dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We are most grateful to  Dr. Christophe Salon  and members of the platform  4PMI  ( Plant Phenotyping Platform for Plant and Microorganisms Interactions ) for the culture of plants and  Pr. Alain Ourry  and the technicians of the platform  PLATIN' (Plateau d'Isotopie de Normandie ) for element analysis used in this study. We also wish to acknowledge  Dr. Laurence Cantrill  for proofreading and English correction.
FUNDING This work was performed, in partnership with the SAS PIVERT, within the frame of the  French Institute for the Energy Transition (Institut pour la Transition Energétique (ITE) P. I. V. E. R. T. (www. institut-pivert. com)selected as an  Investment for the Future ("Investissements d'Avenir "). This work was supported, as part of the Investments for the Future, by the  French Government  under the reference  ANR-001-01.
EEG Recordings Freely moving mice were monitored during kindling via EEG registration-units (DSI), consisting of a receiver plate (RPC-1 receiver, DSI), which acquires radio-frequency transmitted signals from the transmitter and sends it to the data exchange matrix (MX2, Matrix 2. 0, DSI). EEG signals were sampled at a rate of 500 Hz (Ponemah software, DSI). During EEG analyses with Neuroscore software (DSI), a 0. 3 Hz high-pass filter, a 60 Hz low-pass filter, and a 50 Hz notch filter were applied to the recorded traces. Seizures were visually detected and defined as high frequency spikes (>3 Hz) with a duration of at least 3 s, and an increase in baseline amplitude of at least 200%. Not all recordings were suitable for EEG analysis due to EEG contamination by, for instance, heart rate. These recordings were discarded from EEG analyses, explaining the smaller sample sizes for EEG analyses compared to behavioral analyses.
Statistical Analyses Statistical analyses were performed using Graphpad Prism v6. 1. For datasets consisting of multiple groups and time points, two-way ANOVA followed by a Bonferroni post-hoc comparison was performed. For datasets containing multiple groups or time points, one-way ANOVA was performed. For datasets comparing two groups, Mann-Whitney U test was performed. A Fisher's exact test was used for contingency analyses, and a Pearson correlation test was performed for assessing correlations. For all tests, α was set to 0. 05, and P values lower than 0. 05 were considered significant. Results are expressed as mean ± SEM.
Characterization of the association of protein with NP The OVA protein association to the 5 types of NP was evaluated using native polyacrylamide gel electrophoresis (native PAGE). Formulations were supplemented with a non-denaturing buffer (Tris-HCL 125 mm (pH 6. 8), 10% glycerol and 0. 06% bromophenol blue) and run on a 10% acrylamide-bisacrylamide gels for 1. 5 h at 120 V then stained by the silver nitrate method. Gels were scanned and quantified using the ImageJ software, setting the 0% of association over the OVA alone input. Under these conditions, non-associated protein enters the gel while NP-associated protein does not.
1e , brown) (The remaining 11% comprises the 3N form (7%) and the auto-ternary 3N+N His complex (4%). For spectroscopic parameters see Table  S1. Upon stepwise Im additions the band redshifts until the saturation at around 200 mM of Im (λmax = 560 nm, Ԑ =68 L mol -1 cm -1 ) (Fig. 1e , orange). The final band's maximum corresponds to the formation of a ternary Cu(II)-complex where the 3N chelating unit of the peptide AHH was complemented by the external Im (3N+N Im ). 11 The observed red-shift is consistent with the replacement in the copper coordination sphere, where one amide nitrogen is being substituted with a nitrogen from the external Im 10,13,20,21 (Fig. 1 ). Similarly, we monitored the 3N+N Im complex formation via CD spectroscopy. The data are in agreement with the UV-vis spectroscopy.
We acknowledge financial support from the University of Strasbourg Institute for Advanced Study (USIAS), University of Strasbourg (IDEX program), the the Frontier Research in Chemistry Foundation/ Labex CSC, Strasbourg (CSC-PFA-17), the REseau NAtional de Rpe interDisciplinaire (RENARD, Fédération RPE CNRS #3443), the ERC StG-638712 "aLzINK" (C. H. , P. F. and P. G. ) and Narodowe Centrum Nauki (Poland), Grant 2016/23/B/ST5/02253. N. E. Wezynfeld was supported by Start Programme funded by Polish Science No. START 90. 2017 and Mobility Plus Programme funded by Polish Ministry of Science and Higher Education No. 1632/MOB/V/2017/0. The equipment used was sponsored in part by the Centre for Preclinical Research and Technology (CePT), a project co-sponsored by European Regional Development Fund and Innovative Economy, The National Cohesion Strategy of Poland.
