dataesr/scientific-paragraphs-categorization huggingface dataset:
org/10. 1017/S2045796020000633. Data. Due to data-sharing restrictions contained in some individual country agreements with the World Mental Health Surveys Initiative, sharing of the cross-national dataset is not possible. U01-MH60220) with supplemental support from the National Institute of Drug Abuse (NIDA), the Substance Abuse and Mental Health Services Administration (SAMHSA), the Robert Wood Johnson Foundation (RWJF; Grant 044708) and the John W. Alden Trust. None of the funders had any role in the design, analysis, interpretation of results, or preparation of this paper. Conflict of interest. In the past 3 years, Dr Kessler received support for his epidemiological studies from Sanofi Aventis; he was a consultant for Datastat Inc. , Johnson & Johnson Wellness and Prevention, Sage Pharmaceuticals, Shire, Takeda; and served on an advisory board for the Johnson & Johnson Services Inc.
Acknowledgements. We acknowledge the collaborators of the  WHO World Mental Health Survey  (see website for full details https://www. hcp. med. harvard. edu/wmh/). Financial support. The  World Health Organization World Mental Health (WMH) Survey Initiative  is supported by the  US National Institute of Mental Health (NIMH ;  R01 MH070884 ), the  John D. and Catherine T. MacArthur Foundation , the  Pfizer Foundation , the  US Public Health Service  ( R13-MH066849 ,  R01-MH069864 , and  R01 DA016558 ), the  Fogarty International Centre  ( FIRCA R03-TW006481 ), the  Pan American Health Organization, Eli Lilly and Company, Ortho-McNeil Pharmaceutical Inc. ,  GlaxoSmithKline and Bristol-Myers Squibb. We thank the staff of the WMH Data Collection and Data Analysis Coordination Centres for assistance with instrumentation, fieldwork, and consultation on data analysis.
The  Northern Ireland Study of Mental Health  was funded by the  Health & Social Care Research & Development Division of the Public Health Agency. The  Peruvian World Mental Health Study  was funded by the  National Institute of Health of the Ministry of Health of Peru. The  Polish project Epidemiology of Mental Health and Access to Care -EZOP Project  ( PL 0256 ) was supported by  Iceland, Liechtenstein and Norway  through funding from the  EEA Financial Mechanism  and the  Norwegian Financial Mechanism. EZOP project was co-financed by the  Polish Ministry of Health. The  Portuguese Mental Health Study  was carried out by the  Department of Mental Health, Faculty of Medical Sciences, NOVA University of Lisbon , with the collaboration of the  Portuguese Catholic University , and was funded by  Champalimaud Foundation ,  Gulbenkian Foundation, Foundation for Science and Technology (FCT)  and  Ministry of Health.
The  Romania WMH study projects 'Policies in Mental Health Area' and 'National Study regarding Mental Health and Services Use ' were carried out by  National School of Public Health & Health Services Management  (former  National Institute for Research & Development in Health ), with technical support of  Metro Media Transilvania , the  National Institute of Statistics-National Centre for Training in Statistics, SC, Cheyenne Services SRL ,  Statistics Netherlands  and were funded by  Ministry of Public Health (former Ministry of Health)  with supplemental support of  Eli Lilly Romania SRL. The  South Africa Stress and Health Study (SASH)  is supported by the  US National Institute of Mental Health  ( R01-MH059575 ) and the  National Institute of Drug Abuse  with supplemental funding from the  South African Department of Health  and the  University of Michigan.
Dr Stein  is supported by the  Medical Research Council of South Africa (MRC). The  Ukraine Comorbid Mental Disorders during Periods of Social Disruption (CMDPSD)  study is funded by the  US National Institute of Mental Health  ( RO1-MH61905 ). The  US National Comorbidity Survey Replication (NCS-R)  is supported by the  National Institute of Mental Health (NIMH ;.
