 0; Applied Biosystems). As control, 15 reference strains belonging to type I (VAND, ENT, GT1,), type II (PRU, Me49), and type III (VEG, NED), atypical strains of Africa (DPHT, GAB3-GAL-DOM002, GAB5-GAL-DOM001, GAB3-GAL-DOM014, CCH002-NIA, and GAB2-GAL-DOM002), South America (GUY-CAN-FAM001 and TgCatBr5) and Turkey (Ankara and Ege-1 strains) were analyzed in parallel with strains isolated in this study  [3, 9, 22, [26] [27] [28].
Discussion In this study, the first aim was to show the presence of T. gondii DNA in wild birds of İzmir and Manisa provinces of Turkey. The prevalence of Toxoplasma infection was 89. 6% in wild birds based on our molecular screening. A total of 14 strains were genotyped from 43 PCR positive wild birds; Among the 14 strains genotyped, 8 isolates were type II and three isolates were type III. In addition, two samples contained a mix of two type II strains and one was a mixture of type II and type III strain. These mix genotypes which may be due to authentic multiple infections. Nevertheless, these are the first data that show the presence of T. gondii and genotypes of T. gondii in wild birds of Turkey. There are plenty of studies conducted in various wild birds to determine the presence of T.
Study design and treatment This multicenter, open-label, phase II study (HIJAK, NCT01877005) was conducted at ten LYSA centers in France and Belgium, with patients recruited from July 2013 through December 2014. Its primary efficacy endpoint was overall response rate (ORR), defined as the proportion of patients with a complete response or partial response at 6 months of treatment by investigator assessment based on the revised 2007 International Harmonization Project response criteria for malignant lymphoma. 20 Secondary objectives included relief of B symptoms, best ORR (occurring at any time during study), duration of response, progression-free survival, overall survival, as well as the incidence and severity of adverse events. The study was carried out in line with the ethical principles of the Helsinki Declaration and in compliance with the International Conference on Harmonization Guideline for Good Clinical Practice.
05. If seven or fewer patients had a response, the hypothesis of an ORR ≥35% was rejected with a target error rate of 0. 2 and an actual error rate of 0. 187. The ORR estimate and its 90% confidence intervals (CI) were calculated for all patients who completed at least one cycle of the study drug. The Kaplan-Meier method was employed to estimate the median value and its 95% CI for time to response, duration of response, progression-free survival and overall survival. The safety analysis comprised all patients who received at least one dose of the study drug. All statistical analyses were performed using SAS software, version 9. 2. P-values <0. 05 were considered statistically significant. All available data were included in data listings and tabulations, with no imputations of values for missing data.
Acknowledgments The authors would like to thank the patients, their families and their caregivers who made this study possible. We also thank all the study investigators and study staff at each of the clinical sites. For the LYSARC, we acknowledge the project manager and all members of the data monitoring committee. We express our thanks to  Loïc Chartier  and  Sami Boussetta , biostatisticians at the  LYSARC , who contributed to the statistical design and analysis of the study. Editorial assistance was provided by  Cremer Consulting. Ruxolitinib in advanced relapsed/refractory HL haematologica | 2018; 103 (5).
Results-A total of 28 datasets were submitted, from 26 teams with varying levels of self-assessed experience. Wide variability of segmentations, CFD model extents, and inflow rates resulted in interquartile ranges of sac average WSS up to 56%, which reduced to < 30% after normalizing by parent artery WSS. Sac-maximum WSS and low shear area were more variable, while rank-ordering of cases by low or high shear showed only modest consensus among teams. Experience was not a significant predictor of variability. Conclusions-Wide variability exists in the prediction of intracranial aneurysm WSS. While segmentation and CFD solver techniques may be difficult to standardize across groups, our findings suggest that some of the variability in image-based CFD could be reduced by establishing guideli-.
Challenge Study Design As shown in Fig. 1 , five MCA bifurcation aneurysms were selected by one of the authors (K. K. ) for having good 3D rotational angiography (3DRA) image quality, irregular shape, and similar size (~8 mm). The cases included a mix of ruptured and unruptured aneurysms; however, participants were blinded to rupture status. Challenge organizers confirmed that the five cases could be segmented and that CFD simulations could be carried out on the segmented models (those datasets were not included in the present study). Teams were provided only with the DICOM image volumes, which included the ICA and the proximal and distal MCAs. Participants were free to choose their own segmentation methods, CFD solution strategies, flow rate and/or pressure boundary conditions, and material properties, mimicking real-world conditions for aneurysm CFD collaborations between clinicians and engineers.