dataesr/scientific-paragraphs-categorization huggingface dataset:
Randomisation Randomisation will be conducted openly and stratified by centre. It will be performed according to a 1:1 ratio and balanced by blocks. The random numbers will be generated by computer. Subjects are randomised into blocks as the allocation progresses, a block being a subgroup of predetermined size within which there is a random allocation of patients. The software used for the randomisation is SAS version 9. 4. The randomisation key is known only to the biostatistician and the data managers to make it impossible for the investigator to assign a particular treatment. As mentioned above, for logistical reasons the trial is now open-label. As Karanicolas et al. pointed out in their article from 2010, the study should have been masked to the biostatisticians until analyses were performed to reduce the study bias  [31].
The clinical protocol has been writeen according to Spirit check-list (see Additionnal file 5). The amended protocol should be a dated, updated version. If necessary, the information form and consent form should be amended. The updated protocol is at version 10 on 7 July 2018. All the submissions/declarations were made by the Sponsor Department at CHU Nantes which manages the quality of the data collected. The data collected during the study will be processed electronically in accordance with the requirements of the CNIL, the French Data Protection Authority (in compliance with the French Reference Methodology MR001). As required, the sponsor has provided an insurance policy to cover the financial consequences of its civil liability in accordance with the regulations. It has been possible to carry out the protocol and the trial thanks to an Executive Committee which includes a Scientific Committee and a Steering Committee.
An inspection or audit may take place as part of this study, performed by the sponsor and/or by the regulatory authorities. Inspectors will check the documents, logistics, records and any other resources that the authorities consider to be associated with the clinical trial and that may be located at the trial site itself. The trial results will be published in international ophthalmological, medical and scientific journals and presented at national and international conferences. The investigators, who will share the entirety of the final trial dataset, will follow the rules and guidelines of the International Committee for Medical Journal Editors (ICMJE)  [32]. In practice, the Scientific Committee will be among the authors of the publication, as will the investigators who have included the most patients in the trial.
Statistical analysis We used EpiData Analysis (Version 2. 2. 2. 187, http://www. epidata. dk) for dataset restructuring, univariate and bivariate analyses, and R (Version 3. 6. 0, http://www. r-project. org) for time-to-event data analysis. Relevant endpoints were evaluated in three time-to-event analyses: (1) overall treatment success, (2) bacteriological treatment success, and (3) deaths during observation. All outcome probabilities were determined by the KaplanÀMeier estimator. The initial population at risk were all eligible patients who received at least one dose of the regimen. The observation time was censored at 33 months following treatment start. In the first analysis aiming to determine treatment success, loss to follow-up after treatment completion was treated as censored while loss to follow-up and death during treatment were treated as events following international definitions for tuberculosis treatment outcomes  [16].
Acknowledgments We thank  National Tuberculosis  Programme managers:  Jean-Louis Abena ,  S everin Anagonou ,  Georges Bakasway ,  Aoua Hima ,  Kouakou Jacquemin ,  Isidore Moyenga ,  Thadd ee Ndikumana ,  Eucher Yazipo ; laboratory officers:  Dissou Affolabi ,  Souba Diand  e,  Sarah Eyangoh ,  Mourad Gumusboga ,  Zelica Hamidou ,  Eliane Kamanzi ,  Michel Kaswa ,  Raymond Nguessan ,  Manuela Onambele ,  Timoth ee Ouassa ,  Melissa Sander ,  Nyandwi Stany ; clinical officers:  Gis ele Badoum ,  Emile Birba ,  Ibrahim Boukary ,  Yves Habimana-Mucyo ,  Albert Ignaleamoko ,  Ferdinand Kassa ,  Olivier K Adade ,  Olivia Mbitikon ,  L eopoldine Mbulula ,  Soumaila Morou ,  Michel Sawadogo ; data managers:  Nicole Anshambi ,  Wilfried Bekou ,  Marius Esse ,  Lucien Koffi ,  Andr e Nana Yakam ,  Yvon Ngana ,  Tandaogo Saouadogo ,  Valeri Ibode ; members of the Scientific Committee:  Nadia Aït-Khaled ,  Brigitte Gicquel ,  Nicolas V eziris ,  Muriel Vray ; and all patients and families who participated in the nine-country study.
