dataesr/scientific-paragraphs-categorization huggingface dataset:
Several sensitivity analyses were performed. First, analyses were performed only for women with planned pregnancy and without fertility treatment, in order to check that the associations between familial characteristics and compliance with folic acid supplementation guidelines were not driven by the specific cases of unplanned pregnancy or fertility treatment. Then, we calculated weighted multivariate models. Afterward, using multiple imputations to deal with missing data on sociodemographic variables, using the SAS software. We assumed that data were missing at random and generated five independent datasets using the fully conditional specification method (MI procedure, FCS statement, NIMPUTE option) and then calculated pooled effect estimates (MIANALYSE procedure). Imputation model variables included both the potentially predicting non-response and the outcomes.
Compared to mothers who had never smoked, women who had quit smoking before pregnancy were more likely to take folic acid supplementation during the periconceptional period, whereas the relationship was inverse for those women who smoked during the whole pregnancy. Women were more likely to start folic acid supplementation after the periconceptional period when they were older, underweight before pregnancy, or immigrant. The results were very similar when the specific weighting to deal with non-inclusion bias was used in sensitivity analyses (Supplementary Table  S2 ). Moreover, the results were not modified by excluding women with an unplanned pregnancy and those with fertility treatment. In the analysis by multiple imputation with five independent datasets, the findings were very consistent with those described in our main analysis.
Acknowledgments: We would like to thank the scientific coordinators ( Marie Aline Charles ,  Bertrand Geay ,  Henri Léridon ,  Corinne Bois ,  Marie-Noëlle Dufourg ,  Jean-Louis Lanoé ,  Xavier Thierry, Cécile Zaros ), IT and data managers, statisticians ( A Rakotonirina ,  R Kugel ,  R Borges-Panhino ,  M Cheminat ,  H Juillard ), administrative and family communication staff, and study technicians ( C Guevel ,  M Zoubiri ,  L Gravier ,  I Milan ,  R Popa ) of the ELFE coordination team as well as the families who gave their time for the study.
Funding: This analysis was funded by an  ANR  grant within the framework " Social determinants of health " (grant number:  ANR-12-DSSA-0001 ). The Elfe survey is a joint project between the  French Institute for Demographic Studies (INED)  and the  National Institute of Health and Medical Research (INSERM) , in partnership with the French blood transfusion service (Etablissement français du sang, EFS),  Santé publique France , the  National Institute for Statistics and Economic Studies (INSEE) , the  Direction générale de la santé (DGS , part of the  Ministry of Health and Social Affairs) , the  Direction générale de la prévention des risques (DGPR, Ministry for the Environment) , the  Direction de la recherche, des études, de l'évaluation et des statistiques (DREES, Ministry of Health and Social Affairs) , the  Département des études, de la prospective et des statistiques (DEPS, Ministry of Culture) , and the  Caisse nationale des allocations familiales (CNAF) , with the support of the  Ministry of Higher Education and Research and the Institut national de la jeunesse et de l'éducation populaire (INJEP).
This first step is essential to guide data acquisition and make adequate choices in the formalization. Step 2: Data Acquisition Step 2 consists in collecting the data required to characterize both farming system functioning and structure in space and time. Based on the definition of a farming system  [15] , three types of data must be considered: biophysical subsystem data, technical subsystem data, and data on the spatial structure of the farming system (mainly fields). For the biophysical subsystem, the data to be collected relate to the biophysical components of the farming system such as plant, soil or pest and disease components. The phenological stages are identified and the variables associated with these components (e. g. , for the soil component, an associated variable might be soil water status).
Data Collection The data needed to apply the HPD theory to this case study were collected through a two-hour semi-comprehensive interview with one farmer held on one farm. We met the winegrower in person, taking notes on the content of the semi-directive discussion. The interview was organized into four parts: Introduction: presentation of the farm Spatial description: description of the vineyard area, groups of fields and field heterogeneity Technical subsystem description: description of practices over time and space Biophysical subsystem description: explanation of decision rules and elicitation of biophysical indicators In the technical subsystem description, an exhaustive collection of the management practices between budburst and harvest was performed for each field: winter pruning, bud pruning, fertilization, tillage, weeding within and between the rows, the entire pest and disease management process, topping and harvest.
