dataesr/scientific-paragraphs-categorization huggingface dataset:
Study Population Data were collected through a questionnaire distributed to French dog breeders from 2015 to 2019 using direct mailings, Facebook ® messages, during canine exhibitions and via various dog breed associations  [9]. The completion of the questionnaire was voluntary. The recorded data included information about the litter (date of birth, breed, litter size at birth), dam (identity and age at whelping) and puppy (sex, daily weights during the first week of life and eventually death declaration in case of death during the first two months after birth). Weights were measured by the breeders using their own scales. The information collected was transferred anonymously into an Excel table (Microsoft Corporation, Redmond, Washington, DC, USA) for analysis with the breeders' consent. Among all the data collected, only purebred puppies with a known status (dead or alive) at two months of age, with birth weight provided and born beyond the year 2000 were considered for the present study.
Data Management Five growth rates over the first week of life were calculated: between birth (Day 0) and Day 1 (GR 0-1), between Day 1 and Day 2 (GR 1-2), between birth and Day 2 (GR 0-2), between Day 2 and Day 7 (GR 2-7) and between birth and Day 7 (GR 0-7). Growth rates were calculated as: GR (x-y) = [(weight at Day yweight at Day x) ÷ weight at Day x] × 100. Prior to analysis, puppies with extreme values were removed, i. e. , with values higher than the 75th centile plus 1. 5 times the interquartile range (IQR) or lower than the 25th centile minus 1. 5 times the IQR (Tukey's method). Only puppies belonging to a breed represented by at least 100 individuals were included in the analysis.
Birth-Weight Classification Puppies were categorized as low or normal birth weight (LBW or NBW) based on their status at 2 months (dead or alive), using thresholds defined by classification and regression tree (CART) analysis. To deal with the variability of birth weight depending on the breed  [1, 23] , thresholds were breed-specific. For each breed, CART was used to assess the ability of birth weight to discriminate between the survivors and the puppies that would die over their two first months of life. CART is a statistical technique based on the recursive partitioning method well suited to define clinical decision rules  [24]. Briefly, it consists of repeatedly partitioning the data into subgroups leading to the construction of a decision tree  [25, 26]. The method provides a rule (here, a cut-off value) used for predicting the outcome variable (here, dead or alive status at 2 months).
All the statistical analyses were performed using R software, version 4. 2. 1  [27] , and the following packages: ggplot2  [28]  for data visualisation, dplyr  [29]  and tidyr  [30]  for data manipulation and rpart  [31]  and caret  [32]  to perform CART analyses. A value of p < 0. 05 was considered statistically significant, and statistical uncertainty was assessed by calculating 95% confidence intervals (95 CI).
Population Characteristics Data from a total of 8550 live-born puppies from 24 breeds, 1493 litters, and belonging to 127 French breeding kennels were included in this study (Figure  1 ). Litters were born between 2000 and 2019 (71% after 2010). Among the 24 breeds included, 11 were in the top-twenty breeds owned in France, according to the French Kennel Club  [33]. The number of puppies included per breed ranged from 105 for the Greyhound to 1802 for the Labrador Retriever (median: 228). The global mean litter size (alive at birth) was 7. 5 (SD: 2. 6) puppies and the sex ratio was calculated at 1. 0 (4235 males vs. 4287 females). Birth-weight values ranged from 56 g (a Pomeranian puppy) to 940 g (a Newfoundland puppy), with a median of 380 g (IQR: 260-470). Among these 8550 puppies, based on their 0-2 months mortality and CART analysis, 1291 (15%) were classified as LBW and 7259 (85%) as NBW.
5 (Figure  1A ). We next isolated dorsal tissues at the forelimb level at E13. 5, E14. 5, and E15. 5 to perform scRNAseq to characterize early stages of BAT development (Figure  1B ). For stages E13. 5 and E14. 5, the isolated dorsal tissues include epidermis, dermis, mesenchyme, interscapular brown fat and skeletal muscle. For the E15. 5 time point, to enrich BAT precursors, we removed the epidermis and underlying dermis before dissociation. Using the inDrops workflow  (Klein et al. , 2015) , we generated single-cell transcriptomes from at least 6000 cells per stage, resulting in a final dataset of 20,000 cells after quality control filtering (Supplementary figure  1A ). Uniform Manifold Approximation and Projection (UMAP) based 2-dimensional embedding shows that cells from the different time points segregate based on transcriptional similarity rather than with embryonic age (Figure  1C , Supplementary figure  1B ).
