dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall, DNA methylation that could be predicted well in FHS also tended to be predicted well in WHI (a correlation coefficient of 0. 96 for R 2 in two datasets; Supplementary Fig. 1 ). These 77,243 CpG sites were selected for analyses for their associations between predicted DNA methylation and PrCa risk. Associations of genetically predicted methylation with PrCa. Of the 77,243 CpG sites tested, genetically predicted DNA methylation of 759 located at 82 genomic loci were associated with PrCa risk after Bonferroni correction (P ≤ 6. 47 × 10 -7 ) (Table  1 ; Supplementary Table  1  and Supplementary Data 1; Manhattan plot in Fig. 1 ). This included 15 located at 10 genomic loci that were more than 500 kb away from any PrCa risk variant identified in GWAS or fine-mapping studies (Table  1 ).
2 ). This suggests that the identified CpG sites associated with PrCa risk may be enriched in enhancers and may be involved in transcriptional activation. We also observed significant enrichment for the associated CpG sites with positions of genes encoding transcription factors (P = 0. 001). For the identified 759 CpG sites showing an association in the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia, we further evaluated their associations using independent UK Biobank data. In this analysis with far fewer PrCa cases, 554 CpG sites (73%) also showed an association at P < 0. 05 with the same direction of effect (Supplementary Data 2). These suggested that the CpG-PrCa risk associations identified in the main analyses using data of the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia were quite robust.
We performed downstream analyses focusing on these 759 CpG sites. Potential target genes of the PrCa-associated CpG sites. Of the 759 PrCa-associated CpG sites, association analyses were performed for 689 pairs of CpG site-gene, including 613 CpG sites with 244 flanking genes. Overall, associations at a false discovery rate (FDR) < 0. 05 were observed for methylation levels of 42 CpG sites with expression of 28 neighbor genes in blood tissue (Supplementary Table  3 ). Interestingly, we also observed several associations between DNA methylation and expression of genes encoding transcription factors at P < 0. 05 (Supplementary Table  4 ). In the TCGA dataset of tumor-adjacent normal prostate tissue, albeit with a quite limited sample size (n = 34), we observed that 26 of the 37 associations that could be assessed showed the same direction of effect compared with that in the blood tissue (Supplementary Table  5 ).
Among them, 11 showed statistical significance at P < 0. 05 in this small dataset (Supplementary Table  5 ). Associations of potential target genes with PrCa risk. Of the 28 potential target genes of the identified CpG sites based on blood tissue analyses, blood tissue gene expression prediction models were built for 22 genes, and prostate tissue prediction models were built for 14 genes with a prediction performance (R 2 ) of at least 0. 01 (≥10% correlation). Using the S-PrediXcan method, we evaluated associations between the genetically predicted expression of these genes and PrCa risk. Of the 22 genes with blood tissue prediction models built, 18 demonstrated an association at FDR < 0. 05 (Table  2 ). For 12 of them with prostate tissue prediction models built as well, nine showed an association at P < 0.
The statistical power for the model building using TCGA data could also be low due to the relatively small sample size available. In this study, for assessing DNA methylation-gene expression associations to determine potential target genes of identified CpG sites, besides using data from blood tissue (Supplementary Table  3 ), we also leveraged data from tumor-adjacent normal prostate tissue in TCGA. Despite a small sample size, we observed evidence supporting many of the associations identified using blood tissue data (Supplementary Table  5 ). For evaluating predicted gene expression-PrCa risk associations, our analyses using prostate tissue gene expression prediction models also support many of the associations identified using blood tissue prediction models (Table  2 ). In the current work, a large number of subjects (N = 1595) in the reference FHS dataset was used for the DNA methylation prediction model building.
Methods Study design. The overall study design is shown in Fig. 2. First, we built comprehensive genetic prediction models for DNA methylation levels by using data of the Framingham Heart Study (FHS). After external validation, we selected methylation models with satisfactory prediction performance for association analyses of genetically predicted methylation levels with PrCa risk, by using data of the PRACTICAL consortia which involves 79,194 cases and 61,112 controls. For CpG sites showing an association with PrCa risk, we assessed associations of their methylation with expression of adjacent genes (FHS, N = 1367), to identify potential target genes of these CpG sites. For the suggested candidate target genes, we further assessed associations of their genetically predicted expression with PrCa risk.
