dataesr/scientific-paragraphs-categorization huggingface dataset:
Genetic structure patterns Now that we had established that significant differences existed between Pygmies and non-Pygmies for the height-related measurements available in our sample set, we next investigated the levels of genetic differentiation that are present among our Pygmy and non-Pygmy samples. Our genetic dataset consisted of 406 unrelated individuals from 20 populations with genotypes at 153,798 SNPs included on the Illumina Cardio-MetaboChip following the removal of lower quality SNPs and related individuals (see Material and Methods). In agreement with earlier studies  [24] [25] [26] [27] 29, 30, 33] , multidimensional scaling (MDS) analysis of pairwise allele-sharing dissimilarities (ASD) among these 406 unrelated individuals supported three main components to genetic differentiation among Central Africans (Figure  3A ).
585, P=2. 90×10 -8 ; Figure  4A ) and the 57 females (r=0. 485, P=1. 30×10 -4 ; Figure  4B ) with standing height data available among the 406 unrelated individuals in our genotype dataset. The correlations remained significant when restricted to the 61 male and 31 female Pygmy individuals (r=0. 311 with P=0. 015 and r=0. 441 with P=0. 013, respectively). These findings are in agreement with previous studies on the relationship between levels of non-Pygmy admixture and adult standing height in Central African Pygmies  [32] [33] [34]  and provide further support for an appreciable genetic component to the determination of body size differences between Pygmies and non-Pygmies. The correlations of adult sitting height and subischial leg length with non-Pygmy admixture were similar in males (Pearson r=0.
Association analyses of Pygmy/non-Pygmy categorization We have found significant genetic and phenotypic differences between the Central African Pygmies and non-Pygmies in our sample set, with correlations between individual trait variation and inferred levels of non-Pygmy genetic admixture supporting an appreciable genetic component to the determination of their observed phenotypic differences. To investigate the underlying genetic component of their phenotypic differences, we first performed single-and multi-marker association tests to identify genomic regions harboring SNPs with significant allele frequency differences between the Pygmies and non-Pygmies in our genetic dataset of 406 unrelated individuals.
Multi-marker gene-based association tests Because individual SNPs might fail to achieve the Bonferroni significance threshold owing to insufficient power in our modestly sized genetic dataset, we performed multi-marker gene-based association tests using VEGAS  [106] , a multivariate method that combines association signals across all SNPs located within a gene, correcting for non-independence between SNPs. We observed seven genes exhibiting a significant association with Pygmy/non-Pygmy categorization after Bonferroni correction (α=1%; Table  3 ); the genes identified did not differ appreciably if village affiliation was excluded as a covariate (Additional File 1: Table  S2 ).
Single-marker association tests We performed per-SNP association tests to identify genomic regions harboring genes that contribute to variation in adult standing and sitting height and their ratio as well as subischial leg length among the 132-159 individuals with these data available in our dataset (Additional File 1: Table  S1 ) using EMMAX and including sex and Pygmy/non-Pygmy categorization as covariates. Again, EMMAX appeared to provide a reasonable correction for structure and relatedness in these analyses (Additional File 2: Figures  S4E-S4H , respectively) while similar analyses controlling only for sex (Additional File 2: Figures  S4A-S4D ) or for sex and village affiliation (Additional File 2: Figures  S4I-S4L ) instead led to slightly inflated or deflated P-values, respectively.
Nevertheless, gene-based multi-marker test identified nine, ten, nine, and ten genes enriched for SNPs exhibiting an association with standing height, sitting height, sitting/standing height ratio, and subischial leg length, respectively. Importantly, quantile-quantile plots of the P-values created in each association analysis (Additional File 2: Figures  S3  and S4 ) indicate that our approach appropriately corrected for structure and relatedness present among individuals in our dataset (Figure  3 ). Considered together with our identification of significantly associated genes and genomic regions, this would suggest that cross-population GWAS are a viable and powerful approach to identify the genetic basis of human phenotypes that naturally vary across populations. A number of genes and genomic regions identified here potentially provide novel insight into the genetic basis of anatomical and physiological features of the Central African pygmy phenotype.
