dataesr/scientific-paragraphs-categorization huggingface dataset:
Guigue-Rodet  and  V. Martin-Bouret  for providing skin biopsies from some individuals with BDCS; and  M. -C. Hors-Cayla  and  J. -L. Bonafé  for initiating genetic research on BDCS. We are grateful to all subjects and their families for their participation in the study. H. E. was supported by  TUBITAK  (grant no. 112S398 ). H. -S. P. , E. C. , F. K. , D. B. , M. H. and D. H. were supported by the  Swiss National Science Foundation , the  Placide Nicod Foundation  and the  Dind Cottier Foundation. This work was funded by the  Association pour la Recherche contre le Cancer  and the  Société Française de Dermatologie. This work was supported by funding from the  Agence Nationale de la Recherche  ( ANR-10-IAHU-01 ).
9) Γ(s → ηη) = f 2 32πm s c ∂sη m 2 s f 2 + c sη 2 1 - 4m 2 η m 2 s θ(m 2 s -4m 2 η ) , (2. 10) in agreement with results obtained using the decay module of FeynRules  [21, 22]. For the coupling values adopted in our analysis, the total width Γ s is always small. This implies that the narrow width approximation can be used for any cross-section calculation involving a resonant s-contribution. The s-induced dijet cross section can hence be expressed as σ(pp → s → gg) = 1 0 dx 1 1 0 dx 2 f g (x 1 , m s )f g (x 2 , m s ) α 2 s c 2 sg m 2 s 1024πf 2 δ(ŝ -m 2 s )BR s → gg , (2. 11 ) where √ ŝ denotes the partonic center-of-mass energy and f g (x, µ) the universal gluon density that depends on the longitudinal momentum fraction x of the gluon in the proton and the factorization scale µ. For the considered values of m s , the most stringent dijet constraints arise from SppS  [23]  and Tevatron  [24]  data which provides upper limits on the new physics cross section σ for mediator masses of 140 -300 GeV and 200 -1400 GeV respectively.
In comparison, the LHC Run I results further extend the range of covered mediator masses up to 4. 5 TeV  [25, 26]. For f = 1000 GeV, we find that c sg values up to about 100 (which corresponds to an effective coupling of about 10 -3 ) are allowed independently of the other parameters, and we adopt this upper limit henceforth. Turning our attention to the dark matter phenomenology, we first study the η relic abundance Ωh 2 | η. This is numerically computed in Section 3 with the MicrOmegas package  [27] , in which we have implemented our model via FeynRules  [22]. An approximate expression describing the relevant total thermally-averaged annihilation cross section σv can nonetheless be derived analytically. Restricting ourselves to the leading S-wave contribution and ignoring all possible special kinematic configurations featuring, e.
Analysis setup In order to evaluate the LHC sensitivity to our model via monojet probes, we compare our theoretical predictions to official ATLAS results based on early 13 TeV data at an integrated luminosity of 3. 2 fb -1  [3]. This is achieved via an implementation of the analysis of Ref. [3]  in the MadAnalysis 5 framework  [40, 41]. Details on our code and its validation are publicly available on Inspire  [42]  and within the MadAnalysis 5 Public Analysis Database  [43]  2. Our recasted analysis is in agreement with the ATLAS official results for well-defined event samples at the 5% level, and we have also compared, for consistency, our results to those obtained when using LHC Run I data  [8]. The analysis under consideration preselects events featuring one final-state hard jet with a transverse-momentum p T larger than 250 GeV and a pseudorapidity satisfying |η| < 2.
2 in the FeynRules  [22]  package, and generated a UFO library  [44]  that we have imported into MadGraph5 aMC@NLO  [45]. Hard-scattering events describing the pp → ηηj process (with an 80 GeV selection threshold on the jet p T ) have been generated for a center-of-mass energy of 13 TeV and matched to the parton showering and hadronization infrastructure of Pythia 6  [46]. The events are then processed by Delphes 3  [47]  for a fast detector simulation using a tuned parameterization of the ATLAS detector and the Fastjet program  [48]  for jet reconstruction by means of the anti-k T algorithm  [49]  with an R-parameter set to 0. 4. Finally, the MadAnalysis 5 program is used to handle the event selection and to compute the associated upper limit at the 95% confidence level (CL) on the signal cross section according to the CLs technique  [50, 51].
