dataesr/scientific-paragraphs-categorization huggingface dataset:
admixed individuals. We also excluded 21 recent admixed Ainu individuals from the and 4 according to the Ainu-15 sample set described in Jinam et al. (2015). In 6 addition, the five further possible admixed Ainu individuals were excluded based on the 7 ADMIXTURE result (fig. 4) from dataset 3. 3 and D statistics were calculated using qp3Pop and qpDstat, respectively, in the 9 ADMIXTOOLS package (Patterson et al. 2012). PCA was performed using smartpca in 10 the EIGENSOFT package (Price et al. 2006). The most likely number of K in the 11 ADMIXTURE analysis was assessed by five-fold CV (supplementary fig. S13). The 12 qpAdm modeling were also performed using ADMIXTOOLS package. In this modeling, 13 the Ami, Dai, Mixe, Mbuti, Nganasan, and Papuan were used as outgroups and NAT002 14 was modeled by three-way admixture (F23-Itelmen-Oroqen).
The validity of the 15 proposed model was evaluated based on LRT for the proposed admixture model against 16 full rank model. In addition, we explored whether a two-way admixture model can 17 adequately explain the NAT002 genome based on P values for nested models. P values 18 for the nested models were calculated using R 4. 1 based on the difference in chi-squares 19 and the difference in degrees of freedom between each two-way admixture model and 20 three-way admixture model. Haplotype phasing for dataset 3 was performed using Delaneau et al. 2013) based on the phased data and genetic map of the 1000 The 1000 Genomes Project Consortium 2015). Local 23 ancestries were inferred using RFMix (Maples et al. 2013) and ancestry_pipeline (Martin 24 et al. 2017). The SGDP Even, Daur, Hezhen, Oroqen, Xibo, and Mongola were used as Amur reference, F23 (and Ainu in the replication analysis) as the Jomon reference, 2 and the SGDP Itelman, Chukchi, Eskimo-Chaplin, Eskimo-Naukan, and Eskimo-Sireniki 3 as the Chukot-Kamchatka reference.
We determined the HLA type of this individual using HLA-VBSeq (Nariai et al. 2015), 14 TARGT (Pierini et al. 2020), and HLA EXPLORE (Omixon, Budapest, Hungary). 15 Because inconsistent results were obtained for HLA-B (heterozygote of B*40:02 and 16 B*40:06 or homozygote B*40:06) and HLA-DRB1 (heterozygote of DRB1*14:05:01 and 17 DRB1*14:54:01 or homozygote of DRB1*14:54:01) between HLA-VBSeq and the other 18 two programs, we checked the alignment results of these loci with BLAST search using 19 IMGT/HLA database release 3. 43. 0. To confirm the homozygosity/heterozygosity of 20 NAT002 for HLA region, we investigated alternative allele frequencies at 1KG variant 21 sites sequenced with depths ≥ 15× in NAT002 sequence data.
4 5 datasets 1 8 f 21 SHAPEIT2( 22 Genomes Project Phase 3 ( 1 the 518212231 admixed individuals. We also excluded 21 recent admixed Ainu individuals from the and 4 according to the Ainu-15 sample set described in Jinam et al. (2015). In 6 addition, the five further possible admixed Ainu individuals were excluded based on the 7 ADMIXTURE result (fig. 4) from dataset 3. 3 and D statistics were calculated using qp3Pop and qpDstat, respectively, in the 9 ADMIXTOOLS package (Patterson et al. 2012). PCA was performed using smartpca in 10 the EIGENSOFT package (Price et al. 2006). The most likely number of K in the 11 ADMIXTURE analysis was assessed by five-fold CV (supplementary fig. S13). The 12 qpAdm modeling were also performed using ADMIXTOOLS package. In this modeling, 13 the Ami, Dai, Mixe, Mbuti, Nganasan, and Papuan were used as outgroups and NAT002 14 was modeled by three-way admixture (F23-Itelmen-Oroqen).
13 We determined the HLA type of this individual using HLA-VBSeq (Nariai et al. 2015), 14 TARGT (Pierini et al. 2020), and HLA EXPLORE (Omixon, Budapest, Hungary). 15 Because inconsistent results were obtained for HLA-B (heterozygote of B*40:02 and 16 B*40:06 or homozygote B*40:06) and HLA-DRB1 (heterozygote of DRB1*14:05:01 and 17 DRB1*14:54:01 or homozygote of DRB1*14:54:01) between HLA-VBSeq and the other 18 two programs, we checked the alignment results of these loci with BLAST search using 19 IMGT/HLA database release 3. 43. 0. To confirm the homozygosity/heterozygosity of 20 NAT002 for HLA region, we investigated alternative allele frequencies at 1KG variant 21 sites sequenced with depths ≥ 15× in NAT002 sequence data.