, Kanata, ON), following the manufacturer-recommended protocol. For a subset of the individuals from seven Pygmy─Baka (Center), Baka (East), Bezan (South), Bongo (Center), Bongo (East), Bongo (South), and Koya─and three non-Pygmy─Nzime, Tikar, and Bangando─populations, standing and sitting height were measured with a height gauge and weight with a standard weigh scale following standard anthropometric procedures  [132]. Subischial leg length was calculated as standing minus sitting height, while BMI was calculated as body weight in kilograms divided by standing height in meters squared (kg m -2 ). Accurate age data was unavailable for these individuals, as most of the communities do not keep track of birth dates. Though we cannot rule out the confounding effect of osteoporotic age-related shrinking in our study, we expect it to be minimal as our sample set includes only adults and elderly individuals were not considered.
Genotyping and quality control DNA samples for 576 samples─558 individuals from 20 Central African Pygmy and non-Pygmy populations (Table  1 ) and 18 control samples─were genotyped at the University of Michigan Medical School DNA Sequencing Core (Ann Arbor, MI) using the Illumina MetaboChip that interrogates 196,725 genome-wide SNPs  [69]. We focus on 196,091 SNPs whose genomic position had been independently verified (Peter Chines, unpublished data). Genotype calling was performed using the GenomeStudio Genotyping Module (v. 1. 0; Illumina Inc. , San Diego, CA). Following quality control procedures conducted at the genotype-calling level (Stage 1, Figure  S8  [Additional File 2]), the preliminary dataset contained 192,903 autosomal SNPs that were polymorphic in a sample of 543 Pygmy and non-Pygmy individuals.
Next, we conducted population-level quality control procedures (Stage 2, Figure  S8  [Additional File 2]), creating an initial dataset of 154,106 autosomal SNPs polymorphic in a sample of 543 Pygmy and non-Pygmy individuals. Hardy-Weinberg equilibrium was evaluated separately in each population using Yates-corrected chi-squared tests  [133] , and we used the same exclusion criteria as in Pemberton et al. [134]. Pygmy and non-Pygmy neighbors interact socially and economically on a daily basis  [6, 7, 130, 135]. Thus, individuals from neighboring communities might be present at the time of sampling. On rare occasions, some non-Pygmies have fled their communities (to avoid taxes or military recruitment for instance) and taken refuge among neighboring Pygmy populations. Therefore, it is possible that some sampled individuals were wrongly categorized according to our Pygmy/non-Pygmy categorization criteria.
To search for categorization errors, we performed MDS analysis of individual pairwise allelesharing distances (see below) in our initial dataset. We identified 16 individuals─11 initially categorized as Pygmies and five as non-Pygmies─who did not cluster genetically with other individuals sharing the same categorization (data not shown) and who might have been miscategorized during sample collection. To be conservative, we removed these 16 putative misclassified individuals from the preliminary dataset, and repeated the population-level quality control procedures (Stage 3, Figure  S8  [Additional File 2]) to create a dataset containing 154,029 autosomal SNPs polymorphic in a sample of 527 Pygmy and non-Pygmy individuals ("527RELAT" henceforth; Table  1 ); all individuals possessed genotypes at ≥94.
9% of SNPs. Relatedness among all pairs of the 527 individuals in the 527RELAT dataset was evaluated using identity-by-state allele sharing and the likelihood approach of RELPAIR (v. 2. 0. 1)  [136, 137]  following the methods of Pemberton et al. [134]  restricted to sets of 9,999 SNPs. A total of 282 pairs of individuals were inferred by RELPAIR to be related at a level closer than first cousins: 230 intra-population and 52 inter-population pairs. All inter-population relative pairs involved geographically close populations (Figure  1 ); 47 involving the two Bezan populations (BZN and BZS), three involving two Bongo populations (CBG and EBG), and two with individuals from the nearby Fang (CFG) and Ngumba (NGB) populations. A dataset containing no first-or second-degree relatives was created by removing one individual from each of these 282 relative pairs.
To minimize the number of individuals removed, we preferentially omitted individuals present in two or more relative pairs (either intra-or inter-population). In situations where either individual in a relative pair could be removed, we removed the individual with more missing data. After the exclusion of 121 related individuals─in addition to the 16 putative misclassified individuals─from the preliminary dataset of 543 individuals, many of whom were related to multiple individuals in the initial dataset, we repeated the population-level quality control procedures (Stage 4, Figure  S8  [Additional File 2]) to create an unrelated dataset with 153,798 autosomal SNPs polymorphic in a sample of 406 Pygmy and non-Pygmy individuals ("406UNRELAT" henceforth; Table  1 ); all individuals possessed genotypes at ≥94.