The ATLAS analysis finds the latter to be slowly varying with the missing transverse-energy selection and is of the order of a few percent  [3]. We adopt the choice of k 1 = 1. 51 and k 2 = 0. 043, which parametrize the ATLAS results of Ref. [3]  at the percent level, and calculate 95% CL upper limits on the signal cross-section for overlapping signal regions defined by minimum requirements on the missing transverse energy varying in steps of 100 GeV between 500 and 1400 GeV. The statistical procedure relies on a Poisson modelling with Gaussian constraints using the CLs prescription and the asymptotic calculator implemented in the RooStat package  [54]. The lowest upper limit on the fiducial production cross section (with a constraint on the jet transverse momentum of p T > 80 GeV) is then taken to be the final result.
Acknowledgements The authors are grateful to  F. Maltoni  for discussions about the unitarity constraints and to  S. Lacroix  for his fruitful participation to the early stage of this project. We would also like to thank the organizers of the 2015 ' Les Houches -Physics at TeV  colliders' workshop where this work was initiated. MF has been partly supported by the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) and by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sködowska-Curie  grant agreements No  690575  and No  674896. BF has been supported by the  Theorie LHC France initiative  of the CNRS, SK by the  New Frontiers program  of the  Austrian Academy of Sciences , DS by the  French ANR  project  DMAstroLHC  ( ANR-12-BS05-0006 ).
Incubation with secondary antibodies conjugated to infrared fluorophores (goat anti-rabbit IgG Dylight 800 and antimouse IgG Dylight 680 at 1:10,000 from Thermo Scientific) was performed for 1 h. An Odyssey infrared imaging system (LI-COR, Bad Homburg, Germany) was used to scan membranes at a wavelength of 680 nm (anti-mouse) or 800 nm (anti-rabbit). Data were analyzed with Image Studio 1. 1 software (Li-COR). Luciferase assays pCDNA3-GR plasmid was generated by subcloning human GR (NR3C1) into pCDNA3 vector (Invitrogen). Bdnf promoter reporter plasmids were constructed by inserting PCR fragments from Bdnf promoter regions into PGL4-basic luciferase reporter vector (Promega), see Additional file 1: Table  S1  for primer sequences. These fragments, named LP6, SP6, LP4, and SP4, are localized according to the distance to exon VI transcription starting site that was set at +1 (see Fig.
Chromatin shearing was controlled on a 1. 5% agarose gel and typically a smear was visualized ranging from 100 to 500 bp. qPCR amplifications of eluted DNA were performed with primers encompassing a short upstream sequence of exon IV, or on Per1 and Ucp1 promoters (Additional file 1: Table  S1  for primer sequences). Raw data are expressed as percentage of inputs, according to the Percent of Input Method, ChIP analysis; Thermo Fischer Scientific.
Of note, the culture medium with the supplement contains a substantial concentration of corticosterone (~2. 10 -8 M), as measured by LC/MS-MS (data not shown), that is sufficient to partially bind and activate GR. In order to better understand the mechanisms by which GCs decrease neuronal Bdnf expression, it is worth noting that mouse Bdnf gene exhibits a complex genomic structure with 8 untranslated exons (I to VIII) that splice onto the common coding exon IX and two clusters of promoters upstream of I, II, III and IV, V, VI, respectively (Fig. 1c )  [18, 55]. We determined the relative expression of the untranslated exon-containing transcripts by RT-qPCR and found that exon IV containing mRNA isoform was by far the most expressed one followed by exon VI and I containing isoforms (Fig. 1d ).
AP1 and CRE response elements do not mediate GR transrepression To investigate the molecular mechanisms of the glucocorticoid-dependent repression, we analyzed in silico the GR-sensitive SP4 sequence (275 bp; -188, +87 from exon IV transcription start site) using Jaspar database (URL: http://jaspar. genereg. net/cgi-bin/jaspar_db. pl) but were not able to identify any potential GR response elements (GRE). In contrast, we found two Jun/Fos response elements, comprising two potential AP1 binding sites (positions -127 and -101), as well as two cAMP response elements (CRE), for CREB1 (position -38) and CREB3l2 (position -14), see Fig. 5a. Given that GR is able to repress SP4 sequence transactivation ability (see Fig. 4h ), it was tempting to speculate that GR effect was the result of a transrepression mediated by its interaction with AP1 and/ or CRE binding transcription factors as it has been previously proposed  [57].
