dataesr/scientific-paragraphs-categorization huggingface dataset:
Imputation of HLA variants and MHC haplotypes HLA imputation was performed using the SNP2HLA software  [39]. This bioinformatic tool allows researchers to impute from the chromosome 6 genotypic data of each patient, namely: (i) the HLA class I and class IIclassical alleles at a 4-digit level; (ii) the amino acid variants from the imputed 4-digit HLA alleles; and (iii) the SNPs of the entire MHC region. Specifically, we extracted 41,111 chromosome 6 variants from the chip genotype data and imputed a total of 8,961 HLA variants (424 classical HLA alleles and 1276 amino acid variations). We performed a second quality control on imputed markers, namely: the variants with minor allele frequency < 0. 03 and/or p value for the Hardy-Weinberg departure test < 1 × 10 -6 were removed (N = 1546). To ensure the best imputation quality, we applied a filter on the imputation score R2, and kept all variants with R2 > 0.
8 with consequent removal of 12 additional variants. The resulting 7,403 variants, including 106 classical HLA alleles, 930 amino acid variations, and the 6367 SNPs were subjected to the statistical analysis pipeline. As well as the single SNP effect, we also analyzed the potential impact of haplotypes involving the most significant SNPs. In addition, we looked for the possible effects of composite haplotypes involving the SNPs along the neighboring class II-classical HLA alleles genes, namely HLA-DQA1, HLA-DQB1, and HLA-DRB1. We also computed those having significant SNPs and suggestive HLA signals but failing to reach statistical significance. To this end, we used the package R HaploStats  [50]  to compute haplotypes. We obtained 508 haplotypes and as for the imputed variants, haplotypes with minor allele frequency < 0.
Statistical analysis ASD vulnerability. We looked for genetic associations as to the risk to develop ASD among the 7403 imputed HLA variants and the 188 computed haplotypes. We compared ASD and TD for all the variants using a logistic regression implemented in PLINK software  [51]  under the additive, dominant, and recessive models. To correct for possible population stratification, we used the top four principal components (PC) as covariates in the statistical analyses. We also added gender and recruitment centers as covariates in our model. We used the Bonferroni method to correct for multiple testing. The statistical threshold of significance was set at 6. 75 × 10 -6 , which was derived by dividing 0. 05 by 7403 markers tested. This conservative threshold holds particular significance in our investigation due to the prominent presence of linkage disequilibrium (LD) in the MHC region.
Through LD calculations, encompassing all variants in the region, we found a limited number of independent SNPs, i. e. , 1349 (with a pruning based on a r 2 value below 0. 5). When considering the analysis for three different models (3 × 1349 = 4047 tests), the threshold became 1. 24 × 10 -5. However, for the sake of simplicity, we only retained the lowest threshold of 6. 75 × 10 -6 for all tests. ASD severity. To explore if variants or haplotypes implicated in ASD risk can influence ASD severity, we used the psychometric tools assessed in the LEAP cohort (see above) to perform a multivariate linear regression using PLINK software  [51]  for each studied clinical scale under additive and dominant models only for the cases. To avoid spurious associations, we included age, IQ, sex, recruitment centers, and the first 4 PC as covariates for each model with the exception of IQ stratification.
Functional analysis of the associated SNPs Functionally, it is notable that the rs8084 variation creates a splice acceptor site leading to a 25 amino acid deletion of the HLA-DR alpha chain, which affects its molecular conformation and cellular localization  [52]. Further screening of the RegulomeDB database  [53] , which reports information concerning the potential transcriptomic properties of genomic SNPs, indicated that 3 SNPs (rs9268528, rs14004, and rs8084) had a good RegulomeDB rank of 1 f (Supplementary Table  S3 ). This makes their location within regulatory elements plausible and therefore their involvement in gene transcription, given that the probability to have a RegulomeDB score better than 1 f is less than 5% (data not shown). Thus, from the haplotype blocks only rs9268528 and rs14004 (block 1) and rs8084 (block 3) reached this 1 f score (Supplementary Table  2 ).
