dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants For this study, baseline data from the DALI project (ISRCTN70595832) were used. Baseline data were collected between October 31, 2011 and April 30, 2013. The study design and data collection of the DALI project are described in detail elsewhere  [32]. In summary, this project aims to identify the best available measures to prevent GDM in an ongoing pregnancy, and therefore a randomised controlled trial was conducted in nine European countries: the United Kingdom, Ireland, the Netherlands, Belgium, Poland, Italy, Spain, Austria, Denmark (2 study centres), with women from a diverse range of socioeconomic and ethnic backgrounds. For all nine countries, the study design and procedures were approved by the Medical Ethics committee of the respective centres Copenhagen and Odense, Denmark: De Videnskabsetiske Komiteer D for Region Hovedstaden).
The Actigraph accelerometer has previously gained validity and reliability in free living conditions in adults for between 3 and 5 days  [37]. The participants were asked to remove the accelerometer while swimming, showering or bathing. For non-wearing intervals, participants were instructed to write down in a diary how they spent their time. Crude data were obtained using ActiLife 6 (ActiGraph, LLC, Pensacola, Florida, USA). Periods with no counts for at least 60 min were defined as non-wear time. Days with less than 480 min activity were labelled as invalid data and excluded for analysis  [38]. Only women with data for at least three valid days were included  [38]. Using the Freedson cut-off points  [39] , the number of minutes per day in light (100-1951 counts/min), moderate (1952-5724 counts/min) and vigorous (>5724 counts/ min) activity were calculated as well as time spent sedentary (<100 counts/min).
All the variables were entered in the models one by one. Confounding was defined as a change in the regression coefficient of more than 10 %  [46]. After controlling for confounding, the HAPA factors (attitude, self-efficacy, social support and outcome expectancy) were added to the models, in order to assess whether these factors confounded the association between mental health and MVPA and/or sedentary behavior. Significance level of the associations in the final model was set at P-value 0. 05. The data were analysed using SPSS 20. 0 (IBM Corporation, Armonk, NY, USA).
Results A total of 258 women were measured at baseline and 132 of them wore an accelerometer (except in the UK and Denmark). Of those who wore accelerometers, 14 had no valid data for 3 days and 20 had software problems. These excluded women (n = 34) had a higher prepregnancy BMI (mean 40. 6 kg/m 2 ; P < 0. 01) and were further along in their pregnancies (16. 8 weeks; P = 0. 03). Finally, 98 women with valid data were included in the analyses. The mean age was 31. 6 ± 5. 8 years and most were white/Caucasian (75. 5 %) and had at least a secondary education (81. 6 %) (Table  1 ). At baseline measurement, women were 15. 4 ± 2. 8 weeks pregnant. Just over a quarter  (27. 1 %) of the total sample (n = 26) had a depressed mood (WHO-5 score < 50). These women reported a significantly more positive attitude towards physical activity and scored lower on perceived social support (P < 0.
Aim: Our understanding of the ecology and biogeography of microbes, including those that cause human disease, lags behind that for larger species. Despite recent focus on the geographic distribution of viruses and bacteria, the overall environmental distribution of human pathogens and parasites on Earth remains incompletely understood. As islands have long inspired basic ecological insight, we aimed to assess whether the micro-organisms that cause human disease in modern times follow patterns common to insular plants and animals. Location: Global and regional. Methods: Relying on the publically-accessible GIDEON database, we use the spatial distribution of nearly 300 human parasites and pathogens across 66 island countries and territories to assess the current predictive value of the "equilibrium theory" of island biogeography  (MacArthur & Wilson 1967).
, 2005; Orrock et al. , 2011; Svensson-Coelho & Ricklefs, 2011) , but its applicability to human pathogens is unclear. Previous research supports the existence of biogeographical patterns in microbes (e. g. Martiny, 2006; Hanson, 2012) , but these studies were limited to free-living microbial taxa and not focused on host-associated pathogenic species. Recent, more-limited work on historic human populations supports the theorized effect of island size on the diversity of vector-borne pathogens  (Cashdan, 2014) , though the influence of distance and the effect of modern industrial lifestyles, with their enhanced mobility and access to medicine and public health, is less clear. In this study, we use the Global Infectious Disease and Epidemiology Online Network Database (GIDEON) to examine whether the distribution of nearly 300 human pathogens occurring on different islands conforms to the general predictions of island biogeography theory, specifically that pathogen richness is a positive function of island size and a negative function of distance to the nearest mainland.
