dataesr/scientific-paragraphs-categorization huggingface dataset:
If necessary, the SLTs can also access the results online. All the data entered in EON are accessible during the inclusion period and after the end of the study to the clinical research assistant in charge of the follow-up of the study and to the three investigators in charge of the study and who are not involved in the data collection. Data extraction and analyses are allowed at two points of the study, mid-term, and the end of inclusion period. The final trial dataset that will be used for statistical analyses will be available to the three investigators in charge of this study.
Statistical considerations Estimation of samples size The sample size per training group was estimated on the basis of previous protocols and literature reviews  8 49  which show that the number of patients included in the protocols varies between 15 and 150 per group. Taking into account the data of previous studies and expected size effect, we decided to include 55 patients per group. Indeed, the size of each group was estimated to be 45, assuming a small effect of the intervention (Cohen's d=0. 40), with a repeated measures factor Time of assessment (pretraining, immediate post-training, long term post-training) and an independent measures factor of Group (MFG, HFG, REG) to reach a power of 0. 8 with an alpha at 0. 05. We estimated a 10% drop-out of participants. Thus, we estimated the inclusion of 50 patients per group.
The contributions of this paper are: • The introduction of new optimisation criteria -Total Cost of Ownership and Social Cost-Benefitinto the mathematical model. To the best of our knowledge, this is the first HSC model that includes the TCO of vehicles and the SCB in the optimisation phase. • The application of the methodology and model (presented in Sections 4 and 5) to the "Green H 2 in Hungary" project with three scenarios for Hungary that explore several factors (i. e. , demand volume and type, production technology type). The research framework of the study is presented in Fig. 1. The input block corresponds to all the databases, hypotheses and scenarios chosen by the project team. The integration of the mathematical model and the multiobjective optimisation approach constitutes the core of the approach.
Data collection A considerable amount of data was collected for this study. The territorial breakdown considered the internal regions for the discretisation, and a deterministic demand for each region is assumed. The data set includes information related to the hydrogen demand and technical, environmental, economic and risk data associated with each component of the HSC. Information on the purchase and maintenance expenditures for trucks and buses is also included. Some data was collected from recent data and publications (REKK, NECP, IEA, FCH JU, H2 Observatory  [52] , IRENA, etc. ) and from interviews with experts in the energy region and professors (researchers specialized in the energy field and sociologists); however, since the technology is evolving and the market is not yet well defined, there is a high degree of uncertainty on many inputs, the sensitivity of some of them will be investigated with a special focus on demand analysis.
For the case of FCHDTs, trucks above 12 tonnes for long-distance freight (international and national logistics) have been considered, and the data has been taken from the HU-TIMES model  [18]. Data for 4x2 tractor (+trailer) of 40 tonnes gross vehicle weight  [86]  have been used to calculate the TCO with an annual mileage of 110,000 km per FC HDV for Hungary (HU-TIMES model). Hydrogen demand for buses has also been included as currently in the HU-TIMES model. Both short-and long-distance buses have been taken into account, and an annual mileage of 60,000 km per bus is assumed (HU-TIMES model). The database and references can be found in Appendix A. The mobility demand values are indicated in Fig. 5 , as well as the projected demand for mobility from Hungary's National Strategy, which includes not only HDVs but also private cars, trains, ships.
Assumptions The study is based on the following assumptions: • renewable energy is directly used on-site because of grid saturation. This allows to allocate the CO 2 impact to each source; • inter-district transport is allowed, intra-district distribution is not taken into account; • a 10-day LH 2 safety stock is considered; • the safety risk index is calculated by the methodology proposed by Kim et al. ,  [35] ; • the number of plants is initialized at a null value when only electrolysis is allowed. In the scenarios including SMR, the H 2 plants that exist are supposed to be available to supply both industrial and mobility demand. A few plants have been identified based on the REKK database (Fig. 9 -base map); • the cost of migrating a current refuelling station to H 2 fuel is not considered; • the learning rate cost reductions due to the accumulated experience is considered as 2 % per period  [93] ; • For the TCO calculation, neither subventions, tax and road roll exemptions, nor public revenues are taken into account to mitigate the technology migration.
