dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome-wide genotyping For the present study, genome-wide genotyping was performed on DNA from individuals from the Bolivian Andes. Venous blood samples were obtained from the arm with BD Vacutainer Eclipse blood collection needles (Becton Dickinson, USA) in EDTA tubes (Vacuette, Greiner Bio, Austria). DNA was extracted from whole blood samples with the E. Z. N. A. Blood DNA Mini kit (Omega Bio-tek, USA). Genome-wide genotyping was performed at the SNP&SEQ Technology Platform in Uppsala, Sweden on the Illumina Infinium Omni5Exome SNP array. This BeadChip array covers >4. 3 million whole-genome variants down to 1% minor allele frequency (MAF), plus novel functional exonic variants. The data were aligned to the human reference genome build, version 37 (hg19). By using PLINK v1. 90  (Chang et al.
Population structure analyses For population structure analyses, including principal components and clustering analyses, data from the Bolivian study groups (n = 187) were merged with data from (i) South American populations reported by  Barbieri et al. (2019)  (n = 174); (ii) populations of Native American ancestry from North, Central, and South America, as well as Iberian (IBS) and Yoruba (YRI) from the 1000 Genomes Project (n = 312) from a previously published dataset assembled by  Lazaridis et al. (2014) ; and (iii) the SAC population (n = 120) from  Schlebusch et al. (2015). Due to mainly African and European immigration over time in South America, we expected possible admixture with African (represented by YRI) and European (represented by IBS) population groups and they were therefore included in the principal component analyses (PCA) for estimation of admixture.
We used a Hardy-Weinberg equilibrium filter (p-value < 0. 001) to account for genotyping errors in the Bolivian (4763 SNPs excluded) and SAC (4017 SNPs excluded) datasets. First-degree relatives (n = 13 from SAC, 7 Aymara-Quechua, and 8 Uru), as determined with the software KING v2. 1. 4 (Manichaikul et al. , 2010) , were excluded. In total, 236,127 autosomal SNPs from 765 individuals were analyzed. We further pruned SNPs in high linkage disequilibrium (LD, plink -indep-pairwise 200 25 0. 4), resulting in 120,384 autosomal SNPs. Principal component analysis was done using the smartpca program included in  EIGENSOFT (Patterson et al. , 2006; Price et al. , 2006). Admixture fractions were inferred using ADMIXTURE  (Alexander et al. , 2009)  and visually inspected with  PONG (Behr et al. , 2016).
Selection scans 2. 5. 1. Merging with comparative datasets We merged the data from the Bolivian study groups (n = 187) with data of populations from the 1000 Genomes Project (Northern Europeans, CEU, n = 80; Han Chinese, CHB, n = 80; Colombians, CLM, n = 69; Mexicans, MXL, n = 52; Peruvians, PEL, n = 55; Puerto Ricans, PUR, n = 76; and Yoruba, YRI, n = 80). We also merged them with genetic data from SAC (n = 120), genotyped with Illumina HumanOmni 5 M BeadChip that covers 4. 3 million variants, as described in  Schlebusch et al. (2015)  to aid during the haplotype phasing step. The phasing of haplotypes refers to assigning each allele to the maternal or paternal chromosome. To do that, relatives within the Andean study groups were kept to aid the statistical estimation of the haplotypes. We used a Hardy-Weinberg equilibrium filter (p-value < 0.
Haplotype phasing and ancestral state Haplotypes were phased using fastPHASE (v1. 4. 0) with one random start for 25 iterations and 25 clusters. Once haplotypes were phased, to avoid introducing bias due to over-representation of family genotypes during selection scans, we excluded first-and second-degree relatives (n = 30 from SAC, 12 Aymara-Quechua and 12 Uru) identified with KING, resulting in a total of 90 individuals from SAC, 143 Aymara-Quechua and 20 Uru. To compute the ancestral state of each allele, we used as outgroups the hg19-aligned versions of the reference genomes of the chimpanzee, gorilla, and orangutan as described in  Schlebusch et al. (2015). For each SNP, an allele was defined as ancestral if it was present in three of the outgroup genomes. The dataset was then filtered based on this information, with only those SNPs that were the same in the three outgroups retained as their ancestral state was certain.
