dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Analysis Single-unit discrimination was performed offline, and all analyses for single-units and LFP data were performed using custom-written programs in Matlab (Mathworks, Inc. ). Statistical analyses are indicated in the text and in S1 Methods. The proportion of units that exhibited fast-firing properties (including tonic-and burst-firing) were quantified by setting filters based on the peak and mean firing frequencies (see S1 Methods). For LFP data recorded under urethane anesthesia, time-frequency power spectra were computed using the multi-taper method (window size 4s, step 1s). To compare between rats, power values were normalized to the sum of the power spectrum. Theta epochs were extracted with an automatic theta detection algorithm based on the times when the normalized theta power exceeded an empirically-determined threshold of 0.
94 a. u. For analysis of tail pinch events, 30s of LFP data were extracted time-locked to the tail pinch. For theta phaselocking of MSDB units, spikes that occurred outside of theta epochs were discarded. The mean resultant vector length was calculated from the theta phase of the spikes (circular statistics toolbox, Matlab), and units with significant theta phase-locking were identified using the Rayleigh test for non-uniformity  [39]. For LFP data recorded during performance in the T-maze, time-frequency power spectra were computed using the short-time Fourier transform (window size 0. 5s, step 0. 1s). Theta frequency and normalized power were first evaluated as a function of binned running speed. To investigate the dynamic changes in theta rhythm, the maze was divided into 10 position bins and theta values were determined for each bin.
Acknowledgments We thank  David Schubert  ( Salk Institute, San Diego, CA ) for providing the B50 cell line. We also thank the faculty, staff and colleagues at  Dartmouth ,  UVM  and at collaborating institutions for their advice and support ( Ken Orndorff ,  Meijie Li ,  Radu Stan ,  Cliff Kentros ,  Hermes Yeh ,  Pamela Yeh ,  Allan Gulledge ,  Greg Holmes ,  Rod Scott ,  Sophie Sakkaki , and  Sofia Campos).
Blots were developed by chemiluminescence using Clarity western ECL substrate (Bio-Rad) with a luminescence analyser (LAS4000, Fuji) and were analysed using Multigauge software (Fuji). Full blots from Fig. 1b, c  and Fig. 3e  are shown in Supplementary Fig. 6. In vivo pull-down assays. To test the interaction between Mtb-SecB TA and Mtb-HigA1 mutants, E. coli strain BL21 was transformed with pETDuet derivatives containing His6 Mtb-SecB TA and Mtb-HigA1 either WT or mutant. Overnight cultures of transformants were diluted to an OD 600 of 0. 05 and grown at 37 °C to OD 600 of 0. 4 in LB. The expression of Mtb-SecB TA and Mtb-HigA1 variants was then induced for 2 h with 0. 5 mM IPTG. For the interaction between Mtb-SecB TA and luciferase chimeric proteins, E. coli strain W3110 DclpPX-lon::Cm R DclpQ was co-transformed with pSE containing Mtb-SecB TA and and pMPMK6 derivatives harbouring Luc_ChAD Mtb , or Luc_ChAD Mtb W108A/W137A.
2 ng ml À 1 to the PURE system, with or without purified Mtb-SecB TA. Protein synthesis was performed at 37 °C for 60 min with 0. 4 mCi ml À 1 of 35 S-methionine. An aliquot was withdrawn as the total fraction and the remaining reaction was centrifuged at 21,600g for 30 min, and the supernatant fraction was collected. Both fractions were separated on 4-20% Mini-Protean TGX gels (Bio-Rad), visualized using a Typhoon phosphorimager (GE Healthcare) and quantified using Multigauge software (Fuji). Results are presented as the mean and s. d. of triplicate experiments.
Acknowledgements We thank  Olivier Genest  and  Joen Luirink  for valuable discussions,  Alexandre Prudente  and  Thomas Wood  for technical help, and  Dirk Schnappinger  and  Claude Gutierrez  for plasmid gift. This work was supported by  MENRT  and  FRM  ( FDT20140930836 ) fellowships to A. S. , MENRT and FRM (FDT20160435132) fellowships P. T. , an  UPS-2014-113  fellowship to S. A. , and by grants  ANR-13-BSV8-0010-01  to L. M. and P. G. , and  SNF CRSII3_160703  to P. G. The DSF equipment used in this study is part of the  Integrated Screening Platform of Toulouse (PICT, IBiSA ).