com/gbeManuscripts submitted to Genome Biology and Evolution 5 1983; 8 Allele Frequency Net Database (http://www. allelefrequencies. net). 9 No shared HLA alleles were observed between NAT002 and F23 (homozygote 10 of A*24:02:01, B*15:01:01, and C*03:03:01, which were previously genotyped in 11 Kanzawa-Kiriyama et al. 2019); therefore, from the perspective of the HLA gene, we12.
github. io/picard). After indel realignment and base recalibration 11 using GATK IndelRealigner and BaseRecalibrator, the average depth was calculated 12 using GATK DepthOfCoverage (McKenna et al. 2010). Phred scores were rescaled based 13 on damage level using mapDmage 2. 0 (Jonsson et al. 2013). 14 The following genotype calling and depth-based filtering procedures were 15 performed according to the method of the SGDP (Mallick et al. 2016). First, genotype 16 calling using GATK UnifiedGenotyper with the modified prior was performed to avoid 17 reference bias. Sample-specific CNV filtering was executed using bam2cnv 18 (http://reichdata. hms. harvard. edu/pub/datasets/sgdp/filters/sample_specific/bam2cnv/).
Downloaded from https://academic. oup. com/gbe/advance-article/doi/10. 1093/gbe/evab192/6355032 by University of Zurich user on 24 August 2021 1 1. Especially, we considered that detecting the Jomon-related genetic component, which 2 cannot be detected without multi-samples of the Jomon and/or Ainu individuals in PCA 3 and ADMIXTURE, was important to overview the genetic feature of NAT002 in this 4 study. Dataset 2 consists of the variants observed among the 31 individuals of the SGDP 5 samples, two individuals of the Nivkh (Raghavan et al. 2014), F23, and NAT002. The 6 variant sites at filtering level 1 were extracted using cpoly in CTools, resulting in a dataset 7 of 2,556,493 variants for 35 individuals. A phylogenetic tree was constructed based on 8 dataset 2 using TreeMix (Pickrell and Pritchard 2012).
Constructing a phylogenetic tree 9 18 for the modern worldwide populations, F23, and NAT002, and then filtered out the SNP 19 sites with missing rates higher than 2%, resulting in a dataset of 49,079 SNPs for 2,244 20 individuals. Dataset 1 was used to carry out an outgroup f 3 test, D test, PCA, 21 ADMIXTURE analysis (Alexander et al. 2009), and qpAdm modeling. Because the PCA 22 and ADMIXTURE are not robust against sample size, data from various sources were 12 recommendation of Mallick et al. (2016). Dataset 1 consists of the genotypes of the 13 overlapping SNPs among the previously reported DNA microarray datasets for modern 14 worldwide populations (Rasmussen et al. 2010; Jinam et al. 2012; Fedorova et al. 2013; 15 Lazaridis et al. 2016). From the F23 and NAT002 genomes, we extracted genotypes for 16 the overlapping SNP sites among the above studies using cpulldown in CTools (Mallick 17 et al.
Manuscripts submitted to Genome Biology and EvolutionDownloaded from https://academic. oup. com/gbe/advance-article/doi/10. 1093/gbe/evab192/6355032 by University of Zurich user on 24 August 2021 1 1 to detect the individuals recently admixed with European populations (supplementary 2 fig. S21). Some Northeast Asian individuals were plotted between the French and 3 17 missing rates higher than 2%, resulting in a dataset of 499,270 SNPs for 329 individuals. 18 Dataset 3 was used for local ancestry estimation. Although this analysis requires some 19 SNP density, the data of Ainu individuals were also essential to validate robustness of our 20 local ancestry estimation and admixture dating (supplementary figs. S17 and S18). 21 Therefore, we used overlapping sites of WGS data of SGDP samples, F23, NAT002, and 22 the array data of the Ainu (Jinam et al.
2012), to ensure as many SNP sites as possible 23 while including the Ainu in the dataset. 14 2012). From the genomes of the SGDP samples, F23, and NAT002, we extracted 15 genotypes for the SNP sites using cpulldown in CTools while allowing missing at the 16 filtering level 0. Next, we merged these genotype data and filtered out the SNP sites with 24 We performed PCA using the Northeast Asian and French populations of dataset http://mc. manuscriptcentral. com/gbe.