iHS and XP-EHH computation The integrated haplotype score (iHS;  Voight et al. , 2006)  and cross-population extended haplotype homozygosity (XP-EHH;  Sabeti et al. , 2007)  are two statistics used to detect positive selection in the genome. These tests are both based on the fact that extended haplotype homozygosity arises when allele frequencies increase faster than the rate at which recombination can break them down  (Sabeti et al. , 2002). XP-EHH detects alleles that are near fixation in one population compared to another population. We compared the Bolivian study groups (Aymara-Quechua and Uru separately) to Peruvians from Lima. The same tests were also done comparing the Bolivian study groups to Han Chinese (data not shown). The iHS and XP-EHH computations were done using the R package rehh  (Gautier and Vitalis, 2012) , with a maximum distance between two SNPs of 200,000 bp to avoid spurious signals in genomic regions with low SNP density.
Protective haplotype associated with efficient arsenic metabolism Higher %MMA in urine, reflecting less efficient methylation of absorbed iAs, is associated with an increased risk of arsenic-related health effects  (Lindberg et al. , 2008b; Steinmaus et al. , 2006). Therefore, lower %MMA and higher %DMA in urine indicate efficient methylation of iAs to DMA. A haplotype of 52 SNPs surrounding AS3MT that were significantly associated with low %MMA in urine ("protective haplotype") was previously identified in SAC  (Schlebusch et al. , 2015). In the current study, some of the 52 SNPs were filtered out of the dataset because we used different filtering settings in the present study compared with  Schlebusch et al. (2015) , and due to merging datasets genotyped with different arrays. Out of the 52 SNPs, 35 were included in the Bolivian SNP array dataset (located in the interval chr10:104315215-10:104852121, hg19; 537 kb length).
Associations between the 35 SNPs constituting the protective haplotype and arsenic metabolism efficiency phenotypes were evaluated using multivariable linear regression models for the Bolivian study groups combined (n = 163, out of which 143 were Aymara-Quechua and 20 were Uru; relatives excluded based on KING). These models were adjusted for age, sex, and urinary arsenic concentration to facilitate comparison with previous studies  (Balakrishnan et al. , 2017; Pierce et al. , 2012). We also adjusted the models for ethnicity, since we combined both Bolivian study groups to increase the sample size. Furthermore, genome-wide associations were performed with the R package GenABEL (version 1. 8), as described in the Supplementary Material file.
(2015)  (Supplementary Figure  6 ). Minor allele frequencies (MAF; minor allele designated as the least frequent allele in the study groups) of the SNPs constituting the protective haplotype are reported in Supplementary Table  4. The T-allele of rs17115100, associated with lower %MMA in urine, was present at a much higher frequency in the Bolivian study groups (75% for Aymara-Quechua and 90% for Uru) compared to other populations in the 1000 Genomes Project (Peruvians: 41%, Han Chinese: 35%, Yoruba: 4%; Supplementary Figure  7 ). In addition, the C-allele of rs486955, the top SNP associated with lower %MMA in SAC, showed a high frequency in the Bolivian study groups (Aymara-Quechua: 73%, Uru: 82%, Peruvians: 41%, Han Chinese: 47%, Yoruba: 26%; Supplementary Figure  7 ). Based on the gene expression GTEx portal database, rs17115100 is associated with differential expression of AS3MT in several tissues, including pancreas, thyroid, skin, pituitary, and cerebellum, but not in liver, which is the main site of iAs methylation.
From all the genetic variants within the 1. 5-Mb region, we excluded polymorphisms that i) were present in populations of African descent (YRI), where no protective haplotype has been found  (Schlebusch et al. , 2015) ; ii) had a MAF below 0. 4 in Peruvians from Lima, to select alleles d Two participants did not report their smoking habits. Out of those who did report their smoking habits (n = 18), only one was a smoker. with the highest frequency difference between SAC and Peruvians, since we found evidence of selection when comparing with these individuals; and iii) had no indication of gene regulatory effects based on database prediction (Ensembl and Genome Browser). Only seven polymorphisms (indel and SNPs) remained (Supplementary Table  7 ). Interestingly, although these polymorphisms were spread in the haplotype block, the majority have been linked to regulation of AS3MT expression in open-source databases.