Social Cost-Benefit (tri-objective) The methodology for SCB analysis follows the guidelines developed in  [18, 26, 38, 86]. As previously mentioned, one of the main contributions of this paper is the inclusion of this function in the optimisation model and not as an additional post-optimal calculation. In order to adapt the methodology, it was necessary to clearly define the type of technology that could be replaced. Since the case study includes both industry and mobility, it was possible to adapt the methodology to the latter one by comparing current internal combustion buses and trucks using diesel and their equivalence in FCEBs and FCHDTs. For industry, a similar comparison was not possible so far because several industry types are aggregated in the available database. SCB t = ∑ v ( ΔTCO v,t + ΔCA v,t + ΔPD v,t ) (5) In Equation (  5 ), the first term corresponds to the net present value of the economic comparison where the Total Cost of Ownership includes the purchase prices, running cost, maintenance, AD Blue, insurance and road toll costs for a vehicle type v in period t.
[96] , where all but one objective are converted into constraints and only one objective is to be optimised. The AUGMECON code is available in the GAMS library (https://www. gams. com/modlib/libhtml/epscm. htm). By varying the numerical values of the upper and/or lower bounds, a Pareto front can be obtained. In this work, 21 grid points have been tested and the values for the last optimisation iteration have been selected as the preferred solution from the Pareto optimal options.
A total of 60 scenarios have been optimised to test the demand and technology sensitivity. The methodology has been applied to the case study of Hungary, considering a national production without imports. The scenario definition is of utmost importance and has a serious impact on the results. It can be concluded that it is preferred to have a mixed demand (industry and mobility) and a gradual introduction of FCVs and migration to electrolysis technology to ensure a smooth transition, especially in the first periods. The obtained configurations exhibit an average LCOH ranging from $3. 34 to $11. 15 per kg H 2. In a market including industrial and mobility demand (buses and trucks), green hydrogen can result in an LCOH of $6. 33 per kg H 2 in 2030 by using electrolysis. Taxes and surcharges for H 2 fuel will impact the final price at the refuelling station, and by using our current database, the H 2 price can be twice the LCOH.
The TCO gap is larger in the case of buses which can be attributed to the difference in the purchase prices based on the databases used. The inclusion of additional externalities and the reduction of the buses purchase price could help to decrease this gap. Finally, the results of maximisation of SCB and those of multiobjective optimisation were compared. It can be concluded that these approaches are complementary. The main difference is found in the degree of network centralisation and in the relative risk index with a higher risk for SCB maximisation. The multiobjective optimisation by using the ε-constraint method includes LCOH, GWP, risk and SCB, resulting in a trade-off solution after several calculation hours. Solutions for SCB were found in a few seconds, with trade-off solutions for LCOH and GWP implicitly minimising the TCO.
Colocalization analyses demonstrate tissue and cell-specific differential effects of RBD-associated variants We further performed colocalization analyses to determine whether risk variants for RBD are also associated with gene expression in the whole brain or blood. Expression quantitative trait loci (eQTLs) were obtained from PsychENCODE 18 and eQTLGen  19  , large human brain and blood datasets, respectively. In brain, we found strong evidence for colocalization in the SNCA locus with SNCAantisense-1 (SNCA-AS1) expression (posterior probability of hypothesis 4, PPH4 = 0. 89; Fig. 2a  and Supplementary Data 2). SNPs in the region surrounding SNCA-AS1 tended to show an inverse relationship between RBD risk and SNCA-AS1 expression, suggesting that reduced RBD risk is associated with increased SNCA-AS1 expression (Supplementary Fig.
Plot of SNCA-AS1 and MMRN1 specificity in c 35 human tissues (GTEx dataset) and d 7 broad categories of cell type derived from human middle temporal gyrus (AIBS dataset). Specificity represents the proportion of a gene's total expression attributable to one cell type/tissue, with a value of 0 meaning a gene is not expressed in that cell type/tissue and a value of 1 meaning that a gene is only expressed in that cell type/tissue. In c tissues are colored by whether they belong to the brain. In c and d, tissues and cell types have been ordered by specificity from high to low. All data was extracted from the GTEx online portal (https://www. gtexportal. org/). The effect sizes represent the slope of linear regression on normalized gene expression data versus the genotype status using single-tissue eQTL analysis, performed by the GTEx consortium.