The chosen method must enable the farming system to be divided into subgroups of fields via a dissimilarity criterion between fields. Contrary to hierarchical classification methods, which base division on the distance matrix between fields, it was important for each breakdown level to be associated with one meaningful criteria. Accordingly, we used a multiple correspondence analysis (MCA, R®-2. 12. 0 software, package FactomineR, Agrocampus Rennes, Rennes, France). MCA made it possible to extract the most important and structuring variables characterizing a set of individuals according to criteria of dissimilarity, followed by an analysis of the contribution of these variables on a projection plan. One output of MCA is the ordered contribution on the first projection axes. The higher the contribution on the first axe, the more the structuring the variable.
Step 2: Characterization of Specific Crop Management Sequences and Field Diversity The objective of this step is to collect data to characterize the biophysical and technical subsystems and to describe the spatial structure of the studied farm. Data Collection An exhaustive inventory of the management practices between budburst and harvest was drawn up for each field: winter pruning, bud pruning, fertilization, tillage, weeding within and between the rows, pest and disease management, topping and harvest. When information on the crop management sequences for each field was unavailable, the farmer was asked if he had to make adjustments according to the characteristics of each field (e. g. , whether mechanical weeding was done differently in a stony field). The farmer was also asked to explain his decisions on when and how to carry out a given management practice.
Phylogenetic analysis Coding sequences had minimal indels and were aligned manually. The mtCR locus was aligned with MUSCLE 3. 8. 31 (Edgar, 2004)  with minor manual adjustment. Maximum likelihood phylogenetic trees were generated using PhyML 3. 0  (Guindon et al. , 2010) , with 100 bootstrap replicates, which treats gaps in the alignment as missing data, with automatic model selection through Akaike Information Criterion. Trees were visualized using FigTree version 1. 4. 4. All alignments are included as source data, and all sequences available in GenBank (accession numbers: EF1a, MN546736-MN546756; H4, MN546757 -MN546792; mtCR, MN546793 -MN546830; mtCOI, MN546831 -MN546858). qPCR Cancer-associated allele-specific qPCR was performed in M. trossulus, M. edulis, and M. chilensis genomic DNA samples using PowerUp  SYBR Green Master Mix (Applied Biosystems).
SNP array The SNPs used were previously described  (Simon et al. , 2018). They were identified as being diagnostic (nearly fixed for alternative alleles) in a large sequence dataset  (Fraı ¨sse et al. , 2016)  and were subsequently verified to remain diagnostic with larger sample sizes  (Simon et al. , 2019). Non-hybrid individuals can nonetheless occasionally be heterozygous at one, or very rarely at two, of these diagnostic loci because incomplete lineage sorting and introgression is pervasive in the M. edulis complex of species  (Fraı ¨sse et al. , 2018) , but never at more than two loci. Genotyping was subcontracted to LGC-genomics and performed with the Kompetitive Allele Specific PCR (KASP) chemistry  (Semagn et al. , 2014). The results are a combination of two fluorescence values, one for allele 1 (x), and another for allele 2 (y), the M.
Acknowledgements The authors express their gratitude to Lic. Andres Fernandez from  Secretarı ´a de Desarrollo Sustentable y Ambiente of Tierra del Fuego  province for sampling activities. We thank  J Ausio  ´for help in collection of M. trossulus from  Vancouver Island, Canada , and  Jorge Gomez  for help in the collection of M. chilensis from  Calbuco and Castro, Chile.
trossulus fluorescence 0. 50 Cancer Normal Normal (extended dataset) 0. 25 0. 00 2 15 59 801 803 62 88 126 127 605 607 618 802 mean SNP ID 1. 00 0. 75 M. edulis samples M. trossulus fluorescence 0. 50 Cancer Normal Normal (extended dataset) 0. 25 0. 00 2 15 59 801 803 26 47 61 94 117 206 404 617 mean SNP ID Source data 1. Comma separated text file with SNP data from M. chilensis samples. DOI: https://doi. org/10. 7554/eLife. 47788. 014 Source data 2. Comma separated text file with SNP data from M. edulis samples. DOI: https://doi. org/10. 7554/eLife. 47788. 015 B A Figure 6. Identification of M. trossulus-specific SNPs in M. chilensis and M. edulis samples. Cancer and normal samples of (A) M. chilensis from Argentina and (B) M. edulis from Europe were analyzed for the presence of 13 SNPs that are diagnostic for M.
