dataesr/scientific-paragraphs-categorization huggingface dataset:
burnetii was responsible for the abortions  [200]. The vast majority of studies in Brazil are based on serological and molecular detection in convenience samples. Only recently, statistical design studies are being carried out to understand better C. burnetii epidemiology  [64, 79-81, 140, 141 ]. In the state of São Paulo, a study with 1515 cattle samples from 54 cities collected in 9 different slaughterhouses reported a seroprevalence of 23. 8% using an in-house IFA and the detection of C. burnetii by qPCR in 12. 2% of the seropositive animals, highlighting the risk of infection for abattoir workers  [64]. Molecular data suggest the existence of diverse MST and MLVA genotypes of C. burnetii in the state of São Paulo (ST74)  [86]. In the publication, an ST73 was also detected in samples from Argentina.
Methods A prospective, observational, single-center study was conducted from February to June 2021 in the pediatric emergency department (ED) of our University Hospital (NCT04885335, rst trial registration date 13/03/2021). Ethics approval for the study was obtained through our Institutional Ethics Committee of Limoges University Hospital (identi cation number 447-2021-103; rst registration date 10/01/2021). Patients aged from 1 to 17 years whose primary complaint was abdominal pain, a digestive functional sign, or a fever were recruited. Of these, patients with clinical ndings suggestive of AA after an initial examination by a senior doctor or a registrar in the ED were enrolled. Patients referred to the ED for further investigation after an initial examination by a primary care physician < 7 days prior were enrolled (despite ED examination).
Patients with abdominal pain for more than 7 days or with a history of prior appendicectomy were excluded. The enrolled children were allocated into three groups according to the pre-hospital management: 1. Family group (FG): brought in by the family with no previous consultation 2. Physician group (PG): referred by a primary care physician for further investigations 3. Non-referred group (NG): admitted to the ED after a previous consultation in primary care without having been referred by the primary care physician for further investigations The data collected in the ED were the PAS score items and its nal score, characteristics such as age, gender, a record of a previous consultation in primary care < 7 days, WBC counts and CRP values determined other than at the ED < 24 hours prior or during ED hospitalization, duration of pain (< 12, 12-24, 24-48, and > 48 hours), and the presence of abdominal guarding.
De nitive diagnoses were obtained from the ED, radiological, and pathology reports for AA. Patients and characteristics were compared in groups according to whether or not they had a previous consultation in primary care and depending on the nal diagnosis. A clinical PAS score (cPAS) was calculated without WBC and neutrophil counts, scoring a total of 8 points to represent the primary care conditions as previously described  [20]. Statistical analyses were undertaken with SAS Enterprise Guide V7. 1 software. A threshold of 0. 05 was used to de ne statistical signi cance. For the categorical variables, the results of descriptive analyses are presented as numbers and percentages. For the quantitative variables, they are presented as means and standard deviation (SD). The PPV and NPV of PAS scores in diagnosing AA were also estimated with their 95% con dence intervals.
With such limitations and rapid changes in disease management over the time, the results of large population studies provided additional important information regarding the incidence and the prognosis of COVID-19-related VTE. For example, based on the French national administrative database comparing 89 530 patients admitted with COVID-19 from 1 March to 30 April, 2020 with 45 819 patients admitted with influenza from 1 December, 2018 to 28 February, 2019, the incidence rates of VTE and PE in patients with COVID-19 were 4. 9% and 3. 4%, respectively, and 1. 7% and 0. 9%, respectively, for patients with influenza  [16]. Similarly, in the Spanish population-based study of 74 814 patients with COVID-19 admitted to emergency wards, the standardised incidence of PE in COVID-19 patients was 310 per 100 000 person-years compared to an incidence of 35 per 100 000 person-years (OR 8.
6%; OR 0. 96, 95% CI 0. 65-1. 42); and 2), higher than in non-COVID-19 patients with PE (6. 5%; OR 2. 74, 95% CI 1. 66-4. 51)  [17]. Conversely, in the large UK multi-sourced nationwide cohort study of 272 423 patients admitted to 195 hospitals with thromboembolic events between February 2018 and July 2020, the incidence of thromboembolism increased by 45. 9% and the mortality from thromboembolism increased by 6. 7% during the COVID-19 pandemic, as compared to the historical baseline, with more thromboembolism deaths occurring in the community compared with the historical rate (44% versus 33%)  [20]. Facing this uncertainty, the study of HOBOHM et al. [21]  presented in this issue of the European Respiratory Journal provides important additional information. The authors analysed the German nationwide inpatient database that included 176 137 patients hospitalised in 2020 for COVID-19 infection.
