dataesr/scientific-paragraphs-categorization huggingface dataset:
5 µl H 2 O, 3 µl CuSO 4 (0. 1 M), 0. 5 µl Alexa fluor 647-azide (Thermo Fisher Scientific), 10 µl of 100 mM freshly prepared vitamin C) for 30 min at room temperature in the dark. Cells were washed twice with 0. 5 ml PBS/1% BSA and resuspended in 0. 5 ml 1% BSA/PBS containing 0. 1 mg/ml RNase A and 2 µg/ml DAPI. After 30 min at 37 °C samples were analyzed with the Miltenyi Macs Quant analyzer and FlowJo software at the IGH-MRI facility (Montpellier, France). Single-nucleotide polymorphism comparative genomic hybridization. CNVs in sh-Ctrl, sh-CLSP, and sh-TIM cells were determined using Illumina Infinium technology, using BeadChips (HumanHap300 duo) containing 317000 SNP tags. The cnvPartition plug-in of the Illumina BeadStudio software was used to annotate CNVs and estimate copy number. DNA combing and DNA fiber spreading.
Immunodetection of γH2AX in S-phase was performed overnight at 4 °C using a specific antibody (1/100; 05-636, Millipore). Click-it chemistry was performed according to instructions. Images were acquired using an LSM780 Zeiss confocal microscope. Mean fluorescence intensity of γ-H2AX in EdU-positive cells was quantified with CellProfiler. Transcriptome analysis. RNA sequencing (RNA-seq) libraries were prepared using the Illumina TruSeq Stranded mRNA Library Prep Kit. Paired-end RNA-seq were performed with an Illumina NextSeq sequencing instrument (Helixio, France). RNA-seq read pairs were mapped to the reference human GRCh37 genome using the STAR aligner  78. Statistical analyses were performed with R (v3. 2. 3) and R packages developed by BioConductor project. The expression level of each gene was summarized and normalized using DESeq2 R/Bioconductor package  79.
Acknowledgments We thank  Vasilis Gorgoulis  ( University of Athens ) and  Thanos Halazonetis  ( University of Geneva ) for providing the anti-Claspin monoclonal antibody. We thanks  Benjamin Pardo  for discussions and critical comments on the manuscript. We thank  E. Schwob  and the  DNA combing facility of Montpellier  for providing silanized coverslips. We thank the Montpellier RIO Imaging microscopy and cell sorting facility for help with image analysis. We thank  J. -J. Maoret  ( GeT-TQ facility, Genopole Toulouse Midi-Pyrénées ) for help with qRT-PCR analyses. We thank  A. Monteil  and  C. Lemmers  from the Vectorology facility, PVM  Biocampus Montpellier ,  CNRS UMS3426. Work in the Pasero lab is funded by  ANR ,  INCa, MSDAvenir Fund, SIRIC Montpellier Cancer  (grant  INCa Inserm   DGOS 12553 ) and by  Ligue contre le Cancer  (équipe labellisée).
Work in J. S. H. laboratory is supported by funding from  INCa - PLBIO 2016 ,  ANR PRC 2016 ,  Labex Toucan and La Ligue contre le Cancer (Equipe labellisée). JB thanks the  Fondation ARC  and the  French Ministère de la Recherche et de l'Enseignement Supérieur  for support.
All cells were grown at 37 °C and 5% CO 2. HeLa (ATCC CCL-2), HeLaM (RRID:CVCL_R965), HEK293 (Cell Biolabs, LTV-100) and human dermal fibroblasts were grown in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% (vol/vol) fetal bovine serum (FBS), 1 mM L- glutamine and penicillin-streptomycin mix. Non-essential amino acid solution was added to human skin fibroblasts, while HeLa cells stably expressing ST6GALI-HRP (ST-HRP) and HeLa cells stably expressing GFP-GalNAc-T2 (Dr. Brian Storrie, University of Arkansas for Medical Sciences, Little Rock, AK) were supplemented with 1 mg/mL and 0. 5 mg/mL G418, respectively. hTERT-RPE-1 cells (ATCC) were grown in 1:1 mix of Ham's F12 and DMEM supplemented with 10% (vol/vol) FBS, 1 mM L-glutamine, penicillin-streptomycin mix and 10 µM hygromycin B.
