dataesr/scientific-paragraphs-categorization huggingface dataset:
Study sample The International CF GMC consists of individuals from four consortium sites, Johns Hopkins University (JHU), the University of North Carolina/Case Western Reserve study (UNC/Case), the Canadian Gene Modifier Study (CGMS) and the French Gene Modifier Study (FGMS). The North American (NA) data were genotyped in two phases, Phase I-NA with 5,132 individuals and Phase II-NA with 2,830 individuals (before quality control; see below). Data from the FGMS were genotyped separately and include 1,300 individuals with CF. The Phase I-NA data were the basis of the first published meconium ileus genome-wide association study (GWAS) from the International CF GMC  [12].
Genotyping and quality control Genotyping used four different Illumina platforms: the 610Quad, CNV370, 660W and Omni5, where 660W genotyping was performed in three batches (660W-NA, 660W-JHU and 660W-FR). Genotype calling was performed using GenomeStudio V2011. 1. Quality control (QC) of genotypes and phenotypes was performed separately for each of the six genotyping platforms and batches: 610Quad, 660W-NA, 660W-JHU, Omni5, CNV370 and 660W-FR. Sample details after data quality control (QC) are listed in S1 Table, and QC procedures for SNP exclusion are presented in S2 Table. SNP position and annotation information were based on Genome Reference Consortium 37 (GRCh37). The number of probes for each platform before QC was 570,572 for the 610Quad, 655,214 for 660W-NA and 660W-JHU, 4,301,332 for the Omni5, 309,012 for CNV370 and 554,649 for 660W-FR (S2 Table ).
Beagle used a sliding window of 250,000 markers from our genotype data with 25,000 markers overlapping with the next window for the phasing and then imputation steps. The phasing step included all individuals from our samples and from the reference panel to conduct 10 burn-in phasing iterations, followed by additional 5 phasing iterations to ensure accuracy, and the imputation step followed to fill in the markers not genotyped. Imputed SNPs with Beagle quality score AR2<0. 3 were set to missing and SNPs with more than one alternative allele or with a minor allele frequency <1% were excluded from the association analysis.
Acknowledgments We thank the  US CF Foundation  for the use of  CF Foundation Patient Registry data. We thank the  Cystic Fibrosis Canada-SickKids Program for Individualized Therapy  for access to the RNA-sequencing of the nasal epithelia. We thank  Drs. Rhonda Pace  and  Jackie Stonebraker  for their work collecting and curating source documents to confirm meconium ileus status in the  UNC/Case cohort. We thank the patients, care providers and clinic coordinators at CF Centers throughout the  US and Canada  for their contributions to the  CF Foundation Patient Registry and Canadian Gene Modifier Study. Some of the datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www. ncbi. nlm. nih. gov/gapthrough dbGaP accession number phs000424. GTEx. v7. p2.
Typical response of olfactory receptor neurons to pheromone Experimental data were obtained from ORNs by applying different pheromone doses to antennae of the moth Agrotis ipsilon (see Methods for details). To simulate the fluctuating odorant concentration in a natural environment  [27] , the pheromone was applied in short intermittent pulses ( puffs) separated by stimulus-free periods (blanks) of random duration (figure  1a ). Responses of different ORNs to the same pheromone pulse exhibited marked cell-to-cell variability (figure  1b ) as reported in previous studies  [28, 29]. This response heterogeneity of ORNs might be caused, for example, by differences in the density of olfactory receptors (ORs), odorant-binding proteins and odorant-degrading enzymes among ORNs. Nonetheless, averaged responses across cells demonstrated a typical phasic-tonic time course regardless of pheromone dose (figure  1c-f  ).