7% versus 13. 1%) as compared to PE without COVID-19; and 4) COVID-19 infection was associated with a 3. 1-fold increased risk of case fatality in patients with PE as compared to patients with PE and without COVID-19. When comparing 2019 and 2020, the case fatality rate was slightly higher in 2020 as compared to 2019, although the number of hospitalisations for PE was similar. Of note, in such large database, diagnostic screening and assessment has not been adjudicated and a lower prevalence of PE (1. 9%) than that observed previously was reported. Nevertheless, previously recognised risk factors for PE in COVID-19 patients were found, such as male sex, obesity and cancer, these findings reinforcing the validity of the analyses. These findings, based on one of the largest study populations, confirms the poorer prognosis of PE in COVID-19 patients as compared to PE in non-COVID-19 patients and COVID-19 patients without PE, which is complementary to other studies  [20].
Data pre-processing and quality control We computed sequencing quality checks with FastQC v0. 11. 9 and trimming of adapters and low-quality sequences using TrimGalore! V0. 6. 6. Paired-end read alignment was performed onto human reference genome (hg38) with STAR v2. 7. 9a  (Dobin et al. , 2013)  reporting randomly one position, allowing 6% of mismatches. Following previous recommendations  (Teissandier et al. , 2019) , repeat annotation was downloaded from RepeatMasker and joined with basic gene annotation from Gencode v19. The merged file was used as an input for quantification with featureCounts v2. 0. 1. Genes with a minimum of count per million (cpm) > 1 in at least four samples were retained for further analysis. Principal component analyses were implemented with PCAtools v2. 8. 0 on log2(cpm+1) for all genes for single datasets and common genes for multiple datasets, excepting the 10% genes with the lowest variance.
Gene Ontology and gene set enrichment analysis We used Metascape v3. 5 to calculate and visualize overrepresentation of gene ontologies in our list of differentially expressed genes (DEGs)  (Zhou et al. , 2019). Metascape applies hypergeometric tests and FDR corrections to identify ontology terms that comprise significantly more genes in a given gene list than what would be expected with a random gene list. For each gene list tested, we provided an appropriate background gene list corresponding to all expressed genes in all samples for a given experiment. We selected "Express Analysis" to capture relevant gene annotations from multiple sources (GO, KEGG, Reactome, canonical pathways, and CORUM). The p-value cutoff was kept at 0. 01. Gene set enrichment analysis (GSEA) was implemented with the clusterProfiler R package (v4.
Processing of public single-cell RNA-seq datasets in human embryos We compared our data with three early embryos single-cell RNA-seq studies  (Xue et al. , 2013; Yan et al. , 2013; Petropoulos et al. , 2016). Reads alignment and quantification were executed as described previously onto raw reads downloaded from the European Nucleotide Archive (study accessions PRJNA153427, PRJNA189204, and PRJEB11202).
Differential expression along pseudotime We used tradeSeq v1. 10. 0 (van den  Berge et al. , 2020)  to fit a negative binomial generalized additive model (NB-GAM) for each gene. After examining diagnostic plots of the optimal number of knots (k) according to the Akaike Information Criterion (AIC), k was set to 6 as an optional parameter in the NB-GAM model. We selected DEGs along pseudotime with associationTest() function if p-value<0. 05 and meanLogFC>2. This function relies on Wald tests to assess the null hypothesis that the expression of a gene is constant along pseudotime. DEGs with the culture medium were crosschecked with the list of DEGs along pseudotime according to  Petropoulos et al. 's (2016)  dataset. For further investigation, a dataset from  Yan et al. (2013)  was used to visualize these changes in a larger window, from the oocyte to late blastocyst stage.
