dataesr/scientific-paragraphs-categorization huggingface dataset:
Pseudogenes were predicted using the DFAST core workflow (100). We used treeWAS  (72)  to perform a pangenome-wide association study relating data collected in the phenotypic screen to genomic data. Microbiome diversity of polar bear feces. We collected 86 polar bear fecal samples from several regions in Canada, including samples from 10 captive bears, fed various diets or fasted, and 76 samples from an unknown number of wild bears, feeding on unknown diets. The feces from the captive bears were forwarded from institutions within Canada and did not require permitting for their passage to We confirmed that samples collected from wild bears originated from bears by sequencing a cytochrome b gene fragment using metabarcoding approaches based on Ion Torrent (Ion Torrent Systems Inc. , Gilford, NH, USA) and 454 pyrosequencing (454 Life Sciences, Branford, CT, USA) next-generation sequencing technologies  (101).
We cleaned the products using the DNA Clean & Concentrator-25 kit (Zymo Research, Irvine, CA, USA) and checked the concentration using the DS 111 spectrophotometer (DeNovix, Wilmington, DE, USA). We used the cleaned DNA products as the templates for the Sanger sequencing reaction, following the ABI Prism BigDye Terminator cycle sequencing kit (ThermoFisher Scientific, Waltham, MA, USA) protocol with the 27F primer. The sequencing was performed using the 3730xl DNA analyzer (ThermoFisher). Upon sequencing completion, we performed a BLAST search (105) of the sample sequences against the NCBI nonredundant nucleotide database. We examined the top 10 hits to confirm the sample identity. Statistical analysis. We performed all data processing and statistical analysis using R (106) (v3. 6. 2) or Python (v3.
6. 10). We compared means between groups using a Kruskal-Wallis test followed by a pairwise Wilcoxon rank sum when more than two groups were compared. We tested differences in frequencies between groups with more than 10 observations with a x 2 test, repeated 100 times with down-sampling in order to correct for sampling sizes between groups. We measured phylogenetic signal using a log-likelihood ratio test on Pagel's l (Pagel's l fitted using the phylosig function from the phytools R package  [96] [v0. 7-47]  and the null hypothesis being l = 0). When applicable, we tested for the confounding of phylogeny with our groups of interest using the aov. phylo function from the R package geiger (107) (v2. 0. 7). We adjusted all P values for multiple comparisons with the Benjamini-Hochberg (BH) correction method.
Data availability. Raw sequences for the Psychrobacter genome sequencing and polar bear feces 16S rRNA gene sequencing, as well as assembled Psychrobacter genomes, are available in the European Nucleotide Archive under the accession no. PRJEB40380. Annotated Psychrobacter genomes are available at ftp://ftp. tue. mpg. de/pub/ebio/dwelter. Raw data, R notebooks, and Python scripts for the analyses are available at https://github. com/dkwelter/Welter_et_al_2020.
aeruginosa not included). GS, genome size, ranging between 1. 8 Mb and 4. 5 Mb. PCoA, PC1 values from a PCoA based on gene presence/absence data. CD, genome coding density, ranging from 80% to 89%. Growth temperature range data were collected from type strain publications. (B) PC1 and PC2 of a PCoA of a binary matrix of gene presence/absence for 51 species of Moraxellaceae, explaining 32% and 14% of the variation, respectively. Each genome is represented by one point, colored by genus. The P-clade Moraxella spp. are represented by yellow points with red outlines. (C) PC2 and PC3, explaining 14% and 5% of the variation, respectively.
Poorly characterized: [S] function unknown, [X] not in COG database. (D) Percent of genes that are transposases. (E) Percent of genes per genome that are predicted to be pseudogenes. (F) Number of genes per genome. Means were compared using the Wilcoxon rank sum test. *, P value less than 0. 05; **, P , 0. 005; ***, P , 0. 0005.
ACKNOWLEDGMENTS This work was supported by the  Max Planck Society. The Nunavut field work was supported by funds from the  Nunavut Wildlife Management Board  (NWMB), the  Nunavut General Monitoring Plan (NGMP) , the  National Science and Engineering Research Council (NSERC) , and  Environment Canada (Gov. of Canada ). We thank  Jacobo de la Cuesta-Zuluaga ,  Sara Di Rienzi ,  Hagay Enav ,  Angela Poole ,  Jessica Sutter ,  Taichi Suzuki , and  William Walters  for discussions regarding project design and analysis and  Andrea Belkacemi ,  Ilja Bezrukov ,  Pablo Carbonell ,  Silke Dauser ,  Julia Hildebrandt , and  Christa Lanz  for their advice on and assistance with sequencing. We also thank  Jürgen Berger  and  Katharina Hipp  for performing electron microscopy and  Ariadne Papatheodorou  for her assistance with the bile sensitivity assays.
