dataesr/scientific-paragraphs-categorization huggingface dataset:
Long-Standing Population Continuity in Multiple Regions of South America We grouped ancient individuals by location, date range, and genetic similarity, for the most part using italicized labels like Argentina_ArroyoSeco2_7700BP (''country'' followed by ''site'' followed by a ''date'' that for us is the average of the midpoint of the date ranges for the individuals in the grouping rounded to the nearest hundred)  (Eisenmann et al. , 2018). These groupings sometimes span an extensive period of time; for example, the eight Arroyo Seco 2 date estimates range from 8,570 to 7,160 BP. For some analyses, we also lumped individuals into larger clusters, for example grouping individuals from the Andes before and after $4,200 BP into ''Early Andes'' and ''Late Central Andes'' based on qualitatively different affinities to other individuals in the dataset (STAR Methods).
To obtain an understanding of how the ancient individuals relate to present-day ones, we computed f 3 -and f 4 -statistics, which estimate allele sharing between samples in a way that is unbiased by population-specific drift  (Patterson et al. , 2012). The oldest individuals in the dataset show little specific allele sharing with present-day people. For example, a $10,900 BP individual from Chile (from the site of Los Rieles) shows only slight excess affinity to later Southern Cone individuals. In Belize, individuals from two sites dating to $9,300 and $7,400 BP (Mayahak Cab Pek and Saki Tzul) do not share significantly more alleles with present-day people from the region near Belize than they do with present-day groups elsewhere in Central and South America. In Brazil, genetic data from sites dating to $9,600 BP (Lapa do Santo) and $6,700 BP (Laranjal) show no distinctive shared ancestry with present-day Brazilians (Figures  2  and S1 ; Table  S1 ), although the Laranjal individuals do show potential evidence of shared ancestry with a $5,800 BP individual from Moraes (Table  S4 ), confirmed by the statistic f 4  (Mbuti, Brazil_Laranjal_6700BP; Brazil_LapaDoSanto_9600BP, Brazil_Moraes_5800BP) , which is Z = 7.
This paper includes the first ancient DNA data from Cuncaicha, from one Early Holocene and two Late Holocene individuals. Cuncaicha 15-07, a gracile female 18 to 25 years old, is a nearly complete extended burial. Two ultrafiltered (UF) collagen AMS ages obtained on a radius and tibia average to a 95% range of 9240-8770 calBP. Cuncaicha 15-03, a robust male 30-50 years of age, is a seated, flexed burial. Two UF AMS ages on a fibula and tooth give a date of 4290-4080 calBP and genomic analyses confirmed that those two anatomical elements belong to the same individual (data not shown). Cuncaicha 15-04, a robust male 30-55 years of age, is a partial, flexed burial. Two UF AMS ages on the left and right fibulae average 3370-3180 calBP. Lauricocha is a rock shelter in the Huanuco Province of Peru, located at $4050 m above sea level near Lake Lauricocha and the source of the Maran ˜on River (10.
Grouping ancient samples into analysis clusters For most analyses, we grouped samples by site and time period, leading to the ''Main analysis label'' specified in a column of Table  S3. For several analyses we also considered larger pools of samples as indicated by the ''Regional Label'' specified in Table  S3. In particular, we pooled northern Chilean and southern Peruvian samples after $4200 BP into a ''Late Central Andes'' category based on their distinctive pattern of allele sharing with ancient North Americans compared to other ancient South Americans. As a contrast, we also defined an ''Early Andes'' category that included Central Andes samples earlier than $4200 BP as well as all the North Andes samples in our dataset which includes samples that date to the millenium following $4200 BP (Peru_Lauricocha_ 3500BP and Peru_LaGalgada_4100BP) because of the evidence of local continuity in these samples based on f-statistic and qpWave analysis.
