dataesr/scientific-paragraphs-categorization huggingface dataset:
Developmental Stage Specificity of Epigenetically Dysregulated Loci It has been shown that genes upregulated during early postnatal development are often differentially expressed in adolescent and adult ASD brain  (Parikshak et al. , 2013). We therefore asked whether early postnatal genes might also be enriched for the ASD-related acetylation changes we detected in older subjects (R10 years old). Using a database of human RNA-seq profiles (BrainSpan, 2015), we defined the 2,000 genes most upregulated at each developmental stage (fold change relative to median expression across all stages). We then tested for enrichment of DA peaks near each such gene set. This analysis was performed separately for PFC and TC, using expression profiles from the corresponding regions of the developing human brain.
EXPERIMENTAL MODEL AND SUBJECT DETAILS Human Subjects Brain samples from 45 ASD and 49 control individuals were acquired from the Autism Tissue Program (ATP) at the Harvard Brain Tissue Resource Center, the University of Maryland Brain and Tissue Bank and the Oxford Brain Bank. Sample acquisition protocols were followed for each brain bank, and samples were de-identified prior to acquisition. Sample swaps were verified with independent genotyping. Brain sample and individual level metadata are provided in Table  S1.
Quality Control of 229 ChIP-Seq Datasets The set of 229 ChIP-seq datasets contained 13 pairs of biological replicates. The average peak height correlation (Pearson) between replicate datasets was 0. 92, which is similar to replicate correlations in H3K27ac ChIP-seq data from the NHGRI Epigenome Atlas (Inferior Temporal Lobe: 0. 90; Mid-Frontal Lobe: 0. 91)  (Bernstein et al. , 2010). For each of the 229 datasets, we quantified the mean Pearson correlation (mPC) with other datasets from the same brain region. Then, from each of the 13 pairs of replicates, we discarded the dataset with lower mPC. From the remaining 216 datasets, we discarded 7 that had low mPC values relative to the norm for the corresponding brain region (< first quartile -2. 5xinter-quartile range). The remaining 209 ChIP-seq datasets from 94 individuals were used for downstream analysis.
Any ASD sample with Distance_A > 1. 05xDistance_C was discarded (Figure  S3 ). Similarly, any control sample with Distance_C > 1. 05xDistance_A was discarded. A final set of DA peaks was constructed between ASD and control using the remaining samples, with the same Q-value and fold change cutoffs as above (Table  S4 ). To test whether these DA peaks were genuine, we generated 1,000 randomized datasets by permuting sample labels (ASD, control). For each permuted dataset, we called DA peaks using the above-described two-step approach. For each brain region, the P-value of the number of DA peaks in the actual data was calculated as the fraction of permuted datasets with an equal or greater number of DA peaks (Figure  1B ). Functional Enrichment of DA Peaks First, we masked the dup15q locus in the complete peak height matrix.
Enrichment of DA Loci for Expression at Specific Developmental Stages A similar analysis was performed to determine the enrichment of DA peaks in SFARI genes (Tables  S5  and S6 ), DA peaks near individual genes (Tables  1  and S6 ) and DA peaks near developmental stage-specific genes (Figure  4 ). Again, the dup15q locus was excluded. The Refseq gene set  (RefSeq, 2002)  was used in the first two analyses. Gencode v10 gene set  (Harrow et al. , 2012)  was used in enrichment analysis in brain development. Human brain RNA-seq profiles were downloaded from BrainSpan  (BrainSpan, 2015). We defined expressed genes as RPKM > 5 in at least 2 dataset. Then quantile-quantile normalization was performed on the RPKM values across each developmental time point (8 post-conception weeks to 40 years old) in brain regions that develop into PFC and TC.
Motif Analysis For motif enrichment analysis, we used the HOMER ChIP-seq pipeline's findMotifsGenome. pl script with the ''-mknown'' option  (Heinz et al. , 2010). Motif models were drawn from the TRANSFAC vertebrate database  (Matys et al. , 2006)  and the analysis was performed separately on Up and Down DA peaks from each of the 3 brain regions (6 DA peak sets in total), with all peaks from the same brain region as background. Motifs were classified as enriched based on fold enrichment (R1. 3), FDR (%0. 01) and number of foreground peaks that had a motif match (R20). The list of enriched motifs was almost identical when we used the JASPAR database  (Mathelier et al. , 2016)  instead of TRANSFAC (data not shown).
