dataesr/scientific-paragraphs-categorization huggingface dataset:
Sediments that are transported and deposited in dry aeolian environments are also subjected to physical abrasion and less chemical weathering  (Thomas et al. , 2005). As such, sediment grains that are less dense with a shape that is efficient at transportation by saltation or surface creep, and that are resistant to physical weathering are more likely to be transported farther in a dry aeolian environment and concentrated in distal deposits relative to those which do not meet these criteria. The geochemical trends of the majority of ChemCam data between mafic and felsic minerals, and the similarity in composition between concretionary and non-concretionary bedrock, also suggests that alteration was likely minimal away from the unconformity for the Greenheugh pediment capping unit. Minimal alteration at Greenheugh is further supported by the presence of 8 wt% olivine in the Edinburgh drilled sample  (Rampe et al.
The derived data which includes the ChemCam targets used in the study, target classifications, and clustering results, can be found at  Bedford (2022). For our dataset, the number of intervals range between 21 and 34. The black vertical line shows the mean value of the major element oxide at each locality. The y-axis shows the number of data points in each bin (frequency). The histogram for MnO was restricted to 1 wt% but has outliers to higher values which skews the mean, particularly for Greenheugh. White arrows point to light-toned, spherical grains that may also relate to rounded mudstone clasts  (Banham et al. , this issue). Table  2. Mean composition, standard error of the mean (+/-), and standard deviation (std) of the derived clusters. Colors highlight the highest abundances (red), the lowest (blue), and the gradation in between for each of the five clusters.
Study design: We conducted a survey study on a representative sample of the French adult population. Methods: Our data were collected by the Obervatoire R egional de la Sant e (ORS) using a selfadministered online questionnaire. This was completed by a sample of 2,022 people stratified to match French official census statistics for gender, age, occupation, and area of housing. We conducted statistical analysis using Python (Pandas e Scipy -Statsmodels) with chi-squared and Wilcoxon ranksum tests to control for statistical significance. Results: A small majority of respondents used TousAntiCovid (55. 5%), while 41. 0% had never downloaded it. Only one-quarter of the respondents (23. 3%) used it for contact tracing with Bluetooth, while a third (32. 2%) used it only for storing their health pass.
The Anonymous Third-Party Reporting (ATPR) method (or confidantes' method) ATPR was developed to collect quantitative data on induced abortion in restrictive contexts, by asking survey respondents to report anonymously on the recent abortions which occurred to a set of close female relations previously listed, including details on the abortion procedure and the complications experienced. The ATPR method is based on ethnographic work in Burkina Faso showing that information on abortions circulates in social networks in spite of this practice being strongly stigmatized, because women and couples cannot access abortion services otherwise  [11] : information sharing is restricted to close relations and to people who can help in accessing abortion services, i. e. other abortion seekers or abortions providers.
3   The Network Scale-Up Method (NSUM) Recent advancements in another third-party reporting method called the Network Scale-Up Method (NSUM) could provide viable solutions to the first limitation. The NSUM has been used in recent years to estimate the size of epidemiologically hidden and stigmatized populations in HIV research (e. g. sex workers, men who have sex with men, injecting drug users)  [26, 27] , and recently to estimate the number of abortions  [28, 29]. The estimates of the NSUM are generated by collecting information on two elements from a representative sample of respondents: (i) how many members of the hidden population they know, and (ii) estimates of the overall size of their social networks. This data is then aggregated as a ratio to generate population level estimates of the size of these groups.
Second, the transmission bias is obviously much weaker in networks of close relations as opposed to circles of people you "know" or "ate a meal with". Accordingly, plausible data on abortion have been collected via ATPR in some places, while NSUM failed to do so. In this study we will thus apply the ATPR rather than NSUM. However, some features of NSUM can be used to improve on the ATPR, especially its systematic formula to correct for transmission and barrier biases and its attention to the issue of social desirability.
