dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: This work was supported by funding obtained from the  Aix-Marseille Université  and the  Centre National de la Recherche Scientifique (CNRS). J. -D. T. is the recipient of the  Agence Nationale de la Recherche  grant  EZICROM ANR-16-CE14-0011. J. -D. T. expresses here its gratitude to  André Jean  for his trust, his invaluable advice and for the many fruitful and inspiring scientific discussions on the contribution of the DVC to the regulation of energy balance.
11  Single rare variants in the GBA gene increase the risk of developing PDD, and the risk may relate to the pathogenicity of the variant. [12] [13] [14]  Several studies have also reported that the MAPT H1 haplotype is associated with dementia,  9, [15] [16] [17]  although this has not been universally replicated. 10  More recently, the RIMS2 locus has been described in association with progression to PDD, as well as suggestive association signals at the TMEM108 and WWOX loci. 18  Genome-wide association studies in neurodegenerative disease have largely defined casecontrol risk factors for disease susceptibility, but the increasing availability of high-quality longitudinal clinical datasets enables a systematic search for disease modifying factors. Here, we use a genome-wide survival meta-analysis approach to identify new genetic factors that contribute to the progression to PDD.
Patient cohorts We have studied four independent longitudinal PD cohorts: Tracking Parkinson's Disease (TPD, www. parkinsons. org. uk/),  19  Oxford Parkinson's Disease Centre Discovery Cohort (OPDC, www. dpag. ox. ac. uk/opdc),  20  Accelerating Medicines Partnership: Parkinson's Disease (AMP-PD v2. 5, www. amp-pd. org), which consists of harmonized data from multiple cohorts,  21  and Drug Interaction With Genes Downloaded from https://academic. oup. com/brain/article/146/5/1873/6812546 by University of Glasgow user on 06 June 2023 Genome-wide analysis of PDD BRAIN 2023: 146; 1873-1887 | 1875   in Parkinson's Disease (DIGPD, clinicaltrials. gov/ct2/show/ NCT01564992), comprising a total of 3923 participants after clinical and genetic data cleaning (Supplementary Fig. 1  and Table  1 ). Each subject provided written informed consent for participation according to the Declaration of Helsinki and all cohort studies were approved by the relevant ethics committee.
Tissue and cell-type specificity measures Specificity represents the proportion of a gene's total expression attributable to one cell type/tissue. To determine specificity of a gene to a tissue or cell type, specificity values from three independent gene expression datasets were generated. Briefly, these datasets included (i) bulk-tissue RNA-sequencing of 53 human tissues from the Genotype-Tissue Expression consortium (GTEx, v. 8; RRID: SCR_013042)  29  ; (ii) human single-nucleus RNA-sequencing of the middle temporal gyrus from the Allen Institute for Brain Science (AIBS, Allen Cell Types Database-Human MTG Smart-Seq 2018 dataset, available from celltypes. brain-map. org/rnaseq; RRID: SCR_014806)  30  ; and (iii) human single-nucleus RNA-sequencing of the substantia nigra. 31  Generation of specificity values for GTEx and AIBS were previously described in Chia et al.
32  Briefly, specificity values for GTEx were generated using code modified from a previous publication (https://github. com/jbryois/scRNA_disease),  33  to reduce redundancy among brain regions and to include protein-and non-protein-coding genes. Specificity values for the AIBS-derived dataset were generated using gene-level exonic reads and the 'generate. celltype. data' function of the EWCE R package (v. 1. 2. 0). 34  Likewise, specificity values from Agarwal et al. 31  were generated using EWCE. Specificity values for all three datasets and the code used to generate these values are openly available at https:// github. com/RHReynolds/MarkerGenes. 35.
Colocalization analysis To investigate whether there is an overlap between PDD loci and expression quantitative trait loci (eQTLs), we used the coloc R package (v. 5. 1. 0; https://cran. r-project. org/web/packages/colocr/index. html). 37  We also used the R package colochelpR (v. 0. 99. 0)  38  to help prepare datasets for use with coloc. We took a Bayesian inference approach to test the H4 null hypothesis that there is a shared causal variant associated with both progression to PDD and gene expression regulation. The Bayesian inference approach additionally computes the posterior probability that there is no association with either trait (H0), there is association with the PDD trait but not the eQTL trait (H1), there is association with the eQTL trait but not the PDD trait (H2) and that there is association with both traits, but the causal variants are independent (H3).