Data source The OpenSAFELY-TPP database (https://opensafely. org) covers 24 million people registered at GP practices that use TPP SystmOne electronic health record software. It includes pseudonymized data such as coded diagnoses, medications and physiological parameters. No free text data are included. This primary care data is linked (via NHS numbers) with A&E attendance and in-patient hospital spell records via NHS Digital's Hospital Episode Statistics (HES), national coronavirus testing records via the Second Generation Surveillance System (SGSS), and national death registry records from the Office for National Statistics (ONS). Vaccination status is available in the GP record directly via the National Immunisation Management System (NIMS). HCW status is recorded for all vaccine recipients at the time of vaccination, and this information is sent to OpenSAFELY-TPP from NHS Digital's COVID-19 data store.
During the study period, the advice from the UK Chief Medical Officers was that "second doses of both vaccines will be administered towards the end of the recommended vaccine dosing schedule of 12 weeks" (8). Using an intention-to-treat approach, comparative effectiveness estimates beyond 14 weeks were considered to be second-dose effects. We report the actual timing of second doses to assess the extent of any deviation from this recommended treatment strategy, frequency of any cross-brand second doses, and how these may differ between vaccine types. PLR models are computationally expensive to fit, as the input dataset must be arranged as one row per person per day of follow-up. To manage this, a sampling strategy was used such that all those who experienced the event of interest are selected (to retain statistical power), and a random sample of 50,000 event-free participants are selected.
Software, code, and reproducibility Data management and analyses were conducted in Python 3. 8 and R version 4. 0. 2. All code is available at https://github. com/opensafely/comparative-ve-research, and is shared openly for review and re-use under MIT open license. Codelists are available at https://www. opencodelists. org/. No person-level data is shared. Any reported figures based on counts below 6 are redacted or rounded for disclosure control. This study followed the STROBE-RECORD reporting guidelines.
Though we adjusted for region, rurality, and deprivation, we were unable to directly account for occupational differences that may affect both exposure risk and vaccine type. We note also that the GP practices covered in this study are not geographically representative, as TPP SystmOne software is not widely-used in some regions such as London and the North West. We were unable to fully investigate differences in protection against severe disease, in large part due to clear protective benefits of both vaccines, reducing the absolute numbers of events, and therefore statistical power, in the studied cohort. Second-dose effects were considered using an intention-to-treat approach, which does not account for potential in the timing of the second dose between vaccine types. However, we found that by 12 weeks more BNT162b2 recipients had received a second dose than ChAdOx1 recipients, which would bias effect estimates in favour of BNT162b2 assuming higher protection after a second-dose of either vaccine.
Findings in context A number of studies have estimated COVID-19 vaccine effectiveness in observational data (9)  (10)   (11)   (12). Vaccine protection is typically assessed by comparing vaccinated against unvaccinated person-time, or against the early post-vaccination period before anticipated onset of vaccine-induced immunity. Such designs are vulnerable to confounding and selection bias, for example due to vaccine prioritisation and eligibility policies. In addition, differences in vaccine access and acceptance can cause substantial imbalance between vaccinated and unvaccinated groups that may be impossible to account for. Many of these biases can be bypassed when making direct comparisons between recipients of different vaccine types, if the relative performance of two vaccines is of interest.
Information governance and ethical approval NHS England is the data controller; TPP is the data processor; and the key researchers on OpenSAFELY are acting with the approval of NHS England. This implementation of OpenSAFELY is hosted within the TPP environment which is accredited to the ISO 27001 information security standard and is NHS IG Toolkit compliant (14)  (15)  ; Patient data has been pseudonymised for analysis and linkage using industry standard cryptographic hashing techniques; all pseudonymised datasets transmitted for linkage onto OpenSAFELY are encrypted; access to the platform is via a virtual private network (VPN) connection, restricted to a small group of researchers; the researchers hold contracts with NHS England and only access the platform to initiate database queries and statistical models; all database activity is logged; only aggregate statistical outputs leave the platform environment following best practice for anonymisation of results such as statistical disclosure control for low cell counts  (16).