Third, due to data protection in 23andMe, we could not examine for overlap of samples between the iRBD cohort and the PD+pRBD cohort. However, since less than 15% of the iRBD cohort has converted to PD, and since it is highly unlikely that all of them participated in 23andMe, if there is any overlap it is most likely minimal. Finally, this study only includes participants of European ancestry. Future studies in other populations are vital to characterize RBD genetic risk across all ancestries. The results of the current study suggest that the genetic background of RBD, PD and DLB only partially overlap, and larger RBD studies will be required to better elucidate the genetic background of RBD. The present study also suggests that the lysosomal pathway, and more specifically the GBA pathway, could be a crucial target for therapeutic development targeting RBD and aimed to prevent neurodegeneration in this population.
iRBD cases were collected by the International Principal components to adjust for population substructure were used as mentioned below. The cases were aged 68 +/-9 years (standard deviation) on average and were 81% male, and the controls were aged 58. 5 +/-9 years on average, 68% male. 23andMe cases and controls were age-and sex-matched, with 83% over 60 years of age and 64. 5% male. In polygenic risk score (PRS) analyses, we used an independent replication cohort of PD +/-p RBD from McGill (N cases = 502, N controls = 907, average age 61 +/-8 years and 62% male) and 604 PD samples with recorded age at onset (AAO), including the 502 samples with pRBD data (average AAO 58 +/-10 years, 63% male). All cohorts used were confirmed European ancestry with principal component analysis using HapMap 3 as the reference population.
Finally, simulated admixed individuals are used to recalibrate the HMM probabilities so that the reported assignments are consistent with the simulated admixture proportions. GWAS was computed using logistic regression, assuming additive allele effects, adjusted for age, sex, genotype platform, and top 5 principal components (PCs). Imputed dosages were considered for imputed SNPs rather than best-guess genotypes. p-values were computed using a likelihood ratio test and adjusted for LD-score regression intercept to account for sample size mismatch between cases and controls. All other cohorts. The iRBD cases and controls were genotyped on the OmniExpress GWAS chips (Illumina Inc. ). We performed quality control according to a standardized GWAS pipeline (https:// github. com/neurogenetics/GWAS-pipeline).
At the sample level, individuals were screened for high or low heterozygosity (-0. 15 < = F < = 0. 15 for inclusion), call rate (>95%), genetic sex matching reported data (0. 25 < F < 0. 75) using plink 1. 9. Ancestry outliers were detected using HapMap3 PCA data in R version 4. 0. 1. Samples identified with Asian, African, or mixed-race ancestry (N = 139) were removed. We used gcta64 to check for relatedness with other samples closer than cousin (pihat > 0. 125). SNPs were filtered based on variant missingness (<0. 05), disparate missingness between cases and controls (p > 1E-04), missingness by haplotype (p > 1E-04), and deviation from Hardy-Weinberg equilibrium in controls (p > 1E-04) using plink 1. 9. To merge HYPERGENES, Wellcome Trust, and LNG controls with the McGill iRBD genotypes, we performed quality control on each cohort separately, then merged the cohorts using only variants common in all datasets.
Additionally, we used unadjusted linear regression to test whether polygenic risk for RBD is associated with RBD age at onset (AAO) or rate of conversion (AAO or rate of conversion ~PRS), and Kaplan-Meier survival analysis to test whether groups divided into quartiles based on PRS convert significantly faster or slower than others. PRS code can be found on https://github. com/lynnekrohn/RBD_GWAS/blob/main/1_PRS. md.
Pathway analysis Pathway analysis was performed using functional enrichment analysis, specifically gene-set enrichment, using the publicly available online tool WebGestalt (http://www. webgestalt. org/)  68. The gene-set enrichment analysis examines the enrichment of a provided set of genes in predetermined lists of genes involved in various functional pathways, detailed in Supplementary Data 3. An enrichment score is calculated, representing the level to which these genes are over-expressed in the various pathways, and then statistical significance is calculated using permutation testing. RBD genes included were those closest to the most significant GWAS SNP at the GWAS significance level, a single gene from each locus, as including multiple genes from the same locus may lead to false enrichment.