Briefly, strains were grown on triplicate from size-standardized mycelia plugs, and pictures were taken every day using standardized camera settings and lighting environments. Images were analyzed using ImageJ software  (Schneider et al. , 2012). Pictures were stacked and length were scaled manually using 5cm tracks disposed during picture acquisition. All pictures were treated simultaneously using the "montage" option and fungal surfaces were automatically selected using saturation and brightness thresholds. The delimitated surfaces, representing fungal radial growing, were integrated using ROI manager and areas were measured in cm². Radial growing was measured in cm² per day. In vitro biomass: fungal biomasses resulted from MS medium cultures and in vitro production of mycotoxin assay presenting above.
Statistical analysis prior and during QTL detection Statistical difference between the mean values of the parental phenotypes was tested using ANOVA and the post-hoc multiple comparisons of means using the Tukey's Honestly Significant Difference method with the Multcomp package in R. Correlations between experiments and between traits have been tested using Spearman rank order test and considered as significant for p-value < 0. 01. Shapiro Wilk test was used to test the normality of the distribution of the trait in the progeny and considered as normal for p-value > 0. 01. PCA analysis were conducted using FactoMineR and Factoshiny packages in R version 3. 6. 1 (R Core Team; 2020). Mendelian inheritance was tested using Chi-square test and was accepted for p-value < 0. 01. Statistical difference between the mean values of the progeny phenotypes and the parental strain ones was tested using ANOVA and the post-hoc multiple comparisons of means using the Dunnett's method and the Multcomp package in R.
In silico analysis of candidate genes Genomic sequences and genetic variants of parental strains were retrieved from  Laurent et al. , 2017. In order to reduce the number of candidate genes, an analytical process was designed and summarized in Figure  4. This process proposes to identify polymorphic genes (Stage 1), affected by non-synonymous mutations (Stage 2). Variant annotation was conducted using SnpEff software  (Cingolani et al. , 2012) , as previously described  (Laurent et al. , 2017). To predict if an amino-acid substitution or InDels have an impact on the biological function of the protein (Stage 3), Provean algorithm was used  (Choi et al. , 2012)  and polymorphic proteins with a score equal or less than -2 were kept as candidate proteins with highly probable altered activity. In Stage 4, the remaining candidates were filtered out on the basis of the presence of additional deleterious mutation found for both parental strains and compared to the genotypic information from strains, previously characterized  (Laurent et al.
, 2017). Hence, genes having common candidate mutation(s) with other strains should have comparable phenotypes, e. g. if the candidate gene has one mutation in the INRA-171 parent (inducing symptoms of little severity on plant and producing low level of TCTB) and was identify in a strain with a highly aggressive and toxinogenic, this candidate mutation was filtered out (Supplemental file 2B). Gene information and orthologues from other species were retrieved from the FungiDB database  (Stajich et al. , 2012).
Acknowledgement: BL received a PhD fellowship from the  French Research Ministry. Authors would like to gratefully thank  Cyrille Saintenac  for helpful conversations and for assistance during in planta experimentations;  Ross Houston  and  Christos Palaiokostas  for remarkable advices during work conduction and along manuscript writing;  Pierre Chesnot  for providing a valuable help during English writing,  Cécile Robin  for helping reviewing the manuscript. The authors have no conflict of interests to declare.
The rhesus macaque is an abundant species of Old World monkeys and a valuable model organism for biomedical research due to its close phylogenetic relationship to humans. Copy number variation is one of the main sources of genomic diversity within and between species and a widely recognized cause of inter-individual differences in disease risk. However, copy number differences among rhesus macaques and between the human and macaque genomes, as well as the relevance of this diversity to research involving this nonhuman primate, remain understudied. Here we present a high-resolution map of sequence copy number for the rhesus macaque genome constructed from a dataset of 198 individuals. Our results show that about one-eighth of the rhesus macaque reference genome is composed of recently duplicated regions, either copy number variable regions or fixed duplications.
