dataesr/scientific-paragraphs-categorization huggingface dataset:
Computational methods The experimentally resolved structure was completed first. Indeed, due to thermal fluctuations and possible structural disorder, some residues and side chains were not resolved. Missing side chains include the following: Lys 40, Asp 79, and Leu 206 in chain A; Met 34, Asp 79, Lys 175, Leu 206, Phe 207, and Asp 208 in chain B; and missing amino acids, including residues 116-120 for chain A and 116-121 for chain B. After an energy minimization in a vacuum, for all-atom molecular dynamics (MD) trajectories starting from this completed structure were performed at 300 K using the GROMACS 5. 1 package  (Hess et al. , 2008)  and the AMBER99 force field (amber99sb-ildn force field)  (Lindorff-Larsen et al. , 2010). The four MD trajectories were as follows: one 150 ns in duration in the absence of ligand and three 1 μs in duration in the presence of GSH with PITC, AITC, or PEITC.
With regard to their reactivity, a spontaneous conjugation between ITCs and GSH occurred in the absence of enzyme. The chemical reaction rate is Fig. 2. Relative expression of GSTD2 RNAs in chemosensory appendages. Real-time PCR analysis was performed using RNA prepared from olfactory appendages (antennae and palps) (OA), taste appendages (labellum and forelegs) (TA) and heads without chemosensory appendages (H) of male Drosophila. Numbers on the y-axis are arbitrary units that indicate relative level of RNAs. Statistical analyses were performed with REST software (*p < 0. 05, ***p < 0,001; n = 3). proportional to the ITC concentration and led to a glutathione conjugate molecule presenting light absorption properties. To avoid any measurement error, the ITCs were not tested at 100 μM but were directly tested using another enzymatic test.
Isothiocyanates Enzymatic activity towards ITCs was directly measured using the absorbance increase associated with GSH conjugation. In the presence of GSTD2, the spontaneous conjugation reaction was significantly enhanced. An increase in the glutathione-conjugated form was observed using HPLC in the presence of the enzyme compared to that in the absence of the enzyme (data not shown). There is thus a contribution of a non-enzymatic reaction in each enzymatic assay performed. This chemical activity was systematically subtracted from each corresponding enzymatic activity. The activities shown in Table  1  are equivalent or superior to the activity previously determined for D. melanogaster GSTE7  (Mazari et al. , 2014). Indeed, the activity of GSH conjugation measured for GSTD2 with PEITC appears to be 10-fold higher than for GSTE7 at a 10-fold lower PEITC concentration (7.
GSTD2 X-ray structure The crystal structure of GSTD2 was solved at 1. 6 Å resolution (Protein Data bank number (PDB): 5f0g). The overall structure of GSTD2 adopts a canonical GST fold consisting of an N-terminal thioredoxin domain followed by a C-terminal domain primarily composed of α-helices (Fig. 5A ). The structure appears as a crystallographic dimer (Fig. 5A ). This is biologically relevant with regard to both to the large interaction surface (1408 Å 2 ) and the SEC-MALS analysis. To compare this new structure to previously solved insect GST structures, we calculated the root-mean-square deviation (RMSD) for Cα atoms of both monomers after structural alignments (Table  2 ). The RMSD value allowed us to compare the structural proximity between two structures. As expected from the primary structure, the Drosophila Delta GST family includes the nearest structural homologues of GSTD2, with an RMSD between 0.
, 2005; Wongsantichon et al. , 2010 Wongsantichon et al. , , 2012 Wongsantichon et al. , , 2015)). For each structure, the PDB code (parentheses) and RMSD (bold) are indicated in Å. RMSD was obtained after a structural alignment to the D. melanogaster GSTD2 structure. substrate stabilization/accommodation in the active site and the probability of catalysing a given reaction, we can compare these results to those of enzymatic assays. Here, molecular modelling demonstrates that PEITC is the most stabilized substrate, whereas PITC is less stabilized, which is consistent with results from the activity assays even if the activity differences are small (less than 10-fold) (Table  1 ). Our molecular dynamics data suggest that the three tested ITCs adopt three different positions in the active site, but all are compatible with catalysis.
