dataesr/scientific-paragraphs-categorization huggingface dataset:
Within the TM pipeline, we applied a SNP clustering filter with no more than three SNPs per 10 bp. Nevertheless, it allowed quite a number of SNPs in a 100 bp read. For instance in the TM datasets, 56% of SNPs were less than 64 pb away. Since the UNEAK approach only allows 1 SNP per 64 pb, more than 50% of TM SNPs would be automatically discarded by the UNEAK pipeline. However, the two pipelines differ strongly in their rare variant calling rates. Even if base quality is higher than 30 with the ILLUMINA sequencing platform, i. e. , one error every 103 bases, with the amount of data that was generated, it ended up creating numerous errors. Calling rare variants (or not) will depend on the SNP and genotype calling algorithm implemented in the software and on how error sequencing rates are considered  (Han et al.
, 2014). With some pipelines, the error rate estimate is considered to be constant across the genome, while other pipelines estimate an error rate for each base  (Hohenlohe et al. , 2011). Error rate estimates can also account for dependency between sequencing errors, (or not;  Han et al. , 2014). In GATK software, it is assumed that sequencing errors are independent and it takes coverage and base quality into consideration. Thus, unless coverage is about 10× per site per sample, GATK with UnifiedGenotyper can underestimate rare variants  (Han et al. , 2014) , whereas UNEAK handles sequencing errors differently. To deal with this issue, the UNEAK pipeline uses a minimum ETR of 3% to call variants. This ETR has a direct impact on true low frequency variants: with erroneous SNP, true SNP are discarded.
This way of handling the error sequencing rate might be the main reason why UNEAK SFS underestimates low frequency SNPs compared to the expected distribution with a population at equilibrium. It would also explain why so few SNPs are shared, since only frequent SNPs can be found by both datasets, which was confirmed by the distribution of MAF observed for shared SNPs. In summary, we identified two main reasons for the low number of shared SNPs: (i) the constraint of no more than one SNP within 64 bp, and (ii) the uncovering of rare variants by UNEAK, which represent the majority of the polymorphism expected for a population at equilibrium. We ended up with relatively few shared SNPs. However, the allele frequency correlation between these SNP was very high.
, 2014). This could also have an effect on the identification of population structure  (Harvey et al. , 2015; Rodríguez-Ezpeleta et al. , 2016). With the UNEAK pipeline only allowing 1 bp mismatch, it is the maximum stringency level for an RRL pipeline. Yet, we saw no effect on population structure and we observed very high congruence in the population structure in the two datasets and with two different methods: a Bayesian method and a PCA. These results are similar to those obtained by  Rodríguez-Ezpeleta et al. (2016). The main difference we observed between pipelines concerned the identification of low frequency variants. We found that the UNEAK pipeline was not able to recover rare variants while the SFS pattern for frequent variants was similar between pipelines. Thus methods based on "more frequent alleles" such as population structure approaches led to similar results.
On the other hand, several statistics using low frequency variants differed considerably depending on the dataset used. Tajima's D test  (Tajima, 1989 ) is based on the SFS pattern, where an excess of rare variants is the sign of a population expansion or positive selection and inversely, a reduction in rare variants is the sign of a population contraction or balancing selection. Both pipelines gave highly contrasted results ranging from an overall negative value signature of -0. 65 to a positive value signature of 2. 74. Unbiased SFS is crucial for population genetics. Methods used to investigate population history including bottlenecks or expansion events are based on the difference between allelic diversity and heterozygosity and therefore depend on the identification of rare variants. Moreover, SFSs are widely used to test signatures of selection using Tajima's D but so are other tests such as the CLR test  (Nielsen et al.
A number of F ST outlier tests have been developed and extensively used for the discovery of candidate genes  (Beaumont and Nichols, 1996; Vitalis et al. , 2001; Beaumont and Balding, 2004; Foll and Gaggiotti, 2008; Bonhomme et al. , 2010; Günther and Coop, 2013; Duforet-Frebourg et al. , 2014)  and are based on the expected distribution of F ST. Underestimating rare variants will affect the overall distribution of F ST and might therefore have an impact on these selection tests. It is certain that some of the differences in the results of diversity estimates observed between datasets are due to the fact that the UNEAK pipeline interrogates coding and noncoding regions while the TM pipeline only interrogates coding regions. However, like other authors, we previously observed that using APEKI biased SNP discovery toward coding regions  (Combosch and Vollmer, 2015; Guajardo et al.
