dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS The authors thank the patients for their participation in the study and the medical staff who contributed to the study implementation. Especially, the authors thank  Jelena Bakusic ,  Sophie Caterina ,  Andrew Darrow ,  Hugo Loos ,  Sushma Patel ,  Stefano Santamaria ,  Harold M Silverman  and  Josefa Torres  for their contribution to this study. Funding. GlaxoSmithKline Biologicals SA  funded this study (study number  207750 ) and was involved in all stages of the study, including analysis of the data. GlaxoSmithKline Biologicals SA also took charge of all costs associated with the development and publication of this present manuscript, including the Journal's  Rapid Service Fee.
Sample size and its statistical justification A statistician calculated the sample size by estimating by simulation (2000 data sets) under a Weibull distribution using the following hypotheses derived from the BMW trial  11  : a cumulative incidence of successful extubation of 60% by day 7 and 80% by day 28 taking into account a competitive risk of death of 13% by day 7 and 16% by day 28. We determined that the enrolment of 410 patients.
Assignment of intervention and data collection After obtaining consent from the patient or her/his relative, all inclusion/exclusion criteria will be checked by the investigator before randomisation. Randomisation will be stratified on centre and left ventricle ejection fraction (>or ≤45%) at the time of randomisation and carried out by connecting to the centralised electronic-case report form (e-CRF website 'Cleanweb' provided by Telemedicine technologies. Data will be collected on the e-CRF by a trained investigator or research assistant at each centre. Patient follow-up and data collected are detailed in the study flowchart (table  2 ).
Statistical methods All the analyses will be performed by the study statistician according to a predefined statistical analysis plan, using R software (R Foundation for Statistical Computing, Vienna, Austria) or Statistical Analysis System (SAS) latest versions. A two-tailed p value of less than 0. 05 will be considered as indicating statistical significance. In accordance to the CONsolidated Standards Of Reporting Trials (CONSORT) statement, a flow diagram will describe the progress of patients of the two groups through the phases of the trial (enrolment, intervention allocation, intervention received, follow-up and data analysis). The analysis will be performed on an intention-totreat basis. In case of premature stop or withdrawal from the study, patients would not be substituted. Missing value will be described and, according to nature and frequency, multiple imputation methods will be used.
Data monitoring The trial will be overseen by a steering committee (principal investigator, senior investigator and methodologist) regarding the progression and monitoring of the study. Research assistants will regularly monitor all the centres on site to check adherence to the protocol and the accuracy of the data recorded. An investigator at each centre will be responsible for daily patient screening, enrolling patients in the study, ensuring adherence to the protocol and completing the electronic case report form. Since both strategies are currently used in routine practice, no data safety monitoring board was required by the ethical committee.
Dissemination policy Findings will be published in peer-reviewed journals and presented at national and international meetings. Communications, reports and publication of the results of the study will be placed under the responsibility of the principal investigator-coordinator of the study and the steering committee. Reporting will follow CONSORT statement and rules of publication will follow the international recommendations according to The Uniform Requirements for Manuscripts (ICMJE, April 2010). Individual participant data sharing statement Data are available on reasonable request. The procedures carried out with the French data privacy authority (Commission Nationale de l'Informatique et des Libertés) do not provide for the transmission of the database, nor do the information and consent documents signed by the patients.
2 16 24  However, the timing of fluid removal is still a matter of discussion and there are currently no guidelines that recommend the use of diuretics at the initiation of weaning. Of note, a recent retrospective study found that a negative fluid balance on day 3 of ICU stay was associated with lower 30-day mortality whether occurring spontaneously or achieved with deresuscitative measures. 6 A study examining the relationship between loop diuretic use and hospital mortality in critically ill patients with vasopressor support found a reduced mortality in patients who received diuretics. 25 Nevertheless, another study did not confirm this association on 13 000 patients from a national database. 26 here are several advantages for using a preventive fluid removal strategy as tested in the current trial.
