dataesr/scientific-paragraphs-categorization huggingface dataset:
To split branches of a tree, a random sample of m variables was chosen from the full set of p variables. The partition of probes between learning and validation datasets was shown in Supp. Fig. S2. We checked that the three experiments of origin were included in both the training and validation datasets. The GTB prediction models were also generated using 1500 small decision trees for classification or regression, and using a randomly selected bootstrap sample set for learning (n = 74 pigs) and the remaining data (n = 74 pigs) for validation. As recommended, each tree typically contained about six terminal nodes. The model was like to Fourier or Taylor series, which is a sum of factors that becomes progressively more accurate as the expansion continues. After each step of boosting, the algorithm scaled the newly added weights, which balanced the influence of each tree.
The iteration steps were also applied to reduce the number of VIPs in the selected models. At each run, the accuracy of classification models was estimated with the proportion (%) of good classification and the optimal models were selected according to the ROC curve. In regression, RMSE was calculated as the square root of the difference between the realized and the predicted observation within the OOB data after permuting each predictor variable in the training dataset divided by the number of trees for the regression procedure. The adjusted coefficient of determination (R 2 ) was also computed. The predicted (X) values for FCR obtained by the best GTB model and the observed (Y) values measured on the pigs were compared (X-Y) using the GLM procedure. The model was considered unbiased when the intercept obtained by the GLM model was not different from 0 and the slope was not significantly different from 1.
Iterative steps for model reduction to predict FCR values1Random forest (RF) or gradient treenet boosting (GTB) algorithms were applied on a transcriptomic dataset containing 26,687 molecular probes measured in whole blood sampled from 148 pigs. Dataset was split into training (n = 74) and validation test (n = 74) subsets to evaluate model performance in predicting food conversion ratio (FCR). The first rounds led to model stabilization with 604 molecular probes as very important variables (VIP) for FCR prediction using RF and 728 probes for FCR prediction with GTB, respectively, out of the 26,687 expressed annotated probes.
Acknowledgements The authors are grateful to  Annie Vincent  (PEGASE, INRAE) and to  Yannick Lippi  and  Claire Naylies  ( Get-TRiX facility, Genotoul, Toulouse, France ) who performed RNA extraction from blood and/or produced the original transcriptomic datasets. Thanks are also due to staff of  UE3P  (https://doi. org/10. 15454/1. 5573932732039927E12 )and Genesi (https://doi. org/10. 15454/1. 5572415481185847E12 ) Experimental Units (France)  for animal care and line selection procedure.
Methods: The T21RS COVID-19 Initiative launched an international survey for clinicians or caregivers on patients with COVID-19 and DS. Data collected between April and October 2020 (N=1046) were analysed and compared with the UK ISARIC4C survey of hospitalized COVID-19 patients with and without DS. Findings: The mean age of COVID-19 patients with DS in the T21RS survey was 29 years (SD = 18). Similar to the general population, the most frequent signs and symptoms of COVID-19 were fever, cough, and shortness of breath. Joint/muscle pain and vomiting or nausea were less frequent (p < 0. 01), whereas altered consciousness/confusion were more frequent (p < 0. 01). Risk factors for hospitalization and mortality were similar to the general population with the addition of congenital heart defects as a risk factor for hospitalization.
T21RS DS survey An online survey was developed in March 2020, to identify patients with COVID-19 among individuals with DS (a copy of the survey is provided in the supplementary material). Two equivalent surveys were developed, one to be completed by caregivers/family members of COVID-19 affected individuals with DS and one to be completed by clinicians. Most often, either the caregiver or the clinician completed the survey per person. When both forms were completed for the same individual (i. e. , linked surveys), information was combined to maximize the amount of available data per person. The survey collected information on: 1) basic demographics, 2) living situation during the pandemic, 3) pre-existing health conditions, vaccinations and medications, 4) SARS-CoV-2 testing, 5) presenting signs and symptoms, 6) hospital and ICU admission, 7) complications associated with SARS-CoV-2 infection (clinician only), 8) medications and treatments used during COVID-19 illness (clinician only), and 9) status at last evaluation.
