dataesr/scientific-paragraphs-categorization huggingface dataset:
Ocean Database 2005 and World Ocean Atlas 2005 (WOD05 and WOA05), the Global Temperature-Salinity dataset in the tropical Pacific from the Institut de Recherche pour le Development (IRD, France) and the Centennial in situ Observation Based Estimates (COBE) sea surface temperature. The XBT depth bias correction is applied in the current version. The temperature and salinity data are available at monthly intervals over 24 depth levels ranging from the ocean surface down to 1,500 m depth, on a global 1°9 1°grid from January 1945 to December 2012 (see Ishii and Kimoto 2009 for details). • NOAA data set: Available at https://www. nodc. noaa. gov/OC5/3M_HEAT_ CONTENT. As described in Levitus et al. (2012), this 1°9 1°data set uses the World Ocean Database 2009 (WOD09) plus additional data processed since 2009.
Acknowledgments We thank  Don Chambers  and two anonymous referees for their constructive reviews. We also thank  William Llovel  for interesting discussions about our respective studies. H. B. D. PhD  is funded by the  European Space Agency  in the context of the  Climate Change Initiative Sea Level  Project. H. P. is supported by a joint  CNES-CLS  PhD grant. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
Details on the data processing are given in Balmaseda et al. (2013b). Ishii and Kimoto (2009))data set (called IK hereinafter): We used the updated 6. 13 version available at http://rda. ucar. edu/datasets/ds285. 3/. It is based on the World www. metoffice. gov. uk/hadobs/en4/download-en4-0-2. html). This data set is based on the quality controlled subsurface ocean temperature and salinity profiles and objective analyses. The EN4. 0. 2 data set is an incremental development of the previous EN2 and EN3 versions. Data sources include the WOD09, Global Temperature and Salinity Profile Program (GTSPP) and Argo data from Argo Global Data Assembly Centres (GDACs). The EN4. 0. 2 temperature and salinity data are corrected for the XBT and MBT bias. The temperature and salinity data are available at monthly intervals over 40 depth levels ranging from the ocean surface down to 5,350 m depth, on a global 1°9 1°grid from January 1900 to December 2013.
Acknowledgments This work is supported by  National Natural Science Foundation of China, China  (Grant no. 61104144 ). Enrico Zio  would thank the project  Laboratoire Internationale Associé 2MCSI: Modelling  of aging components for system reliability analysis and risk assessment. S. Havlin  acknowledges support from the  Israel Science Foundation , Israel and the  European Projects LINC  (No. 289447 ) and MULTIPLEX (No. 317532 ).
Acknowledgements. This work was supported by  FEDER (QREN)  through  Programa Mais Centro , under projects  CENTRO-07-ST24-FEDER-002002 ,  CENTRO-07-ST24-FEDER-002006  and  CENTRO-07-ST24-FEDER-002008 , through  Programa Operacional Factores de Competitividade -COMPETE  and National funds via  FCT-Fundação para a Ciência e a Tecnologia  under projects  Pest-C/SAU/ LA0001/2013-2014 ,  PTDC/SAU-NMC/120144/2010  and  PTDC/NEU-NMC/0198/2012. We thank  Professor Tadeusz Wieloch  ( Wallenberg Neuroscience Center, Lund University, Lund, Sweden ) for providing the MCAO samples and for the comments on this work, and  Elisabete Carvalho  for the assistance in the preparation of cultured hippocampal neurons.
This region forms a unique ecosystem bordered by the Saharan desert and the tropical rainforests, extending from east to west all across the continent. Its climate is characterized by annual cycles of wet and dry seasons allowing the co-existence of sedentary farmers and nomadic pastoralists who move with their livestock between wet-and dry-season pastures  [51, 52]. In this study, we carried out an investigation of NAT2 sequence variation among six Sahelian populations relying either on nomadic pastoralism or on sedentary agriculture sampled in an area extending from western Burkina Faso to northeastern Chad, in order to explicitly address the hypothesis that different selective pressures associated to these lifestyles acted on the evolution of NAT2. Moreover, by combining our dataset with published African samples of NAT2 sequences (Table  1  and Additional file 1: Figure  S1 ), we also address the hypothesis that selective pressures, stemming from an environmental factor linked to the ecoregion in which populations have been living (i.
