dataesr/scientific-paragraphs-categorization huggingface dataset:
SEG-HCA is however limited to linear motifs including hydrophobic amino acids and thus does not address hydrophilic linear motifs. Another major observation of our work is that in eukaryotic proteomes, the number of H2CD is approximately higher than the number of domains assigned from the conserved domain database, revealing a lot of CD-orphan domains, which are otherwise not considered by other predictive methods based on homology searches. Orphans domains have either evolved too far from the nearest neighbors to be assigned to a domain or they have been created by some de novo mechanisms. Studies have however indicated that most of the solved structures of orphan proteins show structural similarity to already known proteins domains, suggesting that the fraction of orphan domains that have distant homologs is high  [61].
2B ). High and low values are associated with high and low densities of hydrophobic clusters, respectively. SEG-HCA next identifies areas of high hydrophobic cluster density (H2CD), typical of structured or folded regions (Fig. 2B ). To that aim, HCP minimal values are identified for a threshold level of 10%, defining thereby potential hinge regions separating these areas of high hydrophobic cluster density (first rough limits, labeled 1 in Fig. 2B ). This level is considered as the minimal value, for which and below which a linker is predicted to separate two distinct H2CD. This value was first fixed according to numerous case studies. Optimization of this threshold was considered using a non-redundant (40% sequence identity) SCOP database, downloaded from ASTRAL (http://scop. berkeley. edu/).
SEG-HCA was used at different HCP threshold and for each 3D structure, we looked at the values that yield only one H2CD. The distribution of the HCP threshold values was then analyzed, showing an optimum at 22%, thus slightly above 10%. On average, 1. 1 H2CD are observed, with 78% coverage. However, it should be noted that the optimization is made on well-stable, already large globular domains. The used threshold is intended to detect such canonical domains, but also small segments, which may be structured and are not (or poorly) represented in the SCOP structural database. Therefore, as supported by several case studies, using the lower threshold of 10% allows the detection of these small segments, in addition to larger ones. At this level, 1. 04 H2CD are observed, with 86% coverage (thus values close to those observed for the optimized threshold).
Increasing this threshold value may allow the split of multi-domains into domains, but lead to loose small segments, as these last ones generally have low HCP values. Then, SEG-HCA builds a tree, starting from the observed distances between hydrophobic clusters (leafs). The closest hydrophobic clusters are grouped, constituting nodes, the root (labeled 2 in Fig. 2B ), gathering the whole set of hydrophobic clusters contained within the analyzed sequence. SEG-HCA then compares each of the regions identified as the first rough limits (labeled 1 in Fig. 2B ) to those defined by the different nodes of the tree. The best overlap between the two is chosen to define the refined limits of the H2CD segment (labeled 3 in Fig. 2B ). SEG-HCA is fully implemented in python v2. 7. Scripts are available in Supporting Information (Software S1).
Datasets Proteome sequences were downloaded from the National Center for Biological Information (NCBI) (ftp://ftp. ncbi. nlm. nih. gov/ genomes/): Hsapiens, Scerevisiae_uid128, Ecoli_042_uid161985, Archeoglobus_fulgidus_DSM_4304_uid57717 and Pfalciparum. Comparison with assignments from the Protein Data Bank (PDB), Mobi-DB and SCOP, from the Conserved Domain Database (CDD) and with other predictive tools (IUPRED, ANCHOR) Each sequence from each proteome was searched for similarities either with the Protein Data Bank (PDB), using the BLASTPGP program version 2. 26. Only one iteration was done from each entire protein sequence. Hits sharing more than 95% of sequence identity were selected. The disorder was then filtered by using Mobi-DB (  [34] , http://mobidb. bio. unipd. it/), which extends the experimental disorder observations found Disprot database to the whole PDB, or by only considering the classes a, b, c, d, e, f from the SCOP structural databases (  [36] , http://scop.
mrc-lmb. cam. ac. uk/scop/). These classes correspond to all alpha protein, all beta protein, all alpha and beta protein (mainly parallel beta sheet), all alpha and beta protein (mainly anti-parallel beta sheet), multi-domain proteins, membrane and cell surface proteins and peptides, respectively. We obtained information on Conserved Domain(s) (CD) for each protein sequence through the NCBI server (http://www. ncbi. nlm. nih. gov/Structure/cdd/cdd. shtml), which has pre-computed domain architectures. These pre-computed architectures were fixed using the RPBLASTprogram (blast tools version 2. 26) with the Conserved Domain Database (version 3. 1)  [24]. IUPRED was used as a disorder predictor (  [30, 31] , http:// iupred. enzim. hu/). We used the ''long'' mode (IUPRED-L) to predict disordered segments.