It can be applied to any semantic domain and all kinesic forms of expression. The focus on a particular semantic category in specific datasets makes it more feasible to analyze its gestural expressions in detail across gestures and signs. Negation is specifically propitious to this approach as it is deployed both in gestures and signs and as there is a common repertoire of gestures fully integrated in sign languages  (Morgenstern, Blondel, Beaupoil-Hourdel et al. , 2017). The aim of the present study is to indicate that dichotomies between gesture and sign are not as strong as some have claimed. In particular, the description of gestures as idiosyncratic and not decomposable, unlike signs, will be revisited with regards to gestural negation. We approach the expression of negation in its developmental and multimodal aspects according to an integrated model of language that includes "preverbal categories, and takes into account gestures and other kinesic aspects of communication"  (Benazzo & Morgenstern, 2014: 176).
Children's gestures of negation We will review the existing research by starting from the less systematized gestural context to the most grammaticalized context, considering the sign languages of isolated signers (homesign) as an intermediate context. Although we are particularly interested in gestures performed with the upper limbs, we account for the fact that negation can be expressed via all segments of the upper part of our body including the face, the head and the chest. 3 Remarkably,  Franklin et al. (2011: 399)  remind us that in many languages negation and interrogation operators share morphological resources.
Isolated child signers Homesign is a gestural system used by isolated deaf children without exposure to a stabilized sign language  (Cartmill et al. , 2017; Fusellier, 2001; Goldin-Meadow & Mylander, 1998). This acquisition context therefore constitutes a sort of missing link between signing and non-signing children. David is a deaf American child of hearing parents who has not been exposed to any conventional sign language (ASL in this case). Franklin, Giannakidou, and Goldin-Meadow (2011)  have analyzed his productions between 2;10 and 3;11, focusing on interrogative and negative structures 3. The authors have a more logical-formal approach to negation than ours. They consider that negation is defined as a sentential modification. This logical modification in homesign includes both "lexical items" which function as negation markers, and head movements, which the authors consider as negation operators.
Specific-SL signs of negation are more conventionalized than Shared Gestures of negation, but both Shared Gestures and Specific-SL signs respect these polar invariants which therefore constitute potential differentiating semantic features of various types of negation. We thus propose a common origin to what are usually called "co-verbal gestures" of negation, and the SL-core lexicon of negation. This must be tested on larger child datasets in a variety of spoken and sign languages. It could also be extended to the expression of affirmation (as fully complementary to negation). If the analysis of affirmation gestures also brings to light a common formal origin to gestures and signs at the corresponding polar kinesiological level, a system of semantic oppositions could then be coupled with a formal polar opposition system.
Linguistics We collected a new dataset of 3193 datapoints representing 254 basic vocabulary concepts for 98 Transeurasian languages, including dialects and historical varieties (SI 1). We applied Bayesian methods to infer a dated phylogeny of the Transeurasian languages (Extended data Fig. 1 ). Our results indicate a time-depth of 9181 BP (5595 -12793 95%HPD) for the Proto-Transeurasian root of the family, 6811 BP (4404-10166 95%HPD) for Proto-Altaic, the unity of Turkic, Mongolic and Tungusic languages, 4491 BP (2599-6373 95%HPD) for Mongolo-Tungusic, and 5458 BP (3335-8024 95%HPD) for Japano-Koreanic (Fig. 1b ). These dates estimate the time depth of the break-up of a given language family into its subfamilies. We used our lexical dataset to model the expansion of Transeurasian languages in space (SI 3 and 4).
This confirms earlier findings about the dispersal of millet agriculture to Korea by 5500 BP and via the Amur to the Primorye by 5000 BP. 29, 30  Our analysis further clusters Bronze Age sites in the West Liao area with Mumun sites in Korea and Yayoi sites in Japan. This mirrors how during the fourth millennium BP, the agricultural package of the Liaodong-Shandong area was supplemented with rice and wheat. These crops were transmitted to the Korean Peninsula by the Early Bronze Age (3300-2800 BP) and from there to Japan after 3000 BP (Fig. 2b ). While population movements were not linked with monothetic archaeological cultures, Neolithic farming expansions in Northeast Asia were associated with some diagnostic features, such as stone tools  33  and textile technology (SI 7). 31 Domesticated animals and dairying played an important role in the spread of the Neolithic in western Eurasia but, except for dogs and pigs, our database shows little evidence for animal domestication in Northeast Asia before the Bronze Age (SI 6).