9% of SNPs. Because of small sample sizes for the two Bezan populations (11 for BZN, 17 for BZS) in the 406UNRELAT dataset, we combined them into a single population ("Bezan" henceforth; BEZ, Table  1 ). Both the BZN and BZS samples belong to the same Bezan ethnic group located in Central Cameroon, which has a census size <400, divided between two nearby communities (~50 km apart) in frequent contact (P. Verdu and A. Froment, unpublished data).
Multidimensional scaling We performed MDS based on ASD matrices constructed for all pairs of individuals in the 406UNRELAT dataset using asd (v1. 0; https://github. com/szpiech/asd). This program considers in the calculation for a given pair only those SNPs for which neither individual was missing genotypes. We applied classic metric MDS on the ASD matrix using the cmdscale function in R (v2. 11. 0)  [138]. To estimate the proportion of variation in ASD explained by the first two dimensions of the MDS plot, we calculated the Spearman correlation coefficient ρ between the Euclidean distances for all pairs of individuals and their corresponding ASD values (using cor. test in R here and for other correlations). To evaluate dispersion levels among Pygmies and non-Pygmies in the MDS plot, we compared the variance among their ASD values with a one-sided F-test using var.
STRUCTURE To investigate genetic structure in our 406UNRELAT dataset, we performed model-based Bayesian clustering analyses implemented in STRUCTURE (v2. 3)  [97, 98] , which probabilistically assigns proportions of each individual's genotypes to each of K genetic clusters, where K is set a priori, based on allele frequencies and irrespective of individual population categorizations. We used the admixture model with separate Dirichlet parameters in each cluster and correlated allele frequencies, and a burn-in period of 20,000 iterations followed by 10,000 iterations. To minimize the number of linked loci owing to the gene-centric design of the Cardio-MetaboChip, in the STRUCTURE analyses we used 40,424 SNPs that have minimum spacing 18. 75 kb. From these 40,424 SNPs, we created four non-overlapping panels of 10,106 SNPs, each with minimum marker spacing 75 kb.
For SNP panel n (1 ≤ n ≤ 4), every fourth SNP along a vector of the considered 40,424 SNPs was selected, starting at position n -1. In this vector, SNPs were numbered starting at 0 and ordered from chromosome 1 to 22 and by increasing distance along each chromosome (taking genomic positions from NCBI database build 37). For each panel, we computed 10 independent STRUCTURE runs for values of K between 2 and 4, producing 40 independent replicates for each K. We identified common modes among the 40 replicates using CLUMPP  [139]  with the Greedy algorithm and 1,000 random permutations. For each K, all pairs of runs with a symmetric similarity coefficient >0. 9 were considered to belong to the same mode. For each mode, we computed individual membership proportions averaged across runs from that mode, visualizing the most frequent mode at each K using DISTRUCT  [140].
Single-marker tests Per-SNP association tests were performed using EMMAX  [105] , which implements a mixed-effect regression model  [141]  to account for genetic structure in a sample set, incorporating variance components of random polygenic effects  [142, 143]  and genetic relatedness among individuals using a pairwise Balding-Nichols kinship relatedness matrix  [144]  (equation 7 in  [105] ). We constructed the kinship matrix for all pairs of individuals in the 406UNRELAT dataset using EMMAX, including in the calculation only the 94,050 and 93,821 SNPs, respectively, inferred to be in linkage equilibrium (r 2 <0. 5). Association analyses for Pygmy/non-Pygmy categorization were performed by labeling Pygmies as "cases" and non-Pygmies as "controls. " Association analyses were performed separately for each measured trait considering only those individuals with data available.
Multi-marker gene-based tests As individual SNPs may fail to achieve the significance threshold due to insufficient power in our modest sample sizes, we performed multi-marker gene-based tests of association using VEGAS  [106]. Separately for each gene, VEGAS computes a  2 test statistic from observed EMMAX P-values for all SNPs that lie within its boundaries (defined as ±50 kb from the ends of the transcribed region) and evaluates significance via simulations from a multivariate normal distribution with mean 0 and a covariance matrix of pairwise LD between SNPs. The transcribed region of each gene was defined using release hg19 of the UCSC gene database  [145] ; for genes with multiple isoforms, begin and end positions were defined as the outermost positions in the union of the transcribed regions of all isoforms.