MBP-tagged Scyl1 was then captured on the second channel only to a final level of 3000 RUs. Binding of GST-tagged GORAB variants to both channels at 30 nM concentration and a flow rate of 100 μL/min was allowed to occur for 120 s followed by 600 s disassociation, using the first channel as a reference. All binding sensorgrams were collected, processed and analyzed using the integrated ProteOn Manager software (Bio-Rad Laboratories). Liposome recruitment assay. All lipids were purchased from Avanti Polar Lipids. To make 3 mM final 'Golgi lipid' mixture in CHCl 3 , the following lipids were used: 43 mol% phosphatidylcholine from bovine liver, 19 mol% phosphatidylethanolamine from bovine liver, 5 mol% phosphatidylserine from bovine brain, 10 mol% phosphatidylinositol from bovine liver, 7 mol% sphingomyelin from bovine brain and 16 mol% cholesterol from wool grease.
Coverslips were dried before mounting in Mowiol 4-88 (0. 1 M Tris-Cl, pH 8. 5, 10% (wt/vol) Mowiol 4-88, 25% (wt/vol) glycerol). Prepared slides were analyzed using an Olympus BX60 upright microscope equipped with a MicroMax cooled, slow-scan CCD camera (Princeton Instruments) driven by Metaview software (University Imaging Corporation). Images were processed using ImageJ software (MacBiophotonics). Stimulated emission depletion (STED) microscopy. Cells were grown on precision glass coverslips (No. 1. 5H; Paul Marienfeld), fixed and stained as described above. Images were collected on a Leica TCS SP8 AOBS inverted gSTED microscope using a 100×/1. 40 Plan Apo objective. The confocal settings were as follows: pinhole 1 Airy unit, scan speed 400 Hz unidirectional and format 2048 × 2048. STED images were collected using hybrid detectors with the following detection mirror settings: Alexa 488: 498-542 nm; Alexa-549: 564-619 nm; Alexa-647:646-713 nm using the 490 nm, 555 nm and 635 nm excitation laser lines and 592 nm, 660 nm and 775 nm depletion laser lines, respectively.
FRAP was carried out at 37 °C using the FRAP imaging module of the Slidebook application (Intelligent Imaging Innovations). A 5 µm rectangular region of interest was defined and photobleached at a high laser power to result in >80% reduction in fluorescence intensity. Recovery was monitored by measuring fluorescence intensity at 3 s intervals for a total period of 3 min. FRAP recovery curves were analyzed using FRAPAnalyser software (http:// actinsim. uni. lu/; University of Luxembourg, Luxembourg). Metabolic labeling with alkyne-tagged sialic acid. ManNAl was synthesized according to optimized procedures  46. Cells were grown in DMEM supplemented with 10% FBS and 1 mM L-glutamine containing 500 µM of ManNAl for 10 h before fixation of cells with 4% (wt/vol) PFA. Cells were permeabilized in PBS with 0.
Images were acquired and then aligned and stitched using NIS Elements software (Nikon). These stitched images were then exported as a single TIFF image for further processing in Fiji software. The amount of intra-Golgi incorporated alkyne-tagged sialic acid was measured by comparing fluorescence intensity levels with reference to the Golgi marker TGN46. GORAB staining was employed to discriminate between WT and GO fibroblasts. Immunofluorescence-based lectin-binding assays. The following method was adapted from Willet et al. 73. Human dermal fibroblasts were grown on glass coverslips to 90% confluency. Cells were rinsed twice with pre-chilled PBS and incubated with it for 15 min in order to prevent endocytosis of glycosylated plasma membrane proteins. Next, cells were incubated in FITC-conjugated MAL or SNA lectin solution (20 µg/mL; Vector Laboratories) for 20 min in the cold room.
Propidium iodide was added to exclude non-viable cells from the flow cytometry analysis. Data were analyzed using Summit V4. 3 software (Beckman Coulter). Electron microscopy (EM). For morphological analysis, human skin fibroblasts were grown on glass coverslips and flat embedded. Serial thin sections (60 nm) were cut parallel to the coverslip and sections at approximately equal intervals were imaged with Jeol JEM-1400 microscope operated at 80 kV. Images were acquired with Gatan Orius SC 1000B camera. For pre-embedding immuno-EM, cells were fixed with PLP (periodate-lysine-paraformaldehyde) fixative for 2 h, permeabilized with 0. 01% saponin, labeled with anti-GORAB rabbit antibody followed by nanogold-conjugated anti-rabbit IgG F ab -fragments (Nanoprobes), post-fixed with 1% glutaraldehyde and quenched with 50 mM glycine.
