dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, we found a significant positive effect of VS during changing/learnable sessions in the anterior lateral striatum. There was also a significant negative effect of sRPE during stable/unlearnable sessions in the plOFC although we are cautious about overinterpreting this result as it would not survive correction for multiple comparisons. A negative sRPE indicates a stronger response when reward outcomes are worse than expected. Data and code to reproduce the figure can be found at https://doi. org/10. 5281/zenodo. 3993116. a. u. , arbitrary units; n. s. , not significant; plOFC, posterior lateral orbitofrontal cortex; ROI, region of interest; RRE, rare reward event; sRPE, scalar reward prediction error; VS, visuospatial surprise. https://doi. org/10. 1371/journal. pbio. 3000899. g004 either of the other two conditions separately (changeable/learnable sessions versus equiprobable sessions and also changeable/learnable versus stable/unlearnable sessions) (S11 Fig).
Materials and methods Six rhesus monkeys (Macaca mulatta, 1 female) participated in the experiment. The animals weighed between 8. 6 and 13. 5 kg and were 7-8 years of age. They were kept on a 12-h lightdark cycle, with ad libitum access to water for 12-16 h after testing and throughout the day on nontesting days. All procedures were conducted under licences from the United Kingdom Home Office in accordance with the UK Animals (Scientific Procedures) Act 1986 and by the University of Oxford Animal Care and Ethical Review Committee. In the behavioral task, monkeys sat in the sphinx position in a purpose-built MRI-safe chair (Rogue Research, Petaluma, CA). In order to prevent head movements during fMRI data acquisition, an MRI-compatible cranial implant (Rogue Research) was surgically implanted under anaesthesia.
MRI data acquisition and preprocessing Imaging data were collected using a 3T clinical MRI scanner and a four-channel phased-array receive coil in conjunction with a radial transmission coil (Windmiller Kolster Scientific, Fresno, CA). For each monkey, structural images were acquired under general anesthesia, using a T1-weighted MP-RAGE sequence with a resolution of 0. 5 × 0. 5 × 0. 5 mm, repetition time (TR) = 2. 05 s, echo time (TE) = 4. 04 ms, inversion time (TI) = 1. 1 s, and flip angle of 8˚. Two or 3 structural images per subject were averaged. Anaesthesia was induced by intramuscular injection of 10 mg/kg ketamine, 0. 125-0. 25 mg/kg xylazine, and 0. 1 mg/kg midazolam. fMRI data were collected during task performance with a gradient-echo T2 � echo planar imaging (EPI) sequence with a resolution of 1.
5 × 1. 5 × 1. 5 mm, interleaved slice acquisition, TR = 2. 28 s, TE = 30 ms, and flip angle of 90˚. At the end of each session, to aid image reconstruction, a proton-density-weighted image was acquired using a gradient-refocused-echo (GRE) sequence with a resolution of 1. 5 × 1. 5 × 1. 5 mm, TR = 10 ms, TE = 2. 52 ms, and flip angle 25˚. EPI data were prepared for analysis following a dedicated nonhuman primate fMRI processing pipeline  [59]  using tools from FSL  [60] , Advanced Normalization Tools (ANTs)  [61] , and the Magnetic Resonance Comparative Anatomy Toolbox (MrCat; https://github. com/ neuroecology/MrCat). In short, after EPI data were reconstructed offline using a SENSE algorithm  [62] , time-varying spatial distortions were corrected using restricted nonlinear registration, first to a session-specific high-fidelity EPI, then to each animal's T1w structural image, and finally to a group-specific template in CARET macaque F99 space  [63].
fMRI analysis Whole-brain analysis was conducted using a hierarchical GLM approach. Specifically, we first fitted every session individually before combining them for each monkey on a second hierarchical level using fixed effects in F99 standard space. Finally, we combined the data from all monkeys on a third hierarchical level using the FLAME 1 + 2 procedure from FSL  [60]  and using standard cluster-based thresholding criteria of z > 2. 3 and p < 0. 05 cluster-corrected  [64]. Sixteen regressors of interest were designed for each session. Additional confound regressors were used to index head motion and volumes with excessive noise. All regressors were zscore normalized, and the data were analyzed using FSL's fMRI Expert Analysis Tool (FEAT). To model the hemodynamic response function (HRF), each regressor was convolved with a single gamma function (mean lag = 4.