Acknowledgements Open Access funding provided by Projekt DEAL. We greatly appreciate the financial support of Kinderherzen e. V. concerning the performance of Fibrotest ®. We also acknowledge the valuable multi-disciplinary collaboration with  Christian Bassir  from the  Department of Pediatric Radiology at Charité Universitätsmedizin Berlin , and  Tobias Müller  and  Hans-Peter Müller  from the  Department of Gastroenterology and Hepatology, Charité Universitätsmedizin Berlin , concerning the diagnostic and therapeutic approach of Fontan patients with hepatic abnormalities.
Reads quality, alignment and data set generation Reads quality has been performed using FastQC v0. 11. 9 software (http ://www. bioinformatics. babraham. ac. uk/projects/fastqc/). All reads have the same length: 119bp and 125bp for R1 and R2 paired-end reads respectively. Per base sequence quality were high for R1 paired-end sequences (>36) and for R2 paired-end sequences (>32) with a quality score per sequence around 39 for each reads (40 being the maximum). Sequence content per base was accurate for both R1 and R2 paired-end reads. GC content per was slightly higher than the theoretical distribution (calculated with the mean GC rate) for each reads. Thus, the high quality of reads allows avoiding read trimming or deletion. One read alignment was realized using the Stacks V2. 5 software (http ://catchenlab.
life. illinois. edu/stacks/). Parameters have been set using r80 methods which maximize the number of SNPs or loci shared by at least 80% of the samples  (Paris et al. , 2017). The optimized parameters are "max distance between stacks" (inside each sample) and "number of mismatches between stacks" (between samples). Every other parameters have been kept to default values. Data sets were in the form of VCF data file (containing all the SNPs found in the alignment) and fasta data file (which contain the two alleles found for every loci for each sample). A RAD -sequencing locus is thus a sequence composed of 1 to 3 paired-end reads aligned together and delimited by restriction sites. To run DILS-ABC inferences, Stacks fasta file has been converted in another fasta file compatible with DILS (https ://github.
, 1993; Cruickshank and Hahn, 2014; Roux et al. , 2014). On each simulated data set we calculate a vector of means and standard deviations for different summary statistics: intraspecific statistics (π for M. helenor, π for M. achilles, π for M. deidamia, θW for M. helenor, θW for M. achilles, θW for M. deidamia, Tajima's D for M. helenor, Tajima's D for M. achilles, Tajima's D for M. deidamia) and interspecific statistics (gross divergence, net divergence and FST for all three possible pairs; ABBA-BABA D). Statistical comparisons between simulated and observed statistics were performed using the R package abcrf  (Pudlo et al. , 2014; Raynal et al. , 2019).
We measured patrolling time for a total of 295 occasions, including 78 recaptures (i. e. 217 individuals were captured at least once). All captured individuals were males. Individuals M. achilles were the most frequently captured (n = 121), followed by M. helenor (n = 95). Individuals M. deidamia were about half less captured (n = 48), and individual M. menelaus were the least captured (n = 34). Based on capture-recapture histories, we estimated individual abundance for each species using a loglinear model implemented in the R package Rcapture  (Baillargeon and Rivest, 2007)  (Fig. S1 ). Given the short duration the sampling period (22 days) relative to the longevity of adult Morpho butterflies (several months,  Garcia et al. , 2014) , we used a closed-population model assuming no effect of births, deaths, immigration and emigration.
Acknowledgments The authors would like to thank the Peruvian authorities, and in particular SERFOR (the  Servicio Nacional Forestal y de Fauna Silvestre)  for providing the necessary research permits (permit: 373-2017-SERFOR-DGGSPFFS). We thank  Ronald Mori Pezo  for help with the mark-recapture experiment. C. L. R. acknowledges financial support by  Université de Paris  and the  Ecole Doctorale FIRE -Program Bettencourt. This work was also supported by a grant from  Agence National de la Recherche  under the LabEx  ANR-10-LABX-0003-BCDiv , in the program " Investissements d'avenir " n  ANR-11-IDEX-0004-02 " to C. L. R and from the  Emergence program of Paris city council  to V. L.
Histological Analysis of Lung Tissue Microscopic analysis was performed on tissue samples from the right cranial lobes of lungs, fixed in Formalin solution (Sigma-Aldrich, Saint Quentin Fallavier, France) for 48 h, dehydrated in ethanol, and embedded in paraffin. Sections (5 µm) were deparaffinized, counterstained with hematoxylin/eosin/saffron, analysed, and photographed with a NanoZoomer (NanozoomerDigitalPathology. view software, Hamamatsu, Massy, France). Cell subpopulation characteristics and inflammation in each section, evaluated in a blinded manner, were morphologically described. The presence and location of neutrophilic infiltrates were graded subjectively by a diagnostic pathologist considering the amount of infiltrating cells and the extent of the section affected. The scores were -(absent) no neutrophilic infiltrate detected; (+) (insignificant) few scattered solitary neutrophils detected; + (mild) small areas with infiltration of few neutrophils; ++ (moderate) multifocal areas with evident infiltration of neutrophils; or +++ (severe) widespread areas with prominent neutrophilic infiltration.