In mouse, at E14. 5, Ebf2 marks brown fat precursors but its expression is not restricted to these cells  (Wang et al. , 2014). In our scRNAseq dataset, we observed Ebf2 mRNA expression in clusters of brown adipocytes, brown adipocyte precursors, connective tissue and precartilage but not in dermis (Figure  1H , Supplementary Figure  2A ). Immunofluorescence analysis of E12. 5 to E15. 5 embryos confirmed the expression of Ebf2 in the presumptive brown fat, skeletal muscle and precartilage (Supplementary Figure  2A ). To better define the developmental trajectory of brown adipocyte precursors, we analyzed the single cell transcriptomes of different embryonic stages individually. UMAP-based embedding and Leiden clustering revealed similar somitic cell types which clustered together, except for skeletal muscle and endothelial cells, as in the combined temporal analysis (Supplementary figure  2B-D ).
Downregulation of Gata6 in lipid-vesicle containing adipocytes at E15. 5 suggests that Gata6 expression marks the brown adipocyte precursor state. Angueira et al. (2021)  recently identified progenitors that give rise to brown adipocytes in mouse periaortic (pVAT) brown adipose cells analyzed by scRNAseq at E18 and perinatal stages  (Angueira et al. , 2021). We reanalyzed their E18 dataset which contains 16,967 single cell transcriptomes. Following UMAP embedding and Leiden clustering, we identified 21 clusters representing the cell types described in the original study (Supplementary Figure  2E ). After manual annotation of the adipocyte clusters, we isolated three clusters which correspond to adipocytes (Cidea), preadipocytes (Pparg) and adipocyte progenitors (Cd34) (Figure  2I , Supplementary Figure  2F ).
Remarkably, we observed expression of Gata6 in the adipocyte progenitors cluster (Figure2J, Supplementary Figure  2F ). Pi16, Cd34 and Ly6a, which were also identified as brown adipocyte progenitor markers in this report (Figure  2J ) were also differentially expressed in the BApre population identified in our study (Figure  2K ). Immunofluorescence analysis at E15. 5 confirmed the expression of the adipocyte precursors markers Gata6, Dpp4, Cd34 and Ly6a at the periphery of the developing brown adipose tissue (Figure  2H , L-M). Their location suggested that they might correspond to precursor cells involved in the generation of mature brown fat. To further analyze the overlap between the brown adipocyte progenitor cell population identified in the pVAT dataset and our BApre cluster, we trained a k-NN classifier on the brown adipocyte (BA) and BApre cluster identified in our dataset.
, 2015). Using a PAX7-Venus reporter iPSC line, we observed that at day 14-16, cultures harbored large populations of non-overlapping PAX7-expressing and EBF2-expressing cells (Figure  4F )  (Al Tanoury et al. , 2020). To divert the differentiation of the paraxial mesoderm cultures towards an adipogenic fate, we next sought to identify potential signaling pathways involved in brown adipogenesis. We performed differential gene expression analysis with the BApre cluster of the mouse dorsal trunk scRNAseq dataset (Supplementary Table1). Using KEGG pathway analysis, we identified several pathway-specific genes differentially expressed in the BApre cluster (Supplementary Figure  4D ). This included genes involved in BMP signaling (Bmp7, Bmp4, Bmpr1a, Bmpr2), TGFbeta signaling (Tgfbr2, Gdf10), Thyroid hormone signaling (Creb3l1,Gnas, Gpx7, Creb5, Hsp90b1), Wnt signaling antagonists (Sfrp1, Sfrp2, Sfrp4, Dkk2) and FGF/PI3-AKT pathway (Akt1, Igf1, Fgfr1) (Supplementary Figure  4E-G ).
As an outgroup, we used bulk RNAseq data from iPSC-derived skeletal muscle cultures (iPSC-SkM)  (Al Tanoury et al. , 2021). Brown adipocyte-specific genes such as UCP1, CIDEA, PARGC1A and DIO2 were specifically expressed in iPSC-BAs and human fetal BAT (Figure  5J ). Markers for precursors identified in this study such as GATA6 and DPP4 were also detected suggesting the presence of adipocyte precursors in differentiating cultures (Figure  5J ). The top 200 differentially expressed genes in iPSC-BAs corresponded to human WikiPathways related to 'PPARG signaling pathway', 'Estrogen receptor pathway' and 'Thermogenesis' (Figure  5K ). When analyzed using the ProFat database  (Cheng et al. , 2018) , the browning probability of iPSC-BAs was comparable to fetal brown adipocytes (Figure  5L ). Recently, two studies have described the directed differentiation of brown adipocytes from hPSCs  (Carobbio et al.
