dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank the  CGIAR Research Programs: Humidtropics; Agriculture for Nutrition and Health (A4NH) ;  Roots, Tubers and Bananas (RTB) ;  Water, Land and Ecosystems (WLE)  and all the donors who supported this research through their contributions to the  CGIAR Fund. For a list of Fund donors, please see http://www. cgiar. org/our-funders/. BesidesWageningen University & Research also provided strategic funds, under the program 'Global One Health. 'We are grateful to the  Vietnamese Fruit and Vegetable Research Institute (FAVRI) ,  Dr. Pham Hoi ,  Son Nuygen , and  Lan Huong  from  CARES-Vietnam  who facilitated, translated and provided logistical help during data collection. We are most grateful to the  Vietnamese  farmers for their time and patience in supporting this research.
ACKNOWLEDGEMENTS The members of the Review Group are as follows:  Eimear Forbes ,  Temple Street Hospital, Dublin, Ireland ;  Baheerathi van de Bor ,  UCL Great Ormond Street Hospital for Children NHS Trust, London, UK ;  Joanne Olieman ,  Erasmus MC -Sophia Children's Hospital, University Medical Center Rotterdam , the  Netherlands ;  Venetia Simchowitz ,  UCL Great Ormond Street Hospital for Children NHS Trust, London, UK. Thomas Storme, Pediatric Epilepsy & Child Neurology Paris-Diderot University, Paris, France ;  Joerg Klepper ,  Klinikum Aschaffenburg-Alzenau ,  Germany ;  Anastasia Dressler ,  Universit€ atsklinik fur Kinder-und Jugend heilkunde P€ adiatrisches Epilepsiezentrum, Vienna, Austria. We thank  Eric Kossoff  for supporting us during the entire process. The authors have stated that they had no interest that could be perceived as posing a conflict or bias.
Strategic analysis of the institutional processes The final part of our analysis focuses on the governance of the ASUREPs. Our aim was to describe and understand as precisely as possible how these organizations operate, both in principle and in practice. Our data sources included numerous field observations, interviews with key stakeholders and the study of the reports produced by the ASUREPs and ADIR since 2006. Grasping how a system is governed means understanding not only its formal rules, but also the way in which these are reinterpreted for practical application when confronted with informal norms and concrete issues. This approach is much like the strategic analysis used by the sociology of organizations  (Crozier, 1992) , which involves observing and documenting in detail how activities are run, with a particular focus on situations that generate power relationships.
With the mayor's agreement, ADIR then meets the neighborhood leaders to explain this approach to them. The leaders then convene the local notablesmainly the leaders of a street or group of streets -to a meeting where the same information is communicated and awarenessraising sessions are scheduled street by street. Next, after publishing an announcement in the press, ADIR recruits interviewers who are tasked with counting the neighborhood populations, as data from the municipal authorities are often inaccurate. For an average-size Kinshasa neighborhood, four interviewers are needed over four days. On the basis of the numbers collected, ADIR calculates a representativeness coefficient by dividing the number of people registered by the estimated number of general assembly delegates. This ratio determines how many delegates are allocated to a street or whether one or two streets need grouping together to be entitled to a delegate.
STAR+METHODS Detailed methods are provided in the online version of this paper and include the following:   and DK118401 ). Structural analysis was performed by Y. K. and E. Y. J. , who are supported by Cancer Research UK and the UK MRC (C375/A17721 and MR/M000141/1 to E. Y. J. ) and Wellcome (203141/Z/16/Z, supporting the Wellcome Centre for Human Genetics). Whole-exome sequencing was performed as part of the UK10K consortium (a full list of investigators who contributed to the generation of the data is available from https://www. uk10k. org/). Participants in the INTERVAL randomized controlled trial were recruited with the active collaboration of NHS Blood and Transplant England (https://www. nhsbt. nhs. uk/), which has supported field work and other elements of the trial. DNA extraction and genotyping was co-funded by the NIHR, the NIHR BioResource (https://bioresource.
