dataesr/scientific-paragraphs-categorization huggingface dataset:
Expertise documents Altermaker is a young start-up specialized in software development to support design for sustainability. The company led analysis on industrial materials and processes used in mechanical engineering. The results are stocked as MsPowerpoint documents in which several elements are defined for each material or process: advantages, disadvantages, short description and specific comparisons. In these documents (Fig 1 ), several features are interesting to consider and other ones need more analysis. Our study aims at analysing these documents in order to define the concepts that show the main features of given materials and processes.
, 2018b). However, the center of gravity of Denisovan admixture today lies >8000 km south of Denisova Cave in the Papuan populations of tropical eastern Indonesia and New Guinea, where the composition of Denisovan introgression remains poorly understood. We therefore analyzed archaic introgression in a new dataset covering Island Southeast Asia (ISEA) and Papua, a maritime zone of densely inhabited archipelagos larger than Europe. This culturally and linguistically diverse region remains strikingly underrepresented in modern genetic surveys, despite its extraordinary human diversity, and is a major missing link for medical and evolutionary studies  (Horton, 2016). Notably, the area has some of the first traces of anatomically modern humans in Eurasia  (Barker et al. , 2007) ; archaic H. floresiensis likely coexisted with modern humans here  (Sutikna et al.
Results and Discussion The Indonesian Genome Diversity Project fills a gap in regional coverage The Indonesian Genome Diversity Project (IGDP) has been run by the Eijkman Institute of Molecular Biology in Jakarta for over a decade, with the goal of capturing a representative sample of genomic diversity across this understudied region. Spanning a transect of communities across the Indonesian archipelago and neighboring regions of ISEA, populations chosen for whole genome sequencing were selected to reflect the main axes of genomic variation observed in an earlier population genetic study  (Hudjashov et al. , 2017). We sequenced complete genomes to >30x coverage for 161 individuals, from Sumatra in the west to New Britain in the east (Fig. 1 ). We combined this new dataset with 317 additional high-coverage human genomes sampled world-wide, including those few genomes previously available for ISEA and Oceania  (Malaspinas et al.
, 2016; Mallick et al. , 2016) , and three complete archaic hominin sequences, the Altai and Vindija Neanderthals  (Prüfer et al. , 2017; Prüfer et al. , 2014)  and the Altai Denisovan  (Meyer et al. , 2012)  (Fig. S1 , Table  S1 , STAR Methods S1-S5). [Figure  1  here] To confirm that the dataset captures expected genomic patterns, we calculated principal components and determined local ancestry along the genome (Dias-Alves et al. , 2018) (Fig. 1 , Table  S2 , STAR Methods S2 and S6). We observed key features of population diversity in the region, notably a strong cline in Asian to Papuan ancestry across the archipelago with an abrupt transition within the island zone of Wallacea  (Cox et al. , 2010; Hudjashov et al. , 2017). These signals primarily reflect recent events of regional history, particularly the agricultural expansion of Austronesian-speaking populations from ca.
, 2016) , which identifies clusters of linked non-African variation. These haplotypes were then filtered, using a range of protocols (Fig. 2A-C , STAR Methods S9a), to remove blocks that were also similar to the Altai Neanderthal  (Prüfer et al. , 2014)  and optionally the Vindija Neanderthal  (Prüfer et al. , 2017) , as measured by CP, leaving a dataset of high-confidence introgressed Denisovan regions. [Figure  2  here] A clear correlation emerged between loci identified by the three methods. Most archaic introgression was detected in Papua and East ISEA, with the least in West Eurasia. Our multi-step filtering approach enriched detectable Denisovan introgression in Papuans relative to West Eurasians (Fig. 2A-C , STAR Methods S9c and S9d), who are thought to harbor little Denisovan introgression  (Mallick et al.
Denisovan populations introgressed into Papuans twice To determine whether there is structure within the Denisovan sequences, we calculated mismatch distributions between our high-confidence Denisovan blocks and the high-coverage Altai Denisovan genome (STAR Methods S10). Small ancestry blocks can be a problem for mismatch analysis, because the mismatches of individual haplotypes have an imprecise correspondence to genetic divergence caused by the low resolution offered by a small number of stochastic, discrete polymorphisms (STAR Methods S10a) and because small blocks are more often affected by incomplete lineage sorting. We therefore profiled mismatches across a range of block lengths in Papuans, the population in our dataset with the highest Denisovan introgression. Intriguingly, we observe two clearly separate mismatch peaks in Papuans (Fig.