The survey was implemented through REDCap  [21, 22] , a survey and database management system, and was hosted at Emory University. As we include data from the early phase of the COVID-19 pandemic and the testing capacities differ between countries, we use the term "case" to refer to individuals with DS of all ages, who tested positive for SARS-CoV-2 or reported signs or symptoms of COVID-19. The survey was disseminated through clinical routes (e. g. Down syndrome medical interest group listservs and health service providers), Down syndrome associations in the US, India, Spain, UK, France, Italy, Germany, Brazil, and Spanish-speaking Latin America, and DS registries (e. g. , NIH DS-Connect Ò ) as well as via the T21RS website.
Statistical analysis For this analysis, we included data from all cases that were entered between April 9, 2020 and October 22, 2020. Only individuals with information on age and gender were included in the analyses. We used descriptive statistics to show the demographic information, COVID-19 signs, symptoms, outcomes, and comorbidities of the participants included in our analyses. To prevent study participants from being included twice in the analyses (reported by both caregiver and clinician), we excluded duplicated participants based on age, gender and country and other specific demographics. To contrast the COVID-19 related signs, symptoms, and medical complications with those observed in the general population, we compared the T21RS DS cases to data from the UK ISARIC4C survey. The ISARIC4C survey is a prospective observational cohort study engaging 208 acute-care hospitals in England, Wales, and Scotland  [23].
In this study, we included 59,025 hospitalized patients with COVID-19 from the ISARIC4C survey, which were entered between February 2020 and July 9, 2020 (downloaded on July 24, 2020). Among the 59,025 hospitalized patients, 109 individuals had DS (58,916 did not). Of these, 100 had complete data on age, gender and ethnicity. These 100 individuals, referred to as "ISARIC4C DS cases", were matched to 400 COVID-19 patients without DS (referred to as "controls") from the same survey (matching 1:4). To account for differences in study design and country-specific differences between the UK ISARIC4C survey and the international T21RS survey, we used the hospitalized patients with DS from the ISARIC4C survey as starting point and matched those to hospitalized patients in the T21RS dataset based on age, gender and ethnicity.
Only a 1:1 match was possible because of matching criteria (see supplementary tables S2 and S3 for characteristics of the matched samples). While this matching procedure reduced our sample size from 586 to 100 T21RS cases, it added an additional level of confidence because we were able to conduct two comparisons -ISARIC4C patients without DS (i. e. , controls) versus ISARIC4C patients with DS (i. e. , ISARIC4C DS cases) and ISARIC4C controls versus T21RS patients with DS. Fisher's exact test was used to probe for differences in the prevalence of signs, symptoms, and medical complications between matched hospitalized cases with DS (from the ISARIC4C and T21RS surveys) and without DS (controls from the ISARIC4C survey). To compare the mortality rates of hospitalized COVID-19 patients with and without DS in different age groups, we combined data from different sources.
Case counts and mortality rates among hospitalized patients from the general population were estimated from individuals without DS included in the UK ISARIC4C survey combined with published hospital reports from Spain  [24]  and New York City  [25]. Mortality rates among hospitalized patients with DS were based on combined data from the UK ISARIC4C survey and hospitalized individuals from the T21RS survey. In addition, we used the matched samples described above to perform a comparison of mortality rates corrected for age, gender and ethnicity using logistic regression. To test whether differences in mortality rates of individuals with and without DS can be explained by differences in the prevalence of known risk factors for COVID-19-related mortality, we adjusted the association analyses for chronic cardiac disease, chronic pulmonary disease, chronic kidney disease, liver disease, obesity, chronic neurological disorder, dementia, malignant neoplasm  [23].
In a sensitivity analysis, we analysed the association between DS and COVID-19related mortality in the whole ISARIC4C sample (52,142 individuals without DS and no missing data for age, gender and ethnicity, 100 individuals with DS) adjusted for i) age, gender and ethnicity and ii) additionally for chronic cardiac disease, chronic pulmonary disease, chronic kidney disease, liver disease, obesity, chronic neurological disorder, dementia, malignant neoplasm. In addition, we conducted age-stratified association analyses for individuals younger than 40 years versus 40 years or older. To identify risk factors associated with adverse outcomes of COVID-19 in individuals with DS, we conducted adjusted logistic regression analyses in symptomatic COVID-19 cases from the T21RS survey data. Associations between potential risk factors and admission to hospital or all-cause mortality were analysed.