3f, g ), revealing the key role of CB1 receptor-dependent control of drinking in this specific brain circuit. This study reveals an unforeseen circuit mechanism for top-down control of a fundamental life function such as water intake. Specifically, general CB1 receptor activity is necessary to promote water intake, and its control of ACC principal neurons impinging onto BLA is sufficient to promote drinking behavior. These data highlight the complexity of brain control of water intake and underline the importance of top-down regulatory circuits in these processes. positive neurons (Glu-CB1-RS)  19. The mice used in this study were 7-10 weeks old at the beginning of the experiments. Water intake assays. Water intake was observed at 30, 60 and 120 minutes after 24-hour water deprivation and intraperitoneal (IP) injection of 1M sodium chloride (NaCl, VWRV0241) with 10ml/kg body weight.
We thank the Viral Vector Facility (VVF) of Neuroscience Center Zurich (ZNZ) for providing the rAAV2-retro-FLIPo-EBFP and the rAAV2-retro-EBFP viral vectors. We also thank Dr. Karl Deisseroth from Stanford University, Stanford, CA for providing the plasmid of AAV-CaMKIIα-GFP. This work is supported by the China Scholarship Council (to Z. Z. ), INSERM (to G. M. , D. C. , A. B. ), Nouvelle Aquitaine Region (to D. C. , G. M. ), European Research Council (Endofood, ERC-2010-StG-260515 and CannaPreg, ERC-2014-PoC-640923, MiCaBra, ERC-2017-AdG-786467, to G. M. ), Fondation pour la Recherche Medicale (FRM, DRM20101220445, to G. M. ), the Human Frontiers Science Program, Region Aquitaine, Agence Nationale de la Recherche (ANR, NeuroNutriSens ANR-13-BSV4-0006, ORUPS ANR-16-CE37-0010-01 and CaCoVi ANR-18-CE16-0001-02, to G.
Acknowledgments We thank the animal facility and the genotyping platform of the  NeuroCentre Magendie (INSERM U1215 Unit ) for providing assistance in the animal breeding, maintenance and genotyping. We also thank  Drs. Aude Panatier  and  Stéphane Oliet  of  NeuroCentre Magendie  for providing the Osmometer. We thank the  Bordeaux Imaging Center , a service unit of the  CNRS-INSERM and Bordeaux University , member of the national infrastructure  France BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ), for providing the confocal microscope (Leica TCS  SP8 ), the slide scanner (Nanozoomer 2. 0HT ,  Hamamatsu Photonics France) , and  Imaris software (Imaris, Oxford instrument, UK) , the help of  Sébastien Marais  is acknowledged. HHMI Janelie farm research campus is acknowledged for.
providing the rAAV2-retro helper. We thank the Viral Vector Facility (VVF) of Neuroscience Center Zurich (ZNZ) for providing the rAAV2-retro-FLIPo-EBFP and the rAAV2-retro-EBFP viral vectors. We also thank Dr. Karl Deisseroth from Stanford University, Stanford, CA for providing the plasmid of AAV-CaMKIIα-GFP. This work is supported by the China Scholarship Council (to Z. Z. ), INSERM (to G. M. , D. C. , A. B. ), Nouvelle Aquitaine Region (to D. C. , G. M. ), European Research Council (Endofood, ERC-2010-StG-260515 and CannaPreg, ERC-2014-PoC-640923, MiCaBra, ERC-2017-AdG-786467, to G. M. ), Fondation pour la Recherche Medicale (FRM, DRM20101220445, to G. M. ), the Human Frontiers Science Program, Region Aquitaine, Agence Nationale de la Recherche (ANR, NeuroNutriSens ANR-13-BSV4-0006, ORUPS ANR-16-CE37-0010-01 and CaCoVi ANR-18-CE16-0001-02, to G.
For instance, it is well known that HPD are contraindicated in individuals with chronic kidney diseases or at risk for such diseases, as HPD may accelerate kidney dysfunctions  (9, 10). Regarding the impact of HPD on gut health, this remains an emerging but important topic. The aim of the present review is to present the available evidence, including recent data obtained in the MyNewGut European research project, in order to balance the advantages of HPD for weight loss and metabolic health against the potential risks of such unbalanced diets focusing on the gut ecosystem homeostasis. As a matter of fact, there are indications from clinical and experimental studies that dietary changes may modify the large intestine luminal environment with a potential impact on the colonic mucosa  (11). PubMed-referenced publications were analyzed using the following terms in combination: [highprotein diet OR dietary protein OR protein source] / [intestinal microbiota OR bacterial metabolites OR co-metabolites] / [large intestine OR colon OR rectum] / [weight-loss OR overweight OR obesity].