, 2013). To ensure highquality variant calls across all datasets and sequencing batches, only variants with at least 7x coverage in at least 80% of samples were called. We applied further variants QC keeping only variants with a calibrated VQSR tranche above 99. 75% sensitivity, missingness < 20%, Hardy-Weinberg equilibrium c2 p value > 10E-8, mean genotype quality R 30, and variants in low-complexity regions as described previously  (Li, 2014). Further, individual genotypes were set to missing if any of the following was true: GQ < 30, alternate allele read depth (DP1) < 2, allelic balance (AB) < 0. 2, or AB > 0. 8. We used VerifyBamID (v1. 0)  (Jun et al. , 2012)  and a threshold of R 3% to identify contaminated samples, principal components calculated from the 1000Genomes Phase I integrated call set  (Abecasis et al.
Structural analysis To study how the human SEMA3-PLXNA-NRP variants affect signaling, we mapped the mutated residues onto structural models and assessed their potential effects on stability and protein-protein interactions. For SEMA3s, all human SEMA3 sequences (SEMA3A-3G) were first aligned to the sequence of mouse SEMA3A. A homology model encompassing the first three Sema3 domains, the sema, PSI and Ig domains, was made by combining the sema-PSI segments of the mouse Sema3A crystal structure at 3. 3 A ˚resolution (PDB: 4GZ8) together with a homology model of the Sema3 Ig domain generated using the 3. 0 A ˚crystal structure of human Sema4D which includes a more complete Sema-PSI-Ig segment (PDB: 1OL2). The human SEMA3 variants were then mapped onto the equivalent residues in the mouse Sema3A model.
To locate the variants potentially affecting the Sema3-Plxn-Nrp interfaces, the crystal structure of mouse Sema3A-PlxnA2-Nrp1 (PDB: 4GZA) was used as a reference. Similarly, human PlxnA1-A4 sequences were aligned with mouse PlxnA1 and the mouse residues corresponding to the human variants were identified. The human PLXN variants on the extracellular segments (domains sema to IPT6) were mapped onto the 4 A ˚crystal structure of the mouse PlxnA1 ectodomain (PDB: 5L56). Variants in the PlxnA cytoplasmic domain were mapped onto a homology model of PlxnA1 generated using the 3. 3 A ˚crystal structure of the zebra fish PlxnC1 active dimer (PDB: 4M8M). Human NRP1 and NRP2 variants were mapped onto the equivalent positions in the 2. 7 A ˚crystal structure of mouse Nrp1 (PDB: 4GZ9). The MAM domain of the Nrps was not modeled.
Studies in Zebrafish Zebrafish homologs of class 3 Semaphorins, class A Plexins and Nrps were identified using Ensembl GeneTree (ENSGT00760000119048). Phylogenetic trees were constructed from the zebrafish, human, and mouse orthologs using Clustal Omega  (Sievers et al. , 2011). Phylogenetic trees were visualized using the Unrooted software (http://pbil. univ-lyon1. fr/software/ unrooted. html). Two-five non-overlapping short guide RNAs (gRNA) targeting each zebrafish gene were identified using the Zebrafish Genomics track data hub (GA targets) in DanRer 10; gRNA templates were assembled and mRNA produced as described  (Varshney et al. , 2015)  with the exception that the T7 promoter was substituted with an SP6 promoter. sgRNAs (200 ng each) were incubated with 600 ng Cas9 protein (New England Biolabs, #M0646T) for 5 min at 37 C prior to 2 nL being injected into 1-cell stage zebrafish WIK embryos.
Mice were acclimated in the monitoring chambers for 2 days then data were collected for 3 days. These physiological measures were performed at the Rodent Metabolic Core of Children's Hospital of Los Angeles. Glucose tolerance tests (GTTs) were conducted in 10-to 11-week-old mice (n = 9-11/group) through i. p. injection of glucose (1. 5 mg/g body weight) after overnight fasting. Blood glucose levels were measured at 0, 15, 30, 45, 60, 90, 120, and 150 min post-injection. Serum leptin, insulin, T3, T4 levels were assayed in 18-21-week-old fed mice and corticosterone levels were assayed in 18-21-week-old fed mice (n = 8-11/group) using ELISA kits (Millipore, Calbiotech and Enzo Life Sciences, respectively). Fluorescent in situ hybridization Antisense digoxigenin-labeled riboprobes were generated from plasmids containing PCR fragments of Pomc.
