dataesr/scientific-paragraphs-categorization huggingface dataset:
Upstream regulator analysis Using IPA, upstream regulator analysis was performed to identify transcription factors with direct actions on DEGs in the pituitary glands of HG and LG birds. Transcriptional regulators associated with pituitary development and function that potentially contribute to differential expression of pituitary hormones are shown in Fig. 10a. Predicted upregulation of paired box gene 8 (PAX8) and early growth response protein 1 (EGR1) in HG birds is likely responsible for differential expression of genes associated with cell turnover that could change pituitary structure and morphology [collagen, type I, alpha 2 (COL1A2), clusterin (CLU), cyclin dependent kinase inhibitor 1C (CDKN1C), CASP8], as well as microvascular formation (FLT1). Other transcription factors identified as upstream regulators in this network influence pituitary cell-type The number of DEGs between HG and LG birds from our dataset contained within each network c The total number of genes contained within each network differentiation and hormone expression, indicating that these could also be differentially expressed between HG and LG birds.
Despite an apparent increase in the number of pituitary somatotrophs in fast-growing lines of birds during late embryonic development  [29] , it is well established that circulating GH is elevated in slower-growing lines of Fig. 8  Functional gene interaction network associated with pituitary gland structure in HG and LG chickens. A gene network showing direct (solid lines) and indirect (dashed lines) relationships associated with "Organ Morphology and Cellular Compromise" was identified using Ingenuity Pathway Analysis software. Genes which are colored red (upregulated in HG line) or green (upregulated in LG line) are contained within the dataset of 291 genes with a significant line-by-age interaction or a significant main effect of line (P ≤ 0. 05; n = 4 birds per line) and a difference in expression of at least 1.
An increase in ADCY2 expression in these birds may increase intracellular cAMP in an attempt to maintain hormone expression levels in the face of this interference. Further supporting differences in cAMP-mediated intracellular signaling between the lines is the observation that LG birds exhibit elevated levels of AKAP7 mRNA, a scaffolding protein that binds to regulatory subunits of cAMP-activated protein kinase A and influences its activity  [42]. Increased expression of mRNA for a regulatory subunit of calcineurin, PPP3R1, in LG birds indicates that calcium-mediated intracellular signaling may also be altered between the lines and influence pituitary hormone expression levels and/or secretion. Activin-mediated induction of FSH production is regulated by MAP3K7 in sheep pituitary cells  [43] , and elevated levels The number of differentially expressed genes between HG and LG birds from our dataset contained within each category c P-values were determined by IPA software based on the number of DEGs from the dataset within each biological category divided by the total number of known genes assigned to that category within the database Fig.
10  Transcriptional regulators predicted to regulate DEGs observed in the pituitary of HG and LG chickens. Upstream regulator analysis using Ingenuity Pathway Analysis was performed on the dataset of 291 genes with a significant line-by-age interaction or a significant main effect of line (P ≤ 0. 05; n = 4 birds per line) and a difference in expression of at least 1. 6-fold between lines. This analysis identified transcription factors with direct actions on differentially expressed target genes. a Transcriptional regulators associated with pituitary development and function that could contribute to differential expression of pituitary hormones were identified. b A large number of differentially expressed genes between the two lines are also predicted to be regulated by glucocorticoid receptor (NR3C1).
Upstream regulators colored orange are predicted to have higher activity in HG birds, and upstream regulators colored blue are predicted to have higher activity in LG birds. Genes within the dataset that are colored red were more highly expressed in the HG line, and genes within the dataset that are colored green were more highly expressed in the LG line. The color of the line connecting transcriptional regulators to differentially expressed genes indicates IPA prediction of whether transcription factor activity should lead to upregulation (orange line) or downregulation (blue line) of the DEG targets of MAP3K7 mRNA in HG birds suggests that this pathway may play a similar role in birds and also be a positive regulator of pituitary hormones. In addition to differential cell signaling, HG birds exhibited higher levels of mRNA for genes associated with the basic cellular functions of transcription and translation, such as those involved in processing nascent mRNA transcripts [heterogeneous nuclear ribonucleoprotein A2/B1 (HNRNPA2B1)]  [44] , and chaperones involved in the misfolded protein response (CALR and HSPA5)  [45].