QUANTIFICATION AND STATISTICAL ANALYSIS Statistical Method of Computation Statistical methods and software used in this study are cited in the STAR Methods and the Figure legends. The statistical analyses were performed in MATLAB and R. The initial and final sets of DA peaks were constructed using the Wilcoxon rank sum test and Benjamini-Hochberg multiple testing correction with fold-change R 1. 3 and Q % 0. 05 (e. g. , Table  S4 ). The P-value of the permutation test was calculated as the fraction of permuted datasets with an equal or greater number of DA peaks (e. g. , Figure  1B ). In the Venn diagram, the P-values were calculated using the hypergeometric test with the set of all peaks as background (e. g. , Figure  1D ). The Pvalues in the dotplots (e. g. , Figures  1D, 1E , and S4) and the violin plot (Figure  S5 ) were calculated assuming a t-distributed Pearson correlation coefficient.
Numerical tests In this section we present two numerical tests for the infinite horizon problem to illustrate the performances of the proposed algorithm. However, the localization procedure can be applied to more general optimal control problems. All the numerical simulations have been made on a MacBook Pro with 1 CPU Intel Core i5 2. 4 Ghz and 8GB RAM. The codes used for the simulations are written in Matlab. The routine for the approximation of MPC is provided in  [5]. Test 1: 2D Linear Dynamics Let us consider the following controlled dynamics: ẏ(t) = u(t) t ∈ [0, T ] y(0) = x (10) where u = (u 1 , u 2 ) is the control, y : [0, T ] → R 2 is the dynamic and x is the initial condition. The cost functional we want to minimize is: J x (u) := ∞ 0 min{|y(t; u) -P | 2 , |y(t; u) -Q| 2 -2} e -λt dt (11) where λ > 0 is the discount factor.
Phylogenetic analysis and taxon affiliation Sequences used for further sequence analysis corresponded to highquality sequences, i. e. presenting less than 0. 5% undetermined positions. Nucleotide sequences were analyzed using the Blast program in NCBI and Greengenes database. Chimera was detected using Bellorophon software in the Greengenes website (greengenes. lbl. gov). For database comparison, we retained the stringent threshold value of 98. 7% of similarity with a fully defined cultured strain (type or nontype) as recommended for bacterial species delineation  [29] , for the affiliation of a clone to a species-or a genus-level operational taxonomic unit (OTU). When a similarity level of more than 98. 7% was obtained for an uncultured bacterial clone only, the sequence was classified as 16S rRNA gene clone and affiliated to a genus according Greengenes database.
Beside sequences identified herein, the sequences used to reconstruct phylogenies were chosen by Blast analysis as follows. For each clone sequence, we included the most related deposited sequence and the most related sequences corresponding to (1) validated species and (2) human skin clone. The dataset of sequences was aligned using ClustalW software  [30]. The most appropriate substitution model determined according to Akaike information criterion calculated with Modeltest (v. 3. 7) was GTR plus gamma distribution, plus invariant sites  [31]. ML phylogenetic analysis was performed using PHYML v2. 4. 6, gamma shape parameter being estimated from the dataset  [32]. ML bootstrap support was computed using PhyML after 100 reiterations. The Shannon-Wiener (H′) and Simpson (D) diversity indexes were calculated for each phylum and according to the type of method (culture or uncultured approaches)  [33, 34].
Conclusion Inter-ecosystem comparisons suggest that the human skin communities possess strong capacities for interacting with their environment Table  3  Data comparison with the 5 main published studies that characterized healthy skin microbiota by molecular approaches. This table compiles all the genus of Gram-negative bacteria that have been detected on the skin. Data are obtained from 5 published studies and this study. Studies were based on molecular methods in combination or not with culture analysis. D for Dekio et al. [15] , G for Gao et al. [19] , Gr for Grice et al. [22] , H for Human Microbiome Project Consortium  [2] , Z for Zeeuwen et al. [12] , and C for this study. The color intensity is function of the frequency of detection. The core 50 microbiota corresponded to genera found in at least 50% of the published studies.
