dataesr/scientific-paragraphs-categorization huggingface dataset:
Third, the lack of differences in outcomes between patients treated with "matched" or "non-matched" therapy can be explained by the small number of subjects in each group. Our patients were heavily pre-treated, supporting the notion that precision medicine oncology should be preferred in an early stage disease. Finally, the definition of the actionability of a molecular alteration was based on the OncoKB database, as recommended in European guidelines  [52]. However, there was no systematic molecular tumor board assessment to facilitate the interpretation of molecular testing results, and to help the decision of the most appropriate therapeutic strategy. Several studies have shown the impact and importance of MTB in treatment decisions, patient's outcomes and clinical trial enrolment  [53] [54] [55].
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/curroncol29030155/s1, Figure  S1 : Proportions of tumor types. S1 : Targeted molecular alterations and molecularly matched therapy used. Table  S2 : Efficacy parameters in the cohort of patients with level 1-2 AMA (according to OncoKB database  [34] ) (n = 50). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
GWAS meta-analysis The European Alzheimer & Dementia Biobank (EADB) consortium brings together the various European GWAS consortia already working on AD. A new dataset of 20,464 clinically diagnosed AD cases and 22,244 controls has been collated from 15 European countries. The EADB GWAS results were meta-analyzed with a proxy-AD GWASs of the UK Biobank (UKBB) dataset. The UKBB's proxy-AD designation is based on questionnaire data in which individuals are asked whether their parents had dementia. This method has been used successfully in the past  3  but is less specific than a clinical or pathological diagnosis of AD; hence, we will refer to these cases as proxy AD and related dementia (proxy-ADD). EADB stage I (GWAS meta-analysis) was based on 39,106 clinically diagnosed AD cases, 46,828 proxy-ADD cases (as defined in the Supplementary Note), 401,577 controls (Supplementary Tables  1  and 2 ) and 21,101,114 variants that passed our quality control (Fig.
05. This indicates that the inclusion of proxy-ADD cases does not cause disease-relevant biological information to be missed and underlines the additional power of this type of analysis. We next performed a single-cell expression enrichment analysis by using the average gene expression per nucleus (Av. Exp. ) data in the human Allen Brain Atlas (49,495 nuclei from 8 human brains). Only the microglial expression reached a high level of significance (P = 1. 7 × 10 -8 ; Supplementary Table  16 ); greater expression corresponded to a more significant association with ADD. After adjusting for microglial Av. Exp. , the remaining associations became nonsignificant; this indicates that microglial Av. Exp. drives all the other cell-type associations. These results were observed whatever the brain region studied (Supplementary Table  16 ).
A similar result was observed using a mouse single-cell dataset  14  (Supplementary Table  17  and Supplementary Note). Lastly, we looked at whether the relationship between an elevated microglia Av. Exp. and a genetic association with the ADD risk was specific to particular biological processes (Supplementary Table  18 ) by analyzing the interaction between microglia Av. Exp. and pathway membership in MAGMA  15. Of the five most significant interaction signals (q ≤ 10 -3 ), two were directly associated with endocytosis processes (GO:0006898 and GO:0031623); this suggested a functional relationship between microglia and endocytosis, which is known to be involved in phagocytosis (Supplementary Table  18 ). It is noteworthy that we also detected an interaction between GO:1902991 (regulation of amyloid precursor protein (APP) catabolic process) and the gene expression level in microglia (q = 1.
Only the ten strongest associations (according to STriNG software) are presented here. coloc, colocalization; eQTL, expression QTL; eTWAS, expression transcriptome-wide association study; GO, Gene Ontology; haQTL, histone acetylation QTL; Mon. Mac. , monocytes and macrophages; sTWAS, splicing transcriptome-wide association study; m/haQTL, methylation/histone acetylation QTL; sQTL, splicing QTL; FDr, false discovery rate. Importantly, the results of our meta-analysis suggest that the risk of conversion to AD rises with the number of risk alleles from non-APOE risk variants in the GRS by 1. 9-fold in population-based cohorts (HR = 1. 93 (1. 75-2. 13); Fig. 5 ) and 1. 6-fold in MCI cohorts (HR = 1. 63 (1. 42-1. 87); Fig. 6 ) on top of effects of age and the APOE ε4 allele. These observations result from the comparison of hypothetical individuals with a GRS value at the first decile of the distribution versus those with a GRS value at the ninth decile (Fig.
LD r 2 values calculated within EADB-TOpMed dataset (n = 42,140) based on the lead variant rs6586028 (purple) are indicated on a color scale. to note that six of our prioritized (tier 1) genes (ICA1L, EGFR, RITA1, MYO15A, LIME1 and APP) are expressed at a low level in microglia (<10%, relative to the total expression summed across cell types; Supplementary Table  45 ), emphasizing that ADD results from complex crosstalk between different cell types in the brain  23, 40. It is also noteworthy that the EGFR pathway is known to interact with the TNF-α signaling pathway  41  , which suggests interplay between the two signaling pathways during the ADD development. A better understanding of the etiology of ADD might also result from the observation that the risks of developing ADD and frontotemporal dementia are associated with the same causal variants in GRN and TMEM106B.
