dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Data. Participants in this study were obtained from multiple sources, including raw data from case-control samples collected by GR@ACE/DEGESCO, summary statistics data from the case-control samples in the IGAP and the summary statistics of AD-by-proxy phenotype from the UK Biobank. An additional case-control samples from 16 independent cohorts (19,087 AD cases and 39,101 controls) was used for replication, largely collected and analyzed by the European Alzheimer's Disease Biobank (JPND-EADB) project. Full descriptions of the samples and their respective phenotyping and genotyping procedures are provided in the Supplementary Methods. GR@ACE. The GR@ACE study  19  recruited AD patients from Fundació ACE, Institut Català de Neurociències Aplicades (Catalonia, Spain), and control individuals from three centers: Fundació ACE (Barcelona, Spain), Valme University Hospital (Seville, Spain), and the Spanish National DNA Bank-Carlos III (University of Salamanca, Spain) (http://www.
15. 0 software following the Axiom data analysis workflow. The sample quality was determined based on the resolution of AT and GC channels in a group of nonpolymorphic SNPs (resolution > 0. 82). Samples with a call rate greater than 97% and plates with an average call rate above 98. 5% were included for final SNP calling. The samples were jointly called. Markers passing all the QC tests were used in downstream analysis (N SNPs = 729,868; 95. 4%) using the SNPolisher R package (Thermo Fisher). To assess the sample genotyping concordance, we intentionally resampled 200 samples and determined a concordance rate of 99. 5%. We also conducted previously described standard QC prior to imputation  19. In brief, individual QC includes genotype call rates >97%, sex checks, and no excess heterozygosity; we removed population outliers as well (European cluster of 1000 Genomes).
00a). Population-based controls were used; therefore, age was not included as a covariate. Age and gender statistically behave like phenotype proxies (for AD status in this case). Therefore, adjusting for co-variation with age and gender could result in an over-adjustment of GWAS results. After QC steps, we included 6,331 AD cases and 6,055 control individuals and tested 14,542,816 genetic variants for association with AD. IGAP summary statistics. The GWAS summary results from the IGAP were downloaded from the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS, https://www. niagads. org/)  20. Details on data generation and analyses by the IGAP have been previously described  20. In brief, the IGAP is a large study based upon genome-wide association using individuals of European ancestry.
LD information was calculated using the GR@ACE imputed genotypes as a reference. Polygenicity and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in GWAS. To distinguish between inflation from a true polygenic signal and bias we quantified the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD) using the LD Score regression intercept (LDSC software  41  ). Chromosomal regions associated with AD in previous studies were excluded from follow-up (Lambert et al. 3  , Kunkle et al. 42  , and Jansen et al. 38  ). We tested all variants with suggestive association (p < 10 -5 ) located in proximity (200 kb) of genomic regions selected for follow-up to allow for the potential refinement of the top associated variant.
Conditional analyses were performed in regions where multiple variants were associated with AD using logistic regression models, adjusting for the genetic variants in the region (Supplementary Data 13, 14). Regional plots were generated with a mixture of homemade Python (v2. 7) and R (v3. 6. 0) scripts. Briefly, given an input variant, we calculated the LD between the input variant and all the surrounding variants within a window of length defined by the user. The LD was calculated in the 1000 Genomes samples of European ancestry. We used gene positions from RefSeq (release 93); in the case of multiple gene models for a given gene, we reported the model with the largest number of exons. We used recombination rates from HapMap II and chromatin states from ENCODE/Broad (15 states were grouped to highlight the predicted functional elements).
As a reference genome, we used GRCh37. Quantile-quantile plots, Manhattan plots, and the exploration of genomic inflation factors were performed using the R package qqman. Polygenic risk scores. We calculated a weighted individual PRS based on the 39 genetic variants that showed GWS evidence of association with AD in the present study, excluding APOE to check the impact of PRS modulating APOE risk (Table  1  and Supplementary Data 3). The selected variants were directly genotyped or imputed with high quality (median imputation score R² = 0. 93). The PRSs were generated by multiplying the genotype dosage of each risk allele for each variant by its respective weight and then summing across all variants. We weighted this by the effect size from previous IGAP studies [Kunkle et al. 42  (36 variants), Sims et al.