39  We therefore performed conditional analysis beforehand to confirm that there were no additional independent signals, thus meeting the assumption of a single causal variant at each locus. Cis-eQTL data were obtained from (i) eQTLGen, comprising bulk blood-derived gene expression from 31 684 individuals (https:// www. eqtlgen. org/cis-eqtls. html, accessed on the 7 June 2021); and (ii) PsychENCODE, comprising gene expression from bulk RNAsequencing from the prefrontal cortex of 1387 individuals (http:// resource. psychencode. org/, accessed on the 7 June 2021). 40, 41  Next, to understand if LRP1B or BBS9 loci regulate alternative splicing, we used a similar approach using frontal cortex and substantia nigra splicing QTLs (sQTLs) data from the GTEx v. 8 database containing all variant-gene associations from 183 and 100 individuals, respectively, based on LeafCutter (v.
0. 2. 9; RRID:SCR_017639; https:// davidaknowles. github. io/leafcutter/) 42 intron excision phenotypes. For LRP1B, we tested the alternative splicing from eight different introns. In addition, false discovery rate-filtered transcript-per-million transcript expression QTLs (tQTLs) (false discovery rate <0. 001) were obtained from PsychENCODE and used to generate regional association plots overlapping with LRP1B signals. A full colocalization analysis for tQTLs was not possible due to the unavailability of unfiltered tQTL summary statistics from PsychENCODE.
Colocalization analysis We did not identify proxy coding variants in high linkage disequilibrium with the lead variants in LRP1B or BBS9. To determine whether any of the GWSS genome-wide significant signals are involved in the regulation of gene expression, we performed colocalization analysis using eQTLs from eQTLGen  41  and PsychENCODE,  66  which represent large human blood and brain gene expression datasets, respectively. We found no colocalization between PDD GWSS loci and eQTLs from either dataset, indicating that there is currently no evidence of shared causal variants driving both gene expression and the three association signals for PDD progression   5 ). Of note, LRP1B is not expressed in blood, thus no LRP1B eQTLs (significant or non-significant) were available from eQTLGen (Supplementary Fig.
5A ). Next, we explored whether non-coding GWSS significant signals could have a role in alternative splicing by performing colocalization analysis using cortical and nigral sQTLs from the Genotype-Tissue Expression (GTEx) dataset. 29  Again, we found no colocalization of PDD GWSS loci and sQTLs (Supplementary Table  6 ). We also generated regional associations plots for tQTLs from PsychENCODE and PDD GWSS signals in the region surrounding LRP1B, which on visual assessment did not suggest the presence of signal colocalization between LRP1B loci and tQTLs (Supplementary Fig. 9 ). Despite the power limitations of existing QTL datasets, the available data does not currently support LRP1B signals regulating the expression of transcript isoforms via alternative splicing.
93, 94  Other genetic variants previously reported in association to dementia in PD were not confirmed. In particular, a large recent study using a similar genome-wide survival approach identified that a variant in RIMS2 was a stronger predictor of PDD than APOE and GBA. 18  We were unable to replicate this finding, which could be the result of small variations in the post-imputation background allele frequencies in different cohorts. Given the relatively rare minor allele frequency of this SNP in the general population, it is possible that small changes in the allele frequency may significantly change the results of the analysis. The apparent discrepancies between studies will probably be resolved as larger longitudinal datasets become available. Our study has some limitations. First, the analysis was conducted only in individuals of European ancestry, as data from this population was more readily available.
It is therefore possible that individuals who develop dementia early in the disease course are not adequately represented in the dataset analysed. Despite being one of the largest genome-wide survival studies of progression to PDD, sample size and event rates are relatively small, and larger incident cohorts with longer follow-up times are needed to detect variants of small effect size. It is nevertheless reassuring that our study has identified some of the same genetic factors associated with higher risk of progression to dementia as large, incident population-based cohorts with long follow-up times such as APOE ϵ4 and GBA mutations, despite the potential limitations of large non-incident longitudinal cohorts. 95  In conclusion, this large genome-wide study identifies several interesting and plausible new gene candidates associated with faster progression to dementia in PD, while also corroborating the importance of the previously described APOE and GBA variants for cognitive outcomes in PD.