Building of DNA methylation prediction models. We obtained the individual level genome-wide genotyping and white blood cell DNA methylation data from the FHS Offspring Cohort (dbGaP accession numbers: phs000342 and phs000724). The details of the FHS Offspring Cohort have been described elsewhere  36. In brief, DNA was genotyped using the Affymetrix 500 K array, and DNA methylation was profiled using the Illumina HumanMethylation450 BeadChip. The genotype data were imputed to the Haplotype Reference Consortium reference panel  37. SNPs with high imputation quality (R 2 ≥ 0. 8), minor allele frequency ≥0. 05, included in the HapMap Phase 2 version, and those that were not strand ambiguous were used to build DNA methylation prediction models. For DNA methylation data, the "minfi" package  38  was used to filter out low-quality samples, exclude low-quality methylation probes, estimate cell-type composition, and calculate methylation beta values.
Tenfold cross-validation was used for internal validation. Prediction R 2 values, the square of the correlation between predicted and measured methylation levels, were used to estimate the model prediction performance. External validation of the models. To further evaluate the validity of the built methylation prediction models, we performed external validation using data from 883 unrelated healthy female participants of European descent included in The Women's Health Initiative (WHI) (dbGaP accession numbers: phs000315, phs000675, and phs001335). Genotype data and white blood cell DNA methylation data were processed using a similar approach, as described above. The predicted DNA methylation for each CpG site was calculated using the models that were established using FHS data, and then compared with the measured level using Spearman's correlation.
Associations between predicted methylation and PrCa. Considering that our model external validation dataset WHI included females only, and that there is a high concordance of the model performance (R 2 ) in FHS and WHI, we included DNA methylation prediction models (1) with a R 2 ≥ 0. 01 (≥10% correlation between predicted and measured methylation levels) in FHS, a standard criterion used in TWAS for gene expression  27, 39, [42] [43] [44]  , heritability of which tends to be similar to that of DNA methylation in blood  31, 45  , and (2) for probes with no SNPs within the probe-binding site, considering that the measurement of DNA methylation levels for such probes tends to be unbiased  46. Overall, we evaluated associations between genetically predicted methylation levels of 77,243 CpG sites with PrCa risk.
Evaluate associations of genetically predicted methylation levels with prostate cancer (PrCa) risk using iCOGS-Oncoarray-GWAS meta results PrCa associated CpG sites: assess associations with expression of nearby genes in FHS We estimated the association between genetically predicted DNA methylation levels and PrCa risk using S-PrediXcan, which has been described elsewhere  47  (Supplementary Software 1). We used the summary statistics data for the association of genetic variants with PrCa risk that had been generated from 79,194 PrCa cases and 61,112 controls of European ancestry in the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia  26, 48. In brief, 46,939 PrCa cases and 27,910 controls were genotyped using OncoArray, which included 570,000 SNPs (http:// epi. grants. cancer. gov/oncoarray/).
Also included were data from several previous PrCa GWAS of European ancestry: UK stage 1 and stage 2, CaPS 1 and CaPS 2, BPC3, NCI PEGASUS, and iCOGS. These genotype data were imputed using the June 2014 release of the 1000 Genomes Project data as reference. Logistic regression summary statistics were then meta-analyzed using an inverse variance fixed effect approach. A Bonferroni-corrected threshold of P < 6. 47 × 10 -7 (0. 05/77,243) was used to determine a statistically significant association. For CpG sites showing a significant association between genetically predicted methylation levels with PrCa risk, we further evaluated whether the observed associations were independent of nearby PrCa risk variants identified in GWAS or fine-mapping studies, by performing GCTA-COJO analysis  49. For this analysis, the risk SNP showing the most significant association with PrCa risk in the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia was adjusted for calculating association betas and standard errors of DNA methylation predicting SNPs with PrCa risk.