Calibration of radiocarbon dates All calibrated 14 C ages were calculated using OxCal version 4. 3  (Ramsey and Lee, 2013). The IntCal13 northern hemisphere curve  (Reimer et al. , 2013)  was used for four samples from Belize, while the remainder were calibrated using the SHCal13 curve  (Hogg, 2013). Dates from two coastal sites-Los Rieles in Chile and Jabuticabeira II in Brazil-were calibrated according to a mixture with the Marine13 curve  (Reimer et al. , 2013)  based on an estimate of a 40% marine dietary component. For each site, DR values were calculated based on the most proximate sample locations in the 14CHRONO Marine Reservoir Database  (Reimer and Reimer, 2001)  (see Table  S3  for details). To define genetic group labels we in general used the following nomenclature: ''Country_SiteName_AgeBP''  (Eisenmann et al.
, 2015) , or including at the terminal nucleotides (UDGplus)  (Briggs et al. , 2010). We enriched for sequences overlapping 1,233,013 SNPs ('1240k SNP capture')  (Fu et al. , 2015)  and sequenced the DNA on Illumina NextSeq500 or HiSeq 4000 instruments. We removed adapters from the sequences using SeqPrep (https://github. com/jstjohn/SepPrep) or AdapterRemoval v2  (Schubert et al. , 2016) , mapped the data to hg19 with BWA  (Li and Durbin, 2009) , removed duplicates with bamrmdup  (Stenzel, 2018)  or Dedup  (Peltzer et al. , 2016)  and merged data from different libraries of the same individual using SeqPrep. The damage patterns were quantified using mapDamage2. 0 (Jonsson et al. , 2013). We extracted genotypes from the ancient genomes by drawing a random sequence at each position, ignoring the first and last 2 bp of every read as well as any read containing insertions or deletions in their alignment to the human reference genome.
For samples not treated with UDG (UDGminus) (e. g. , USA_Anzick1_11400BP and Brazil_ Jabuticabeira2_2000BP), we also clipped 10bp and created a second dataset to represent the sample following this processing. If the randomly drawn haploid genotype of an ancient individual did not match either of the alleles of the biallelic SNP in the reference panel, we set the genotype of the ancient individual as missing. For the great majority of analyses we analyzed all autosomal SNPs from the $1. 2 million SNP enrichment reagents. For a subset of analyses we restricted to transversion SNPs which are unaffected by the characteristic ancient DNA errors that occur at transition SNPs.
QUANTIFICATION AND STATISTICAL ANALYSIS Contamination estimation in mitochondrial DNA, the X chromosome, and the autosomes We tested for contamination in mtDNA using schmutzi (parameters:-notusepredC-uselength)  (Renaud et al. , 2015) , which iteratively determines the endogenous mitochondrial genome while also estimating human mitochondrial contamination given a database of potential contaminant mitochondrial genomes. For males we estimated contamination on the X chromosome with ANGSD  (Korneliussen et al. , 2014) , which creates an estimate based on the rate of heterozygosity observed on the X chromosome. We used the parameters minimum base quality = 20, minimum mapping quality = 30, bases to clip for damage = 2, and set all other parameters to the default. Finally, we measured autosomal contamination using a recently developed tool based on breakdown of linkage disequilibrium that works for both males and females (N.
, 2015)  after applying a quality filter of R 10 (or R 11 for LapaDoSanto_Burial28, LapaDoSanto_Burial17 and ArroyoSeco2_AS6) for a total of 48 newly reported sequences, including samples for which no nuclear data was obtained (Table  S3 ). We produced a multiple genome alignment of our newly reconstructed sequences (excluding LagunaChica_SC50_L763 because of low coverage) along with 17 previously published ancient sequences older than $4000 BP (Table  S3 ) and 230 present-day sequences  (Llamas et al. , 2016)  using MUSCLE (parameter: -maxiters 2)  (Edgar, 2004). We thus analyzed a total of 295 mtDNAs and used an African sequence as outgroup. We used the program MEGA6  (Tamura et al. , 2013)  to build a Maximum Parsimony tree with 98% partial deletion (16518 positions) and 500 bootstrap iterations, and visualized it in FigTree (http://tree.
