dataesr/scientific-paragraphs-categorization huggingface dataset:
Metabarcoding Data Processing and Analysis Reverse and forward reads generated from the 36 samples were processed using the open-source software  Mothur (v. 1. 42. 3 )  (Schloss et al. , 2009). Reads were paired and trimmed according to their length and their content in homopolymers and ambiguous bases. The criteria were one single error in the primer sequence, a maximum of 8 homopolymers and 0 ambiguous bases. Short (<370 nt) and long (>380 nt) reads were discarded. Chimeras were removed with Uchime implemented in Mothur  (Edgar et al. , 2011). Cleaned sequences were aligned using the kmer searching function in SILVA alignments database (release 132) and were assigned in a taxonomic classification (SILVA database)  (Quast et al. , 2013). Any affiliations to chloroplasts, mitochondria or Eukarya were removed from the dataset.
Processed sequences were then clustered into operational taxonomic units (OTU) at 97% identity. To eliminate erroneous sequences and assignments, OTUs with relative abundance <0. 005% were discarded as recommended in  Bokulich et al. (2013). Samples were rarefied to the number of sequences present in the smallest sample dataset (6100 sequences). The Fastq files generated in this study have been deposited at the National Center for Biotechnology Information (NCBI) 1  under the project accession number PRJNA590493 (submission ID: SUB6567135).
Bacterial Community Diversity and Composition Rarefaction curves were calculated from the non-rarefied OTU table, with samples pooled according to their type and site of provenance, using the rarefaction. single function implemented in Mothur, and were then visualized in R with the ggplot package. The 95% confidence intervals of observed richness were calculated directly in Mothur with 5000 iterations (randomization). Richness, diversity indices and Bray-Curtis dissimilarity matrix were calculated from the rarefied OTU table using the vegan package in R  (Oksanen et al. , 2011). Richness and diversity indices were compared among sample types using the non-parametric Kruskal-Wallis test followed by a post hoc Dunn's test in R with a correction of p-value using the Holm's method (significance threshold p < 0.
These functions are implemented in the vegan and pairwiseAdonis R packages, respectively,  Oksanen et al. (2011 ), Martinez Arbizu (2017). Pairwise comparisons were performed using the R package limma, with moderated t-tests on log-transformed OTU relative abundance and corrected for multiple hypothesis testing  (Smyth, 2005; Bulgarelli et al. , 2012)  to identify the differentially abundant OTUs in each of the three sample types. Focusing on the significantly enriched OTUs in each sample type compared to the two others (p-value < 0. 05), a heatmap and a ternary plot were produced [vegan package and personal R script adapted from  Bulgarelli et al. (2012) ].
Functional Predictions Functional predictions of the bacterial communities from the gut content and the gut tissue were determined through the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt v2. 0. 0) as described in  Douglas et al. (2019). The PICRUSt pipeline was run using the representative sequences of the OTUs from the rarefied OTU table using the get. oturep function in Mothur. Hidden state predictions were performed through the hsp. py script by the Maximum Parsimony method for 16S rRNA gene copy number, Enzyme Commission (EC) numbers and Kyoto Encyclopedia of Genes and Genomes Orthology (KO) abundances, along with the calculation of the Nearest Sequenced Taxon Index (NSTI). Any OTU-representative sequence with an NSTI value higher than two was classified as uncharacterized phyla and was discarded for further analysis.
Then, the copy numbers of gene families were predicted for each OTU with the metagenome_pipeline. py script, providing the OTU abundances per sample normalized by the predicted 16S rRNA gene copy numbers per OTU. Finally, pathway-level abundances were predicted with pathway_pipeline. py function assigning EC numbers to MetaCyc reactions and KO abundances in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Potential functional profiles were analyzed using the STAMP (v2. 1. 3) software  (Parks et al. , 2014). Comparisons between sample type community features were achieved through Welch's t-test, p-values were adjusted with the Benjamini-Hochberg FDR multiple-test correction, and comparisons with p-value < 0. 05 were considered significant.