QUANTIFICATION AND STATISTICAL ANALYSIS All values were represented as the mean ± SEM. Studies in cellular models are from at least 3 independent experiments. Numbers for every experiment are found in the relevant part of the STAR Methods. Explant co-culture assays are derived from 4-28 explants in 3-6 independent experiments. Mice studies are from 3-11 animals per group. Statistical analyses were conducted using GraphPad Prism (version 6. 07). Datasets with only two independent groups were analyzed for statistical significance using unpaired two-tailed Student's t test. Datasets with more than two groups were analyzed using one-way analysis of variance (ANOVA) followed by the Bonferroni posthoc test. For statistical analyses of body weight and GTT (mice), we performed two-way ANOVAs followed by Bonferroni's posthoc test.
u. , arbitrary units). (C) Effect of WT/mutant SEMA3A-G on cell collapse normalized to amount of semaphorin secreted. (D) Structural analysis of SEMA3 mutants affecting cell collapse (increased, blue; decreased, red). Mutants are mapped on the crystal structure of the mouse Sema3A-Nrp1-PlxnA2 complex (PDB: 4GZA). Data represented as mean ± SEM from at least three independent experiments. *p < 0. 05; **p < 0. 01; ***p < 0. 001 for all experiments. See also Figure S1 and TableS3.
Participatory Rural Appraisal to identify local perceptions In-depth fieldwork was conducted to characterise the relative importance of household and community capitals, to explore livelihood dynamics and to draw up a profile of livelihood systems. This was also used to identify indicators that stakeholders, experts and local residents perceive as representative and robust to examine the effects of each capital on livelihood opportunities. A Participatory Rural Appraisal was used as the principal suite of tools for data collection to highlight the perceptions and opinions of rural dwellers. This suite of tools enables local people to share their knowledge, and discuss and analyse their situation using their own terms  (Mukherjee, 2005). Literature evidence highlights the hierarchical social complexity of class, caste and gender in India, which are reflected in both men and women's roles and participation in the labour market, including engagement in agricultural activities (Ray-Bennett, 2009;  Savath et al.
Districts were selected according to their level of vulnerability  (Nathan et al. , 2008)  and their location, so as to cover the whole geographical extent of the delta. Villages were then sampled based on their socio-economic characteristics (using census data provided by the Registrar General and Census Commissioner, 2011) and on the main livelihood activities conducted by households (based on the key informant interviews). The activities were conducted over 4 to 5 days in each village. The social, economical and political status in India relies upon a class-based structure with landlords at the top and landless labourers at the bottom  (Kannabiran & Kannabiran, 1991; Kapadia, 1998). Inequalities are perpetuated by the system of castes (Ray-Bennett, 2009), which is "an essential ingredient in the study of stratification patterns in Indias population"  (Deshpande, 2001).
Data used for the quantification of livelihood capitals Several sources of data were needed to proxy the different capitals highlighted by participants during the Participatory Rural Appraisal, such as demographic data, infrastructure and amenities and environmental data. Census and satellite remote sensing data were chosen as they are publicly accessible online, they are available at a fine resolution (village-level or finer) and cover a large spatial extent. The demographic data used in this paper were a subset of the 2011 Census of India and included all rural villages located within the five districts (Bhadrak, Jagatsinghpur, Kendrapara, Khorda and Puri) located within 5-meter contour of the Mahanadi river delta (9,829 villages in total) (Registrar General and Census Commissioner, 2011).
The data are at the village-level and consist of three sets of tables: "village amenities", "house-listing" and "population enumeration". The "village amenities" dataset includes area in hectares, total income, total expenditure and the different infrastructures available related to education, medical, drinking water, communication, banking, recreational and cultural facilities, accessibility to the village, power supply and natural resources. The "population enumeration" tables provide comprehensive information on the population, with all the information recorded based on the twelve months preceding census enumeration. For instance, these tables provide figures about the main livelihood activity of each individual (cultivator, agricultural labourer, entrepreneur, other) and its frequency ("main" for more than six months per year, "marginal" for less than six months per year).
Quantification of livelihood capitals During the Participatory Rural Appraisal, participants identified the factors that influence their choice of a livelihood activity. Proxy variables from the secondary data available described previously were selected to represent the factors identified by the participants. The variables were then assigned to livelihood capitals (natural, physical, human, financial, social) according to the type of good they fall into: private goods being classified as household capitals and common-pool resources as community capitals. Given the high number of variables falling under each type of capital and their collinearity, extraction methods were used to reduce the information to a lower dimensionality space. Principal Component Analysis was used to decrease the amount of redundant information by de-correlating the input vectors, as suggested by  Filmer & Scott (2012).