We compared the fit of different models by estimating the marginal likelihoods using nested sampling 8 (SI 18) and conclude that the pseudo Dollo covarion model with a relaxed clock has the best fit, and covarion with relaxed clock the next best fit. Both models produce compatible time estimates, though covarion estimates tend to have larger uncertainty (that is, have larger 95% HPD intervals). Time estimates of the CTMC model with relaxed clock are still compatible but even wider, and tend to have a higher mean. All posterior estimates were performed using BEAST v2. 6 9 using adaptive coupled MCMC  10. Detailed specification of the models, priors, hyperpriors and settings used to run these models can be found in the BEAST XML files (SI 19). The results of our Bayesian analysis are visualized as a dated phylogenetic tree of the Transeurasian languages (Extended data Fig.
Linguistic palaeontology We compiled comparative agropastoral vocabularies for each Transeurasian subfamily, i. e. , Turkic (SI 5a), Mongolic (SI 5b), Tungusic (SI 5c), Koreanic (SI 5d) and Japonic (SI 5e). We applied linguistic reconstruction, a procedure for inferring an unattested ancestral state of a language on the evidence of data that are available from a later period, to corresponding words (SI 5). In order to distinguish between inherited and borrowed correspondence sets, we used standard criteria based on the phonology, semantics, morphology and distribution of the word involved, as specified in SI 5. Dividing our dataset into inherited versus borrowed subsistence vocabulary, we determined distinctive spatiotemporal and cultural patterns for each category (SI 5). We applied linguistic palaeontology to our subsistence vocabulary, a historical comparative method that enables us to study human prehistory by correlating our linguistic reconstructions with information from archaeology about the culture of the ancient speech communities that used these words.
Archaeological database We scored 172 cultural traits for a total of 255 Neolithic-Bronze Age archaeological sites/phases from the West Liao river basin (36), the Amur (Jilin, Heilongjiang and inland Liaoning) (32), the Primorye (4), the Liaodong peninsula (37), the eastern steppes (1), the Shandong peninsula (4), the Yellow River basin (2), the Korean peninsula (58) and the Japanese Islands (85). Sites with several major cultural phases were scored separately. The sites date from 8400-1700 BP and include the Early Neolithic to Bronze Age in northeast China, the Middle Neolithic Zaisanovka culture in the Primorye, the Middle-Late Neolithic Chulmun and Bronze Age Mumun cultures in Korea, and the Late Neolithic/Bronze Age Final Jomon and Yayoi cultures in western Japan. Categories of cultural traits scored comprised ceramics (70), stone tools (38), buildings and houses (9), plant and animal remains (26), shell and bone artefacts (17), and burials (12).
Definitions of scored features are found in SI 6 (sheet 2) and further discussion of scoring methods can be found in SI 7. All features were scored as present (1) or absent (0) following published site reports or other literature. The database was used to analyse changes in the distribution of Neolithic and Bronze Age artefacts over time, especially in relation to the spread of agricultural systems in Northeast Asia (SI 7). In addition, the cultural data in our archaeological database were analyzed using Bayesian phylogenetic methods. The cultural data are encoded as a binary alignment, and we applied the same substitution and clock models as for the lexical data. The pseudo Dollo model with relaxed clock fits the data best (SI 20). Since the coefficient of variation of the relaxed clock exceeded 1, which indicates a considerable amount of variation, we also ran the analysis with the standard deviation capped at 1, which only slightly affected time estimates.
Archaeobotanical database In addition to the database of archaeological features, we also compiled a list of the earliest cereal remains from each region of Northeast Asia directly dated by radiocarbon (SI 9). This list comprises 268 samples (China: 82; Primorye: 12; Korea: 31; Japan (excluding Ryukyus): 119; Ryukyu Islands: 24). Radiocarbon dates in this database were re-calibrated using OxCal 4. 4. Our databases were further supplemented by published datasets for faunal remains  16, 17  , dolmens  18  , and spindle whorls  19. We used kernel density mapping to plot the spread of.
Sequence data processing Raw sequencing reads were processed by an automated workflow with the EAGER v1. 92. 55 programme. 21 Illumina adapter sequences were trimmed from the sequencing data and overlapping pairs were merged with AdapterRemoval 2. 2. 0. 22 We mapped the merged reads with a minimum of 30 bp to the human reference genome (hs37d5; GRCh37 with decoy sequences) using BWA v0. 7. 12. 23 We removed PCR duplicates by DeDup v0. 12. 2. 17 To minimise the impact of post-mortem DNA damage on genotyping, we masked 2 bp for non-UDG libraries and 10 bp for half-UDG libraries on both ends per read using the trimbam function on bamUtils v1. 0. 13. 24 The cleaned reads with both base quality (Phred-scale quality) and mapping quality (Phred-scale mapping quality) over 30 were piled up by SAMtools 1.
3  19  with the mpileup function. We called pseudo-diploid genotypes using the pileupCaller program [https://github. com/stschiff/sequenceTools] against SNPs in the '1240K' panel  25, 26  under the random haploid calling mode. For C/T and G/A SNPs, we used the masked BAM files, and for the rest we used the original unmasked BAM files.