Core Microbiota, Co-occurrence Networks and Neutral Model A core microbiota was defined for each gut-related sample type -gut content and gut tissue -as the fraction of the community common to both Ardley and China sites. Shared OTUs were defined as OTUs prevalent in 75% of the samples from the same type in both sites. Co-occurrence analysis was performed on the gut core microbiota of each site through the Co-occurrence Network software (CoNet, v1. 1. 1) (Faust and Raes, 2016) , following the approach described in  Hakim et al. (2019). Briefly, OTUs with a cumulative sum of relative abundance per sample lower than 100 (i. e. , <15% sequence relative abundance) were discarded from the analysis. Co-occurrence scores were computed using two correlation (Pearson, Spearman), two dissimilarity (Kullback-Leibler, Bray-Curtis) and one similarity (mutual information) measures detailed in  Faust and Raes (2016).
7) implemented in Cytoscape. For each taxon of both networks, three-node parameters, the number of edges, the closeness centrality, and the betweenness centrality, were plotted using the ggplot R package  (Wickham, 2006). Hub taxa were manually identified based on their high centrality and connectivity within each network compared with the other taxa  (Berry and Widder, 2014; Agler et al. , 2016). The potential contribution of neutral processes (i. e. , stochastic factors such as ecological drift and passive dispersion) in microbiota assemblies from gut content, gut tissue and the combination of both was assessed by the neutral community model proposed by  Sloan et al. (2006) , applying the R code of  Burns et al. (2016). The migration rate estimator (m) was used to evaluate if the observed frequencies and mean relative abundances of OTUs fit the neutral model.
Changes in Alpha Diversity Along the Host Gradient All alpha diversity analysis performed (observed richness, Shannon and Simpson indices) led to the same gradient of microbial diversity among the external sediments (highest diversity), the gut content (intermediate diversity) and the gut tissue (lowest diversity) on China site (Dunn test, p-values < 0. 05) and pooled sites dataset (Dunn test, p-values < 0. 02) (Table  1  and Supplementary Table  S1 ). The samples from Ardley site tend to harbor the same pattern of diversity (gut content < gut tissue and external sediment < gut tissue, Dunn test, p-values < 0. 02), at the exception of the external sediment and the gut content samples that did not showed any significant difference on richness, Shannon and Simpson indices (Dunn tests, p-values = 0.
Acknowledgements This work was supported by  BBSRC  (grant  BB/M009513/1 ),  EPSRC  (grants  EP/ N02396X/1 ,  EP/L015552/1 ) and the  Rutherford Foundation Trust. The Flatiron Institute is a division of the  Simons Foundation. C. W. gratefully acknowledges the support of  NVIDIA Corporation  with the donation of the Tesla K40 GPUs used for this research. For computational resources, we were supported by the  ARCHER UK National Supercomputing Service  and the  UK Materials  and  Molecular Modelling Hub  for computational resources ( EPSRC Grant  No. EP/P020194/1 ). We are grateful to  Prof. Christopher Dennison  ( Newcastle University ) for comments.
As this article is being written, French authorities estimated that 140,000 people have been infected and 27,000 died. All donors tested in the current study provided informed consent for a non-therapeutic use of their blood samples. Data were analyzed according to sex, age and blood types. Neutralizing antibodies were detected using a virus neutralization test (VNT) as previously described for Zika virus  (Nurtop et al. , 2018)  and adapted to SARS-CoV-2. We used VeroE6 cells cultured in 96-well microplates, 100 TCID50 of the SARS-CoV-2 strain BavPat1 (courtesy of Pr. Drosten, Berlin) and serial dilutions of serum (1/20-1/160). Dilutions associated with CPE (cytopathic effect) were considered as negative (no neutralization) and those with no CPE at day 4 post-infection were considered positive (complete neutralization), respectively.
data. gouv. fr/fr/datasets/donnees-hospitalieres-relati ves-a-lepidemie-de-covid-19/, downloaded on May 8th , 2020) indicating that COVID-related deaths (M/F ¼ 1. 50), admissions to intensive care unit (ICU, M/F ¼ 2. 82), and to a lesser extent hospitalizations (M/F ¼ 1. 22) are significantly more frequent in men than in women (p < 0. 001). Seroprevalence values did not differ significantly among age groups, but it was noted that they were slightly higher (over 3%) in donors <30yo and �60yo, which deserves attention for future studies with larger numbers. The median age of seropositives was 36yo (range 19-64) and not different between men and women. Finally, we analyzed the relation between sero-status and blood type. In a recent non-peer-reviewed report,  Zhao et al. (2020)  observed that among Chinese in-patients there was an over-and under-representation of groups A and O, respectively.