Images Acquisition and Quantification of c-Fos + Nuclei Quantification was performed by identifying spot positions. c-Fos + were counted with ICY software (http://icy. bioimageanalysis. org/) after acquired images using a digital camera (Nikon DXM 1200) of an Olympus BX600 microscope coupled to a software (Mercator Pro; Explora Nova, La Rochelle, France). The constant use of a X10 Plan Apo objective allowed to have a good resolution for c-fos immunochemistry. The focus was set on the upper face of each section before digitization. Each region of interest (ROI) was delimited on the screen for each picture based on the mouse atlas  (41). ICY software directly counts the number of cells in the ROI. Cell density per square micrometer was thereafter calculated. The ROIs chosen included the prelimbic (PrL), infralimbic (IL), orbitofrontal lateral, median, dorsolateral and ventral cortex (OFC), the NAcc, caudate putamen (CPu), basolateral nucleus of amygdala (BLA), the hippocampus (Hipp), motor cortex (M) and agranular and granular insular cortex, and dorsal and ventral (CIns).
Experiment II When considering all animals (i. e. , before subgroup separation), we used ANOVAs usingVAR3 statistical software  (42)  with an alpha level of 0. 05. In order to test global differences from chance level (50%) we use Wilcoxon rank sum test, paired version (Z of the Wilcoxon test is displayed in Statistica software). Once subgroups were made and number of animals was below 30, we considered that data would not follow a normal law of distribution. We, thus, used Mann-Whitney or Kruskal-Wallis non-parametric test when appropriate.
Experiment III Non-parametric analyses were performed using R software (version 2. 13. 2 (2011-09-30) copyright (c) 2011 the R foundation for Statistical computing with Rcmdr-package), as some of the scored behavior would not follow a Gaussian distribution. We used Wilcoxon rank sum test for two samples, the    43 ) was applied to extract E and I. For each recording (e. g. , Figure  3C ) the total input conductance (gT) was first extracted (Figure  3D ) and its decomposition allowed to further evaluate the relative contribution of evoked excitatory and inhibitory inputs reaching the soma of the recorded L5PyN (Figure  3D ). Typical layer 2-3 or 6 electrical stimulation produces a fast excitatory conductance (gE) elicited before a long-lasting inhibitory conductance (gI). Quantification of these somatic conductances showed that the control stimulus-locked composite signal at the soma of L5PyNs is composed of 18% of E and 82% of I whatever the stimulated layer was (Figure  3E , n = 25 cells and n = 11 cells for stimuli in layer 2-3 or 6, respectively, p = 0.
Metabolomics Analysis. Metabolomics analyses have been performed by Criblage Biologique Marseille (CRIBIOM) platform. For the analysis of CSF, protein of 50 L was removed by methanol precipitation using 200 L of cold methanol (-20 ∘ C) followed by 5 min centrifugation at 14000 rpm. The supernatant was recovered and filtered through 10 kDa filters to remove all proteins. The extracts were evaporated to dryness under a stream of nitrogen at room temperature and redissolved with 25 L of water/acetonitrile = 90/10 (v/v). To check for data quality, a blank sample (deionised water) and a pool sample (a mixture of all CSF samples) were extracted/diluted and analysed repeatedly along with the sample series  [24]. The samples were analysed on a Dionex UltiMate 3000 (Thermo Fisher Scientific, France) coupled to a Q-Exactive Plus mass spectrometer (Thermo Fisher Scientific, France).
This result demonstrates for the first time that NU has an effect on metabolism of CSF. These metabolites are important for the proper physiological functioning of the brain. For instance, the two first belong to the metabolic pathway of arginine and proline and N-methylsalsolinol is implicated on the balance impairment between dopamine and acetylcholine. These results could be paralleled to studies using metabolomics as a diagnostic marker in neurodegenerative diseases and/or cognitive impairment  [44]. Butyric acid which can be found in CSF (database accession number hmdb00039) is also an end-product of ketone bodies metabolism (Kegg pathway map00650), which are compounds used by brain for alternative energy production to glucose. When injected in CSF it has been associated with memory function  [45]  as well as mood stabilization  [46]  in rats.