It was decided not to combine multiple factors as it would have distorted what the component captured and would have made it difficult to interpret (McKenzie, 2005). The first step consisted in normalising the data stored as a matrix (X) into X. Then, the eigenvectors (U) and eigenvalues (Λ) of the covariance matrix (C) were calculated using Singular Value Decomposition. Finally, to reduce the dimensionality, the data was projected onto the largest eigenvectors P = U T • X. The first eigenvector was kept and checked how the component captured the initial variables. The new vector was kept only if, for each variable, the loading's direction matched the findings from the Participatory Rural Appraisal. The index for each capital was then computed by weighting each variable using its factor loading and then summing them.
(2013) to get a coherent land cover dataset for 2011. The main features extracted from this dataset are built-up (urban/rural), forest cover (evergreen/deciduous/shrubs/mangroves), agricultural land (cropland/plantation/fallow), wasteland, wetland and waterbodies. Detailed maps of water-logged areas (seasonal and permanent), erosion process and salinity intrusion for 2008 -2009 were also used to evaluate environmental stresses (Ministry of Rural Development, 2011).
Acknowledgements This work was carried out under the  Deltas, vulnerability and Climate Change: Migration and Adaptation (DECCMA)  project under the  Collaborative Adaptation Research Initiative in Africa and Asia (CARIAA)  programme, with financial support from the  UK Government's Department for International Development (DFID) , the  International Development Research Centre (IDRC) (Canada)  [grant number  107642 ] and the  UK Economic and Social Research Council  [grant number  1501613 ]. The views expressed in this work are those of the creators and do not necessarily represent those of  DFID ,  IDRC ,  ESRC  or its Boards of  Governors. Prior  to commencing the study, ethical clearance was obtained from the  University of Southampton  [ERGO number 15234]. Data used in this research come from the  Census of India  provided by the  Office of the Registrar General & Census Commissioner of India.
39 Such ring structures were observed in low resolution electron microscopy structures of 2C proteins from poliovirus and foot-and-mouth disease virus, a picornavirus from the aphthovirus genus. 45, 46 The published EV-A71 2C structure (PDB: 5GRB) shows a C-terminal interaction between two adjacent 2C molecules to form a bipartite binding site for the ATP. In total, there are six protein chains in the crystal structure of which only chains A and B are fully resolved, whereas all the others have at least one gap. 39 he cocrystallized ATP molecules adopt different conformations for each chain, and only chains A and F crystallize in a conformation resembling a bipartite binding site with both monomers forming hydrogen bonds with the ATP. Therefore, chain A was used as a template to generate the homology model of CVB3 2C (Figure  4A ).
Pictures of molecular modeling were prepared using MOE. 3. 7. Homology Modeling. The protein sequence of CVB3 (strain Nancy) 2C protein was downloaded from Uniprot (ID: P03313 amino acids 1101-1429). The structures reported in ref 39, especially 5GRB, were used as a starting point in this study and were retrieved from the Protein Data Bank. 5GRB contains ATPγS and was the structure used for the computational studies. The sequence of CVB3 was aligned to the sequence of the crystallized EV-A71 using MOE. 5GRB chain A was used as a structural template for the homology model. The homology model was built with the Amber12: EHT force field. 59, 60 Automatic detection of disulfide bridges was disabled. Ten intermediate models were generated and refined using a medium refinement by molecular mechanics (highly tethered minimization to relieve steric strains).
This work was supported by research grants from The  Netherlands Organisation for Scientific Research  ( NWO-ECHO-711. 017. 002  to F. J. M. v. K. and J. R. P. M. S. ;  NWO-VICI-91812628  to F. J. M. v. K. ) and the  European Union  ( Horizon 2020 Marie Skłodowska-Curie ETN "ANTIVI-RALS ", grant agreement number  642434  to T. L. , B. C. ,.
