dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, a variable selection procedure was applied using stepwise regression to find significant coefficients for each gene (step. method = "backward", alpha = 0. 01). The list of differentially expressed genes according to the slope was generated and allowed us to define patterns of changes in expression over time (Rsquared cutoff = 0. 8). These patterns were then clustered using a hierarchical classification analysis with the correlation distance and complete linkage method using cluster v3. 0 and displayed with the JavaTreeView v1. 1. 5r2 [20, 21]. Y ¼ b 0 þ b 1 time þ b 2 time 2 þ ε with: • Y: normalized expression value For a functional analysis of the defined clusters, each cluster was analyzed using the webbased tool Funspec (http://funspec. med. utoronto. ca/; adjusted p. value = 0. 05 with a Bonferroni correction method) and genes were classified into functional categories, biological processes and protein cellular localizations using the GO database  [22].
In order to obtain individual patterns for some stress (or stress-related) gene expressions (HSP30, HSP12, TPS1, HSP26, HSP104, SOD2, MSN2, MSN4), the corresponding data were extracted from the transcriptomic analysis. The complete microarray data set was deposited in the Gene Expression Omnibus (GEO) public repository (accession number GSE95152). Microarray description is under GEO accession number GPL17690.
Surprisingly, we did not find specific enrichments in stress genes in any dataset whatever the type of starvation. Specifically, stress-responsive genes were not detected, except for some isolated ones, in those clusters that differentiated the conditions allowing maintenance of viability from those leading to cell death. We therefore examined individually the expression patterns of a large set of stress genes. All these genes are involved in the overall stress response under the control of MSN2/ MSN4 indicating that a global stress response took place in all conditions whatever was the yeast growth-limiting nutrient limiting. Therefore the activity of the nitrogen-signaling pathway did not block the expression of stress genes. This apparent stress response in micronutrients limited conditions is not consistent with the lack of resistance of yeast cells.
Main effects of Ras/MAPK polymorphisms in ASDs We generated an idiopathic ASD GWAS dataset by utilizing each available published dataset [Autism Genetic Resource Exchange (AGRE), Autism Genome Project (AGP), Simons Simplex Collection (SSC)] in addition to in-house generated data [University of California, San Francisco (UCSF)], all of which were comprised of family trios with one affected offspring and both parents. We performed a transmission disequilibrium test (TDT) for ASD association in our final dataset of 4,109 trios. We first performed set-based analysis for polymorphisms within 5kb of each of the known RASopathy genes in the Ras/MAPK pathway (S1 Table ). Then, we compared the proportion of SNPs exceeding a false discovery rate (FDR) threshold of q = 0. 2 to permutations using length-matched random gene sets, showing that common polymorphisms within Ras/MAPK genes are significantly enriched for association with idiopathic ASDs (P = 0.
1, P < 2. 2x10 -16 ). We observed an excess of association at several additional P-value thresholds in this analysis in ASD cases compared with matched pseudo-controls (P < 2. 2x10 -16 ) (Table  1, Fig 2). We also identified individual epistasis signals surpassing a gene-based significance threshold set by dividing the GWAS significance threshold (P = 5. 0x10 -8 ) by the number of genes in the Ras/MAPK set for an approximate independent hypothesis-testing estimate (P = 2. 9x10 -9 ) considering all SNPs in a single gene part of the same hypothesis (i. e. LD-independent SNPs relevant to the same gene considered non-biologically independent due to potentially similar functional consequences) (Table  1 , S1A-S1F Fig). The data underlying these results show that the primary driving genotype-combination category is double-heterozygotes, which shows dramatic increase compared with expected counts in cases, but similar to expected counts in pseudo-controls (S3 Table ).
05), but also used matched-sex parent controls, which provided similar results to pseudo-controls (no epistasis P < 10 -6 for SNP pairs in Table  1 ; significant overrepresentation genome-wide in cases at all P-value thresholds considered from P < 10 -6 to P < 7. 6 10 -10 ). As a positive control, we selected the small homogeneous set of cases and matched unaffected siblings from unique families (N = 1,136) from the SSC dataset, and similarly to pseudo-controls, we observed significant epistasis enrichment in cases and diminishing odds ratios at varying P-value thresholds down to P < 10 -6 (S3 Fig). Finally, we validated the top results (P < 1. 0x10 -6 ) with the trio correlation test for epistasis. This independent method had highly correlated P-values with the caseonly approach, suggesting the results are consistent with epistasis and not marginal effects (rho = 0.