, 2003). Either the GSTD2 X-ray structure or the molecular dynamics modelling with ITC allows us to propose a residue for the correct orientation and stabilization of the deprotonated thiolate anion. However, an alternative mechanism to activate GSH was proposed: the base-assisted deprotonation model. In this deprotonation model, glutamyl α-carboxylate of GSH contributes to the thiolate anion formation  (Caccuri et al. , 1999; Widersten et al. , 1996). This base-assisted deprotonation model was already proposed for a Delta GST: the A. dirus GSTD3  (Winayanuwattikun and Ketterman, 2004, 2005). However, the catalytic Ser residue is conserved and well positioned in the active site of this A. dirus GSTD3 (PDB: 1JLV and 1JLW). The Ser residue substitution is a recent molecular event in the melanogaster subgroup that is also apparent in Drosophila simulans and Drosophila sechellia GSTD2s but not any other GSTD2s from the 9 other Drosophila species fully sequenced to date.
The opinion expressed by Eriksson and colleagues' fails to recognise that there are no standard experimental designs for academic investigations involving omics analyses of genetically modified crops and that the only valid comparator to determine the effect of the process of transgenesis is a near isogenic variety grown at the same time and location, as was the case in our investigation of NK603 maize. Eriksson does not acknowledge that the quality of the rat liver tissues in our chronic Roundup toxicity study has neither been questioned nor branded as unsuitable for further investigation. In addition, Eriksson fails to appreciate that the statistical methods we used to analyse the liver metabolomics dataset are recognised as appropriate as some of a number of approaches that can be taken.
However, the point of our study was to assess the effect of the GM process on the composition of NK603. Thus the only scientifically valid comparator is the non-GM closest (isogenic) relative. Comparisons with different maize varieties that do not constitute near-isogenic strains, and which are grown at different locations and times, would only serve to increase variation in the dataset and thus mask rather than highlight the effects of the GM process, negating the purpose of the study. Additional valid comparisons could be made between other maize strains harbouring the NK603 event (produced by out-crossing of the original genetically engineered parent) and their near-isogenic maize variety(s), always with the caveat that they must be grown at the same time and location. Such an investigation would determine if our findings are unique to the NK603 strain we have studied or occur generally with this genetic modification event.
In this situation, we expect that asexual population genetic dynamics will be followed and that they will depend on standing GxE fitness variance. At several periods we collected genome-wide single-nucleotide polymorphism (SNP) data and used these to infer the fitness reaction norms of the genotypes that were present in the ancestral population as well as their expected frequency changes during experimental evolution due to selection. We performed a second set of evolution experiments where we test for the repeatability of adaptation in the most extreme environment to show that genetic drift and founder effects during prior gradual evolution can lead to the loss of the best genotypes and impact selection efficacy.
elegans is diploid with six similarly-sized chromosomes, for a genome of 100 Mbp), with the objective of sampling haplotypes at relatively low frequencies (Fig 2A ). With self-fertilization and complete linkage disequilibrium, the number of observed chromosome-wide haplotypes (CWH) should be similar to the number of observed "region-wide" haplotypes (RWH), each defined by a pair of homozygous chromosomes in each hermaphrodite (Fig  2A ). We estimate, however, that linkage disequilibrium is not complete since when we take the data from all populations and time points into consideration between 5% and 21% more RWHs than CWHs are found, depending on the region (Fig 2B ), with the majority of them being at low frequencies across the dataset (S2 Fig). We estimate that the ancestral population must have segregated at least 212 RWHs (the minimum observed number), and by extrapolation at least the same number of whole-genome haplotypes although many more are possible (see S1 Text section 1.
The alleles observed in the target sites in the respective pair of chromosomes define the region-wide haplotype (RWH) for the individual, equivalent to concatenating the two corresponding CWHs. The figure shows the number of distinct CWHs and RWHs observed within each region, computed considering the full dataset (across time points and populations sampled). Since a RWH is defined by the CWHs in each of the two chromosomes in a region, the number of distinct RWHs is bounded below by the minimum of the number of distinct CWHs observed in the two respective chromosomes (reflecting the existence of linkage disequilibrium), and bounded above by the product of these two numbers (no linkage). The data indicate that linkage is strong since the number of distinct RWHs is comparable to those of distinct CWHs but not complete.