NAT2 diversity in the Sahel We analyzed a total number of 287 samples from six well-defined Sahelian populations relying on two different modes of subsistence, namely pastoralism (Fulani from Banfora and from Tindangou in Burkina Faso, Fulani from Ader in Niger, Fulani from Bongor in Chad, and Daza in Chad) and agriculture (Kanembou in Chad). We completed this new dataset of NAT2 sequences with those from other African population published samples (Table  1  and Additional file 1: Figure  S1 ). For each of the 287 Sahelian samples, 1,396 base pairs (bp) encompassing the 870 bp NAT2 coding exon were successfully sequenced (Additional file 2). In total, 15 polymorphic positions were observed, 11 of which are located in the coding exon, and 9 being non-synonymous (Fig. 1 ). From the 15 single nucleotide polymorphisms (SNPs) observed, a total of 21 haplotypes were inferred with the expectation-maximisation (EM) algorithm of Arlequin for the six Sahelian populations (Fig.
71 to 0. 78). For nucleotide diversity instead, four of our Sahelian samples display intermediate values (around 0. 0029), while the Fulani from Bongor display a slightly lower value (0. 0028), and the Kanembou a slightly higher one (0. 0030). However, these estimates are associated with large standard deviations thus precluding finding significant differences in diversity levels between populations. This is especially the case of many of the published datasets, which include samples of less than 20 individuals. In support of this, we found a high and significant correlation between sample size and number of haplotypes detected (r = 0. 738, P < 0. 00001, Additional file 6: Figure  S3 ), and it remained  P-value associated with Tajima's D test for departure from selective neutrality: it is given as the proportion of random D values generated under the neutral equilibrium model that are smaller than, or equal to the observed value.
NAT2 population structure in Africa To investigate for the presence of a genetic structure differentiating populations with distinct food-producing subsistence modes (and for the influence of samples of small size on the results as well), analyses of population structure were performed on three subsets of the population data (Table  1 ), namely: (1) on the African populations dataset (AFR, i. e. , 38 samples, excluding African Americans), (2) on the African food-producing populations dataset (FP, i. e. , 29 samples, excluding huntergatherers), and (3) on an African food-producing populations dataset comprising only those samples with size ≥ 20 individuals (FPLS, i. e. , 13 samples, excluding both hunter-gatherers and all other samples with sizes smaller than 40 chromosomes). Taking into account the molecular diversity of NAT2 sequences (in terms of the number of pairwise differences between NAT2 haplotypes), the global level of population structure estimated for the African continent is of 3.
3% (AFR dataset, Φ ST = 0. 033, P< 0. 0001), which means that 3. 3% of the total genetic variation is attributed to differentiation among populations, while 96. 7% is due to differences among individuals within populations. In the AFR dataset, 33. 4 % of the pairwise genetic distances between populations were found to be significant at the 5 % level. This proportion drops to 19. 5 % in the FP dataset, thus suggesting substantial NAT2 genetic differentiation both among hunter-gatherer populations and between huntergatherer and food-producing societies. Consistently, the proportion of genetic variance explained by differences among populations drops to 2. 2% in the FP dataset, but it is still significant (FP dataset, Φ ST = 0. 022, P< 0. 0001). Thus, while differentiation of hunter-gatherer populations contributes to NAT2 population structure in the African continent, population structure among foodproducing communities is also significant.
Among the non-significant pairwise genetic distances, a rather large proportion could actually be due to lack of power in significance testing because of small sample sizes. Indeed, the proportion of significant distances increases again to 34. 6% in the FPLS dataset, i. e. , in the dataset that excludes food-producing population samples of less than 20 individuals. In line with this, the proportion of genetic variance explained by differences among populations also raises to 2. 6% (for the FPLS dataset, Φ ST = 0. 026, P < 0. 0001), thereby suggesting that the inclusion of samples of small size in the AFR and FP analyses leads to a lowering of the power to differentiate populations. Most of the significant pairwise genetic distances in the FPLS dataset differentiate the Yoruba and Akele populations from the others (Additional file 9: Figure  S6 ).