This association might be due to the misclassification of clinical diagnosis of AD and the presence of proxy-ADD cases in the UKBB. However, GRN and TMEM106B have also been linked to brain health and many other neurodegenerative diseases. For instance, GRN and TMEM106B are reportedly potential genetic risk factors for differential aging in the cerebral cortex  42  and cognitive impairment in amyotrophic lateral sclerosis  43  and Parkinson's disease  44, 45. Lastly, both GRN and TMEM106B have already been associated with neuropathological features of AD  [46] [47] [48]. Taken as a whole, these data may thus emphasize a potential continuum between neurodegenerative diseases in which common pathological mechanisms are driven by GRN and TMEM106B. Interestingly, both GRN and TMEM106B are reported to be involved in defective endosome/lysosome trafficking/function  49, 50  , a defect that is also observed in AD.
In conclusion, we have validated 33 previous loci, doubled the total number of genetic loci associated with the ADD risk, expanded our current knowledge of the pathophysiology of ADD, identified new opportunities for the development of GRSs and gene-specific treatments and opened up a pathway to translational genomics and personalized medicine. Pathway analysis. A total of 10,271 gene sets were considered for analysis (Supplementary Note). Gene set enrichment analyses were performed in MAGMA v1. 08 (refs. 65, 66 ), with correction for the number of variants in each gene, LD between variants and LD between genes. LD was computed from the EADB-TOPMed dataset using high-quality (probability ≥0. 9) imputed genotypes. The measure of pathway enrichment was the MAGMA 'competitive' test (in which the association statistic for genes in the pathway is compared with those for all other protein-coding genes), as recommended by De Leeuw et al.
67. We used the 'mean' test statistic, which uses the sum of -log(variant P value) across all genes. The primary analysis assigned variants to genes if they lay within the gene boundaries, although a secondary analysis used a window of 35 kb upstream and 10 kb downstream to assign variants to genes (as in Kunkle et al. 5  ). The primary analysis included all variants with an imputation quality above 0. 8. We used q values 68 to account for multiple testing. Expression in various cell types. The expression of genes was assigned to specific cell classes of the adult brain, as described previously  69. Briefly, middle temporal gyrus single-nucleus transcriptomes from the Allen Brain Atlas dataset (49,555 total nuclei derived from 8 human tissue donors aged 24-66 years) were used to annotate and select six main cell classes using Seurat 3.
Furthermore, eQTLs in monocytes and macrophages from various datasets  [77] [78] [79] [80] [81] [82]  (as prepared by eQTL Catalogue 83 ) were included in the analyses. Data on pQTLs  84  , mQTLs  85  and haQTLs  85  were available for DLPFC. Using each molecular QTL catalogue, the effect of the lead variants was queried and significant associations were reported. Moreover, genetic colocalization studies were conducted by comparing ADD association signals with the eQTL/sQTL signals from AMP-AD bulk brain, MiGA microglia and EADB LCL cohorts. We also conducted eTWASs and splicing TWASs (sTWAS) of the ADD risk, along with fine mapping of the eTWAS results. To this end, we trained functional expression and splicing reference panels based on the AMP-AD bulk brain and EADB LCL cohorts, and we leveraged precalculated reference panel weights  86  for the GTEx dataset  75  in tissues and cells of interest.
Articles NATure GeNeTiCs domain, we also tested for associations between ADD and genetically driven DNA methylation (MetaMeth analysis) in blood (with blood-brain methylation correlation estimates obtained from BECon 87 ) using the procedures described by Freytag et al. 88  and Barbeira et al. 89. A detailed description of the datasets and methods used for each of these analyses is given in the Supplementary Note. APP metabolism domain. We assessed the functional impact of gene underexpression on APP metabolism for all candidate genes based on a genome-wide high-content short interfering RNA screen 17 (Supplementary Note). Gene prioritization score. We computed a gene prioritization score for each candidate gene as the weighted sum of the evidence identified in the seven domains. We specified a weight for each type of evidence, as detailed in Supplementary Table  19.