Results Molecular Dynamic simulation of the XRCC4/ Lig4 C-ter complex. To get quantitative insights into the dynamics of critical residues involved in the interaction between XRCC4 and the Lig 4 C-ter domain (residues 654-911), we performed a 10 ns Molecular Dynamics (MD) simulation on this complex. From this run, we analyzed the evolution of the root mean square deviation (RMSD) of the atomic coordinates for both interacting proteins as well as persistent intermolecular contacts, defined as the distances between protein/ligand heavy atoms, which do not exceed 3 Å and exist in at least 80% of all trajectory frames. As a starting model, we used the crystallographic structure of the complex (PDB entry 3II6) (Fig. 1A ). The solvated system with both proteins equilibrated after around 3 ns and RMSD was stable until the end of the simulation.
Therefore we needed to establish the structure of the Lig4 clamp domain separated from XRCC4. Since the C-terminal structure of the Lig4 was only available with its XRCC4 partner (PDB entry 3II6), we first virtually assessed its intrinsic flexibility when present alone by running a 15ns MD simulation in a periodic box with explicit solvent. To simulate different clamp conformations, we kept both BRCT domains to account for structural constraints. After 3 ns, RMSD for the clamp domain remained stable. After equilibration, Helix1 and Helix2 became closer mainly by a hydrophobic contact between F778 from Helix1 and L799 from Helix2 (data not shown). This new link created a large cavity inside the tripartite structure, whose surface varied with the simulation and the side chains flexibility but which was very well suited to accommodate inhibitor molecules (Fig.
2A, B ). We set the cluster radius to 2 Å and extracted eleven major main chain conformers for subsequent docking. These clamp structures thus likely represented the most probable conformations of Lig4 in solution when not in complex with XRCC4 (Fig. 2A ). This conformationally sampled Lig4 clamp receptor was then used to dock a filtered library of around ~95,000 molecules. These molecules were provided by a subset of the ZINC chemical database ("Clean Drug-Like" subset)  32  , further enriched by an in-house library. The initial set of molecules collection was first filtered according to specific physico-chemical properties (molecular weight < 450 Da; cLogP and Solubility Forecast Index (SFI)  33  between -3 and 1; polar surface area <110 Å 2 ; H-bond donors and acceptors between 0 and 5; rotatable bonds between 0 and 6 and charges between -1 and 1).
Purified XRCC4 was incubated with increasing amounts of purified Lig4 C-ter domain and the reaction mixture was run on a native gel. At equimolar concentrations, both components were fully engaged in complex formation, thus confirming their functionality (Fig. 5A ). In the interaction buffer used, molecules #231, #3101, #36972 and #3091B were shown to be soluble up to 3 mM by NMR. Each candidate inhibitor was first pre-incubated with Lig4 C-ter and XRCC4 was then added in the mixture. The activity of each compound was assessed as the extent of disappearance of the XRCC4/ Lig4 C-ter complex. As shown in Fig. 5B, C , only molecule #3101 had a dose-dependent inhibitory effect on the XRCC4/ Lig4 C-ter association. Notably, when #3101 was pre-incubated with XRCC4 instead of Lig4 C-ter, no inhibitory activity was found on XRCC4-Lig4 C-ter interaction (data not shown), arguing against promiscuity linked to compound aggregation.
We used AutoDock Vina  34  as docking program and the Relaxed Complex Scheme (RCS) method  28  to account for protein flexibility during the screening of potential inhibitors. RCS relies on molecules docking on several protein conformations computationally extracted by MD simulation. RCS has previously proven successful in several attempts to predict disruption of protein-protein interaction by small molecules  [29] [30] [31] 45. Her , we carried out a Lig4 C-ter protein conformational sampling and a subsequent virtual screening campaign of ~95. 000 filtered molecules on 11 clamp domain conformers. From a first set of 29 compounds selected according by a Vina score (≥ 7 kcal/mole) and clustering around critical XRCC4 interacting residues, only 11 were finally conserved according to persistent contacts analysis by MD simulations on the full Lig4 C-ter domain.