Data collection Analyses were based on a subset of data extracted and compiled from GIDEON (http://www. gideononline. com/). GIDEON provides clinical, geographical, and epidemiological information on 332 unique viruses, bacteria, fungi, protozoans, and helminths infecting humans in each of the 222 countries and administrative territories of the world. The database is updated regularly using publications from Medline based on a list of keywords and search information published by national Health Ministers, the World Health Organization (WHO), and the U. S. Centers for Disease Control and Prevention (CDC). As such, GIDEON is the most current, global database available for human infectious disease. For simplicity, we use the term "pathogens" in this manuscript to cover both pathogens and parasites and consider disease names (e.
g. measles) as synonymous with the infectious agents that cause them. Following Guernier et al. (Guernier et al. , 2004)  and  Smith et al. (Smith et al. , 2007) , we excluded pathogens causing infectious diseases that did not meet the following three criteria: (i) those with multiple etiological origins, (ii) those with major uncertainties surrounding national presence/absence, and (iii) vector-and reservoir-borne pathogens with imprecise information on hosts. The resulting database included 271 pathogens: 85 viruses, 87 bacteria, 15 fungi, 64 helminths, and 20 protozoans. We categorized pathogens three ways to assess the importance of different ecological and evolutionary processes: by host association, by transmission mode, and by taxonomy. We assigned host associations following  Smith et al.
Our geographic choices are driven by island biogeography theory. From 222 administrative territories recorded in GIDEON, we extracted data from 66 island countries and territories (Fig. 1. A), the largest being Madagascar and the smallest Tokelau (Appendix S1). Inclusion or exclusion of islands was driven by the completeness of information for a set of geographic, socioeconomic and demographic indicators based on previous, complementary work  (Guégan & Broutin, 2009). Since including all islands in this sample together could introduce confounding effects, such as those related to latitude, and because the equilibrium theory was originally elaborated for a group of islands within an archipelago, we extracted from the whole island dataset two regional island subsets, one for Caribbean islands (n=25, Fig.
1. B) and one for Pacific islands (n=21, Fig. 1. C). Territorial surface areas (in square-km) and total human population size were extracted from the 2010 World Factbook, published by the U. S. Central Intelligence Agency and updated yearly. ArcGIS software, version 9. 3. 1 (Esri, Redlands, CA, USA) was used to compute the centroid of each island and the distance, in km, from that centroid to the nearest mainland shoreline.
Complementary analysis on regional sub-datasets As for the dataset as a whole, we found that larger islands supported greater pathogen diversity in the Caribbean and Pacific subsets (p<10 -3 and p=0. 001, respectively). However, a significant negative relationship between SR and distance to the nearest mainland was only observed for the Pacific islands (p=0. 02). The effect of island size was driven by zoonotic and vector-borne pathogens in both Caribbean and Pacific islands and, for Pacific islands only, by protozoans and helminths (Table  2 ). For both Caribbean and Pacific islands, we did not find significant differences across pathogens categories in the relationship between SR and distance to the nearest mainland.
Discussion We have shown here that the distribution of known human pathogens on islands follows the main predictions of MacArthur and Wilson's equilibrium theory of island biogeography  (MacArthur & Wilson, 1967) : pathogen species richness increases with island area and decreases with distance to the nearest mainland. However, the relative influence of area is much greater than that of isolation, and the extent and strength of the associations vary by host requirement, transmission pathway, and pathogen taxonomy. Importantly, pathogens whose primary hosts are not humans are more strongly affected by island biogeography than are those that primarily afflict people. A limitation of this study is the relatively small sample size, an inherent constraint of focusing on a relatively small subset of the larger GIDEON dataset.