Statistical analysis STATA software version 14. 2 (Copyright 1985-2015 StataCorp LLC, http://www. stata. com) was used for statistical analysis. The main outcomes of interest were the relationships between respiratory pathogens among pilgrims during the Hajj. We evaluated the carriage of HRV, HCoV, S. aureus, S. pneumoniae, H. influenzae, K. pneumoniae and M. catarrhalis using logistic mixed models. Because each pilgrim provided four successive samples, we used a repeated measures design to take into account the variability of series samples from each pilgrim. To evaluate the effect of covariates on each respiratory pathogen carriage, we modelled carriage of HRV, HCoV, S. aureus, S. pneumoniae, H. influenzae, K. pneumoniae and M. catarrhalis separately. We did not separately model the outcome of carriage of influenza A and B viruses because of the low prevalence of these viruses.
The physical and biogeochemical controls on the biological carbon pump in these regions are markedly distinct  (18). As a result, the evolution of each system under future environmental stressors, such as acidification  (33)  and sea-ice cover changes  (34) , may be substantially different. The primary limitation of this study is that the observational dataset used, while regionally relatively extensive, is specific to a particular window in time. However, broad consistency of the carbon budget with independent measures of carbon uptake suggests that the results are not prohibitively biased by the timing of measurements, on either a seasonal or interannual basis  (17). With regard to spatial representativeness, it is reasonable to assume that the mechanism of carbon enrichment within the recirculating gyre interior also operates in the other cyclonic gyre systems around the subpolar Southern Ocean (an assumption supported by observations of elevated remineralized carbon in the central Ross Gyre; fig.
Hydrographic and biogeochemical data Data were collected on three oceanographic transects, plotted in Fig. 2A , that took place over a period of 2 years between 2008 and 2010. Two of the cruises were conducted as part of the Antarctic Deep Water Rates of Export (ANDREX) project in December 2008 to January 2009 and March to April 2010, from the Antarctic Peninsula to 30°E around the northern rim of the Weddell Gyre  (36, 37). A third cruise was completed as part of the U. S. CLIVAR (Climate Variability and Predictability) program in February to March 2008 along the quasi-meridional I6S section at 30°E  (38). Temperature and salinity were measured in 2-dbar pressure bins across the full depth range at each station. Water was collected at an array of depths for onboard measurement of a number of chemical species.
It is separated into "preindustrial" and "anthropogenic" components to account for the changing levels of atmospheric CO 2 since the industrial revolution. The preindustrial saturated component, C pi sat , is determined from potential temperature, salinity, and preformed alkalinity (see below) and was evaluated using the CO2SYS MATLAB program  (42) , assuming a preindustrial atmospheric CO 2 level of 278 ppm. We did not attempt to evaluate the anthropogenic saturated component directly but instead included it in a residual C res along with the disequilibrium component, DC, which accounts for the extent to which the water parcel was out of equilibrium with the overlying atmosphere when it left the surface ocean. The regenerated component comprises contributions from the remineralization of soft tissue organic matter, C soft , and the dissolution of hard shell carbonates, C carb.
Acknowledgments: We are grateful to all scientists and support staff involved in the collection and analysis of samples, both at sea and on shore, especially  K. Speer , chief scientist of the I06S line occupied as part of the  U. S. CLIVAR repeat hydrography program , and  H. Venables  and  P. Abrahamsen  for their work on the ANDREX cruises. We are grateful to two anonymous reviewers, whose contributions improved the quality of this manuscript. Funding: The  ANDREX  project was supported by the  National Environmental Research Council  ( NE/E01366X/1  and  NE/E013538/1 ). G. A. M. acknowledges support from  NERC , the  U. K. Met Office , and a  Radcliffe-Graduate scholarship  from  University College, Oxford. A. C. N. G. acknowledges the support of a  Philip Leverhulme Prize , the  Royal Society , and the  Wolfson Foundation.
Bioinformatics Analysis The HLA-G mapping, genotyping and haplotyping strategy used here has already been published elsewhere  [30]. In brief, prior to mapping, all DNA segments (reads) produced by NGS were trimmed on both ends for primer and adapter sequences using the Cutadapt software. Mapping was performed by hla-mapper  [34]  version 2. 2, function dna, database version 2. 1, with the error threshold set to 0. 03, minimum read size length set to 50 and the human hg38 used as a reference. The Genome Analysis Toolkit (GATK, version 3. 7) HaplotypeCaller was used for genotype calling in the GVCF (genomic variant call format) mode, applied for each sample separately  [35]. Then, a VCF (variant call format) file was generated by the concatenation of all samples together with the GATK GenotypeGVCFs algorithm.