Within the framework of the EU project OFFICAIR, a large field investigation was carried out, resulting in an extensive database that comprises not only data about physical building characteristics, but also on individual and occupational factors as well as on health symptoms. Thus, the aim of this study was to investigate the associations between the office environment (especially physical building characteristics) and self-reported dry eye complaints, taking into account individual and occupational risk factors, as a first important step towards unravelling the office related causes of dry eye symptoms.
Study population and study design OFFICAIR is a European collaborative project. The procedure is described in detail elsewhere  [5] , and is therefore only briefly be summarized here. A cross-sectional study was performed during the winter of 2011e2012, collecting data from 167 office buildings in eight European countries (Greece, France, Finland, Hungary, Italy, Portugal, Spain and the Netherlands) simultaneously with questionnaire data from 7441 office workers. Office buildings were selected based on a range of criteria including: new or recently retrofitted buildings (e. g. preferably <10 years old, use of modern equipment and access to internet), operating in their current form for a minimum of 1 year prior to the start of the study (preferable 2 years), with no major renovation planned before the autumn of 2012.
Worker questionnaire The digital questionnaire was voluntary, anonymous and available in the national language of the participating countries. It was based on standardized and validated questionnaires such as the HOPE questionnaire  [4]  and the effort reward imbalance and overcommitment questionnaires  [24]. Socio-demographic data (e. g. sex, age, education level), lifestyle (e. g. smoking status, alcohol consumption), work related data (e. g. VDU use), psycho-social environment (work-related stress), and health complaints were collected. The health outcome of interest was the occurrence of dry eye complaints defined by the following question: 'During the past four weeks, on how many days did you experience dry eyes when you were at work at your workstation (including today)? The answer options ranged from 'not in the last 4 weeks', '1e3 days in the last 4 weeks', '1e3 days per week in the last 4 weeks', and 'every or almost every workday'.
Building checklist Data on physical building characteristics were collected by a checklist filled out for each office building by a local investigator of the OFFICAIR consortium along with a building manager. Parameters potentially affecting different indoor environmental conditions (e. g. indoor air quality, air velocity, temperature, humidity, lighting conditions) were included in this checklist, such as: The outdoor environment (e. g. busy road, rural/surroundings), the building (construction materials (e. g. type of ceiling/floor/wall coverings), ventilation (e. g. type of ventilation, ventilation rates, re-circulating air, cleaning of ducts, localization of outdoor air intake), heating and cooling (e. g. type, control of temperature and humidity), lighting conditions (e. g. solar protections, type of artificial light, way of controlling main lights), the equipment (e.
Strengths and limitations One strength of this study is its large scale, 7441 office workers in 167 modern office buildings with a wide geographical coverage and spread over Europe were investigated. Furthermore, information was collected on a broad range of individual risk factors and other potential confounders, including socio-demographic characteristics, lifestyle factors, as well as work related factors, and psycho-social characteristics of the working environment. In addition, data was collected on a large number of building characteristics (e. g. location, size, construction materials, systems e aspects of operation, maintenance and control, equipment, cleaning activities e type and frequency), which potentially may affect different indoor environmental conditions (e. g. indoor air quality, temperature, acoustic and lighting conditions).
Acknowledgement The project OFFICAIR (On the reduction of health effects from combined exposure to indoor air pollutants in modern offices) was funded by the  European Union 7th Framework  ( Agreement 265267 ) under the Theme: ENV. 2010. 1. 2. 2e1. Part of this work was funded by the fellowship of  Prof. P. M. Bluyssen  provided by the  Delft University of Technology , under the Chair of indoor Environment.
Predictive models for retransplantation In order to predict post-reLT survival and, therefore, aid in patient selection, several scores have been developed  [30, 31]. That most widely employed is the Rosen score  [32]. However, like many others, this model was based on reLT cases in general and not just on cases of HCV recurrence. The first score specifically designed for HCV-positive patients was published recently by Andres et al  [29]. This study analyzed registry data from the Scientific Registry of Transplant Recipients on 1422 individuals transplanted for HCV and retransplanted at least 30 d after the first transplant. In order to design a score that could predict survival after reLT, they identified six predictive variables associated with survival: recipient age at the time of the first transplant, interval elapsing between the two transplants, donor age, creatinine levels, international normalized ratio (INR), and serum albumin values before the second transplant.