Pairwise LD estimates in our datasets considered in the calculation only unrelated individuals. List of Abbreviations Used AR: androgen receptor; ASD: allele-sharing dissimilarity; BMI: body mass index; DRC: Democratic Republic of Congo; GWAS: genome-wide association study; HGH: human growth hormone; IGF1: insulin-like growth factor 1; MDS: multidimensional scaling; SD: standard deviation; SNP: single nucleotide polymorphism. Population codes follow Table  1. Population ranges were inferred from ethnographic field work  [7]. [146]  representations of the distributions of (A) standing heights, (B) subischial leg lengths, (C) sitting heights, (D) sitting/standing height ratios, and (E) BMI shown separately for males (blue) and females (pink). The number (n) of males and females with data available for each measurement is provided above each plot.
The mean and SD of each group are reported in Table  S1  (Additional File 1). Each ''violin'' contains a vertical black line (25%-75% range) and a horizontal white line (median), with the width depicting a 90º-rotated kernel density trace. 3B ) and standing height for (A) 76 males (r=0. 585, P=3×10 -8 ) and (B) 57 females (r=0. 485, P=3×10 -5 ), sitting height for (C) 71 males (r=0. 431, P=8. 90×10 -5 ) and (D) 57 females (r=0. 226, P=0. 040), and subischial leg length for (E) 71 males (r=0. 475, P=1. 41×10 -5 ) and (F) 57 females (r=0. 574, P=6. 53×10 -7 ). Only individuals included in the genetic dataset with a measurement for that trait were included in each comparison. Individuals are indicated by the symbols in Figure  3A.
B B E Z C B K A K A E B K S B K A K M K O Y C B G K O L N S U S B G E B G A K L B G D N Z I N G B T IK B K J C F G • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Membership propor�on in the blue "non-Pygmy" cluster 0 0. 2 0. 4 0. 6 0. 8 1       S1 ). (C) LocusZoom plot  [147]  of the genomic region surrounding the single marginally significant SNP (rs13097517, P=6. 35×10 -7 ). The figure follows the same format as Figure  6. • • • • • • • • Male si�ng height (cm) • • • • • • • • • • • • • • • • • • • • • • Male BMI (kg m -2 -log 10 (P) Recombina�on rate (cM/Mb)  Numbers of males (M) and females (F) appear in parentheses. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • rs13097517 WNT5A ERC2 ERC2-IT1 MIR3938 CCDC66 FAM208A ARHGEF3 ARHGEF3-AS1 a The two Bezan populations were merged into a combined BEZ population in the 406UNRELAT dataset.
Acknowledgements The authors thank the volunteers from  Cameroon ,  Central African Republic, Gabon , and  Uganda  who participated in this study. The authors thank  Peter Chines  for providing verified Cardio-MetaboChip SNP positions,  Sen Li  for providing ancestral and derived allelic states for the MetaboChip SNPs,  Hyun Min Kang  for assistance with EMMAX, and  Frédéric Austerlitz ,  Erkan Buzbas ,  Zachary Szpiech , and.
Funding This research was supported by the  France-Stanford Center for Interdisciplinary Studies  (N. A. R. ); the  French Assistance Publique Hôpitaux de Paris ,  ATM-MNHN 'Les relations Sociétés-Natures dans le long terme '  2009-2012  and the  Fondation pour la Recherche Médicale  and the  ANR-Blanc program  'GrowingAP' (E. H. ); a  University of Michigan Center for Genetics in Health and Medicine postdoctoral fellowship  (T. J. P. ); and a  Natural Sciences and Engineering Research Council of Canada   Discovery Grant  ( RGPIN-2015-04739 ; T. J. P. ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In a previous work, the monitoring performance was assessed in terms of the magnitude of completeness (M c ) at the Wysin site using noise reference levels from recording data before HF operations  26. To account for the changed noise conditions, the M c is recalculated according to the estimated SNAI ratios (Figs  2  and S3 , and Method M2) following the same approach described in previous works  26. The noise conditions change during day hours, because HF operations took place between 6:00 and 18:00 h only, increasing the M c from 0. 55 to 0. 80 around the HF area (Fig. 4 ). Induced seismicity. Continuous seismic recording were processed at the Wysin site before, during and after the HF operations that took place on June and July 2016. During the seismic monitoring period, automatic event detection procedures were performed to assess the background seismicity and identify the seismic signals caused for the HF stimulations, which could be related to induced or triggered seismicity.