Errors and outliers became more likely as each session progressed and TrialNo increased. Data and code to reproduce the figure can be found at https://doi. org/10. 5281/zenodo. 3993116. a. u. , arbitrary units; GLME, generalized linear mixed-effect model; RRE, rare reward event; SEM, standard error of the mean; sRE, scalar reward expectation; TrialNo, trial number; VS, visuospatial surprise. https://doi. org/10. 1371/journal. pbio. 3000899. g002.
The orofacial area activity reflects reward amount, and thus all three time courses exhibit an initial positive peak. In contrast, ventrolateral striatum and SN/VTA process sRPEs, and thus, the time course for receiving 1 drop of juice-which is associated with a negative sRPE-results in supressed activity (a negative activity change). Data and code to reproduce the figure can be found at https://doi. org/10. 5281/zenodo. 3993116. a. u. , arbitrary units; BOLD, blood-oxygen-level-dependent imaging; n. s. , not significant; ROI, region of interest; RRE, rare reward event; SN/VTA, ventral tegmental area and substantia nigra; sRPE, scalar reward prediction error; VS, visuospatial surprise. https://doi. org/10. 1371/journal. pbio. 3000899. g003.
2 (1) = 0. 134, p = 0. 714; right S9 Fig. Neural effects of VS by session type. We also examined the extracted z-statistics shown in Fig 5 split up by session type. Labelling conventions are the same as in Fig 3. Data and code to reproduce the figure can be found at https://doi. org/10. 5281/zenodo. 3993116. (PNG) S10 Fig. Comparing neural RRE effects between session types. As an alternative to the analysis shown in Fig 4,.
= 0. 002, p = 0. 965) was significant. Data and code to reproduce the figure can be found at https://doi. org/10. 5281/zenodo. 3993116. (PNG) S11 Fig. Locations of neural RRE activity. RRE activity in plOFC in changing/learnable sessions (red) is located in the same area as activity when we contrast changing/learnable and equiprobable sessions (blue) or changing/learnable and stable sessions (green). All effects shown here are at a threshold of 2 and are shown without applying any cluster correction. Data and code to reproduce the figure can be found at https://doi. org/10. 5281/zenodo. 3993116. (PNG) S12 Fig. Summary of the neural results.
2 ( 1 ) 21 = 0. 002, p = 0. 965) was significant. Data and code to reproduce the figure can be found at https://doi. org/10. 5281/zenodo. 3993116. (PNG) S11 Fig. Locations of neural RRE activity. RRE activity in plOFC in changing/learnable sessions (red) is located in the same area as activity when we contrast changing/learnable and equiprobable sessions (blue) or changing/learnable and stable sessions (green). All effects shown here are at a threshold of 2 and are shown without applying any cluster correction. Data and code to reproduce the figure can be found at https://doi. org/10. 5281/zenodo. 3993116. (PNG) S12 Fig. Summary of the neural results.
MiSeq Illumina metagenomic sequencing Metagenomic library preparation. Metagenomic libraries were prepared from 1 ng of DNA using the Nextera XT Library Prep Kit and indexes following the protocol in Illumina's reference guide for the Nextera XT DNA Library Prep Kit with some modifications for samples with DNA concentrations below 1 ng as follows. The tagmented DNA was amplified over 13 PCR cycles instead of 12 PCR cycles, and the libraries (after indexing) were resuspended in 30 µL of RSB (resuspension buffer) buffer instead of 52. 5 µL. Metagenomic sequencing was performed using the MiSeq and V2 technology of Illumina with 2 × 250 cycles. At the end of the sequencing, the adapter sequences were removed by internal Illumina software. Reads quality filtering. Reads 1 and 2 per sample were not paired but merged in a common file before filtering them based on read quality using the tool FASTX-Toolkit (http: //hannonlab.
Downloading of public metagenomes Public metagenomes were downloaded from the MGRAST (Metagenomic Rapid Annotations using Subsystems Technology;  Keegan et al. , 2016)  and SRA (Sequence Read Archive;  Leinonen et al. , 2011; NCBI Resource Coordinators, 2018)  databases as quality-filtered read-containing FASTA files and raw read-containing FASTQ files, respectively. The FASTQ files containing raw reads underwent the same quality filtering as our metagenomes (as discussed above). The list of the metagenomes, type of ecosystem, number of sequences and sequencing technology (i. e. , MiSeq, HiSeq or 454) are summarized in Table  S2. The sampling sites are positioned on the map in Fig. 1.