Methods To attain these two aims, we propose to use the following methodological strategy: 1. We will obtain population-level data on abortion care seeking (method, provider, setting and post-abortion symptoms and care-seeking) in two sites in Kenya and Burkina Faso where access to legal abortion services is highly restricted, by applying Anonymous Third Party Reporting (ATPR) (also known as confidantes' method) and Respondent-Driven Sampling, both modified with features that can potentially improve their implementation or accuracy. 2. We will modify the Anonymous Third Party Reporting (ATPR) method by refining and validating the method for future use in large scale surveys by: 2a. developing a formative study to determine whether the ATPR is pertinent in that context; 2b. identifying the best network-generating question; 2c.
Overall study design: two phases The two HDSS sites will obtain approvals from their local institutional review boards prior to the start of the project. The project is divided into two phases (Table  2 ). In a first phase, each HDSS will conduct a formative research, where the existence of the transmission bias will be probed in a qualitative study and the other biases will be investigated by conducting a quantitative pre-test for the ATPR and for the RDS. The study will then move to phase 2 (main survey: ATPR and RDS) if the data collected during phase 1 shows that the ATPR and RDS can be implemented for abortion in that site. For RDS, this means that phase 1 can produce at least 6 seeds that can successfully recruit 30 respondents. For ATPR this means that abortion rate obtained fall within in a plausible range.
Pre-tests of the ATPR and RDS In a location outside of the HDSS area (but similar in features), the questionnaire for the ATPR will be tested on 100 women aged 15-49. The questionnaire will include two successive network generator questions (the internationally validated "people you have discussed important matters with during the past year" as well as "up to three women you share secrets with"). The test of ATPR will advance knowledge on the network generating question best suited to investigate abortion and verify that the different biases (transmission, barrier, selection, social desirability) do not preclude the collection of data on abortion. The RDS pre-test will be conducted in a second location, also outside of the HDSS area, but also similar. The aim of the pre-test for the RDS is to fine-tune the RDS procedures (seeds, location for the interviews, etc.
Data collection: Anonymous Third Party Reporting method Sampling, inclusion criteria, and interview setting We will conduct a cross sectional representative household-based survey among women aged 15-49. We will draw a simple random sample from the sampling frame of women 15-49 usually residing in the HDSS and randomization tables. We will use Excel to generate random numbers that will be apply to the ordered list of women in the area. The definition of residence may vary slightly across sites. To calculate sample size, we used estimates of abortion rates from published numbers in national studies or global estimates; numbers indicate a low 22 abortions per 1000 women aged 15-49 in rural Burkina Faso to a high of 48 abortions for 1000 15-49 in Kenya. The number of women aged 15-49 living in the HDSS area varies from 8500 in Nairobi to 10,000 in Kaya.
Using these inputs and a random sample design, we determined that 1500 women in each site were enough to yield reliable abortion safety estimates across the two sites, assuming a reporting rate of 50% (one in two women knowing about the abortion of their close relation will report it in the survey) and two close relations. Along these assumptions, we will collect data on about 150 abortions in Kaya, and the double in Nairobi. To anticipate on possibly lower reporting rates and number of relations or abortion rates, we increase the sample size to 2000 in each site. All women  6  who consent to participate in the study will be interviewed using the self-report/ATPR tool, in a private setting in their home or close surroundings, face to face by an interviewer recording answers on tablets. Questionnaire The questionnaire will include five sections.
Data collection: respondent-driven sampling Recruitment, inclusion criteria and interview setting When the ATPR is done, a different team of fieldworkers will start the RDS. To begin the RDS sample in the HDSS area, following the tested procedures, we will purposively select a sample of 8 to 12 women who reside in the area and have had abortions in the last 3 years as seeds to recruit our target population; they will be selected to represent the demographic, economic, residential and occupational diversity of the sites (see section on ethics in the discussion for details on seed recruitment). After providing informed consent, each seed will be interviewed face-to-face and provided with 3 coupons to recruit their peers who should meet the same inclusion criteria (reside in the site and have had an abortion, or in a multiplex design, reside in the site, and be close to someone who has had an abortion; pilot results will determine in which circumstances to recruit a social referent).