The OpenSAFELY research platform adheres to the obligations of the UK General Data Protection Regulation (GDPR) and the Data Protection Act 2018. In March 2020, the Secretary of State for Health and Social Care used powers under the UK Health Service (Control of Patient Information) Regulations 2002 (COPI) to require organisations to process confidential patient information for the purposes of protecting public health, providing healthcare services to the public and monitoring and managing the COVID-19 outbreak and incidents of exposure; this sets aside the requirement for patient consent  (17). Taken together, these provide the legal bases to link patient datasets on the OpenSAFELY platform. GP practices, from which the primary care data are obtained, are required to share relevant health information to support the public health response to the pandemic, and have been informed of the OpenSAFELY analytics platform.
This work was supported by the Medical Research Council MR/V015737/1 and the Longitudinal Health and wellbeing strand of the National Core Studies programme. The OpenSAFELY platform is funded by the Wellcome Trust. TPP provided technical expertise and infrastructure within their data centre pro bono in the context of a national emergency. BGs work on clinical informatics is supported by the NIHR Oxford Biomedical Research Centre and the NIHR Applied Research Collaboration Oxford and Thames Valley. BG's work on better use of data in healthcare more broadly is currently funded in part by: NIHR Oxford Biomedical Research Centre, NIHR Applied Research Collaboration Oxford and Thames Valley, the Mohn-Westlake Foundation, NHS England, and the Health Foundation; all DataLab staff are supported by BG's grants on this work.
LS reports grants from Wellcome, MRC, NIHR, UKRI, British Council, GSK, British Heart Foundation, and Diabetes UK outside this work. KB holds a Wellcome Senior Research Fellowship (220283/Z/20/Z). HIM is funded by the NIHR Health Protection Research Unit in Immunisation, a partnership between Public Health England and London School of Hygiene & Tropical Medicine. AYSW holds a fellowship from the British Heart Foundation. EJW holds grants from MRC. RM holds a Sir Henry Wellcome Fellowship funded by the Wellcome Trust (201375/Z/16/Z). HF holds a UKRI fellowship. IJD holds grants from NIHR and GSK.
| 2020 field season sampling The 2020 dataset focused solely on D. tinctorius tadpole counts and pH measurements of weekly resampled ground access phytotelmata (N = 26) over the time period of a month (February 2020). Rainfall data were provided by the Nouragues Ecological Research Station from an above-canopy weather station in the study area. High arboreal pools (N = 8, 2020) were only measured once. pH was recorded using a pH meter (AMTAST Waterproof pH Meter). The pH meter was calibrated once per day, prior to pool sampling, using both acidic (pH = 4) and neutral (pH = 7) calibration solutions. The pH of ground access pools was taken directly by submerging the pH probe into the pool, and the measurement was recorded once read-out stabilized. For arboreal pools, a sample of water was collected using a syringe, which was then sealed at both ends.
| Statistical analyses The presence of D. tinctorius in pools was analyzed using 2019 field data. As a result of the high collinearity between variables in the 2019 dataset (see Figure  S1 ), we used a principal component regression to analyze phytotelm ecology data. We first checked data for a nonrandom structure following  Björklund (2019) ; then, we established that the correlation matrices were significantly different from random (ψ = 10. 22, p = 0; = 0. 238, p <. 001) to ensure they were suitable for a PCA. Based on each PC difference from random matrices, we selected the first three principal components as predictors of probability for D. tinctorius tadpole presence as a binomial response in the principal component regression (PC1-3 explained about 53% of the variability of the data (where PC1 = 0.
24 ± 0. 48, PC2 = 0. 17 ± 0. 40, PC3 = 0. 11 ± 0. 33 (variance explained ± SE)). We evaluated the fit of negative binomial GLMs based on second-order AIC ranks (AICc) using the package AICcmodavg  (Mazerolle, 2020)  which are specialized for smaller sample sizes (Akaike, 1974; see Table  S1 ). Models within two AIC scores of each other were further evaluated by assessing the significance of interactions between model terms. To better understand which variables contributed significantly to each principal component, we calculated which variables had index loadings larger than random data. Following the methods outlined by  Björklund (2019)  and  Vieira (2012) , we randomized the data and calculated new correlation matrices which we permuted 1,000 times. We then compared the index of loadings (IL ij = u 2 ij × 2 i , see  Vieira (2012)  for details) between each observed PC and the randomly generated data to see which variables contributed significantly to each principal component.