The spectrometer was operated in positive reflectron mode by delayed extraction with an accelerating voltage of 20 kV and a pulse delay time of 200 ns and a grid voltage of 66%. All the spectra shown represent accumulated spectra obtained by 400-500 laser shots. Sample was prepared by mixing a 1 μL aliquot (5-10 pmol) with 1 μL of 10 mg/mL 2,5-dihydroxybenzoic acid (in 50% methanol). The assignment of glycan species for both human fibroblast and mouse skin samples was based on accurate m/z measurements, precisely matching to theoretical masses of the glycan species measured, taking into account the known ionization of these glycans, and on the basis of the well-accepted biosynthetic route for N-glycans  74, 75. Statistical analysis. Statistical analyses were conducted using GraphPad Prism software (GraphPad Software).
Acknowledgements T. M. W. was supported by a PhD studentship from the  Wellcome Trust  ( 096601/Z/11/Z ) and M. L. by an  MRC  research grant ( MR/N000366/1 ). We thank the  Wellcome Trust  for equipment grant support to the  EM Facility. Bioimaging Facility  microscopes used in this study were purchased with grants from  BBSRC ,  Wellcome  and the  University of Manchester Strategic Fund. Special thanks go to  Peter March  for his help with the light microscopy and  Mike Jackson  from the  Flow Cytometry Core Facility  for his help and advice. The  York Centre of Excellence in Mass Spectrometry  was created thanks to a major capital investment through  Science City York , supported by  Yorkshire Forward  with funds from the  Northern Way Initiative. U. K. received funding from the  EU (E-Rare  project  EURO-CDG-2 ) and from the  German Federal Ministry of Education and Research (BMBF)  (DIMEOs ( 1EC1402B )).
Published atomic data for He II In addition to the previously mentioned collisional excitation rate coefficients, a collisional-radiative model requires transition probabilities for spontaneous emission (Avalues) for each transition. These radiative decay rates for the intershell transitions are found in the NIST database  (Kramida et al. , 2015) , which incorporates the extensive listing of  Wiese and Fuhr (2009). Additional A-values for the fine-structure transitions within an n shell are given by  Aggarwal et al. (2017)  and  Jitrik and Bunge (2004). Aggarwal et al. adopt  the fully relativistic GRASP (General-purpose Relativistic Atomic Structure Package) code to generate the atomic structure, which gives wave functions, energy levels and A-values, while Jitrik and Bunge use a point-nucleus Dirac Hamiltonian model.
For He II, the available data are expected to be reliable, being more accurate than the collisional results and therefore not limiting the accuracy of the population model. For electron collisions, R-matrix calculations are expected to be more accurate than older methods and hence we focus on these. The earliest R-matrix calculation for He II is that of  Aggarwal et al. (1991) , while  Callaway (1994)  reviews the various calculations for H and hydrogen-like ions available up to the time of publication, the latter forming the basis of the 1997 dataset within the ADAS database  (Summers, 2004). Both  Aggarwal et al. and  Callaway present data for the so-called 'spectroscopic levels', n = 1-5. These data are lresolved, therefore including 15 levels, and are for the intershell transitions only.
(2017) , for which this assumption is unnecessary. This will highlight differences that a user of the atomic data will encounter. The most reliable calculations are expected for transitions between the n = 1 to 4 shells. Hence our comparison will focus on these, even if results are also given for transitions to n = 5. These calculations should provide data accurate to ~10-20% for the stronger transitions, and the compensation already noted between the different components when deriving a line intensity should ensure that the latter show a similar degree of reliability. However, what is very apparent in this comparison is the variation between the results of different calculations. The l-resolved, He II atomic data contained in the ADAS 97, ADF04 dataset (hlike_mom97#he1. dat) are based on the assessment given by  Callaway (1994)  of the calculations available at that time.
8, 2. 9 and 2. 6, respectively. These comparisons show systematic differences in that the effective collision strengths for the earlier calculations for transitions from the 2 S levels tend to be smaller than the later results, whereas those for transitions from other levels tend to be larger. Ballance et al. (2003) , whose full results are available in the ADAS database (ADF04 dataset hlike_cpb03#he1. dat), investigate the importance of continuum coupling using the R-matrix with pseudo-states method for several hydrogenic ions, again presenting l-resolved effective collision strengths. There is agreement for all transitions to within a factor of 2 and typically within 30%. However, again, there is a tendency for the effective collision strengths for transitions from the 2 S levels to be smaller than those of  Aggarwal et al.