Figure1. Delineation and comparison of globular domains using HCA. The sequence is written on a duplicated alpha helical net and the hydrophobic amino acids (V, I, L, M, F, W, Y) are contoured [14, 15]. These form hydrophobic clusters, which mainly correspond to regular secondary structures [17]. This is illustrated here with the chromo and chromo-shadow domains of the mouse chromobox homolog 1 (CBX1_MOUSE, UniProt P83917), for which the observed secondary and tertiary structures are shown below the HCA plot (pdb identifiers: 1AP0 and 1DZ1) [66]. Globular domains (boxed), containing approximately one third of hydrophobic amino acids gathered into clusters, are separated by a hinge, which is clearly less hydrophobic. The comparison of the HCA plots of the two domains indicates similar shapes of clusters (shaded green and red), suggesting a structural relationship.
Acknowledgments We thank  Jean-Paul Mornon  for insightful developments associated with Hydrophobic Cluster Analysis, his enthusiastic suggestions and comments. We also thank  Raphae ¨l Guerois  and  Stephane Marcand  for critical reading of this manuscript, the three reviewers for their constructive remarks, as well as  Jean-Pierre de Villartay  and  Patrick Revy  for their interest in our methodological approach.
ACKNOWLEDGMENTS We are grateful to  Prof. George Daley ,  Prof. Ian Wilmut ,  Dr. In-Hyun Park , and  Dr. Gareth Sullivan  for providing the iPSC line. We are grateful to  Judy Fletcher  and  Dr. Gareth Sullivan  for providing guidance on iPSC culture and hepatocyte differentiation. We would like to thank  Prof. Roland Wolf ,  University of Dundee , for supplying the CYP3A antibody. This project was funded by awards from the  Engineering and Physical Sciences Research Council Follow on Fund  (M. B. , D. C. H. , J. P. I. ),  Edinburgh Bioquarter  (D. C. H. ),  Medical Research Council   PhD Studentship  (B. L. -V. , D. S. ), the  Research Councils UK  Fellowship (D. C. H. ), a generous donation from  Toni  and  Emmet Stephenson , Doctors. com(D. C. H. ), and  Medical Research Council program  grant funding (J.
Such results seem to be consistent with the linguistic and ethnic changes described above. Whether Central Asia was a source or a convergent zone of subcontinent gene flows remains unresolved. To address this issue, we present mt-DNA and Y-chromosome data on more than 500 Afghan samples from 5 main ethnic groups inhabiting the mountainous region of the Hindu Kush: Tajik, Turkmen, Pashtun, Hazara and Uzbek. Mt-DNA analysis consisted in HV1 sequence and polymorphism analysis in the coding region. Y-chromosome analysis covered 102 binary SNPs, among which there were 6 new markers, and 39 Y-STRs. A representative subset of each ethnic group was also analyzed for autosomal markers by Illumina 650 K SNP. This Afghan data was completed with the same Ychromosome analyses of 672 original male samples from Iran, Kyrgyzstan, Mongolia and updated Pakistani samples (HGDP-CEPH).
Autosomal genetic analyses Autosomal genetic variation was analyzed in a subset of 5 Hazara, 5 Tajik, 5 Uzbek, 4 Turkmen and 5 Pashtun samples using Illumina 650 K SNP array. In addition 1485 samples were taken from published data  [43, [48] [49] [50] [51] [52] [53]  (See Figure  1  and Table  S2  for population description from published data). Admixture analyses were performed including African populations given the African component in some populations in Pakistan (Makrani, Balochi, Brahui). No Native American populations were included in the Admixture analyses. Autosomal genotypic data may be accessed through The National Center for Biotechnology Information-Gene Expression Omnibus (NCBI GEO), by request to the authors or on the website http://evolutsioon. ebc. ee/MAIT/ public_data/index.
Quality control We used PLINK 1. 07  [54]  in order to only retain SNPs on the 22 autosomal chromosomes that had a minor allele frequency. 1%, a genotyping success. 97%; additionally, only individuals that had a genotyping success rate. 97% were used. Also, since Linkage Disequilibrium (LD) can affect principal component and structure-like analyses, the marker set was further thinned by excluding SNPs in strong LD (pairwise genotypic correlation r 2. 0. 4) in a window of 200 SNPs (sliding window of 25 SNPs)  [52]. On average, approximately 232,000 SNPs were left depending on the populations/samples retained for the specific analyses. Genetic clustering analysis. To study the population's genetic structure in the dataset of over 232,000 genome-wide SNPs, we used a structure-like  [55]  model-based maximum likelihood (ML) clustering approach implemented in ADMIX-TURE  [56].