Thus, identifying modifiers of an ASD-related trait in individuals ascertained for a RASopathy could identify interactors with the Ras/MAPK pathway relevant to ASDs, as we consider the RASopathy locus and the modifier locus interacting to influence the ASD-relevant trait. We performed quantitative trait locus (QTL) mapping within-disorder and metaanalysis across the four disorders, as well as QTL mapping in sibling controls. We did not observe any SNPs meeting criteria for genome-wide significance (P = 5. 0x10 -8 ), however, the Table  1. Ras/MAPK ASD epistasis top results. The unique epistatic SNP pairs with P < 2. 9x10 -9 are listed in the table. For each SNP, the following is listed in order of columns: rsID (Epistatic SNP), chromosome (CHR), position (BP, reference version hg19), minor allele frequency in the ASD dataset (MAF), nearest gene to the epistatic SNP, Ras/MAPK gene associated with the interacting SNP, and P-value for epistasis in cases (Epistasis ASD P) and pseudo-controls (Epistasis Control P).
Locus pairs meeting genome-wide significance criteria (P < 7. 6 x 10 -10 ) are bolded. Main effects for epistatic and Ras/MAPK SNPs listed here are listed in S4 most significant SNP in this QTL analysis (rs62621010, P = 5. 6x10 -7 , Table  2 ) is 0. 39 Mb from the locus with the second-most significant epistasis signal in ASDs (rs114490548, Table  1 ). The rs62621010 putative modifier SNP does not show evidence for association with SRS in sibling controls. The region flanking and between rs62621010 and rs114490548 (chr7:37,749,392-38,139,570) contains genes ELMO1, GPR141, NME8, SFRP4, EPDR1, and STARD3NL. The two SNPs have low LD in 1000genomes measured by r 2 , although there is high D' variably across the region (Fig  3 ). Neither SNP is represented in both ASD and RASopathy datasets (or can be imputed) for direct comparison.
In addition, our results in idiopathic ASD SNP datasets strongly support our findings in rare RASopathies. One of the highly significant epistasis loci is near the top result (albeit non genome-wide significant) in a QTL modifier mapping approach in RASopathy subjects. A gene in this region, GPR141, demonstrates reproducibly reduced expression in RASopathy neural cell lines. Although this is merely circumstantial with regards to the observed epistasis and QTL results, it provides biological plausibility for interaction with the Ras/MAPK pathway. The function of GPR141, an orphan g-coupled protein receptor, is currently unknown. Sequence-structure based phylogeny suggests potential ligand association with N-arachidonylglycine (NAGly)  [100]. The well-studied GPR18 in this class has been shown to mediate concentration-dependent phosphorylation of ERK 1/2 in the presence of NAGly  [101].
ASD datasets ASD genotype data sets were collected from multiple sources. We obtained previously published genotype data as study investigators (UCSF-Weiss) or by application to AGRE, SSC, and dbGAP (AGP). Genotyping of each dataset was previously performed on Illumina or Affymetrix genotyping arrays as described in S6 Table. Diagnostic criteria were previously described in the respective references (S6 Table ), but in summary, the Autism Diagnostic Interview-Revised (ADI-R)  [122] , and/or Autism Diagnostic Observation Schedule (ADOS)  [123]  criteria was used for diagnosis for the AGRE, AGP and SSC datasets, and clinician diagnosis for the UCSF-Weiss dataset. The ASD affected child and both parents were included in each study. All samples were anonymized for analysis.