nlm. nih. gov/gds (accession ID: GSE23636). Cis-QTL data were obtained from eQTLGen (https://www. eqtlgen. org/cis-eqtls. html) and PsychENCODE (http://resource. psychencode. org). False discovery rate-filtered tQTL data were obtained from PsychENCODE (http://resource. psychencode. org/). Cortical sQTL data were accessed from the GTEx v. 8 database (https://gtexportal. org/home/). GTEx bulk-tissue RNA-seq data are available at https://www. gtexportal. org/home/datasets. AIBS human single-nucleus RNA-seq data are available at https://portal. brain-map. org/atlases-and-data/rnaseq. Human single-nucleus RNA-seq of the substantia nigra data can be accessed from https:// www. ncbi. nlm. nih. gov/geo/ (accession ID: GSE140231). Summary statistics from the PD GWAS (Nalls et al. 47  ) used to perform the GRS analysis are available from https://pdgenetics.
Then, odorant concentrations were presented in descending order until participants had incorrectly discerned the odorant, and so on. The last four staircase reversal points were used to estimate the IDT. Once the IDT was determined, the experimenter listed the concentrations above the subject's detection threshold for the given odor. Concentrations above the IDT of the participant, in step of 3 (threshold, threshold + 3, threshold + 6. ), were selected before creating a random presentation order for the following part of experiment, i. e. , the odor hedonic ratings. This part lasted from 15 to 25 min. Odor hedonic ratings. Participants sat on a chair in an individual booth facing a screen computer. OHRs were performed with FIZZ Biosystems (Biosystèmes, Couternon, France), a sensory analysis software allowing automated data collection.
During the test, participants were not aware of the name of the odor used and that the different concentrations presented corresponded to the same odorant. Odor presentation order was randomized between participants. This part lasted from 10 to 15 min. Data analysis. Data for IDTs and OHRs were statistically evaluated with Statistica software ® , using Spearman correlations, Wilcoxon tests, Mann-Whitney U tests, Fisher Z-transformation and polynomial regressions. The level of significance was p < 0. 05.
Phase 3: Virtual panel training during COVID 19 pandemic and sample evaluations in office settings The panel received additional training evaluating various sweetpotato genotypes for 6 days in February 2020. Shortly thereafter, work with the sensory panel was challenged by the COVID 19 pandemic. Under these circumstances, home and office settings were identified as alternative sites for conducting the sensory tests. The panel was divided into two groups and participated in one 3hour virtual training session held via Microsoft Teams. Members who would be evaluating samples from home attended a special training session on how to prepare samples. Data were collected virtually using Compusense Cloud software  (Academic Consortium, Compusense Cloud, Compusense Inc. , Guelph, ON, Canada). Four members of the panel were trained to prepare and evaluate the samples at their individual homes using cookware available in their kitchens.
Trained interviewers obtained consent from the participants in English or a local language where necessary. The interviewers then administered a questionnaire and entered responses via Compusense software. All samples were prepared as described in 2. 2. 4 and presented monadically to respondents who evaluated each sample once. All personal information that was collected was discretely stored by the research team. The full length of the questionnaire is presented as Appendix S. 1 in Supplementary Materials. Most of the respondents were men (56. 5 %) (Table  S. 2 in Supplementary Materials) and working in the informal sector (43. 5 %). The age range of the participants was 19 to 48 years, with an average of 31 years. Most of the respondents ate sweetpotato for lunch (78. 3 %) several times a week (52.
2 %). Following this pilot study, the questionnaire was modified (Appendix S. 2 in Supplementary Materials) and used for a consumer study in phase 6 where 106 men and women who were regular consumers of sweetpotato (Table  S. 3 in Supplementary Materials) were interviewed to identify their attitudes towards 7 sweetpotato genotypes. Due to poor internet connection, data were entered on printed ballots then transferred to Compusense. Panelists rated overall liking, color liking and aroma liking of the samples on a 9-point hedonic scale ranging from 1 (dislike extremely) to 9 (like extremely). They also rated sweetness, mealiness and firmness on just-about-right scales ranging from 1 to 5 centered by just-about-right (JAR) at 3. These attributes were previously identified as the most important drivers for consumer preference of boiled or steamed sweetpotato in Uganda  (Mwanga et al.