Based on the gene expression GTEx portal database, two of these SNPs (rs56060736 and rs72846190) are associated with differential expression of AS3MT in several tissues including esophagus, lung, hypothalamus, and whole blood. Furthermore, the open-source database JASPAR CORE collection included in Genome Browser, a track representing predicted binding sites for transcription factors, indicated that all 7 polymorphisms were located at predicted transcription factor binding sites.
Acknowledgments We warmly thank all the participants who took part in this study. We would also like to thank the  Servicio Departamental de Salud de Oruro (SEDES) , the program of  Salud Familiar Comunitaria e Intercultural (SAFCI) , as well as the fieldwork teams and workers at local health centers in Argentina and Bolivia for assisting during recruitment. We are thankful to  Gabriela Concha  for her contribution during the recruitment in Argentina, to  Michael Levi  for measuring the arsenic metabolites, to  Chiara Barbieri  for sharing her data prior to publication, to  Yanjun Zan  and  Örjan Carlborg  for their insights regarding genotype-phenotype associations, and to  Helena Korres de Paula  for her help with the DNA extraction. Support by  Vincent van Hoef  as part of the  National Bioinformatics Infrastructure Sweden (NBIS)  is gratefully acknowledged.
Funding This work was supported by  HydroSciences laboratory (IRD-University of Montpellier, France) , the  Eric Philip Sörensens Stiftelse (Sweden) , the  Swedish Research Council  ( 2016-05799  and  2017-01239 ),  Karolinska Institutet (Sweden) , the  Swedish International Development Cooperation Agency , and the  Genetics Institute at Universidad Mayor de San Andrés (Bolivia). In addition, CS was funded by the  European Research Council  ( ERC StG AfricanNeo , grant no. 759933 ). The  SNP&SEQ Technology Platform  and the  Uppsala Genome Center  are supported by the  Swedish Research Council for Infrastructures and Science for Life Laboratory (Sweden). The  SNP&SEQ Technology Platform  is also supported by the  Knut and Alice Wallenberg Foundation.
Statistical analysis All the data were tested by ANOVA for a 4 Â 4 Latin square design using the mixed procedure of SAS 9. 4 statistical software  (SAS, 1988). The statistical model included period (1, 2, 3 and 4), species (cow and goat), diet (CTL, COS, MAP and HPO), and species Â diet interaction as fixed effects. For lipid class analyses, animals in groups (i. e. the three cows or three goats allocated to the same diet for each of the four periods) were included as random effects. For analysis performed on samples composed of the three animals on the same diet during the same period (FA analysed by GLC; TAG and ceramides analysed by LC-MS), the group was included as random effect. The normality of data distribution was tested (Shapiro-Wilk, Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling tests), and the data were log-transformed and re-analysed in case of significance (P < 0.
Acknowledgements This work is issued from PhD thesis of  Hélène Fougère  (https:// tel. archives-ouvertes. fr/tel-02094454). The authors thank the staff of  Unité Expérimentale Herbipôle de Theix (INRAE, Saint-Genès-Champanelle , France; https://doi. org/10. 15454/1. 5572318050509348 E12 ) for their diligent care of the experimental animals;  Sylvie Bardou-Valette  and  Martine Tourret  ( UMR 1213 Herbivores, INRAE ) for their assistance during sample collection;  Sylvain Emery  ( UMR 1213 Herbivores, INRAE ) for valuable assistance with laboratory analysis by HPTLC and GLC and the subsequent data processing;  Aurélie Batut  and  Pauline Le Faouder  from  MetaboHUB-MetaToul Core facility ,  UMR1048  ( INSERM, MetaboHUB - ANR-11-INBS-0010 ) for the laboratory analyses by HPLC/HRMS or MS/MS.
Acknowledgements The authors would like to thank  Dr Vincent Petit  (Metafora) and  Dr Julien Textoris  (bioMérieux) for fruitful discussions as well as the clinical teams from ICUs in HCL who were involved in this project as well as patients and their families who agreed to participate to this clinical study. The authors would also like to thank  Valérie Cerro  and  Laurie Bignet , ( Services de Réanimation, Hospices Civils de Lyon ). Finally, the authors would like to thank  Virginie Moucadel ,  Estelle Peronnet  and  Elisabeth Cerrato  for technical support.