DNA sequence data processing The raw DNA sequences were first assigned to each sample via their Mid tag using MOTHUR version 1. 33  (Schloss et al. , 2009) , and reads were removed if at least one of the following criteria was found: (i) length <200 nt or >600 nt, (ii) one mismatch to the barcode sequences or ≥1 mismatch to the primer, and (iii) the presence of homopolymers of >8 nt in length. Adaptor sequences were removed from the sequences using the "Cutadapt" tool version 1. 8  (Martin, 2011)  implemented in the Galaxy server of the Institut de Génétique et Microbiologie (IGM) of the Université Paris-Sud (http://galaxy. igmors. u-psud. fr) and now available at: https://github. com/galaxyproject/tools-iuc/tree/master/tools/cuta dapt. Then, the sequences were checked for quality scores by Con-DeTri version 2.
2  (Smeds and Kunstner, 2011) , using a 90 % quality score and those below this score were removed. We used UCHIME  (Edgar et al. , 2011)  and Decipher  (Wright et al. , 2012)  to detect chimeric sequences. Sequences detected as chimeras by either program were removed from the data sets. The raw sequences have been deposited in the GenBank sequence read archive (SRA), under the accession number SRP063276. Taxonomy assignments of the remaining 16S rDNA reads were conducted using SILVAngs classifier (online server) with a similarity threshold of 90, using the Silva database release 123  (Cole et al. , 2014; Yilmaz et al. , 2014). Sequences classified as chloroplast, eukaryotes and/or mitochondria, or that could not be classified as belonging to the Bacteria Kingdom, were removed. Diversity analyses were performed using the software package MOTHUR.
Statistical analyses The clean reads were clustered into operational taxonomic units (OTUs) using UPARSE version 7 at a cutoff value of 97 % sequence identity  (Edgar, 2010 (Edgar, , 2013)). The Chao1 and Shannon indices were calculated to estimate taxon richness and diversity  (Schloss and Handelsman, 2008). Bray-Curtis distance was used for calculating the distances of multiple samples. The Principal Coordinate Analysis (PCoA) was conducted in the "ade4 ′′ R package (version 3. 1. 2) (Chessel et al. , 2004)  to ascertain groups in the microbial communities of the different samples. The beta_diversity. py command was used to estimate the beta diversity of bacterial communities using Qiime version 1. 80  (Caporaso et al. , 2010). Pearson correlation coefficients between relative abundances of sequences classified to specific phyla of different sample sites and environmental parameters were calculated using SPSS version 22.
DNA sequencing data analyses We obtained 483,364 sequences of raw reads in total from the different samples after 454 pyrosequencing (Microsynth AG, Switzerland), with an average read length of 480 nt. After bioinformatic cleaning with MOTHUR, approximately 95 % of the sequences remained (460,475 reads). The sequences were further filtered for quality score and chimeric sequence removal, leaving 65 % of the total reads (312,358 reads). The average length of the sequences after processing was approximately 400 nt. The number of remaining reads for each sample are presented in Table  3.
Bacterial community composition In total, 305,167 sequences from the 18 samples were able to be classified as belonging to the Bacteria domain (sequences classified as chloroplast, eukaryotes and/or mitochondria were removed from the DNA sequence dataset). The sequences were classified at 6 taxonomic levels using SILVAngs, resulting in the delineation of sequences belonging to 37 Phyla, 136 Classes, 136 Orders, 254 Families and Genera. The sequences (97 %), classified to 37 phyla, were found to represent 13 phyla found to be present in all the samples (Fig. 2a ). The predominant phyla of the samples represent members belonging to the Proteobacteria, Actinobacteria, Bacteroidetes, and Gemmatimonadetes, at 28. 3 %, 25. 7 %, 19. 7 %, and 6. 5 %, as the respective averages for the samples, followed by members of the Acidobacteria (3.
Normalization of sequences To better approximate the composition of the bacterial population in the samples, sequences were normalized based on the estimated 16S rRNA gene copy numbers of different species using the program Tax4fun  (Aßhauer et al. , 2015). We found that, using this program, the bacterial community composition was altered at all taxonomic levels. At the phylum level (Fig. 2b ), the relative abundance of most phyla was found to be reduced, as sequences that could not be aligned to the reference database of 16S rRNA gene copy number were assigned to unclassified sequences (15. 5 % of total sequences at the phylum level). However, the relative abundance of the Actinobacteria increased from 25. 7 % before normalization to 35. 3 % after normalization, suggesting that the Actinobacteria population was likely underestimated by simply counting classified 16S rRNA genes.