For this, donor age was categorized as more or less than 60 years considering the scarcity of young donors, and MELD score superior or inferior to 25  [37]. Data were compared between retransplanted and nonretransplanted patients using the χ 2 test for categorical data and the independent samples t-test for continuous data. Survival comparisons between the two groups were performed by taking account of the date of the first decompensation, considering that this could be the moment at which re-listing would be discussed. Differences between fibrosis progression rates were calculated using a paired t test. A P value of 0. 05 or lower was considered to be significant. Statistical analyses were performed using SAS software version 9. 1. 3 (SAS Institute Inc. , Cary, NC, United States). inclusion criteria were: patients aged 18 or older receiving LT between January 1994 and June 2012 for HCV-related disease and experiencing HCV recurrence in the form of Metavir F3, F4, or fibrosing cholestatic hepatitis (FCH) (confirmed histologically), presenting with clinical hepatic decompensation (defined as the presence of ascites, encephalopathy, variceal hemorrhage, or jaundice), who had not undergone reLT, and with positive serum HCV RNA after the first transplantation.
Case identification Retransplanted patients were identified by consulting prospectively maintained databases in the Spanish center and in five of the French centers or the database operated by the French Biomedicine Agency (Agence de la Biomédecine), which covered the two other French centers. This agency is a public organization under the supervision of the French Ministry of Health, whose responsibilities include organizing the procurement and transplantation of organs, tissues, and cells. All transplant centers in France are required to report all their cases to this agency. To identify cases of non-retransplanted patients with advanced liver disease/FCH and graft failure in all centers, all patients transplanted for HCV-related disease were identified first. Subsequently, all post-LT biopsies revealing F3, F4, or FCH were included, and a chart review was performed in order to identify those patients presenting with hepatic decompensation (defined as the presence of ascites, encephalopathy, variceal hemorrhage, or jaundice).
Data collection and definitions After case identification, data were collected by consulting the prospectively maintained databases when available, which included several variables. Variables not available in the databases were collected through chart review in all centers. Regarding the first and second transplantations: Donor age and gender, living or deceased donor, donor HCV serology, recipient age and gender, concomitant kidney transplantation, acute rejection episodes after LT, receipt of corticosteroid bolus, receipt of OKT3, maintenance immunosuppressive regimen, HCV treatment (medication and duration), reason for treatment discontinuation, HCV treatment response (as previously defined  [34]  ), presence of hepatocellular carcinoma (HCC) prior to LT1, presence of alcoholic disease prior to LT1, human immunodeficiency virus (HIV) co-infection, diabetes mellitus pre or post-LT, Metavir fibrosis score on biopsies post-transplant, presence and timing of FCH, HCV viremia levels before LT1 and before reLT, HCV genotype, date and type.
RESULTS Between January 1994 and June 2012, 11341 LTs were performed in the eight study centers, and in 2586 (23%) patients, the main indication was HCV-related disease. Of these, 372 (14%) patients progressed to F3 or F4, and 91 patients were retransplanted. Fortythree patients (2%) presented FCH, and 17 of these were retransplanted, totaling 108 retransplanted patients. Figure  1  shows all cases that led to the final case selection. We also identified 164 patients with hepatic decompensation who did not undergo reLT (141 with F3 or F4 and 23 with FCH) prior to the data collection period. The center-based distribution of advanced fibrosis and FCH cases with and without reLT is described in Table  1. The mean interval elapsing between reLT re-listing and actual reLT was 151 d (1-1393), with no statistical difference between the groups (P = 0.
028) (the data refer to 71 treatments in reLT group and 87 treatments in the no reLT group). Variables found to be similar in both groups included: acute rejection episodes after LT1, use of OKT3 after LT1, number of antiviral treatments, results of biochemical and hematological investigations up to 30 d after hepatic decompensation following LT1 (bilirubin, creatinine, INR, hemoglobin, platelets, sodium and albumin), genotype distribution, type of antiviral treatment, treatment duration, and rate of treatment discontinuation. In the non-reLT group with hepatic decompensation, 20 (12. 1%) of the 164 patients were re-listed or were undergoing a pre-reLT work-up for relisting at the time of data collection. The reasons for not replacing the remaining 144 patients on the waiting list were: death due to hepatic decompensation before relisting (30.