Acknowledgments We are indebted to blood donors who participated in the study; to directors and medical staff of each regional blood services for their implication and support (EFS Grand Est:  C. Gachet ,  C. Claudel ,  C. Forny ;  EFS Ile de France :  S. Noel  and  E. Jacquot ;  EFS Nord de France :  R. Courbil  and  N. Delemer ;  EFS Provence, Cote d'Azur et Corse: C. Lazaygues ); to  C. Isnard  for invaluable technical contribution.
Background: Three large trials of fluoxetine for stroke recovery (FOCUS (fluoxetine or control under supervision), AFFINITY (the Assessment oF FluoxetINe In sTroke recovery) and EFFECTS (Efficacy oF Fluoxetine-a randomisEd Controlled Trial in Stroke)) have been collaboratively designed with the same basic protocol to facilitate an individual patient data analysis (IPDM). The statistical analysis plan for the three individual trials has already been reported in Trials, including a brief description of the IPDM. In this protocol, we describe in detail how we will perform the IPDM. Methods/design: Data from EFFECTS and AFFINITY will be transferred securely to the FOCUS statistician, who will perform a one-stage IPDM and a two-stage IPDM. For the one-stage IPDM, data will be combined into a single data set and the same analyses performed as described for the individual trials.
FOCUS published its results among 3127 patients in December 2018: there was no effect of fluoxetine on the primary outcome of modified Rankin scale (mRS), but those allocated to fluoxetine were less likely to develop depression by 6 months and were more likely to have had a bone fracture  [5]. AFFINITY (n = 1280  [6] ) and EFFECTS (n = 1500  [7] ) published their 6-month data in August 2020. The findings were nearly identical to those in FOCUS: there was no effect of fluoxetine on the mRS, but those allocated to fluoxetine were less likely to develop depression in the EFFECTS trial and were more likely to have had a bone fracture in the AFFI NITY and EFFECTS trials.
Aim We will perform an IPDM of FOCUS, AFFINITY and EFFECTS data. Initially, we will include only the 6month follow-up data (which includes our primary outcome measure). We will report the 12-month data in a subsequent publication. This decision was made before knowing the results of AFFINITY and EFFECTS. We will therefore be able to expedite the IPDM and thus provide important analyses that will inform clinical practice and help researchers make decisions about whether further trials of SSRI for stroke are needed. The objectives below describe the analyses at 6 months and 12 months.
Data-driven exploratory analyses Data-driven analyses will be conducted to address heterogeneous treatment effects. The aim is to look at subgroups of patients who may respond differently from others to fluoxetine, e. g. at higher risk of adverse effects, of gaining more benefit etc. This was considered for FOCUS, but there were insufficient data to perform these analyses. Subpopulations with different average treatment effects will be identified using 'regression tree' or 'recursive partitioning' methods  [14]. These data-driven analyses will be identified as such when we report the IPDM results.
Patient eligibility criteria for inclusion in the IPDM We will include all the patients who were randomised in the three trials. We will exclude those who consented but were not subsequently randomised. In FOCUS, there was a small number of patients where the 6-month outcome data were obtained too early (less than 90 days) or too late (more than 1 year from randomisation); in the IPDM, these will be dealt with in the same way as in FOCUS, i. e. if the mRS score was available between 90 days and 1 year, we included it within our primary analysis based on the 6-month follow-up. If it was not between these times, it was not used. If after 1 year, it would have been used for 12month analyses. We will do a sensitivity analysis sequentially excluding patients, starting with those who did not meet the entry criteria for the trials.
Data items In the IPDM, we will include the core data items (baseline and outcome data) which have been collected in all three trials and any measures that are common to at least two of the individual trials. The following outcome domains are common to all three trials, but different variables have been collected: a) Cognition. The SIS which incorporates an assessment of memory and thinking that was collected for all three trials. An IPDM will be performed for those data. FOCUS and EFFECTS also report the Mental Health Inventory 5. These will be combined in the IPDM. There are also data from the PHQ-9 in AFFINITY and MADRS in EFFECTS; these scales will not be combined in this IPDM. When we update the Cochrane review of SSRI for stroke recovery, we will use SMD to combine these scales.