We calculated an FPDS for each household by summing the following crop and livestock groups: 1) cereals; 2) tubers; 3) beans, peas, and pulses; 4) vegetables and fruits; 5) cattle; 6) poultry; 7) goats and sheep; 8) pigs; and 9) camels. The categories of foods were based on available data on crop production, and the livestock groups were computed based on taxonomy (small ruminants, large ruminants, pseudo-ruminants, monogastric, or poultry), like that used in a study in Kenya  (37). In our analysis, the FPDS was used as a continuous variable and could range from 1 to 9.
Data management and statistical analyses Data were collected using Android tablets. Data quality was ensured by quality checks at data entry and by post-survey data cleaning. Data management and analyses were performed using R software version 3. 4. 3. All analyses took into account the sampling design (stratification, clustering, and sampling weights) using the Survey package. Unless otherwise specified, the type I error risk was set at 0. 05. A descriptive analysis was conducted of the characteristics of the study sample. MDD-W and WDDS-10 were used as response variables to analyze the relation between women's dietary diversity and household food security and/or farm production diversity. Variables identified as potential confounders for these relations were grouped in 3 dimensions (characteristics of women, characteristics of heads of households, and socioeconomic and demographic characteristics of households).
If production diversity is measured using a different unit (e. g. , species) than the one used to assess dietary diversity (count of groups), associations are less likely to be identified  (49). Because of data constraints, the construction of an ideal FPDS based on the exact same groups used for the MDD-W as seen in India  (29) , or of other relevant indicators such as nutritional functional diversity  (50)  or modified functional attribute diversity (51), was not possible. In a context in which agricultural policies encourage diversification of production rather than relying on a small number of staple crops (52), our study helps document the relation between individual diet quality and on-farm production diversity assessed from a nutritional perspective. Finally, several conceptual frameworks (3-7) hypothesize that farm production diversity may affect women's dietary diversity through its positive effects on household food security.
Despite the cross-sectional nature of our data, the period of time to which our indicators referred-FPDS during the past 12 mo, HFIAS and HHS during the past month, and MDD-W during the past 24 h-led us to expect we would be able to identify such a plausible pathway. However, our results revealed that food security mediated only a very small part of the total effect of FPDS on women's dietary diversity. In addition, we hypothesize that this pathway may vary with the season. Our study has some limits. The market may play a role in the relation between agricultural diversification and individual dietary diversity  (35) , but it was not possible to specifically assess its contribution in the current study. We used baseline data from a randomized controlled trial in which women received either cash transfers or lipidbased nutrient supplements for their children.
Acknowledgments We thank our colleague  Saidou Magagi  from  UNICEF  for assistance with the training and supervision of the interviewers. The authors' responsibilities were as follows-YMP, MS, ALP, and TM: contributed to the conception and design of the study; TM, NEK, and KF: responsible for program implementation; YK, SF, MS, ALP, NEK, and KF: conducted the research; LA and SF: performed statistical analysis; LA, YMP, MS, and ALP: wrote the manuscript; and all authors: read and approved the final manuscript.
Background: Unravelling the genetic architecture of agronomic traits in walnut such as budbreak date and bearing habit, is crucial for climate change adaptation and yield improvement. A Genome-Wide Association Study (GWAS) using multi-locus models was conducted in a panel of 170 walnut accessions genotyped using the Axiom™ J. regia 700K SNP array, with phenological data from several years. These accessions come from the INRAE walnut germplasm collection which is the result of important prospecting work performed in many countries around the world. In parallel, an F1 progeny of 78 individuals segregating for phenology-related traits, was genotyped with the same array and phenotyped for the same traits, to construct linkage maps and perform Quantitative Trait Loci (QTLs) detection. Results: Using GWAS, we found strong associations of SNPs located at the beginning of chromosome 1 with both budbreak and female owering dates.