Recording of vegetation and insects Surveys of the % cover of all vascular plant species were conducted in June and September 2007. Trees and shrubs were recorded on the entire plots, while herbaceous species were recorded at five 2×2m 2 subplots located in the center and at the four corners of each plot. Data from both seasons were combined, allocating the maximum % cover recorded during either of the two surveys to each plant species to establish their maximum 'importance' in the plot vegetation. Automatic light traps were used for moth sampling between May and October in both 2006 and 2007. These traps consisted of a 12 V battery-operated UV light tube (Sylvania black light-blue, F15W/BLB-TB; Osram, Munich) placed above a plastic funnel leading into a plastic bucket  (Axmacher et al. , 2011).
Acknowledgements The authors are greatly indebted to the  National Natural Science Foundation of China  ( NO. 41271198  and  NO. 31470514 ). We also want to thank  Dr. Axel Hausmann  ( Zoological State Collection in Munich ) for his invaluable assistance and help in identifying Geometridae, and many thanks to the PhD students  Xiaotong Zhang ,  Xingtong Li  and  Ying Pan  at the  China Agricultural University  for their invaluable assistance during the field sampling.
Results The unique Austronesian origin of the Ma'anyan. Following quality control, we obtained genotypes for 701,211 SNPs in a new set of 202 individuals from Borneo: 162 Ma'anyan and 40 South Kalimantan Dayak (SK-Dayak). To characterize the Ma'anyan and SK-Dayak gene pool within an Asian context, we focused our analyses on Island Southeast Asian, East Asian and Mainland Southeast Asian populations (Fig. 1 ). In a Principal Component Analysis (PCA) using a subset of the SNPs that intersect with published data from an extensive range of regional populations (the low density dataset) (Supplementary Fig. S1 ), the first principal component (explaining 19. 3% of the variance) separates Island Southeast Asian populations from East Asian and Mainland Southeast Asian groups, while the second principal component (explaining 17.
5% of the variance) splits the Igorot on the positive axis and the Ma'anyan on the negative axis, with other Austronesian-speaking populations falling in between, such as Taiwanese aborigines, Filipinos, Borneo populations (Murut, Dusun, Lebbo' and South Kalimantan Dayak) and Sumatran populations (Sumatran Malay and Karo). Other Austronesian-speaking groups, like the Bidayuh, Javanese and Malaysians cluster towards mainland Southeast Asia, likely due to the historical influence of that region on these groups. Interestingly, the Ma'anyan form their own pole on the plot and do not cluster closely with other populations from Borneo, although the genetically closest population is still the South Kalimantan Dayak group, which is also geographically the nearest neighbour to the Ma'anyan. A similar population clustering pattern is observed with both the low-and high density SNP datasets (Supplementary Fig.
S2 ). This observation also agrees with F ST values calculated on the low density dataset (Supplementary Table  S1 ). This unique genetic placement of the Ma'anyan is supported by admixture estimates, also performed on the low density dataset (Fig. 2 ), especially at K = 14 where it achieves its lowest cross-validation value (Supplementary Fig. S3 ). The main ancestral components observed in Southeast Asian populations are: i) an Austronesian Igorot and indigenous Formosan component (C3; light green), ii) a Mainland Southeast Asian (MSEA) component (C11; light brown); and iii) a Papuan component (C2; light blue). However, our analysis reveals a major new component in Island Southeast Asia, representing 80% to 95% of the ancestry in Ma'anyan individuals (C8; dark blue). This Ma'anyan component is also found using an ADMIXTURE analysis on our high density SNP dataset (Supplementary Fig.
S4a, b ). The remaining ancestry components in the Ma'anyan also occur in most of the other Indonesian populations, and may result from shared history and/or limited gene flow between the Ma'anyan and neighbouring populations. In return, the new C8 component identified in the Ma'anyan is also found at much lower frequencies in many other Indonesian groups, reaching its highest frequency in surrounding populations of Ma'anyan in Borneo (~40%), but also appearing in some mainland Southeast Asian populations. To determine whether this distinct and homogenous genetic component in the Ma'anyan results from genetic drift (due to geographic isolation and/or endogamy), we inferred the extent of 'Runs of Homozygosity' (ROH) in the full high density dataset. Homozygosity in the Ma'anyan is similar to that of other Borneo populations (Supplementary Fig.
S5 ), Figure  2. ADMIXTURE plot using the low density database with K = 14 (the optimum determined by cross-validation). Each component is identified by a specific color and a C label which corresponds to its order of appearance from K = 2 to K = 14. even though these show much higher levels of admixture (Fig. 2 ). However, homozygosity in the Ma'anyan is lower than in the Igorot, an isolated, indigenous Austronesian-speaking population living in the Philippine highlands. This suggests that the unusual homogeneity and unique ancestry component found in the Ma'anyan reflects the population's migration history, rather than simply resulting from high levels of genetic drift. Genetic drift has also potentially occurred in the Igorot, and other isolated ethnic populations that exhibit low genetic diversity and have small population size (such as the Mlabri)  27, 28  , or that show a high level of consanguinity (such as the Malay Negritos)  29.