A total of 263 candidate genes with a potential role in altering growth and metabolism were identified, and the results clearly demonstrate that molecular events associated with all levels of pituitary gland function were impacted by divergent selection. Among the candidate genes were five of the six pituitary hormones. Organization of the dataset using SOMs identified clusters of genes which could be driving phenotypic differences through altered regulation of pituitary hormone production. GO categorization and pathway analyses confirmed that processes and gene networks related to signal transduction, transcriptional regulation, and membrane trafficking and vesicle-mediated transport were different between lines and may be influencing hormone expression and secretion. Differential expression of genes involved in immune regulation was observed, suggesting that processes such as inflammation and response to cellular stressors may compromise pituitary function.
This design necessitated the use of 32 microarray slides (one per individual pituitary gland). Labeling with Cy3 and Cy5, microarray hybridization, and image scanning were performed at the University of Maryland Biotechnology Institute's Microarray Core Facility as previously described  [68, 69]. Data from the microarray analysis were processed, normalized, and trimmed as described earlier  [28, 68, 69]  using software that is part of the TM4 suite of microarray data analysis applications  [74]  freely available from The Institute for Genomic Research (TIGR, Rockville, MD). During image processing, cDNA spots that were flagged due to lack of detection, detection below background, pixel intensity saturation, or malformation were rejected from further processing and normalization and removed from the dataset.
Spots whose pixel intensity for all slides was < 90% of the lowest median pixel intensity for the salmon DNA control spots (background; 8 spots/slide) among all slides were also eliminated from further consideration, as were any spots that were not detectable on at least half of the slides. Microarray data files for the study were deposited in the GEO data repository (accession no. GSE122519, sample accession nos. GSM3473153 -GSM3473183). The trimmed dataset consisted of 10,437 cDNA spots that were submitted for statistical analysis by two-way analysis of variance (ANOVA) using the general linear model (GLM) procedure of Statistical Analysis System (SAS Institute, Cary, NC) v. 9. 4. Data were analyzed as log 2 (normalized Cy3/raw Cy5), or log 2 -ratio, for each spot, and differences were considered statistically significant at P ≤ 0.
Gene ontology and self-organizing maps analyses DEGs were analyzed for GO enrichment terms based on biological process and molecular function using AgBase (http://agbase. arizona. edu/), a resource for functional analysis of agricultural gene products  [75]. Of the 260 functionally annotated DEGs, 249 and 236 were successfully placed into GO categories based on biological process and molecular function, respectively. GeneCluster 2. 0  [76]  was used to organize the 291 DEGs into 16 SOMs clusters in a 4 × 4 configuration. Data were entered into the program as log 2 ratio HGmeanlog 2 ratio LGmean at each age, in order to identify changes in relative expression between the lines which may be correlated with divergence in growth and body composition. A 4 × 4 grid was chosen in an iterative process to minimize both the variance within individual clusters and the redundancy of similar clusters, while still maintaining profiles that reflected divergence of HG and LG phenotypes.
Gene interaction network and upstream regulator identification The 260 DEGs that were functionally annotated using GeneBase were submitted to IPA in order to identify gene interaction networks and upstream regulators. Of these, 249 DEGs mapped to the IPA annotated database and were subjected to further analysis by the program. Data were entered into IPA as relative expression between HG and LG birds, (log 2 ratio HGmeanlog 2 ratio LGmean ), for each mapped DEG at each of the four ages.