In order to depict the spatial distribution of the ACs detected in Eurasia, the proportions of ACs 3, 4, 6, 7, 8 and 9 as resolved at K = 9 were then displayed on a color-graded map. Geographic correlation. Correlation between spatial distribution of the ACs detected in Eurasia and each proportion of ACs 1 to 9 as resolved at K = 9 was tested with the Pearson test (significance alpha value = 0. 05) using XLSTAT 7. 5. 2 software. Principal component analysis and F ST. As the inclusion of African samples results in the first Principal Component (PC) sorting African samples versus non-African samples, the former were excluded from the analysis. The LD pruning procedure was repeated on the 1183 samples left. Pairwise genetic differentiation between populations with sample size. 5 was estimated with the F ST index.
Additionally, a total of 39 Y-STRS (DYS385a-b, DYS388, DYS389a, DYS389B, DYS390, DYS391, DYS392, DYS393, DYS394/19, DYS426, DYS437, DYS438, DYS439, DYS441, DYS442, DYS444, DYS445, DYS446, DYS447, DYS448, DYS449, DYS452, DYS454, DYS455, DYS456, DYS458, DYS459a-b, DYS460, DYS461n(TAGA)n, DYS462, DYS463, GGAAT1B07, YCAIIa-b, YGATAA10, YGATAC4/Y_DYS635, YGATAH4) were genotyped using two multiplex reactions. Electrophoresis of the amplified fragments, mixed with formamide and 500 LIZ internal Size Standard was carried out in an ABIPRISM 31030XL Genetic Analyzer. Interpretation was performed by GeneScan ID 3. 2 fragment analysis software.
Haploid database construction We gathered mtDNA and Y-chromosome haplogroup frequency data from published data focused on Central Asian populations. Concerning mtDNA, a total of 14,308 HV1 sequences from 214 populations were included (Table  S4 ). Haplogroups were assigned according to Phylotree. org (build 11, February 7 th )  [59]. Concerning the Y-chromosome, the data set was initially built from 442 Eurasian populations totalizing 23,800 men from 68 bibliographic references. Since the studies have not all used the same level of resolution for SNP genotype samples, we needed to determine the consensus level of phylogenetic depth in the Ychromosome tree. We therefore agreed to 34 male lineages and summed all frequencies within each:   S4 ). C-M130(xPK2), C3a-PK2, D-M174, E-M96(xP147xM75), E1-P147, E2-M75, F-M89, G- M201, G1-M285, G2-P287, H-M69, H1a-M82, K-M9, I-M258, I1-M253, I2-M438, J-M304, J1-M267, J2-M172, J2a-M410, L- M11, L1a-M76, L1b-M317, M-P256, N-M231, O-M175, P-M74, Q-M242 (xM25), Q1a2-M25, R-M207 (xM449, M343, M479), R1a-M449, R1b-M343, R2-M479,.
Concerning the Y-chromosome, we used the 37 populations from Afghanistan, Iran, Kyrgyzstan, Mongolia and Pakistan screened for the high-resolution 102 Y-SNPs. In order to fairly compare the genetic structure of the female population with that of the male one, we selected a subset of 27 populations from Iran, Mongolia and Kyrgyzstan (totalizing 3067 HVS-I sequences) from the mtDNA database described above (Table  S4 ) and compared them with our Afghan data. Factorial Correspondence Analysis. We ran two levels of factorial correspondence analysis (FCA) using XLSTAT 7. 5. 2 software. Given the depth of resolution of the 102 Y-SNPs herein examined, we first focused on the genetic relationships between the 84 most-derived male lineages in 37 populations from Afghanistan, Iran, Pakistan, Kyrgyzstan and Mongolia.
Afterwards, we extended the comparison between populations to a subcontinental scale using our databases described above (Tables  S3 ). Median Joining Network. Networks were constructed by the median joining method using Network 4. 5. 0. 2, where e = 0 and microsatellite loci were weighted proportionally to the inverse of the repeat variance observed in each haplogroup  [65]. Spatial correlation of Y-chromosome data. To further explore the links between Y-chromosome distribution and geography, we first conducted a Mantel test using 37637 matrices of Fst and geographic distance (in kilometers using version 1. 2. 3 of the Geographic Distance Matrix Generator). To elucidate the nature of this association, we investigated the role of latitude and longitude on the frequency distribution of the major Eurasian Y haplogroup, suggested as genetic markers of the most striking historical events (Mongol expansion, Neolithic demic diffusion, Indo-Iranian expansion): C3b2b1-M401, J2a1-Page55 and R1a1a-M198  [35, 46, 66].
