dataesr/scientific-paragraphs-categorization huggingface dataset:
Data that is clearly needed in face of the fact that the landscape of human society is changing with an estimate that in 2050 almost 70% of the world's population will live in urban regions (http://esa. un. org/unpd/wup/). A major limitation of the present study is that our georeferencing data only assesses the environment 1 km around the place of residence of the participants. It does not allow us to infer where the participants actually spend most of their time. Due to the fact that almost all participants were retired, work-related commuting is unlikely to influence the results, however there might still be significant differences in the extent that participants actually spent time in and make use of their direct surrounding. Another limitation of the study certainly is that the land use data used in this analysis refer to the year 2006, whereas the brain data has been acquired in 2014/2015.
Voxel-based morphometry (VBM) preprocessing. Structural data was processed by means of VBM8 (http://dbm. neuro. uni-jena. de/vbm. html) and SPM8 (http://www. fil. ion. ucl. ac. uk/spm) using default parameters. VBM8 involves bias correction, tissue classification and affine registration. The affine registered GM and white matter (WM) segmentations were used to build a customized DARTEL (diffeomorphic anatomical registration through exponentiated lie algebra) template. Then warped GM and WM segments were created. Modulation with Jacobian determinants was applied in order to preserve the volume of a particular tissue within a voxel leading to a measure of volume of GM. Diffusion tensor imaging preprocessing. Diffusion-weighted images were pre-processed using the FSL software package  34  , version 5.
ROI extraction. Based on prior studies exploring differences between brain function in participants currently living in cities compared to more rural residents we focussed on bilateral amygdala, pACC  21  and DLPFC  23  as defined by the automated anatomical labelling atlas  36. Urban land use data. Within the BASE-II study we merged data assessed within the georeferencing dataset from Berlin city and its surrounding together with MRI data. The georeferencing data used was taken from the European Urban Atlas, provided by the European Environment Agency and constitutes a comprehensive and comparative cross-section study of urban land use in Europe, including data for major German cities  37. The dataset contains polygon features representing real-world features characterised by areas. All areas greater than 0.
The focal question was whether geocoded land use variables quantifying characteristics of the environment surrounding the households of participants are specifically associated with the structural integrity of the amygdala, pACC and DLPFC. It should be noted that due to cross-sectional characteristic of this dataset we cannot clarify the causal direction of effects. However, we used SEM to define a model based on previously reported negative effects of urbanicity on amygdala, pACC  21  and DLPFC  23  where the four characteristics of urban land use predicted structural integrity of our three regions of interests. Latent factor models were established by using MPlus v7 (Muthen LK, Muthen BO: Mplus User's Guide. Los Angeles, Muthen & Muthen, 2010). To ease optimization, the data were z-standardized correcting for the extreme inter-variable differences in scale and location.
Acknowledgements We are grateful for the assistance of the MRI team at the  MPI Berlin consisting of Sonali Beckmann ,  Nils Bodammer ,  Thomas Feg ,  Sebastian Schröder ,  Nadine Taube. This article uses data from the  Berlin Aging Study II (BASE-II ) which has been supported by the  German Federal Ministry of Education and Research  under grant numbers  16SV5537/16SV5837/16SV5538/16SV5536K/01UW0808/01UW0706. The Responsibility for the contents of this publication lies with its authors. SK has been funded by a  Heisenberg grant  from the  German Science Foundation  ( DFG KU 3322/1-1 ), the  European Union  ( ERC-2016-StG-Self-Control-677804 ) and the  Jacobs Foundation  ( JRF 2016-2018 ).
The framework can be used as a tool to prioritize future research and to develop policies to reduce sedentary time. and/or related term definitions, cut-points, measurement challenges/recommendations or standard processing techniques for accelerometer data in the title or abstract, with no regard to the population (e. g. , child or adult; healthy or unhealthy), type of study, intervention, or comparison being explored. After reviewing the included full-text articles, data were extracted from relevant articles including the aim of the study, defined/discussed terms, targeted population, and definitions/relevant information. In addition to gathering background information for the project, the literature search allowed for the identification of authors of key papers who were invited to form a Steering Committee for the SBRN Terminology Consensus Project (MST (Chair), TJS, VC, AEL, SFMC, TA, MJMC and project management support from SA and JDB from SBRN).