Proteins were identified and quantified by using the quantification software MaxQuant 1. 5. 1. 2 (free software developed by Prof. Jürgen Cox at the Max Planck Institute of Biochemistry, Münich, Germany, https://www. maxquant. org) [41] and the Bos taurus proteome database extracted from Uniprot, released September 2016. The search parameters were set to Bos Taurus taxonomy, trypsin enzyme, carbamidomethylation of cysteine as static modification, and oxidation of methionine and deamidation of asparagine and glutamine as variable modifications. A decoy search database including common contaminations and reverse database was used to estimate the false discovery rate (FDR). An FDR of 1% was accepted for peptides and protein identification. The criteria for protein identification were set to at least two identified peptides per protein.
Statistical Analysis Statistical analysis was performed with the software GraphPad Prism 8. 3. 1 (GraphPad Software, San Diego, CA, USA). Data shown are individual data, with mean and SD. Non parametric tests were always used: Mann-Whitney two-tailed or Wilcoxon with corrected p-values (Benjamini-Hochberg). As stated in the figure legend, * indicates significant differences between controls and vaccinated calves (both preF and preF+N), # indicates significant differences between the two vaccinated groups (preF versus preF+N). # ,* p < 0. 05; ## , ** p < 0. 01, ### , *** p < 0. 001, #### , **** p < 0. 0001. Analysis of correlates of protection was done using Spearman two-tailed analysis between the immune response data versus the clinical and virological data of the 12 vaccinated calves. For principal component analysis, R (free software, https://www.
(b) Biopsies were taken from the cranial lobe, and lung sections were stained with H&E. (c) Bronchoalveolar lavage (BAL) supernatants from vaccinated calves and controls, obtained on D7 after BRSV challenge, were analysed by liquid chromatography coupled to tandem mass spectrometry (the shotgun method, LC-MS/MS, Orbitrap, Thermo Scientific, Bremen, Germany). Proteins were identified, and a total label-free intensity analysis was performed using MaxQuant 1. 5. 1. 2 software. Histological examination of lung sections from cranial lung lobes confirmed the protection of vaccinated calves. Whereas four out of five controls had a marked consolidation of the lung parenchyma and a broncho-to bronchiolar-interstitial inflammatory reaction with a marked purulent exudate in the airways, such exudate could not be observed in calves vaccinated with either of the vaccines (Figure  2b ).
Correlation of Protection against BRSV Infection with Immune Effectors Induced by Vaccination To gain further insight into the immune parameter(s) associated with protection against BRSV induced by the subunit vaccines, we took advantage of the fact that the two subunit vaccines induced a relatively wide range of values for immunological, virological, and clinical parameters (21 parameters measured for 17 calves). These parameter values were loaded in a principal component analysis (PCA) in order to identify the main axes of the data variance as well as the key variables. In a first analysis where data from the control and vaccinated groups were used, the Dim 1 axis, which explained 54. 65% of the variance, clearly separated the data from the control group and the two vaccinated groups, with the data from the two vaccinated groups overlapping on the x-axis (Figure  6a ).
Only the significant correlations (p < 0. 05) and the correlations with p < 0. 1 are shown (Figure  6e ). Virus replication in nasal secretions (RNA nasal AUC) and in BAL (RNA BAL) were negatively correlated with PreF IgG titers and nAb titers, respectively (Figure  6e ). Interestingly, the presence of eosinophils in lungs was negatively correlated with BRSV IgA titers in serum whereas a positive correlation was observed for % of PMN in BAL. Together, the global analysis of the immune, virological, and clinical data of calves vaccinated with preF or preF+N indicated that BRSV-neutralizing Ab and preF Ab are associated with virological protection and suggest that these Abs were instrumental in the protective immunity induced by our subunit vaccines. a relatively wide range of values for immunological, virological, and clinical parameters (21 parameters measured for 17 calves).