LP6-driven activity was significantly repressed by DEX yet prevented by addition of GR antagonist RU. Functional characterization of the transcriptional activity of LP6 construct fragments by luciferase reporter assays led to identify a short 275 pb sequence (SP4) directly upstream of exon IV, and encompassing its transcription start site, which might account for the inhibitory effects of GCs. Analysis by Jaspar online software (URL: http://jaspar. genereg. net/ cgi-bin/jaspar_db. pl), revealed that two Jun/Fos response elements, constituting AP1 binding sites, were located on this region as well as two cAMP response elements (CRE), a CREB1 and a closely related CREB3l2 response element, all being located just upstream of the exon IV transcription start site (see Fig. 6a ). Chromatin immunoprecipitation assays demonstrated that GR binds to this sequence, GR recruitment being prevented by GR antagonist RU.
However, promoter constructs with deletion of AP1 and CREB binding sites were still repressed by DEX excluding their involvement in GR regulation of Bdnf expression. Altogether, we propose that one of the mechanisms responsible for the repression of Bdnf expression by DEX is the binding of GR just upstream of exon IV, through ternary complexes with transcription factors that are still to be determined. Such a repression of gene expression by GR tethered to various transcription factor complexes has been reported in several studies  [57, 63]. Nonetheless, no other response elements for proteins susceptible to interact with GR, such as NFκB, were identified on the SP4 sequence using Jaspar software. Another proposed mechanism of direct GR repression was uncovered recently in the form of negative GRE (nGRE) with a specific sequence (CTCCXGGAG) that clearly differed from positive GRE  [64].
Acknowledgements We are indebted to  Patrice Penfornis  for the initial derivation and subcloning of the BZ cell line (present address:  Cancer Institute, University of Mississippi Medical Center, Jackson, MS, USA ). We thank  Fanny Jung  for part of the BZ cell line characterization during her practical training for her advance technology degree (BTS) in biotechnologies. We also thank  Simon Travers  ( Service de Génétique Moléculaire, Pharmacogénomique et Hormonologie, Bicêtre Hospital ) for corticosterone measurements by LC/MS-MS.
Functional in vitro study The Neuro-2a (N2a) mouse neuroblastoma cell line (catalog #400394/ p451_Neuro-2A, CLS; RRID:CVCL_0470) was grown at 37°C, with 5% CO 2 in DMEM containing GlutaMAX (Thermo Fisher Scientific) and was supplemented with 10% fetal bovine serum, 100 U/ml penicillin, and 100 g/ml streptomycin. The plasmid containing the 784 bp upstream of the SNAP25 transcription start site has been cloned in the pGL3-Basic vector (Promega) upstream of the luciferase reporter gene as described previously  (Cai et al. , 2008)  and was provided by Professor Weihong Song (The University of British Columbia, Vancouver, Canada). The rs6039769 polymorphism was generated by in vitro mutagenesis using the QuikChange II XL Site-Directed Mutagenesis Kit (Agilent Technologies). N2a cells were seeded onto 24-well plates 1 d before transfection and were transfected with 50 ng of plasmid DNA per well using 1 l of Lipofectamine 2000 (Thermo Fisher Scientific) and 1 ng of Renilla luciferase vector to normalize the transfection efficiency.
Experimental design and statistical analysis Statistical testing for allelic and genotypic associations was performed using the PLINK version 1. 07 software (RRID:SCR_001757; http://zzz. bwh. harvard. edu/plink/). We performed statistical analyses of expression using the statistical software package R version 2. 9. 2 (RRID:SCR_003005; http://cran. r-project. org). Groupwise comparisons included parametric tests (Student's t test/ANOVA) and nonparametric tests (Mann-Whitney U test/Kruskal-Wallis test), according to the normality of distribution, and were tested using the Shapiro-Wilk method. A potential correlation between SNAP25a and SNAP25b expression levels and age, postmortem interval, refrigerator interval, and brain pH has been tested using Spearman's rank correlation test in case patients and control subjects.
Automated volumetry, as implemented in FIRST 1. 2, part of FSL 4. 1 software  (Patenaude et al. , 2011) , was applied on T1 images to segment the hippocampi and amygdala. Registrations and segmentations were visually inspected as recommended by the authors of the software. The volumes of hippocampi and amygdalae were measured on segmented images. As our results with the discovery sample concerned only the amygdala, we took advantage of the larger replication sample to test for lateralized associations of the amygdala volumes with the genotype. The rs-fMRI images were preprocessed using Data Processing Assistant for Resting-State fMRI (DPARSF) toolbox (RRID:SCR_002372; http://www. restfmri. net) with Statistical Parametric Mapping (SPM8; RRID:SCR_007037; http://www. fil. ion. ucl. ac. uk/spm/).