This is supported by data from the GTex database showing BTNL2 to be overexpressed in the small intestine in correlation with the rs9268528-G SNP allele. This is likely to be the underpinnings of the protection against ASD afforded by this SNP and by the protective block 1 haplotype that carries it. The gut-brain axis is intimately associated with many neuropsychiatric and neurodevelopmental disorders, including ASD  [67, 68]. This has relevance to the digestive symptoms commonly observed in a substantial subset of autistic people, which will often be concurrent to alterations in the gut microbiome in ASD  [7] , and supported by a recent metaanalysis  [69]. However, in a study of pre-pubertal Chinese Fig. 3  Logistic regression analysis of main genetic variants, a summary of Odds Ratios, and 95% Confidence Intervals.
ACKNOWLEDGEMENTS The work was supported by grants from the  EU-AIMS  ( European Autism Interventions)  and  AIMS-2-TRIALS programs  which receive support from  Innovative Medicines Initiative Joint  Undertaking Grant No. 115300  and  777394 , the resources of which are composed of financial contributions from the  European Union 's FP7 and  Horizon2020 programs , and from the  European Federation of Pharmaceutical Industries and Associations (EFPIA)  companies' in-kind contributions, and  AUTISM SPEAKS ,  Autistica  and  SFARI ; and by the  Horizon2020  supported program  CANDY  Grant No. 847818 ). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Any views expressed are those of the author(s) and not necessarily those of the funders (IHI-JU2).
Genome dataset and species phylogeny Seventeen chondrichthyan genome assemblies, corresponding to thirteen species were downloaded from NCBI: Amblyraja radiata (GCF_010909765. 2), Callorhinchus milii The completeness of these genomes was assessed with BUSCO v5. 1. 2 using the vertebrata_odb10 database  (Manni et al. , 2021). For species with multiple genome assemblies, we retained only the one for which BUSCO retrieved the highest number of complete genes (supplementary table S1, Supplementary Material online). Leucoraja erinacea was removed from the phylogenetic analysis described below, as only 7% of BUSCO genes could be retrieved complete from its genome assembly (supplementary fig. S6 , Supplementary Material online). We then extracted protein sequences of 1068 BUSCO genes that were retrieved in common in single copy in the best assemblies for each species, and align these sequences individually using MAFFT (auto, v7.
We verified that predicted genes were true olfactory receptors with a BLASTX against a custom database of olfactory, taste and other G-proteincoupled receptors and with phylogenetic trees, retaining only sequences that clustered with known olfactory receptors. Retrieved sequences were then classified into four categories: 1) "complete" if a complete coding sequence was retrieved; 2) "pseudogene" if the coding sequence contained at least one loss-of-function mutation (a premature stop codon or a frameshift); 3) "incomplete" if the gene was found incomplete and with no loss-of-function mutation; 4) "edge" if the gene was found incomplete and near a contig or scaffold border, which are most likely assembly artifacts. For nucleotide sequences for all validated olfactory receptor genes see (supplementary file S2, Supplementary Material online).
Patterns of gene birth and death A gene tree -species tree reconciliation method was used to infer the number of gene duplications and gene losses in every branch of the species tree. We first collapsed nodes with low bootstrap values (<90%) in gene phylogenies for the four olfactory receptor families using the R package ape v5. 0  (Paradis and Schliep 2019). Treerecs was then used to find the best root and reconcile gene trees with the species tree, with default parameters  (Comte et al 2020).
Acknowledgements We are grateful to  Sylvie Mazan  ( Genoscope-Centre National de Séquençage, Evry, France ) and  Ronan Lagadec  ( Observatoire Océanologique de Banyuls-sur-mer UMR7232 BIOM -CNRS/UPMC, France ) for kindly providing the catshark olfactory tissues used in this work. We are thankful to  Daniel Kowatschew  for expert technical advice. This work was supported by the  German Science foundation  (grant  KO1046/12-1  to S. I. K. and grant  SH1768/1-1  to K. S. ).