Study periods This retrospective 'before/after' cohort study was conducted over two time periods within 5 years totaling 3501 patient days in the ICU for 275 patients. The preinterventional period ('before period') took place from November 2009 to March 2011, the post-interventional period ('after period') from November 2011 to November 2014. We compared two chronological cohorts: the 'before group' with unmonitored nutritional care and the 'after group' with updated nutritional care (computer assisted, guideline updated, immunonutrient supplemented). Patients and most of clinico-biological data were retrospectively identified and analyzed using our prospective ICU database included in our ICU information system (MetaVision ® , iMDsoft, Tel Aviv, Israel). Following the 'before period, ' a CDSS module was locally designed and added to our ICU information system as a tool to provide clinicians with essential guideline-based information concerning nutritional support in the ICU and hematology patients  [7, [11] [12] [13].
Clinical and biological data All clinical and biological data were prospectively collected, except for caloric and protein balances in the 'before group'; however, for this group, intakes were prospectively computed by our medical system information. The following clinical data were collected during the ICU stay (Table  1 ), among them: age and gender; height; weight; calorie and protein intakes; caloric and protein deficit at day 3, day 5, and ICU discharge; the number of days in compliance with caloric and protein targets during ICU stay; loss of weight at ICU discharge; characteristics of the malignancy; neutropenia (absolute neutrophil count of < 0. 5 × 10 9 l -1 ); characteristics of sepsis; chronic health status as evaluated using the Knaus scale  [18] ; severity-of-illness scores using Simplified Acute Physiology Score II (SAPS II)  [19]  at admission, cause of ICU admission; therapeutic interventions in ICU, including vasopressor use, need, and duration of IMV, RRT, antimicrobial treatment; ICU and hospital length of stay; and hospital mortality.
Statistical analysis Individual data collected prior to ICU were summarized using counts (frequencies) for qualitative variables and medians [25th-75th percentiles] or means [standard deviations (SD)] for quantitative variables. The primary objective was to compare the number of days in compliance with calories target between the study cohorts enrolled prior and after initiation of CDSS intervention, respectively. Analysis of the primary endpoint was performed using Poisson regression including terms for cohort (after vs. prior CDSS) and length of ICU stay as an offset term to account for individual duration of exposure. A multivariate regression was then performed to confirm the results, by adding as covariates neutropenic, severe and allogeneic SCT statuses. Estimate of intensity rate ratio (IRR) derived from Poisson regression was used to assess the difference in the number of days in compliance between the two cohorts.
Analysis of the metal content in organs The iron (Fe) and zinc (Zn) content was determined by inductively coupled plasma atomic emission spectroscopy (ICP-AES, ICPE-9000 from Shimadzu Scientific Instruments). Experiments were conducted as follows: organs were rapidly collected and weighted before being dried at 60°C for 24h on a DigiPREP Block digestion system. Dried tissues were mineralized overnight in 65% nitric acid. Mineralized samples were then diluted with purified water to attain a final concentration of 10% nitric acid. A multi-element standard (Sigma-Aldrich) was used for calibration. Ytterbium (Sigma-Aldrich) was added at 100 µg/L to all solutions (samples and standard) as an internal control. The data obtained were analyzed with the ICPE-9000 analysis software by selecting wavelengths displaying minimal interference with other elements.
Retro-transcription and quantitative real-time PCR (RT-qPCR) Total RNA was isolated from murine placentas at embryonic age E13 using the Nucleospin® RNA Kit (Macherey-Nagel) according to the manufacturer's instructions. RNA was quantified with a NanoDrop ND1000 spectrophotometer by means of the ND1000 software (Labtech). 0. 5µg of total RNA was retrotranscribed into cDNA using the Affinity Script cDNA Synthesis kit (Agilent Technologies) and the C1000 TM Thermal Cycler (Biorad). Gene expression levels were determined by qPCR using Brilliant II SyBR Green qPCR Mastermix (Agilent) and the CFX 96 TM Real-Time system (Bio-Rad). Data were analyzed with the Bio-Rad CFX Manager software (version 2. 0). All qPCR primers (Table  2 ) were designed with NCBI Primer3/ BLAST (Basic Local Alignment Search Tool) and purchased from Eurofins Genomics.
Histological analysis of placentas The analysis of placental histology was done as described previously  [18]   [19]. Briefly, fourmicrometer-thick sections were cut from paraffin-embedded placenta blocks, mounted on silanized slides, deparaffinized, and rehydrated. The general morphology of the placenta with its 3 typical layers (decidua, junctional zone, and labyrinth) was analyzed on selected tissue sections after staining with hematoxylin and eosin. The thickness of each layer was measured using ImageJ (Image J software, 1. 46r).