Associations with age and gender were adjusted for the data source (caregiver versus clinician survey) and associations with living situation, level of intellectual disability, and comorbidities were adjusted for age, gender, data source and country of residence. We only included comorbidities that were present in at least 15% of our study samples to reduce the burden of multiple testing and the chance of false positives due to small numbers of cases. We did all data analyses using R (version 4. 0. 0).
Description of study population Of the 1906 records in the T21RS database entered between April 9, 2020 and October 22, 2020, 1103 individuals had signs and symptoms of COVID-19 or a positive test result and provided information on age and gender. After removing potential duplicates, the final sample size was 1046 COVID-19 patients with DS (Figure  S1 ). The majority of these cases were symptomatic (981 (94. 5%)) and 861 (83%) were tested for COVID-19 (750 (91%) with a positive test result) (Table  S4 ). Of the 1046 COVID-19 patients with DS, 591 (57%) were reported by a clinician and 455 (43%) by a family member or caregiver (Table  1 ). The largest number of cases were from India (405 (40%)), followed by the United States (163 (16%)), Spain (155 (15%)) and Brazil (75 (7%)). The mean age was 29 years (SD=18); cases reported by a clinician were on average 15 years older than cases reported by a family member or caregiver.
ACSC is supported by the  Alana USA Foundation ,  Awakening Angels Foundation , and the  Infectious Diseases Society of America (IDSA). MD is supported by the  Centre for Genomic Regulation Severo Ochoa  excellence grant, the  CIBER of Rare Diseases ,  DURSI 2017SGR595 , and acknowledges the  Spanish Ministry of Science, Innovation and Universities (MSIU)  to the EMBL partnership, the  Centro de Excelencia Severo Ochoa and CERCA (GenCat). AS is supported by the  MRC  ( MR/ S011277/1 ;  MR/S005145/1 ;  MR/R024901/1 ),  Lumind IDSC ,  The LeJeune Foundation  and the  European Commission  ( H2020   SC1 Gene overdosage and comorbidities during the early lifetime in Down Syndrome   GO-DS21-848077 ). ML was supported by the  National Institute for Health Research (NIHR) Biomedical Research Centre  based at  UCL Great Ormond Street Institute of Child Health/Great Ormond Street Hospital NHS Foundation Trust ).
DRdeA was partially supported by  Spanish Fondo de Investigaci on Sanitaria-Instituto Carlos III (FIS-ISCIII) , grant no. PI19/00634 ; BAC has research support from  Various Grateful Families of Patients. The  Research Programme on Biomedical Informatics (GRIB)  is a member of the  Spanish National Bioinformatics Institute (INB) , funded by  ISCIII  and  EDER  ( PT17/0009/ 0014 ). The DCEXS is a ' Unidad de Excelencia Mari ́a de Maeztu' , funded by the  AEI  ( CEX2018-000782-M ). The GRIB is also supported by the  Ag encia de Gesti o d'Ajuts Universitaris i de Recerca (AGAUR) ,  Generalitat de Catalunya  ( 2017 SGR 00519 ). This study was also supported by the  Fondo de Investigaciones Sanitario (FIS) ,  Instituto de Salud Carlos III  ( PI18/00335  to  MCI ,  PI14/ 01126  and  PI17/01019  to JF), partly jointly funded by  Fondo Europeo de Desarrollo Regional, Uni on Europea , Una manera de hacer Europa; the  J erôme Lejeune Foundation  (No.
Funding This work is supported by grants from:  Down Syndrome Affiliates in Action, Down Syndrome Medical Interest Group-USA, GiGi's Playhouse ,  Jerome Lejeune Foundation ,  LuMind IDSC Foundation , The  Matthew Foundation ,  National Down Syndrome Society, National Task Group on Intellectual Disabilities and Dementia Practices. AH is supported by the  HERCULES Center  ( NIEHS P30ES019776 ) and the  LuMind IDSC Foundation. The REDCap survey and database management system at  Emory University  was supported by  Library Information Technology Services  grant support ( UL1 TR000424 ).