Starting with WGS data and a masked genome reference, we followed a novel approach in order to call copy number intervals for each non-repetitive 1 kbp window and sample based on depth of coverage. We performed a copy number allele calling per window to classify all non-diploid windows in fixed duplicated regions (FD regions), copy number variant regions (CNV regions), or unclassified non-diploid regions. (B) To assess the functional implications of non-repetitive windows, we crossed the protein-coding genes in the corresponding genome with the three different types of non-diploid regions. From all the genes that were related to FD or CNV regions, we distinguished between: partial FD genes or partial CNV genes, whose length is partially covered by an FD or a CNV region; composite FD genes or composite CNV genes which are within several FD or CNV regions (with 90% or more of their coding length covered by several FD or CNV regions); and complete FD genes or complete CNV genes which are within a single FD or CNV region (i.
Although we have observed some discrepancies between our human and rhesus macaque maps of structural variation, our results show that the maps are highly comparable at the gene level. For the sake of completeness, we also considered the annotation of segmental duplications in the human genome and the human CNV dataset from Sudmant et al. [63]. Annotated segmental duplications include duplications more divergent than the ones targeted by our approach. Consequently, most of the genes within human segmental duplications with one-toone orthologs in rhesus macaque appear to be diploid in the latter (68 genes). We retrieved four complete FD genes in macaque that have one-to-one orthologs within human segmental duplications (Fig 4D ). The Sudmant et al. [63]  CNV dataset used a different methodology, sample size and selection criteria of individuals than our human panel.
Sudmant et al. [63]  included a larger number of human genomes and was intended to detect intra-population copy number variability. In contrast, our panel of human samples was intended to be compared with the rhesus macaque genome and was designed to detect inter-population copy number variability. In this way, Sudmant et al. provide a more comprehensive dataset that helps to discriminate completely fixed regions from those with low-frequency CNV alleles in humans. Therefore, it is not surprising that most of the genes within the Sudmant et al. [63]  dataset with one-to-one orthologs in rhesus macaque are diploid in rhesus macaques (Fig  4D ). We also identified nine complete FD genes (APOL2, ZNF418, PPP2R5C, ZNF33B, MT1E, TXN, TMEM211, MT1G, and ZNF92) and four complete CNV genes (CCDC115, IMP4, KRTAP1-5, and KRTAP1-1) in the rhesus macaque genome that have one-to-one orthologs within the Sudmant et al.
Disease associations for genes with different copy number between humans and rhesus macaques Copy number variation is one of the main sources of phenotypic variability between individuals in a population. Consequently, differences in the copy number profile between rhesus macaque and human orthologous genes have the potential of leading to interspecies differences in specific phenotypes. In particular, we studied the relevance for human disease of the few one-to-one gene pairs that have a different copy number in the two species. We explored complex disease-related genes by intersecting our genes of interest with the GWAS catalog database  [64]  and the DisGeNET database  [65]. Table  1  shows the list of the 18 identified associations of human disease and genes having relevant differences in their copy number profile between human and macaque.
Focusing on one-to-one gene orthologs between the two species allows for a more defensible comparison yet more limited in number. About one-third of the human orthologs of our macaque complete CNV and FD genes could be detected with an equivalent classification in at least one of the analyzed human datasets. Due to the extensive characterization of the human genome, the rest of the complete nondiploid proteins in rhesus macaque (10 complete CNV genes and 13 complete FD genes) are highly likely to exhibit a true species difference. In fact, we found that, after controlling for trivial conservation of diploid states, changes from diploid to non-diploid states between human and macaque orthologs are more common than expected by chance. This excess of changes in copy number suggests a relevant role of copy number variants in the divergence of genic repertoires between both species.
Interestingly, we found that at least 18 of the genes with different copy number levels between human and macaque are associated with various diseases in human  [64, 65] , although none of them showed human inter-population copy number variability. Among these diseases, we found several neuropsychological diseases, such as Alzheimer's disease, autism spectrum disease, ADHD and schizophrenia, as well as male infertility and chronic kidney disease. Moreover, we found that, for both human and macaque, non-diploid genes are enriched in human complex-disease gene datasets. Similarly, changes in copy number profile between human and macaque are also significantly enriched in complex-disease genes. Different changes in copy number profile represent different challenges for using macaque as a model organism to study a given disease.