The 2020 dataset consisted of weekly resampled pools throughout February 2020. Models took repeated measures of pool ID into account as a random effect. Both the presence of D. tinctorius tadpoles (count; negative binomial family) and pH (Gaussian family) from resampled pools were modeled using a mixed effects generalized linear model in the package "glmmTMB"  (Magnusson et al. , 2020). Predictor structure for both pH and D. tinctorius models was built based on biologically relevant variables (pool substrate, time, D. tinctorius tadpole count (for pH model), water capacity, surface area:depth ratio). Using these variables, models were first fit with relevant interactions (see Tables  S2  and S3 ), which were then removed if they did not contribute significantly to the model using single term deletions (using base R function, drop1;  Zuur et al.
| Dendrobates tinctorius across temporal scales Using both 2019 and 2020 datasets, we were able to follow phytotelmata across multiple timescales: 13 weekly resampled ground access and low arboreal pools, 13 annually resampled ground access and low arboreal pools, and 7 annually resampled high arboreal pools. Overall, we found that pools can persist over multiple sampling seasons. High arboreal pools appear to be the most stable with respect  The bold text was to emphasize column names in original tables. When considering pools resampled weekly over the course of a month, two trends emerge: (a) pH is consistently higher in pools contained in "dead" substrates than in "live" substrates (odds ratio = 1. 47, Table  2 ). For all substrate types, however, pH values decreased over the one-month sampling period (Figure  7a ; odds ratio = -0.
): Dendrobatidae: Dendrobates tinctorius and Ranitomeya amazonica; Aromobatidae: Allobates femoralis; Hylidae: Osteocephalus oophagus, Trachycephalus resinifictrix, and T. hardroceps; and Bufonidae: Rhinella castaneotica. The tadpoles of only three species (D. tinctorius, O. oophagus, and A. femoralis, present in N = 34, N = 7, and N = 10 pools, respectively) were detected frequently enough for further analysis. The species-wide dataset is based on the sampling of 70 unique pools in 2019. Differences in pool accessibility are highlighted in Figure 3. Compared to A. femoralis and O. oophagus, one of the most striking aspects of D. tinctorius ecology is its flexibility with respect to site F I G U R E 3 Tadpole presence across the vertical landscape. Panel (a) shows all sampled pools. For the "All" category, colored/empty triangles represent presence/absence data of at least one of the three species in the pool.
d'Avenir" grants managed by Agence Nationale de la Recherche (AnaEE France ANR-11-INBS-0001; Labex CEBA ANR-10-LABX-25-01), for logistic support in the field and for providing the meteorological data. Thank you to Rick Lehtinen and L. M. Schulte for their constructive and insightful reviews, which greatly contributed to the quality of this manuscript. This work is part of a partnership between BR, AP, and the Nouragues Nature Reserve aimed at improving and spreading the knowledge about amphibians. We thank the staff of the Nouragues Nature Reserve for their commitment to preserving our natural world; Walter Hödl for his ongoing mentorship and inspiration of this work and collaboration; Valentine Alt for the essential canopy access training and equipment; and Lauren A. O'Connell for her generous support of AP, SJSR, and MTF.
Funding information This project was partially funded by the  Investissement d'Avenir funds of the ANR (AnaEE France   ANR-11-INBS-0001 ;  Labex CEBA   ANR-10-LABX-25-01 ) in the framework of the  Nouragues Travel Grant  granted to BR, AP, SJSR, and JDCC. BR, JV, and CF are funded by the  Academy of Finland  ( Academy Research Fellowship  to BR, Project No. 21000042021 ). AP is supported by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement no. 835530. AP, SJSR, and  MTF  were also supported by  Lauren A. O'Connell  with  Stanford University  and the  National Science Foundation  ( IOS-1845651 ) funds.
0 kit according to the manufacturer's instructions. Libraries were quantified at each step with the Qubit HS DSDNA system (Thermo Fisher Scientific, Villebon sur Yvette, France). Sequencing: samples were prepared with the Sequel binding and internal control kit 3. 0 and sequenced with the Sequencing kit by placing 6 pM in the SMRT cell 1M v3 Tray and using 600-min movies on the Sequel Platform (Pacific Bioscience, Menlo Park, CA, USA). Data analysis: the A6 amplicon consensus sequences were built from the PacBio reads using a home-made Snakemake pipeline. First, the HiFi reads were generated from the PacBio subreads. bam using PacBio CCS tool (v. 6. 0. 0, https://github. com/PacificBiosciences/ccs, accessed on 26 October 2021) and demultiplexed with Lima (v. 2. 0. 0, https://github. com/ PacificBiosciences/barcoding, accessed on 26 October 2021).