To assess the quality of our generated single-embryo RNA-seq datasets, we relied on previous high-quality studies that performed single-cell RNA-seq (scRNA-seq) in human early embryos. According to the criteria that an expressed gene should have a count per million (cpm) value greater than 1 in at least half of the samples in each embryo stage, we found consistent numbers of 12,056, 10,022, and 11,213 genes being expressed in four-cell stage embryos in the work of  Yan et al. (2013) , the work of  Xue et al. (2013) , and our own dataset, respectively (Supplementary Figure  S1 ). In blastocysts, we identified 12,790 expressed genes in our data, compared with 8,204 genes in  Yan et al. 's (2013)  dataset in which blastocysts were collected a day later, at day 6. Principal component analysis (PCA) and hierarchical clustering of global gene expression further confirmed the high similarity of our data with those two previous studies: our day-2 embryo samples clustered near four-cell samples, and our day-5 embryos samples clustered beyond morula and before late blastocyst stages (Supplementary Figures  S2A, B ).
's (2013)  reference dataset (Supplementary Figure  S5B ). Top 10 most dysregulated genes including ZBED4, SLC30A1, AL139384. 2, SRARP, VIM, IRX3, ERVK13-1, TDRD12, PHLDA1, and NIPBL-DT are shown in Figure  2B  for each individual embryo according to the culture medium group. Six of them can be considered as mixed maternal/embryonic transcripts, while the four others appear to be transcribed from the embryonic genome. Gene Ontology (GO) analysis with Metascape revealed an over-representation of DEGs related to development (pattern specification process, reproductive structure development, skeletal system development, and kidney development), regulation (regulation of the mitotic cell cycle, regulation of cyclin-dependent protein kinase activity, negative regulation of the intrinsic apoptotic signaling pathway in response to DNA damage, and positive regulation of the transforming growth factor), ribonucleoprotein biogenesis complex, and response to nutrients (Figure  3 ; Supplementary Figure  S6 ).
Effects of the culture medium on the transcriptional trajectory of early embryos Early embryos undergo profound transcriptional changes during the first stages of development. In an attempt to address whether the embryo culture medium may affect these sequential modifications of gene expression, we used a public dataset of 1,529 single-cell RNA-seq of human embryos from days 3 to 7 (cultured in either CCM (Vitrolife) or G-1 Plus (Vitrolife) media)  (Petropoulos et al. , 2016) , which previously allowed delineating the transcription signature of each embryonic lineage and their dynamics during embryonic lineage segregation  (Meistermann et al. , 2021). Our objective was to identify genes whose expression dynamically changes across early embryonic development, in a continuous manner, independent of the embryo stage.
For that purpose, we applied the PHATE dimensionality reduction method  (Moon et al. , 2019)  and inferred existing lineages and pseudotime (van den Berge et al. , 2020)-a metric that could be interpreted as a timing distance between 1 cell and its precursor cell. On the public scRNA-seq dataset  (Petropoulos et al. , 2016) , we were able to identify eight clusters by using k-means clustering to group cells with high transcriptomic similarities. We also identified three distinct lineages (Supplementary Figure  S10A ) that shared the same structure when considering cells from clusters 1 to 5 but separated into clusters 6, 7, and 8. Using the cell classification adopted in previous studies  (Petropoulos et al. , 2016; Meistermann et al. , 2021) , the three lineages corroborated with the demarcation into epiblast (EPI), primitive endoderm (PrE), and trophectoderm (TE) cells (Supplementary Figures  S10B, C ).
While the 19 DEGs at day 2 that were also differentially expressed pseudotime showed a diverse profile of expression (Figure  6C ), the 9 DEGs at day 5 that are also differentially expressed along pseudotime displayed a declining expression over embryo development in the reference pseudotime (Figure  6D ). To confirm these results, we used an independent public scRNA-seq dataset from the work of  Yan et al. (2013)  obtained with a broader window, from the oocyte to late blastocyst (Figure  7 ). When considering our own datasets, embryos continuously cultured in Global up to day 5 also showed this declining trend of expression from days 2 to 5, with levels that were congruent with the reference dataset from the work of  Yan et al. (2013)  (Figure  7 ). However, day-5 embryos previously cultured in Ferticult until the four-cell stage showed over-expression for all DEGs, suggesting that these embryos retained abnormally high levels for their embryonic stage.
Acknowledgments We thank  Fuchou Tang  for hosting PF in his lab to learn the scRNA-seq method,  Nicolas Lieury  for his assistance in preparing and obtaining the samples and  Maud Carpentier  of the " Direction de la Recherche Clinique et de l'Innovation" of Dijon University Hospital  for the adminitrative management of the study. We acknowledge the  ICGex NGS  platform of  Institut Curie  (supported by grants  ANR-10-EQPX-03 ,  Equipex  and  ANR-10-INBS-09-08 ,  France Génomique ).