7  (2 variants), Jun et al. 43  (MAPT locus), Supplementary Data 5]. The generated PRS was validated using logistic regression adjusted by four principal components in a sample of 676 AD cases diagnosed based on clinical criteria and 332 pathologically confirmed AD cases from the European Alzheimer's Disease Biobank-Fundació ACE/Barcelona Brain Bank dataset (EADB-F. ACE/BBB, Supplementary Information). This dataset was not used in prior genetic studies. In this dataset, all pathologically confirmed cases were scored for the presence or absence of concomitant pathologies. In all analyses, we compared the AD patients to the same control dataset (n = 1386). We performed analyses to test the robustness of the PRS. We tested the effect of adding variants below the genome-wide significance threshold using a pruning and thresholding approach.
Risk stratification of the validated PRSs. We searched for the groups at the highest risk of AD in the GR@ACE dataset (6331 AD cases and 6055 controls). We stratified the population into PRS percentiles, taking into account survival bias anticipated at old age  18. To eliminate selection bias, we calculated the boundaries of the percentiles in the control participants aged 55 years and younger (n = 3546). Based on the boundaries from this population, the rest of the controls and all AD cases were then assigned into their appropriate percentiles. We first explored risk stratification using only the PRSs. For this, we split the PRSs into 50 groups (2 percentiles) and compared all groups with that which had the lowest PRS. Second, we explored risk stratification considering both the APOE genotypes and the PRSs.
The APOE genotypes were pooled in the analyses as APOE ɛ2ɛ2/ɛ2ɛ3 (n = 998, split into 7 PRS groups), APOE ɛ3ɛ3 (n = 7611, split into 25 PRS groups), APOE ɛ2ɛ4/ɛ3ɛ4 (n = 3399, split into 15 PRS groups), and APOE ɛ4ɛ4 (n = 382, split into 3 PRS groups). We studied the effect of PRS across groups of individuals stratified by the APOE genotypes with the lowest-PRS group (APOE as the reference group using logistic regression models adjusted for four population ancestry components). Finally, we compared the median AAO using a Wilcoxon test. We implemented a Cox regression model on AAO in the GR@ACE/DEGESCO dataset case-only adjusted for covariates as APOE group, the interaction between the PRS and APOE and four population ancestry components. All analyses were done in R (v3. 4. 2). Functional annotation.
Acknowledgements We would like to thank patients and controls who participated in this project. The present work has been performed as part of the  doctoral program  of  I. de Rojas  at the  Universitat de Barcelona (Barcelona, Spain)  supported by national grant from the  Instituto de Salud Carlos III   FI20/00215. The  Genome Research @ Fundació ACE  project ( GR@ACE ) is supported by  Grifols SA, Fundación bancaria "La Caixa", Fundació ACE , and  CIBERNED. A. R. and M. B. receive support from the  European Union/EFPIA Innovative Medicines Initiative  Joint undertaking ADAPTED and MOPEAD projects (grant numbers  115975  and  115985 , respectively). M. B. and A. R. are also supported by national grants  PI13/02434 ,  PI16/01861 ,  PI17/01474 ,  PI19/01240  and  PI19/01301. Acción Estratégica en Salud  is integrated into the  Spanish National R + D + I Plan  and funded by  ISCIII (Instituto de Salud Carlos III)-Subdirección General de Evaluación  and the  Fondo Europeo de Desarrollo Regional (FEDER-"Una manera de hacer Europa").
Some control samples and data from patients included in this study were provided in part by the  National DNA Bank Carlos III  (www. bancoadn. org, University of Salamanca, Spain ) and  Hospital Universitario Virgen de Valme (Sevilla, Spain) ; they were processed following standard operating procedures with the appropriate approval of the  Ethical and Scientific Committee. Amsterdam dementia Cohort (ADC) :  Research of the Alzheimer center Amsterdam is part of the neurodegeneration research program  of  Amsterdam Neuroscience. The Alzheimer Center Amsterdam is supported by  Stichting Alzheimer Nederland  and  Stichting VUmc  fonds. The clinical database structure was developed with funding from  Stichting Dioraphte. Genotyping of the Dutch case-control samples was performed in the context of EADB (European Alzheimer DNA biobank) funded by the  JPco-fuND   FP-829-029  ( ZonMW  project number  733051061 ).