Acknowledgments Trisomy 21 Research Society (T21RS) COVID-19 Taskforce developed the survey, with the financial and dissemination support of Down Syndrome Affiliates in Action (DSAIA), Down Syndrome Medical Interest Group-USA (DSMIG-USA), GiGi's Playhouse, Jerome Lejeune Foundation, LuMind IDSC Foundation, The Matthew Foundation, National Down Syndrome Society (NDSS), and the National Task Group on Intellectual Disabilities and Dementia Practices (NTG). These and other international Down syndrome organizations are members of the T21RS COVID-19 stakeholders advisory group that provided advice to inform the design of the survey questions and interpretation of results, including the Global Down Syndrome Foundation (USA), DSA (UK), DSMIG (UK), DSMIG (USA), DSRF-UK, DSi, DSE international, Trisomie21-France, Down España, National Down Syndrome Congress (NDSC), Down Madrid, Fundaci o Catalana Síndrome de Down (Spain), EDSA, Royal College of Psychiatrists, Coor-Down (Italy), Associazione Italiana Persone Down (AIPD; Italy), AFRT (France), Fundaci on Iberoamericana Down 21 (Spain), FIADOWN (Latin America), Federação Brasileira das Associações de Síndrome de Down (Brazil) and the European Down Syndrome Association.
0) 100 0. 094 Vomiting/nausea 92 (27. 0) 341 8 (10. 7) 75 0. 003 14 (14. 0) 100 0. 008 Diarrhoea 77 (23. 1) 334 10 (12. 3) 81 0. 034 17 (17. 0) 100 0. 216 Skin rash or skin ulcers 5 13 (4. 0) 321 4 (5. 1) 78 0. 754 2 (x) 44 0. 700 dataset) and 92% (95%-CI 84À96%; T21RS dataset) lower in the younger individuals compared with those over 40 (table.
imagej. nih. gov/ij/ last access: 4 May 2021) and was automatically performed by using the MorFo_. ijm (Morphometrics on Foraminifera) Java macro developed for this study (available at https://github. com/microfossil/ ImageJ-LabView-Scripts, last access: 4 May 2021, and in the Supplement). Once installed (simply put the MorFo_. ijm file into the plugin directory of your ImageJ software) and selected, the MorFo plugin will automatically perform the following:.
Acknowledgements. We thank the  Agence Nationale de la Recherche  for its financial support to  Luc Beaufort  and  Martin Tetard  under projects  ANR-12-BS06-0007-CALHIS  and  JPI  Belmont  ANR-15-JCLI-0003-05-PACMEDY. We thank  IODP France  for its financial support to  Martin Tetard. This work is a contribution to the  EU   FP7  project  MedSeA  (grant agreement no. 265103 ). Samples from the WNP were studied in the framework of the  Shirshov Institute state assignment  (theme no. 0128-2021-0006 ,  Ekaterina Ovsepyan ). Many thanks to the professional captains and crews of the research vessels  Marion Dufresne ,  Sonne, Meteor , and  Olaya. We would also like to thank  Franck Bassinot ,  Frans Jorissen , and  Helena Filipsson  for their valuable contributions to this paper. Ekaterina Ovsepyan is thankful to  Elena Ivanova  for collecting core top samples from the  WNP.
Code availability. The code use for the morphometric analysis of images is available at https://doi. org/10. 5281/zenodo. 4740079 (Tetard, 2021)  and also in the Supplement of the present publication as a file named "MorFo_. ijm". Data availability. All the data used for calibration of the methods presented in this study are available in Tables  1  and 2. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-18-2827-2021-supplement.
imagej. nih. gov/ij/ last access: 4 May 2021) and was automatically performed by using the MorFo_. ijm (Morphometrics on Foraminifera) Java macro developed for this study (available at https://github. com/microfossil/ ImageJ-LabView-Scripts, last access: 4 May 2021, and in the Supplement). Once installed (simply put the MorFo_. ijm file into the plugin directory of your ImageJ software) and selected, the MorFo plugin will automatically perform the following: Schneider et al. , https://doi. org/10. 5194/bg-18-2827-2021 Biogeosciences, 18, 2827-2841, 2021 2012; http://.
Background: Alternative splicing (AS) produces transcript variants playing potential roles in proteome diversification and gene expression regulation. AS modulation is thus essential to respond to developmental and environmental stimuli. In grapevine, a better understanding of berry development is crucial for implementing breeding and viticultural strategies allowing adaptation to climate changes. Although profound changes in gene transcription have been shown to occur in the course of berry ripening, no detailed study on splicing modifications during this period has been published so far. We report here on the regulation of gene AS in developing berries of two grapevine (Vitis vinifera L. ) varieties, Gewurztraminer (Gw) and Riesling (Ri), showing distinctive phenotypic characteristics. Using the software rMATS, the transcriptomes of berries at four developmental steps, from the green stage to mid-ripening, were analysed in pairwise comparisons between stages and varieties.