For analyses of volumes, we used the total intracranial volume as an additional covariate. Total intracranial volumes were computed as the sum of gray matter, white matter, and CSF volumes obtained after segmentation of the T1 images with the "new segment" procedure implemented in SPM8 software. We checked for the normality of residuals using Q-Q plots. For significant results, we additionally checked for an interaction between genotype and gender, as such interactions have previously been described on emotional neuroimaging variables; when significant, we performed post hoc stratified analyses by gender. We also tested for an interaction between genotype and status (control/BD) and could not find any interaction.
Recherche (ANR) under reference. This work also received financial support from the  ANR  (Grant  ANR-08-MNPS-041 ), the  Institut National Pour La Sante et la Recherche Medicale , the  Reseau Thematique de Recherche et de Soins en SanteMentale (Fondation FondaMental) , and the  Institut Thematique Multi Organismes Neurosciences, Sciences Cognitives ,  Neurologie et Psychiatrie. The Jamain group is affiliated with the  Paris School of Neuroscience  and the  Bio-Psy Laboratory of Excellence. We thank the patients and control subjects for their participation. We also thank  Pr. W. Song  for SNAP25 promoter constructs. In addition, we thank  E. Abadie , the  Cochin Hospital Cell Library  ( Professor J. Chelly ), the  Clinical Investigation Centre  ( Dr. P. Lecorvoisier ). and the  Biological Resources Platform  ( Professor B.
For this situation of the mix at time t, the hourly cost of the electricity, the hourly amount of CO 2 emitted, the amount of power exported or imported from outside can be calculated. Average values of these quantities are then calculated after many samplings of the random variables. The MIXOPTIM simulation also enables one to calculate the probability of a power cut, e. g. the occurrence of the event where the power demand cannot be satisfied by an undersized power mix. MIXOPTIM is an open-access software, which is available on http://www. mixoptim. org. Details on the model have been described in a previous paper  [1]. 3 Input data for the scenarios simulation MIXOPTIM needs the following input data: the cost of the MWh for each production source has been split into a variable component, proportional to the produced power, and a fixed component, proportional to the installed power.
Sensitivity of the performance of the mix to small changes in its composition Three performance criteria can be calculated with MIXOPTIM: an economic performance indicator of the mix, defined as the average cost C of the MWh, divided by the average power demand: P economy ¼ C=D, expressed in €/MWh; the CO 2 emissions of the mix, defined as the average CO 2 emissions divided by the average power demand: P CO 2 ¼ CO 2 =D , expressed in kilograms of CO 2 /MWh; the probability of occurrence of a power cut P cut at any time t is also provided by the MIXOPTIM software. In this first sensitivity study, these three criteria were calculated for the 2013 EU mix (taken as a reference), and for mixes modified by the addition of 20 GW of installed power for a given power source (Tabs. 4 and 5). The addition of 20 GW was made separately and successively for each power source, and the performance indicators of the modified mix were calculated with MIXOPTIM.
This choice can be viewed as the attribution of an economic value to an avoided ton of CO 2 , and to an avoided yearly hour of power cut. Value of an avoided ton of CO 2 : €=tCO 2 ¼ 1000: j CO 2 j eco P ecoref P CO 2 ref : Value of an avoided yearly hour of power cut: €=MWh ¼ 1 8760 j cut j eco P ecoref P cutref : The values of the weighting coefficients j proposed here correspond to giving a value of 47 € to the avoided ton of CO 2 , and of 0. 79 €/MWh to the avoided yearly hour of power cut. Other values for the weighting coefficients can be used by interested readers if they wish to use the open access MIXOPTIM software to perform their own calculations. By definition, this global figure of merit is dimensionless and equal to unity for the reference mix (here, the mix EU 2013). It is interesting to see how this figure of merit varies for small variations of the composition of the mix by calculating the sensitivity to changes in the installed power a i of the source i: ∂P global ∂a i ¼ 0:8 ∂P economy =∂a i P ecoref þ 0:15 ∂P CO 2 =∂a i P CO 2 ref þ0:05 ∂P cut =∂a i P cutref : Here, the partial derivatives ∂P ∂a i have been approximated as DP Da i with Da i = 20 GW.