100-Plus study: We are grateful for the collaborative efforts of all participating centenarians and their family members and/or relations. This work was supported by  Stichting Alzheimer Nederland  ( WE09. 2014-03 ), Stichting Diorapthe , horstingstuit foundation,  Memorabel  ( ZonMW  project number  733050814 ,  733050512 ) and  Stichting VUmc Fonds. Genotyping  of the 100-Plus Study was performed in the context of EADB (European Alzheimer DNA biobank) funded by the  JPco-fuND   FP-829-029  ( ZonMW  project number  733051061 ). Longitudinal Aging Study Amsterdam (LASA) is largely supported by a grant from the  Netherlands Ministry of Health, Welfare and Sports, Directorate of Long-Term Care. The authors are grateful to all LASA participants, the fieldwork team and all researchers for their ongoing commitment to the study.
This work was supported by a grant ( European Alzheimer DNA BioBank, EADB)  from the  EU Joint Program-Neurodegenerative Disease Research (JPND)  and also funded by  Inserm ,  Institut Pasteur de Lille , the  Lille Métropole Communauté Urbaine , the  French government 's  LABEX DISTALZ program  (development of innovative strategies for a transdisciplinary approach to AD). Genotyping of the German case-control samples was performed in the context of EADB (European Alzheimer DNA biobank) funded by the  JPco-fuND (German Federal Ministry of Education and Research, BMBF :  01ED1619A ). Full acknowledgments for the studies that contributed data can be found in the Supplementary Note. We thank the numerous participants, researchers, and staff from many studies who collected and contributed to the data.
We thank the  International Genomics of Alzheimer's Project (IGAP)  for providing summary results data for these analyses. The investigators within IGAP contributed to the design and implementation of IGAP and/or provided data but did not participate in analysis or writing of this report. IGAP was made possible by the generous participation of the control subjects, the patients, and their families. The i-Select chips was funded by the  French National Foundation  on AD and related disorders. EADI was supported by the  LABEX (laboratory of excellence program  investment for the future) DISTALZ grant,  Inserm ,  Institut Pasteur de Lille, Université de Lille 2  and the  Lille University Hospital. GERAD was supported by the  Medical Research Council  (Grant  n°503480 ),  Alzheimer's Research UK  (Grant  n°503176 ), the  Wellcome Trust  (Grant  n°082604/2/07/Z ) and  German Federal Ministry of Education and Research (BMBF) :  Competence Network Dementia (CND)  grant  n°01GI0102 ,  01GI0711 ,  01GI0420.
In addition, young branches of Beldi almond trees were taken from the current year to minimize browning. In this experiment, some contaminations and browning problems of the tissue in the culture occurred without affecting a big percentage of the explants, with a contamination rate (data not shown). Endogenous or exogenous microbiological contaminants have always limited the in vitro establishment of explants from woody plant material; in fact the proliferation of the starting plant material depends on several factors linked essentially to the physiological state of the explants but also to the nutritional and environmental conditions of the culture  [42]. e losses recorded in this study are mainly due to the age of local almond trees, since the age of "Beldi" almond cultivated in eastern Morocco is estimated for more than 50 years, and this issue could be overcome by the use of a sterile cold antioxidant solution like citric acid and ascorbic acid at the time of explants rinsing or by their inclusion in the medium  [43] , or even by using less expensive methods such as the natural antibacterial properties of some plants, taking, for example, the infusion of olive leaves  [44].
QUANTIFICATION AND STATISTICAL ANALYSIS A brief overview of data processing and in silico EC selection (related to Figures  5  and 6 ) 1. Generate gene expression matrices using the CellRanger software (10x Genomics). 2. Aggregate sample data using CellRanger software, and process raw data further in R (version 3. 4. 4). 3. Perform the following quality control steps on the pooled tissue datasets: a. genes expressed by fewer than 10 cells or with a row average of < 0. 002 should not be considered and therefore removed; b. cells that expressed fewer than 300 genes (low quality), and cells that expressed over 4,000 genes (potential doublets) should be excluded from further analysis; c. cells in which over 10% of unique molecular identifiers (UMIs) were derived from the mitochondrial genome should be removed.
4. Normalize the data using the NormalizeData function as implemented in the Seurat package  (Satija et al. , 2015). 5. Cluster the cells per organ prior to in silico EC selection for each organ separately. 6. First, for EC selection, identify highly variable genes using the Seurat FindVariableGenes function (mean lower threshold = 0. 0125, mean higher threshold = 8, dispersion threshold = 0. 5. 7. Auto-scale the data (using highly variable genes only) and summarize by principal component analysis (PCA) using the flashPCA package  (Abraham et al. , 2017)  8. Visualize the data using t-Distributed Stochastic Neighbor Embedding (t-SNE, Rtsne package; top 8 principal components (PCs))  (Van Der Maaten and Hinton, 2008). 9. Perform graph-based clustering to cluster cells according to their gene expression profile using the FindClusters function in Seurat (clustering resolution = 1, k-nearest neighbors = 10).