This scalable solution can freely adjust the coverage without other, additional investment. 2 Data collected for personal use only; that is, not shared with other people. 3 Data collected for sharing with a group of people. ing  4  , by considering the different types of phenomena being monitored. • In individual sensing  [23] , the phenomena being monitored pertain to individuals; for example, the monitoring of individual movement patterns (e. g. , running, walking, exercising, and driving). The individual mentioned here is not specific. Considering the sensing from a large number of individuals, this can be seen as crowd sensing. Such sensing enables large amounts of information to be obtained from different individuals and shared among these individuals. • Group sensing is used to monitor group phenomena that cannot be easily measured by a single individual  [24] ; for example, in virtual teaching, students' mobile phones are used to sense and share the physical information around them.
Carbapenemase genes were sought using in-house multiplex PCRs  28, 29  and confirmed by WGS using a Hiseq sequencer (Illumina) for the retrospective isolates. The resulting WGS data were analyzed using an in-house bioinformatics pipeline with resistance genes identified by mapping reads to an in-house library curated from publicly-available databases  30. Any discrepancies between the BYG Carba v2. 0 and molecular assay results obtained in centres B, C, D, were reanalyzed in centre A following its own procedure for bacterial identification, antibiotic susceptibility testing by disc diffusion and PCR for carbapenemase genes. The isolates were also retested with the BYG test v2. 0 in centre A. The results subsequently obtained in centre A, were not used for the calculation of the performance of the test but only to investigate the potential sources of discrepancies.
The K * + amplitude model extracted from our data is consistent with expectations from the quark model and from the previous experimental results on such resonances. We determine the J P C quantum numbers of the X(4140) structure to be 1 ++. This has a large impact on its possible interpretations, in particular ruling out the 0 ++ or 2 ++ D * + s D * - s molecular models  [3] [4] [5] [6] [7] [8]. The X(4140) width is substantially larger than previously determined. The below-J/ψ φ-threshold D ± s D * ∓ s cusp  [9, 18]  may have an impact on the X(4140) structure, but more data will be required to address this issue, as discussed in more detail in the companion article  [30]. The existence of the X(4274) structure is established and its quantum numbers are determined to be 1 ++. Molecular bound-states or cusps cannot account for these J P C values.
2 M for UK. Imputations were performed on all scans using 1,094 subjects from the 1000 Genomes Project (phase 1 release 3) as the reference panel (Supplementary Online methods). Each discovery-stage data set was analysed individually assuming log-additive (trend) SNP effects, with the exception of the two IARC scans which were pooled and analysed together (Supplementary Online methods). We then performed a fixedeffects meta-analysis of 7,437,091 SNPs that were polymorphic in at least two data sets. Quantile-quantile plots of the combined results showed little evidence for inflation of the test statistics compared to the expected distribution (l ¼ 1. 034; Supplementary Fig. 1 ). For visual representation, we provide a Manhattan plot summarizing the genome-wide SNP results in Supplementary Fig.
FAS-associated factor 1 facilitates the degradation of b-catenin, a transcriptional co-activator that stimulates expression of genes driving cell proliferation  25. Constitutively activated b-catenin, induced by VHL inactivation, is an important pathway in ccRCC oncogenesis  26. The rs4381241 risk allele is weakly correlated (r 2 ¼ 0. 12 in CEU) with the allele of another FAF1 variant (rs17106184) associated with reduced risk of type-2 diabetes and lower serum insulin post oral glucose challenge  27, 28. The risk variant rs67311347 maps to a region of 3p22. 1 that harbours several genes. Within the KIRC tumour tissue data, the risk-associated allele of the surrogate SNP rs9821249 (r 2 ¼ 0. 97 with rs67311347 in CEU) was weakly associated with higher expression of CTNNB1 (P ¼ 0. 03). This gene, located 706 kb away centromeric, is a strong candidate as it encodes the RCC protooncogene b-catenin, although this association was not seen within the IARC data set.