Reference datasets We compared our ancient individuals to two sets of world-wide genotype panels, one based on the Affymetrix HumanOrigins Axiom Genome-wide Human Origins 1 array ('HumanOrigins'; 593,124 autosomal SNPs)  27  , the '1240k' panel. 20, 28 We augmented both data sets by adding the Simons Genome Diversity Panel 29 and published ancient genomes (SI 11).
Ancient DNA authentication We applied multiple criteria to confirm the authentication of the newly published ancient genomes from northern China, Korea and Japan. First, we characterized the post-mortem chemical modifications characteristic for ancient DNA using mapDamage v2. 0. 6. 30 Second, we estimated mitochondrial contamination rates for all individuals using Schmutzi v1. 5. 1. 31 rd, we measured the nuclear genome contamination rate in males based on X chromosome data as implemented in ANGSD v0. 910. 32 Since males have only a single copy of the X chromosome, mismatches between bases, aligned to the same polymorphic position, beyond the level of sequencing error are considered as evidence of contamination.
Population structure analysis We performed a Principal component analysis (PCA) with the smartpca v16000 33 using a set of 2,077 present-day Eurasian individuals from the 'HumanOrigins' dataset and the '1240k-Illumina' dataset with the option 'lsqproject: YES' and 'shrinkmode: YES'. We used outgroup-f3 statistics  34, 35  to obtain a measurement of genetic affinity between two populations since their divergence from an African outgroup. We calculated f4 statistics with the 'f4mode: YES' function in the admixtools. 31 Both f3 and f4 statistics were calculated using qp3Pop v435 and qpDstat v755 in the admixtools package.
Admixture modeling with qpAdm We modelled the ancient individuals in this study using the qpWave/qpAdm framework (qpWave v410 and qpAdm v810) in the admixtools v5. 1 package. 22 We used the following 9 populations in '1240k' datasets as outgroup ("OG"): Mbuti, Natufian, Onge, Iran_N, Villabruna, Mixe and, Ami. This set includes an African outgroup (Mbuti), early Holocene Levantine hunter-gatherers (Natufian), Andamanese islanders (Onge), early Neolithic Iranians from the Tepe Ganj Dareh site (Iran_N), late Pleistocene European hunter-gatherers (Villabruna), Central Native Americans (Mixe), and an indigenous group native to Taiwan (Ami).
Building on previous applications of triangulation in anthropology  37  , we applied the method to the dispersal of the Transeurasian languages, integrating linguistics, archaeology and genetics to contribute to a better understanding of the phenomenon. We collected different datasets and applied the variety of methods described above to draw independent inferences with regard to a number of variables such as location, chronology, migratory dynamics, continuity vs. diffusion, and subsistence patterns (Extended data Table  1 ).
cereals in this database over time across Northeast Asia. The results are shown in SI 7 and Extended data Fig. 10. 3. Genetics 3. 1. Laboratory procedures Ancient DNA wet lab work, including the DNA extraction and library preparation was performed in a dedicated ancient DNA clean room facility at the MPI-SHH in Germany and in an ancient DNA lab at Jilin University in China following established protocols. 20 A double-stranded library was built with 8-mer index sequences at both P5 and P7 Illumina adapters. Four individuals from China characterised in Jilin were directly shotgun sequenced on the Illumina HiSeq X10 instrument in the 150-bp paired-end sequencing design to obtain an adequate coverage. 54 double-stranded libraries for 33 individuals from Korea and Japan were generated and characterised in the MPI-SHH either by shotgun sequencing or by insolution capture at approximately 1.
Aligning the evidence offered by the three disciplines, we gained a more balanced and richer understanding of Transeurasian migration than each of the three disciplines could provide us with individually. SI 1. Comparative dataset including 3193 datapoints representing 254 basic vocabulary concepts for 98 Transeurasian languages 16_Eurasia3angle_synthesis_SI 1_BV 254. xls SI 2. Basic vocabulary etymologies across the Transeurasian languages, underlying semantically equivalent cognate sets scored as (1) in SI 1 17_ Eurasia3angle_synthesis_SI 2_basic etymologies. doc SI 3. Bayesian phylogeographic analysis modelling the spatiotemporal expansion of the Transeurasian languages 18_Eurasia3angle_synthesis_SI 3_phylogeography. klm SI 4. Integration of qualitative assessment methods and Bayesian phylogeography in identifying the ancestral homelands of Transeurasian 19_Eurasia3angle_synthesis_SI 4_homelands.