5E ). The overall genetic similarity and relatively recent divergence of these Papuan groups (Fig. 1  and 5C, STAR Methods S10h, S10j) has implications for the past distribution of D1 Denisovan populations and the process of archaic introgression. First, our data suggest that the D1 Denisovans, in contrast to D2, contributed additional DNA to the mainland New Guinea population after the mainland and Baining populations diverged from their common Papuan ancestor (Fig. 5E ). This together with the nearly complete absence of D1 in continental Asia is most consistent with the scenario that D1 Denisovans were present in New Guinea or East ISEA (e. g. , Wallacea). In turn, this would imply that at least some Denisovan populations had the ability to cross large bodies of water, such as the one represented by the Wallace Line.
Limited evidence of further introgression complexity in East ISEA and Papua Given the recent presence of Homo floresiensis in our study area  (Brown et al. , 2004; Sutikna et al. , 2016) , and the possibility that late Homo erectus was contemporary with the earliest anatomically modern humans in ISEA  (Yokoyama et al. , 2008) , we investigated whether there might be any hints of archaic hominin ancestry, other than Denisovan or Neanderthal in the dataset. We attempted to detect such signals by analyzing S* windows that exhibit minimal overlap with Denisovan or Neanderthal blocks as identified by CP and HMM (residual S*, STAR Methods S12). We first note a pronounced excess in total S* signal in our Papuan samples (97. 2 Mb) compared to East Asians (50. 9 Mb), South Asians (48. 3 Mb) and West Eurasians (40.
erectus, as has been recently suggested for other Asian populations  (Mondal et al. , 2016). For instance, the Altai Denisovan is also thought to have some H. erectus ancestry  (Lipson and Reich, 2017; Mallick et al. , 2016; McColl et al. , 2018; Prüfer et al. , 2017; Skoglund et al. , 2016) , although it is not yet clear whether this is also true for introgressing Denisovan populations. Equally, however, these genomic signals could arise without further introgression events, notably through balancing selection or incomplete lineage sorting, and so warrant careful further study. Finally, our dataset includes Rampasasa, a village on Flores that is close to the cave site where the H. floresiensis bones were found  (Sutikna et al. , 2016) , and whose inhabitants were the subject of a recent genetic study  (Tucci et al.
J Hum Evol 55, 274-277. S2 , STAR Methods S6). Inner pie charts indicate archaic introgression estimates: green -unambiguous Denisovan introgression, yellow -unambiguous Neanderthal introgression, light grey -introgression consistent with either Neanderthal or Denisovan ancestry (Table  S3 , STAR Methods S9); the total area of each pie chart corresponds to the total amount of unambiguous Denisovan introgression signal, with Papua 13. 7-fold greater than West Eurasia. (B) PCA of the new dataset including non-African reference samples, showing two major axes of variation -a 'Papuan' axis stretching from New Guinea Papuans/New Britain Baining to West Eurasians, with ISEA intermediates, and an 'Asian' axis stretching from Papuans/West Eurasians to East Asian samples (STAR Methods S2). S2  and S3 , STAR Methods S9).
S1 -Sequencing and SNP calling Sequencing libraries were prepared using TruSeq DNA PCR-Free and TruSeq Nano DNA HT kits depending on DNA quantity. 150 bp paired-end sequencing was performed on the Illumina HiSeq X sequencer. Individuals were sequenced to expected mean depth of 30x, with an achieved median depth of raw reads across samples of 43x. These newly generated whole genome sequences were combined with the following published genomes (raw reads): a) 292 genomes from the Simons Genome Diversity Project (SGDP)  (Mallick et al. , 2016 )) b) 25 Papuan genomes from the  Malaspinas et al. (2016)  study SNP calling was performed on the combined dataset, with published genomes analyzed from raw reads exactly as for the new sequence data. Trimmomatic v. 0. 38  (Bolger et al. , 2014)  was used to cut adapters and low-quality sequences from the reads.