Data dictionary We have developed a data dictionary based on FOCUS records, which has ~500 variables coded. This will be sent to the AFFINITY and EFFECTS trialists. The FOCUS data dictionary will enable EFFE CTS and AFFINITY trialists to align their data dictionaries to FOCUS, so that the same variables are coded in the same way for each trial. Thus, when AFFINITY and EFFECTS data are made available to FOCUS statisticians, minimal manipulation will be required to merge the data sets.
Transfer of data from AFFINITY and EFFECTS to the FOCUS statistician After the data dictionaries have been aligned, AFFI NITY and EFFECTS trialists will transfer their anonymised data to the FOCUS statisticians. Initially, this will include all data collected up to 6 months. When the 12-month data become available, these will also be provided. The process of data transfer will be determined by the governance processes in each country; this is likely to be using file exchange via secure FTP (SFTP).
Merging the trial data sets FOCUS own the FOCUS data. AFFINITY and EFFECTS own their respective data but have given permission for these data to be combined with FOCUS. The merged data sets will be stored in the University of Edinburgh Data store, along with a detailed data dictionary and descriptors and a description of how the merging was done. FOCUS data will be shared as part of the researchers' responsibilities to the funder. Further discussion is needed about whether, and if so, how, to share the merged data set with other researchers; we will follow the recommendations of the three funders and the institutions which hosted the three trials. Whilst the statistician performing the IPDM works on the combined data set, it will be password protected and the analysis will be done in a locked office.
Missing data for single questions within scores will be handled as described by each scoring method. Immediately before database lock for each trial, a blinded review of the completeness of primary and secondary outcomes is performed. For items such as SF36, if there are at least 50% of questions within a domain that have responses, we are able to use the methods detailed by the standardised tools to calculate a value for the domain. If higher levels of missing mRS data than expected (> 5%) are seen, we will consider additional sensitivity analyses to assess the robustness of the results against various assumptions about the missing data mechanism as outlined in Jakobsen et al. [17]. Given the exact nature of these additional sensitivity analyses is not pre-specified, they will be considered exploratory.
Subgroup analyses We will perform subgroup analyses as we have done for the individual trials. The mRS data at 6 months will be compared using an ordinal logistic regression using our adjusted model, and comparison will be made across variables listed in Table  1  in the Appendix. Each subgroup will be considered separately by adding the subgroup variable and its interaction with the treatment variable to the main model. This includes examining individual and group covariates of substantive interest such as stroke severity (NIHSS) and the six simple variable model for prognosis  [4]. Potential covariates and other baseline data are shown in Table  1  in the Appendix. Additionally, we will explore the impact of trial (FOCUS, AFFINITY, EFFECTS), country and ethnicity. This will enable us to explore variation in effects.
Further possible analyses of the combined data set There are several other research questions that can be addressed using the combined data set. Each of these questions will require a separate statistical analysis plan to be developed. These questions include: a) What are the types of bone fractures after stroke? What is their impact on mRS? What factors are associated with fracture risk? We have already published these data from FOCUS and can repeat the same analyses on the combined data set. Note that falls will be included in the interpretation of these results. b) What are the baseline predictors of seizure risk after stroke? What drugs are associated with increased seizure risk? How does fluoxetine modify this effect? c) What are the baseline predictors of depression after stroke? How is depression at 6 months associated with disability and dependency? d) What factors are associated with pain after stroke (using pain item from EQ5D-5L)? This includes baseline predictors and factors that might influence pain severity, e.
Acknowledgements We acknowledge  Dr. Veronica Murray  who was instrumental in designing and obtaining funding for EFFECTS and who sadly died in December 2014 as EFFECTS commenced. We are most grateful to the patients and their families for agreeing to participate, to our service users for providing advice and guidance on the study protocol, to the  Dundee Speakeasy group  for designing easy access patient information materials for FOCUS, to the sites who participated by recruiting patients, to all the members of our collaborative groups and to the Data Monitoring Committees.