Similarly, the number of lineages present in the lab-adapted ancestral population is bounded below by the product of the number of distinct RWHs in each of the three regions, indicating that the number of lineages in the ancestral population must be greater than or equal to 212. Data available from the Dryad Digital Repository: https://doi. org/10. 5061/dryad. 76n6f7c [54]. https://doi. org/10. 1371/journal. pgen. 1007731. g002 being described by λ k (x)  (Fig 4) , corresponding to the expected absolute number of live offspring produced under environment x. Selection is defined by the per generation growth multiplier (growth rate) of each lineage relative to mean population fitness-with the frequency of each lineage expected to follow a deterministic logistic frequency trajectory  [25, 26].
E i , L i and B i are categorical variables, while g i is a continuous variable. In this model, the transgenerational value g i is given by: g i ¼ c i À 25 305 À 25 � � t i À 1 ð Þ where c i is the NaCl concentration, in mM, and t i 2 {1,2} is the generation assayed. The various terms of the model correspond to: i) β(E i ,L i ), the statistical interaction between environment and line; ii) α(L i ), the line-dependent transgenerational effect; and iii) the intercept-based effect of block. The data was analyzed in R  [47] , using the following formula to specify the model in function lmer from package lme4  [49] : log(growthRate) ~saltTreatment � line + line � tGenComp + (1 | block) With the R package lsmeans  [48]  being then used to obtain estimates of interest: • expected log-growth rates L28 and L11 in each of the three assay environments: ~saltTreatment � line • expected selection coefficient of L28 relative to L11: pairwise ~line | saltTreatment In both cases, the estimates obtained do not include contributions of transgenerational effects (by evaluating the model at g i = 0, via parameter tGenComp = 0).
L28 and L11 fitness assays under competitive conditions L28 and L11 were further assayed in head-to-head competitions (Fig  6D ). Lineages were revived and reared for two generations at 25 mM NaCl before they were set up at three NaCl concentrations: 25 mM, 225 mM and 305 mM. On the third generation, L1 larvae from the two lineages were mixed in 1:1 ratio, at a density of 10 3 L1s in each of two Petri dishes per replicate assay. Each replicate assay was maintained for two generations. At both the assay generations, L1 samples were collected for pool-genotyping of single nucleotide polymorphisms (SNPs). Assays were performed in three blocks, with 3 replicates per salt concentration in each of two blocks, and 4 replicates in the third block. The data for analysis was based on the L28 and L11 SNP frequency values obtained after doing calibration curves where the ratio of both lines was known (S12 Fig).
For analysis, the estimated frequencies for L28 were forced to be in the interval (0. 005, 0. 995). To estimate relative fitness we calculated the selection coefficients of L28 with respect to L11, for the three assay environments considered, using a mixed effects model per SNP  [49]. Each model included salt treatment and generation as fixed effects, and replicates as a random effect: y i ¼ b 0 þ aðE i Þ t i þ gðR i Þ where y i is the logarithm of odds-ratio of the L28 allele: y i ¼ logðp i =ð1 À p i ÞÞ where E i denotes the assay environment (E i = 0, for 25 mM NaCl; E i = 1, for 225 mM NaCl and E i = 2, for 305 mM NaCl), t i denotes the generation, and R i is the replicate (R i 2 {1,2,� � �,30}). The data was analyzed in R  [47] , using the following formula to specify the model in function lmer from package lme4  [49]  log(OdssRatioL28Allele) ~generation : saltTreatment + (1 | replPop) The selection coefficients in each of the three assay environments were obtained via the point estimates for the corresponding parameters of the model.
Archiving All data and code for analysis has been archived in Dryad. org: doi:10. 5061/dryad. 76n6f7c. The archive consists of the following sets of files, each with a README. md for instructions on setting up the analysis and running the code: 1) input_data-genotp_data_NaCl. zip: raw genotyping data on the initial NaCl experiment (50 generations); 2) analysis_code-genotp_data_NaCl. zip: R code for preparing and summarizing the genotyping data on the initial NaCl experiment in changing environments. 3) input_data-growth_rate_data_NaCl. zip: raw growth-rate data on the ancestral population and the lines L28 and L11; 4) analysis_code-growth_rate_data_-NaCl. zip: R code for the analysis of the growth-rate data. This is necessary for inference of the RWH parameters and the lineages, since the inference relies on fitness data on the ancestral population.