Autosomal analyses Autosomal variation in Eurasian populations was analyzed via genetic structure in a dataset of over 232,000 genome-wide SNPs, depicted by a structure-like clustering approach implemented in ADMIXTURE. None of the genetic structure simulations (K = 2 to K = 15, see Figure  S1 ) show any ancestral component (AC) specific to, or even dominant in Central Asia, except for the Kalash (see below). We identified nine ACs which reflect geographically localized sets of SNPs with shared genetic ancestry in these regions. To get a better idea of the spatial distribution of the so-defined autosomal ACs, the proportions of AC 3, 4, 6, 7, 8 and 9 as resolved at K = 9 (Figure  S2 ) were depicted on a map (Figure  2 ). The proportions of AC 3, 4, 6, 7, 8 and 9 as resolved at K = 9 displayed high correlation with geography, either with latitude or with longitude, or both (Figure  S3 ).
Afghan populations are placed inbetween the Kyrgyz, Iranian and Pakistani populations suggesting a genetic influence across these parts of East Asia. FC plots in Figures S9 show 34 Y-chromosome haplogroup frequencies from our Central Asian database (see Table  S4 ) and from the samples in this study (Afghanistan, Iran, Mongolia, Pakistan, Kyrgyzstan). Language affiliation showed that Altaicspeaking populations stretched from peripheral Tungusic, and Mongolic to Turkic, which merge with Dravidian and Indo-European groups. The Indo-European-speaking populations were more spread out, Indo-Aryan and Balto-Slavic were in tight formation, whereas Indo-Iranian speakers were the most dispersed. When geographic affiliation was considered, populations respectively from East, North, South and West Eurasia and from Caucasus displayed well-defined clusters.
Discussion Central Asia, defined as the region containing Kazakhstan, Uzbekistan, Turkmenistan, Kyrgyzstan, Tajikistan, Afghanistan and the northern part of Pakistan, has gathered a growing and ongoing interest from archaeologists and anthropologists. Retracing the main historical events in the gene pool of the present Afghan populations has been strongly restricted, because of sampling work in this country being inadvised, with the exceptions of recent Y chromosome studies  [30] [31] [32]. Herein, we contribute to fill this gap by providing a detailed genetic picture of the five main ethnic groups inhabiting the mountainous region of the Hindu Kush. Autosomal, mtDNA and Y-chromosome data (including 6 new Y-SNPs) was enriched with 672 original male samples from Iran, Kyrgyzstan, Mongolia and Pakistan and three exhaustive databases from published work.
Here we improve the phylogenetic resolution within the Ychromosome haplogroup C3-PK2 by identifying SNPs describing two bifurcating subclades, C3a-M386 and C3b-M532 that accounted for all C3-PK2 derived chromosomes in our dataset. Another improvement to C3 topology involves new sub-haplogroups within the C3b-M532 component including C3b2b1-M401 that circumscribes the Mongol 'star cluster' YSTR haplotype  [61]. The amplified C3b2b1-M401 signal found in Afghan Hazara and Mongols as well as in the Kyrgyz shows a correlation with latitude and longitude. The enhancement of resolution within haplogroup C3 has important implications for future studies. First, it should allow tracking of the Mongol invasions by Genghis Khan and identification of affiliated descendants since the 13th century, as well as detection of possible dispersal of C3 lineages during prehistoric migrations  [81, 82, 84].
Sample sources We assayed the gut microbial communities, as present in fecal samples, of gorilla subspecies Gorilla gorilla gorilla (western lowland gorilla) from Cameroon and the CAR, and of gorilla subspecies Gorilla beringei graueri (eastern lowland gorilla) from the DRC. These data were merged with sequences generated previously from the gut microbiomes of chimpanzee subspecies Pan troglodytes ellioti from Cameroon, Pan troglodytes troglodytes from Cameroon, Pan troglodytes schweinfurthii from Tanzania, and Pan paniscus from the DRC  (Ochman et al. 2010; Degnan et al. 2012) , yielding a data set containing 1,321,276 processed reads and averaging 15,544 reads per sample. These reads clustered into 2,392 99% operational taxonomic units (OTUs) (i. e. , phylotypes). A map of field collection sites is presented in Figure  1 , and detailed sample information is presented in Supplemental Table  S1.