Annotation of the metagenomic reads Firstly, to access the overall functional potential of each sample, all the filtered sequences per sample were functionally annotated using DIAMOND  (Buchfink et al. , 2015)  and the nr (non-redundant) database; then the gene-annotated sequences were grouped in the different SEED functional classes (around 7000 functional classes, referred simply to as functions) using MEGAN6 (Metagenome Analyzer;  Huson et al. , 2016). Functional classes that were present ≤ 2 https://doi. org/10. 5194/bg-17-6081-2020 Biogeosciences, 17, 6081-6095, 2020 times in a sample were removed from this sample. In parallel, the Kraken software  (Wood and Salzberg, 2014)  was used to retrieve the bacterial and fungal sequences separately from the filtered sequences using the Kraken bacterial database and FindFungi  (Donovan et al.
, 2018)  fungal database (both databases included complete genomes), respectively (and using two different runs of Kraken). Separately, both the bacterial and fungal sequences were also functionally annotated using DIAMOND and MEGAN6 (number of sequences functionally annotated in Table  S3 ). Secondly, for specific metabolic and stress-related functions, we annotated the sequences using eggNOG-Mapper version 1 (DIAMOND option; Huerta-Cepas et al. , 2017), then examined specific GO (Gene Ontology; Gene Ontology Consortium, 2019) terms chosen based on their importance for microbial resistance to atmospheric-like conditions. The different GO terms used were the following: GO:0042744 (hydrogen peroxide catabolic activity) and GO:0015049 (methane monooxygenase activity) as specific metabolic functions and GO:0043934 (sporulation), GO:0009650 (response to UV), GO:0034599 (cell response to oxidative stress) and GO:0009269 (response to desiccation) as stressrelated functions.
Some samples from soil, sediments and seawater such as French agricultural soil (61 %), Peruvian sediments (53 %) and Celtic seawater (53 %) had relatively high percentages of fungal sequences, while other samples had less than 50 %. The approximated number of fungal and bacterial cells in air and soil was also estimated using 16S rRNA and 18S rRNA gene copy numbers per cubic meter of air and gram of soil, respectively. Air samples showed ratios between 16S and 18S rRNA gene copy numbers from around 4. 5 times up to 160 times lower than soil samples (Table  S4 ; some qPCR data have already been published in  Tignat-Perrier et al. , 2020). The qPCR data were used to see if similar results or the same trend of the estimated ratio between fungi and bacteria in air compared to the planetarybound ecosystems could be obtained from metagenomic data and qPCR.
In order to incorporate weekly variation when comparing sites, we used the microbial functional profile averaged per site in the subsequent multivariate analyses performed with the data from other ecosystems (Fig. 3 ). The PCo multivariate analysis showed that terrestrial atmospheric sites (GRE, NAM, STP, PDD, PDM, CHC and New York) grouped with the soil, sediment and snow samples, while the marine and coastal atmospheric sites (AMS, CAP and San Diego) were situated between the datasets from soil, seawater and river water (Fig. 3 ). The polar site STN did not group with the other sites. When considering only the bacterial sequences (i. e. , excluding the fungal sequences), the distribution of the terrestrial atmospheric sites did not change, while the marine Amsterdam Island, coastal Cape Point and polar Station Nord atmospheric sites were further from the seawater and river water datasets than when the fungal sequences were included (Fig.
, 2019). Similarly, the sequencing technology (Illumina MiSeq) could not have been responsible for the larger percentage of fungal se-quences observed in our datasets, as the Beijing, New York and San Diego air sample datasets originated from Illumina HiSeq and 454 sequencing technology, respectively. qPCR results on the 16S rRNA gene (bacterial-cell concentration estimation) and on the 18S rRNA gene (fungal-cell concentration estimation) on our air samples in comparison to agricultural soil samples evidenced that the ratio between fungal and bacterial cell number might be much higher (from 4. 5 to 160 times higher for the most vegetated site, Grenoble) in air than in soil (Table  S4 ; see  Tignat-Perrier et al. , 2020 , for more qPCR data on air samples). The ratio between fungal and bacterial cell number might be higher in the planetary boundary layer (PBL) than in other environments like soil  (Malik et al.
