dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to analyze a bit more quantitatively these results, the values of χ 2 /ndf obtained for the E866 and PHENIX data sets are quoted in Table  4. As can be seen, the E866 data do not allow one to differentiate the various energy loss predictions including (or not) EPS09/DSSZ nPDF corrections. On the contrary, the agreement at RHIC is considerably better when energy loss is supplemented by saturation effects (χ 2 /ndf = 0. 3) rather than by nPDF (χ 2 EPS09 /ndf = 2. 7, χ 2 DSSZ /ndf = 1. 1), as mentioned above. For completeness we also quote the values of χ 2 /ndf assuming no energy loss but only saturation 36  or nPDF corrections. As can be seen from Table  4 , saturation without energy loss still gives a fair description of PHENIX data, χ 2 /ndf = 2. 7 (as well as EPS09 and DSSZ to a lesser extent), although saturation and nPDF effects alone would totally fail to reproduce E866 data.
A change in expression in a large number of genes was observed after abexinostat treatment (1012 genes upregulated and 476 genes downregulated with a P-value of 1 Â 10 À 5 and an absolute fold change 42). Gene set enrichment analysis between the treated and untreated cells reveals that the great majority of pathways in MSigDB database was not affected. Indeed in the three subsets pathways KEGG, Biocarta and Reactome only 14/186, 0/217 and 18/430, respectively, appears differential. DNA repair, DNA DSB, DNA recombination and response to DNA damage were the most prominent signatures of abexinostattreated cells (Figure  5a ), underscoring the key role of HDAC on the DNA repair mechanism. 9, 10 In contrast, genes implicated in DNA repair such as RAD51, AP1, Exo1, TIMELESS-interacting protein or BRCA1 were decreased by twofold to nearly threefold (Table  1 ).
Abexinostat treatment induces p53 activation in MKs. To understand if the p53 pathway was induced, purified MKs were treated with increasing doses of abexinostat during 48 h and qRT-PCR and western blots were performed. Subsequently to ATM phosphorylation (Figure  6a ), we found a p53 increase in MKs treated with abexinostat (data not shown) as well as its phosphorylated form on Serine 15 (Figure  6c ). This was associated with an increased apoptosis as shown by the complete cleavage of caspase 3 at 100 nM abexinostat (Figure  6b ). These results indicate that abexinostat may lead to caspase-mediated apoptosis through DSB induction that activates a p53-dependent pathway. Two p53 target genes, P21 and BAX, were induced after HDACi as shown by qRT-PCR, further demonstrating the functional activation of p53 (Figure  6d ).
7. 3. 1) was used to quantify the intensity of fluorescent images and to apply a linear normalization to correct for artifacts caused by nonlinear rates of dye incorporation and inconsistent relative fluorescence intensities between some green and red dyes. All data were imported into Resolver software (Rosetta Biosoftware, Boston, MA, USA) for database management, quality control, computational recombination of dye-swaps and statistical analysis. Gene set enrichment analysis was performed between the two groups of samples (treated and non-treated by abexinostat). Data availability. The microarray data related to this paper have been submitted to the Array Express data repository at the European Bioinformatics Institute (http://www. ebi. ac. uk/arrayexpress/) under the accession number EMTAB-1180.
Acknowledgements. We are grateful to  F Wendling  for helpful suggestions on the manuscript. Labex GR-Ex is funded by the  program 'Investissements d'avenir' , an  ANR Blanc (I Plo)  and a research grant from  Servier  ( N Debili ). This work has also been supported by a grant from the  Ligue Nationale contre le Cancer  (Equipe labellisee 2013). O Bluteau  and  S Boukour  were supported by a postdoctoral fellowship from  ANR  and AP by a PhD fellowship from the  GEHT. N Debili ,  H Raslova  and  W Vainchenker  are recipients of a research fellowship from  CHU Bordeaux-INSERM ,  AP-HP-INSERM.