5) analysis_code-inferring_RWH_params. zip: R code for the inference of RWH parameters and the lineages, given the genotyping data during the NaCl experiment and the fitness data on the ancestral. 6) analysis_results. zip: the overall results of the analysis in the paper, which was the source for the figures; 7) input_data-genotp_data_NaCl_continuation. (A) 100 lines were derived from gradual populations (GM1 and GM3) at generation 50 and whole-genome sequenced in  [21]. Most of the CWHs and RWHs observed in the present study are observed as well in the lines of  [21]. Shown are the number of distinct CWHs and RWHs observed within each region, considering the lines derived from each replicate population (rows 1 and 2), and considering all lines (last row). (B) RWHs detected in high frequencies in the lines were also observed in the populations, having been assigned most often as major RWHs.
Details on the SNPs density and number of individuals genotyped in each of the populations can be found in S1 Fig. (B) Given the genotyping data, a CWH consists of the alleles observed in each of the target sites in a single chromosome. The alleles observed in the target sites in the respective pair of chromosomes define the region-wide haplotype (RWH) for the individual, equivalent to concatenating the two corresponding CWHs. The figure shows the number of distinct CWHs and RWHs observed within each region, computed considering the full dataset (across time points and populations sampled). Since a RWH is defined by the CWHs in each of the two chromosomes in a region, the number of distinct RWHs is bounded below by the minimum of the number of distinct CWHs observed in the two respective chromosomes (reflecting the existence of linkage disequilibrium), and bounded above by the product of these two numbers (no linkage).
The data indicate that linkage is strong since the number of distinct RWHs is comparable to those of distinct CWHs but not complete. Similarly, the number of lineages present in the lab-adapted ancestral population is bounded below by the product of the number of distinct RWHs in each of the three regions, indicating that the number of lineages in the ancestral population must be greater than or equal to 212. Data available from the Dryad Digital Repository: https://doi. org/10. 5061/dryad. 76n6f7c [54].
The second set of evolution experiments (described below, Fig 7), were run for 30 generations from the 7 replicate gradual populations at generation 35 (right vertical dashed line). Detailed trajectories for other inferred lineages are shown in S8 Fig. Shaded colors correspond to the credible intervals obtained when sampling the ancestral population 20 times, with the line showing the median. S9 Fig shows the RWHs trajectories and S10 Fig shows lineage trajectories under quadratic fitness reaction norms. S11 and S12 Figs show the expected mean and variance in population fitness under linear and quadratic functions. Data available from the Dryad Digital Repository: https://doi. org/10. 5061/dryad. 76n6f7c [54]. https://doi. org/10. 1371/journal. pgen. 1007731. g005.
[21], the two inferred L28 and L11 lineages were identified after genome-wide sequencing of 100 lines derived from two gradual populations at generation 50 (S13 Fig and S2 Table). For the competitions shown in (D), frequency estimates of L28 and L11 were obtained using pooled-genotyping data on 18 SNPs that differ in L28 and L11 (see S14 Fig for calibration curves on these SNPs, and S15 Fig for our ability to differentiate L28 from L11). Data available from the Dryad Digital Repository: https://doi. org/10. 5061/ dryad. 76n6f7c [54]. https://doi. org/10. 1371/journal. pgen. 1007731. g006.
Red crosses indicate the likely position of the L28 lineage in this PC space. Analysis of the probability of a sweep by L28 in each population is shown in S17 Fig. Data available from the Dryad Digital Repository: https://doi. org/10. 5061/dryad. 76n6f7c [54]. https://doi. org/10. 1371/journal. pgen. 1007731. g007.
Acknowledgments We thank  J. Costa ,  A. Crist ,  H. Gendrot  and  I. Theologidis  for support with worm handling and sample preparation,  L. Noble  for help with the genomic data analysis. We thank  R. Gomulkiewicz ,  J. Hermisson ,  S. Matuszewski  and  L. Noble  for discussion, and  J. Hermisson  and the reviewers for helping improve the presentation of this work.