Stress-related functions were present in airborne bacteria but rarely in higher concentrations compared to the bacterial communities in other ecosystems. However, the constant flux of microbial cells to the planetary boundary layer might have complicated the de-termination of a physical selection and/or microbial adaptation of airborne microorganisms, especially bacterial communities. Meta-omics investigations on air with a deeper sequencing are needed to confirm our results and explore the functionality of atmospheric microorganisms further. Data availability. Sequences reported in this paper have been deposited in ftp://ftp-adn. ec-lyon. fr/Tignat-Perrier_2020_air_ metagen_INHALE/, last access: 30 November 2020. A file has been attached explaining the correspondence between filenames and samples.
Acknowledgements. The chemical analyses were performed on the IGE AirOSol platform. This work was hosted by the following stations:  Chacaltaya, Namco, Puy de Dôme, Cape Point, Pic du Midi ,  Amsterdam Island ,  Storm Peak  and  Villum Research Station. We thank  I. Jouvie ,  G. Hallar ,  I. McCubbin ,  B. Jensen ,  A. Nicosia ,  M. Ribeiro ,  L. Besaury ,  L. Bouvier ,  M. Joly ,  I. Moreno ,  M. Rocca  and  F. Velarde  for sampling and station management. We thank our project partners:  K. Sellegri ,  P. Amato ,  M. Andrade ,  Q. Zhang ,  C. Labuschagne ,  L. Martin  and  J. Sonke. We thank  R. Edwards ,  J. Schauer  and  C. Worley  for lending their high-volume sampler. We thank  L. Pouilloux  for computing assistance and maintenance of the Newton server. Financial support. This work was supported by the  Agence Nationale de la Recherche  (grant no.
ANR-15-CE01-0002-03 IN-HALE ),  Région Auvergne-Rhône-Alpes  (grant no. ARC3 2016 ),  Campus France  (grant for program XU GUANGQI) and the  French Polar Institute  (IPEV; program no. 1028 ; grant no. 399 ). Review statement. This paper was edited by  Denise Akob  and reviewed by two anonymous referees.
Study Population We used data from UK Biobank, a prospective cohort study, the sampling and procedures of which have been well described. 14 Baseline data collection took place between 2006 and 2010 across twentytwo research assessment centres in the UK giving rise to a sample of 502,655 people aged 40 to 69 years (response rate 5. 5%). 14 Ethical approval was received from the North-West Multi-centre Research Ethics Committee, and the research was carried out in accordance with the Declaration of Helsinki of the World Medical Association, and participants gave informed consent. For the present analysis, participants residing in Scotland, Wales, or Northern Ireland were excluded as COVID-19 test data were only available for England.
Hospitalisation for COVID-19 Provided by Public Health England, data on COVID-19 status covered the period from 16 th March to 26 th April 2020 (http://biobank. ndph. ox. ac. uk/showcase/field. cgi?id=40100), during which testing was largely restricted to those with symptoms in hospital. COVID-19 tests were performed on samples from combined nose/throat swabs using real time polymerase chain reaction (RT-PCR) in accredited laboratories. 15 These data can therefore be regarded as a proxy for hospitalisations for severe COVID-19 cases for England only.
Lifestyle Measures Physical activity, smoking, and alcohol consumption were assessed by questionnaire. Participants were categorised into never, previous, and current smokers. We grouped alcohol intake into three categories: never/rarely, and below or above current UK guidelines (≥14 units in women and ≥21 units in men). Leisure time physical activity was assessed using the short form version of the International Physical Activity Questionnaire (IPAQ). 17 Measuring duration and frequency of moderate-to-vigorous physical activity in the last week, data were grouped in 3 categories: inactive, somewhat active below the guidelines, and meeting activity guidelines (≥150 min/week moderate-tovigorous physical activity or ≥75 min/week vigorous activity). 18.
Results Ethnicity data were available for 428,494 participants (235,528 women, 55%) who were alive prior to COVID-19 testing (up to 5 March 2020). The main analytical sample comprised 340,966 participants (640 COVID-19 cases) with complete data on the core set of covariates listed in Table  1. After adjusting for age and sex, compared to White participants, being from a Black ethnic background was associated with over a four-fold risk of hospitalization for COVID-19 (odds ratio; 95% confidence interval: 4. 32; 3. 00-6. 23), while a doubling was apparent in Asian (2. 12; 1. 37, 3. 28) and Other (1. 84; 1. 13, 2. 99) (Table  1 ). Gradual attenuation of the association after inclusion of groups of confounders can be seen in Figure  1. The greatest attenuations were observed when socioeconomic factors were added to the multivariable model: 24.