's (2016) dataset. The curve corresponds to the NB-GAM fitted normalized counts (TMM-adjusted cpm). Left panel surrounded by a blue line corresponds to genes upregulated with Ferticult. Right panel surrounded by a red line corresponds to genes downregulated with Ferticult. (D) Expression dynamics of the nine day-5 Ferticult-to-Global DEGs that are differentially expressed along pseudotime according to Petropoulos et al. 's (2016) dataset. The curve corresponds to the NB-GAM fitted normalized counts (TMM-adjusted cpm).
Data sources and participants This study was a retrospective analysis of data from the Antiretroviral Therapy Cohort Collaboration, which combines data from various European and North American cohorts of people with HIV,  10  and data from the UK Collaborative HIV Cohort Study (UK CHIC). 11 verall, 20 cohorts were included in our study (appendix p 1). Our study included people with HIV who were aged 16 years or older when starting combination ART. They had to have either started combination ART between 1996 and 2014 and were still taking it in 2015, having survived for at least a year since treatment initiation, or started combination ART between 2015 and 2019 and subsequently survived for at least a year. We chose 2015 as the cutoff because it was the year when treatment guidelines changed to recommend ART for all people with HIV irrespective of CD4 cell count.
Life expectancy from age 40 years was estimated for various sex-stratified population groups, with the expected age at death then calculated. Briefly, for each ART start year group, mortality rates were calculated in 5-year age bands (up to 80-84 years; the subsequent group was ≥85 years) from Poisson model coefficients and then entered into a life table to produce estimated expected remaining years of life for each age group. The variables selected for inclusion in the life-expectancy calculations were chosen through assessment of the magnitude of associations and consideration of which data are commonly available from the cohorts and from people with HIV and their clinicians. The methods for estimating life expectancy are described in more detail in the appendix (p 2). The same methods were used to estimate life expectancy from age 20 years, for comparison with previous literature.
In the age groups in which mortality estimates among people with HIV were lower than those among the general population, the general population rate was used instead (appendix p 2). Standardised estimates of remaining years of life at ages 40 years and 20 years were derived by weighting the lifeexpectancy estimates in each population group by the proportion of the sample in that group starting ART between 2015 and 2019. These derivations were done by sex, and were also stratified by CD4 cell count at the start of follow-up. General population mortality rates and life expectancies were taken for 2015. Sex-specific rates were calculated for each country and then weighted to correspond to the countries of residence of the people with HIV in our dataset. Analyses were done in Stata (version 16.
Our analysis was based on a large, detailed longitudinal dataset containing data for over 200 000 adults with HIV on ART across cohorts in Europe and North America. Our findings should therefore be generalisable to adults on ART in other high-income countries, although they might be less relevant in low-income and middle-income settings or settings where access to ART is restricted or costly. We were able to produce life-expectancy estimates for subgroups defined by sex, HIV acquisition route, viral suppression status, AIDS status, ART start year, and CD4 cell count. This study is, therefore, one of the most detailed analyses of life expectancy among people with AIDS treated in the modern ART era. Our study had some limitations. Follow-up was short in the oldest age groups, and therefore there were few deaths.
Contributors JACS and AT conceived and designed the study. AT combined, checked, and cleaned the datasets. CAS, GB, HC, Ad'AM, ME, MJG, SG, JLG, IJ, FCL, NO, JMR, CS, TRS, RT, GT, J-CW, FW, LW, RZ, MJS, and AJ-the individual cohort representatives-provided cohort data. AT did all statistical analyses. All authors contributed to the interpretation of the data. AT and JACS drafted the Article, which was critically revised for important intellectual content by all authors. AT and JACS accessed and verified the data, and had final responsibility for the decision to submit for publication.