Data analysis was done at the  Center for Scientific Computing  from the  CNSI , MRL, at  UC Santa Barbara , an  NSF MRSEC  ( DMR-1121053 ) and  NSF   CNS-0960316  supported facility, SD is a fellow of the  Labex MemoLife  ( ANR-10-LBX-54   MEMO LIFE  and  ANR-IDEX-0001-02-PSL ). Financial support from the  National Science Foundation  ( EF-1137835 ) to SRP, Financial support from The  Human Frontiers Science Program  ( RGP0045/2010 ), the  European Research Council  ( FP7/2007-2013/243285 ) and  Agence Nationale de la Recherche  ( ANR-14-ACHN-0032-01 ) to HT. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
From ref. [21], the two inferred L28 and L11 lineages were identified after genome-wide sequencing of 100 lines derived from two gradual populations at generation 50 (S13 Fig and S2 Table). For the competitions shown in (D), frequency estimates of L28 and L11 were obtained using pooled-genotyping data on 18 SNPs that differ in L28 and L11 (see S14 Fig for calibration curves on these SNPs, and S15 Fig for our ability to differentiate L28 from L11). Data available from the Dryad Digital Repository: https://doi. org/10. 5061/ dryad. 76n6f7c [54]. https://doi. org/10. 1371/journal. pgen. 1007731. g006.
Database To collect, store and standardize data from many different sources, input tools and a relational database were developed and made accessible to all consortium partners. Provision of data was regulated by a data transfer agreement and uploading was facilitated through a userfriendly graphical interface that allowed users to drag and drop files to the server. A special focus was placed on security to ensure that the data were not compromised in any way. The first phase of submission and study, to which the series of AdaptMap papers in GSE refer to, accepted data until November 2015. At that point, the AdaptMap raw dataset included 4653 animals sampled from across the world and represented 148 populations, 35 countries and five continents. 43 populations were sampled, which comprise commercial international transboundary breeds (e.
Pakistani breeds were also represented. The samples from North America (Fig. 4 ) included both original local breeds, such as La Mancha, exotic breeds such as the New Zealand Kiko, and some populations with peculiar characteristics such as the island goat San Clemente, summed to six breeds. Climates varied from the arid region of Texas to the more temperate northern east coast. South American (Fig. 5 ) contributions totalled six breeds from Brazil and Argentina, which originate from very different latitudes, including the local Canindè and Moxoto. Finally, Australia and New Zealand (Fig. 6 ) samples from three breeds were provided, with rangeland uniquely represented in the dataset by sampling sites in the western part of the continent. For 11 breeds, populations sampled in more than one geographical location.
In particular, the Boer and Saanen represented seven and six populations, respectively. Samples were available from four Angora populations and three each from Alpine, Landrace and Small East African breeds. Admixed The Illumina GoatSNP50 BeadChip  [3]  that includes 53,347 SNPs was used to genotype all the animals. Prior to analyses described in the papers of this series, marker positions were re-mapped on the new goat reference sequence ARS1  [1]. After populating the database, quality control was performed on the whole set using the Plink 1. 9 software  [2]. Individuals and SNPs that did not pass the following thresholds were removed: individual genotype call rate higher than 0. 96, SNP call rate higher than 0. 98, and identity-by-state (IBS) between genotypes (compared pairwise based on all markers) less than 0.
99. Moreover, SNPs that were completely monomorphic over the whole dataset were also removed. To identify individuals with high relatedness (e. g. parent-offspring pairs), we calculated the number of Mendelian errors (ME) in pairwise comparisons of all individuals with a locally-developed script. Animals that were most frequently found in pairs with less than 100 ME were removed. The dataset included 12 crossbred populations: admixed Boer, Galla x Saanen, Matabele cross, admixed Mubende, Old Irish Goat cross, Saanen x ANB, Saanen x Creole, admixed Small East African, Small East African x Alpine, Small East African x Galla, Small East African x Saanen, and Small East African x Toggenburg. These populations were excluded from the analyses for the study of diversity and selection signatures but they do represent an interesting resource for further investigation Fig.
The evolutionary history of the thirteen genes is consistent with an horizontal transfer from an ancestor of the virus LbFV (or a virus closely related to this ancestor) to Leptopilina species (Figure  3 ). Indeed, in all phylogenies, the three wasp genomes formed a monophyletic clade with LbFV as a sister group. In addition, when other sequences with homology to the proteins of interest were available in public databases, they confirmed this interpretation  (ORFs 58, 78, 92, 60, 68, 85, 96). Notably, it appeared from this analysis, that before being transfered to Leptopilina wasps, ORF60 has probably been acquired by the donor virus from an ancestral bacteria (Figure  3 ). The clustering of most of these loci on the same scaffold in L. boulardi (8 out of 13 on scaffold 159, N=75550 scaffolds, see Figure  1 ) strongly suggests that a single event is at the origin of the phenomenon.