was consistent with those of published P. pentosaceus genomes (https://www. ncbi. nlm. nih. gov/genome/genomes/1109). The Unicycler assembler yielded 28 contigs with an N 50 value of 364,301 bp and a largest contig size of 435,252 bp (using only contigs larger than 200 bp). The draft genome had 1,924 predicted coding sequences, 54 tRNA genes, and 3 rRNA operons. The 16S rRNA gene showed 100% identity with that of P. pentosaceus strain SL4  (15)  according to a BLAST search with the "align two or more sequences" option (https://blast. ncbi. nlm. nih. gov/). MLST analysis identified known allele numbers for pyc-4, pgm-2, and pgI-6, while previously undescribed alleles were detected for gyrB, leuS, glnA, and dalR genes (sequences were submitted to the Pediococcus pentosaceus PubMLST database on 5 March 2019).
The database of L8-OLI and S2-MSI images collected over contrasted coastal and inland water areas, and dedicated to the development and validation of the algorithm, is first presented. The new algorithm is then described for L8-OLI. Finally, WiPE is evaluated from an independent validation database, and the results are compared with those obtained by Fmask. While the present algorithm is fully described for the OLI sensor, the same methodology is applied to MSI on Sentinel-2a (launched on 23 June 2015) which present some slight differences in terms of spectral bands and spatial resolutions (Table  1 ). The adaptation of WiPE on S2-MSI is discussed at the end of the paper from illustration examples. This adaptation, performed for S2a-MSI, can fully be applied on S2b-MSI, as the spectral bands of S2a-MS1 and S2b-MSI only differ by about less than one nanometer in the visible and NIR spectral domains.
3. Spectral database for each selected object Classified objects are manually selected on Rayleigh-corrected color image composites. Polygons are drawn over homogenous areas of the considered objects, and pixels are counted using the QGIS software. Mixed situations are manually disregarded, except those specifically considered here as cloud shadows and thin clouds over land and water. All TOA reflectance and Rayleigh-corrected spectra are stored for each of the following objects: clouds over land and water (664 pixels), thin clouds over land and water (9493 pixels), shadows over land and water (13,582 pixels), water (164,681 pixels), vegetation (4817 pixels), barren land (2103 pixels), and constructions (1398 pixels). The whole database of the ρrc(λ) spectra over the six non-water selected objects is provided in Fig.
3. Similarly to the water pixels (Fig. 2 ), each of the previous object is characterized by a large range of variability of ρrc(λ) spectra, as shown by the large standard deviations observed around each mean spectrum. Based on this latter pattern, we can assume that the database covers a broad range of situations for each object, which makes it quite suitable for the algorithm development phase. The benefit of using ρrc(λ) instead of ρTOA(λ) to detect water pixels is illustrated in Fig. 4 , where typical spectra over water bodies are displayed. From clear (Fig. 4a ) to very turbid waters (Fig. 4d ) the spectral shape (as well as the amplitude) of ρrc(λ) significantly differs from one water type to another. These latter spectral shape differences are less pronounced for ρTOA(λ), especially between clear (Fig.
Interestingly, this test also removed almost all thin clouds (Fig. 7d ) over land and waters (98. 9%). The remaining thin-cloud pixels (1. 1%) after the application of this first step are only found over water pixels (Fig. 7d ). Shadow pixels are by essence difficult to remove, due to their relatively low reflectance value and the strong impact of the spectral shape of the overlaid object. Based on the development database, identified shadow pixels over land present a minimum ρrc(2) value, while almost all land objects have ρrc(6) > ρrc(2). For that purpose the ρrc(6)/ρrc(2) ratio, which presents a large range of variability, is plotted against ρrc(1), for which shadow pixels over land show the lowest variability compared to other bands (Fig. 5f ). Based on these observations, pixels with ρrc(1) higher than -0.
This issue can only be addressed by adding a flag cautioning for potential confusion in the processing scheme for clear and very shallow aquatic environment. Adjacency effects may also be another source of uncertainty in the border areas between different objects. The detection of water pixels in mountain areas is also very challenging, due to numerous shadow areas and adjacency effects. The application of WiPE to a complex mountain area in the North West of China shows however that water pixels are still well identified in such complex environment (Fig. 12 ). Being aware of these specific and complex situations, the performance and limitation of the WiPE algorithm are now addressed in details using the second data set gathering images collected by the OLI sensor over contrasted coastal and inland environments.