ASD dataset SNP quality control Data preparation, quality control, and imputation were conducted as described previously in Mitra, I. et al. [124]. First, SNPs were filtered using PLINK  [63, 64]  (see web resources) for Hardy-Weinberg equilibrium (HWE), call rate, minor allele frequency (MAF) and Mendel errors separately in each ASD dataset (S6 Table ). Next, imputation was performed separately for each dataset using IMPUTE2  [125]  (see web resources), following the recommended pipeline. Lastly, each ASD dataset was combined together, and the following quality control steps were performed: SNPs with severe departure (P < 1. 0x10 -6 ) from HWE in Caucasian founders were removed; SNPs were removed if they had different MAF (P < 1. 0x10 -6 ) in Caucasians between multiple datasets; SNPs were removed if they had MAF < 1% in Caucasians, or MAF < 2% in the combined dataset.
Individual quality control for ASD datasets Initial data preparation and quality control for ASD individuals was conducted for each dataset, as described previously in Mitra, I. et al. [124]. For each dataset, the following individual quality control filters were applied using PLINK  [63, 64] : genotyping rate, heterozygosity rate, verifying individual sex, verifying known relationships, removing individuals contributing to confounding relationship, and keeping one instance of individuals present in multiple studies. After combining the multiple datasets, sex and family structure were re-checked. To avoid population stratification, we selected only Caucasian individuals for the analysis. Ancestry was determined using the first two principal components resulting from multidimensional scaling with PLINK  [63, 64]  (-mds-plot option) (S8 Fig).
A proband and both parents were required to fall within the Caucasian cluster for inclusion in the analysis. Only unrelated complete trios were used. The final dataset included 4,109 ASD affected cases (3,517 males and 592 females) with both parents. A QQ plot for main effect association is shown in S9 Fig. As our data were family-based, we used non-transmitted parental alleles, commonly known as pseudo-controls, generated by the-tucc option in PLINK  [63, 64]  instead of unrelated healthy individuals. These 4,109 pseudo-controls are perfectly matched to cases for ancestry, thereby serving as a control for any population confounding. the GWAS significance threshold (P = 5. 0x10 -8 ) by the number of genes in the Ras/MAPK set for an approximate independent hypothesis-testing estimate (P = 2.
We conducted a second negative control analysis to rule out results due to interactions associated with viability by using 4,109 parents that were of the same sex as the ASD cases. In addition, we performed epistasis testing in our sample for a permuted SNP set included in a non-candidate pathway selected set as a negative control and for Ras/MAPK set in a homogeneous set of cases (N = 1,136) and matched unaffected siblings (N = 1,136) from unique families from the SSC dataset as a positive control. For both, we measured odds ratios at varying P-value thresholds down to P < 10 -4 (S2 and S3 Figs).
Trio correlation test To statistically validate the results from the case-only epistasis test, we used an independent test, called the trio correlation test  [128]. The trio correlation test leverages information from the parental genotypes to compute the expected distribution of the offspring genotypes then used in a correlation test. This test was provided to us as an R script (see web resources). To individually test candidate SNP pairs for interaction, we tested the nominally significant interaction results (P < 1. 0x10 -6 ) from the PLINK  [63, 64]  epistasis test. To confirm the absence of false-positive results, we performed a Spearman correlation test in R (see web resources) between the P-values of the nominally significant PLINK  [63, 64]  epistasis test results (P < 1. 0x10 -6 ) and their corresponding P-value from the trio correlation test (S4 Fig).
RASopathy dataset We have previously described in detail the recruitment and phenotype data collection in RASopathy subjects  [56]. In summary, patients with a physician (medical geneticist or neurologist) confirmed NF1, NS, CS, CFC diagnosis were included in the study. We recruited subjects from the NF/RAS Pathway Genetics Clinic UCSF, UCSF NF Symposium, RASopathy support groups (NF, Inc. , Children's Tumor Foundation, Noonan Syndrome Foundation, CFC International, Costello Syndrome Family Support Network, and Costello Kids), and three national RASopathy family meetings (Chicago, Illinois, USA, July 2011; Berkeley, California, USA, July 2009; Orlando, Florida, USA, August 2013). In addition, NF1 patients were recruited at University of California, Los Angeles (UCLA) through online postings (NF, Inc.