Sequence filtering Pyrosequencing reads were processed in QIIME version 1. 5. 0  (Caporaso et al. 2010). Flowgrams were obtained in SFF file format and converted to FASTA and QUAL files with process_sff. py. Reads were assigned to their respective samples based on their identifying barcode with split_libraries. py, allowing a minimum quality score of 28 and no errors in the barcode. Reads attaining these quality standards were assigned to 99% OTUs with pick_otus. py using the uclust algorithm and classified with assign_taxonomy. py using the RDP classifier. An OTU table was produced with make_otu_table. py. To eliminate pyrosequencing errors and to focus our analyses on primary constituents of the gut communities, the OTU table was filtered to remove all OTUs represented by fewer than 50 reads.
Acknowledgments We thank  Kim Hammond  for assistance with the preparation of figures;  Eitel Mpoudi Ngolle  and the  SIV team of Project PRESICA  for field work and logistical support in Cameroon; the  Cameroonian Ministries of Health, Forestry and Wildlife , and Research for permission to collect samples in Cameroon; the Water and  Forest Ministry  for permission to collect samples in the Central African Republic; the  Ministry of Scientific Research and Technology , and the  Department of Ecology and Management of Plant and Animal Resources of the University of Kisangani  for permission to collect samples in the DRC. This work was supported by grants from the  National Institutes of Health  ( R01 AI50529 ,  R01 AI58715  to B. H. H. and  R01 GM101209  to H. O. ), the  Agence Nationale de Recherche sur le Sida  ( 12152/12182 ), and a  National Science Foundation Graduate Research Fellowship  to A.
The median value of the CIRRUS flag in our sample is 0. 093 at 857 GHz, a low value compatible with no cirrus contamination when used in conjunction with the EXTENDED=0 flag (e. g. Herranz et al. 2012). 3. We query the NED and SIMBAD databases at the positions of all our ERCSC sources using a 2. ′ 5 search radius. Each Planck source has many matches (many of them completely unrelated, e. g. foreground stars), and the identification is more complex at higher frequencies. However, as we show later, our N(> S ) cumulative distribution of sources is always less than 200 sources per steradian, i. e. less than 3. 3 × 10 -4 ERCSC sources per 2. ′ 5 search radius on average. We thus search for the most probable match by identifying the source type in this order: Galactic, then extragalactic. The Galactic types include supernova remnants, planetary nebulae, nebulae, Hii regions, stars, molecular clouds, globular/star clusters.
We call a source "Galactic unsecure" when one of the two databases returns no identification and the other a Galactic identification. We do not use "Galactic secure" or "Galactic un-secure" sources in Here α 217 143 is shown as a dotted line, α 353 217 as a dashed line, and α 857 545 as a solid line. The region 2 ≤ α ≤ 4 is typical of thermal dust emission. In red we show the dusty sources, whereas in blue we show the synchrotron sources. The sources in all three samples (deep, medium, and shallow) are combined here. Note that, as expected, the 100 GHz, and 143 GHz samples are dominated by radio galaxies, whereas the 545 GHz and 857 GHz samples are dominated by dusty galaxies. At 217 GHz and 353 GHz we observe the transition between the two populations, with significant numbers of both types being present in the samples.
Nature of the Galaxies at submillimetre and millimetre wavelengths The change in the nature of sources (synchrotron dominated vs. dusty) with frequency was first observed in the Planck data in Planck Collaboration VII (2011). Our new sample allows a more precise quantification because of its completeness. The statistics of synchrotron vs. dusty galaxies are summarised in Fig. 8 , showing the fraction of galaxy type as a function of frequency. We estimate the uncertainty in the classification to be of the order of 10 % (see Appendix A). The striking result is the almost equal contribution of both source types near 300 GHz. The high frequency channels (545 and 857 GHz) are, unsurprisingly, dominated (> 90 %) by dusty galaxies. The low frequency channels are, unsurprisingly, dominated (> 95 %) by synchrotron sources at 100 and 143 GHz.
Planck Number Counts Compared with Other Datasets The number counts are in fairly good agreement at lower frequencies (100 to 217 GHz) with the counts published in the Planck early results, based on a 30 GHz selected sample (Planck Collaboration XIII (2011); represented as diamonds in Fig. 9  and 10). The effect of incompleteness in the latter is seen in the fainter flux density bins, below about 500 mJy. We also notice a slight disagreement around 400 mJy at 217 GHz, where our counts of synchrotron galaxies exceed the Planck early counts by a factor of 1. 7 (13. 7 ± 1. 5 vs. 8. 2 ± 0. 9 Jy 1. 5 sr -1 ). This discrepancy can be easily understood: our current selection of syn-chrotron sources is not the same as the one adopted in the Planck Early results paper Planck Collaboration XIII (2011), in which a more restrictive criterion was used (α 217 143 < 0.