The resulting lack of statistical power did not allow us to account simultaneously for different categorical factors or to take into account other factors previously identified as important drivers of pathogen richness, such as climate  (Guernier et al. , 2004; Dunn et al. , 2010). However, our purpose was not to identify and assess the relative influence of a large set of variables but rather to test how well an influential biogeographical theory describes a pattern of contemporary human ecology. Conducting a complementary analysis on regional sub-datasets (Caribbean and Pacific islands) was a way to control for shared characteristics of islands from the larger sample, such as latitude and regional biotic influences. The fact that the results of these regional analyses were similar to those for the whole dataset supports the validity of the relationships we found.
Another limitation is that GIDEON is an evidence-based database, so the data could, potentially, reflect a reporting bias. Indeed, wider sampling or research efforts on larger or less-isolated islands could contribute to the results described here. Hypothetically, although such a reporting bias for this island dataset could influence our findings, it is unlikely this bias would produce the patterns we observed across pathogen categories. Furthermore, healthcare expenditure is a poor predictor of human pathogen SR at the country-level  (Dunn et al. , 2010)  even if it does predict infectious pathogen prevalence. Thus, our results are likely independent of any reporting effect. According to the equilibrium theory of island biogeography, the positive relationship we found between human pathogen species richness and island area is due to lower extinction rates on larger islands.
ACKNOWLEDGEMENT We are thankful to  François Guilhaumon  from  Institut de Recherche pour le Développement (IRD)  and  Dov Sax  from  Brown University , for useful comments on an earlier draft. This research has benefited from an " Investissement d'Avenir " grant managed by  Agence Nationale de la Recherche  ( CEBA ANR-LABX-10-2501 ). In addition, JFG received support from both  IRD  and  CNRS.
The LAT exposure was calculated for the adopted photon selection with the RELEASE-09-32-05 of the LAT Science Tools 3. Systematic errors on the LAT sensitive area increase from 5 % to 10 % with energy over the 0. 4-100 GeV range selected for the analysis  (Ackermann et al. 2012c). We have calculated the effective point-spread-function (PSF), the exposure map, the γ-ray emissivity spectrum q LIS of the local interstellar gas  (Casandjian 2012)  and the spectrum of the isotropic background in 12 energy bins, with a bin width of 0. 2 dex, and centred from 10 2. 7 to 10 4. 9 MeV. To ensure statistics robust enough to follow details in the spatial distributions of the different interstellar components, we have analysed the data in 4 broader and independent energy bands, bounded by 10 2. 6 , 10 2.
H i data and kinematical component separation The H i Galactic All Sky Survey (GASS) is the most sensitive and highest resolution survey of 21 cm line emission of the southern sky  (McClure-Griffiths et al. 2009 ). We have used the GASS data corrected for stray radiation, instrumental baselines and radio interference contamination, and with both IFs to remove the negative ghosts occasionally caused in frequencyswitching mode by the presence of high-velocity-cloud lines in one of the bands  (Kalberla et al. 2010). We have used the GASS data server 5  to resample the original data cubes onto our spatial grid. Our choice of 0 •. 1 for the Gaussian interpolation kernel gives an effective FWHM resolution of 14. 5 and an rms noise of 0. 07 K per channel. We have kept the original velocity resolution of 0.
2014) , of 390-500 pc for the Orion clouds  (Ackermann et al. 2012e; Schlafly et al. 2014) , and of 100-200 pc for the R CrA, Chamaeleon, and IVA clouds  (Mizuno et al. 2001; Corradi et al. 2004; Ackermann et al. 2012a ). We separate the clouds in Galactocentric radius, but do not expect a CR gradient over such a small distance range. The data points indicate that an equivalent CR flux pervades the nearby Chamaeleon, R CrA, Cepheus, and Polaris clouds. We note that the low CR flux anomaly reported earlier for the Chamaeleon complex was due to an error in evaluating the exposure  (Ackermann et al. 2012a (Ackermann et al. , 2013)). The emissivity in Orion may be ∼ 25 % lower than in the other nearby complexes of the Chamaeleon, R CrA, Cepheus, and Polaris. This difference is small and commensurate with systematic uncertainties in component separations, so a careful re-analysis of the region with three times more γ-ray data (now available), higher H i resolution, and iterative construction of the DNM map is required to assess this difference.