Conversely, none of the six Sahelian populations was found significantly differentiated from the others, irrespective of language affiliation or lifestyle, and most of the genetic distances between our samples and the East African samples were also found statistically not significant. Accordingly, no correlation of genetic distances with geographic distances was found for any of the AFR and FP datasets (r = 0. 054, P = 0. 223, and r = -0. 012, P = 0. 531, respectively), whereas the Mantel test with the FPLS dataset led to a significant negative correlation (r = -0. 218, P = 0. 034). This somewhat surprising result stems from both a set of large genetic distances between populations located in close geographic proximity and a set of small genetic distances between populations geographically far apart (Additional file 10: Figure  S7 ), and does not fit the expectation of an isolation-by-distance model of evolution.
In line with this, no significant correlogram of Moran's I spatial autocorrelation indices was found with any of the NAT2 haplotypes observed in Africa, except for haplotype NAT2*5C, a rather infrequent haplotype. Association of NAT2 genetic structure with geography, culture, or climatic zone and biome Hierarchical analyses of molecular variance (AMOVA) were carried out to gain further insight into a possible association of the genetic structure of populations with factors related to their demographic and cultural history. Four categorization criteria were tested (Table  1 ): geography, language, subsistence mode and ecoregion (biome). The results highlight a marked genetic structure associated with the fourth categorization criterion that considers whether populations live within the dry savanna biome or outside of it (Table  3 ).
However, none of the negative Φ CT values in the table are statistically significant, thus indicating that they are equal to zero 7 Only two linguistic families are considered here (Niger-Congo and Nilo-Saharan, Table  1 ) because the third family (Afro-Asiatic) is represented by one population sample only (SOM) greater than Φ SC indices in all cases (corresponding Φ SC for AFR, FP and FPLS of 2. 2 %, 0. 7 % and 0. 6 %, respectively, all P-values < 0. 05). Significant genetic structure was also detected with a classification according to subsistence strategy, but under this categorization criterion and for each of the three datasets, the Φ CT index is lower than the Φ SC index, thereby meaning that more differentiation is found among populations in groups with matching subsistence mode than between those groups, even if only slightly more so.
Finally, the results indicate that neither differentiation among geographic groups nor among linguistic groups does associate with the genetic structure of populations displayed by NAT2 sequences, since Φ CT indices were never found significant for any of the AFR, FP, or FPLS datasets (Table  3  and Additional file 2). The two-dimensional plots resulting from the nonmetric multidimensional scaling (MDS) analyses of genetic distances among populations are shown in Fig. 2  for the AFR dataset, and in Additional file 11: Figure  S8  and Additional file 12: Figure  S9  for the FP and FPLS datasets, respectively. In each of these figures, the MDS plot is displayed four times, with populations being highlighted according to our four categorization criteria, namely geography, language, subsistence mode and biome.
0014). This difference remains both when hunter-gatherers and when small samples are excluded from the analysis (FP and FPLS datasets, P = 0. 0161, and P = 0. 0152, respectively). When huntergatherers are excluded (FP dataset), only three subsistence modes are represented in the data, namely agriculturalists, agro-pastoralists, and pastoralists (Table  1 ). When small samples are excluded (FPLS dataset), only agriculturalists and pastoralists are represented, thus implying that slow acetylation prevalence is significantly higher among pastoralist than among agriculturalist populations. These results were confirmed by pairwise Wilcoxon tests of equality in average slow acetylation frequencies among subsistence strategies (Additional file 14: Table  S4 ). A significant difference in slow acetylation prevalence was consistently found between pastoralists and agriculturalists (and between pastoralists and hunter-gatherers as well).