5%) samples from this scan and three samples from the Omni5 scan. From the Omni5 scan, genotypes for 4,276,196 SNPs were obtained, of which we excluded 127,523 SNPs because of low (o95%) success rate, 14,513 SNPs for departure from Hardy-Weinberg disequilibrium (HWE) (Po10 À 7 ) in controls, 65,300 with ambiguous strand issues, and 37,319 non-autosomal SNPs. The final Omni5 analytical data set included 4,031,541 SNPs on 2,781 cases and 2,526 controls. For the same criteria, the 951,117 SNPs obtained from the OmniExpress array were pruned from 16,409, 1,132, 24,370 and 20,715 SNPs, respectively, leaving a final data set of 888,491 SNPs on 414 controls. Imputation of genotypes was performed on these data sets after the exclusion of 2,485,185 SNPs from the Omni5, and 742 SNPs from the OmniExpress scans, when minor allele frequency (MAF) was o0.
05. For the new NCI-2 scan, a total of 3,168 samples were initially genotyped by the OmniExpress array. A total of 22,775 (3%) SNPs with call rate o90% were excluded, as were 282 samples (9%) with completion rateo94%. After this exclusion, the concordance rate was 499. 9% for 66 pairs of blind duplicate pairs. After removing duplicates, a data set including 2,820 unique samples was advanced to further assess quality control at the subject level. In addition, we excluded 10 sex-discordant individuals and two individuals with excessively low mean heterozygosity for ChrX SNPs. For the cleaned data including genotypes for 2,808 individuals, we next pooled in a total of 4,221 previously scanned controls (HumanOmni2. 5M or HumanOmniExpress array) from the ATBC, CPSII, HPFS, NHS, PLCO and WHI studies (Supplementary Table  1 ).
After merging the newly scanned data with the previously scanned controls, we obtained genotypes for 7,029 individuals. Subsequently, we excluded data for 204 non-CEU individuals (admixture proportion for CEUo80%), both members of a pair of unexpected within-study duplicate samples, one from each of eight unexpected cross-study duplicate pairs, and one from each of eight related pairs (two parent-child pairs and six sibling pairs). The final analytic data comprised 6,808 individuals (2,417 cases, 4,391 controls) for 678,580 loci. Statistical analysis. The statistical analysis included summary data from four previously published scans conducted at the NCI (NCI-1)  9  , IARC (IARC-1)  9  , the University of Texas MD Anderson Cancer Center (MDA)  10  , and the Institute of Cancer Research, UK (UK)  11  , as well as the two new scans from NCI (NCI-2) and IARC (IARC-2).
The IARC-1 and IARC-2 data were pooled, resulting in five separate discovery-stage data sets. Imputation was performed separately for each scan data set using SNPs of minor allele frequency Z0. 01 (Z0. 05 for the IARC data set), with 1000 Genomes Project data (phase 1 release 3) used as a reference set. IMPUTE2 version 2. 2. 2 was used for imputation of the NCI-1, NCI-2, MDA and UK data sets, while Minimac version 3 was used for the IARC data set  42, 43. Imputed SNPs with sufficient accuracy as assessed by r 2 Z0. 3 for both IMPUTE2 and Minimac were retained for the analysis. We further assessed the quality of imputation by randomly selecting 10% of genotyped SNPs on chromosome 1 within the IARC-1 series (which used the least-dense chip across the different scans) and removing them before running the imputation algorithm.
MAFs calculated from the genotyping data correlated with r 2 40. 99 with MAFs calculated from the imputed dosage data. Finally, top SNPs were technically validated through Taqman genotyping in the IARC and NCI-2 scan (Supplementary Table  4 ). After imputation, genotypes for 7,437,091 SNPs were available for analysis. Association testing with RCC was conducted separately for each data set assuming log-additive (trend) SNP effects using SNPTEST version 2. 2 at NCI and R version 3. 2. 3 at IARC. The model covariates varied by data set; for the previous scans, we used the same covariates as in the initially published analyses. The covariates were as follows: sex and study for NCI-1 (no statistically significant eigenvectors present in null model); sex and four significant eigenvectors for NCI-2; age, sex and two significant eigenvectors for MDA; no covariates for the UK; and sex, study, and 19 significant eigenvectors for IARC-1 and IARC-2.
Eigenvectors were considered significant if Po0. 05 from the Tracy-Widom statistics. In the IARC series, all 19 eigenvectors were significantly associated with the country of recruitment. We additionally conducted analyses restricted to ccRCC. The SNP association results from each data set were combined by meta-analysis using a fixed-effects model. Heterogeneity in genetic effects across data sets was assessed using the I 2 and Cochran's Q statistics. Analysis of heritability. We estimated GWAS heritability, h l 2 , using the GCTA software  44, 45  and data from the NCI-1 and NCI-2 scans. Analyses assumed a disease prevalence of 1. 66%, included only SNPs with MAF 40. 05, removed subjects missing more than 5% of genotypes and adjusted for sex, substudy and the top 20 eigenvectors. In addition to quality control steps taken for the original GWAS, we removed SNPs with a missing rate 410% or a HWE P value o10 À 5 in the control group in any study.