Data and statistical analysis 2. 5. 1. Descriptive sensory analysis (Phase 1 to 6) All sensory data were first organized in Microsoft Excel (Microsoft 365 Apps for enterprise, version 2102). For descriptive sensory analysis, data from genotypes served in duplicate (sensory replicates) were used to evaluate panel reliability in SPSS version 22 (IBM Corp. in Armonk, NY, 2013) by Fisher's test with genotypes and panelists as fixed factors  (Canul et al. , 2011). Data for descriptive sensory analysis at phase 3, phase 4, and phase 6 were analyzed in SPSS analysis of variance (ANOVA) models with genotypes as fixed variable and panelists as a random factor. To visualize relationships between the sensory attributes and the genotypes, principal component analysis (PCA) using mean scores for each replicate by the trained panel was run in XLSTAT (2020.
Instrumental texture analysis (Phase 4 to 6) With all texture data, means for each texture parameter, specifically average peak positive force and average positive area for the first and second compressions, were calculated. When analyzing data from descriptive sensory panel and instrumental texture analysis, a panel mean for each sensory attribute evaluated by the panel was calculated per genotype including those that were served in duplicate. In phase 4, instrumental texture parameters were correlated with sensory firmness (descriptive sensory analysis) using Pearson correlation analysis. In phase 5, a linear regression model predicting sensory firmness was developed in XLSTAT with the best model procedure using 59 clones which had complete data from the DDBIO multi-location advanced field trial planted in 2021 as the training set and the 12 genotypes of the DDBIO advanced field trial from phase 4 as the validation set.
Acknowledgements This article is based on research conducted from 2019 to 2021, under the  RTBfoods  project  Breeding RTB  products for end-user preferences (RTBfoods) https://rtbfoods. cirad. frthrough a grant  OPP1178942 , to the  French Agricultural Research Center for International development (CIRAD) ,  Montpellier, France , by the  Bill and Melinda Gates Foundation (BMGF). This research was also funded by The  Sweetpotato Genetic Advances and Innovative Seed Systems (SweetGAINS ) https://hdl. handle. net/10568/106838with funding by  BMFG  to the  International Potato Center (CIP) ,  Lima Peru. The views expressed in this article do not necessarily reflect those of BMGF and CIP. The team would also like to acknowledge the  National Agricultural Research Organisation (NARO) ,  Uganda  for their contribution towards the study by providing sweetpotato roots and facilities.
Data collected The clinical characteristics of patients were prospectively recorded. When multiple values were available, we selected the closest one to the genetic testing prior to any iron depletion. The following data were recorded: date of diagnosis, age, gender, family history of iron overload disorder, alcohol consumption, tobacco smoking, body-mass index, blood pressure, presence of diabetes, clinical symptoms (fatigue, cardiomyopathy, liver disease, hypogonadism, arthropathy), iron status (serum ferritin, serum transferrin saturation), blood count, lipid profile, liver enzymes, and C-reactive protein. High blood pressure and dyslipidemia were defined using the same criteria than criteria for the diagnosis of metabolic syndrome. We also recorded liver iron concentrations (LIC) as assessed by Magnetic Resonance Imaging (MRI) as previously described.
The predicted value obtained by the model was used to plot the Receiver Operating Characteristics (ROC) curve. The model was first determined in patients without missing data, then to assess the impact of missing values, multiple imputation was performed. The datasets were generated (n=100) with fully conditional specification imputation methods for missing values for the following variable: age, serum ferritin, BMI, LIC, transferrin saturation, ALAT, GGT, hemoglobin, c reactive protein, high blood pressure, diabetes, alcohol consumption, dyslipidemia. Logistic regression was then performed with the previously defined model for each dataset and then pooled. We created a clinical and biochemical score using the rounded odd ratios of the logistic regression as the weights for each of the predictors.
Performance of this new predictive model J o u r n a l P r e -p r o o f value of the score to respect a predefined sensitivity of at least 90% in order to avoid underdiagnoses. Point estimate sensitivity, specificity and likelihood ratio with confidence intervals were determined for the selected for cut-off. The relevance of the final model was assessed in a replication cohort to ascertain its generalization. A p value <0. 05 was considered significant. Statistical analysis was performed using SPSS 23. 0 (IBM Corp, Armonk, NY).