For the molecular QTL-GWAS integration domains, we gave more weight to replicated hits (i. e. , evidence in several datasets) than to single hits. We also gave more weight to hits observed in brain (the bulk brain and microglia datasets) than to hits observed in other tissues/cell types (LCLs, monocytes, macrophages and blood). To avoid score inflation, several specific rules were applied: (1) for the results of sQTL-and mQTL-based analyses, multiple splice junctions or CpGs annotated for the same genes were aggregated prior to weighting due to correlated data; (2) if we observed a fine-mapped eTWAS association for a gene, its other significant (but not fine-mapped) eTWAS associations were not considered; (3) for genes having several significant CpGs (prior to aggregation) in MetaMeth analyses, the associated CpGs with a low (<75% percentile) blood-brain methylation correlation estimate were not considered if the gene also had associated CpGs with a high (≥75% percentile) blood-brain methylation correlation estimate.
Furthermore, we approximated the probability of conversion to AD at 3 and 5 years in memory clinic patients with MCI by using Cox models implemented in the 'PredictCox' function from the 'riskRegression' (2020. 12. 8) R package  97. We did not derive AD conversion probabilities for two cohorts with very small sample sizes (N < 50). Predicted AD conversion probabilities were derived and averaged for all patients in each of the groups formed by the decile of the GRS distribution in each cohort. The difference between the groups with the highest and lowest GRSs was computed in each cohort. We report the median (range) results in each group formed by the GRS deciles. Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank the many study participants, researchers and staff for collecting and contributing to the data, the high-performance computing service at the  University of Lille  and the staff at  CEA-CNRGH  for their help with sample preparation and genotyping and excellent technical assistance. We thank  Antonio Pardinas  for his help. We thank the  Netherlands Brain Bank. This research was conducted using the UKBB resource (application number  61054 ). This work was funded by a grant (EADB) from the  EU   Joint Programme -Neurodegenerative Disease Research. INSERM UMR1167  is also funded by the  INSERM ,  Institut Pasteur de Lille, Lille Métropole Communauté Urbaine and French government 's  LABEX DISTALZ program  (development of innovative strategies for a transdisciplinary approach to AD).
Data Availability Genome-wide summary statistics have been deposited to the European Bioinformatics Institute GWAS Catalog (https://www. ebi. ac. uk/gwas/) under accession no. GCST90027158. The significant eQTLs/sQTLs mapped and eTWAS/sTWAS functional reference panel weights generated for this study (in AD-relevant bulk brain regions from AMP-AD cohorts and in LCLs from the EADB Belgian cohort) are publicly available at https://doi. org/10. 5281/zenodo. 5745927 and https://doi. org/10. 5281/ zenodo. 5745929. Anonymized aligned reads of the amplicon-based long-read Nanopore cDNA sequencing experiment conducted for the TSPAN14 splicing analysis are available through the European Nucleotide Archive under accession PRJEB49234.
Misreporting is a widely recognised bias in self-reported dietary data  [16]. Less described are the flawed outcomes that can ensue from the method in which food records are coded. Errors can be induced from several areas, including poorly chosen food codes, number displacement, and difficulties in interpreting written participant records  [15]. Inaccuracies in nutrient estimations related to static food composition tables can reflect variations in coder interpretation and entry of diet records, as well as the nutrient database in use, in opposition to tangible differences between individual nutrient intakes  [15]. As such, enhanced efforts are required to minimise errors related to the collection and processing pipeline. STrengthening the Reporting of OBservational studies in Epidemiology -Nutritional Epidemiology (STROBEnut) recommendations emphasise the importance of full-reporting of data-source measurement  [17].
The TwinsUK cohort is one of the largest epidemiological research databases in the United Kingdom (UK) and the largest UK twin registry encompassing over 14,000 twins  [18]. The twin structure of this cohort allows for the estimation of the genetic susceptibility of dietary aspects, with insight into the interplay between genes and diet crucial to understanding the pathophysiology of health and disease outcomes  [19]. FFQs allowing for the characterisation of habitual diet and dietary patterns have traditionally been the sole method of dietary data collected in the TwinsUK cohort. In order to determine acute dietary intake and eating behaviours, EFR were collected on 2184 participants of this cohort between 2013 and 2017. This paper aims to; (1) describe data collection, processing and quality assessment of the EFR from the Twins UK cohort, (2) characterise nutrients, eating patterns and co-occurrence of foods, and (3) to test the heritability of diet behaviours, (4) and lastly to compare heritability of nutrient intake from the two dietary assessment methods (EFR and FFQ).
, McCance and Widdowson Food Composition Tables  5th  and 6th  Editions and all supplemental volumes  [22] ) with 60 dietary variables, defined as nutritive and non-nutritive food components. The second batch (Batch 2) was composed of 936 records (936 participants, January 2016-October 2017) and processed by Abacus Ltd. (Dietplan7, Foresoft Software Ltd. , McCance and Widdowson database 7th Edition and the revised Composition of Foods Integrated Data Set) with 48 dietary variables. To assess quality of data processing, 30 randomly selected paperscan EFR (> 10% of total number of EFR) were processed by a Research Dietitian using Dietplan7 nutrition software (Foresoft Software Ltd. , McCance and Widdowson database 7th Edition and the revised Composition of Foods Integrated Data Set  [23] ). Where not described, portion sizes were allocated on the following basis; (1) if stipulated within the software, (2) according to The Food Standards Agency Food Portion Sizes, 3rd Edition  [23] , or (3) as detailed online by common supermarket suppliers.