(2017) , and those from other levels to be larger. Since the introduction of pseudo-states is expected, if anything, to lead to a decrease in the effective collision strength, the differences found cannot simply be due to the use of pseudo-states. The coupling to high bound and continuum states is expected to be greatest at higher temperatures for transitions with the largest energy separation and which end in states close to the ionization limit. Similar discrepancies are observed at all temperatures. The wide variation between different calculations is illustrated in figure  18a , where we plot ratios of the effective collision strengths from these three datasets to the results of  Aggarwal et al. (2017)  for transitions from the ground state, these being crucial for accurately modelling the populations of the excited levels.
Although they only have a small temperature range (0. 137-1. 72 eV), the results of  Kisielius et al. (1996)  are of interest as they are the only other calculations of effective collision strengths that are J-resolved. Over this limited temperature range good agreement (within ~10%) is found for many transitions with the results of  Aggarwal et al. (2017). The most significant exceptions are transitions to the 4d 2 D 5/2 and 4f 2 F 5/2 levels, and in two cases to 4f 2 F 7/2 , where the differences can be up to a factor of 3. 7. Figure  18b  illustrates this by showing the ratio of the effective collision strengths of  Kisielius et al. to those of Aggarwal et al. for transitions from the ground state. In the case of the line intensities, results using the calculations of  Ballance et al. (2003)  and the widely-employed ADAS 97 dataset are compared with those of  Aggarwal et al.
(2017). For the former datasets, line intensities are calculated assuming that states within an n shell are statistically populated, although the comparison is with line intensities derived from the calculations of  Aggarwal et al. (2017)  for which this assumption is not used. Heavy particle collisions are also included in the latter. Again, concentrating on transitions between the n = 1 to 4 shells, there are differences between the results of  Ballance et al. (2003)  and  Aggarwal et al. (2017)  of up to a factor of 2, with the former being smaller. Although not expected from a comparison of the effective collision strengths, the agreement of  Aggarwal et al. with the ADAS 97 data is somewhat better (within ~35%). Except for the Lyman-α, n = 1-2 transition, the ADAS 97 line intensities are again found to be smaller than those of  Aggarwal et al.
(2017)  data, whereas the differences are smaller (factors of up to 4) for the comparison with the Ballance et al. results. Of more importance for users are comparisons of the line intensities. At densities of 10 18 -10 20 m -3 , the ADAS 97 and Ballance et al. intensities differ by typically a factor of 2 from those of Aggarwal et al. The discrepancies among the various calculations of effective collision strength summarised above are discussed further and explained in detail by  Aggarwal et al. (2017). These authors also assess the accuracy of their results, which they estimate to be probably better than 20% for the majority of transitions, and 30% for the remainder. Hence the He II models presented here based on the Aggarwal et al. atomic data should be the most reliable.
It is noted, however, that all the calculations agree to within 10% for the intensity of the Lyman-α line and there is good compensation between the two components of this line when a statistical population is supposed. This is an important result given the contribution of this line (~90%) to the total He II line power, which is widely used to predict the power loss term in transport modelling. This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 under grant agreement No. 633053 and from the RCUK [grant number EP/P012450/1]. To obtain further information on the data and models underlying this paper please contact PublicationsManager@ukaea. uk. The views and opinions expressed herein do not necessarily reflect those of the European Commission.
Genome-wide association study identified prostate cancer risk variants explain only a relatively small fraction of its familial relative risk, and the genes responsible for many of these identified associations remain unknown. To discover novel prostate cancer genetic loci and possible causal genes at previously identified risk loci, we performed a transcriptome-wide association study in 79,194 cases and 61,112 controls of European ancestry. Using data from the Genotype-Tissue Expression Project, we established genetic models to predict gene expression across the transcriptome for both prostate models and cross-tissue models and evaluated model performance using two independent datasets. We identified significant associations for 137 genes at P < 2. 61×10 -6 , a Bonferroni-corrected threshold, including nine genes that remained significant at P < 2.