After trimming, the vast majority of reads were longer than 145 bp; those below 60 bp were excluded. We aligned the reads to the 'decoy' version of the GRCh37 human reference sequence (hs37d5) using BWA MEM  (Li, 2013). We removed duplicate reads with picard-tools v. 2. 12. 0 (http://broadinstitute. github. io/picard) and performed local realignment around indels with GATK v. 3. 5  (Poplin et al. , 2017). After alignment, and keeping only properly paired reads that mapped to the same chromosome, the sequencing depth across the samples used in downstream analyses was as follows: min = 18x, Q1 = 35x, median = 38x, Q3 = 43x, max = 48x. Only three samples had median coverage rates below 30x: CBL34, RAM005 and RAM067. Base calling was undertaken with GATK v. 3. 5 following GATK best practices. Per-sample gVCF files were generated using GATK HaplotypeCaller (using only reads with mapping quality ≥20).
Additionally, we did not apply the call rate filter in the motif-counting analysis in STAR Methods S12). Applying this 99% call-rate filter yielded a total of 36,462,963 SNPs in the combined dataset. We removed sites within segmental duplications, repeats and low complexity regions. These masks were downloaded from the UCSC and Broad Institute genome resources: http://hgdownload. soe. ucsc. edu/goldenPath/hg19/database/genomicSuperDups. txt. gz http://software. broadinstitute. org/software/genomestrip/node_ReferenceMetadata. html In the filtered and masked VCF files, we examined several statistics across the samples: the percentages of no-calls and singletons; the average depth; transition/transversion ratio; the number of variants; and heterozygosity. One highly heterozygous sample from the SGDP (LP6005441-DNA_A09, Naxi-2) was excluded based on these metrics, as well as on the basis that the original authors determined that this sample had been contaminated  (Mallick et al.
S2 -Kinship and outlier analysis We performed sample kinship analysis using KING v. 2. 1  (Manichaikul et al. , 2010). Of the 161 new genomes, 6 were excluded due to the presence of a first-degree relative in the dataset, leaving a total of 155 genomes for downstream analysis. This relatedness is a consequence of the village-scale sampling strategy employed in this study. In addition, 7 sample pairs display second-degree relatedness (BNA05 / BNA12-F, BNA21-F / BNA26-F, CBL018 / CBL019, RAM045-F / RAM067, RAM022 / RAM039-F, NIAS08 / NIAS12, NIAS01 / NIAS10). These samples were kept for further analyses, and the final dataset comprised 471 genomes: 155 newly generated complete genomes, 25 genomes from the  Malaspinas et al. (2016)  study and 291 genomes from SGDP. Principal component analysis (PC) was used to detect sample outliers and characterize regional diversity.
S3 -Adding archaic data and ancestral information The combined primary dataset of 471 modern human genomes was merged with two high-coverage archaic hominin genomes: a) Denisovan  (Meyer et al. , 2012). Downloaded from http://cdna. eva. mpg. de/denisova/VCF/hg19_1000g/ b) Neanderthal  (Prüfer et al. , 2014) ). Downloaded from http://cdna. eva. mpg. de/neandertal/altai/AltaiNeandertal/VCF/ Positions with missing or low-quality calls (marked as 'LowQual' in the original VCF files) in one of the archaic samples were excluded during the merging procedure. In addition, heterozygous archaic SNPs were also removed to improve phasing quality (but see later analyses). Both types of SNPs were masked for both archaic and modern individuals. These additional filters resulted in a dataset comprising 35,395,615 SNPs.
A second merged dataset was produced by excluding missing and low-quality archaic calls but retaining SNPs that were heterozygous in archaic individuals. This dataset was used to cross validate our results and assess potential bias (if any) introduced by trimming archaic heterozygous positions. A third dataset was produced by merging the primary dataset containing 471 modern and 2 archaic genomes without heterozygous archaic SNPs with the Vindija Neanderthal genome data (Vindija33. 19, downloaded from http://cdna. eva. mpg. de/neandertal/Vindija/VCF/Vindija33. 19/). As with the main dataset, positions that were missing, low-quality or heterozygous in the Vindija individual were masked for all archaic and modern individuals. We added ancestral allele information to our dataset using the Ensembl Compara 71 database (ensembl_compara_71@ens-staging2:3306, MethodLinkSpeciesSet: 6 primates EPO (  548 )).
S4 -Phasing and phasing assessment The combined dataset comprising both modern and archaic samples was phased using read aware phasing with SHAPEIT v. 2. r837  (Delaneau et al. , 2013). Phase informative reads (PIRs) were extracted using software guidelines from both modern and archaic BAM files. The HapMap phase II b37 recombination map  (International HapMap Consortium et al. , 2007)  was used and the phasing was performed using the following arguments: --states 400 --window 0. 5 --states-random 200. We assessed the performance of our phasing approach, which incorporated phase informative reads, by comparing our phased haplotypes to two experimentally phased individuals  (Prüfer et al. , 2014)  that overlap with the Simons Genome Diversity Project genomes included in our dataset -WON,M (corresponding to sample B_Australian-4 in the Simons dataset, Illumina ID SS6004477) and BUR,E (corresponding to sample B_Australian-3 in the Simons dataset, Illumina ID SS6004478).