ensembl. org/Gallus_ gallus/Info/Index), where possible. Primers for genes with missing or problematic annotations were designed using sequences in GenBank (https://www. ncbi. nlm. nih. gov/genbank/). Primers (Additional file 4) with parameters as described previously  [77]  were designed to span an intron within the 3′-end of the transcript, whenever possible. PCR reactions (15 μl) contained 1 μl diluted cDNA, 400 nM each primer, PCR buffer (50 mM KCl, 10 mM Tris-HCl, 0. 1% triton-X-100), 0. 12 U/μl Taq Polymerase, 200 nM dNTPs, 40 nM fluorescein (Invitrogen), and SYBR Green I Nucleic Acid Gel Stain (Invitrogen) diluted 1:10,000 and were carried out in the MyiQ Single-Color Real-Time PCR Detection System (Bio-Rad, Hercules, CA). Each PCR reaction was conducted in duplicate, and the Ct value used in subsequent calculations was the mean of the values from these duplicate reactions.
Acknowledgements The longitudinal animal study and associated sample collection required the participation of several scientists and technicians from laboratories and breeding facilities at the  Unite de Recherches Avicoles and Unite Experimentale Pole d'Experimentation Avicole de Tours, INRA, Nouzzily, France. Production of microarrays used for this study also required the participation of several scientists, post-doctoral associates, technicians, and students from laboratories at  University of Maryland, University of Delaware , and  Institut National de la Recherche Agronomique. Although not individually named, each person's contribution towards this project was essential and is gratefully acknowledged.
Strengths Our study has several strengths. The long period data collection allowed the inclusion of a large number of women, which gave a substantial power to highlight a difference between both groups of maternal age if it exists. Attending practitioners prospectively collected the data about the management of the pregnancy, labor and delivery, and these data were ascertained routinely by medical staff the day after the delivery, so that thorough and accurate information was available. We have detailed information about the patients labour. The hospital database allowed us the collection of numerous covariates to address many potential confounding factors in the logistic regression models and to estimate the association between maternal age and caesarean delivery rate with greater precision.
Joule Effect Measurement In order to measure the Joule effect, the Keithley 2461 SourceMeter (Beaverton, OR, USA) and a thermal camera FLIR (Wilsonville, OR, USA) were used. The SourceMeter was connected to two clamps, which maintained the sample at 5 cm above the ground. The distance between the clamps on the sample was 1 cm. A thermal camera, C2 FLIR, connected to the software FLIR (Wilsonville, OR, USA) was placed at a height of 5 cm above the sample. Thanks to the thermal camera and the software, the sample temperature could be measured at any time and everywhere on the sample. The SourceMeter was programmed to deliver a voltage of 20 V for 300 s. Thus, the temperature and the electrical conductivity of the sample could be measured. At least five samples were measured for each blend.
m. and 11 a. m. in a quiet room maintained at ≥ 22 °C. The blood pressure signal was digitized (500 Hz) using a 12-bit A/D converter, and processed by an algorithm based on feature extraction to detect and measure the characteristics of a blood pressure cycle (AcqKnowledge® 3. 0 software; BioPac Systems, Goleta, CA, USA). Systolic blood pressure and heart rate were stored on a hard disk and sampled at 10 Hz. BRS was evaluated using a specifically designed programme that analysed simultaneous fluctuations in systolic blood pressure and heart rate using both a time-domain technique (sequence method) and a frequency-domain technique (transfer function analysis). The sequence method is based on the detection of three or more cycles with increases or decreases in systolic blood pressure associated with parallel changes in the RR-interval of the following cardiac cycle.
The COLIBRI cohort: general design The COLIBRI project has been described previously  [17]. Its primary aim is to propose a standardised and structured web-based medical consultation. Participants are voluntary respiratory physicians in France. All entered data are stored in a secured central server certified for health data storage (OVH Healthcare, Claranet). The database has been authorised by the French national commission on personal data privacy (Commission Nationale de l'Informatique et des Libertés, CNIL, authorisation number # 2013-526) after a positive advice from the committee on health data management for research purposes (Comité Consultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la Santé, CCTIRS). The requirement for written consent was waived in this observational cohort study in accordance with French law.