-statistics of the form f 3 (Mbuti; America 1 , America 2 ) for both ancient and present-day Americ an groups for all individuals with over 100,000 SNPs of coverage. (A) Heatmap of the matrix of statistics. (B) Neighbor joining tree of the matrix of inverted statistics (distances = 1/outgroup f 3 -statistic). (C) MDS plot of the matrix of 1-outgroup f 3 -statistic. (D) PCA of ancient individuals projected onto present day variation. PCA built with the ''unadmixed unmasked'' version of the Illumina dataset (STAR Methods); newly reported ancient individuals are projected in black outline.
ACKNOWLEDGMENTS In Belize, we thank the  Institute of Archaeology , our Maya collaborators, and the  Ya'axcheConservation Trust  for providing opportunities to consult with their community. In Argentina, we thank the  Comunidad Indı ´gena Mapuche-Tehuelche Cacique Pincen  for supporting sampling of skeletal material from  Laguna Chica. In Peru , we thank  Johny Isla-Cuadrado  and the  National Museum of Archaeology, Anthropology  and  1194 Cell 175 ,1185118611871188118911901191119211931194119511961197November 15,2018 History for permission to sample skeletal material. In Brazil , we thank all of the individuals who participated in the Lagoa Santa studies;  J. Hein ,  R. Tavares de Oliveira ,  J. Barbara Filh ; and institutional support from the  IEF, IPHAN IBAMA , and the cities of Matozinhos,  Lagoa Santa  and  Pedro Leopoldo.
We thank  S. Fiedel ,  Q. Fu ,  C. Jeong ,  T. Kivisild ,  M. Lipson ,  V. Narasimhan ,  I. Olalde ,  B. Potter ,  A. Scally ,  C. Scheib ,  O. Semino ,  A. Torroni , and  T. Stafford  for critical comments. We are grateful to  M. O'Reilly  for graphic design help. We thank the wet laboratory and computational teams at  MPI-SHH  and at  Harvard Medical School. L. F. -S. was supported by the  Wenner-Gren Foundation  ( SC-14-62 ), a  Hellman Foundation  fellowship, and the  NSF  ( A15-0187-001 ). N. N. was supported by the  NIGMS  ( GM007753 ). P. S. was supported by  Cancer Research UK  ( FC001595 ), the  UK Medical Research Council  ( FC001595 ), and the  Wellcome Trust  ( FC001595 ). C. M. and A. N. D. were supported by  FONDECYT  # 1170408  and  CONICYT R17A10002. Archaeological research in Argentina was funded by the  National Geographic Society  ( 9773-15 ),  ANPCYT  ( PICT 2014-2070 ), and  CONICET  ( PIP 0414 ).
B. L. , W. H. , and A. C. were supported by the  ARC  and received sequencing support funding from The  Environment Institute at Adelaide University. Funding for the Belize research to K. M. P. and D. J. K. came from the  NSF  ( BCS1632061 ,  BCS1632144 ), to K. M. P. from the  Alphawood Foundation , and to D. J. K. and B. J. C. from the  NSF   Archaeometry program  ( BCS-1460369 ). The Cuncaicha work was supported by an  Alexander von Humboldt Foundation  fellowship (K. R. ), the  DFG  ( FOR 2237 ,  INST 37/706 ,  FOR 2237 ), the  Pontifical Catholic University of Lima , and  Northern Illinois University. D. C. is Superior Researcher of  CONICET. Financial support for research in Brazil was provided by  FAPESP  ( 99/12684-2 ,  04/01321-6 ,  04/11038-0 ,  2016/1237-1 , and  2017/16451-2 ).