Acknowledgments: We thank the experimental farm  INRAE Le Pin  (https://www6. rennes. inrae. fr/domaineexperimental-du-pin_eng/)for providing the calves and  INRAE PFIE  (https://www6. val-de-loire. inrae. fr/pfie_eng/)for housing the vaccine trial. We thank the histology facility of  @Bridge  of  UMR GABI  and the MIMA2 platform for access to the virtual slide scanner (Pannoramic SCAN,  3DHISTECH ), Jouy-en-Josas, France. We also thank the  Cells for Life Platform , partly funded by the  Infrastructure Committee at SLU , ( Uppsala, Sweden ), for providing facilities and equipment. This work was supported by the  Mass Spectrometry-based Proteomics Facility at Uppsala University.
For the IHC, a goat polyclonal antibody (VMRD, Pullman, WA) against N. caninum was used as a primary antibody, antigoat IgG horseradish peroxidase (HRP)-labeled polymer (Vector polymer enzyme detection kit, Burlingame/CA) as the secondary antibody, and 3-amino-9-ethylcarbazole as the chromogen (DAKO, Santa Clara/CA). The PCR for N. caninum was done following the procedure described by  Yamage et al. (1996). PCR products were visualized on 1% agarose gel electrophoresis stained with SYBR safe (Invitrogen, USA), purified with QIAquick PCR Purification Kit and sequenced in house at the sequencing service of the "Institut Pasteur de Montevideo", Uruguay. All tests were performed with appropriate positive and negative controls for each run. Statistical methods. The results of the pathologic examinations and diagnostic tests, as well as the fetal gestational age, degree of autolysis and the department where the dairy farm was located were recorded in a database using Microsoft Excel, and descriptive statistics were obtained.
PCR for BHV-1 was negative in all cases. RT-PCR for BVDV was positive in three cases; two of them were in co-infection with N. caninum, and the protozoon was determined as the causal agent based on the presence of typical lesions and the positive results for N. caninum PCR and IHC. Due the absence of lesions, the etiology on the third BVDV-positive case was categorized as undetermined. Two of the three BVDV PCRpositive fetuses were aborted in the second trimester and the other in the third trimester of gestation. To assess whether these fetuses congenitally infected with BVDV harbored high antigenic viral loads and thus were persistently infected, IHC for BVDV antigen detection was performed in several tissues (brain, lung, heart, thymus, liver and small intestine), with negative results in all three cases (data not shown).
Acknowledgments. -This research was financially supported by " Agencia Nacional de Investigación e Innovación" (ANII) , grant  FSSA_X_2014_1_105696. The authors thank  Yisell Perdomo ,  Cecilia Monesiglio ,  Anderson Saravia ,  Bruno Lopez , and all graduate students from the animal health platform at  INIA ,  Jennifer McKenna  from  UW, Francisco Uzal ,  Karen Sverlow  and  Juliann Beingesser  from  CAHFS-UC Davis  for valuable assistance. We are grateful to  Luis Corbellini , for his keen and valuable suggestions preparing the manuscript,  Ximena Salaberry  from the  "Division de Laboratorios Veterinarios" (DILAVE) of the Uruguayan "Ministerio de Ganadería, Agricultura y Pesca" (MGAP) ,  Santiago Mirazo  from  UdelaR , and  Cecilia Nieves  and  Camila Hamond  from the " Institut Pasteur", Montevideo.
Standard model signal modelling Central exclusive production of W + W -pairs is simulated with the Forward Physics Monte Carlo (FPMC) event generator  [62]. FPMC simulates diffractive exchanges and photon exchange in pp collisions. Photon exchange is simulated within the equivalent photon approximation described in Sec. 2. 1. The process pp → pW + W -p accounts for scattering amplitudes induced by the SM γW + W -and γγW + W -. These amplitudes were calculated with the CalcHEP package  [63] , and coded into FPMC  [12, 64]. The SM cross section is 95. 6 fb for central exclusive W + W -production, and 5. 9 fb for a W + W -invariant mass above 1 TeV for √ s = 14 TeV  [12, 64].
Transcription factors (TFs) regulate the expression of gene expression. The binding specificities of many TFs have been deciphered and summarized as position-weight matrices, also called TF motifs. Despite the availability of hundreds of known TF motifs in databases, it remains non-trivial to quickly query and visualize the enrichment of known TF motifs in genomic regions of interest. Towards this goal, we developed TFmotifView, a web server that allows to study the distribution of known TF motifs in genomic regions. Based on input genomic regions and selected TF motifs, TFmotifView performs an overlap of the genomic regions with TF motif occurrences identified using a dynamic P-value threshold. TFmo-tifView generates three different outputs: (i) an enrichment table and scatterplot calculating the significance of TF motif occurrences in genomic regions compared to control regions, (ii) a genomic view of the organisation of TF motifs in each genomic region and (iii) a metaplot summarizing the position of TF motifs relative to the center of the regions.