Acknowledgments The Antiretroviral Therapy Cohort Collaboration is funded by the  US National Institute on Alcohol Abuse and Alcoholism  (grant  U01-AA026209 ). UK CHIC is funded by the  UK Medical Research Council  (grants  G0000199 ,  G0600337 ,  G0900274 , and  M004236/1 ). JACS is funded by the  National Institute for Health and Care Research Bristol Biomedical Research Centre  and by  Health Data Research UK. AT is funded by the  Wellcome Trust  under a  Sir Henry Wellcome Postdoctoral Fellowship  ( 222770/Z/21/Z ). Funding for the individual Antiretroviral Therapy Cohort Collaboration cohorts was provided by  Alberta Health ,  Gilead , the  French National Agency for Research on AIDS , the  French Ministry of Health , the  Austrian Agency for Health and Food Safety, Stichting HIV Monitoring , the  Dutch Ministry of Health, Welfare, and Sport  (through the  Centre for Infectious Disease Control  of the  National Institute for Public Health and the Environment ), the  Translational Platform-HIV  by the  German Centre for Infection Research  ( NCT02149004 ), the  Instituto de Salud Carlos III  through the  Red Temática de Investigación Cooperativa en Sida  ( RD06/006 ,  RD12/0017/0018 , and  RD16/0002/0006 , as part of the  Plan Nacional I + D + i and co-financed by ISCIII-Subdirección General de Evaluación and the  Fondo Europeo de Desarrollo Regional) ,  ViiV, Preben og Anna Simonsens Fond ,  Institut National de la Santé et de la Recherche Médicale ,  Bristol Myers Squibb ,  Janssen ,  Merck Sharp & Dohme , the  US National Institute on Alcohol Abuse and Alcoholism  ( U01-AA026230 ), the  Spanish Ministry of Health , the  Swiss National Science Foundation  ( 33CS30_134277 ), Centers for  AIDS Research Network of Integrated Clinical Systems  ( 1R24 AI067039-1  and  P30-AI-027757 ), the  US Department of Veterans Affairs , the  US National Institute on Alcohol Abuse and Alcoholism  ( U01-AA026224 ,  U01-AA026209 , and  U24-AA020794 ), the  US Veterans Health Administration Office of Research and Development , and the  US National Institute of Allergy and Infectious Diseases  ( P30 AI110527 ).
Ten new reference-quality chromosome-level genomes of bats We used long-read and long-range sequencing technologies to generate highly contiguous and complete reference genome assemblies that meet the standards of the Bat1K project  38. We used samples from museum collections as a source of genetic material for eight of the 10 bat species (Supplementary Table  2 ), highlighting the importance of museum collections for biodiversity genomics  39. Nine of these 10 species were sequenced to 27-42X coverage of PacBio circular consensus (HiFi) reads, providing long and accurate reads for contig assembly, and ~60X coverage of chromosome conformation capture (Hi-C) Illumina read data for scaffolding. The Megaderma spasma genome -generated from a 25-year old tissue sample-was assembled from Oxford Nanopore long reads (81X coverage), Bionano optical maps, and Hi-C read pairs, and short read (10X Genomics Illumina) data was used to correct base errors.
However, compared to other mammalian orders, bats exhibit the strongest enrichments for immune gene selection, providing genomic evidence that bats possess unique immune system adaptations. Using our comprehensive dataset comprising 115 mammalian species and 228 phylogenetic branches, we observed that branch length is significantly correlated with the number of selected genes. A regression model to estimate the expected number of selected genes showed that the ancestral Chiroptera branch exhibits more immune gene selection than expected. This supports that the evolution of powered flight and immune system adaptations are linked  22, 116, 117  ; however, it remains to be determined whether flight is directly or indirectly linked to immune system changes. While we applied the regression model specifically to immune genes, this approach could be a generally-applicable strategy to reveal lineages (branches) having an excess of selection on genes belonging to particular functional categories.
Scaffolding of Megaderma spasma - We first scaffolded the contigs created by ONT reads using the 10X Genomics data. To this end, we mapped the 10X Genomics reads using longranger v2. 2. 2 and scaffolded using Scaff10X v4. 2 and Break10X v3. 1. Next, we used Bionano optical maps to further scaffold the assembly after 10X scaffolding. We created an optical map de-novo assembly and then created the scaffold using Bionano Hybrid Scaffold using tools within Bionano Solve v1. 6. 1. The resulting assembly was further scaffolded with HiC data, as described below. HiC Scaffolding --To scaffold contigs into chromosome-level scaffolds, we first mapped the Arima V2 HiC data to the genome assemblies using bwa-mem v0. 7. 17-r1188 125 and filtered reads based on mapping quality and proper-paired alignments following the Arima mapping pipeline from the VGP: (https://github.