Patients provided oral consent following information by their physician. The project was launched in March, 2013 in the Rhône-Alpes French Region and was subsequently extended to other participants on the French territory. Altogether, at present the project comprises 145 respiratory physicians working in hospitals (78%) or private practices (22%). Among hospital-based physicians, 83 (73%) work in tertiary care university hospitals. All patients with a spirometry-confirmed physician diagnosis of COPD can enter the database.
Data collection The COLIBRI project collects data on treating physicians and patients. Physicians' data include type of activity (university hospital, general hospital, private clinic, mixt), type of area of activity (town, rural). The main patients' data include demographic and anthropometric characteristics, risk factors (smoking history, professional exposure, occupation), comorbidities, respiratory symptoms, exacerbation history, findings at physical examination, self-estimated time spent walking outside the home, modified Medical Research Council dyspnea scale (mMRC), Epworth Sleepiness Scale, COPD assessment test (CAT), Hospital Anxiety and Depression (HAD) scale, Disability Related to dyspnea COPD Tool (DIR-ECT)  [18] , lung function tests, arterial blood gases and pharmacological and non-pharmacological treatments.
Statistical analysis All analyses were performed using the R statistical software, version 3. 2. 4 and the SAS statistical software, version 9. 2 (SAS Institute, Cary, NC, USA). Results were considered statistically significant when the probability of a type I error was below 5%. The analyses were done with data from all individuals with complete records for age, height, weight, gender, FEV1, FVC, exacerbation history, comorbidities, mMRC, CAT, HAD and DIRECT scores, and pharmacological treatments. Continuous data are presented as means and standard deviations while categorical data are presented as percentages. The characteristics of the patients in the two populations, the one with complete records and the one with incomplete records, were compared with the nonpaired independent samples Student's t-test for continuous variables and Chi-2 or Fischer's exact test for proportions.
In a second phase, since in reality the prescribed therapeutic treatment is a multi-categorical variable, we have used a multinomial multiple logit regression for nominal multi-category responses and parameters glyphs to visualize the effect strengths by star plots, where one star collects all the parameters connected to each selected term (R-package EffectStars)  [19]. Finally (third phase), to investigate and confirm, in a descriptive way, the impact of the predictors on the response variable and examine the degree of their correlation, we have also used a mosaic display which is appropriate for the analysis of multiway contingency tables  [20]. More precisely, we have fitted a regression tree model to the data to predict the response's status from a selected set of predictor variables and then, used the splits on the selected variables, to recode all variables as categorical before applying the mosaic methodology.
Patients Among the 4140 patients in the COLIBRI database on April 3rd, 2017, 1171 had complete data for all the required variables and were included in the analyses. Their characteristics are described in Table  1  and compared with those of patients who could not be included in the analyses due to incomplete data. Patients with complete data comprised a slightly lower proportion of GOLD 4 and a slightly higher proportion of GOLD 3 category. Slightly less patients were on LTOT (long-term oxygen therapy) and CPAP (Continuous Positive Airway Pressure) but more patients were on NIV (Non Invasive Ventilation). A greater percentage of patients were at-risk of future exacerbations following the GOLD criteria. Some other statistically significant differences were found but their magnitude was of marginal clinical significance.
Study Sample The data analyzed here originate in the third survey wave of the longitudinal Cohort Study on Substance Use Risk Factors (C-SURF) 1. From 2010 to 2012, 7556 young males, who attended their mandatory recruitment for the Swiss army, gave written informed consent and 5987 participated in the first wave. Data for this study were derived from the third wave which has been conducted between April 2016 and March 2018 and which has included 5516 males (see 2  for Questionnaire No. 3).
Statistical Analyses Continuous data are presented as the median and interquartile range (IQR) and nominal data as frequencies (FREQUENCIES function in SPSS). For missing data points, the corresponding study subjects were excluded from the specific analyses and the number of individuals included in these analyses is reported. Correlations were calculated using Spearman's method, because normal distribution was rejected for all variables. We used the Mann-Whitney U test to compare independent groups. For twosided tests, p < 0. 05 was considered to be statistically significant. All reported p-values are uncorrected for multiple comparisons. Data were analyzed using IBM SPSS Statistics Version 21 for Windows (SPSS Inc. , Chicago, IL, United States) and Graph Pad Prism 5 (Graph Pad Software Inc.