Experimental Crystal data   Data collection: APEX2  (Bruker, 2008) ; cell refinement: SAINT  (Bruker, 2008) ; data reduction: SAINT; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and PLATON  (Spek, 2009) ; software used to prepare material for publication: PLATON and publCIF  (Westrip, 2010). We are grateful for financial assistance from the DG-RSDT and ANDRU (Diretion Generale de la Recherche Scientifique et du Developpement Technologique et l ' Agence Nationale pour le Developpement de la Recherche Universitaire, Algeria) through the PNR project. Supplementary data and figures for this paper are available from the IUCr electronic archives (Reference: SJ5322).
Computing details Data collection: APEX2  (Bruker, 2008) ; cell refinement: SAINT  (Bruker, 2008) ; data reduction: SAINT  (Bruker, 2008) ; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and PLATON  (Spek, 2009) ; software used to prepare material for publication: PLATON  (Spek, 2009)  and publCIF  (Westrip, 2010).
They can also exchange local CBTC information over the UWB radio link. When arriving in the range of the UWB communication, the train computes its absolute localization to the beacons using time of flight information. Localization algorithms such as TDOA (Time Difference of Arrival) and TOA (Time of Arrival) can be used. Moreover, multiple UWB transmitters can be located in a beacon to enhance focusing and availability. The local Channel State Information (CSI) between any beacon transmitter and the virtual optimal beacon localization between the rails is identified a single time during the initial installation. This information is then introduced as pre-filtering data in the different UWB transmitters. Therefore, focusing is obtained in the required position, along the track, potentially improving the absolute localization process  [4].
Genome-Based Strategy With the progress in DNA sequencing technology, genomic sequences have been determined for a large number of species. According to the Genome Online Database (GOLD), 6330 complete bacterial genomes are now available and 14,986 are about to be published. Bioinformatics tools have undergone rapid improvement that has led to -genome mining‖ approaches. Most studies have focused on NRPS, PKS, or hybrid NRPS-PKS pathways, because the large size and repeating motifs in their genomic nuclear sequences make them relatively simple to identify and analyse  [26]. Although the Basic Local Alignment Search Tool (BLAST) is widely used for genome mining and NRPS discovery, specific NRPS-PKS bioinformatics tools have been developed; for more details on the software available for NRPS-PKS analyses, see  [27].
A study on a NRP monomer has highlighted that certain residues generate specific biological activities of the peptide  [77]. A search in the Norine database reveals that many NRPs containing Glu, Gln or D-Ser are known to display antibiotic activity. For instance, among the 199 NRPs containing Gln, 187 appear to exert antimicrobial activity. Furthermore, D-amino acids are frequently found in antibiotic NRPs  [24]. D-Amino acid-based peptides seem to be particularly resistant to enzymatic degradation, which is of great interest for in vivo applications  [78]. All these features suggest that this putative NRP has antibacterial activity. More structural information would allow the development of a purification protocol to confirm and study the antibiotic activity of this NRP from Pseudoalteromonas.
To date, the main characterised NRPS enzymes come from terrestrial organisms and particularly from Actinobacteria such as Streptomyces species. Nonetheless, the genomic approach based on NRPS homology cannot be a universal method to discover and analyse marine Proteobacteria NRPSs. The exclusive use of genomic strategies precludes discovering any non-databank-indexed structures. This highlights the need for a combination of genomic and microbiological strategies, such as complete genome analyses combined with natural NRPs purification and characterisation. However, this coupled approach requires culturable bacteria. Despite the predominance of Proteobacteria in bacterioplankton, only a few NRPs have been isolated from this phylum when compared to the less abundant Cyanobacteria and Actinobacteria.
Acknowledgements This research was supported by the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement No. 311975. This work was partly funded by the  Brittany Regional Council  ( Biprobio  project,  ARED 6227 ). Florie Desriac  was supported by a PhD grant from the -Quimper communauté‖. Hervé Bourdon  and  Carolyn Engel-Gautier  are warmly thanked for their improvement in the manuscript.
This report is from the Whitehall II cohort study. 6 In our analysis of up to 4630 adults without chronic disease, we used repeat measures of inflammatory markers and mental disorder. We measured the proinflammatory cytokine interleukin 6 (IL-6) in 1992, 1997 and 2003 and common mental disorder, based on the General Health Questionnaire (GHQ), in 1997, 2003 and 2008. The IL-6 distribution was categorized as: p1. 0 pg ml À 1 (low), 1. 1-2. 0 pg ml À 1 (intermediate) and 42. 0 pg ml À 1 (high). Details of study designs, methods and the included/excluded study populations are presented in Supplementary Material, Supplementary Figures  S1-S4  and Supplementary Tables  S1  and S2. Cross-sectional analysis, using data from 1997, showed IL-6 not to be associated with common mental disorder (age-and sex-adjusted odds ratio for high versus low IL-6 ¼ 1.