The index SNPs for these independent signals range across a 350-kb region from 202,036,478 to 202,379,828. To determine whether these CASP8-associated signals accounted for the ER-negative associations in the meta-analysis, conditional analyses were conducted using the BCAC-iCOGS data. After accounting for the four CASP8 signals, rs74943274 retained evidence of an association with overall breast cancer (P ¼ 1. 44 Â 10 À 3 ) and a strong association with ER-negative breast cancer (P ¼ 1. 34 Â 10 À 5 ; Supplementary Table  16 ; Supplementary Fig. 2 ), suggesting that rs74943274 and rs115635831 represents a novel locus associated with ER-negative breast cancer. Further consideration of the BCAC-iCOGS data found no association for rs115635831 at 2q33 with ER-positive breast cancer (P ¼ 0. 23) but identified a significant difference (P diff ¼ 2.
Methods Study populations. Details of the subjects, genotyping and quality control measures for the BCAC GWAS and iCOGS data  3  , BPC3 (ref. 16), EBCG  37  , TNBCC  14, 38   Genotype data. Genotyping and imputation details for each study are shown in Supplementary Table  1. Imputation. We performed imputation separately for BRCA1 carriers, 11 GWAS, BCAC-iCOGS and TNBCC-iCOGS samples. We imputed variants from the 1000 Genomes Project data using the v3 April 2012 release  39  as the reference panel. Imputation was based on the 1000 Genomes Project data with singletons removed. Eight BCAC GWAS were imputed in a two-step procedure, with prephasing using the SHAPEIT software and imputation of the phased data in the second with IMPUTEv2 (ref. 40). For the remaining three GWAS (BPC3, TNBCC and EBCG), imputation was performed using MACH (version 1.
0. 18) and Minimac (version 2012. 8. 15)  41. The iCOGS data were also imputed with two-stage procedure involving SHAPEIT and IMPUTEv2. To perform the imputation we divided the data into segments of B5 Mb each. The iCOGS samples were divided into 10 subsets, keeping all subjects from individual studies in the same set. Estimates and s. e. 's were obtained using logistic regression adjusting for study and 9 principal components. GWAS SNPs were excluded if the imputation accuracy was r 2 o0. 3 or if the minor allele frequency (MAF) was o0. 01, TNBCC SNPs were excluded when the imputation accuracy was r 2 o0. 9 and MAFo0. 05, iCOGS SNPs were excluded when r 2 o ¼ 0. 3 and MAFo0. 005. Regions with evidence of genome-wide significant associations (Po5 Â 10 À 8 ) were reimputed in iCOGS, using IMPUTEv2 but without prephasing in SHAPEIT to improve imputation accuracy.
Samples with call rates below 80% were excluded. Identity by state was computed with the R GenABEL package  47  and closely related samples with IBS40. 95 were removed. Imputation was performed on the iCOGS and Affymetrix6 germline genotype data using the 1000 Genomes Project March 2012 v. 3 release as the reference data set. A two-stage imputation procedure was used as described above. The influence of SNPs on gene expression was assessed using a linear regression model. An additive effect was assumed by modelling copy number of the rare allele, that is, 0, 1 or 2, for a given genotype. Candidate gene analysis. TCGA has performed extensive genomic analysis of tumours from a large number of tissue types including over 1,000 breast tumours. All genes in the novel loci were evaluated for coding somatic sequence variants in TCGA.
Breast tumours with log2 copy-number data in the TCGA data were analysed for deletion and amplification of each candidate gene using the cBio portal  48, 49. Informatics and chromatin biofeatures. Candidate SNPs were evaluated using SNPInfo (http://snpinfo. niehs. nih. gov) and SNPnexus (http://snp-nexus. org/test/ snpnexus). The presence of SNPs in transcription factor binding sites using TRANSFAC and miRNA binding sites using TargetScan were noted. Regulatory potential scores (ESPERR Regulatory Potential) were obtained from the UCSC genome bioinformatics browser (http://genome. ucsc. edu/). RegulomeDB (http:// regulomedb. org) was used to assess SNPs for transcription factor recognition motifs, open chromatin structure based on FAIRE and DNAse-seq analysis and protein binding sites based on ChIP-seq data.