Background: Yam (Dioscorea alata L. ) is the staple food of many populations in the intertropical zone, where it is grown. The lack of phenotyping methods for tuber quality has hindered the adoption of new genotypes from breeding programs. Recently, near-infrared spectroscopy (NIRS) has been used as a reliable tool to characterize the chemical composition of the yam tuber. However, it failed to predict the amylose content, although this trait is strongly involved in the quality of the product. Results: This study used NIRS to predict the amylose content from 186 yam flour samples. Two calibration methods were developed and validated on an independent dataset: partial least squares (PLS) and convolutional neural networks (CNN). To evaluate final model performances, the coefficient of determination (R 2 ), the root mean square error (RMSE), and the ratio of performance to deviation (RPD) were calculated using predictions on an independent validation dataset.
Near infrared spectroscopy measurement The NIR spectroscopy measures were carried out in the food processing laboratory of INRAE's Tropical Animal Research Unit, UR143, in Guadeloupe. Two replicates of yam flour samples were scanned with a FOSS-NIRSystems model 6500 scanning monochromator (FOSS-NIRSystems, Silver Spring, MD, USA) equipped with an auto cup. The spectroscopic procedures and data recording were conducted with ISIscan (TM) software (FOSS, Hillerød, Denmark). Each flour was placed in a small ring cup 36 mm in diameter and 9. 7 mm in height, and reflectance spectra from 400 to 2500 nm were recorded at 2 nm intervals. Each spectrum represented the average of 32 scans.
8. 5, https://www. python. org) with the scikitlearn 0. 23. 2 library. 34 he CNN model was implemented using Python language (v3. 6, https://www. python. org) with a Keras framework (v2. 1. 5, https://keras. io/) and a TensorFlow backend (v1. 6. 0, https:// www. tensorflow. org). For this model, the raw spectra, the 12 datasets from individual preprocessing, and those from their second-order combination were all merged and kept together for calibration as input data (i. e. , 157 datasets). The convolutional neural network was composed of three convolutional layers followed by two dense layers. Mean squared error was used as the loss function. A 20% feature dropout was used between layers to prevent overfitting. Threefold cross-validation was used to calibrate the model. 35 he coefficient of determination (R 2 ), root mean square error (RMSE), and the ratio of performance to deviation (RPD) were computed to assess the performances of both models using the following equations: R 2 = 1- ∑ n i =1 y i -b y i ð Þ 2 ∑ n i =1 y i -y ð Þ 2 ð2Þ where y i and b y i are respectively the observed and the predicted amylose content of the i-th sample, and y is the average observed amylose content RMSE = ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi ∑ n i = 1 y-b y i ð Þ 2 n s ð3Þ RPD= SD RMSE ð4Þ where SD is the standard deviation of the observed amylose content in the calibration set.
RESULTS Table  1  presents the variation in the amylose content in the 21 D. alata L. genotypes. The amylose content of our samples varied from 12. 62 to 39. 75% for Kabusa and Tagabe respectively. The SEL of the amylose content reference method was 1. 05%. The mean standard deviation within genotypes was 1. 52% while the standard deviation of the mean amylose content between genotypes was 2. 6%. The descriptive statistics of the calibration and validation data are presented in Table  2. The calibration and validation datasets exhibited similar ranges, means, and standard deviations (Table  2 ). Figure  1  illustrates the performance of each preprocessing combination after the optimal number of components had been identified. The best combination of preprocessing includes a first-order Gaussian (Gaussian 1) followed by a fourth-order Sav-Gol (SavGol 4) and retains 24 components.
ACKNOWLEDGEMENTS The authors are grateful to  Elie Nudol  (CIRAD),  Christophe Perrot  (CIRAD),  David Lange  ( INRAE ) and  Jocelyne Leinster  ( INRAE ) for field work and sample preparation. The authors would also like to thank the  Bill & Melinda Gates Foundation (BMGF)  for grant opportunity  OPP1178942 :  Breeding RTB Products for End User Preferences (RTBfoods).