Colocalization Coloc (version 4. 0. 1; https://github. com/chr1swallace/coloc)  69 was used to evaluate the probability of RBD loci and expression quantitative trait loci (eQTLs) sharing a single causal variant. Cis-eQTLs were derived from eQTLGen (accessed 19/02/2020; https://www. eqtlgen. org/cis-eqtls. html)  70  and PsychENCODE (accessed 20/02/2020; http://resource. psychencode. org/)  71 , which represent the largest human blood and brain expression datasets, respectively (eQTLGen, N = 31,684 individuals; PyschENCODE, N = 1387 individuals). We additionally investigated eQTLs from individual CNS cell types (astrocytes, endothelial, microglia, oligodendrocytes and precursors, and pericytes) using eQTLs generated by Bryois et al. (https://malhotralab. shinyapps. io/brain_cell_type_eqtl/)  22.
Cell-type and tissue specificity measures Specificity represents the proportion of a gene's total expression attributable to one cell type/tissue, with a value of 0 meaning a gene is not expressed in that cell type/tissue and a value of 1 meaning that a gene is only expressed in that cell type/tissue. To determine specificity of a gene to a tissue or cell-type, specificity values from two independent gene expression datasets were generated, as described in Chia et al. 8. Briefly, these datasets included (1) bulk-tissue RNAsequencing of 53 human tissues from the Genotype-Tissue Expression consortium (GTEx; version 8) 20 and (2) human single-nucleus RNAsequencing of the middle temporal gyrus from the Allen Institute for Brain Science (AIBS; https://portal. brain-map. org/atlases-and-data/ rnaseq/human-mtg-smart-seq)  21.
Specificity values for GTEx were generated using modified code from a previous publication (https:// github. com/jbryois/scRNA_disease)  73 , and modified to reduce redundancy among brain regions and to include protein-and non-proteincoding genes. Expression of tissues was averaged by organ (except in the case of brain). Thus, specificity values were generated for a total of 35 tissues. Specificity values for the AIBS-derived dataset were generated using gene-level exonic reads and the "generate. celltype. data" function of the EWCE package (https://github. com/NathanSkene/ EWCE)  74. Specificity values for both datasets and the code used to generate these values are openly available at: https://github. com/ RHReynolds/MarkerGenes.
Acknowledgements HYPERGENES Project: https://cordis. europa. eu/project/rcn/86758_en. html Wellcome Trust Case Control Consortium: https://www. wtccc. org. uk/. We thank the members of the  NINDS Neurodegenerative Diseases Research Unit  and the  NIA Laboratory of Neurogenetic  for their collegial support and technical assistance. We would like to thank the research participants and employees of 23andMe for making this work possible This work was financially supported by the  Michael J. Fox Foundation  ( MJFF-020700: Z. G-O. ) , Fonds de recherche du Québec  (Doctoral Training Award: L. K. ), and  Parkinson Canada  (Pilot Project Grant: L. K. ). It was additionally supported by the  Canadian Consortium on Neurodegeneration in Aging (CCNA) , and the  Canada First Research Excellence Fund (CFREF)  awarded to  McGill University  for the  Healthy Brains for Healthy Lives (HBHL) program , and was supported in part by the  Intramural Research Program  of the  National Institute on Aging (NIA) ,  National Institutes of Health, Department of Health and Human Services  (project number  ZO1 AG000535 ), as well as the  National Institute of Neurological Disorders and Stroke.
Data availability The iRBD summary statistics are publicly available on GWAS catalog (https://www. ebi. ac. uk/gwas/, study accession GCST90204200). The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Please visit https://research. 23andme. com/collaborate/# dataset-access/ for more information and to apply to access the data. The GWAS summary statistics for traits analyzed for genetic correlation with RBD can be found on LDHub (http://ldsc. broadinstitute. org/ ldhub/) or GWAS Catalog (https://www. ebi. ac. uk/gwas/). The quantitative trait loci data used for fine-mapping can be accessed on: eQTLGen https://www. eqtlgen. org/cis-eqtls.