Sample Our sample includes women and men and girls and boys. We conducted 82 free listing interviews to collect wild edible data among adults (n = 42) and children (n = 40). We used a convenient sampling method to obtain a balanced sample considering gender and life stage. Our sample includes 22 women, 20 men, 22 girls and 18 boys from 52 households. We also selected eight key informants, five adults and three children, to collect additional data with semi-structured interviews  [55]. The five adults (two women and three men) were ombiasa (traditional healers working with plants and animals), well-known in the valley for their expertise. As children's key informants, we selected two boys and one girl around 10 and 12 years old able to cite and identify more than 20 plant species in our free listing exercise (which represents twice the average number of plants cited by children).
Ethnobotanical data Ethnobotanical data were collected using free listings and semi-structured interviews with key informants. We collected ethnobotanical data using free listings because this tool gives a good overview of local knowledge and can be adapted to children, so data collected using free listings allow us to compare adults' and children's knowledge  [18]. Because people of the study area do not have a unique term for the concept "wild edible plants'', our prompt for free listing was: "Which non-cultivated plants do you know that are edible?" We used the word sakafo, which means food, meal or edible. To facilitate the work with children and to build a relationship of trust with them, before starting data collection we spent two weeks of direct and participant observation  [56, 57].
To avoid that children identified the interview with schoolwork, free listing interviews with children were mostly conducted outside school, mostly at home  [58, 59]. If parents wanted to assist in the interview, we asked them not to influence children's responses  [58]. Free listing interviews with adults were conducted at home or in communal spaces, like the marketplace. To obtain more detailed data on each WEP listed in free listing, we conducted semi-structured interviews with adults and children key informants. During semi-structured interviews with key informants, and for each WEPs reported in free listing, we asked about i) synonymy, ii) parts consumed, and iii) wild edible plant location and hamlets-to-location walking time. To collect this information with children, we complemented our semi-structured interview with walks in the surrounding forests.
Botanical data In February 2019, with the help of local experts, we collected and made vouchers for WEP reported during free listings. We prepared plant vouchers following the field guide used by Missouri Botanical Garden  [60]. WEPs were identified by comparing the local names, vouchers and photographs taken by the first author with species identified in literature and Malagasy flora database  [61] [62] [63]. Family and scientific names were checked with the World Flora Online  [64]. For each species reported during free listing, we determined its life form. We considered "trees" as plants erected from the ground with a single trunk, "shrubs" as multiple woody stems plants, "climbers" as support-dependent plants, and "herbaceous" as annuals plants without a trunk, usually non-woody. Based on the type of vegetation and disturbance levels on the area  [65, 66]  (see S1 Table in S1 File for habitats description), we described five ecological habitats where WEP grow: Grassy Secondary Vegetation, Shrubby Secondary Vegetation, Mountain Forest and Highland vegetation.
Data analysis We started the analysis using a MANOVA to test the relation between the habitats and the biogeographical characteristic of the WEPs cited. This test allows us to assess the number of endemic (to Madagascar), native (to Africa including Madagascar and the Mascareignes region) and introduced (voluntarily or accidentally) species in each habitat. Then, to assess the level to which WEPs knowledge varies within people in the study site, we used gender (i. e. , women vs. men), life stage (i. e. , adults vs children), and the combination of both to build different subsamples for the analysis.
Number of WEPs cited and estimated To get an overview of the WEP knowledge distribution, we used free listing data to calculate the total number of WEP cited-i. e. , observed richness -, differentiating between citations by the full sample and by each subsample. We also analyzed the number of WEPs shared and not shared by the four subsamples. Then, we estimated the theoretical number of WEP knowni. e. , the estimated richness-using the non-parametric estimator Chao 2, i. e. , the asymptote of the accumulation curve of species cited  [67]. Estimated richness was calculated for the full sample and for subsamples and compared to the respective observed richness. To assess variation on the number of wild edible plants listed across gender and life stage, we compared the length of the lists generated by the four subsamples (i.
Acknowledgments We thank the  Betsileo community  for their warm welcome and all the participants of our fieldwork interviews. We extend our gratitude to Gaston and  Silvain Michel ,  M. Lova  and  A. DePaul  for their assistance in the field and  Clementine Durand-Bessart  for encouragement and support. We are also grateful to our Malagasy partners, the  University of Antananarivo , the  Centre National de Recherches sur l'Environnement. We particularly thank  Claude-Anne Gauthier IRD  representative in Madagascar, who provided logistical support to facilitate our research. We would also like to thank  Santiago Alvarez Fernandez  for his support and help during the data analysis and all the participants of the writing workshop we attended for their constructive comments. Finally, would like to thank the Academic editor and the two reviewers for their insightful comments.