Chromatin biofeatures in HMEC and MCF7 cells were assessed using ENCODE layers on the UCSC browser (http:// genome. ucsc. edu/). Enhancers active in the mammary cell types MCF7 and HMEC were cross-referenced with candidate SNPs. Luciferase reporter assays. Genomic tiles spanning regions containing SNPs with indication of regulatory activity by RegulomeDB were generated. Regions containing the major and minor alleles within the 2p23. 2 region spanning 2,229 bp (chr2:29,117,333-29,119,561) were generated by PCR using BAC DNA CTD-3216P10 as template. Forward and reverse primers contained attB1 and attB2 sequences, respectively, to aid in recombinational cloning. Tiles were cloned in both a forward and reverse orientation upstream of the SV40 promoter by recombination in the firefly luciferase reporter vector pGL3-Pro-attb vector designed to test for enhancer regions.
Acknowledgements We thank all the researchers and administrative staff who have enabled this work to be carried out and all of the individuals who took part in these studies. B. C. A. C. was funded through a  European Community Seventh Framework Programme  under grant agreement no  223175  ( HEALTH-F2-2009-223175 ; COGS);  Cancer Research UK  ( C1287/A10118 ,  C1287/A10710 ,  C12292/A11174 ,  C1281/A12014 ,  C5047/ A8384 ,  C5047/A15007 ,  C5047/A10692 ); the  National Institutes of Health Specialized Program of Research Excellence (SPORE) in Breast Cancer  ( CA116201 ), R01 grants ( CA128978 ,  CA176785 ,  CA192393 ), and  Post-Cancer GWAS initiative  ( 1U19 CA148537 ,  1U19 CA148065  and  1U19 CA148112  -the  GAME-ON initiative ); the  Canadian Institutes of Health Research (CIHR)  for the  CIHR Team in Familial Risks of Breast Cancer , the  Breast Cancer Res.
Roads are known to act as corridors for dispersal of plant species. With their variable microclimate, role as corridors for species movement and reoccurring disturbance events, they show several characteristics that might influence range dynamics of both native and non-native species. Previous research on plant species ranges in mountains however seldom included the effects of roads. To study how ranges of native and non-native species differ between roads and adjacent vegetation, we used a global dataset of plant species composition along mountain roads. We compared average elevation and range width of species, and used GLMMs to compile their range optimum and amplitude. We then explored differences between roadside and adjacent plots based on a species' origin (native vs. nonnative) and nitrogen and temperature affinity.
However, the sizes and directions of such shifts likely depend on species-specific characteristics, as different species will profit or suffer differently from the altered environment in roadsides. In this paper we compare differences in the elevational range of species in roadsides and the adjacent vegetation. Understanding the effects of mountain roads on species' elevational ranges is not only important from a theoretical point of view, but also crucial to improve species distribution models to forecast future climate change impacts on mountain biota and to decide on informed management strategies for mountain ecosystems. We used a dataset based on a large-scale monitoring effort of plant species distributions along roadsides and within adjacent natural vegetation across elevation gradients in eight mountain regions  (MIREN 2005) , and applied two different modelling approaches to study general and species-specific range patterns.
Elevational range differences between roadsides and adjacent plots Ranges based on the elevation of occurrence of species were calculated separately for roadsides and adjacent plots. Although elevation differences might not have exactly the same ecological meaning in different study regions, e. g. because of regional differences in adiabatic lapse rates and precipitation gradients, elevation currently is the best available variable to study range shifts in mountains. The use of climatic data would explain shifts in a more ecological way, but the current scale of globally available climatic datasets is too coarse (~1 km²) to explain differences in elevational ranges on a scale.
In the first approach, average, minimum and maximum values of each species' elevational range in the roadside and the adjacent plots were calculated per region (pooling all roads in a given region), hereafter called "dataset A1" (N = 510 region-specific values, for 438 different species, Supplementary material Appendix 1 Table. A1). The difference between a species' elevational occurrence optima was defined as the difference between its average elevation of occurrence in the roadside and in the adjacent plots. This resulted in positive values for species with a higher optimum in roadsides than in the adjacent plots, and vice versa. Differences between range amplitudes were defined as difference between ranges from maximum to minimum elevation of occurrence in roadside plots and adjacent plots.