Then, genotypes were processed by vcfx checkpl (www. castelli-lab. net/apps/vcfx) with the minimum genotype likelihood set to 99. 9%, which guarantees that only high-quality genotypes are maintained for a further imputation step. The physical association between alleles from different variation sites was assessed by inferring the physical phase among variants from the sequencing data using the GATK ReadBackedPhasing routine, coupled with a Bayesian probabilistic model implemented in the PHASE software  [36] , as described elsewhere  [30]. The PHASE algorithm also imputed the missing alleles observed after the vcfx treatment. The scripts used to infer haplotypes are available online at https://github. com/ erickcastelli/phase-readbackedphasing. It should be highlighted that all singletons have been removed before the haplotype inference step using the PHASE algorithm.
Singletons were manually included later in the phased VCF file, when they met the following criteria: (a) there was no missing allele for any sample regarding this particular position; and (b) there was a straightforward relationship between the singleton and a neighboring heterozygous site, either visually detected using the BAM file of that sample or because it is the only heterozygous site detected on that particular sample. The phased VCF file was converted into HLA-G CDS sequences using the hg38 reference sequence as a draft and replacing the correct nucleotide in each position, two sequences per sample, by using the application vcfx [function fasta] (www. castelli-lab. net/apps/vcfx). By using a local BLAST server with databases containing all known class I and II HLA CDS sequences described so far, downloaded from the IPD-IMGT/HLA database (https://www.
Methods We developed a systematic review protocol based on PRISMA-P guidelines, an evidence-based minimum set of items for reporting systematic reviews  [26] , and registered it with PROSPERO, an international database of prospectively registered systematic reviews (registration number CRD42018094012). PRISMA checklist is available in Multimedia Appendix 1. Apps were identified through two different approaches: a search of peer-reviewed articles reporting on development or validation of mHealth apps for self-management of chronic conditions, and a search in the Android commercial marketplace for smartphones. We used Android marketplace Google Play store as it detains 88% of the global mobile phone market  [27]  and most apps are developed for both operating systems (Android and iOS). A systematic search of PubMed (MEDLINE), IEEE and Web of Science electronic bibliographic databases was conducted (all search terms are available in Multimedia Appendix 2).
For each keyword, the 20 first apps shown in the marketplace (n = 240) were extracted. The complete list (n = 795) was assessed independently by two investigators. After screening, selected apps were divided into 5 groups: 4 according to the condition they targeted ('Cancer', 'Diabetes', 'Respiratory diseases', 'Cardiovascular diseases') and one generic group ('Other'), comprising apps targeting behaviours like medication adherence and physical activity irrespective of medical condition. All searches, data extraction and coding were done between March and April 2018. Second coding by a different investigator, along with inter-rater reliability, reconciliation and coding review were performed in October 2018. A smartphone Samsung J7 with Android version 6. 0. 1 was used for downloading and evaluating all selected apps.
Screening and selection Reference management software (Zotero) was used to identify/remove duplicate records in the literature search. Titles and abstracts of remaining records were screened by two independent reviewers to establish eligibility. If at least one reviewer recommended study or app inclusion, the full text or app availability was sourced for review and appraisal, and/or to reaching consensus of study eligibility. If reviewer discordance persisted, consensus was reached through discussion and arbitration with a third investigator.
Data extraction and analysis The following app characteristics were extracted from Google Play store: information on the presence of sales on app (paid app; free app with/without paid features), number of downloads (from 10+ to 10 000 000+), user ranking (from 1 to 5 stars), number of ratings, version, last update and developer information were extracted from the marketplace along with app's name and available description. Developers were then categorized in three groups, "Private company", comprising single app developers and dedicated app developing companies, "Non-private", comprising NGO, public institutions or European projects, and "Pharmaceutical/Medical Devices companies", comprising bigger players in the market such as pharmaceutical laboratories and other medical technology companies.