Setting Mpilo Opportunistic Infections (OI) Clinic is within Mpilo Central Hospital in Bulawayo (the second largest city in Zimbabwe). It is a tertiary facility, managing complicated referrals, including patients on second and third line treatment. VL testing is offered as per national guidelines at the hospital HIV laboratory, adjacent to the clinic. Patient data are routinely entered in the electronic point of care database, ART register and patient care booklet  8. First line ART regimen for children <3 years is ABC+3TC+LPV/ R or AZT+3TC+LPV/r. Children 3-9 years and adolescents <35kg receive AZT+3TC+NVP or ABC+3TC+EFV, while adolescents >35kg receive TDF+3TC+NVP or EFV  8. Those with VL ≥1000 copies/ml at six-months are offered enhanced adherence counseling (EAC), and a second VL test after three months.
Data extraction and analysis We extracted anonymized patient data from electronic patient and laboratory databases, analyzed using STATA (version12. 1 STATA Corp. , College Station, TX, USA). If data were missing in electronic databases, we referred to paper-based registers and booklets. We defined low CD4 count as follows: CD4 count ≤350 cells/mm 3 for children and adolescents >5 years, and CD4% <25% of total lymphocytes for children <5years  8. We defined 'undergoing VL testing at six months' as those with first VL tests done within six to nine months of starting ART. Comparisons were made between children and adolescents using chi squared test.
/doi. org/10. 5256/f1000research. 25113. r61601 © 2020 van Wyk B. This is an open access peer review report distributed under the terms of the Creative Commons , which permits unrestricted use, distribution, and reproduction in any medium, provided the original Attribution License work is properly cited. Brian van Wyk School of Public Health, University of the Western Cape, Cape Town, South AfricaThe study reports on viral load monitoring at 6 months for of children and adolescents who were initiated on HIV treatment in a tertiary hospital in Bulawayo. The study is important because of the HIV epidemic in Zimbabwe, and the need to reach the third 90 of UNAIDS 90-90-90 targets.
Data availability Underlying data Figshare: Dataset for Moyo S et al. study: https://doi. org/ 10. 6084/m9. figshare. c. 4884726. v1  13 le 'silungiledata' contains all de-identified variables extracted for this study, alongside a codebook explaining all fields and field values. Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1. 0 Public domain dedication).
Aims This study aimed to identify relationships in recently diagnosed atrial fibrillation (AF) patients with respect to anticoagulation status, use of guideline-directed medical therapy (GDMT) for comorbid cardiovascular conditions (co-GDMT), and clinical outcomes. The Global Anticoagulant Registry in the FIELD (GARFIELD)-AF is a prospective, international registry of patients with recently diagnosed non-valvular AF at risk of stroke (NCT01090362).
[3] [4] [5]  For example, in a Swedish registry study of 272 186 patients with AF and 544 344 matched controls, 69. 5 vs. 27. 2% had any concomitant disease, with rates of 26. 0 vs. 8. 0% for ischaemic heart disease, 12. 2 vs. 3. 6% for MI, 24. 8 vs. 2. 9% for CHF, 25. 4 vs. 6. 8% for hypertension, and 13. 4 vs. 6. 2% for DM. 4  Similarly, in a US populationbased study, comorbidities occurring at significantly higher rates than controls included hypertension (71. 1 vs. 57. 2%), CHF (18. 2 vs. 4. 9%), coronary artery disease (CAD) (39. 0 vs. 21. 0%), and DM (30. 6 vs. 26. 7%). 3  This study explored whether treatment of these comorbidities, using guideline-directed medical therapies (GDMT), optimizes management of AF patients. 1  The Global Anticoagulant Registry in the FIELD (GARFIELD)-AF registry (ClinicalTrials.
gov Identifier: NCT01090362) is a prospective, international, multicentre, observational study of adults with recently diagnosed non-valvular AF and at least one risk factor for stroke. 6  The GARFIELD-AF registry captures information on the management of unselected patients in everyday clinical practice, providing the opportunity to assess treatment patterns and explore relationships between management and outcomes. Oral anticoagulant (OAC) therapy is GDMT for stroke prevention in patients with AF with a CHA 2 DS 2 -VASc score of ≥2. 1  This study investigated the use of GDMT in patients in GARFIELD-AF with a CHA 2 DS 2 -VASc score of ≥2 (excluding sex) and one or more of five comorbidities-CAD, DM, CHF, hypertension, and PVD. The objectives are to explore whether appropriate use of GDMT for comorbidities (co-GDMT) is associated with increased use of anticoagulation for AF and whether the association between OACs and clinical outcomes differs according to appropriate GDMT use.