These components are visible in The core of the method is based on fitting every spectrum in brightness temperature, T B (v), with a sum of lines across the whole -164. 1 ≤ v ≤ 409 km s -1 velocity interval exhibiting significant line emission. The first step is to find the number and approximate velocity of the lines to be fitted. For each (l, b) pixel direction, we have smoothed the spectrum in velocity with a Gaussian kernel of 1. 48 km s -1. We have measured the rms dispersion in temperature, T rms , outside the bands with significant emission. We have clipped the data to zero outside regions with T (v) > T rms in 3 adjacent channels. The clipping limits the number of fake line detections triggered by strong noise fluctuations in bands devoid of emission. We have then computed the curvature d 2 T B /dv 2 in each channel by using the 5-point-Lagrangian differentiation twice.
Acknowledgements. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN , JA, and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU). A description of the Planck Collaboration and a list of its members, including the technical or scientific activities in which they have been involved, can be found at http://www. sciops. esa. int/index. php?project=planck&page=Planck_Collaboration. The  Fermi LAT Collaboration  acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT as well as scientific data analysis.
coli 536, a strain from the B2 phylogroup, that is known to be an excellent competitor in the mouse gut  (Diard et al. 2010). The E. coli 536 strain is susceptible to amoxicillin, whereas the ROAR strains were amoxicillin resistant (supplementary table  S3 , Supplementary Material online). This allowed us to plate the feces on media containing amoxicillin to distinguish the strains. Furthermore, the strain identity was confirmed by performing specific polymerase chain reaction (PCRs) on isolated clones on plates (B2 phylogroup and O6-type determination for the 536 strain and B1 phylogroup determination and fyuA detection for the ROAR strains) (data not shown). The tested ROAR strains were able to colonize and persist in the mouse gut, even in the presence of the 536 strain (median: 410 7 CFU/g of feces for both strains) (fig.
2010). Our second step was to confirm any signal found during the initial screening and to determine if these results could explain differences found in conjugation abilities of strains that also result in changes in antimicrobial resistance profile. For the screening part of our approach, we used the commercially available Phenotype MicroArray (Biolog). We obtained data from 25 96-well plates in which nearly every well had different metabolites, substrates, or conditions. This allowed us to quickly generate phenotypic data differentiating the selected CC87 (ROAR61) and non-CC87 (ROAR394) strains. Out of the 1,152 compounds and conditions tested (supplementary table  S6 , Supplementary Material online), 55 were associated with an increased fitness of the non-CC87 strain and 108 with a positive fitness of the CC87 strains (fig.
This test was validated on 185 B1 phylogroup strains from the ROAR collection for which MLST data were available (n = 129) and from two other previously reported collections, one including the ECOR strains  (Clermont et al. 2011) (n = 49)  and the other the completely sequenced Broad Institute strains (n = 7). In addition to the 17 CC87 strains from the ROAR collection, we found seven additional CC87 strains, including the ECOR34 strain and four strains sequenced at the Broad Institute (B574, B799, H591, and TA271), in agreement with the data of the complete genome sequencing (fig. 7 ). These strains originated from wild animals (birds and mammal), a dog, and humans and were all except two commensals. Altogether, our test showed a specificity and a sensitivity of 100%. Furthermore, this analysis also suggests a widespread of CC87 in nature, and major impact it can play in a spread of antimicrobial resistance to other, pathogenic bacteria.
2015). In the Achtman MLST database (http://mlst. warwick. ac. uk/mlst/dbs/Ecoli last accessed November 12, 2015), we found 52 strains from various continents (Europe, Africa, North and South America, and Asia) belonging to ST58 and 155 and originating from humans, wild, and domestic animals, and the environment, confirming that these strains are widespread. Of particular interest, six of these strains produce an ESBL CTX-M enzyme, usually encoded by a plasmid-borne gene. Similarly, two ST155 strains with plasmid-borne antibiotic resistance genes have been reported from small rodents living in close contact to humans in French Guiana  (Grall et al. 2015). Nevertheless, our findings need to be confirmed on additional strain collections. Overall, in a context of an increasing concern about the impact of human activities on the environment, careful analysis of a unique collection of E.