Data analysis Coding was done using Microsoft Excel and all statistical analyses were performed using RStudio version 1. 1. 383. We examined app characteristics, behavioural content and PEMAT scores via descriptive statistics. We performed nonparametric tests to compare groups and to investigate the correlation between the variables of interest, given their distribution properties. Associations between behavioural content and PEMAT scores were investigated via bivariate correlations (Spearman's rank correlation coefficient, ρ). Additional exploratory analyses regarding relationships between app characteristics and these content and format properties are reported in Multimedia Appendixes 3 and 4 for interested readers.
Search Of the total 704 unique apps identified in Google Play store, 167 (23. 72%) had descriptions in languages other than French; 104 apps (14. 77%) were considered as targeting chronic conditions in general and 50 apps (7. 10%) focused on chronic conditions in the 4 groups of diseases included in this review. Other chronic conditions (not considered in this work) were back pain, migraine and headache, sleeping apnea, depression, among others. For app selection, agreement between reviewers was substantial (kappa = 0. 62). Reconciliation was done by a third reviewer when agreement was not reached after discussion between the first two. Fifty apps met inclusion criteria. Nonetheless, 3 of the selected apps required other connected objects (e. g. , connected glucometer or blood glucose sensor, smart watch), 3 had descriptions in French but app content was entirely in English, and 2 pairs of apps had different names but same content (1 app of each pair was removed; total n excluded = 8).
Discussion The use of mHealth apps for supporting health-related behaviour change and patient empowerment is being widely discussed as solution to healthcare challenges worldwide, particularly in chronic conditions. This review showed that in 2018 in France the potential of mHealth is far from being achieved. The apps available for download on Google Play had relatively limited behaviour change content and, although moderately easy to understand for diverse audiences, they did not commonly point to clear actions users may take to self-manage the condition targeted. To better support patients with chronic conditions, apps can be improved by building on more solid behaviour change research and applying it in ways that are easier to understand and act upon. By searching directly on Google Play, we found only 39 apps available in French targeting selfmanagement in the 4 groups of chronic conditions with highest mortality rates (cardiovascular diseases, cancer, respiratory diseases and diabetes), among 704 apps with different purposes and languages.
In addition, the predominantly low actionability of the apps in our study shows that we are still far from fulfilling the potential of mHealth tools to increase patient autonomy. More than half of the analyzed apps did not present any clearly stated action or had any suggestions concerning the data recorded by users on health-related events. For mHealth apps to fully achieve their potential to support chronic conditions treatment, clearly indicating actions is imperative (for example, diabetes apps need to indicate that patients need to intervene immediately if high or low blood glucose is recorded and give concrete physical activity suggestions to users). In our study, apps with more TBs and BCTs were also more understandable, indicating developers who consider behavioural content may also be more careful with being comprehensible for users; levels of actionability were low irrespective of behavioural content.
Secondly, we only considered peerreviewed papers and conference articles published in English, even though we were looking for apps available in France and in French. A future study of the iOS marketplace and French databases may be useful to complement our findings. We have also downloaded and assessed both behavioural content, and understandability and actionability by two independent coders interacting directly with the apps, not only the descriptions available in the commercial marketplace. Thus, we were able to obtain a comprehensive assessment of the properties examined and reflect also on the assessment tools used. We identified several issues for further improvement. First, while the BCT taxonomy enables systematic coding with good inter-coder reliability, there is no consensus to date on classifying TBs apart from broad domains  [45].
1c). Subsidence 123 of momentum is not included in the current LES cases. For simplicity, and to limit computational expense, the local vertical gradient (x 3 being the vertical coordinate) of the (filtered) potential temperature is replaced by the vertical gradient of its horizontally-averaged value indicated by. In future work, the impact of these simplifications will be assessed in more detail. In addition, the term is calculated within the model using a first-order upwind scheme. A short note on the implementation of subsidence can be found in Appendix 1. We use the open-source code MicroHH (http://microhh. org), which is a combined finitedifference LES/direct numerical simulation code supporting two-dimensional parallelization using the message passing interface standard. For the advection of momentum and scalars, a second-order finite-difference scheme is used.