67). In the subgroup of participants without common mental disorder at the third inflammation measurement (2003, Study Design D), odds ratio of new common mental disorder in 2008 for high IL-6 in 1992, 1997 and 2003 was 1. 42 (0. 78-2. 57) (details in Supplementary Material). These findings support the hypothesis that persistently elevated levels of IL-6 contribute to the development of common mental disorder. 1, 2 The fact that previous studies relied on a single measurement of IL-6 may partially explain the mixed findings: 7 a one-off measure does not reliably capture the chronicity of inflammation. 8 The present study has limitations: common mental disorder measured by a questionnaire is not the same as clinical diagnosis of depression or anxiety. 9 Our data are from an occupational cohort where participants are likely to be healthier than the general population.
This study was funded by grants from  Swedish Brain Power , the  Swedish Research Council  (projects  14002 ,  2006-6227 ,  KP2010-63P-21562-01-4  and  K2011-61X-20401-05-6 ), the  Wolfson Foundation , the  Alzheimer's Association  ( NIRG-08-90356 ), the  JPND  Project  BIOMARKAPD, Swedish State Support for Clinical Research  ( ALFGBG-144341 ), the  Swedish Brain Fund , the  Alzheimer Foundation, Sweden , the  Dementia Association, Sweden , the  National Institute for Health Research (NIHR) Biomedical Research Unit in Dementia based at University College London Hospitals (UCLH) ,  University College London  ( UCL ). The Dementia Research Centre is an  Alzheimer's Research UK Coordinating Centre. HH was supported by the  Katharina-Hardt Foundation ,  Bad Homburg ,  Germany. AW thanks the  Gothenburg MCI Study  team and was supported by the  Swedish Research Council  (project  K2010-61X-14981-07-3 ).
III. Data The population of interest consists of importing firms, since the theoretical predictions apply to them and not to firms sourcing only domestically. We use data on the two sourcing modes-either arm's length or intrafirm-of French imports in 1999. The observation unit is a firm-country-product triple: firm i sourcing product p from country c either at arm's length or intrafirm. In what follows we describe the construction of the sample and the variables used in the analysis.
A. Primary Data Sources We rely on three primary data sources. First, the EIIG (Échanges internationaux intra-groupe) database documents the sourcing mode in a firm's yearly imports by origin country and by CPA96 or HS4 four-digit product codes in 1999. Intrafirm trade is defined as trade with an affiliate controlled by a single French entity with at least 50% of its equity capital. The data cover 4,305 firms and come from a survey conducted in 1999 by the French Ministry of Industry's SESSI (Service des études statistiques industrielles). The survey was addressed to all firms incorporated in France and trading more than 1 million euros, owned by manufacturing groups that control at least 50% of the equity capital of an affiliate based outside France. We refer to this group of firms (8,236 units) as the EIIG target population.
7 The response rate was 52. 27%, but the 4,305 respondent firms represent more than 80% of total exports and imports of French multinationals. Nonrespondent firms are excluded from our analysis because information on the sourcing mode is not available. We discuss and address sample selection issues in the online appendix. These data have been previously used by  Defever and Toubal (2007)  and  Carluccio and Fally (2009) , who do not deal with sample selection. Although some firms in the EIIG data set source some of their imports at arm's length, by construction they all have an affiliate so that limiting ourselves to these firms would bias our results toward intrafirm trade. For instance, SESSI estimates that around 36% of the total value of manufacturing imports is intrafirm  (Guannel & Plateau, 2003) , while in the EIIG data, the corresponding value is much higher (55.