Data management All data collection in the main survey will be conducted on handheld android tablets using the mobile data collection software SurveyCTO collect. SurveyCTO is a platform that facilitates offline electronic data collection. Electronic data collection will be used to allow for increased security of collected data; tablets will be password-protected and completed consent forms and surveys will be uploaded to a secure server at the end of each day. Only the research team (University of Geneva and HDSS sites) will have access to the server. University of Geneva researchers will set up the secure server for the study and provide access to each site for the duration of the study.
Plans for analyses The analyses will focus on the two network methods applied to measure abortion safety (RDS and ATPR). The RDS data will be analyzed using the Respondent-Driven sampling analysis tool (RDSAT) or alternative tools in Stata or R, modified to take into account the multiplex structure if needed. For each site, we will examine recruitment patterns amongst respondents, and generate both crude point estimates for the sample recruited and population adjusted estimates of variables of interest including: women's demographic characteristics, the proportion of women obtaining abortions from different types of providers, those experiencing complications, those seeking care etc. Additional analyses may include multivariable analyses examining the relationship between abortions with various degrees of safety or care seeking for complications and sociodemographic variables.
We will also estimate the transmission rate using information on the number of respondents' close relations and the number who know about her induced abortions and its sequelae. We will measure the barrier bias by comparing the network sizes/structure of RDS respondents to that of all women in the ATPR survey. Using the ATPR data and for each site, we will describe women's demographic characteristics and that of their network of close relations. We will check which networkgenerating question yields the less selected sample of friends, and whether one question yields a sample that contains more abortion seekers, at constant characteristics. We will correct for selection bias. We will estimate the proportion of close relations obtaining abortions with different methods, different types of providers, those experiencing complications, those seeking care etc.
A final potential risk is that women could feel psychologically distressed during the interviews when evoking their abortions. However, the literature clearly shows that psychological distress is not a usual outcome of abortions; in the opposite, abortions usually bring relief to women who are pregnant at an inappropriate time  [45]. Moreover, in this study, we will only collect data on the conditions of the abortions as they relate to their safety; we will not explore the reasons for pregnancy termination or the unintended pregnancy. If the woman appears to the interviewer that she requires additional support or if she herself asks for more information or seeks support, we will arrange for referral to psychological counselling in each site. For each site, there is an established collaboration with the local hospital so that referrals can be made smoothly.
Quantitative data collection software Data collection for the ATPR and RDS component of the study will be conducted on handheld android tablets using the mobile data collection software SurveyCTO collect. SurveyCTO is a platform that facilitates offline electronic data collection. Electronic data collection will be used to allow for increased security of collected data; tablets will be password-protected and completed consent forms and questionnaires will be uploaded to a secure server at the end of each day. Interviewers will also carry paper copies of the instruments however, in the case of power or mechanical failure. Paper copies will be entered as soon as tablets are functional again and will be destroyed thereafter either by shredding or through incineration. Interviewers will pick up and return their tablets daily to the HDSS offices where they will be stored in locked cabinets.
Only the lead scientist on each site will have access to these cabinets. Interviewers' passwords will allow them to access the data collection tool but not to see or edit previously entered questionnaires on the tablet. Backups are automatically stored on each tablet in case they were are unable to transmit the data immediately and automatically to the remote server. The backup data will remain on the password-protected tablets until the end of field activities, and until all the data have been synced with the SurveyCTO Server, at which time each tablet will be securely and permanently wiped clean. When not in use, all tablets will be secured in lockable cabinets or containers/cases in the HDSS field offices accessible only to the lead scientist of each site.
Data security Encryption and decryption keys will be generated by security software that comes with the SurveyCTO package and which is automatically available to the project through the study's SurveyCTO account. The encryption key will be programmed into all questionnaires to ensure that the responses are encrypted prior to transmission to the remote server. The study team will have the private decryption key, which will be saved by the study investigators in a file on a secure computer accessible only to the PIs. Once a questionnaire is completed, all the sensitive data it contains (identification number and nicknames or initial for network numbers, mention of abortions) will be automatically encrypted during the transmission to our secure, private, remote server space within the Sur-veyCTO Server.