Code availability The code for all analyses are publicly available on https://github. com/ lynnekrohn/RBD_GWAS/releases/tag/October2022 77 except for colocalization analyses, which can be found on https://github. com/ RHReynolds/RBD-GWAS-analysis/, including adaptations of previously published code (https://github. com/jbryois/scRNA_disease and https://github. com/NathanSkene/EWCE).
During this study, we tested the co-selection hypothesis at different genomic locations in an exceptionally large and comprehensive database and identified the most favored and the most disadvantaged combinations of AR and VF. It is an innovative work because it generates knowledge that allows the study of evolutionary forces and those driving the co-mobilization of every AR and VF type or class, in bacterial genomes, on a very large scale. We consider the bacterial genome as two types of molecular replicon: the chromosome and the plasmids. Most of the housekeeping genes are chromosomally encoded. Plasmids, however, are especially important mobile genetic elements, part of the accessory genome responsible for local adaptation. They can encode for supplementary biochemical pathways, which allow the expression of the different phenotypes and lifestyles, adaptative traits enabling cells to respond to local competitive or environmental pressures, such as exposure to antibiotics, bacteriocins, heavy metals, or other xenobiotics.
Results To try to capture evolutionary patterns on an overly broad scale, we retrieved all 16,632 complete closed bacterial genomes from the NCBI RefSeq database and classified all their replicons as chromosomal or plasmid. An additional genomic category was considered that included genes that have copies in the chromosome and in a plasmid of the same genome, and which could potentially be ICE. Each gene can only belong to one of these three categories-chromosome, plasmid, or 'both'-as every gene that appears on both chromosome and plasmid is only considered in the category 'both'. Note that a single cell may harbor more than one plasmid; therefore, even if two genes co-localize in plasmids, they may localize in different plasmids.
Genomic Organization of Antibiotic Resistance-Encoding Genes in Bacterial Genomes To assess the identification and localization of antibiotic resistance-encoding genes within the bacterial genomes, we identified and classified all proteins from the dataset. To do so, we aligned all these protein sequences against antibiotic resistance databases: ResFinder, which consists of 3160 genes of acquired antibiotic resistance organized into 17 categories, including disinfectant resistance  [24] , and MUSTARD, which is a catalogue of 3. 9 million proteins from the human gut microbiome organized into 41 categories  [25]. For each genome, we identified all orthologs of each antibiotic resistance category, in each replicon type: chromosome or plasmid. We then counted the number of AR orthologues of each category that are located only in the chromosome, only in their plasmids, or simultaneously in both replicons.
We also found large amounts of disinfectant-resistant genes. Of note, no fosfomycin, nitroimidazole, pseudomonicacid, or glycopeptide orthologues were found in the 'both' location. Interestingly, most orthologues for disinfectant resistance also appear to be preferentially encoded in the bacterial mobilome (here represented by the plasmids and ICE). Figure  1b  represents the relative number of AR orthologues per genomic location. c, d ) data from the MUSTARD database. (a) For the ResFinder orthologues, the highest amount of antibiotic resistance orthologues is in the 'both' location (yellow). Beta-lactamase, sulphonamides, and disinfectant orthologues are more often (more than 50%) located in both replicons than in only a plasmid or the chromosome. (b) For the ResFinder orthologues, weighted bar plot shows that there are no fosfomicin, nitroimidazole, pseudomonicacid, fusidic acid, or glycopeptide orthologues in the 'both' location (yellow).
Orthologues located in chromosomes (green) have a lower proportion compared to the other two genomic locations. (c) For the MUSTARD orthologues, there is a higher number of antibiotic resistance orthologues located simultaneously in both a plasmid and the chromosome ('both' location in yellow). There is a higher amount of blaa, rnam, and sul and orthologues. (d) For the MUSTARD database, there is a higher proportion of orthologues located in plasmids (blue). There is a lower proportion of orthologues located in 'both' a plasmid and the chromosome (yellow) than in a plasmid   c, d ) data from the MUSTARD database. (a) For the ResFinder orthologues, the highest amount of antibiotic resistance orthologues is in the 'both' location (yellow). Beta-lactamase, sulphonamides, and disinfectant orthologues are more often (more than 50%) located in both replicons than in only a plasmid or the chromosome.