The list of disease-associated genes with different copy number profiles presented here is necessarily incomplete and further research remains to be done to define the actual impact of each of these genes and others with differences in copy number. Consequently, we believe that our dataset of genes with different copy number profiles between human and rhesus macaque represents a valuable resource for those researchers using rhesus macaque as a model organism. The genomic differences between human and rhesus macaque that we report here may be relevant to the use of rhesus macaques as a model system, in particular, for those diseases that we have identified as being related to genes with structural variant differences. One possible strategy for determining the significance of these observed differences would be to compare levels of mRNA or protein expression for the genes that show copy number differences.
Sampling and data generation We use sequence data from an initial panel of 315 rhesus macaques. This set includes 113 previously published samples  [31]  and 202 newly sequenced samples. WGS was performed using the Illumina HiSeq 2000 platform, generating 100-bp paired-end reads. The final dataset contains 198 individual rhesus macaques from eight research colonies (S1 Table ). For comparison, with humans we used an initial panel of WGS data from 50 human samples specially selected from the Simons Genome Diversity Project  [51]  to cover a great part of human diversity.
Emission probabilities were extracted from the corresponding read-depth distributions. For low copy number states, these distributions were defined as normal distributions with mean equal to the corresponding copy number (μ N = N) and standard deviation s N ¼ s CR ffi ffi ffi ffi ffi ffi ffi ffi ffi N=2 p , where σ CR is the standard deviation in control regions. For copy numbers above 20, emission distributions were a mixture of the corresponding normal distributions weighted proportionally to the estimated frequencies of each copy number. We used the Baum-Welch algorithm  [79]  to train the transition matrix of this HMM until convergence with a random set of samples that were excluded from further analysis. Finally, in order to predict the probability of each of the hidden states for each window in each individual, we used the forward-reverse algorithm coded in the Pomegranate Python package.
Sample quality control Many factors can affect the coverage of a given sample apart from its copy number and GC content. Cell immortalization, library preparation, sequencing biases, and divergence to the reference, among other factors, can alter copy number inferences from the depth of coverage  [6, 81]. Since our method is very sensitive to the width of copy number intervals, we needed to control for external (i. e. , experimental) factors affecting variance in coverage, noise, sequencing artifacts, outliers, hidden population substructure, among other factors. To avoid confounding factors in our copy number estimations as much as possible, we performed a very strict sample quality control, which is described below. Our starting dataset includes 387 samples from three different batches: 113 high-coverage published samples (batch 1) with a mean raw coverage of ~35x; 74 low coverage published samples (batch 2) with a mean raw coverage of ~8x; and 202 high-coverage new samples (batch 3) with a mean raw coverage of ~32x.
We excluded samples according to the following criteria: 1. Quality control filters of the pipeline removed 66 samples. These filters detect systematic deviations of the expected behavior of genome-wide read coverage distribution affecting the assumptions of our CNV calling approach: a. Standard deviation of the coverage in diploid control regions: excluded samples higher than 0. 5 (S26A and S26B Fig). b. Kolmogorov-Smirnov statistic  [82]  for normality of the distribution of coverage: excluded samples lower than 0. 03 (S26A Fig). c. Pearson correlation coefficient  [83]  between neighboring windows: excluded samples greater than 0. 15 (S26B Fig). 2. 5 samples were used for HMM training and hence, removed from the final dataset. 3. We detected a batch effect leading to a systematic increase of non-diploid calls in batch 2 (S26C Fig).
This filter removed 28 samples. As a result of this quality control, our final dataset is composed of 198 samples, including 51 samples of batch 1 (mean raw coverage of ~35x) and 147 samples from batch 3 (mean raw coverage of ~32x). Application of the same filters to the initial set of 50 human samples from the Simons Genome Diversity Project  [51] , filtered out 18 samples (including HMM training samples), and ended up with a final set of 32 human samples.