In grapevine, like in other plant species, these different stress factors have previously been shown to regulate gene AS  [22] [23] [24] [25]. SR genes, which play a major role in gene splicing, are themselves subjected to AS modulation, especially under stress conditions, and may regulate the splicing of multiple downstream target pre-mRNAs, at the same time, to modify the transcriptome  [26, 27]. The extent of AS in grapevine has been first explored using cDNAs/ESTs collections available from public databases or merged RNAseq data to identify as many events as possible  [22, 28]. The analysis of pooled RNAseq data from leaves, roots and berries exposed to various stress conditions, together with the comparison of two rootstock varieties, suggested that tissue, environmental conditions and even genotype may contribute to the diversity of AS profiles  [22].
Total differential AS events detected in stageand variety-comparisons The comparisons between consecutive stages in Gw and Ri, and between the two varieties at each stage, were performed using replicates of RNAseq data obtained from berries harvested at four developmental stages, i. e. green berry, 6 weeks after flowering (S1), hard berry and soft berry at the mid-véraison stage (respectively, S2 and S3), and mid-ripening (S4). Four types of AS events were considered: usage of 3′-and 5′-alternative splice sites (A3SS and A5SS), exon skipping (ES) and intron retention (IR). For each AS event, the rMATS software distinguished two alternative isoforms and estimated the inclusion level (IL) as the proportion of the longest isoform among the total, which indicated the degree of AS. ILs were then automatically compared between two conditions to determine an IL difference (ILD) (significant at FDR ≤ 5%).
We used the rMATS software, shown effective in plants for accurate AS analysis from replicate data  [37] , to analyze and statistically validate the differential events occurring between two conditions, either consecutive stages, or the two varieties at each stage. Thereafter, we applied stringent threshold values for the number of reads covering the splice junction and for the ratio of splice isoforms (ILs), in order to select only the most reliable results. Indeed, the number of AS events can be overestimated when taking into account the many low abundant and incompletely spliced (IR) transcripts constituting a noise in the splicing process  [40]. Remarkably, our analysis highlighted many differential exon skipping/inclusion events (≈ 50%), that have often been described as the least frequent AS events occurring in plants, although this assertion was mainly based on studies in Arabidopsis  [12, 41].
The investigation of AS in other plant species suggest that this type of event occurs much more frequently than generally reported. For instance, ES events were found to be only 1. 5 fold less frequent than IR events in maize leaves  [42]  and slightly more frequent in the kiwi fruit  [43]. These findings argue in favor of a sofar underestimated importance of ES events in plants. It is worth mentioning that almost half of the differential ES events detected by rMATS were newly identified AS events, suggesting that this software was particularly well-suited to this task.
AS analysis and gene expression quantification For AS analysis, the rMATS software version 4. 0. 2 (rnaseq-mats. sourc eforge. net/) was used to compare replicate biological samples representing two different conditions, either consecutive stages or different varieties. This fast computer program has been conceived to model exon inclusion levels and to evaluate, simultaneously, the individual replicates uncertainty and the variability among replicates, which leads to highly accurate validation of DAS events between the compared conditions. AS events were detected by rMATS using both the RNAseq data BAM files and the grapevine reference genome GTF file. DAS events were statistically validated (FDR ≤ 0. 05) for a difference between isoform ratios exceeding the defined threshold of 5% (c). The different types of AS events considered were A3SS, A5SS, ES and IR events.
v3 annotation, and to compare the genomic sequence of splice sites between the two varieties. Sashimi plots representing the alignment of RNAseq reads to the genome annotation were automatically generated by a dedicated tool of the IGV application. For gene expression quantification, read counts (normalized by the size of the library and the gene length), were generated with featureCounts (v1. 5. 3) (acade mic. oup. com/ bioin forma tics/ artic le/ 30/7/ 923/ 232889) and according to the VCost. v3 annotation. The comparison of gene expression between consecutive developmental stages was then performed with the Sartools R package (v1. 7. 3) (journ als. plos. org/ ploso ne/ artic le? id= 10. 1371/ journ al. pone. 01570 22) and the DESeq2 R package (v1. 22. 2) (pubmed. ncbi. nlm. nih. gov/ 25516 281/).