QUANTIFICATION AND STATISTICAL ANALYSIS S5 -Dataset genetic diversity and SNP novelty As an exploratory analysis of our phased dataset, we retrieved lists of SNPs in the 1000 Genomes Project (Phase 3) (1000 Genomes Project  Consortium et al. , 2015) , dbSNP (Build 150), ESP (ESP6500SI-V2-SSA137)  (Tennessen et al. , 2012)  and ExAc (ExAcRelease 1. 0)  (Lek et al. , 2016). We counted the number of SNPs defined when considering each population group separately, and determined how many were observed in one or more of these datasets, and how many were not observed. Across all of our newly reported samples, we observed 11,859,578 SNPs, of which 21% (2,525,213) were novel. Existing datasets are better able to capture variation in West ISEA (9. 3% SNPs novel) than East ISEA (14. 3%) or Papua (21.
S6 -Estimating Asian-Papuan admixture proportions To estimate Asian-Papuan admixture proportions in ISEA and Papua, we used local ancestry inference implemented in LOTER  (Dias-Alves et al. , 2018). LOTER has been shown to outperform similar methods, such as HAPMIX  (Price et al. , 2009)  and RFMix  (Maples et al. , 2013) , and its accuracy is greatest when admixture is more ancient than 150 generations. This timeframe (ca 4500 ya) is directly relevant for Indonesian prehistory -linguistic, archaeological and genetic evidence all point to the spread of Austronesian speakers beginning 4000-4500 ya from Taiwan, reaching eastern Indonesia 3500-3000 ya  (Hudjashov et al. , 2017). We specified two reference datasets: Papuans from the current study (N = 72; see Table  S1   S2.
b. Hidden Markov Model To explore the behavior of the CP approach, we additionally implemented a hidden Markov model (HMM, https://github. com/guysjacobs/archHMM). Our approach is inspired by that of Racimo and colleagues  (Racimo et al. , 2017; Seguin-Orlando et al. , 2014) , and we follow their notation where possible. The problem is to partition the genome into blocks of DNA that have introgressed from an archaic hominin and non-introgressed DNA. To do this, we consider a test haplotype vector of SNPs ℎ = (ℎ * ,. , ℎ -) ∈ 0,1 of length k, with each SNP indicated by either a 0 (if ancestral) or 1 (if derived). The SNPs have an associated genetic map vector  = ( * ,. ,  -) where  3 ≥  5 if  >. We assume that each SNP has one of two hidden states (human, 0, or archaic, 1), which form the ancestry vector  = ( * ,.
c. S* We used the S* method  (Plagnol and Wall, 2006; Vernot et al. , 2016; Wall et al. , 2009) , which seeks to detect introgressed haplotypes from archaic hominins without using an archaic reference genome, following the implementation of  Vernot et al. (2016). S* is sensitive towards highly diverged sequences with high LD, and thus is adequate to detect hominin introgressed haplotypes without having a reference sequence of the hominin in question. To calculate empirical S* in our non-African samples, we used 35 sub-Saharan Africans as a reference population and analyzed one non-African sample at a time. We removed any non-segregating sites from our data. We used the HapMap phase II b37 recombination map  (International HapMap Consortium et al. , 2007) , and ancestral information from the Ensembl Compara 6 primates EPO (548) database.
Empirical S* values were estimated in 50 Kb genomic windows. After calculating the S* value from the real dataset, we used simulations to assign statistical significance to our empirical estimates. We performed simulations using different combinations of the number of segregating sites and recombination rates from the previous step. We simulated a previously described demographic model  (Henn et al. , 2015)  with the simulator ms  (Hudson, 2002)  We then used the computationally efficient strategy employed to estimate the S* value for every region as in  Vernot et al. (2016). Finally, we assigned a p-value for each 50 Kb genomic window in every individual sample in our non-African dataset using the neutral distribution of S* from the simulations without archaic introgression. Genomic windows with a recombination rate less than 0.