Acknowledgements We thank all the members of the  Laboratoire de Génomique et Biodiversité Microbienne des Biofilms (LGBMB) of the Institute for Integrative Biology of the Cell (I2BC)  and the Editor and two anonymous Reviewers for their interesting discussions, comments and suggestions. JO was a doctoral scholarship recipient from  CONICYT Becas Chile. YW was a doctoral scholarship recipient from the  China Scholarship Council (CSC). This work was supported by the  Centre National de la Recherche Scientifique (CNRS), France.
These examples illustrate the emerging and probably highly diverse molecular roles of non-conventional ORFs in vivo. Accordingly, ribosome profiling analyses  [33, 34] , together with the re-annotation of transcriptomes (based on algorithm for ORF prediction from transcriptions sequences) to re-analyze proteomic datasets  [34, 35] , confirmed that several non-conventional mRNAs could lead to the production of proteins. For example, the re-annotation of proteomic datasets showed that as many as 174 771 non-conventional ORFs coding for 71 705 proteins could exist in Drosophila  [35]. These observations definitively established the importance of considering non-conventional ORFs to better understand the basic products of the genome. The re-annotation analyses also revealed a novel category of non-conventional ORFs that has been called "alternative ORFs" (altORFs).
altORF Prediction altORFs were predicted in the whole BX-C locus of D. melanogaster (chr3R:12,481,489-12,801,607 in dm3 assembly) and in orthologous regions of D. simulans (droSim1), D. sechellia (droSec1), D. yakuba (droYak2), D. erecta (droEre1), D. ananassae (droAna2), D. pseudoobscura (dp3), D. persimilis (droPer1), D. mojavensis (droMoj2), D. virilis (droVir2) and D. grimshawi (droGri1) identified with blastn. This region spans ca. 320 kb. altORFs were identified on both strands using a home-made BioPython script requiring a minimal length of 30 amino acids. Briefly, this script searches for sequences longer than 90 nucleotides, beginning with an ATG and with no interruption by a stop codon. It sorts out the corresponding translated sequences. The full script can be provided upon request.
Transcription and Translation Features Transcription promoters were predicted in the whole D. melanogaster BX-C region using the Neural Network Promoter Prediction program available on the Berkeley Drosophila Genome Project (BDGP) website (https://www. fruitfly. org/seq_tools/promoter. html, Reese 2001, last accessed on 14 October 2021), with default parameters. altORFs were attributed the closest upstream promoter, annotated by the intervening distance and its prediction score. PolyA sites were searched in 150 nt downstream of the altORF in all the species were it was found using the PolyA_SVM program  [42]. altORF expression was assessed in 8-10 h whole embryos by blasting them against RNA-seq data retrieved from modENCODE (SRX008010, SRX008249, SRX008252, SRX008273, and SRX008274 experiences).
Overlap of altORFs with Other Genomic Features Gene annotations (transcripts, exons, CDS) were retrieved from FlyBase (https://fly base. org/, accessed on 10 March 2015) and overlapped with DroMel altORF coordinates. DroMel altORFs were also overlapped with cDNAs retrieved from ModENCODE (http: //www. modencode. org/, accessed on 5 August 2015), in sense or antisense orientation. altORF peptide sequences were finally compared to protein sequences Ubx, AbdA, or AbdB using BlastP to identify altORFs in frame with Hox coding sequences. Transposable element annotations were retrieved from the Natural Transposable Element Project of the BDGP website (https://www. fruitfly. org/p_disrupt/TE. html, version 9. 4. 1. accessed on 17 September 2015), and overlapped with altORF coordinates. Predicted altORFs were finally compared with different sets of peptides already published in the literature, including peptides from the PeptideAtlas database (http://ww w.