4%). We must thus complement the EIIG with import data on nonmultinational firms. To this end, we use a second database, from the French Customs Office, documenting the universe of import and export flows in 1999 at the firm, origin country, and product level. These data were used (among others) by  Eaton, Kortum, and Kramarz (2004). The data are collected from custom declarations. 8 The total value of imports in the database represents about 99% of French aggregate imports in 1999 as reported by EUROSTAT, with the 1% difference being due to the imputed trade of firms not obliged to report information to the French Customs Office. Regrettably, this data set does not provide information on whether imports come from a related party (unlike U. S. customs data, for example). Finally, the EAE (Enquête annuelle entreprise) database provides balance sheet data on manufacturing firms.
The data come from a census of all French firms with at least twenty employees whose primary activity is in the manufacturing sector (NACE rev1 D category), conducted by the French Ministry of Industry's SESSI and the Ministry of Agriculture's SCEES (Service central des enquêtes et des études statistiques). Firms in the EAE database represent 9. 8% of the total number of French manufacturing firms but 87. 2% of production in 1999, as reported by EUROSTAT. By merging information from customs data with the EIIG data on respondent firms, we get our baseline estimation sample. In our analysis, we refer to this sample as the large sample. It has 281,419 firm-country-product triples spanning over 14,711 firms, 219 countries, and 272 CPA96 four-digit products. Matching the large sample with the EAE data generates what we refer to as the small sample: 98,168 triples spanning over 5,175 firms, 185 countries, and 270 products.
The same applies to skill intensity (88. 58%). This holds despite the fact that we trimmed observations to exclude outliers in value added and capital per worker. Within-industry heterogeneity in factor intensity is in fact even more pronounced than its TFP counterpart, which is well documented in the trade literature. This echoes  Bernard et al. (2003) , who observe that "industry. is a poor indicator of factor intensity" in data on U. S. manufacturing firms. Summarizing, firm characteristics such as capital and Summary statistics on firm productivity TFPi, capital intensity ki, and skill intensity hi refer to either the full EAE firm data (top panel) or the subsample of EAE firms used in estimations (bottom panel). % Intra-NACE3 SD refers, for each variable considered, to the ratio between the standard deviation within NACE three-digit industries and the overall standard deviation.
Specification 3 allows for firm fixed effects by means of a conditional fixed effects logit model. In this case, identification of the coefficients of X c and X p relies on firms that import different products from several countries under different sourcing modes. This reduces drastically the number of observations actually used by the conditional fixed effects logit procedure. Another drawback is that we cannot identify the impact of the contractibility of the final good μ f , which is firm specific. Finally, specification 4 is estimated by a probit model on the small sample for which firm-level information from the EAE database is available. The vector of firm controls FC i corresponds in this case to the firm characteristics used in the previous section. In all specifications, a few observations are lost during estimations because of the lack of data for some countries or products.
With the conditional firm fixed-effects logit, column 4, the identifying variation is provided by those observations  (35, 802)  referring to firms engaging in, depending on the country or product, both intrafirm and outsourcing. In this case, μf , which is firm specific, cannot be estimated. Finally, column 5 corresponds to observations for which firm-level controls are available from the EAE database. IM1 is the inverse Mills ratio, coming from the estimation of selection into response to the EIIG survey, which is set to 0 for firms outside the EIIG target population. Marginal effects are presented in all cases. In the fixed-effects logit case, marginal effects are obtained by setting fixed effects to 0. Firm-clustered standard errors (except for the random effects probit and fixed effects logit) in brackets.
A. France Is Not Different from the United States We start by replicating U. S. findings with our French data. Table  5  reproduces some of the cross-industry (column 1) and cross-country (column 2) regressions of  Antràs (2003b)  for France. The dependent variables Share s and Share c represent the share of intrafirm imports value at the industry and country 21 For instance, the definition of affiliate trade differs in the two countries. Our French data record imports from affiliates where the parent holds more than 50% of the stock. In the United States, the equivalent thresholds are 6% in Customs data and 10% in the Bureau of Economic Analysis survey of multinationals. Besides, the EIIG covers only about 80% of French multinationals' imports due to nonresponse, while U. S. Customs data are in principle exhaustive.