Acknowledgements The authors thank  Beatrice Saggiorato ,  Jonathan Izett  and  Stephan de Roode  for their valuable suggestions and feedback. Bart van Stratum  is acknowledged for his help in adapting MicroHH for this study. The authors also thank  S. Argentini ,  G. Mastrantonio ,  A. Viola  and  A. Conidi  for their assistance in preparing the sodar observations. Concordia station at Dome C is jointly managed by the  French (IPEV)  and Italian (PNRA) polar institutes and programs. In situ boundary-layer observations used here are obtained in the framework of the  CALVA  project supported by  IPEV  (project  1013 ). This work was sponsored by  NWO Exact and Natural Sciences  for the use of supercomputer facilities. We gratefully acknowledge funding by the  European Research Council  through an  ERC-Consolidator grant  ( 648666 ).
Methods: We will search for studies that tested the effect of changes in (i) biological communities, and (ii) habitats and landscapes, on diseases. Scientific literature from 5 publication databases will be screened in a 3-rounds process: title, abstract and full-text screening. At each stage, articles will be either rejected or kept for the next round, depending on whether they fall in the exclusion or inclusion criteria. We will present results in two parts: a systematic map and a systematic review. The systematic map will present, for the 14 diseases, the number of publications, their geographical distribution, the type of ecosystem component/function they studied, as well as the host(s) in which epidemiological measurements have been performed. From this systematic map, we will identify groups of articles that allow for critical appraisal, i.
Search strategy Our search strategy has been designed to retrieve articles that cover a broad range of ecosystem components/ functions and diseases. We will search for publications in 5 bibliographic databases, identified as relevant by the review team and the expert panel, and listed in "Bibliographic databases". In each database, we will perform one search per disease. We will search in the "topic" section or in the title/abstract/keywords, whenever this option is possible. These databases are either available in openaccess, or through a pre-existing subscription of FRB or research institutions of experts. Unpublished research articles will also be searched for, in an effort to minimize publication bias, and scientists from the expert panel and the enlarged committee have been asked to share unpublished articles.
Search string The search string has been built based on a scoping exercise conducted on Web of Science in May-June 2018. This search string is structured into 3 elements related to Population and Outcomes. We chose not to use Intervention-or Exposure-related search terms, as it seemed impossible to exhaustively list them and thus retrieve all the possible range of them. The 1st element targets articles mentioning the required disease or pathogen agent(s). It is the only part of the search string that will differ between searches for different diseases. Synonyms of disease names, including vernacular names, were listed using the Mesh database and Google and will be included in the search string. This list can be found in Additional file 1, along with the list of pathogen agents identified for each disease.
The '*' wildcard will allow to retrieve plurals and words sharing the same root such as the words "predator", "predation", "predated" (etc. ) from the single word predat*. Detailed search string, as designed for the Web of Science database: This search string was validated by the expert panel. It will be adapted to the specific requirements of each bibliographic database (Boolean operator accepted, maximum number of words). If it must be reduced, priority will be given to the 1st and 2nd elements of the search string. The search string used in each database will be reported in an Additional file attached to the systematic review to ensure replicability and facilitate future upgrading.
Search update We will provide the date of the searches performed in each database as well as the exact search string to facilitate a future upgrading of the work. Given that we will present both a systematic map and a systematic review, the subsequent part of this protocol is divided in two: the sections "Article screening and study inclusion criteria", "Study validity assessment", "Data coding strategy", "Study mapping and presentation" detail the protocol for the systematic map, and the sections "Study inclusion criteria", "Critical appraisal", "Data extraction", "Potential effect modifiers/reasons for heterogeneity", "Data synthesis and presentation" describe the protocol for the systematic review.