Data collection and variable definitions. A full census of the study populations was conducted in April-July 2018. Individual and household data on socio-demographics (age, gender, education, occupation), self-reported previous history of clinical malaria, and structural characteristics of the household were collected. All households and participants were encoded and geo-referenced using a Global Positioning System (GPS) handheld device (Garmin's GPSMAP 60CSx, Garmin International Inc. , USA). A blood sample of 6 mL for adults or 3 mL for children of whole blood was collected by venipuncture in tubes with EDTA (BD Vacutainer, BD Franklin Lakes, USA) as a preservative. Venipuncture blood samples were separated by centrifugation (3500 rpm) into plasma and packed red blood cells (PRBC) for serological analysis.
Such quantities are estimated parametrically. The following 4-step process was adopted. First, expansion of the original dataset; a new set of analytic datasets was created by repeating the original dataset in three blocks. The first block was identical to the original dataset, the second block was modified and set the values of A (of out-ofvillage working activities) to unexposed (A = 0), the third block was modified and set the values of A to exposed (A = 1). In the second and third blocks, the values of the outcome (Y -malaria exposure) were removed and set as missing. Second, a regression model (a modified Poisson regression  40  to consider the highly prevalent outcome) was fitted for the outcome (i. e. malaria) given exposure A (out-of-village working activities) and confounders L (including villages as fixed effects).
Acknowledgements We would like to thank all inhabitants and local authorities from  Rumococha, Santo Tomás, Quistococha, Gamitanacocha, Libertad ,  Primero de Enero, Salvador ,  Lago Yuracyacu ,  Puerto Alegre , and  Urco Miraño in Loreto, Peru  for their enthusiastic participation in the study as well as all field workers and laboratory technicians for their dedication during the study.
2021). Given that environmental factors strongly affect grain Zn concentrations, it is of interest to determine to what extent GP can be employed in target environments that are less homogenous compared to the well-managed trials conducted on research stations. While the polygenic nature of grain Zn may favor a GP approach, it is possible that main effect single loci are more stable across environments and therefore possess greater predictive power in less controlled environments. We have grown a set of 253 rice gene bank accessions sampled from the 3 K genome project  (Mansueto et al. 2017)  in two farmer's fields in Madagascar and determined the variation for grain Zn concentrations and grain yield. Using this dataset, the objectives of this study were to: (i) Conduct genome-wide association studies  (GWAS)  in an attempt to detect alleles associated with high grain Zn concentrations, (ii) Develop a GP model for grain Zn concentrations based on above training set and employ this model to predict grain Zn concentrations among the 3000 sequenced rice accessions available at the IRRI gene bank, (iii) Identify potential donors with high grain Zn concentrations and confirm their suitability through confirmatory experiments.
3 g of whole rice seed, which had been ovendried at 80 °C for 4 h to remove remaining moisture, was acid-digested in a closed tube as described in  Wheal et al. (2011). Elemental concentrations of samples were measured using ICP-MS (8900; Agilent, Santa Clara, CA) according to the method of  Palmer et al. (2014). The grain Zn concentration is given in μg g -1 on a dry weight basis. In each of 10 digestion batches, a blank and a certified reference material (CRM; NIST 1568b rice flour) were added for quality assurance. Samples with Al present at > 5 μg g -1 were considered to have unacceptable levels of purported soil contamination  (Yasmin et al. 2014 ), thus they were eliminated from the dataset.
Statistical analysis for phenotypic values Given the experimental design, the following linear model was fitted for each trait: where y ijk is the phenotypic value (i. e. , observed Zn concen- tration) of i-th genotype evaluated at the k-th block in j-th site, is an intercept, g i is the genotypic value of i-th geno- type, s ij is the interaction effect between the i-th genotype and the j-th site, j is the effect of j-th site, jk is the effect of k-th block in the j-th site, and e ijk is the residual. The interaction effects s ij and the block effects jk were modeled as random effects, and the other model terms were modeled as fixed effects. This model was implemented in lmer function in the lme4 package. To test the statistical significance between two sites, site effect ( j ) for each trait was tested based on the type-III analysis of variance with Satterthwaite's method using the anova function in the lmerTest package  (Kuznetsova et al.