Associations with the PRS and individual SNPs selected for replication were computed for the following RCC phenotypes: papillary and chromophobe RCC histologies (through case-control analyses); age at onset (o60 versus 60 þ years at diagnosis; case-only analyses) and stage (2, 3 and 4 versus 1; case-only analyses). The stage-stratified analyses were restricted to the IARC data sets, for which these data were available. Technical validation of imputed SNPs. To technically validate our imputation findings, we genotyped the 32 SNPs carried over for replication by Taqman assay in a subset of samples from the NCI-2 and IARC-1/2 scans (n ¼ 566 and 6,402 respectively). The concordances between imputed and directly assayed genotypes are detailed in Supplementary Table  4. Gene expression data and eQTL analysis.
KIRC: Genotyping and RNAseq data for the KIRC TCGA samples (481 tumours and 71 normal renal tissues) were downloaded from The Cancer Genome Atlas database (http://cancergenome. nih. gov/, accessed on 15 January 2016). We quantified expression as normalized read counts and removed outlier samples with expression values exceeding 1. 5 times the interquartile range. Linear trend tests were used to test for allele-specific increases in gene expression for genes within a 6 Mb window. Analyses were performed using R v3. 1. IARC: For a subset of cases from the IARC K2 and the CE studies (Supplementary Table  1 ), gene expression analysis of renal normal and tumour tissue samples were conducted using Illumina HumanHT-12 v4 expression BeadChips (Illumina, Inc. , San Diego) for samples with RNA integrity (RIN) 45.
0. Raw expression intensities of samples with signal-to-noise ratio 49. 5 were processed with variance-stabilizing transformation and quantile normalization with lumi package  47  as reported by Wozniak et al. 21. The 50 mer sequences of probes were mapped to human reference genome hg19 downloaded from UCSC Genome Browser database (http://genome. ucsc. edu/, accessed on 15 November 2014) using BWA  48  to demarcate positional relationships between corresponding probes/genes and SNPs. In total, 234 normal and 555 tumour tissue samples from confirmed clear cell RCC cases with available genotyping data were used to test for allele-specific increases in gene expression for genes within a 6 Mb window under linear trend assumption. Analyses were performed using R v3. 1. 3. Data availability. The scan IARC-2 obtained Institutional Review Board certification permitting data sharing in accordance with the US NIH Policy for Sharing of Data Obtained in NIH Supported or Conducted GWAS.
Data are accessible on dbGaP (study name: 'Pooled Genome-Wide Analysis of Kidney Cancer Risk (KIDRISK)'; url: http://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/ study. cgi?study_id=phs001271. v1. p1). Similarly, the NCI-1 scan is accessible on dbGaP (phs000351. v1. p1). Data from IARC-1 and MDA scans are available from Paul Brennan and Xifeng Wu, respectively, upon reasonable request. The UK scan data will be made available on the European Genome-phenome Archive database (accession number: EGAS00001002336). The NCI-2 scan will be posted on dbGaP. TCGA data were accessed at the following url: https://gdc-portal. nci. nih. gov/ projects/TCGA-KIRC.
Acknowledgements The authors thank all of the participants who took part in this research and the funders and support staff who made this study possible. Funding for the genome-wide genotyping was provided by the  US National Institutes of Health (NIH) ,  National Cancer Institute  ( U01CA155309 ) for those studies coordinated by  IARC  and by the intramural research program of the  National Cancer Institute, US NIH , for those studies coordinated by the  NCI. Funding for the IARC gene expression and eQTL study was provided by the  US National Institutes of Health (NIH) ,  National Cancer Institute  ( U01CA155309 ). Additional acknowledgements can be found in Supplementary Note.