An f3-statistics analysis reveals more clearly that the Ma'anyan is not an admixed population (Supplementary Table  S2 ). Defining the Ma'anyan as the daughter group, all possible combinations of populations in the low density dataset returned positive f3 statistics with Z-scores > -2, indicating no significant gene flow. In addition, a TreeMix analysis supported eight migration events, none of which involved gene flow to or from the Ma'anyan (Supplementary Fig. S6 ). In contrast, a migration event was supported from the basal cluster of MSEA Austroasiatic-speaking H'tin and Mlabri to the Bidayuh, a population in northwest Borneo. This suggests that MSEA gene flows reached the west of Borneo, but not the east. To test whether the Ma'anyan gene pool has drifted from its original Austronesian or MSEA ancestry, we performed an f3-outgroup statistics analysis (Fig.
3a ). All Island Southeast Asian populations, except the Bidayuh, Javanese and Sundanese, were pulled to the Austronesian side (as defined by the Formosan aborigines). Conversely, mainland Southeast Asian groups were pulled to the MSEA side (as defined by the H'tin). The Ma'anyan fall in the upper left diagonal of the plot, indicative of genetic similarity with Austronesian rather than MSEA groups. To determine the closest population to the Ma'anyan, the configuration f3(Yoruba; Ma'anyan, x) was explored, where x represents all populations in turn in the low density dataset. The highest value was obtained when x was the Igorot from the Philippines or non-Ma'anyan Borneo populations (Fig. 3b ), a result that is also obtained when using the high density dataset (Supplementary Table  S3 ). These results place the genetic diversity of the Ma'anyan within the broader Austronesian gene pool.
This Austronesian connection is also highly supported by an Identity-by-Distance (IBD) analysis performed with Refined IBD on the high density dataset. The Ma'anyan share more haplotypes with surrounding Borneo populations and the Igorot than with Mainland Southeast Asian groups (e. g. , Cambodians, Burmese and Vietnamese) (Fig. 4  and Supplementary Fig. S7 ). When filtered for a total shared haplotype length greater than 20 cM (~20 Mb) between two individuals, links were still observed between the Ma'anyan and Mainland Southeast Asian groups, as well as other Indonesian populations. However, the links with Mainland Southeast Asian groups disappear with larger haplotype lengths, while connections with Austronesian groups (including the Igorot) are maintained up to a threshold of 40 cM, indicating more recent common ancestry (the hypothesis of recent gene flow can be discarded from earlier analyses).
At higher thresholds (i. e. , longer shared haplotypes), only connections within Borneo remain. Together, these analyses (ADMIXTURE, PCA, Runs of Homozygosity, f3 statistics, TreeMix and IBD) suggest that the unique Ma'anyan genetic component is an undetected part of the broader Austronesian genetic diversity. The Ma'anyan harbour a unique Austronesian genetic component, thus allowing us to raise the question: did the Ma'anyan gene pool contribute strongly to Malagasy, as suggested by linguistic evidence? The Island Southeast Asian ancestries of the Malagasy. We performed PCA using the low density dataset, finding that the first two components described 54. 6% of the observed variance (Supplementary Fig. S8 ). The first component (PC1; explaining 39. 1% of the variance) largely separated the continental groups of Africa, Explicit admixture analysis on the low density dataset confirms this assessment, showing that the three Malagasy populations have ~70% African ancestry (red) versus ~30% Asian ancestry (mixed colours; Fig.
These results are also consistent with the general nature of Island Southeast Asian gene flow into Malagasy, as determined by TreeMix (Supplementary Fig. S10 ). Since the African ancestry in Malagasy may hinder the precise identification of Asian parental sources, we performed a PCAdmix analysis on the full high density dataset to mask African variants in the Malagasy data. An Asian ancestry-specific PCA, run on the filtered set of 17,043 SNPs, explained 46. 8% of the observed variance in the dataset (Fig. 5 ). The first principal component separated eastern Indonesians from western Indonesians and mainland East Asians. The second principal component separated the mainland Asian groups from those in Island Southeast Asia. As observed previously (Supplementary Fig. S1 ), the Ma'anyan are positioned away from the other Island Southeast Asian groups and form their own pole on the graph.