, and Children's Tumor Foundation), and the Neurofibromatosis and Neurocutaneous Disorders clinic at the Children's Hospital, Los Angeles. We enrolled the unaffected siblings of RASopathy subjects as controls. To measure ASD symptoms, we used the SRS questionnaire  [129]. The SRS questionnaire is a quantitative and continuous measure of social ability. Parents or persons well-acquainted with the study participant answered the 65-item SRS questionnaire regarding traits characteristic of ASD. Following the SRS manual, we calculated the raw score for each individual, and then calculated the sex-normalized T-scores. A total of 257 RASopathy patients and 142 RASopathy-unaffected full siblings of RASopathy patients with SRS phenotype data were recruited. Further detailed information about the SRS questionnaire data for the sample population can be found in Adviento et al.
RASopathy dataset genotyping and quality control All participants provided blood or saliva samples for DNA extraction. Blood samples were collected by venipuncture using standard procedures. Saliva samples of families involved in the study were collected by mail or in person at family meetings. Participants provided saliva samples using the Oragene Discover kit (OGR-250 for children and OGR-500 for adults) by DNA Genotek (see web resources). DNA was extracted using the manufacturer's standard protocol. All specimens were anonymized for analysis. All DNA samples were genotyped in the Genomics Core Facility (GCF) of UCSF on the Affymetrix Axiom EUR array following standard manufacturer protocols. The Axiom EUR array contains approximately 675,000 SNPs across the genome  [130]. Genotype calling was performed using Axiom GT1 algorithm as part of the Affymetrix Genotyping Console™ (GTC) Software (see web resources).
Data analysis in RASopathy dataset We performed a multidimensional scaling analysis of genome-wide pairwise identity-by-state (IBS) distances in PLINK  [63, 64]  for all individuals in the dataset. We used the first five dimensions as covariates in the analysis to correct for population stratification and batch effects. To accurately compare between the RASopathy and control sibling groups, we scaled the T-scores within each group (CFC, CS, NF1, NS, and sibling) so that the mean of the values was 0 and variance was 1, and excluded outlier values greater or less than 3 standard deviations (SD) from the mean (S5 Fig). For each group (CFC, CS, NF1, NS, sibling), we performed QTL mapping by implementing in PLINK  [63, 64]  a linear regression analysis using the scaled SRS scores as a quantitative trait (-linear).
-A language and environment for statistical computing: http://www. R-project. org/ RefSeq Genes Database-UCSC: http://hgdownload. cse. ucsc. edu/goldenPath/hg19/ database/knownToRefSeq. txt. gz SFARIgene: https://gene. sfari. org/autdb/Welcome. do Trio Correlation Test R script: https://github. com/BrunildaBalliu/TrioEpi.
Acknowledgments We acknowledge  Dina Bseiso ,  Jessica Gage ,  Brigid Adviento ,  Keren Messing-Guy ,  Julie Lustig , and  Allison Kiang  for their helpful discussion, or assistance in revision or subject recruitment. We acknowledge  Noah Zaitlen  and  Brunilda Balliu  for providing the trio correlation statistical test R program. We gratefully acknowledge the resources provided by the  Autism Genetic Resource Exchange (AGRE) Consortium , the  Simons Simplex Collection (SSC) , the  Autism Genome Project (AGP) ,  NF Inc. ,  Children's Tumor Foundation ,  Noonan Foundation ,  CFC International, Costello Syndrome Family Support Network , and  Costello Kids  for their contribution to our recruitment efforts, and the families participating in our research.
Table. R. -A language and environment for statistical computing: http://www. R-project. org/ RefSeq Genes Database-UCSC: http://hgdownload. cse. ucsc. edu/goldenPath/hg19/ database/knownToRefSeq. txt. gz SFARIgene: https://gene. sfari. org/autdb/Welcome. do Trio Correlation Test R script: https://github. com/BrunildaBalliu/TrioEpi.
e. that are more or less frequent than expected under the assumption of independent interactions of different types. In the dataset we used for illustration purpose, we found higher prevalence and over-representation of plants with highly asymmetrical combinations of degrees (i. e. those plants which only established interactions with larval or adult lepidopterans and plants that were eaten by few species of herbivores but visited by several species of adult lepidopterans). Extremely specialized plants (i. e. those interacting with few Lepidoptera species in both networks) were under-represented. As far as we know, there are only two datasets exploring the visitation and herbivory interaction patterns of plant species in multispecies assemblages and using the network approach. Pocock et al.