Illumina Sequencing and Mapping and Phylogenetic Analysis Chromosomal DNA was extracted from seven ROAR strains of the CC87: 4 strains belonging to ST21 (ROAR47, 61, 72, and 82) and three strains belonging to ST24 and ST87 (ROAR205, 334 and 419) using DNeasy Blood and Tissue Kit (Qiagen) from 1 ml of overnight culture grown in LB. DNA quality and amount was measured using Nanodrop 1000 spectrophotometer (Thermo Scientific). One microgram of genomic DNA was sheared using Q500 sonicator (QSonica), and the fragments were used to prepare libraries using a NEBNext Ultra DNA library kit (New England Biolabs). Libraries were barcoded and sequenced as 50-nucleotide single-end reads on the Illumina HiSeq2000 flow cell. Reads were aligned using CLC-Genomics Workbench software. At least 17 million reads were obtained for each sample.
The seven ROAR sequencing data sets were assembled from seven fastq files containing 50 base pair length single reads using SOAPdenovo version 2. 04  (Luo et al. 2012 ). The contigs were then mapped, using BWA 0. 7. 10 (Li and Durbin 2010) to a data set of 128 complete E. coli genomes that are listed in  Lescat et al. (2014). For each contig, we identified the reference E. coli genome with the highest similarity. We extracted from that reference the gene annotations within the contig mapping region. We could therefore identify the gene repertoire of each sequenced genome and extract their sequence. Gene presence and absence were recorded in the 128 reference genomes using homology and synteny to define the homologous gene categories and extended to include the strains we sequenced. We also extracted and aligned the sequences of 500 genes from the core E.
Acknowledgments This work was originally supported by the  Alliance for the Prudent Use of Antibiotics (APUA)  in the frame of the  Reservoirs of Antibiotic Resistance (ROAR)  project  2006-2007. We are grateful to  Guillaume Arlet  for providing us the clinical E. coli strains exhibiting the bla TEM-1 gene. We thank  Jennifer O'Malley  for her technical support.
1% (final dilution 1:1000) as primary antibodies overnight at 4°C. Finally, the membranes were further incubated for one hour in anti-rabbit IgG (H+L) (DyLight 680 Conjugate) secondary antibody (final dilution 1:2000). Bands were visualized by Infrared Fluorescence using the Odyssey Imaging System (LI-COR Inc. Biotechnology, Lincoln, NE, USA) and quantified by Odyssey infrared imaging system software (Application software, version 1. 2). AMPKα was used as loading controls.
Acknowledgments This study was performed with the financial support of the  French Agence Nationale de la Recherche  (http://www. agence-nationale-recherche. fr/) and the  French National Science Infrastructure CRB-Anim. Thi Mong Diep Nguyen  is a PhD student supported by a grant from the  Vietnam Education Ministry  (http://www. moet. gov. vn/). Francois Seigneurin received funding in the form of salary from the non-profit SYSAAF. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We also thank  Isabelle Grasseau ,  Sabine Alves , and  Aurore Thélie  ( INRA ) for help concerning methodology,  Nicolas Marcoux  for help regarding the english, and the staff of the  INRA experimental unit PEAT  for the animal breeding and participation to semen collections.
This study was performed with the financial support of the  French Agence Nationale de la Recherche  (http://www. agence-nationale-recherche. fr/), and the  French National Science Infrastructure CRB-Anim. Thi Mong Diep Nguyen  is a PhD student supported by a grant from the  Vietnam Education Ministry  (http://www. moet. gov. vn/). Francois Seigneurin received funding in the form of salary from the non-profit SYSAAF. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
3. 2. 6. Sequencing and analysis. Inserts were recovered from the plasmids of representative clones by PCR and purified as described above. Purified PCR products were sequenced by Macrogen Inc. (Kumchun-ku, Seoul, South Korea) in both forward and reverse directions with pGEM-F and pGEM-R primers, using a 3730 • 1 DNA Analyzer (Applied Biosystems). Forward and reverse sequences were aligned and edited with ABI Autoassembler 2. 0 (Applied Biosystems) or Vector NTI Advance 9. 0 software (Invitrogen Life Technologies, CA, USA) to obtain consensus sequences. Consensus sequences were compared with GenBank database sequences using BLASTN  (Altschul et al. , 1990).