Of note, the data processing and in silico EC selection (steps 4-9) was performed using algorithms implemented in the BIOMEX software  (Taverna et al. , 2020). All raw sequencing data referred to in this study are available at ArrayExpress (ArrayExpress: E-MTAB-8077; brain, muscle, lung)  (Kalucka et al. , 2020)  and ArrayExpress: E-MTAB-8119: choroid  (Rohlenova et al. , 2020).
ACKNOWLEDGMENTS We acknowledge the help of  A. Bouche  ´and  P. Vanwesemael  for technical assistance. J. K. , N. V. C. , K. V. , L. T. , M. B. , P. d. Z. , and K. R. are supported by the  Fonds voor Wetenschappelijk Onderzoek (FWO) ; J. K. by AIAS-CO-FUND II: GA: MSCA:  754513  and The  Aarhus University Research Foundation ; Lundbeckfonden:  R307-2018-3667 ,  Carlsberg  Fonden:  CF19-0687 ,  Riisfort Fonden ,  A. P. Møller Fonden  and  Steno Diabetes Center Aarhus (SDCA). L. -A. T. by  University of Antwerp ; V. G. by  Strategisch Basisonderzoek Fonds voor Wetenschappelijk Onderzoek-Vlaanderen  ( SB- FWO ); S. J. D. and K. D. F. by a  Marie Curie-IEF   Fellowship ; P. C. is supported by a long-term structural  Methusalem  funding by the  Flemish Government, Fonds Wetenschappelijk onderzoek (FWO) ,  Foundation Against Cancer  ( 2016-078 ),  European Research Council (ERC)   Advanced Research Grant  ( EU-ERC743074 ),  ERC Proof of Concept  ( ERC-713758 ),  VIB TechWatch  and by the  Novo Nordisk Foundation  ( NNF19OC0055802 ).
Data Collection MC performed the face-to-face interviews in a private office (NH or GP studio) to ensure confidentiality. Interviews were audio-recorded to facilitate transcription. The interviews aimed to obtain a detailed description of i) HP experiences concerning drugs prescription/distribution for NH residents, and ii) their expectations and views of the proposed TMR. Interviews follow a questionnaire based on the framework detailed in Figure  2. This questionnaire was built by exploring the literature on similar topics.
Data Analysis Interviews were transcribed as intelligent verbatim using Microsoft Word, and de-identified by MC, who then performed a thematic analysis of the data. 24 An inductive approach was chosen to allow themes to emerge spontaneously. First, MC read the transcripts several times and then proceeded to coding. Higher-order themes were determined by discussion between MC and PV. In order to ensure coding consistency,  25, 26  researchers met regularly to discuss their progress with the analysis. Any discrepancies were discussed until consensus was reached. The main challenge of this analysis was to identify common themes which would group different professionals' concerns while at the same time expressing the variability of their points of view. Data saturation was reached after 13 interviews.
Acknowledgments The sponsor is represented by  Assistance Publique ,  Hôpitaux de Marseille, France. The authors thank the NH patients recruited to date in the TEM-EHPAD controlled trial, and all the healthcare professionals involved in Phase 1 of the mixed-methods study. We also thank the directors of the following nursing home groups: Korian ( Ms. Hakopian ,  Ms. Amara ,  Ms. Lescaudron ),  Medeos (Ms. Charles, Ms. Bichout) ,  Domusvi  ( Mr. Virgile ,  Mr. Hardy ,  Mr. Solheim ),  Orpéa  ( Ms. Feist ,  Ms. Mousis ,  Ms. Laurent ),  Mgen (Ms. Santangéli), Colisee (Ms. Alemany) and Les Opalines (Ms. Putzu). Our special thanks for the contributions made by all the management and coordinating doctors in the various nursing homes ( Dr Teissier ,  Dr Clot-Faybesse ,  Dr San José ,  Dr Manes ,  Dr Gauthier ,  Dr Michel ,  Dr Labarriere ,  Dr Drancourt ,  Dr Morel-Roux ,  Dr Mayan ,  Dr Alves de Quiera ,  Dr Blanc Fattin ,  Dr Benedetti ,  Dr Di Léo Massiani ,  Dr Gil ), and for the support of the  EMAI Clinical Research Network (Sanchez D, Dimier C, Hugues E, Verdier V, Agnelli L).