In contrast, the skeletal muscle cluster is clearly segregated from the mesenchymal cluster in our dataset. This is consistent with the early divergence of the Pax7-expressing multipotent progenitors into skeletal muscle lineage by E11. 5 revealed by lineage tracing experiments in mouse  (Lepper and Fan, 2010). Lineage tracing analyses in mouse demonstrated that interscapular brown fat derives from Pax3/Myf5+ and Pax3/Pax7/Myf5+ cells  (Sebo et al. , 2018). Our lineage tracing experiments confirm that some of the mouse interscapular brown fat derives from Pax7-expressing precursors  (Lepper and Fan, 2010; Sanchez-Gurmaches and Guertin, 2014)  which can give rise to a Gata6-positive brown adipocyte precursor population. These early Pax7 precursors could correspond to the En1-positive cells of the central dermomyotome previously identified in mouse  (Atit et al.
, 2006). The relatively low contribution of Gata6-tdTomato labeled cells to the adipose tissue could result from waves of adipocytes precursors differentiation as suggested by En1 lineage tracing  (Atit et al. , 2006). We reanalyzed a scRNAseq dataset of developing mouse periaortic BAT and observed a population of Gata6positive BA precursors closely related to the one identified in the interscapular BAT. Interestingly, perioaortic BAT receives little contribution from Pax3+ cells and has no contribution from Myf5+ cells  (Sanchez-Gurmaches and Guertin, 2014). This therefore suggests that different embryonic populations can contribute to the Gata6-positive brown adipocyte precursors. Gata6 (GATA binding protein 6), is a zinc finger transcription factor which plays an essential role in the development of extraembryonic tissues  (Koutsourakis et al.
, 1999). Its null mutation in mouse leads to lethality shortly after implantation. Gata6 has also been implicated in the development of several organs including heart, lung, gut, pancreas and skin but its expression during brown fat development has so far not been reported  (Freyer et al. , 2015; Morrisey et al. , 1996). Gata6 was however identified in silico as a transcription factor potentially regulating the expression of brown fat specific genes such as Zic1 and the TCA cycle enzymes such as citrate synthase (Cs) in the ProFat database  (Cheng et al. , 2018). Also, a GATA recognition motif was identified in ATACseq data of differentiating human adipocytes in vitro  (Zhang et al. , 2020). In cardiomyocytes, GATA-6 was shown to associate with Ppara to regulate the expression of the glucose transporter Glut4  (Yao et al.
ACKNOWLEDGEMENTS FIGURES Figure1 Figure1 A -Immunofluorescence analysis of developing interscapular brown fat in mouse. Images represents transverse section of mouse embryos at the forelimb level showing interscapular region. Perilipin (Plin1) expressing adipocytes start to appear on embryonic day (E) 14. 5 and perilipin is widely expressed in interscapular fat on E15. 5. Staining for Pparg antibody illustrating expression of Pparg starting at E13. 5. Interscapular fat develops in between muscle bundles stained using a Myosin heavy chain (Myhc) antibody. NT = Neural tube. B -Schematic representation of cell isolation strategy for single cell RNA sequencing. Dorsal interscapular region was dissected out with intact dermis and epidermis for embryonic day (E) 13. 5 and 14. 5. For E15. 5, skin and underlying dermis was removed before cell dissociation.
py pipeline (available at github. com/indrops/indrops). Single cell transcriptomes were mapped to mouse (GRCm38/mm10) and human (GRCh38/hg19) reference transcriptomes. Samtools version 1. 3. 1, rsem version 1. 3. 0 and Bowtie version 1. 2. 2 was used with parameter -e 100. A weighted histogram of transcript counts per cell barcode vs cell barcode abundance was used to identify transcripts originating from abundant cell barcodes. Only transcript counts originating from abundant cell barcodes were included in downstream analysis. Basic filtering parameters were used to exclude cells expressing <500 genes and genes expressed in less than 3 cells. The filtered counts were normalized by total number of counts for each biological sample. Top 1000 variable genes were identified according to Satija 2015.
Top 500 differentially expressed genes were identified by a Wilcoxon rank-sum test by comparing cells of each cluster with cells of all the other clusters. Genes were considered differentially expressed based on foldchange, minimum expression, and adjusted p-value cutoffs, as indicated. To calculate the RNA velocity, we processed the E14. 5 dataset BAM files through the velocyto pipeline to enumerate spliced and unspliced transcripts and obtained loom files  (La Manno et al. , 2018). We processed the matrix and filtered out cells and genes based on our standard criteria mentioned above. UMAP was used to embed the neighborhood graph (PCs = 50) and cell clusters were identified using Leiden method. RNA velocity analysis was performed on sub-setted somitic cluster using default parameters as described in the scVelo tutorial  (Bergen et al.