Acknowledgement: We thank the  UK Biobank  study members for their generosity in participating. Access to data: Data from  UK Biobank  (http://www. ukbiobank. ac. uk/) are available to bona fide researchers upon application. This research has been conducted using the UK Biobank Resource under Application 10279. GDB and CL generated the idea for the present paper; CRG prepared the data set; CL carried out all the data analyses; CL, BG, and GDB wrote the manuscript; studies had no role in study design, data collection, data analysis, data interpretation, or report preparation. CRG/CL had full access to  UK Biobank  manuscript for publication.
Recruitment The study flow chart is shown in Figure  1. Eligible patients will be identified by a clinical research assistant who will regularly contact hospital wards (emergency, geriatric, infectious NOSO-COR Protocol, Version 1 28/02/2020 diseases, etc. ) and will review the results of the virology laboratory. The clinical research assistant will meet eligible individuals to explain the purpose of the study and obtain written informed consent. Nosocomial cases will be defined as infected patients hospitalised for more than 48 hours. Identification of infected HCP will be based on data from the department of occupational medicine. A confidential interview with the symptomatic HCP will be organized to describe the purpose of the study and obtain written informed consent. Identification of infected CG will be based on self-reporting.
Data collection Clinical data Data will be collected using case-report forms (CRF) designed especially for the purpose of this project for patients, CG, and HCP. According to the operating modes of each participating hospital and their organization in terms of clinical or epidemiological research, the CRF will be completed during a face-to-face interview and the use of medical records. All three CRF will include demographic characteristics, underlying comorbidities, medical history, clinical, biological and laboratory data on the SARS-CoV2 infectious episode. In case of HCP or CG hospitalisation, a patient's CRF including additional data regarding information on hospitalisation (ward specialty, type of room, etc. ) and biological parameters (blood cell counts, etc. ) will be completed. The characteristics of the hospital and participating services (specialties, number of beds, number of nurses and doctors, presence of an infection control unit, etc.
Statistical analyses Data will be analysed using statistical methods to describe the characteristics of the healthcare setting and study participants, the percentage of Covid-19 of those presenting influenza-like illnesses, the percentage of nosocomial Covid-19 patients among the confirmed cases, etc. The primary criteria will be the proportion of patients, CG and HCP with confirmed nosocomial SARS-CoV2 infection relative to all patients, CG and HCP with syndromes suggestive of 2019-nCoV infection during the study period. NOSO-COR Protocol, Version 1 28/02/2020 Secondary outcome criteria will be to i) describe the delay of onset of suspected or confirmed nosocomial Covid-19 infection for hospitalised patients; ii) estimate the attack rate of confirmed Covid-19 cases in hospitalised patients according to their length of stay; and iii) estimate the attack rate of confirmed Covid-19 cases among CG and HCP.
Case Report Form All required information will be recorded on a paper-based CRF in a clear and legible manner and justification must be provided for all missing data. Erroneous data noted in the CRF will be clearly crossed out and the correct data will be written next to the crossed-out information, accompanied by the initials of the investigator or authorized person who made the correction, the date and, if possible, and a justification for the correction. To reduce data-entry errors by predefining plausibility checks and facilitate the rapid transfer of data, the electronic CRF (e-CRF) version of the study CRF will also be available. Transfer of the data to the coordinating centre in Lyon (France) will be performed via the e-CRF following approval by the French "Commission nationale de l'informatique et des libertés (CNIL) -National Commission for Data Protection".
Rights to access source documents Source documents are defined as any original documents, data, records in which data collected for a clinical trial is first recorded. Source documents will be kept by the investigator for 10 years or, if hospital medical records, by the hospital for 10 years. Each centre will have access to its own data during the study. To ensure quality control and auditing, the sponsor will be responsible for obtaining the agreement of all parties involved in the research to guarantee direct access to all places where the research is carried out and to source data, documents and reports. In accordance with current laws and regulations (articles L. 1121-3 and R. 5121-13 of the French public health code), all documents and personal data required for monitoring, quality control or auditing will be available to the persons in charge of these activities.
Informed consent Patients, CG and HCP will be informed of the objectives and their rights to refuse to participate in the study or withdraw at any time using simple, understandable terms. This information will be provided by an information and consent form given to each participant. According to French law, voluntary, oral informed consent will be obtained by the investigator before inclusion for the epidemiological data while signed consent is needed for the collection of biological banks. Consenting will be performed following the country's ethical guidelines. Informed consent will be obtained from the parents of included children (<18 years old).