54 (2H, t, J = 7. 5 Hz, Ar), 7. 68 (1H, bs, Ar), 8. 15 (2H, d, J = 7. 2 Hz, Ar). 13CNMR (DMSO-d6, 75 MHz) δ ppm: 168. 99, 162. 25, 158. 86, 155. 12, 142. 54,  137. 32, 133. 90, 130. 31, 128. 79, 128. 61, 128. 24, 128. 02, 127. 56, 126. 19, 71. 75, 70. 26, 70. 07, 68. 90, 65. 76, 59. 39,  55. 39, 26. 51. ES-HRMS (positive mode) calcd for C32H41N4O10 [M+ H]+ : 641. 2823. Found: 641. 2828; [M+ Na]+ : 663. 2647. The synthesis of methylated pyrimidinone (compound 8) has been previously described  [51] [52] [53]  and the characterization data obtained in this work were in accordance with those of literature. Raltegravir was purchased from Euromedex (Souffelweyersheim, France). Compounds #231 and #36972 were purchased from Enamine (Riga, Latvia).
Samples were heated from 20 to 89 °C. Each fluorescence measurement was taken every 0. 3 °C/tps. Data were analyzed using the CFX Manager Software (Bio-Rad Laboratories, USA) and the Tm value estimated from the transition midpoint of the fluorescence curve, which corresponds to the temperature at which half of the protein population is unfolded. High-Throughput Docking. A screening campaign by high-throughput docking was performed using AutoDockTools v1. 5. 4 and Autodock Vina 34. 11 MD-extracted clamp conformation structures of the Lig4 (residues 771-803) were used as targets with the coordinate pdbqt file prepared with AutoDockTools v1. 5. 4. During target preparation polar hydrogens were added and partial charges calculated according to the Kollman method  54. The grid had a 40 Angstroms size in the x, y and z dimensions.
A spacing of 1 Angstrom was used with the center of the grid placed at the center of gravity of the protein. During the docking, the side chains of the clamp were kept as rigid. An ensemble of ZINC database  32  and in-house molecules was used. Raltegravir, a potent selective orally bioavailable HIV-integrase inhibitor is based on the dihydroxypyrimidine scaffold. This scaffold, and also the carboxamide derived compounds are characterized by improved drug like qualities  51. Thus, in-house series of molecules of hydroxypyrimidinone family were included in the chemical database. Molecules were converted via Open Babel 2. 2. 3 into pdbqt files with added hydrogens and proper charge (at pH7. 2) and 3D coordinates. The screening library has been filtered taking into account the ADMET properties of the compounds.
Reactive compounds (covalent binders), warheads (non-covalent binders) as well as the PAINS compounds have been eliminated  [55] [56] [57]  , and the rest of the library has been filtered for druglikeness  58, 59. The initial part of the work was done using the Screening Assistant v. 60. The final filtered database contained around ~95. 000 molecules. Docking was performed on eight CPUs with an exhaustiveness value of 80 and a maximum output of 20 conformers. Ligands from the rigid docking were ranked according to the Vina score and a threshold value of -7 kcal/mol was chosen to select the most promising hits. From this, 29 molecules were selected for a MD simulation step and these top compounds were selected based on the criteria of number of non-polar and H-bond contacts inside the clamp structure.
MD simulations of 10ns for each 29 C-ter Lig4/molecule complexes were performed to determine the stability over time (RMSD) of the interaction, persistent contacts for each molecule inside the clamp domain as well as details on the molecule binding mode and energy. The binding energy was calculated from the last 4ns of simulation to rank each candidate and with the generalized-born method  61. Molecular Dynamic simulations. MD simulation was done on the XRCC4/C-ter Lig4 protein complex (PDB entry 3II6, residues 654-911) for 10 ns. It was also done with each 29 C-ter Lig4/molecule complexes for the same calculation time. These simulations were performed with the Amber 9 software (1-3) including the all-atom ff03 force field for protein parameterization and the generalized AMBER force field (GAFF) for ligands.
2 NMR software (Bruker). For TR-NOESY spectra, protein buffer was the same as for STD-NMR experiments. Samples were prepared with 165 μL in 3 mm capillaries containing 1 mM of ligands from 100% DMSO d 6 stock solutions and with or without 14 μM of C-ter Lig4 protein. Experiments were measured with 8 scans and a mixing time of 200 ms. NMR spectra were recorded at 298 K (25 °C) using a Bruker Avance 600 spectrometer equipped with a TXI cryoprobe. NMR data were processed and analyzed using Topspin 3. 2 NMR software (Bruker). Native gel-based assays for protein-protein interaction. 10 pmol of purified recombinant C-ter Lig4 protein was combined with an increasing amount of each tested molecule at room temperature during 15 min. The final DMSO concentration was maintained constant for each sample.