1 %; 56. 7 %]), thus suggesting a possible association of acetylation status with ecoregion, namely with the climatic zone and biome populations live in (Fig. 4c ). In line with this observation, a highly significant difference in average slow acetylation frequency was consistently found between populations living within the dry savanna biome or outside of it (Clim, P = 0. 0003, P = 0. 0054, and P = 0. 0112, for the AFR, FP, and FPLS datasets, respectively, Table  4 ), irrespective of subsistence strategy. Single factor analyses of variance (ANOVA) corroborated the results of the Kruskal-Wallis rank sum tests in that predicted slow acetylation frequency is observed to differ significantly between populations classified both by ecoregion (Clim) and subsistence mode (Subsist), but not by geography (Geo) or linguistic classification (Lang) (Additional file 15: Table  S5 ).
Intermediate to low NAT2 molecular diversity in Sahelian populations Twenty-one haplotypes were parsimoniously inferred for the six Sahelian samples, of which three haplotypes not yet described to the best of our knowledge (NAT2*12N, NAT2*13D and NAT2*14K, Fig. 1 ). In order to compare our newly generated dataset with published NAT2 sequences from other African samples, we restricted the analysis to the 870 bp of the NAT2 single coding exon. We have thus shown that levels of genetic diversity in the six Sahelian populations are amidst the low to intermediate range of values in the scale of NAT2 polymorphism known for the African continent, especially so when measured by expected heterozygosity (Additional file 5: Table  S2 ). This result could be due to faster genetic drift in these populations compared to others, which is congruent with the idea, based on analyses of polymorphisms in uniparentally transmitted DNA (mitochondrial DNA and Y chromosome) showing that some Sahelian populations experienced a demographic contraction in their history  [54, 55].
Thus, this trend towards Tajima's D positive values could also point to a reduction in population size  [60] , particularly so in the pastoral nomads. However, irrespective of subsistence mode, most African populations analyzed here displayed the same trend towards positive D values, which are also expected if the gene is submitted to balancing selection. The signature left by balancing selection should be detected by the Ewens-Watterson homozygosity test, since this selective regime should lead to an excess of heterozygotes, as has been shown for the Human Leukocyte Antigen (HLA) loci  [61]. As already stated, among the 39 Ewens-Watterson tests performed on the complete population dataset, a single case of significant rejection of neutrality was found for the Mbuti Pygmies (Additional file 5: Table  S2 ), because of heterozygosity in excess (which would fit the balancing selection model), but not anymore after adjustment for type I error rate.
3 % for the AFR dataset of 38 populations). Here we further showed that this structure is not only attributable to the genetic differentiation of hunter-gatherer populations but is also found among African food-producing communities, as attested by the significant Φ ST value of 2. 6 % for the FPLS dataset of 13 populations. This significant genetic structure is mainly due to the variation in frequencies of three major haplotypes, two of which are categorized as decreased function alleles (NAT2*5B and NAT2*6A), which are particularly frequent in populations living in the dry savanna biome, and one fast allele (NAT2*12A), more frequently detected outside this region (i. e. , in populations living in tropical humid environments) than within it. Accordingly, both the AMOVA (Table  3 ) and MDS analyses (Fig.
Differences in slow acetylation prevalence across the southern Sahelian limit The frequency of slow acetylators in each population sample was predicted from individuals' NAT2 genotypes (i. e. , diplotypes). Kruskal-Wallis tests of homogeneity in proportions of slow acetylators among groups of populations did not evidence significant variation between geographic groups, nor between linguistic groups. In turn, significant difference of slow acetylators were found between subsistence strategies, and specifically between pastoralist and agriculturalist populations (Table  4 , P = 0. 016 and P = 0. 015, for the FP and FPLS datasets, respectively; see also Additional file 14: Table  S4  and Additional file 15: Table  S5 ). Considering only those populations for which sample sizes included at least 20 individuals, average frequency of slow acetylators among pastoralists is of 63.
005 and P = 0. 011, for the FP and FPLS datasets, respectively). Multi-factor ANOVAs further showed that no significant interaction between subsistence mode and ecoregion explained the observed differences in predicted mean slow acetylation frequency (Additional file 15: Table  S5 ). Altogether, these results point to a possible independent influence of dietary habits on one hand (as reflected by subsistence modes), and of the chemical environment on the other hand (as reflected by climatic zones), in the evolution of NAT2 diversity. Thus, this hypothesis does not necessarily call for the existence of specific dietary components associated with subsistence modes, and hence has the advantage of being compatible with the opposite pattern of variation observed in sub-Saharan Africa with respect to Central Asia, where higher slow acetylation prevalence is observed in sedentary agriculturalists than in nomadic pastoralists  [43].