2021). Scale bar represents log normalized read counts. K -WikiPathway 2019 Human analysis of top 200 differentially expressed genes (FDR<0. 05, log2|FC|>6) in iPSC-BAs compared to undifferentiated iPSCs. L -Estimation of browning probability of iPSC derived brown adipocytes (iPSC-BA), iPSC derived skeletal muscle (iPSC-SkM) and human fetal brown adipose tissue (fBAT) using PROFAT database. Numbers represent replicates. M -Heatmap comparing transcriptomic profiles of iPSC derived brown adipocytes (iPSC-BA), human fetal brown adipose tissue (fBAT), human embryonic stem cell derived brown adipocytes inZhang et al. 2020 (H9-d50)  and iPSC derived brown adipocytes inCarobbio et al. 2021 (KOLF2-C1-d25).
Light was delivered to the tissue using optic fiber of 200 μm diameter placed at a maximum distance of 0. 5 mm. Image data analysis. Single Z-plane 1-minute movies were acquired every 5 minutes during 60 minutes. Blood flow in basal conditions was measured for 10 minutes before switching on the laser. A movie was then acquired every 5 minutes during 40 minutes of continuous laser stimulation (50 ms flashes at a frequency of 1 Hz). Acquisition rate was set to 150 frame/s. Movies were stabilized using ImageJ (NIH). Measurements were performed in at least 3 independent experiments per condition. The displacement of RBC shadows was visualized by contrast and quantified using previously described image analysis methods to calculate RBC velocity  (45). RBC velocities were measured in 5-10 vessels per movie in at least 3 different mice per condition.
Images were acquired using a Zeiss LSM 780 confocal microscope and analyzed using Imaris (Bitplane) and ImageJ (NIH). Single-cell RNA-Seq data analysis. Data from Campbell et al. 2017  (23)  were imported in R 4. 1. 3 from the scRNAseq Bioconductor package version 2. 7. 2, then converted to a Seurat object, for plotting using the Seurat package, version 4. 1. 1. The cell labels were imported from the metadata and renamed according to what was reported in the original article.
Acknowledgments The authors would like to thank  P Samper  and  E Galibert ,  Institute of Functional Genomics, Montpellier, France , for technical assistance; the animal facility staff; and Montpellier in vivo Imaging Platform. Authors were supported by grants from the  Agence Nationale de la Recherche  ( ANR BETA-DYN JCJC13  to MS,  France-BioImaging   ANR-10-INBS-04 ,  ANR-15-CE14-0012  and  ANR-18-CE14-0017  to PM),  Société Francophone du Diabète, INSERM, CNRS, University of Montpellier , and  Région Languedoc-Roussillon. DJH was supported by  Medical Research Council  ( MR/S025618/1 ) and  Diabetes UK  ( 17/0005681 ) Project Grants, as well as a  United Kingdom Research and Innovation European Research Council (ERC)   Frontier Research Guarantee Grant  ( EP/X026833/1 ). This project has received funding from the  ERC  under the European Union's  Horizon 2020 research and innovation programme  (Starting Grant  715884  to DJH).
, 2014 , Nehring et al. , 2015 , Shizukuda et al. , 2018 , Diószegi et al. , 2019). Bitter taste perception in humans is primarily mediated by TAS2Rs, a family of are G-protein-coupled receptors expressed on the surface of taste buds  (Wooding et al. , 2021). Compounds such as QHCl act as agonists for these receptors and trigger a series of biochemical signalling cascades and neural activity resulting in bitterness perception. There are 25 subtypes of TAS2Rs, each recognising multiple bitter substances, while a single bitter compound can activate several TAS2Rs. Quinine has shown to activate different receptors in the micromolar range  (Meyerhof et al. , 2010) , and sensitivity to its taste intensity has been linked to TAS2R19 genotype  (Reed et al. , 2010 , Deng et al. , 2022). The aim of this study was to better understand how taste perception data from adults can be used to support the development of medicines for children, therefore age was the main variable investigated.