Symmetry statistics and admixture tests (f-statistics) We computed D-statistics, f 4 -statistics and f 3 -statistics with ADMIXTOOLS  (Patterson et al. , 2012)  using the programs qp3Pop and qpDstat with default parameters and ''f4mode: YES. '' We computed standard errors with a weighted block jackknife over 5-Mb blocks. For f 3 -statistics we set the ''inbreed: YES'' parameter to account for the fact that we are representing the ancient samples by a randomly chosen allele at each position rather than using their full diploid genotype which we do not have enough data to discern. The details of the inbreeding correction, which computes the expected value of statistics taking into account this random sampling, are presented in the section 1. 1 of the Appendix of  Reich et al. (2009). We computed ''outgroup'' f 3 -statistics of the form f 3 (Mbuti; Pop 1 , Pop 2 ), which measures the shared genetic drift between population 1 and population 2.
Where relevant we plot the statistics on a heatmap using R (https://github. com/pontussk/point_heatmap/blob/master/heatmap_Pontus_colors. R). We also created a matrix of the outgroup f 3 values between all pairs of populations. We converted these values to proxies for distances by subtracting the values from 1 and generating a multi dimensional scaling (MDS) plot with a custom-made R script. We converted the original values to distances by taking the inverse of the values and generating a Neighbor joining tree using PHYLIP version 3. 696's  (Felsenstein, 1989)  ''neighbor'' function and setting USA_USR1_11400BP as the outgroup (default settings were used for the rest of the analysis). We displayed the tree using Itol  (Letunic and Bork, 2011).
We used the default settings of qpGraph for all runs exept for the options ''outpop: NULL'' instead of setting an outgroup population and ''allsnps: YES'' to compute each f-statistic on the common SNPs present in the populations involved in the statistic, rather than the intersection of all SNPs present in the dataset. To reduce the impact of damage-induced substitutions in UDGminus data of the Anzick-1 individual we restricted the analysis to a version of this sample where sequences were 10bp trimmed on both sides before genotyping. In addition, we performed all analyses with the transitions at CpG sites removed, and we also report the maximum Z-scores of many of the analyses with all transition sites removed. Lastly, for the graphs in Figures  S6A-D  we computed standard errors for the lengths of different graph edges by performing a block jackknife by dropping each of 100 contiguous blocks (with an equal number of SNPs) in turn  (Lazaridis et al.
Outgroup f 3 3 -statistics of the form f 3 (Mbuti; America 1 , America 2 ) for both ancient and present-day Americ an groups for all individuals with over 100,000 SNPs of coverage. (A) Heatmap of the matrix of statistics. (B) Neighbor joining tree of the matrix of inverted statistics (distances = 1/outgroup f 3 -statistic). (C) MDS plot of the matrix of 1-outgroup f 3 -statistic. (D) PCA of ancient individuals projected onto present day variation. PCA built with the ''unadmixed unmasked'' version of the Illumina dataset (STAR Methods); newly reported ancient individuals are projected in black outline.
The Wars2 c. 349G>T Mutation Disrupted Exon Splicing and Caused Tissue-Specific WARS2 Deficiencies The Wars2 c. 349G>T lesion causes a missense substitution (p. V117L) in the encoded protein. In silico prediction of the functional effects of the p. V117L missense substitution did not predict that it was deleterious. However, the mutated nucleotide is the first coding nucleotide of exon 3, and the NetGene2 splice site prediction program indicated that substitution of G to T at the first nucleotide of the third exon of Wars2 would affect the efficiency of exon 3 splicing (Figure  4A )  (Hebsgaard et al. , 1996). We modeled the predicted consequence of exon 3 skipping and found that three a helices, required for substrate binding and release, are missing, which would likely lead to a loss of WARS2 function (Figure  S1F ).