Thereafter encrypted data will be downloaded onto computers by the study investigators using a desktop application called SurveyCTO Sync. Data will be downloaded from the study server servers to cold room computers by the research team or persons appointed by them where they will be decrypted using the private key (during transmission it undergoes double encryption again). Thereafter all personal identifying information will be removed from the dataset before it is made available to other members of the research team for analysis. Although local backups of the data are saved on the computer and servers, just in case of a crash, it remains encrypted and therefore unreadable until the private decryption key is entered. At the end of the study period, all the data on the tablets will be wiped, final data collection tools, datasets and stata do files for importing data will be downloaded from the server and the server wiped and closed.
Decryption and storage of raw data will occur on one secure computer, accessible only to the PIs, for the duration of the study. All decrypted data will be stored on regularly backed up secure drives on password protected computers within the study institutions. Data will only be accessible to members of the study team for analysis. A master data set containing data from all countries will be held centrally at the University of Geneva, with the Geneva research members having access to this data set, and made available to HDSS sites as needed for the central products. Country specific data files will be available to researchers from the HDSS sites for future analyses, beyond the currently promised products. WHO, the University of Geneva and each center running the HDSS sites are the primary owners of each sites study data.
Emerging evidence indicates that the gut microbiome contributes to endurance exercise performance, but the extent of their functional and metabolic potential remains unknown. Using elite endurance horses as a model system for exercise responsiveness, we built the first equine gut microbial gene catalog comprising more than 25 million non-redundant genes representing 4,696 genera spanning 95 phyla. The unprecedented resolution unrevealed functional pathways relevant for both the structure of the microbiome and the host health and recovered 369 novel metagenome-assembled bacterial genomes, providing useful reference for future studies. Integration of microbial and host omic datasets suggested that microbiomes harboring rare species were functionally dissimilar from those enriched in Lachnospiraceae taxa.
Taxonomic assignments were made from clean read pairs searching for maximum exact matches between translated sequences and a reference database  20. This approach yielded an annotation for 61% of all sequences (Fig 1b ) and revealed a diverse community of 95 phyla encompassing 1,110 families and 4,696 genera. Bacteria (95%) defined most of the assemblage in terms of abundance and diversity, followed by a handful of eukaryotes (3. 27%), archaea (1. 14%) and viruses (0. 15%). At the phylum level, the bacterial phyla Firmicutes (44. 3%) and Bacteroidetes (26. 5%) greatly outnumbered Proteobacteria (7. 5%), Spirochaetes (6. 2%), Actinobacteria (4. 6%), Euryarchaeota (1. 1%), Ciliophora (0. 90%) and Ascomycota (0. 90%; Fig  1c ). Consistent with a recent metagenomic study in the wild Przewalski's horses  21  , Ascomycota and Basidiomycota were among the top Eukaryota phylum in the gut.
44% ± 3. 71), Treponema (5. 96% ± 1. 59), Clostridium (4. 91% ± 0. 49) and Ruminococcus (4. 04% ± 0. 79). All of them are in full agreement with the core microbiome of endurance horses inferred from 16S rRNA-based sequencing  [23] [24] [25] [26]. To gain functional insights, genes and functional modules were annotated using the nonsupervised orthologous groups (EggNOG) database. We identified a total of 12,060 KEGG orthologous groups (KOs) and 137 different carbohydrate-active enzymes (CAZymes), which encompassed 44% (n = 11,132,404) and 3. 38% (n = 665,235) of the gene catalog, respectively. The majority of KOs had functions related to essential microbial gut functions, including genetic information processing and signaling, carbohydrate and amino acid metabolism. However, we also identified a number of pathways (i.