The resulting reads were analyzed by a PacBio open-access tool: pbAA (v. 0. 1. 3, https://github. com/PacificBiosciences/ pbAA, accessed on 26 October 2021) which clustered similar reads together and generated one or several haplotypes depending on the diversity of the viral population of each sample. The resulting haplotypes were then mapped on the SARS-CoV-2 reference genome (Wuhan-Hu-1 isolate, Genbank accession number NC_045512. 2) with Minimap2 (v2. 17)  [28]  to discard eventual chimeric reads. In parallel, a consensus sequence was built from the reads and the files were generated by pbAA with another PacBio tool: CoSA (Coronavirus Sequence Analysis, v9. 0. 0, https://github. com/Magdoll/CoSA, accessed on 26 October 2021). Then, the haplotypes sequences and the consensus sequence (for each sample) were treated separately.
Each haplotype/consensus sequence was aligned on the reference SARS-CoV-2 spike region (NC_045512. 2) with MAFFT (v. 7. 475)  [29]  and trimmed from the beginning of the spike gene (nt 21563) to the end of the A6 amplicon (nt 23823). Aligned sequences were translated into amino acids and again aligned with MAFFT to the spike amino acids reference sequence (YP_009724390. 1). We used a variant table that included the variant strains and their key mutations identified by the French SARS-CoV-2 National Reference Center (NRC) (Table  1 ). A python script retrieved the mutations (SNP, indel) carried by the haplotype or consensus sequences and compared them to the NRC profiles. A profile was validated if the analyzed sequence harbored 80% of the mutations from the spike region profile covered by the A6 amplicon, as some of the mutations were not present or covered, especially with the Illumina whole genome amplification protocol.
2N NaOH and 400 mM Tris-HCl (pH-8). Replicates of each 384-sample pool were placed in the S4-flow cell according to the NovaSeq-XP workflow (Illumina, USA) and subjected to dual-indexed on a NovaSeq 6000 platform. The 141 samples of January 2021 were sequenced with single-end 35 pb read length while the 45 samples collected in July 2021 were sequenced with paired-end 150 pb read length. Data analysis: the resulting sequence data were processed using DRAGEN COVID Lineage (v3. 5. 1) (Illumina, CA, USA) to obtain consensus sequences, Nextstrain clades (https://nextstrain. org/sars-cov-2, accessed on 31 August 2021), and Pangolin lineages (https://cov-lineages. org/pangolin. html, accessed on 31 August 2021) when at least 5 SARS-CoV-2 probes were detected. Among them, samples covering at least 75% of the SARS-CoV-2 genome and 85% of the spike region were analyzed for alignment.
Consensus sequences were aligned to the SARS-CoV-2 Wuhan-Hu-1 reference genome (NC_045512. 2) and 109 GISAID sequences of Pangolin lineages detected in France using MAFFT (v. 7. 475). Maximum likelihood trees were then calculated from these alignments with IQTREE (v. 2. 0. 3, evolutionary model: GTR + F + R3)  [30]  to confirm any clade and lineage results. D253G/del G del R346K/S K S Q414K K K417N/T (N) N * T N440K K * Y449H N450K K L452R/Q R * (R) R * R * R * R * R * R * R * Q * S477N N * N NT 478K K * (K) V483A A E484K/Q K * Q * K * K * (K) K * K * K * K * KˆQ * Q * K * F490S S N501Y/T Y * Y * Y * Y * Y * Y Y * T * Y * Y * A570D D D D D D614G G G G G G (G) G G G G G G G G G G G G G G G G G G G A653V V H655Y Y Y Y Y * G669S S Q677H H (H) H N679K P681H/R H H H R (R) H H * H H H R R A701V V V T716I GISAID accession numbers are provided in the Supplementary Table  S1.
Supplementary Materials: The following are available online at https://www. mdpi. com/article/ 10. 3390/v13122544/s1, Table  S1 : GISAID Accession Numbers. Author Contributions: Conceptualization, S. L. , P. T. , and J. I. ; methodology, N. J. , N. R. , J. L. , G. S. and C. D. ; formal analysis, S. L. , J. L. , N. J. , P. T. , J. L. and J. I. ; data curation, N. J. , N. R. , J. L. , G. S. and C. D. ; writing-original draft preparation, S. L. ; writing-review and editing, S. L. , N. J. , P. T. , M. M. , J. L. and J. I. ; Supervision, S. L. and J. I. ; funding acquisition, J. I. All authors have read and agreed to the published version of the manuscript. Funding: ANRS-MIE0152 Emergen/Quasicov. Institutional Review Board Statement: These analyses were part of the national SARS-CoV-2 surveillance.