3  studied multispecies interactions in an agroecosystem in England (hereafter the Norwood dataset) and found that some individual plant species were disproportionately well linked to many visitor and herbivore species, but these plants differed between the visitation and the herbivory networks. Sauve et al. 22  explored plant degree correlations in the Norwood dataset and found that the number of flower visitors was positively correlated to the number of herbivores that interacted with plants. Since the reported degree-correlations are low  22  and different from what can be expected by degree distribution, taken together these results suggest that asymmetrical interaction patterns may be prevalent and over-represented among plants of the Norwood dataset, which is in accordance with our results.
Past work comparing the plant as herbivore hosts and nectar sources of species that can be herbivores and floral visitors studied a subset of the data analysed in our study  5. They found that the number of plant species on which Lepidoptera species feed as larvae was positively correlated with the number of plant species on which adults look for nectar, this relationship being stronger for diurnal Lepidoptera species  5. Similarly, the degree of adult lepidopterans was also higher for oligophagous and polyphagous species than for monophagous and strictly oligophagous ones in diurnal Lepidoptera species  5. Our results complement these findings by showing the highly asymmetrical nature of the interactions established by larval (herbivores) and adult (flower visitors) lepidopterans. As far as data quality is concerned, the plant-insect interaction dataset we used is virtually complete, and thus highly robust (see also Pearse & Altermatt  37  for an analysis on the robustness of the dataset when removing interactions), but also does not underrepresent the interactions of rare species.
Indeed, a correlation between interaction records/degree of interactions and the rarity/commonness of a species would be problematic  38. However, our dataset is based on the sum of observations of hundreds of entomologists, and rare species often received disproportionate attention (see also  5  ), such that plant-insect interactions are very well resolved for all species, regardless of its rarity. In fact, some species may be rare because they have only few interactions, and thus are limited by their host plant use (see Pearse & Altermatt  39  on that dataset). Differences between insect herbivores and flower visitors in their level of generalism seem to be widespread. Fontaine et al. 40  studied the interactions established by insect herbivores and flower visitors of species belonging to 44 plant-insect networks describing either visitation or herbivory communities.
Since species degree constrains the interaction pattern of species  53  , the degree distribution of multilayer networks may also limit their similarity in module composition. Considering the range of similarity values that can be achieved by randomized networks with the same size, connectance and degree distribution, we showed that similarity in module composition between the studied herbivory and visitation networks was high, although not different from random expectations. The importance of degree distribution in predicting the similarity in species interaction partners between herbivory and visitation networks has been little explored in previous studies. In the Norwood dataset, degree distribution predicted that the similarities in flower visitors and in herbivores among pairs of plant species were unrelated  22.
In both the Norwood and the Doñana datasets, the similarity in plant groups between the herbivory and visitation networks was higher than expected by the number of plant groups of each individual network (i. e. groups being subsets of plant species interacting with more similar sets of herbivores and flower visitors)  20. However, how degree distribution is associated with this similarity remains unknown for the Norwood and Doñana datasets. Indirect evidence of how widespread the role of degree distribution might be in influencing the similarity of herbivory and visitation networks can be found in the results reported by Fontaine et al. 40. According to this study, herbivore species interacted with plants that were more phylogenetically related than flower visitors did, and plant phylogenetic relatedness was negatively associated with the degree of herbivores, but unrelated to the degree of visitors  40.
j is the sum over all rows of the N ij elements and represents the size of module j of the visitation network. N is the total number of species. The normalized mutual information measure I is calculated as: = -∑ ∑          ∑ + ∑ = = =. = ( ) ( ) I H V N N N log ( , ) 2 l og log (7) i m j m ij N N NN i m i N N j m j N N 1 1 1 1 H V ij i j H i V j where m H is the number of modules found in the herbivory network and m V is the number of modules found in the visitation network. When the two classifications coincide, then I (H, V) takes its maximum value of 1. When the classifications are so different that they are effectively independent (i. e. when N ij = N i. N j /N), then I (H, V) = 0. The calculation of I is already implemented through function 'compare. communities' , option = nmi, in the 'igraph' package under R.