2. 1-Literature search Relevant citations for this review were identified by searching the databases PubMed/Medline and Google scholar between January 2000 and July 2022. A combination of search terms "socioeconomic", "socio-economic", ''social position'', ''social class'', ''socioeconomic position'', "determinant*", "health disparities", "sleep", ''sleep disorders'', ''sleep disturbances'', ''sleep complains'', "sleep outcome", "sleep health" and "India*" was used. All included articles were identified on the basis of relevance to the association between SES determinants and sleep outcomes following the PRISMA guidelines (Fig. 1 ).
2. 2-Inclusion and exclusion criteria Empirical studies were defined as peer-reviewed scientific articles of any design (cross-sectional, retrospective or longitudinal) that assess the relation SES and sleep, including a human sample of any sex, race/ethnicity, gender or age from the general population of India. The study had to include an objective such as education, income, assets, occupation, employment status, perceived SES or a qualitative measure of SES including self-reported items by participants. Aggregate measures of SES (neighbourhood SES or area deprivation indices) were included if participant's data were not available or reported by authors. For studies with children or adolescents' participants, perceived family SES measures such as parental education, parental profession or household income were used.
IMAGE addressed a wide range of issues, including gene bank management, reproductive physiology and cryopreservation, genomic characterization of stored material, data management, and utilization of stored material in management of in vivo populations. FAO was among the partners in the IMAGE project. One of the deliverables 5 of IMAGE was a review of the previous guidelines and recommendations for content in a revised document. This publication addresses these recommendations, while also considering the results from other IMAGE deliverables. To ensure a comprehensive and global perspective, further expertise was gathered through the contributions of scientists from all FAO regions. The document was then reviewed by members of the Commission on Genetic Resources for Food and Agriculture and its Intergovernmental Technical Working Group on Animal Genetic Resources for Food and Agriculture.
Section 7 addresses the health and sanitary issues that must be considered when establishing and operating gene banks for animal genetic resources, including regulatory issues. Section 8 describes documentation and database requirements for storing information on individual animals and on the samples of genetic material stored in the gene bank. It also addresses the integration of gene bank data with other relevant publicly available data systems, while making key information available to stakeholders. Section 9 addresses the legal issues associated with cryoconservation. The section considers the legal aspects of cryoconserved germplasm as a private and public good and outlines the issues to be accounted for in the agreements that govern transfer of material to and from the gene bank, including transfer across national borders.
Data policy As mentioned previously, information about samples in the gene bank collection should be regarded as an integral part of the gene bank. Therefore, a data policy must be developed as part of the overall gene banking strategy. This topic will be elaborated in more detail in Section 8. The following are potential issues that may be addressed in a data policy: • types of data to be collected and managed; • system for organization of data, and standards for documentation and metadata; • protection of privacy, security, confidentiality, intellectual property or other rights; • access to and sharing of data: how and when to share data and with whom; and • data storage: where to be maintained, and how to be secured.
By using molecular tools such as single nucleotide polymorphism (SNP), gene banks have developed collections that have captured up to 98 percent of the variation within a breed  (Wilson et al. , 2019) , and have identified subpopulations within breeds  (Hulsegge et al. , 2019a). Gene banks can use a number of approaches to capture genetic diversity based upon information on hand. Box 1. 1 shows how simple random sampling of donor animals can be effective in capturing a wide array of diversity. When a breed is dispersed among production environments with no or little migration, it may be desirable to sample within and among the environments. Geographic coordinates of collection locations should be recorded and stored in the gene bank database, as they provide links to extensive information in environmental databases.
Such an approach is useful in ensuring that the gene bank collection is genetically balanced, but requires sufficient organization and budget, and data availability can be a limitation. Genomic information can be used in the methods previously mentioned, either instead of or in conjunction with pedigrees. In addition, genomic data can be used in other ways to select animals within a breed, including absence of known genetic abnormalities, ex post assessment of the collection versus in situ population diversity, and targeting animals for collection that have genotypes of interest for traits such as adaptability to climate change. Utilization of estimated breeding values or phenotypes for traits of interest can also play a role in identifying animals for the collection. For instance, the gene banks in France and the United States of America have been collecting samples from animals that represent extremes.