The dependent variables Shares and Sharec represent the ratio of intrafirm imports value over total imports value in industry (NACE rev1 three-digit) s and country c, respectively. Estimation is carried out by OLS. Robust standard errors in brackets. Significantly different from * * * 1%, * * 5%, and * 10%. levels, respectively. Industry-level covariates are NACE rev1 three-digit sector averages of capital and skill intensity (k s and h s ) and the final good contractibility measure μ f. 22 Country covariates are capital and skill abundance (k c and h c ), as well as the log of country c population in 1999 (Population c ), taken from the IMF World Economic Outlook database. Our estimations confirms findings on U. S. data by  Antràs (2003b)  and other authors. In particular, the intrafirm share increases with industry capital intensity as well as with the capital abundance of the origin country.
That makes outsourcing relatively more profitable in capital-abundant countries. That conjecture would also imply lower variable costs and greater imports under outsourcing in capital-rich countries. Regrettably, we do not have data on the number of available suppliers to test this conjecture. The first stage of the Heckman procedure, column 1, is a probit where the variable yipc equals 1 if imports by firm i of product p from country c are intrafirm and 0 otherwise. Estimations are carried out on the small sample for which firm-level data are available from the EAE database. The excluded variable in the second stage is firm multinational status in 1994. The second stage of the Heckman procedure, columns 2 and 3, is an OLS regression on the values of (log) imports for a given mode (either intrafirm or outsourcing) and contains the inverse Mills ratio coming from the first stage (IM3) as well as the inverse Mills ratio coming from the selection into response for EIIG firms (IM1).
This information comes from the LIFI (Liaisons financières) database collected by the French Statistical Office (INSEE), which describes ownership ties between firms that have a legal entity in France. These data exhibit strong persistence of multinational status, which suggests the presence of substantial sunk costs of creating a foreign affiliate. For this reason, we argue that, conditional on other firm variables, past multinational status conveys information on a firm's incentives to engage in intrafirm imports without directly affecting their value. The logic echoes analyses of the persistence of export status in Roberts and Tybout (1997)  or Bernard and Jensen (2004). In our data set the correlation multinational status in 1994 and 1999 is 0. 38. The correlation between multinational status in 1994 and y ipc is 0.
Network limitations There are several non-trivial circumstances under which our network models may fail. First, we notice that the spiking rule that , as a function of the number of recorded neurons, K, for a spiking network with N~400 neurons. For the blue line, all K neurons were recorded simultaneously, for the red line, each neuron is recorded in a different trial (red). The red line follows perfectly the prediction for K independent Poisson processes. Data are from an homogeneous integrator network with s V ~10 {3 and s s ~0, other parameters as in Fig. 1  C. (F) Effective connectivity filters of two randomly chosen pairs in the network, as measured through a GLM analysis. (G) Consequence of suddenly inactivating half of the active neurons for the network shown in B. Blue bar: unit 1 to 100 inactivated (membrane potential set to 0).
(D) Spike trains (dots), state x (blue) and estimate x x (red) when each spike train is recorded from a different trial of the network shown in (B). (E) Estimation error, ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi E½(x{x x) 2 q, as a function of the number of recorded neurons, K, for a spiking network with N~400 neurons. For the blue line, all K neurons were recorded simultaneously, for the red line, each neuron is recorded in a different trial (red). The red line follows perfectly the prediction for K independent Poisson processes. Data are from an homogeneous integrator network with s V ~10 {3 and s s ~0, other parameters as in Fig. 1C. (F) Effective connectivity filters of two randomly chosen pairs in the network, as measured through a GLM analysis. (G) Consequence of suddenly inactivating half of the active neurons for the network shown in B.
Transcriptomes were compared for each annotated feature between the wild type strain and the fliZ mutant (GEO accession number: GSE47365). We observed significant differences in expression between the fliZ mutant and the wild type strain for 278 coding sequences (|log 2 fold change| $1; adjusted Pvalues#0. 05; Table  S1 ), 235 of which were downregulated and 43 of which were upregulated in the fliZ mutant relative to the wild type strain. These genes were either isolated or clustered into 23 genomic regions scattered throughout the bacterial chromosome. The genes on which fliZ inactivation had the strongest effect are listed in Table  1. The protein-coding genes significantly downregulated in the fliZ mutant included all 47 flagellar protein-encoding genes, clustered in three flagellar regions (loci 13, 14 and 17; Table  S1 ).