INTRODUCTION The regulation of gene expression is mediated by the binding of transcription factors (TFs) to regulatory regions  (1). They contain a DNA binding domain, which recognizes short specific DNA sequence motifs  (6-12 bp). Hundreds of TF binding motifs have been determined experimentally using in vitro assays such as systematic evolution of ligands by exponential enrichment (SELEX)  (2)  or high-throughput SELEX (3) and protein-binding microarrays (PBM)  (4, 5) , as well as in vivo approaches such as chromatin immunoprecipitation followed by high throughput sequencing (ChIPseq)  (6). They have been summarized as position-weight matrices (PWMs)  (7)  and are freely available in TF motif databases such as JASPAR  (8) , cis-BP (4) and HOCO-MOCO  (9). In order to identify TF motifs in genomic regions of interest, several approaches are available.
A de novo motif search allows to identify TF motifs enriched directly from the input sequences without prior knowledge. For example, it can be applied to derive motif PWMs from TF ChIP-seq data. Several methods have been developed for de novo motif search such as MEME-ChIP from the MEME Suite  (10) , peakmotifs from RSAT  (11)  or HOMER  (12). Once a motif is identified, it can be compared to known TF motifs found in databases, which is often directly performed by the de novo motif search methods. As an alternative, a known motif search can be performed by taking advantage of the hundreds of motif PWMs from public databases to identify TF motifs enriched in genomic regions of interest. Several methods have been developed for known motif search such as AME from the MEME Suite  (13) , i-cisTarget  (14) , Pscan-ChIP  (15)  or HOMER  (12).
The use of known motifs, allows to perform further analyses using the genomic coordinates of the TF motif occurrences such as the identification of motifs at specific positions within the genomic regions. Such analyses can for example be performed by CENTRIMO from the MEME Suite  (16). Despite the large amount of TF motifs available in public databases, their integration with genomic regions of interest remains non-trivial for researchers with no coding experience. Furthermore, the availability of online tools, which take advantage of such valuable data, remains sparse and often lacks graphical representation of the results. Here, we present TFmotifView, a web server that allows to study the distribution of known TF motifs in genomic regions. Using the genomic coordinates of the regions of interest and selected TF motifs, TFmotifView performs an overlap of the regions with TF motif occurrences already pre-processed on our servers.
Transcription factor motif occurrences TFmotifView relies on known TF motifs from the curated, non-redundant, vertebrates JASPAR CORE 2020 database  (8). JASPAR position-weight matrices were used to scan each reference genome (human hg38, hg19 and mouse mm10, mm9) for motif occurrences using MAST from the MEME Suite  (17). Since each motif has a different complexity, we used the motif information content  (18)  to derive a dynamic P-value threshold for calling motif occurrences (Supplementary Figure  S1A-C ). Each motif information content IC is defined using the frequency f i,b at each position i for each base b assuming an equal base frequency in the whole genome p b of 0. 25: IC = i b f i,b × log 2 f i,b p b The P-value threshold is then derived following: p-value = 1 2 IC The P-value thresholds derived from the motif's information content were tested using ChIP-seq datasets of TFs expressed in mouse embryonic stem cells (see Supplementary Figure  S1 ).
We used the following publicly available datasets: KLF4 (ChIP-Atlas ID ERX1965624), MYC (GEO ID GSM1171649), GABPA (GEO ID GSM3258761), NRF1 (GEO ID GSM1891641), OCT4 (ChIP-Atlas ID ERX1965633), SOX2 (GEO ID GSM288347), SP3 (GEO ID GSM3258757), ESRRB (GEO ID GSM288355), CTCF (GEO ID GSM747534), SP1 (GEO ID GSM3258754) and REST (GEO ID GSM671094) and processed them following the same pipeline as our example data (see below). We further clustered highly similar motifs using TOM-TOM from the MEME Suite  (19)  and the hclust function in R. This led to 747 motifs distributed in 180 clusters (see supplementary data). Additionally, specific motifs can be provided as input using their exact genomic sequence (limited to 6-12 bp).