We also performed a principal component analysis (PCA) using both the SNP array and whole-genome data (Fig. 2a, b  and Supplementary Note 9), with both data sets showing highly similar population structure patterns, despite having very different ascertainment schemes (SNP array data are expected to be biased towards European breed dogs). The larger SNP array reference data set shows that village dogs primarily separate into five distinct geographic clusters: Southeast Asia, India, Middle East, Europe and Africa. Breed dogs fall mostly within European village dogs' variation with the exception of basal or 'ancient' breeds  20. Consistent with NJ tree analysis, all three ancient samples fell within the range of modern dog variation. HXH and NGD are the ancient samples found closest to the major European cluster, both lying adjacent to the cluster of Pacific Island dogs that are thought to be derived almost completely from European dogs  9.
Fundamental differences in the overall distribution of genetic variance in PC space between the two studies are due to the overall ascertainment of samples. Since our data set contains a substantially more diverse collection of samples, we assert that our PCA results are more reflective of the true range of dog diversity. We further examined the genetic relatedness between ancient and modern dogs by performing an f3-outgroup analysis  21, 22  on both the SNP array and whole-genome sequence data sets. We used the golden jackal and Andean fox as outgroups for the SNP array and the whole-genome data sets, respectively. Our results corroborated NJ tree and PCA findings, and showed that all three Neolithic European samples are genetically most similar to modern European dogs (Fig. 3 , Supplementary Figs  13  and 14  and Supplementary Note 10).
31 ). Any site with o7 Â coverage was reported as missing. In addition, any position where the highest likelihood is a heterozygote must have a minimum Phred-scaled genotype quality of 30 or the next highest homozygote likelihood genotype was chosen instead. The code is available at https://github. com/kveeramah/aDNA_GenoCaller. This protocol substantially decreased the overrepresentation of C4T and G4A sites identified by GATK UnifiedGenotyper 44 , which does not account for postmortem damage. In addition, base calls with a quality score o15 and reads with a mapping quality o15 were not included during genotype calling. Base calls with a quality score 440 (which can occur during paired-end read merging) were adjusted to 40. See Supplementary Note 5 and Supplementary Figs 32 and 33 for more details.
Reference data set. To construct a genome sequence data set, in addition to the three ancient samples, we examined whole-genome sequence data from 96 modern canids. Additional genomes were generated using Illumina sequencing for a Great Dane and Iberian wolf (SRP073312). We also posted sequencing reads to the SRA for a Portuguese village dog, Chinese Mongolian Shepherd village dog and a Sub-Saharan African village dog (SRP034749). All remaining genome data were acquired from previously published data sets deposited on SRA. As above, reads for all modern canids were aligned to CanFam3. 1 using BWA, followed by GATK quality score recalibration, and genotype calling using HaplotypeCaller  44. These data were supplemented with genotype data for six canids from Freedman et al. 26  (basenji, dingo, golden jackal, Croatian wolf, Israeli wolf and Chinese wolf).
Ancient sample mtDNA consensus sequences were aligned to the canid alignment from Thalmann et al. 5  , which contain whole mtDNA genomes for both modern and ancient canids. A NJ tree was built with a TN93 substitution model (500 bootstraps) using MEGA 6. 06 (ref. 45). A further NJ tree was built with additional C1 and C2 samples from Duleba et al. 19. See Supplementary Note 7 for more details. NJ trees were constructed for the whole-genome SNP set using the ape R package  46  using distance matrices based on the metric of sequence divergence from Gronau et al. 25. One hundred bootstrap replicates were generated by dividing the genome into 5 cM windows and sampling with replacement to determine node support. See Supplementary Note 8 for more details. PCA was performed on both the SNP array data set and genome SNP call set 3 using smartpca, part of the EIGENSOFT package version 3.
Outgroup-f3 statistic maps were created using the R packages ggplot2 and maps using the public domain Natural Earth data set (http://www. naturalearthdata. com). D-statistics were used to identify potential ancient dog-wolf admixture and f4-ratio tests to estimate dog-dog and dog-wolf admixture proportions were calculated using Admixtools  22. See Supplementary Note 10 and Supplementary Fig. 46  for more details. Both MixMapper  23  and ADMIXTUREGRAPH  22  were used to perform model-based inference of specific admixture events involving the three ancient dogs. MixMapper was performed on both the SNP array and wholegenome SNP data sets, whereas ADMIXTUREGRAPH was performed on the whole-genome data set only. Significance was assessed using a weighted block jackknife procedure for all five analysis types.