The allele T of this SNP is linked to a late budbreak date (R² = 30. 6%, allelic estimated effect = 5. 9) (Table  3 ). We also ran a Kruskal-Wallis test to nd if the phenotypic differences were signi cant among the three genotypes using the different phenotypic datasets, and this allelic effect remains consistent (p-values = 1. 84E-13 for two-year data, 6. 63E-12 for 2018, 9. 76E-13 for 2019, and 2. 61E-09 for legacy data; Fig. 5 ). The co-localizing major QTLs found in 2019 in LG 1 in 'Franquette' and 'UK 6 - 2' explain 23. 9% and 34. 8% of the budbreak date variance, respectively. In addition, GWAS with two-year data found four additional associations on chromosomes 2, 4, 8 and 15, while the classical linkage mapping analysis identi ed minor QTLs on linkage groups 6, 11, 12 and 14. The high power of our gene tagging approach based on both GWAS and QTL mapping, was also con rmed for beginning, peak, and end, of female owering dates.
The SNP 'AX-170990138' (physical position: 9,298,520 bp) on Chr1 was systematically found associated with all stages of female owering (Table  3 ). For beginning female owering date, we found this SNP associated using two-year data, and using each year separately. We also observed this marker-trait association for peak female owering date using legacy data, and for end female owering date with two-year data and with 2019 data. The SNP 'AX-170990138' is 2. 8 Mbp apart from the most signi cant marker-trait association found for the budbreak date on Chr1, and the allele G of this SNP is linked to a delayed female owering, with a R² ranging from 34. 8-39. 6%, and an allelic estimated effect ranging from 3. 4 to 4. 5, depending on the stage and the dataset. We identi ed additional QTLs for all three stages of female owering but the most signi cant ones, segregating in both parental maps, co-localize with those previously found associated with the budbreak date on Chr1 (Table  3 ).
Besides the major QTL on Chr1 identi ed with both GWAS and QTL mapping, we found three signi cant associations also on Chr7 for all three stages. These three SNPs are located in a region of about 23 to 25 Mb (Table  3 ). In addition, we found QTLs on LGs 2 and 14 in 'Franquette' map, and on LGs 3, 9, 11 and 12 in 'UK 6 - 2' map. Association and Linkage Mapping for Heterodichogamy and Male Flowering Dates Results for male owering are similar to the female owering results in that a few SNPs in a very close region are associated with all three stages (Table  4 ). On Chr11, we found four associated SNPs depending on the stage and the dataset, in a region of about 31. 8 Mbp and a window of 52 kb. The most signi cant QTLs for all three stages of male owering for both parental maps co-localize with those previously identi ed as associated with budbreak date and the three stages of female owering.
Two additional QTLs on 'UK 6 - 2' map were found on LG 11 for peak male owering date, using two-year data (26,141,527-35,537,934 bp), and for the end of male owering using 2019 data (27,685,560-35,537,934 bp), supporting the GWAS results. For heterodichogamy trait (computed by subtracting peak female date from peak male date; Table  S5 ), the signi cant associations found with GWAS using two-year data and legacy data co-localized with the associations identi ed for male owering dates on Chr11 in the region of about 31. 8 Mbp. Candidate Genes for Bearing Habit and Phenology-Related Traits Using the Walnut Genome By combining GWAS and QTL results and considering their consistency over phenotypic datasets, we decided to focus on a robust subset of eight loci to nd candidate genes for bearing habit and phenologyrelated traits (Table  5 ).
Using HaploView v4. 2 software and the new chromosome-scale reference walnut genome v2. 0  [30] , several interesting coding sequences were found within the de ned Linkage Disequilibrium (LD) blocks for different traits. Primers related to the marker-trait association found on Chr1 for budbreak date (SNP 'AX-171179714') were developed. These primers were used to genotype 96 unreleased breeding line accessions of the Walnut Improvement Program of the University of California, Davis. Among them, 48 are early lea ng accessions and 48 are late lea ng accessions. Lea ng date occurs 3 days after budbreak date. Our GWAS results showed that the allele T of the SNP 'AX-171179714' (versus G) has an allelic estimated effect of 5. 9 (R² = 30. 6%), linked with a delayed budbreak date. Since we designed the KASP primers using the complementary strand, we expect to nd the allele A (versus C) as involved in late lea ng genotypes.