Output energy stability An average output power of ~4W was measured by a powermeter (UP25N-40S, Gentec) over 25 min. In order to monitor the pulse-to-pulse stability of the pump, SC and output pulses, fractions of these beams are sampled out and sent to amplified photodiodes: InGaAs photodiode for the pump pulses, extended-InGaAs photodiode for the SC pulses, HgCdTe photodiode for the output pulses. The output signals are simultaneously recorded at 100 kHz (synchronous acquisition) and the resulting data are stored and processed. For 100 successive shots, recorded data show an output pulse-to-pulse energy variations of ~1. 3% rms, while the energy variations of the pump pulses are ~0. 9% rms. Remarkably, the energy stability improves from DFG (~4. 7% rms) to OPA3 (~1. 3% rms) indicating the increasing level of saturation within the successive nonlinear stages.
Equal amounts of pure tubulin and MTC (10 µmol. L -1 ) were mixed in a phosphate-based buffer (10 mmol. L -1 NaPi, 0. 1 mmol. L -1 GTP). After addition of increasing concentrations of PP-13, the displacement of MTC binding to tubulin was measured by fluorescence changes using Fluoromax-2 (Jobin-Yvon SPEX, Longjumeau, France) (excitation wavelength: 350 nm, emission spectra: 372-525 nm). Tubulin and MTC alone served as negative controls for fluorescence normalization. Assuming 0. 8 MTC sites per tubulin heterodimer and 4. 7 × 10 5 M -1 as the MTC binding constant  36  , the PP-13 binding affinity constant was evaluated using the Equigra v5 program  25.
Acknowledgements This work was supported by the  Institut Curie , the  CNRS , the INSERM, and by grants from  La Fondation de France ,  La Ligue contre le Cancer (comité de l'Isère) ,  Cancéropôle CLARA (Lyon Auvergne Rhône-Alpes) , and  BFU2016-75319-R (AEI/FEDER ,  UE)  from  Ministerio de Economia y Competitividad (JFD). The authors acknowledge networking contribution by the  COST Action CM1407  "Challenging organic syntheses inspired by nature -from natural products chemistry to drug discovery". We kindly thank  Dr. D. Gerlich  and  Pr A. Musacchio  for generously providing HeLa cells expressing mEGFP-α-tubulin and mRFP-H2B and HeLa cells expressing GFP-BubR1 respectively. We also acknowledge with much gratitude  Lauralie Perrone  for help with IC 50 ,  Francisco de Asis Balaguer  for help with TEM studies and  Dr I.
Inherent in clinical trials are four principal sources of variation: (1) sampling of trial participants from a relevant population; (2) intra-subject (individual) variation in the effects of compared interventions; (3) betweensubject variation in these effects; and (4) random assignment of trial participants to treatments. Only patients who meet the study entry criteria and give informed consent are enrolled in clinical trials. This and many other reasons cause sampling of subjects from the population to be non-random. This cannot be mitigated by even the best statistical analysis of trial data and thus is not a subject of this study. However, non-random sampling represents a major obstruction to extrapolation of trial findings to the population and their reproducibility. Variation types 2, 3 and 4 will be addressed in subsequent sections.
Classification of ELM types Dataset. A dataset comprising 74 type I and 26 type III ELMy plasmas spanning over the shot range [50564-76483] was assembled from the JET CW experiments. This is an extension of the data set used earlier by Webster et al. [1]  and is the same dataset that was used earlier for the visualization of the tokamak operational space in  [7]. The analysis, in this work, has been restricted to time intervals in which the plasma conditions are quasistationary with approximately constant heating, gas fueling and central density. Further, all experiments dealing with ELM control and mitigation techniques have been excluded. Predictive capability of each plasma parameter. LDA is performed on the plasmas represented by each global 115 plasma parameter individually. In this case, the estimated covariance matrices coincide with the variances of the two classes and the discriminant function is reduced to a discriminating value (DV).
Human epidermal growth factor receptor 2-positive breast cancer is a subtype of interest regarding its outcome and the impressive impact of human epidermal growth factor receptor 2 targeted therapy. Constitutional variants may be involved in the aetiology of human epidermal growth factor receptor 2-positive breast cancer, and we propose a case-case study to test the hypothesis that single nucleotide polymorphisms may be associated with human epidermal growth factor receptor 2 status. A Genome-Wide Association Study was used in a cohort of 9836 patients from the SIGNAL/PHARE study (NCT00381901-RECF1098). The main goal was to identify variants specifically related to human epidermal growth factor receptor 2-positive breast cancer. A two-staged genotyping strategy was carried out to cover as large a proportion of the genome as possible.