Sequence alignments The consensus sequences generated for individual genome segments were combined with sequences from one Pakistani and other Indian BTV genomes available from GenBank in November 2017 (and for which metadata was sufficient), bringing the total number of genomes used in this study to ninetytwo. Spatial distribution of samples and other metadata are summarised in Supplementary Fig. S1  and Table  S1. For each segment, the main coding region-VP1, VP2, VP3, VP4, NS1, VP5, VP7, NS2, VP6, and NS3 for the segments 1-10, respectively-was aligned using PAL2NAL  (Suyama, Torrents, and Bork 2006)  according to protein sequences aligned with Clustal Omega  (Sievers et al. 2011). For the remainder of the manuscript, these coding regions are referred as Seg-1 to Seg-10. Alignments were also obtained for NS4 and NS5 (alternate ORFs on Seg-9 and Seg-10) but were only used for selection pattern analyses, as described below.
We used Tracer v1. 6  (Rambaut et al. 2013)  to visualise the posterior distribution of each parameter and to obtain an estimate of the effective sample size (ESS) after removal of the initial 10 per cent burn-in. We assumed a run had reached sufficient mixing if the ESS of all the parameters was above 200. For each BTV segment, a maximum clade credibility (MCC) tree was produced after removal of the initial 10 per cent burn-in based on common ancestor heights method (Heled and Bouckaert 2013) using the auxiliary program TreeAnnotator included in the BEAST package.
Sequence clustering and reassortment In order to quantify functional genetic diversity present in Indian BTV genomes and its changes through time, we used the Bayesian clustering algorithm provided by the program K-Pax2  (Pessia et al. 2015). This approach overcomes problems encountered with standard clustering tools relying on arbitrary criteria that are challenging to calibrate in cases where evolutionary histories and levels of divergence may vary considerably across the genome. For each segment, clusters of sequences based on shared amino acid changes were defined via K-Pax2. Ten independent runs were carried out from different starting configurations under the default prior settings and upper bound values for the number of clusters corresponding to the number of samples (n ¼ 92). The optimal clustering of each segment was identified using the log posterior scoring function of the method.
Delineated clusters were highlighted on the plotted phylogenies using the R package ggtree  (Yu et al. 2017). We used multi-dimensional scaling (MDS) plots summarising tree-to-tree variation in branch lengths to assess how reassortment might have broken up associations among segments  (Rambaut et al. 2008; Bahl et al. 2013). For each BTV segment, 500 trees sampled from the MCMC chain were used to determine the time to most recent common ancestor (TMRCA) for any pair of Indian isolates sampled within the same year (from 2003 onwards). The correlation coefficient of TMRCA estimates across all pairwise comparisons of trees was calculated and used to estimate the tree-to-tree distance (inverse). The matrix of tree-totree distances was then plotted in two dimensions using MDS. In this analysis, the spread of each point cloud represents the statistical uncertainty in the phylogenetic history of each BTV segment, where overlapping point clouds represent cases for which the null hypothesis of no reassortment cannot be rejected.
Reassortment events were further identified by the graph incompatibility-based reassortment finder (GiRaF) program originally developed for influenza viruses  (Nagarajan and Kingsford 2011). Fifty repeats of the following two-steps procedure were performed. First, 1,000 unrooted candidate trees were inferred for each segment with MrBayes v3. 2. 7  (Ronquist and Huelsenbeck 2003; Ronquist et al. 2012 ) using a GTRþIþG substitution model, a burn-in of 50 per cent (100,000 iterations), and sampling every 200 iterations. This tree sample was used to account for uncertainty in the phylogenetic inference for each segment. GiRaF, which identifies sets of taxa with different phylogenetic placement across the trees, was applied to each tree set using default settings across all forty-five possible combinations of segments to comprehensively catalogue reassortment events.