b. Filtering supported by SGDP introgression values The substantial increase in the enrichment of Denisovan signal in Papuans over West Eurasians following our iterated filtering approach is a strong indication that we are successful in removing spillover signal from Neanderthal introgression. This increased accuracy comes at the cost of decreased power to detect true Denisovan introgression. While the filtering method reduces the Denisovan signal in West Eurasia from 9. 5 to 0. 7 Mb (93%), the signal halves in Papua from 59. 2 to 32. 3 Mb, which is a greater signal depletion in absolute terms despite broadly similar proposed levels of Neanderthal introgression in both regions  (Mallick et al. , 2016). To profile this behavior in real data, we turn to genome-wide estimates of Denisovan and Neanderthal introgression reported for the SGDP samples in our dataset, calculated using counting statistics  (Mallick et al.
, 2016)  as independent variables, and either the total Denisovan sequence identified for each sample by CP alone or our high confidence Denisovan introgressed sequence as the dependent variable. In this analysis, our introgression estimates are expressed as a proportion of introgressed sequence per individual genome, such that the units are the same in the two datasets. Given the high correlation coefficients, the factors in Table  S3C  provide an indication of the amount of spillover signal (significant correlations of our Denisovan introgression estimates with SGDPNean, or of our Neanderthal estimates with SGDPDeni) and the relative power of our methods to detect overall levels of introgression. We assume that the published introgression estimates from the SGDP data are accurate, which is supported by the strong consistency of their results with other published findings (e.
We calculated the scale factor by summing the total number of mismatches between each of the 75 samples × 2 = 150 West Eurasian haploid genomes and the Altai Denisovan, and dividing this by 150 times the total length of unmasked sequence in the dataset. Plotting these mismatches by continent yields the distribution pictured in Main Text Fig. 3C. The variation in the number of blocks >50 Kb between continental groups reflects varying sample size, as well as the total amount of Denisovan introgression and the time since Denisovan introgression in different groups. Two primary patterns emerge. Firstly, we replicate the results of  Browning et al. (2018)  in identifying a mainland Asian-specific peak with relatively low divergence to the Altai Denisovan. This peak is not limited to East Asian populations (in whom it was originally detected), but also extends into Siberia and the Americas.
e. D1/D2 signal is not a bioinformatic bias The set of Papuan samples that we study come from three different sources -30 newly generated sequences, 25 samples from  Malaspinas et al. (2016)  and 17 samples from  Mallick et al. (2016). We used exactly the same pipeline for mapping and base calling on all three datasets, and while we did not observe any obvious differences between data from the three sources during our quality control steps, we thought it possible that the bimodal distribution could conceivably be caused by this batch effect. However, blocks assigned to D1 and D2 were common over all three sample sources, and the number of blocks assigned to D1 and D2 in sequences from the three sources showed no significant deviation from random expectations (χ 2 = 0. 0997; p = 0. 95). While the high coverage of the Altai Denisovan genome argues for high confidence in SNP calls, a bimodal distribution of mismatch distances could be generated by certain quality biases.
122%, D2: 0. 124%; of the 23. 1 Mb and 19. 2 Mb of D1 and D2 sequence identified, respectively). While low-quality SNP calls were masked in the dataset used to identify the two block sets (see STAR Methods S3), a strong bias in the amount of low-quality calls in the genomic regions covered by one block set could imply relevant biases in region quality. We did not observe this. These checks, along with the lack of a bimodal mismatch distribution when comparing introgressing blocks retrieved using the Altai Neanderthal genome against the Altai Neanderthal (Fig. S3C-E ) and the fact that our filtering process to retrieve high confidence Denisovan blocks requires that blocks overlap S* output (which does not use a reference archaic genome), suggest that genetic properties of the Altai Denisovan genome do not substantially contribute to the mismatch difference between D1 and D2 blocks.
We compressed the sets of blocks in the two components to their respective overlapping subsets using the bedtools function 'merge', leaving 86 D1 regions and 68 D2 regions. For each region we calculated the average recombination rate using the same combined HapMap phase II b37 genetic map as used for phasing  (International HapMap Consortium et al. , 2007) ; the distribution of recombination rate between the two chunk sets was not significantly different (standardized 2-sample Anderson-Darling test statistic -0. 60, asymptotic p = 0. 71). Ideally, we would directly profile switch point errors by comparing our Denisovan introgressed blocks to those retrieved using experimentally phased haplotypes. However, experimentally phased data are only available for two Australian samples in our dataset, and given the low Australian sample size, we only called D1 and D2 blocks in Papuans.