Ervina et al. , 2020  conducted an in-depth study investigating taste perception in 11-year old children using 10 mL solutions of both quinine and caffeine as bitter tastants  (Ervina et al. , 2020). Interestingly, hedonic taste liking, captured on seven-point pictorial scale including a happy and grim face, did not correlate with sensitivity measures including detection thresholds and recognition thresholds  (Ervina et al. , 2020). It is therefore important that affective responses and palatability data are captured independently of determining taste detection thresholds or measuring the intensity of bitter tasting APIs. In an era of patient-centric drug development, there is an important need to develop tools to safely and accurately gauge taste perception in children themselves, as well as with adults.
Acknowledgments The authors acknowledge and thank all children, parents/carers, and teachers at  Henry Maynard  Primary School (www. henrymaynardprimary. co. uk/) for their support and participation in this study as well as all the adult panellists for their valuable input into this project. This work was supported by the  European Paediatric Formulation initiative (EuPFI)  grant reference number:  EuPFIRF2021-1.
Data capture and Analysis Information on age, gravidity status, residence, marital status, educational level, occupation and knowledge on malaria, prevention and risks during pregnancy were obtained using a structured questionnaire and analysed with STATA. Information on malaria infection status at ANC was obtained from participant's own account during questionnaire interview. At delivery, malaria infection statuses were obtained from hospital records in participant's ANC booklets. Total ANC attendance was also obtained from ANC booklets. For associations, the various variables/factors were compared with choice of facility using Chi-square tests. Where assumptions for Chi-square fail (frequencies <5), Fisher's exact tests were used. Differences in means of continuous variables were tested between facilities using t-tests and where requirements for t-tests failed, particularly normality of the data, Wilcoxon Rank Sum tests were used.
Material and methods: We used amniotic fluid samples of singleton pregnancy, collected by amniocentesis between 16 and 20 weeks' gestation, without stigmata of infection (i. e. , all amniotic fluid samples were tested with broad-range 16 S rDNA PCR to distinguish samples with evidence of past bacterial infection from sterile ones), during a randomized, double-blind, placebo-controlled trial to perform a nested case-control laboratory study. Cases were women with a spontaneous delivery before 37 weeks of gestation (preterm group). Controls were women who gave birth at or after 39 weeks (full term group). Amniotic fluid concentrations of the extracellular matrix-related proteins and cytokines measured by immunoassays were compared for two study groups. Clini calTr ials. gov: NCT00718705. Results: Between July 2008 and July 2011, in 12 maternal-fetal medicine centers in France, 166 women with available PCR-negative amniotic fluid samples were retained for the analysis.
| Study groups Among the 1043 women included in the PREMYC study, 951 of the 980 (97. 0%) with complete PCR data had negative PCR results for all tests and were eligible for this analysis. Women with spontaneous deliveries before 37 weeks, with or without preterm premature rupture of the membranes (PPROM), comprised the "preterm group. " The control or "full-term" group contained the four women with negative PCR and a full-term birth (≥39 weeks) recruited just after each case to compare the preterm births with full-term (between 39 weeks and 42 weeks) rather than simply term births (>37 weeks) that may be associated with more obstetrical complications. We also.
| Clinical data For each woman, the data potentially related to preterm birth were collected, including general characteristics (age, body mass index, country of birth, isolated women, professional activity during pregnancy, use of tobacco during pregnancy); medical and surgical history (eg preexisting diabetes and hypertension, previous conization); obstetrical history (eg previous curettage, previous fetal loss and preterm delivery). Data of the pregnancy course (prophylactic cerclage, gestational hypertension, bleeding during pregnancy, gestational age at birth).
| Statistical analyses All data were collected in a computerized database and analyzed with Stata 13. 0 (StataCorp). Data are presented as means ± SD or medians (interquartile ranges), as appropriate. Because the distributions of the amniotic fluid procollagen, osteopontin, IL-19, and IL-6 concentrations were non-Gaussian, statistical comparisons between the two groups were performed with the Mann-Whitney U test. Pearson's χ 2 or Fisher's exact test, as appropriate, were used for the categorical variables (for IL-20, IL-33, TNFα, TGFβ, and IL-1β). Each variable was studied independently, and no multitest procedures were used. Statistical significance was defined by a p-value <0. 05.
The trial was approved by the national data protection authority (Commission Nationale de l'Informatique et des Libertés, CNIL) and by the committee for the protection of people participating in biomedical research (Comité de Protection des Personnes [CPP], CPP Poissy Saint Germain, no. 07051) on July 21, 2008. It was registered at Clini calTr ials. gov (no. NCT00718705). The study was performed in accordance with the Declaration of Helsinki and Good Scientific Practice guidelines. All patient records were anonymized and de-identified before the analyses. All participants gave written informed consent before enrolment.