Then, 10 pmol purified recombinant XRCC4 protein was added in the mix with the reaction buffer (20 mM Tris pH 8. 0, 50 mM KCl, 0. 1 mM DTT and 5% glycerol) in 10 μl final reaction volume (the final concentration of each protein was 10 μM). The reaction was incubated during 15 min at room temperature. Reactions were loaded on a 6% native PAGE in TBE and run at 4 °C (with a 30 minutes pre-running of the empty gel). The gel was colored with Instant Blue (Expedeon) and image was captured with an Epson Perfection 2480 Photo scanner. Quantitative analysis of the gel was performed with the ImageJ software (version 1. 4).
Acknowledgements We thank  Pascal Demange  and  Pascal Ramos  for assistance in the NMR experiments. The authors thank  Dr. Neil Johnson  for the careful reading of the manuscript. This work was supported by grants from the  « Institut National du Cancer »  ( PLBio2010 XXL-screen program ) and the  « Ligue Nationale Contre le Cancer  » (Equipe labellisée 2013). GM was supported by  PhD fellowships from Ministère et de l' enseignement supérieur et de la Recherche  (3 yr) and from  Ligue Nationale Contre le Cancer  (1 yr). OB was partly supported by a postdoctoral fellowship from  Region Midi-Pyrénées  (6 months). These studies and the IPBS NMR equipment were financed by the  French Research Ministry ,  CNRS ,  Université Paul Sabatier , the  Région Midi-Pyrénées  and European structural funds.
Thus, they can be implemented without a priori knowledge on the nature of adaptive traits  (Luikart et al. 2003; Stinchcombe and Hoekstra 2008). Combined with the use of large molecular marker datasets, these so-called genome scan methods have accelerated the discovery of putatively adaptive variants in natural populations. However, the lists of candidate loci identified in genome scans are sometimes difficult to link with a particular mechanism of adaptation. Valuable insights into the selective agents can be obtained using methods that specifically search for associations between allele frequencies and environmental variables (e. g. ,  Coop et al. 2010; de Villemereuil and Gaggiotti 2015). Ultimately, however, a more thorough understanding of the adaptive role played by candidate loci requires further assessments of their functional effects on phenotype  (Storz and Wheat 2010; Barrett and Hoekstra 2011).
Population Genomics Methods to Detect Polygenic Selection The advent of next-generation sequencing  (Shendure and Ji 2008)  has made possible the generation of large datasets consisting of dense arrays of markers, typically single nucleotide polymorphisms (SNPs), covering the whole genome of a species. This in turn has led to a renewed interest in the development and application of statistical methods aimed at making inferences about the genetic architecture of adaptive traits. Given the inherent difficulties associated with the study of natural populations, and in particular marine species, the most popular methods adopt a population genomics approach based on collecting samples from natural populations and generating large number of molecular markers in order to scan the genome of species in search of so-called "outlier loci" whose behavior departs from the neutral expectations  (Luikart et al.
This type of information is unfortunately not available in large populations of unrelated individuals, which is a quite common situation in marine populations. A powerful alternative to detect QTL in populations that do not contain closely related individuals is to use a GWAS approach  (Goddard and Hayes 2009). GWAS methods test for association between genetic markers and phenotypic variation on a SNP by SNP basis. Although they can be used to map QTLs with a much higher resolution than linkage mapping, they usually require a higher density of markers because recombination breaks down the statistical associations between QTL and neighboring markers over time. Large population genomic datasets now allow to conduct GWAS in nonmodel natural systems, providing invaluable information on the genetic architecture of important fitness traits, including the number, genomic distribution, effect size, and dominance patterns of QTL alleles  (Barson et al.
2011; Robinson et al. 2013). Under the polygenic inheritance model, the proportion of phenotypic variance explained by each chromosome is proportional to its length or gene content (Figure  3B ). This expectation provides a direct way to assess the hypothesis of polygenicity by evaluating the strength of the correlation between chromosome size and its contribution to overall additive genetic variance  (Santure et al. 2013; Jensen et al. 2014). In practice, genome-wide and chromosome partitioning approaches will most often provide underestimates of the true heritability due to imperfect linkage between markers and causative variants. However, this effect should not affect the correlation expected between chromosome size and heritability for polygenic architectures. High-density SNP datasets should enable even further partitioning of heritability by narrowing down the estimation of additive genetic variance to genomic segments within chromosomes (Figure  3C ).