Duplicated genes by age and segmental duplications We compared our estimated chromosomal content of all types of non-diploid windows with independent proxies: duplicated genes and the segmental duplications annotated in the rhesus macaque genome. We used data of precise phylostratification of rhesus macaque duplicated genes  [56]  to classify duplicated genes by three different age categories: first, macaque specific genes only present in the macaque species; second, other primate genes present in other primates but not only in macaques; and third, other mammalian duplicated genes found also duplicated in rhesus macaque that appeared before the primate diversification within the mammal class. For segmental duplications annotations, we used rheMac2 annotations from the UCSC table browser.
Definition of genes implicated in FD regions, CNV regions or unclassified regions We used Ensembl (release 92) annotations of rhesus macaque (rheMac8) protein-coding genes and a list of reliable protein-coding genes in the human genome from Abascal et al. [62]. For those genes with more than one protein-coding transcript annotated in Ensembl (release 92), we selected only the exons of the transcripts used in the Ensembl Compara multi-species database (the main isoform). Genes with more than one annotated protein-coding transcript and no annotation in Ensembl Compara multi-species were not considered. According to these criteria, we considered 20,946 rhesus macaque protein-coding genes and 17,000 human proteincoding genes. The difference in the number of the considered protein-coding genes reflects the differences in the quality of the annotations of these two species reference genomes.
Disease association exploration The GWAS Catalog  [64]  was parsed in order to retrieve genes of interest with variants associated with a complex disease or phenotype (downloaded 2018/09/26). We recovered the associations considering both Reported genes (gene names reported by the paper) and the Mapped genes (mapped gene given the position of the SNV). In order to obtain even more possible associations between genes and diseases, we used the DisGeNET tool  [65]  that compiles information from several public databases and uses the power of text mining to find links between genes and diseases in the literature.
Acknowledgments We wish to thank  Harshavardhan Doddapaneni ,  Donna Muzny ,  Richard Gibbs , and the staff of the  Human Genome Sequencing Center  for their efforts in generating the primary sequence data used in these analyses. We also sincerely thank the following people and institutions for the generous contributions of DNA and/or blood samples from rhesus macaques:  Zach Johnson  (formerly of  Yerkes National Primate Research Center (NPRC) ),  Roger Wiseman  ( Wisconsin NPRC ),  Betsy Ferguson  ( Oregon NPRC ),  H. Michael Kubisch  ( Tulane NPRC ),  Eric Valender  ( New England NPRC and Tulane NPRC ),  John Capitanio  and  Jeffrey Roberts  ( California NPRC ),  Stanton Gray and Beth Dray (Keeling Center for Comparative Medicine and Research, MD Anderson Cancer Center). We also wish to acknowledge  Lukas F.
Mathematical modelling We extended our previously described epidemiological model 5 , to model the population dynamics of bacteriophages that encode anti-CRISPR proteins (Acr) in an initially sensitive host population that can evolve CRISPR resistance. All simulations were performed in the software Mathematica version 11. 2. Bacteria may either be sensitive (the density of these bacteria is denoted   ), or may have evolved CRISPR-resistance after the incorporation of a spacer targeting the phage (the density of these bacteria is noted   ), or may be in an immunosuppressed state (the density of these bacteria is noted   ). Initially the host population is homogeneous, consisting exclusively of sensitive bacteria (except for the data presented in Fig. S2 , where all bacteria are initially already CRISPR resistant).
Bioinformatic analyses Sequenced reads were trimmed for the presence of Illumina adapter sequences using Cutadapt version 1. 2. 1  27. The option -O 3 was used, so the 3' end of any reads which match the adapter sequence for 3 bp or more are trimmed. Reads were further trimmed using Sickle version 1. 2 (https://github. com/najoshi/sickle) with a minimum window quality score of 20. Reads shorter than 20 bp after trimming were removed. Reads were merged with Flash version 1. 2. 11  28 and a further 5 bases were trimmed from the 5' end of each read, following additional quality checks. The resulting read length distributions were determined directly with an Awk expression. Merged reads were then processed using the Qiime2 platform (version 2018. 2). Additional quality filtering was done using the default settings based on sequence quality scores (minimum phred score = 4, maximum number of consecutive low scores = 3, minimum length of sequence after filtering = 75% of the original read).