The most relevant results are here shown for a 4-month period between June and September 2016. We apply a recently developed automated full waveform detection and location algorithm based on waveform stacking and coherence analysis (Method M4). This technique has been successfully applied in previous work, showing an improvement with respect to classical detection methods both for natural and induced seismicity  21, 36. Moreover, the detection algorithm performance at Wysin site, in terms of M c , was verified by processing a realistic synthetic dataset  26   Detections showing such a temporal pattern of arrival time will be referred hereafter as "local HF detections". However, most of the local HF detections correspond to very weak events, which signals are only visible at the three operational borehole stations and often showing a low signal to noise ratio (Fig.
The location results has a better resolution and show a sharper coherence peak for the June 25 th , 2016 event (Fig. 6c, d ), with respect to the August 31 st , 2016 event (Fig. S8c, d ), what reflects their different magnitude and the quality of recordings (Fig. S9 ). The first event is located 1500 m SSW of the wellhead; the second one is located closer to the HF area, just 220 m ESE of the wellhead. The magnitudes (Method M6) of both events are successfully estimated to be M w 1. 05 ± 0. 07 and M w 0. 48 ± 0. 09 (Fig. 7 ), taking advantage of the synthetic seismogram database computed for a range of locations, depths and moment magnitudes  26.
Acknowledgements This work is funded by the  EU H2020 SHale gas Exploration and Exploitation induced Risks (SHEER)  project (www. sheerproject. eu-grantagreement no. 640896 ). We thank  R. Bauz ,  M. Kriegerowski ,  J. Mirek ,  W. Białoń ,  S. Cielesta  and  M. Lasak  for supporting the development, installation and maintenance of the seismic monitoring. We also thank  M. Galis  for providing the data used in Fig. S14. W. Białoń  and  H. Marcak  are also acknowledged for providing the data used in Fig. S2 and Table  S1.
Human studies Institutional ethics committee approved the study and consent was obtained from each patient. Patients were included from the NUMEVOX cohort, referenced at Clinicaltrials. gov (N : NCT00997165). The investigation conforms to the principles outlined in the Declaration of Helsinki. Patients were eligible for inclusion if they met criteria for the National Cholesterol Education Program-Adult Treatment Panel III; they had at least three of the following five criteria: (i) waist circumference >102 or 88 cm for men and women, respectively; (ii) systolic and diastolic pressures !130/85 mmHg; (iii) fasting glycemia !1. 1 g/l; (iv) triglycerides !1. 5 g/l; and (v) high-density cholesterol lipoprotein <0. 4 g/l in men or <0. 5 g/l in women (Table  A. 1). Patients with a history of cardiovascular disease (CVD), preexistent chronic inflammatory disease, and cancer were excluded.
Protein arrays Human adipokine array kit (#ARY024, R&D systems) were incubated with 150 mg of each EV subtype (MVs or EXOs), and processed according to manufacturer's protocol (R&D systems). Protein signals were revealed using ChemiSmart 500 imager (Vilber Lourmat). Signal intensity of spots (measured as pixels number) was quantified using Image J Software and expressed as pixels/mg of protein loaded on membrane. Only detectable protein signals are presented.
PKH26-EV internalization MVs isolated from WT and MIF À/À MEF supernatants were labeled with PKH26 dye using the red fluorescent cell linker kit (Sigma Aldrich) according to manufacturer's protocol. Briefly, 100 mg MVs diluted in PBS were added to 0. 5 ml Diluent C. In parallel, 4 ml PKH26 dye was added to 0. 5 ml Diluent C and incubated with the MV solution for 4 min at RT. To bind excess dye, 2 ml 0. 5% bovine serum albumin/PBS was added. The labeled MVs were washed at 21,000Âg for 30 min, and the MV pellet was suspended with PBS and used for uptake experiments. Twenty mg of PKH26-labeled MVs (20 mg/ml) were incubated for 0, 0. 5, 1, 2, and 4 h on MIF À/À MEFs cultured on glass coverslips. MV internalization was imaged using a LSM 710 confocal (Zeiss). Internalization kinetics graph represents the mean fluorescent intensity (MFI) measured by Image J software on 30 different cells per MV condition (10 cells taken for each condition from three different images).
D. 9) and their IgG control isotypes (kindly provided by R. Bucala, Yale University, USA). 2. 13. Quantification and statistical analysis Significant differences were assessed using GraphPad Prism software following statistical analysis by an ANOVA test corrected for multiple comparisons by Tukey's test are indicated by *p < 0. 05, **p < 0. 005, ***p < 0. 001, ****p < 0. 0001. For linear correlations, Pearson coefficient (R) is indicated, and association is considered significant when p < 0. 05.