Transcription factor A, mitochondrial (Tfam), required for transcription and associated with mtDNA copy number, was reduced in heart and BAT (Figure  6G ). (C) To quantify Wars2 mRNA missplicing in vivo, RNA was extracted from tissues from mice at 12 months of age and qRT-PCR was performed using 3 TaqMan probes targeted to alternate Wars2 exon-exon boundaries and expressed relative to Wars2 +/+ : boundaries 2/3 (Wars2 FL ), 4/5 (Wars2 Total ), and 5/6 (Wars2 Total ). Wars2 V117L/V117L and Wars2 +/+ littermate numbers were 8 and 8 (5 male and 3 female), respectively; mean ± SEM. Data were analyzed using a Mann-Whitney twotailed t test. Significance differences between Wars2 V117L/V117L and Wars2 +/+ samples are shown as *p < 0. 05, **p < 0. 01, and ***p < 0. 001. (D-J) Immunoblot analysis of (D) WARS2 protein levels in multiple tissues and (E-J) mitochondrial respiratory chain subunit protein levels in (E) heart, (F) liver, (G) brown adipose tissue (BAT), (H) kidney, (I) inguinal white adipose tissue (iWAT), and (J) skeletal muscle from female mice at 12 months of age.
OCR and ECAR measurements were taken at baseline and following oligomycin (Oligo), carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP), and rotenone and antimycin (Rot/Anti) treatment. OCR and ECAR measurements were normalized to live cell number. (C) Relative oxygen consumption rates of basal respiration, proton leak, ATP production, maximal respiration, and spare respiratory capacity in Wars2 +/+ and Wars2 V117L/V117L MEF cultures. There were 9 replicates of each genotype; mean ± SD. Data were log transformed and analyzed using an unpaired two-tailed t test or a Mann-Whitney t test. (D) MEFs were stained with MitoTracker green, and fluorescence in 30,000 cells per sample was quantified by fluorescence-activated cell sorting (FACS). There were 3 replicates of each genotype; mean fluorescence ± SD.
Ppa2 Y123F/Y123F mice were generated from Ppa2 +/Y123F x Ppa2 +/Y123F matings (47 mice total). Mice were randomly assigned to cages at weaning before subsequent genotyping of individual mice. Downstream phenotyping experiments were performed blinded to the genotype of the mice. For body composition three cohorts were analyzed, two with multiple time points and one at 1 month only. In the first cohort one wild-type mouse was humanely killed because it was sick and one found dead and all data from these animals was excluded. Final cohort sizes were Wars2 +/+ n = 11 and 3, Wars2 +/V117L n = 13 and 17, Wars2 V117L/V117L n = 7 and 8, male and female respectively. In the second cohort data from one homozygous mouse was excluded after being found dead before the 6-month time-point and two heterozygous mice humanely killed to reduce cage numbers prior to starting phenotyping.
WGS was performed as previously described  (Potter et al. , 2016). Briefly, following DNA extraction a library was generated and a single lane or paired-end sequencing (100nt) was performed using the Illumina HiSeq platform (Oxford Genomics Centre, Wellcome Centre for Human Genetics). The 100nt paired-end reads were aligned to the reference mouse genome (NCBIM38/mm10) using Burrows-Wheeler Aligner software  (Li and Durbin, 2009). Single-nucleotide variants (SNVs) were identified for each alignment using the unified GenotypeCaller tool in the Genome Analysis Toolkit (GATK) as previously described  (Potter et al. , 2016). Here the mouse dbSNP version 137 was used as the background SNP set using default parameters. Identified SNVs were then given a quality score (Phred scaled quality score, À10 x log(1-p), p is the probability of a SNV being called incorrectly).
, 2010). Briefly, mice were anaesthetized via administration of an intra-peritoneal injection of anesthetic (1 mL Ketamine, 0. 5 mL Xylasine, 8. 5 mL sterile H 2 O) at a rate of 0. 1 ml/10 g of body mass. Once unconscious, the mouse was placed on a heated mat in a sound proof booth. Electrodes were then placed sub-dermally below the right pinna (reference), into the muscle mass below the left ear (ground) and on the midline of the skull (active). Mice were placed with their auditory canal 1 cm from the speaker and were exposed to a broadband 'click' stimulus, followed by tones at 8, 16 and 32 kHz. The electrodes recorded the auditory brainstem responses to the tones. The recorded data was calibrated, generated and processed using the Tucker Davies Technology (TDT) system III. Following the ABR testing an IP injection of Antipamezole (0.