Phenotypic Evaluation and Data Analysis Phenotypic evaluation for the following 10 traits was conducted in two years (2018 and 2019) for both the GWAS panel and the F 1 progeny: budbreak date, beginning, peak, end, and duration of female owering, heterodichogamy, and beginning, peak, end, and duration of male owering (Table  S5 ). Heterodichogamy, degree of male and female owering overlap, was computed by subtracting peak female owering date from peak male owering date. A negative value (categories '1' and '3', Table  S5 ) means the accession is protandrous since peak male owering in Julian days occurs before the peak female date. Categories '7' and '9' indicate protogyny. For the GWAS panel, we also used phenotypical data based on the same scoring scales used for many years previously (mainly between 1989 and 2011).
These legacy data are available from the INRAE walnut germplasm collection database  [15]. Moreover, as bearing habit is not affected by environment conditions, this trait was recorded only in 2019. This trait did not segregate within the F 1 progeny, so only the GWAS panel was evaluated. Data management and visualisation, and correlation matrixes were performed using "R" software  [44] , with the packages "tidyverse"  [45]  and "corrplot"  [46] , respectively. For the GWAS panel, the means of genotypic effects were obtained for each accession by adjusting for known environmental factors using the BLUPs. When using two-year data, the means were predicted using a mixed linear model considering the year effect (a). When using phenological data from many years and three sites  [15] , the means were predicted using a mixed linear model considering the effects of year, site and combined of year and site (b): P ik = µ + Y i + g k + e ik (a) P ijk = µ + Y i + S j + (Y i × S j ) + g k + e ijk  (b)  where P i(j)k refers to the observed phenotype of the k th accession in the i th year in the j th site; µ is the mean value of the trait; Y i is the xed effect of the i th year, S j is the xed effect of the j th site, g k is the random effect of the k genotype; and e i(j)k is the residuals of the model.
The BLUPs were performed using "R" package "lme4"  [47]. Based on the previous linear models, broad-sense heritability of each trait was estimated as follows: H² = σ 2 G / [(σ 2 G + (σ 2 ε / n obs/g )] QTL is present in the LG) and H0 hypothesis (no QTL in the LG). The threshold for MIM was 0. 05 and computation of Type I error rate for each QTL (α chr ) was performed as follows: α chr = 1 -{1 -[1 -(1 -0. 05) 1/M } m where M is the total number of markers used for the QTL detection on each parental map, and m is the number of markers in the LG carrying the QTL  [54].
Population Structure and Kinship Analyses For the GWAS panel, the R packages "SNPRelate" and "gdsfmt"  [55]  were used to perform PCA and relatedness estimations based on a LD pruned set of 29,825 SNPs to avoid capturing too much variance of high LD regions. For the PCA, the ten largest eigenvalues were obtained to check for structure. The KING method of moment was used to estimate identity-by-descent (IBD) proportions between all pairwise comparisons  [56]. Population structure was also investigated using the "fastSTRUCTURE" software  [57] , and the most likely K was determined using the ΔK method  [58]. Then, "CLUMPP"  [59]  and "distruct"  [60]  softwares were used for clustering accessions and graphical presentation respectively.
Search of Annotations Within LD Blocks of Associated Loci LD levels around the most associated loci were estimated using HaploView v4. 2 software  [65]. Each physical position of these trait-SNPs associations was investigated to explore the extension of the surrounding LD blocks and determine the genomic regions where to search for candidate genes. The LD blocks were investigated using the method of con dence interval  [66]   List Of Abbreviations.
Acknowledgments First, we would like to thank the late  Eric Germain , former head of the breeding program at the  INRAE of Bordeaux  from  1977  to 2007. His remarkable work, then continued by  Francis Delort , has given us the opportunity to study a rich set of plant material. The latter helped us to phenotype the bearing habit of the walnut trees. We thank the  Fruit Tree Experimental Unit of the INRAE in Toulenne and the Prunus/Juglans Genetic Resources Center  for the maintenance of the collection and for helping us to collect the samples. We acknowledge the  BioGEVES laboratory  for DNA extraction and KASP marker development. Then, the CTIFL, holder of the project " INNOV 'noyer", in partnership with the  INRAE of Bordeaux , want to thank the " Région Nouvelle-Aquitaine", and "Cifre" convention of "ANRT" (Agence Nationale de la Recherche et de la Technologie).