Tengkawang butter's fatty acid profile The fatty acid content of tengkawang butter samples from Sintang, Nanga Yen, Bengkayang, and Kapuas Hulu was determined using the fatty acid profile analysis technique. Instrument gas chromatography (GC) with a mass detector (MS) or flame ionization detector (FID) is a widely used technique for the analysis of fatty acids contained in oil and butter in foods. [19, 20] s can be seen from the fatty acid profile data shown in Figure  1 , in general, the dominant types of fatty acids in the four TB samples are the same: stearic acid, oleic acid, and palmitic acid, whose composition was 40. 05%, 32. 34%, and 24. 05%, respectively, for Bengkayang TB; 43. 12%, 31. 71%, and 22. 04%, respectively, for Nanga Yen TB; 45. 77%, 31. 14%, and 17. 23%, respectively, for Sintang TB; and 46.
The molecular weight and concentration of fatty acids in the fat/oil determine the saponification number. [36] Therefore, an adequate saponification number was used to calculate the average relative molecular mass of a fat/oil. The saponification number of tengkawang fat follows the standards of tengkawang (SNI) and cocoa butter (FAO). The saponification numbers of Bengkayang TB, Nanga Yen TB, Sintang TB and Kapuas Hulu TB were 200. 99, 201. 88, 198. 74, 196. 51, respectively. DMRT analysis indicates that the saponification number of Kapuas Hulu TB and Sintang TB were significantly different from those of Bengkayang TB and Nanga Yen TB. The data shown in Table  1  indicate that several parameters are follow the SNI tengkawang standard and CB standard, such as water content, IN, saponification number, melting point, and peroxide number.
Acknowledgments The  Ministry of Research and Higher Education of Indonesia  provided financial assistance for this study through the  research grant "Hibah Program Menuju Doktor Mahasiswa Unggul" (PMDSU) 2021  ( NKB-354/UN2. RST/HKP. 05. 00/ 2021 ). The authors also gratefully acknowledged the financial support for publication by  Universitas Indonesia  through the  scheme of Hibah Riset Kolaborasi Internasional WCU 2021  ( NKB-643/UN2. RST/HKP. 05. 00/2021 ).
The aim of this study was to investigate the influence on individuals' time perception of observing a range of foods differing in calorific content. In a first experiment, 92 adult participants performed a temporal bisection task with stimulus durations presented in the form of high-or low-calorie food pictures as well as matched non-food control pictures. In a second experiment, 102 participants performed a strict replication of Experiment 1, without the low-calorie pictures condition as it showed less pronounced effects. Across the two experiments, the data revealed common results. An overestimation of time was observed in relation to high-calorie food pictures when compared with non-food pictures (Experiment 2), and the effect was a function of participants' diet control (Experiments 1 & 2).
The task remains unchanged for the participants, who must continue to judge the durations. The first results reported by Gil and colleagues  28  showed an underestimation of time duration in relation to food pictures compared to control pictures, and that this underestimation was more pronounced for disliked food. The authors attributed this effect to an attentional bias mechanism, whereby food cues distract attention away from the processing of time. In contradiction to this attentional hypothesis, a recent meta-analysis  30  conducted in non-clinical samples on a set of 57 datasets found little experimental evidence that greater dietary restriction is related to impaired cognitive control in general, or to increased cognitive bias for food. Thus, when examining specific attentional bias tasks, it appears that in healthy individuals without eating disorders, increased food restriction is not associated with increased attentional bias or distraction by food cues, regardless of the scale used to measure food restriction.
Experiment 1 Participants. A total of 92 participants took part in this experiment. As will be described more fully below, seven of them were removed from the database. The final sample therefore consisted of 85 participants, 68 of whom were females (80%). The participants' age ranged from 18 to 27 years (M = 19. 42, SD = 2. 37). They were enrolled via an invitation communicated in the psychology courses of a French University and on social networks. The participants gave their consent via an electronic form presented at the beginning of the online experiment. The experiment was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans, and was approved by the research ethical committee of the University of Nantes.