Holo-omics: rare microbiomes with lower abundances of Lachnospiraceae taxa associated with improved cardiovascular fitness and points toward enhanced mitochondrial capacity To characterize the microbiome-host crosstalk and identify molecular differences between the two types of cardiovascular outcomes in elite horses, we integrated multi-omic datasets from host and associated gut microorganisms through a multivariate matrix factorization approach using DIABLO (Data Integration Analysis for Biomarker discovery using Latent Components). To achieve this integrated perspective coined as holo-omics  34  , we combined pair host-centered omic and phenomics data with the shotgun metagenomics, fecal SCFAs composition and the concentrations of bacteria, anaerobic fungi and protozoa. First, we observed strong covariation between the dominant phylotypes and the genetic functionalities derived from both KOs (r 2 = 0.
The Divisive Amplicon Denoising Algorithm (DADA) was implemented using the DADA2 plug-in for QIIME 2 (v. 2021. 2) to perform quality filtering and chimera removal and to construct a feature table consisting of read abundance per amplicon sequence variant (ASV) by sample  67. Taxonomic assignments were given to ASVs by importing Greengenes 16S rRNA Database (release 13. 8) to QIIME 2 and classifying representative ASVs using the naive Bayes classifier plug-in  68. The phyloseq (v. 1. 36. 0)  69  , vegan (v. 2. 5. 7)  70  and microbiome (v. 1. 14. 0) packages were used in R (v. 4. 1. 0) for the downstream steps of analysis. A total of 364,026 high-quality sequence reads were recovered for the 11 horses of the study (mean per subject: 33,093 േ 17,437, range: 12,052 -62,670). Reads were clustered into 5,412 chimera-and singleton-filtered ASVs at 99% sequence similarity.
The ASV taxonomic assignments and ASV counts for each individual are presented in the Suppl Table  S4 ). Fecal metagenome: Shotgun sequencing data production and analysis Metagenomic sequencing was performed using the same DNA extractions. For each individual, a paired-end metagenomic library was prepared from 100 ng of DNA using the DNA PCR free Library Prep Kit (Illumina, San Diego, CA, USA) and size selected at about 400 bp. The pooled indexed library was sequenced in an Illumina HiSeq3000 using a pairedend read length of 2x150 pb with the Illumina HiSeq3000 Reagent Kits at the PLaGe facility (INRAe, Toulouse).
MAG assembly and annotation Raw metagenomics reads were quality-trimmed, assembled, binned and annotated using the ATLAS pipeline, v. 2. 4. 4  71. In short, using tools from the BBmap suite v. 37. 99  72  , reads were quality trimmed and contamination from the horse genome were filtered out (available at NCBI sequence archive with the accession number GCA_002863925. 1; Equus_caballus. EquCab3. 0). Reads were error corrected and merged before assembly with metaSPAdes v. 3. 13. 1  73. Since a high diversity between individuals was described through 16S rRNA amplicon analysis, we first assembled each sample independently. QUAST 5. 0. 2  74  was used to evaluate the quality of each sample assembly. Contigs from single samples were binned using MetaBAT 2 (v. 2. 14)  75  and Maxbin 2. 0 v. 2. 2.
The abundance of each MAG was then quantified across samples by mapping the reads to the non-redundant MAGs and determining the median coverage in 1 Kb windows along each genome. For the taxonomic annotation, ATLAS predicted the genes of each MAG sequence using Prodigal v. 2. 6. 3 80 with default parameters. Robust taxonomic annotation was assigned to bins according to the genome taxonomy database (GTDB-tk  81  ) release 95, v. 5. 0 (July 17, 2020). As such, GTDB-Tk taxonomy names are used throughout this paper. In addition, MAG phylogenetic trees were built based on markers from GTDB-Tk and CheckM and visualized using ggtree (v. 3. 0. 2) in R package. To assess the contribution of the constructed MAGs to the functional potential of the gut microbiome, the predicted gene and proteins extracted by Prodigal during the CheckM pipeline were compared to the EggNOG database 5.