These association statistics were then used for re-running the S-PrediXcan analyses. Familial relative risk of PrCa explained by novel CpG sites. For PrCa-associated CpG sites that were located at novel loci or independent from known PrCa risk variants, we used the linkage disequilibrium (LD) score regression method  50  to evaluate the proportion of familial relative risk of PrCa that could be explained by predicted methylation levels of these CpG sites. In brief, we firstly applied the prediction models of these CpGs to the genetic data of male controls included in the pancreatic cancer GWAS data (N = 3655) to generate the predicted methylation of these CpGs for each of the participants. Detailed information for this dataset, quality control, and imputation has been described elsewhere  51.
We further used the formula Z 2 = 1 + (N T l/M)/h 2 to estimate the heritability explained by these CpG sites. Here for each CpG, Z represents the Z score of the association between the predicted methylation and PrCa risk; N T represents the number of individuals included in the GWAS of the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia, namely, 140,306; l represents the LD score of the CpG of interest; M represents the number of CpG sites that were significantly associated with PrCa risk; and h 2 is the estimated heritability of PrCa risk that could be explained by the predicted methylation of the CpG sites of interest. The LD score for each CpG was estimated by adding up the squared Pearson correlation coefficient (R 2 ) of the CpG of interest with all the other CpG sites. Finally, after fitting a linear regression model using data of all these CpGs, the estimated heritability of PrCa risk that could be explained by the predicted methylation of the CpGs of interest, along with the standard error and P value, were estimated.
Given that the heritability of PrCa was estimated to be 57%  52  , the familial relative risk of PrCa that could be explained by predicted methylation levels of these CpGs was calculated as ℎ 2 /0. 57. Validation of identified CpG sites using the UK Biobank. Individual level data of the UK Biobank were used to validate the identified associated CpG sites. The UK Biobank released GWAS data on ~500,000 individuals  53. PrCa cases were determined by combining Hospital Episode Statistics (HES) data and self-reported data. Specifically, cases were defined as hospital admission, type of cancer, or cause of death due to ICD-9 185. 9 or ICD-10 C61 or a self-reported cancer code. We calculated associations of genetically predicted DNA methylation of the identified CpG sites with PrCa risk, adjusting for age, age 2 , and top 20 PCs provided by the UK Biobank.
We also evaluated whether the associated CpG sites were enriched in loci of genes encoding transcription factors  59. Determine genes associated with identified CpG sites. For CpG sites with genetically predicted DNA methylation levels significantly associated with PrCa risk, we evaluated associations between methylation and expression levels of genes flanking their loci by using data from the FHS Offspring Cohort (dbGaP accession numbers: phs000363 and phs000724) and The Cancer Genome Atlas (TCGA). Details of the FHS Offspring Cohort, DNA methylation, and gene expression data have been described elsewhere  36, 60, 61. Overall, DNA methylation and gene expression data were available for 1367 unrelated individuals. For the CpG sites showing a significant association with PrCa risk, associations between the normalized methylation levels in beta values and normalized expression levels of genes flanking the CpG sites were estimated, after adjusting for age, sex, top PCs, and estimated cell-type compositions based on methylation data.
We further assessed significant methylation-gene expression associations identified in blood tissue analyses in adjacent normal prostate tissue of PrCa patients in the TCGA (N = 34). The processing of DNA methylation and gene expression data has been described elsewhere  62, 63. Associations of potential target genes with PrCa risk. For genes whose expression levels were associated with DNA methylation levels, we assessed whether the genetically predicted expression levels of these genes in blood and prostate tissue were also associated with PrCa risk  44, 64, 65. We used prediction models developed using the PrediXcan method (Elastic Net) and leveraging data from the v8 version of the Genotype-Tissue Expression dataset (GTEx) project (http:// predictdb. org/). Details of the methods of building gene expression prediction models using SNPs have been described elsewhere  44, 47, 66.