For each selected image, a reference water pixels map (referred to as the reference WPM hereafter) was derived as follows. First the WiPE algorithm has been applied on the image. Then, based on a visual analysis of the image, polygons have been drawn using the QGIS software on the missing water pixels areas (P1) as well as on the areas where WiPE wrongly classify pixels as water pixels (P2). The reference WPM for each image is then generated using the results of WiPE corrected by adding (removing) water pixels from the P1 (P2) polygons. The Mean Absolute Percentage Difference (MAPD) is then calculated between the reference number of water pixels (WPM), and the number of water pixels generated by WiPE and Fmask (Table  2 ). The evaluation of WiPE, and the inter-comparison with Fmask (version 3.
FUNDING The online survey and focus groups were funded by the  Geran Kursi Endowmen MPOB-UKM Malaysia , and the  Royal Geographical Society UK. The residential workshop was supported from by  British Council  and  Academy Science Malaysia  via the  UK Newton Ungku-Omar Fund. ZD, JB, and MS are supported by the  UK Natural Environment Research Council  ( NE/K016407/1 ; http://lombok. nerc-hmtf. info/).
Experimental Section General. Chromatography: All reactions were monitored by TLC: Merck silica gel 60 (0. 25mm). Visualization of the TLC was performed by UV light or stained with Dragendorff reagent. Solvents were purified by standard methods. High resolutions (HR) were obtained on a GC-HRMS Micromass Autospec (IE). 1 H and  13  C were recorded on a Bruker 400 and a Bruker AC 300, using CDCl3 as solvent. Data are reported as follows: chemical shifts, multiplicity (s = singlet, d = doublet, dd = doublet of doublets, t = triplet, dt = doublet triplet, m = multiplet), coupling constants (Hz), and integration. Melting points (mp) were determined under microscope with a Leitz Wetzlair device. All reagents and solvents were used as received. Imines, oxaziridines and nitrones were prepared according to the literature.
Study identification and procedures Inclusion criteria for PROG-IMT have been described elsewhere. 4 A more detailed description can be found in the Supplementary files (online). Briefly, a comprehensive PubMed search for the following criteria was performed: longitudinal observational studies, sample of or similar to the general population, well-defined inclusion criteria and recruitment strategy, at least two visits with assessment of cIMT, clinical follow-up after the second visit recording myocardial infarction (MI), stroke, death, vascular death, or a combination of these. Publications in all languages published until 10 January 2012 were included. Furthermore, articles referenced in reviews on cIMT were manually searched. When a study satisfied the inclusion criteria, the study teams were invited to participate in the project and contribute a predefined individual participant dataset.
11 Initially, 22 population studies were identified as potential participants. However, two of these declined participation in the project, one did not reply to the invitation, and three accepted but did not submit their data on time to be included in the analysis. 3] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]  All datasets underwent central plausibility checks, the variables were harmonised, transformed to SI units, and ordinal variables were recoded into balanced binary categories. 4 The clinical endpoints (MI, stroke, vascular death and total mortality) were defined as in the original studies (Supplementary Table  1 ). Probable or definite MI and any stroke (symptoms lasting more than 24 hours, including non-traumatic haemorrhages) were included.
Acknowledgements The authors used a restricted access dataset of the  Atherosclerosis Risk In Communities (ARIC) Study. The  ARIC Study  was supported by the  National Heart, Lung and Blood Institute (Bethesda, MD, USA)  in collaboration with the ARIC Study investigators. This article does not necessarily convey the opinions or views of the ARIC Study or the  National Heart, Lung and Blood Institute. and funding agencies. The Bruneck study was supported by the  Pustertaler Verein zur Praevention von Herz-und Hirngefaesserkrankungen ,  Gesundheitsbezirk Bruneck , and the  Assessorat fuer Gesundheit (Province of Bolzano, Italy). The  Carotid Atherosclerosis Progression Study  was supported by the  Stiftung Deutsche Schlaganfall-Hilfe. The Cardiovascular Health Study research reported in this article was supported by contracts ( HHSN268201200036C ,  N01-HC-85239 ,  N01-HC-85079  to  N01-HC-85086 ,  N01-HC-35129 ,  N01 HC-15103 ,  N01 HC-55222 ,  N01-HC-75150 ,  N01-HC-45133 ) and grant ( HL080295 ) from the  National Heart, Lung and Blood Institute , with additional contribution from the  National Institute of Neurological Disorders and Stroke (Bethesda, MD, USA).