Funding This trial is being supported by the  French Ministry of Health  through institutional grants from the  French 2017 National Programme for Clinical Research (Programme de Recherche sur la Performance du Système de Soins ). The Assistance  Publique-Hôpitaux de Marseille  is the study promoter and is in charge of all administrative measures. The trial is registered at ClinicalTrials. govunder the number  NCT03640845.
Background: Although several studies suggest the benefit of a low-protein diet supplemented with amino acids and keto acids (sLPD) in delaying the initiation of hemodialysis, evidence on whether these nutritional approaches could delay the timing of preemptive transplantation is lacking. Methods: Retrospective nationwide cohort study, from Taiwan's National Health Insurance Research Database. Patients having undergone a first preemptive kidney transplantation between 2001 and 2017 were identified and divided into two groups according to the presence of sLPD treatment or not. The primary outcome was the time between the diagnosis of advanced CKD and transplantation. Secondary outcomes were post-transplantation adverse events. Results: A total of 245 patients who received their first preemptive kidney transplantation were identified from the nationwide database; 63 of them had been on an sLPD prior to transplantation (sLPD group).
Second, the exposure to immunodepressive drugs is not without side effects, which are cumulative over time. Delaying the need for such exposure, and associated risks of developing infections or cancer, may be an advantage. Third, immunosuppressive treatments are improving over time, and delaying the need for transplantation may mean having access to better drugs, or drug combinations  [23]. Thus, this study aimed to elucidate the effect of sLPDs in delaying the need for preemptive kidney transplantation, and to evaluate if such diets could have a detrimental long-term influence after kidney transplantation. Taking advantage of the comprehensive nationwide medical records collected in Taiwan by the National Health Insurance Research Database (NHIRD), this study investigated the association between the use of sLPDs and the time to surgery among patients who received a preemptive kidney transplantation.
Data Source This retrospective cohort study utilized the National Health Insurance (NHI) Research Database (NHIRD) collected by the National Health Informatics Project (NHIP) and managed by the Health and Welfare Data Science Center (HWDC) of Taiwan. In March 1995, Taiwan launched a nationwide, single-payer, compulsory health care program called the NHI program, which covers approximately 99. 8% of Taiwan's population (nearly 24 million people in 2017). Comprehensive health care information on insured patients, including outpatient visits, disease diagnoses, drug prescriptions, and procedures, is included in the NHIRD. Disease diagnoses in the NHIRD follow the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) before 2015, and ICD-10-CM after 2016. Although examination reports and laboratory data are not available, the populationwide comprehensiveness is the strength of the database.
Patient Selection Patients who received their first kidney transplantation between 2001 and 2017 were identified from the NHIRD, covering the entire Taiwanese population. The diagnosis code 55. 69 (ICD-9-CM), Z94. 0 (ICD-10-CM), the procedure code V42. 0 (ICD-9-CM), 0TY00Z0, 0TY00Z2 (ICD-10-PCS) and the Taiwan reimbursement code 76020 were used in combination to identify kidney transplantation according to hospitalization data of NHIRD. The discharge date after hospitalization for kidney transplantation was defined as the index date. Patients who had a history of kidney transplantation or had received dialysis before the index date were excluded. Finally, patients whose information of advanced CKD was not available were also excluded (Figure  1 ). Nutrients 2021, 13, x FOR PEER REVIEW 3 of 11 information regarding the NHI and NHIRD is available in previous publications  [27] [28] [29].
Patient Selection Patients who received their first kidney transplantation between 2001 and 2017 were identified from the NHIRD, covering the entire Taiwanese population. The diagnosis code 55. 69 (ICD-9-CM), Z94. 0 (ICD-10-CM), the procedure code V42. 0 (ICD-9-CM), 0TY00Z0, 0TY00Z2 (ICD-10-PCS) and the Taiwan reimbursement code 76020 were used in combination to identify kidney transplantation according to hospitalization data of NHIRD. The discharge date after hospitalization for kidney transplantation was defined as the index date. Patients who had a history of kidney transplantation or had received dialysis before the index date were excluded. Finally, patients whose information of advanced CKD was not available were also excluded (Figure  1 ). The kidney transplant donors were not identifiable from the NHIRD.