Characterization of the Predicted Peptides Predicted peptides were submitted to InterProScan (https://www. ebi. ac. uk/interp ro/search/sequence/, last accessed on 3 October 2015) to search for putative conserved protein domains such as signal peptides or transmembrane helix. Potential signal peptides were predicted using the SignalP-5. 0 Server  [44]  (http://www. cbs. dtu. dk/services/Signal P/, last accessed on 13 October 2015). Transmembrane helices were predicted using the TMHMM Server v. 2. 0  [45]  (http://www. cbs. dtu. dk/services/TMHMM/, last accessed on 13 October 2015). Intrinsically disordered regions were predicted using UIPred2A server (https://iupr ed2a. elte. hu/plot_new, [46] , last accessed on 11 May 2021). Predicted altORF peptides were also compared to the full proteome by submitting them to a BlastP search against the nr database, with default parameters.
Eukaryotic Linear Motifs (ELMs) were identified using the ELM database (http://elm. eu. org/, last accessed on 7 June 2021) and FuzzPro (https://www. bioinformatics. nl/cgi-bi n/emboss/fuzzpro, last accessed on 26 October 2015). The search was restricted to ELMs found in one of Ubx, AbdA, or AbdB proteins. All ELMs found in each altORF are given in Table  S1. Description of ELMs found in Ubx, AbdA, and/or AbdB is given in Table  S2. A global "ELM score" was computed, for each ORF, as follows: ELMscoreORF i =   ∑ j (moti f sjinORF i /moti f sjinallORFs)/aminoacidlengtho f ORF i   × 10 6 (1) This score attempts to quantify the mean occurrence of ELMs in each altORF by taking into account the length of the altORF as well as the frequency of each ELM in all the predicted altORFs.
Annotation of altORFs in BX-C We have considered the 320 kb region of BX-C and applied a home-made BioPython script (see materials and methods) to search for all possible altORFs with two required criteria for altORFs: a minimal length of 30 codons and an ATG start codon  [36]. In contrast to the previous annotations of altORFs, our prediction analysis was not restricted to ncRNAs and mRNAs (therefore considering the entire intergenic regions). This first step led to the prediction of 2086 altORFs (Figures  1A  and 2 ). We next considered altORFs that were not matching the conventional proteins Ultrabithorax (Ubx), Abdominal-A (AbdA) or Abdominal-B (AbdB) to discard altORFs that could correspond to any Hox isoform, achieving 2075 predicted altORFs in total (Figure  1A, B ). In the second and third filtering steps, we arbitrarily decided that altORFs should be present in at least two different Drosophila species that are distanced by 10 M years or more during evolution (Figure  1A-C ).
Here, we only considered RNA-seq data of stage 8-10 h embryos for considering the number of reads, which is a stage of high RNA expression and during which Hox genes are specifying the epidermis along the anterior-posterior axis. The rational was to identify altORFs that could potentially be involved in these early Hox patterning functions. The reference threshold for altORF covering by RNA-seq was deduced by considering both S2 cells and stage 8-10 h embryos. These last filtering steps led to a final list of 48 altORFs for which we also calculated the dN/dS ratio (i. e. , the ratio of non-synonymous mutations on synonymous ones, a value smaller than 1 testifying for a negative selection pressure) to assess whether they were under purifying selection. The score indicated a negative selection (dN/dS < 1) for 74% of ORFs for which the calculation could be performed (with sequences that were not too similar (dS < 0.
Conclusions Our study revealed the presence of 48 altORFs with a high potential to encode for functional alternative proteins in BX-C. Probably many more non-conventional proteinencoding ORFs are present in BX-C. For example, our stringent selection criteria did not allow retrieving all previously predicted altORF proteins (Figure  1  and https://openpr ot. org/, last accessed on 18 October 2021), highlighting that alternative proteins could be less conserved within the Drosophila evolutionary tree and/or produced using more flexible genomic annotation marks. Along the same line, we did not consider smORFs. For example, 5098 smORFs with a minimal size of 10 codons could be predicted in BX-C. Several of those predicted smORFs could likely encode for small peptides, as described for MSAmiP  [32].