Associated data The database is essential for managing routine gene bank operations and to support management decisions. The database serves as the primary tool for receiving, storing and exchanging information about samples in the collection. Therefore, proper and accessible documentation is vital for the operational management of the gene bank, for optimization of gene bank collections, and for future use of any stored gene bank material. Gene banks need to develop and implement a database for this purpose. Basic information about gene bank collections should be easily accessible without the need for any additional information from outside the database (see  Section 8).
Tracking sample use and impact Tracking the utilization is important for gene banks. Data on utilization are evidence of the value of banks and can be used in funding activities. They are also useful in planning for the future. Figure  1. 2 shows the yearly utilization of samples from the US gene bank, according to birth year of the donor. The gene bank has released samples from more than 11 000 animals since 2004. The data also shows that samples of animals of nearly all birth years are being continually used by the various stakeholders. If samples are used for genotyping, the gene bank should require the user to provide the data and other results and inform the bank about how the genotyping helped solve a problem (see  Box 1. 3). Furthermore, such examples are extremely useful in articulating the value of gene banking to the stakeholders, administrators, and the public at large.
Priority setting Strategy informed by characterization data. Genomic and phenotypic characterization and storage of results in the database greatly facilitate future development and use of collections. Characterization data can also be used to compare collections with those already existing in other gene banks, and determine uniqueness of material to be stored to avoid duplication (e. g. when collecting material from transboundary breeds). Vibrant gene banks will offer stakeholders a range of services by expanding collections to include blood, DNA, or tissue samples for further research on livestock genetic diversity. The largest use of the Dutch (over 1 000 animals of local breeds) and US collections (over 6 000 animals) has been for DNA analyses. Economic optimization. For rationalization, costs of collection decisions should be optimized against their benefits.
Continuous monitoring Continuous monitoring of the status, development and use of the collection is critical. The genetic profile of material already collected in the gene bank should be assessed in the context of genetic diversity of in situ populations. Allelic frequencies within commercially vibrant breeds are in a continuous process of evolution and, therefore, a gene bank needs to keep abreast with these genetic changes to keep the collection viable. Regular gap analysis based on genomic and other relevant data can be used for adapting the strategy and its implementation. The gene bank may wish to budget for regular genomic analyses of its key collections, or should develop partnerships with relevant stakeholders (i. e. breeders and breed associations, research institutes and breeding companies) to obtain such data.
Genetic material database Section 8 details the issues related to data management, but the following issues are critical for quality management: • Databases are indispensable infrastructure for efficient operation of the gene bank. • The gene bank's database shall make publicly available a set of data defined in agreement with material providers and complying with regulations. This means they need to be accessible via the internet, according to FAIR principles (where applicable, see  Section 8). The database will generally also have a private section to keep information needed for internal use, such as sample storage in a specific tank location, or privacy-sensitive information. • Spreadsheets are not sufficient to efficiently and reliably run gene banks, i. e. they are not a substitute for a formal database.
• Security of data is a critical issue that merits maximum attention. One or more levels of redundancy should be maintained on machines in different physical locations or units of cloud storage. If a country has multiple gene banks, all locations must be connected to a common portal or utilize the same database, to facilitate data sharing. • The QMS documentation should describe all of the pertinent features of the database, including software utilized, data fields stored, security procedures and access rights. Information about the samples is as important as the samples themselves (see also  Section 8). There are two data streams per sample that gene banks should be acquiring (see  Figure 2. 2 below). From outside the gene bank, information such as species, breed, breeder, animal ID (which should follow a standard as much as possible, since there may be more than one ID), date of birth, pedigree, date of sampling, sampling protocol, phenotypic measurements, genotypic information, environmental descriptors or potentially the geographic information system (GIS) coordinates for where the animal was born, and management system (extensive, mixed crop-livestock, or intensive) the animal was produced.