Availability of data and materials The phenotypic raw datasets generated and analyzed during the current study for the GWAS panel and for the F 1 progeny are available respectively in Table  S6 , and Table  S7. The SNP genotyping raw datasets in "hapmap" format are freely and openly accessed on the "Portail Data INRA" repository, via the identi er "INRA's Walnut Hapmap" and the following Digital Object Identi er (DOI): https://doi. org/10. 15454/XPKII8. The dataset called "GWAS_hapmap. txt" is related to the GWAS panel, and the one called "Progeny_hapmap. txt" is related to the F 1 progeny. The additional le called "List of ID. tab" allows to link the array identi er name of the accessions with the identi er name used in this study.
BFP is associated with improvements in children's educational outcomes  [8] , parental participations in the labor force  [9] , childhood mortality  [10] , and leprosy incidence  [11]  among other social and health related outcomes. Also, Family Health Strategy and BFP had a positive impact on the TB mortality rate in Brazil  [12]. A recent cohort study of people with TB showed that BFP has a direct effect on improving TB treatment outcomes  [13] , in agreement with a previous found from a longitudinal database study  [14]. In order to understand the role of these governmental benefits on people with TB, since 2014, the Brazilian National Tuberculosis Program requires indication of the BFP beneficiary status on the TB notification forms. This study was aimed at assessing the effect of being beneficiary of a Brazilian governmental cash transfer program (BFP) on TB treatment cure rates.
Design and population We investigated the association between TB determinants and the outcomes of TB treatment in people from Brazilian Notifiable Disease Information System (SINAN) through a longitudinal database study. We included people �18 years old with confirmed incident TB who started and finished their treatment in 2015. People with a previous TB treatment were excluded, as well as those from the state of São Paulo because, in 2015, this state did not incorporate the new set of SINAN variables including beneficiary status into their surveillance systems. SINAN is a public database for all notifiable diseases in the Brazilian Unified Healthcare System (SUS-Sistema U ´nico de Saude). The World Health Organization (WHO) uses SINAN data in estimating Brazilian TB incidence. These data also include socio-demographic and clinical characteristics, features of treatment and outcome for individuals diagnosed with TB  [15, 16].
Variables Since 2014, in the beginning of treatment, people diagnosed with TB were asked about their governmental social programs benefits. A cash transfer beneficiary is considered any household member in a beneficiary family. These beneficiaries were divided into two groups based on classification within the SINAN database: a cash transfer group (people who answered as being beneficiaries of cash transfer program), and a non-cash transfer group (people who answered as not being beneficiaries of cash transfer program). Concerning the outcomes of TB treatment, studied participants were prospectively followed through the treatment, from diagnosis to outcome, with TB outcome categorized as cure, dropout, death (from TB or from another cause), change of drug regimen, development of drug-resistant TB, and treatment failure.
Data analysis Missing data. Missing data are a serious concern in epidemiologic studies, particularly those using secondary data. SINAN has reasonable completeness as previously reported, but missing data is a problem for some covariates  [17, 19]. S1 Table shows that most variables had more than 80% completeness, however among 25,084 subjects in our sample, only 5,993 (24%) had completed all information in the dataset. Incomplete datasets can be analyzed using multiple imputation through random draws from distributions inferred from observed data. Multivariate imputation by chained equations (MICE) is one of these recommended methods for multiple imputation in electronic health-record data  [20, 21]. We treated missing data by using an algorithm based on random forest MICE called "missForest".
This algorithm predicts individual missing values more accurately rather than take random draws from a distribution and it can be used to impute continuous and/or categorical data including complex interactions and nonlinear relations  [22]. Random forest is an extension of classification and regression trees that uses bootstrap aggregation of multiple regression trees to reduce the risk of overfitting. It combines the predictions from many trees to produce more accurate predictions  [23]. We choose MICE random forest with 1,000 trees. Evidence has suggested that random forest MICE produces less biased parameter estimates and confidence interval with better coverage  [23]. The imputation analysis was performed using the program R Project, version 3. 3. 3 (R Group, Vienna Austria). Assessing the determinants of TB treatment outcome.