Acknowledgements The authors thank  Wanqing Wen  of the  Vanderbilt University School of Medicine  for his help with this study. The authors also would like to thank all of the individuals for their participation in the parent studies and all the researchers, clinicians, technicians and administrative staff for their contribution to the studies. This study used resources at the  Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University, Nashville , TN ( NIH   S10 Shared Instrumentation Grant   1S10OD023680-01  (Meiler). A full description of funding and acknowledgments for the PRACTICAL,  CRUK ,  BPC3 ,  CAPS , and PEGASUS consortia are included in the Supplementary Note. Lang Wu is supported by the  University of Hawaii Cancer Center Seed Grant. Yanfa Sun  is partially supported by the  Department of Education of Fujian Province ,  P R China.
Data availability The OncoArray genotype data and relevant covariate information (i. e. , ethnicity, country, principal components, etc. ) for prostate cancer study are available in dbGAP (Accession no. : phs001391. v1. p1). In total, 47 of the 52 OncoArray studies, encompassing ~90% of the individual samples, are available. The previous meta-analysis summary results and genotype data are currently available in dbGaP (Accession no. : phs001081. v1. p1). The datasets of FHS Offspring Cohort and WHI are publicly available via dbGaP (www. ncbi. nlm. nih. gov/gap): dbGaP Study Accession: phs000342 and phs000724 for FHS, and phs000315, phs000675, and phs001335 for WHI. TCGA data can be accessed through the Genomic Data Commons Data Portal.
At the international level, the main stakeholder involved is the Commission for the Conservation of Antarctic Marine Living Resources, which sets conservation measures that determine the use of marine living resources in the Antarctic, based on the best available scientific information. The French Government is in charge of managing the fishery. Stakeholders include scientists, environmental NGOs, and the fishing industry. Conservation objectives require minimizing depredation-type interactions by non-lethal measures, such as implementation of vessel avoidance strategies and/or development of catch protection systems  (Tixier et al. , 2015). Current coordination between stakeholders and sectors exists and is substantial. Several research programs are co-funded by the industry, in collaboration with the regional administration (Terres Australes et Antarctiques Francaises); depredation is taken into account in toothfish stock assessments; and the funding provided by the selling of fishing rights related to toothfish fishing are used, among other things, to support scientific activities and monitoring of authorized fisheries.
19 F and 7 Li Solid-State NMR Spectroscopy. 19  previously. [17]  The room temperature 19 F MAS spectra of the electrochemically prepared samples (charged and discharged electrodes) were recorded using a Hahn echo sequence with an interpulse delay equal to one rotor period. The 90° pulse length was set to 1. 25 μs, and the recycle delay was set to 10 or 20 s. The room temperature 7 Li MAS spectra of the charged and discharged electrodes were recorded using a single pulse sequence. The 90° pulse length was set to 2 μs, and the recycle delay was set to 10 s or 30 s. 19 F and 7 Li spectra are referenced to CFCl 3 and 1 M LiCl aqueous solution, respectively, and they were fitted by using the DMFit software. [46]  Electrochemistry: The composition of the electrode was 80 wt. % active materials, 10 wt.
3 V vs. Mg 2+ /Mg, and the temperature was set at 25 °C using a thermostatic chamber. The cells were assembled in an argon-filled glovebox, and the data was collected on SP-200 or BCS-805 (Bio-Logic Science Instruments). Galvanostatic-intermittent-titration-technique was applied during the first discharge. A constantcurrent discharge pulse at the current density of 10 mA g -1 was applied to the cell for 1 h, followed by a relaxation period for 10 h. This protocol was repeated until the cell potential reached +0. 05 V vs. Mg 2+ /Mg. Computational: We performed our density functional theory calculations using the code VASP  [47, 48] , with valence electrons described by a plane-wave basis with a cutoff of 500 eV. Interactions between core and valence electrons were described using the projector augmented wave (PAW) method  [49]  models with all fluoride ions occupying anion sites adjacent to the titanium vacancies, which we have previously shown minimizes the DFT-calculated energy.
[17, 26, 32]  Individual calculations were deemed optimized when all atomic forces were smaller than 0. 01 eV Å -1. All calculations were spin polarized, and used a 2 × 2 × 2 Monkhorst-Pack grid for sampling k-space. To calculate intercalation energies, reference calculations for metallic Li and metallic Mg were performed using the same convergence criteria as above. For both metals we considered 2-atom cells, and used a 16 × 16 × 16 Monkhorst-Pack grid for Li and a 18 × 18 × 18 Monkhorst-Pack grid for Mg for k-space sampling. A data set containing all DFT calculation inputs and outputs is available at the University of Bath Data Archive, published under the CC-BY-4. 0 license. [39]  The dataset also includes analysis scripts, published under the MIT license, used to post-process the DFT data and to generate Fig.