In this more refined analysis, the Asian markers found in the three Malagasy populations overlap closely with those from coastal Borneo (South Kalimantan Dayak, Murut and Dusun), although they do not obviously show any specific affinity with the Ma'anyan. Additionally, some Malagasy individuals are closely clustered with Bajo individuals, which may indicate that sea-nomads are relevant factors in the migrations to Madagascar, as suggested earlier  10. Despite this general link between Malagasy Asian ancestry and Borneo groups, an F ST analysis using the same dataset  highlights that the South Kalimantan Dayak still have the lowest genetic distance to the three Malagasy groups (average F ST = 0. 022) (Supplementary Table  S4 ), thus suggesting that this is a likely Asian source population. Together, these analyses confirm that Malagasy are a mixture of African and Island Southeast Asian populations, as suggested by much previous research  [6] [7] [8] [9] 30, 31.
This outpost was established because the coastline might have extended over 100 kilometres, and perhaps as much as 200 kilometres, further inland that at present  25, 26  , and possibly laid near Tanjung-Amuntai region, the auto-identified Ma'anyan's original homeland  24, 32. It is conceivable that this settlement might then have provided sea contact to what is now land-bound Ma'anyan. As the coastline move southwards, the trading post were also moved south and later formed the city of Banjarmasin, which is the dominant city, commercial state, and centre of activity in the trading network of this region. The inhabitants of Banjarmasin, the Banjar people, might then have constituted a mix of individuals from south Borneo under the cultural influence of the Malay Srivijaya kingdom. Based on this historical source, together with linguistic work on the ancestral states of the Malagasy language showing a substantial number of Malay loanwords  11, 26  , we postulate that the Asian source population of the Malagasy constituted admixed Ma'anyan individuals (best represented in our dataset by the South Kalimantan Dayak), who lived in the Srivijaya area of influence, integrating Malay and Javanese cultural traits and favouring a large degree of gene flow, before migrating to Madagascar.
Although the cause of their migration remains elusive, our data tend to favour an origin for the Malagasy in southern Borneo. Curiously, the group with the closest genetic affinity to Malagasy in our dataset is the South Kalimantan Dayak, a composite population of several ethnic groups located in southeast Borneo today (Supplementary Table  S4 ). This suggests that an in-depth analysis of these ethnic groups, including the Banjar people and other southeast Borneo ethnic communities, might be a promising direction to better identify the (possibly mixed) genetic sources of the Malagasy and to determine the ultimate causes of the Malagasy expansion. Our study shows that the Ma'anyan have genetic diversity that is unique in Southeast Asia, complicating existing scenarios of dispersal during the Austronesian expansion.
Genome-wide SNP genotypes for the two groups were generated using the Illumina HumanOmniExpress-24 v1. 0 Bead Chip (Illumina Inc. , San Diego, CA), which surveys 730,525 single nucleotide markers regularly spaced across the genome. Genotyping data are available upon request. Dataset integration. Two datasets were compiled from previous published data to fulfil key analytical criteria: i) the low density dataset has wide geographical coverage, but includes relatively few SNPs; while ii) the high density dataset has greatly increased SNP density, but includes fewer populations. This approach, which is necessitated by the wide range of DNA genotyping chip technologies used by the scientific community (Supplementary Table  S5 ), allows us to address the widest range of questions. Filtering and quality controls were performed using PLINK v1.
9  43  : i) to avoid close relatives, relatedness was measured between all pairs of individuals within each population using an Identity-by-Descent (IBD) estimation with upper threshold of 0. 25 (second degree relatives); ii) SNPs that failed the Hardy-Weinberg exact (HWE) test (P < 10 -6 ) were excluded; iii) samples with an overall call rate < 0. 99 and individual SNPs with missing rates > 0. 05 across all samples in each population were excluded; and iv) variants in high linkage disequilibrium (r 2 > 0. 5; 50 SNP sliding windows) were also removed for the low density dataset. The final low density dataset contained 9,743 SNPs in 1,817 individuals from 73 populations, after excluding 7 Ma'anyan and 2 South Kalimantan Dayak individuals for reasons of low data quality. This low density dataset includes East and Southeast Asian populations (Mörseburg et al.