Because of their small effect size, these variants are difficult to identify in a typical GWAS, even with a very large sample size. Transcriptome-wide association studies (TWAS) can be used to systematically assess the association of genetically predicted gene expression levels with disease risk throughout the transcriptome, providing a powerful approach to identify novel disease risk genes and uncover possible causal genes at loci identified previously by GWAS  (17) (18) (19) (20) (21) (22) (23). Instead of evaluating each specific genetic variant as conducted in GWAS, TWAS uses gene-based approaches that aggregate the effects of multiple SNPs into one testing unit and thus may increase power for identifying novel disease risk loci. Because it is expensive and often infeasible to profile the transcriptome of the target tissue in a large number of cases and controls, reference datasets containing both genotyping and gene expression data are used to establish genetic predictors for gene expression, which are then used to impute gene expression levels for subjects with genotype information available in a typical GWAS for association analyses of predicted gene expression with disease risk  (18).
Building gene expression prediction models We used transcriptome and high-density genotyping data from the Genotype-Tissue Expression (GTEx) study to establish gene expression prediction models using SNPs  (25). In brief, genomic DNA samples obtained from study participants were genotyped using Illumina OMNI 2. 5M or 5M SNP Array, and RNA samples from 51 tissue sites were sequenced to generate transcriptome profiling data. We used genotyping and prostate tissue transcriptome data from 73 European descendants to build prostate tissue gene expression prediction models. The genetic ancestry of GTEx subjects was determined based on the first two principal components, with reference to populations in the 1000 Genomes Project. Considering that the regulatory mechanisms for a large proportion of genes are similar across most human tissues  (25) (26) (27) , to increase the statistical power of building models that aim to capture genetic effects on gene expression of normal prostate tissue, we also generated cross-tissue models using gene expression data generated in all tissues from 369 GTEx participants of European descent  (28).
Genotyping data were processed according to the GTEx protocol (http://www. gtexportal. org/home/documentationPage). Briefly, SNPs having a call rate < 98%, with differential missingness between the 5M and 2. 5M Array experiments, with Hardy-Weinberg equilibrium P-value < 10 -6 (among subjects of European ancestry), or showing batch effects were excluded; also one participant diagnosed with Klinefelter disease, one participant with trisomy 17 mosaicism, and three related individuals were excluded. The genotype data were imputed in our study to the Haplotype Reference Consortium reference panel(29) using Minimac3 for imputation and SHAPEIT for prephasing  (30, 31). SNPs with high imputation quality (RSQR ≥ 0. 8), minor allele frequency (MAF) ≥ 0. 05, those that were included in HapMap Phase 2 for CEU population, and those on autosomal chromosomes were retained for the construction of gene expression prediction models.
Further, adjustments were made for the top three principal components (PCs) derived from genotype data and the top 15 probabilistic estimation of expression residuals (PEER) factors  (33)  for prostate models, and the top three PCs, the top 35 PEER factors  (33) , and sex for cross-tissue models. The PEER analyses were used to further control for unmeasured determinant of gene expression variation, including batch effects  (33). In GTEx data, there are expression measurements in different tissues for each individual. A mixed effect model was used to decompose the expression level of a gene at a given tissue for individual i into a subject-specific cross-tissue component and a subject-by-tissuespecific component  (28) , as Y i, t = Y i CT + Z i ′β + ϵ i, t Here Y i CT represents the cross-tissue component, Z′ i represents a vector of covariates (e.
g. , PEER factors, genetic ancestry, and sex) that have effects of β on the expression levels of the gene, and the subject-by-tissue-specific component was estimated as the difference between the expression levels and cross-tissue components (Y i CT ) given the lack of replicated measurement for a specific tissue/subject pair. The mixed effect model parameters were estimated using the lme4 package in R. Posterior models of the subject level random intercepts were used as estimates of the cross-tissue components. The whole tissue gene expression data of 6,124 GTEx tissue samples from 369 unique European ancestry individuals with genotyping data available were used. Using both genotyping and gene expression data, an expression prediction model for each gene was built by applying the elastic net method as implemented in the glmnet R package, with α=0.
Evaluating performance of gene expression prediction models using Mayo Clinic and TCGA data To further assess the external validity of the models we built using GTEx data, we performed external validation experiment using Mayo Clinic dataset comprising genetic data and gene expression data of fresh frozen normal prostate tissue obtained from patients with either radical prostatectomy or cystoprostatectomy (N=471)  (8) , and TCGA dataset comprising genetic data and gene expression data of tumour-adjacent normal prostate tissue from European-ancestry prostate cancer patients (N=45). Genotype data were imputed using the 1000 genomes phase 3 data as reference. Gene expression data were processed and normalized using a similar approach as described above. The predicted expression level for each gene was calculated using the models established using GTEx data and then compared with the observed level of that gene using the Spearman's correlation.