Comprehensive Laboratory Animal Monitoring System The Comprehensive lab animal monitoring system (CLAMS) was used to measure mice energy expenditure at home cage temperature (22 C) according to standard protocols. Briefly mice were placed in individual cages for a total of 72 hours. Measurements of oxygen (O 2 ) and carbon-dioxide (CO 2 ) in-flow and out-flow concentrations were automatically monitored and recorded along with food consumption and water intake throughout the 72-hour period. Data from the first 24 hours was removed from the analysis as this period was used to allow the mice to acclimatize to their new environment. Data collected from the second 24-hour period was used for all subsequent analysis. Energy expenditure was calculated as follows EE = CV x VO 2 (where CV = Calorific value = 3.
Prediction of WARS2 3D structure The crystal structure of human WARS2 (PDB: 5EKD, Human mitochondrial tryptophanyl-tRNA synthetase bound by indolmycin and Mn*ATP. Williams, T. L. , Carter Jr. , C. W. ) was downloaded from the PDB database (PDB; http://www. rcsb. org/). The predicted protein structure of human Wars2 was generated using PHYRE2 Protein fold recognition server  (Kelley et al. , 2015). The alignment and visualization of the protein structures was performed by PyMOL by Schro ¨dinger (https://pymol. org/2/). Western blot bands were analyzed and quantified using ImageJ  (Schneider et al. , 2012). Energy Expenditure adjustment for lean mass by ANCOVA using SPSS.
ACKNOWLEDGMENTS We thank  Gavin Falkous  for technical assistance. T. A. is supported by a  Medical Research Council UK  (MRC) doctoral training studentship. M. G. , C. A. , A. M. , M. S. , H. H. , C. E. , Y. W. , H. C. , L. B. , C. S. , S. D. M. B. , M. R. B. , and R. D. C. are supported by  MRC  funding ( MC_U142661184  and  MC_U142684175 ). R. W. T. is supported by the  Wellcome Centre for Mitochondrial Research  ( 203105/Z/16/Z ), the  Medical Research Council (MRC) Centre for Translational Research in Neuromuscular Disease, Mitochondrial Disease Patient Cohort.
Supplemented embryo media was made fresh and changed daily. Zebrafish were monitored for survival and heart rate as indicators of toxicity. For controls, H 2 O was added to the embryo media instead of L-tyrosine. Four independent treatments were performed for each experiment with 30 fish per treatment. Zebrafish swimming assays and resting heart rate determination. The resting heart rates were measured at 2 dpf by counting the number of heart beats in 10 sec. Heart rate measurements were performed in triplicate with 10 fish per experiment. Assay of swimming ability, as well as the subsequent data analyses performed on 6 dpf WT zebrafish treated with increasing doses of L-tyrosine, TgACTA1 D286G -eGFP and their control WT siblings treated with 10 μM L-tyrosine were as per  34. Total voluntary distance travelled in a 10-minute period in the dark was measured in mm using zebraboxes (Viewpoint Life Sciences).
The EZ:faast AAA-MS 4 μ column 250 × 2. 0 mm provided with the kit was used and the acquisition method of the kit was followed. The internal standard was homophenylalanine. Data were acquired in positive ion mode and the transition for L-tyrosine was 396-136. Bodyweight of NM mouse models. Male 6-week old TgACTA1 D286G mice treated from pre-conception were weighed prior to individually being housed with access to Low-Profile Wireless Running Wheels (Med Associates Inc, USA) for 6 consecutive days. For both TgACTA1 D286G and KIActa1 H40Y mice, bodyweight was measured after 1 month of exposure to the 2% L-tyrosine supplemented diet or to the normal diet. Voluntary running wheel analyses and rotarod assessment of TgACTA1 D286G mice. The daily distance travelled, daily time spent running (summary of 1 min intervals in which at least one wheel revolution was recorded), average speed and maximum speed values were calculated.