Acknowledgments: We thank  Benoît Vanderperre  for helpful comments on the manuscript and  Jean-Nicolas Volff  for early discussions on the project. Work in the laboratory of S. M. is supported by the  Centre National de la recherche Scientifique (CNRS) ,  Ecole Normale Supérieure (ENS) de Lyon ,  Association pour la Recherche sur le Cancer (ARC) ,  Région Auvergne-Rhône-Alpes and Institut National du Cancer (InCA).
spinifolia. I also have some points I would like you to address: 1) Regarding the discussion on the role forest cover for seed dispersal (lines 497-509), it is not obvious to me why patterns of mtDNA and cpDNA haplotype networks would be incompatible with the signal found by IBR analysis on organelle data. I would appreciate if you could develop this point. 2) Supplementary data availability: In addition to making available the missing Table  S1 , please consider making supplementary tables S6, S7 and S8 available through reputed repositories (such as institutional repositories, Zenodo, DataDryad, supplementary material in bioRxiv. ) that can assure long term accessibility to those files (instead of Google Drive/Docs service). Also, I could not find reference PRJNA632767 in the Sequence Read Archive (SRA) NCBI database, make sure the reference is correct and it is accessible.
Additional requirements of the managing board: We would like to receive your revision within 2 months. If you need more time, just tell us. As indicated in the 'How does it work?' section and in the code of conduct, please make sure that: -Data are available to readers, either in the text or through an open data repository such as Zenodo (free), Dryad or some other institutional repository. Data must be reusable, thus metadata or accompanying text must carefully describe the data. -Details on quantitative analyses (e. g. , data treatment and statistical scripts in R, bioinformatic pipeline scripts, etc. ) and details concerning simulations (scripts, codes) are available to readers in the text, as appendices, or through an open data repository, such as Zenodo, Dryad or some other institutional repository.
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Library size and quality was assessed on an Agilent Bioanalyzer and library yield was quantified by qPCR using the KAPA Biosystems Library Quantification kit (Kapa Biosystems, Inc. , Wilmington, MA, United States) prior to sequencing on (single end, 75 bp fragment size) on the Illumina HiSeq 2000. The number of aligned reads per sample varied between 40 and 50 million, and represented ∼78% of the total reads per sample. Raw sequence data in Fastq format were assessed for quality (FastQC, 1 ) and ribosomal RNA content. Fastq files and processed files for this experiment have been deposited on the Gene Expression Omnibus (GEO) database (Accession number GSE172000). Libraries for a second batch of samples were prepared using a NEBNext rRNA Depletion Kit (New England Biolabs, #6310) to deplete ribosomal RNA from the total RNA.
RNA Sequencing Data Analyses FASTQC (see text footnote 1) and multiQC v1. 9  (Ewels et al. , 2016)  were used to determine the quality of sequencing data for all samples. Adaptor sequences were trimmed using Cutadapt 3. 1 with Python 3. 6. 2 and the -m 1 option ( 2 STAR) (Spliced Transcripts Alignment to a Reference, version 2. 5. 3a)  (Dobin et al. , 2013)  was used to align the reads to the reference genome (GRCm38/mm10) using the default settings, and the genome index was built based on the GENCODE VM22 annotation (GRCm38/mm10). rRNA and tRNA were filtered using bedtools version 2. 26. 0, using the GTF files downloaded from the UCSC Table Browser (GRCm38/mm10). HTSeq  (Anders et al. , 2015)  was used to count the reads per transcript (HTseq counts) with a default setting over the union with -s no option.
For differential gene expression analysis, DESeq2 version 1. 30. 1  (Love et al. , 2014)  was performed in the R environment (Version 4. 0. 3), using the HTseq counts and the significance cutoff set by default to an adjusted P < 0. 05. ComBat-seq  (Zhang et al. , 2020)  was utilized to adjust for batch effects between the Cofactor Genomics and Tufts University sequencing datasets. Heatmap, MA, PCA, UpSet  (Conway et al. , 2017)  plots were created in R, based on both coding and non-coding transcripts. Functional ontology and pathway analyses of differentially expressed genes were performed using Ingenuity Pathway Analysis (IPA) software (Qiagen, Valencia, CA, United States), and the Database for Annotation, Visualization and Integrated Discovery (DAVID) 3. 1 http://www. bioinformatics. babraham.
Statistical Analyses Statistical analysis of data other than RNA-sequencing data was performed using the statistical tools of GraphPad Prism 6 (GraphPad Software, Inc. ). A Student's t-test, and one-way ANOVA or two-way ANOVA followed by Tukey's test were used to determine statistical significance, which was defined as P < 0. 05. Significance between different distribution frequencies of genes was determined using a standard chi-square test.