Acknowledgements The research leading to these results has received funding from the  French National Research Agency  under Idex@ Sorbonne University  for the  Future Investments program  (No. ANR-11-IDEX-0004-02 ) and " Sino erman TU9 network for electromobility"  under the grant reference number  16N11929. The work done at the  Advanced Photon Source , an  Office of Science User Facility  operated for the  U. S. Department of Energy (DOE) Office of Science by Argonne National Laboratory , was supported by the  U. S. DOE  under Contract No. DE-AC02-06CH11357. B. J. M. acknowledges support from the  Royal Society  (Grant No. UF130329 ), and the  Faraday Institution  (faraday. ac. uk; EP/S003053/1 , grant number  FIRG003 ). Calculations were performed using the  Balena High Performance Computing Service at the University of Bath , and the  ARCHER supercomputer, through membership of the UK's HPC Materials Chemistry Consortium , funded by  EPSRC  grant  EP/L000202.
Shielding benchmarks were extensively used for the validation and improvement of nuclear data since many years. Recent evaluations however mostly rely on the validation using critical benchmarks, which can introduce biases and compensation effects. A new Working Party on International Nuclear Data Evaluation Co-operation Subgroup 47 (WPEC SG47) entitled "Use of Shielding Integral Benchmark Archive and Database for Nuclear Data Validation" was started in June 2019 with the objective to promote further development and use of Shielding Integral Benchmark Archive and Database (SINBAD) and thus contribute to the diversification of the nuclear data validation practice by including more extensively other types of integral measurements, such as shielding benchmarks, in the validation and evaluation procedure.
Introduction The Shielding Integral Benchmark Archive and Database (SINBAD) project  [1] [2] [3] [4] [5] [6]  started in the early 1990's as a collaboration between the Organization for Economic Cooperation and Development's Nuclear Energy Agency Data Bank (OECD/NEADB) and the Radiation Safety Information Computational Center (RSICC) at the Oak Ridge National Laboratory (ORNL) with the goal to preserve the information on the performed radiation shielding benchmarks and make them available in a standardised form to the international community. The latest version of SINBAD comprises over 100 shielding benchmarks, divided into three categories, covering both low and inter-mediate energy particles applications: fission reactor shielding (48 benchmarks), fusion blanket neutronics (31), and accelerator shielding (23) benchmarks.
In addition to the characterization of the radiation source, description of shielding set-up, instrumentation and the relevant detectors, most sets in SINBAD contain also the deterministic or probabilistic (Monte Carlo) radiation transport computer model used for the interpretation of the experiment and, where available, results from uncertainty analysis. The set of primary documents used for the benchmark compilation and evaluation are provided in computer readable form. The database is intended for different users, including nuclear data evaluators, computer code developers, exper-.
WPEC SG47 Integral benchmark experiments have proven to be valuable for validation of nuclear cross-sections. In recent years, the evaluations have been predominantly validated against criticality benchmarks, and are 'tuned' to better match the measurements. However, the effective multiplication factor (k e f f ) is a very global parameter, dependent on many 'free' parameters. Besides criticality, there are other important concerns, of which shielding is a priority for many applications. Including shielding benchmarks in the validation process is expected to provide a complementary view and would allow broader evaluation of the performance of the nuclear data. This would ultimately contribute to a production of general-purpose cross-section evaluations. A new Working Party on International Nuclear Data Evaluation Co-operation Subgroup 47 (WPEC SG47) entitled "Use of Shielding Integral Benchmark Archive and Database for Nuclear Data Validation"  [7]  was started in June 2019 with the consider the above challenges and objectives, such as: • To provide feedback on the existing database and contribute in this way to the quality review, concentrating on the verification of the completeness and the consistency of the experimental information (on the geometry, material composition, the procedure to derive data-unfolding, etc.