Causative factors driving NAT2 evolution Consistent with previous investigations on NAT2 molecular variation  [42]  (and references therein), no statistically significant signal of selection was found in our datasets after correction type I error rate. This suggests that if positive directional selection for slow-causing NAT2 haplotypes in specific dietary and environmental conditions has been acting on the genetic evolution of populations, such selective pressures might have acted on multiple standing genetic variation rather than on specific new NAT2 mutations  [25, 26, 38, 43, 77]. We acknowledge indeed that classical selective neutrality tests on population molecular data (such as the Ewens-Watterson homozygosity, Tajima's D and Fu's F S tests used here) are known to have little power to detect a signal of selection on standing genetic variation  [62, 78].
As Fulani groups move frequently, the samples were secured in the temporary camps during the dry seasons, when the nomads rested at the southern extremity of their migration routes or transhumance corridors. The seminomadic Daza people (n=41) were collected in the northernmost regions of Lake Chad basin  [56]. The Daza also rely mainly on animal husbandry, although some groups are semi-sedentary, cultivating palms in small oases. The sedentary Kanembou (n=49) from the northern fringes of Lake Chad represent agriculturalists in our dataset  [54]   Niger). In Burkina Faso and Chad, no ethics approval was required by National Authorities, possibly because ethics commissions were not operating, but the same study protocol was followed in all three countries. Only the geographic location of sampling sites was recorded (i.
Inference of NAT2 haplotypes The sequence haplotypes and their associated maximum likelihood (ML) frequencies were inferred separately for each population sample, using the Bayesian approach based on an approximate coalescent model implemented in the software PHASE v. 2. 1  [93, 94] , and the maximum likelihood (ML) approach based on the expectationmaximisation (EM) algorithm implemented in Arlequin ver. 3. 5  [95]. To this end, PHASE and Arlequin were run independently, and the resulting inferences were compared. Because we observed that PHASE results were less stable among runs (slightly different haplotype calls output with different seed numbers but with the same settings) than Arlequin results (identical haplotype calls with the same settings), we chose the latter software to obtain the haplotype calls and their associated ML frequencies (see also Additional file 2).
Following the standards of the official nomenclature of human NAT2 alleles  [37] , haplotype NAT2*4 (GenBank accession X14672) was used as a reference for the coding exon. However, because of known variation in the flanking region of the coding exon of NAT2*4 alleles  [26, 42, 44] , we chose the human genome reference sequence (GRCh37/hg19, http://genome. ucsc. edu/) as a reference for the upstream and downstream flanking regions, and created a NAT2*4/hg19 construct. The sequences generated in this study were thus aligned with this construct, using ClustalW  [96]. Two in-house programs were designed so as to (1) identify and map variant positions with respect to the NAT2*4/hg19 construct, and (2) classify inferred phased haplotypes according to the official NAT2 gene nomenclature.
Collection of published NAT2 sequences from African populations Our new dataset was completed with published NAT2 sequences from sampled African populations obtained through a comprehensive search of publications. Only populations represented by samples including at least 10 individuals (i. e. , 20 chromosomes) were considered. Sequence genotypes from  [97] , obtained through personal communication with the authors, had to be phased in order to infer sequence haplotypes. The new dataset also comprises NAT2 sequences reconstructed from the phased variant call file extracted from the 1000 Genomes Project (phase 1, release version 3 of April 2012,  [53] ), which were obtained with a Python program developed for this purpose. Together with the new 574 NAT2 sequences generated in this study (i.
e. , 287 individuals), the complete dataset assembled includes 38 African population samples and one sample of African Americans, totalizing 1,192 individuals (Table  1  and Additional file 1: Figure  S1 ). Geographic maps reporting the samples (Fig. 3 , and Additional file 1: Figure  S1  and Additional file 7: Figure  S4 ) were created with the QGis open source software  [98] , and climatic zones were defined according to  [99].