Assessing quality of food record participant entry To assess participant recording completeness, 366 EFR were selected at random (> 15% of sample). Data with missing electronic scans of the paper entry (n = 7) were excluded. The remaining records (n = 359) were assessed via previous measures outlined by  Goff et al. [25] , applying a score of 0 = no information, 1 = incomplete information and 2 = complete information to variables portion size, food item description and time of consumption. A summed total score of ≥5 (out of 6) was deemed an acceptable quality on consensus of two dietitians.
Food record: food item frequency and co-occurrence For all analyses, R version 3. 6. 2 and RStudio version 1. 1. 463 were employed. From the individual food items recorded, 386 items were considered similar or exact matches to other food items (e. g. 'Rice, Eggfried, Takeaway' , 'Eggfried Rice, Takeaway'). Therefore, a 'food key' was created for the McCance and Widdowson database (7th edition) and additional food items present within the EFR (total n = 4957). One thousand one hundred thirtyeight food items were manually aggregated into 542 'food key' food items, overviewed by two dietitians. Nutrient composition of aggregated 'food key' items were determined by; (1) those with the largest number of nutrients complete were selected, (2) for those with the same number of complete nutrient values the mean was applied if within a 5% difference of variation.
The food key condensed 2785 to 2399 food items across 1974 records filled by 1858 participants. Word clouds were generated for the 100 most frequently described individual food words and 100 most frequently consumed food item phrases (Supplementary Table  1 ). Two networks of commonly cooccurring foods were created using R package 'igraph'  [26]  after removing duplicate food items per participant, (1) all foods consumed within 1 day period (beverages milk, coffee, tea and wine were excluded), (2) those consumed at 'breakfast' (> 6:00 am and < 11:00 am)  [27]  (Supplementary Fig. 1 ).
Food record: impact of eating behaviours on nutrient intake and BMI The impact of (1) breakfast consumers vs. skippers (breakfast consumed between 6:00 am and 11 am with a minimum intake of 100 kcal  [27] ), (2) length of eating window (time (hrs) from first consumption of food/drink to last) on energy, residual energy adjusted macronutrient and fibre intake  [29] , on BMI, were investigated using univariate analysis of variance (ANOVA) (null fit model, R package 'lme4'  [30]  and 'stats') adjusting for covariates of age (years), with sum of energy intake (kcal) as fixed effects and family and zygosity as random effects. EFR (n = 14) with < 4 h eating window were excluded due to likely implausible recording.
Food record and FFQ: heritability The twin aspect of the data available, with two dietary assessment methods, allowed for characterisation of the heritability of nutrient intakes by the EFR and by the FFQs (720 EFR, 720 participants, 203 monozygotic (MZ) pairs, 157 dizygotic (DZ) pairs) (Supplementary Table  3 , Supplementary Fig. 2 ). The heritability of eating behaviours was explored through the EFR (1216 records, 1216 participants, 372 MZ pairs, 246 DZ pairs). Linear structural equation modelling was applied, adjusting for age, sex and BMI, to estimate the genetic and environmental components of variance in the nutrient intakes. Heritability assessment considers (A) additive genetic effects, (C) environmental effects in common, (E) environmental effects not in common/error; with univariate ACE, AE and CE models applied using the R package 'mets'  [31].
Quality of food record participant entry and data processing EFR (n = 329) were assessed for quality of participant entry. 97% of EFR had complete information for meal timings, 3% contained incomplete meal timing information. 55% of portion sizes described by the participants contained complete information, with 45% of EFR containing incomplete information on portion sizes. 84% of the description of food items provided complete information, with 16% providing incomplete description of one or more food items. A quality score ≥ 5 (out of 6) was deemed acceptable quality, reviewed by two dietitians, and based on previous work by Goff et al. [25]. The mean quality score of the participants food record entry was 5. 36 (±SD 0. 76, range 3-6), with 75% of EFR considered an acceptable quality. Participants are disproportionately twin females (88% vs.
Characterisation of acute dietary intake A total of 2398 foods were consumed within 47,635 occasions in 1974 EFR. Tea and milk were the most frequently consumed food items, on both an individual and paired basis. 'Tea, black, infusion, average' comprised 8. 27% (n = 3938) of food items, on average 1. 99 occasions per food record, and 'milk, semi-skimmed, pasteurised, average' described 6. 72% times (n = 3200), on average 1. 62 occasions per food record. Two-word clouds were generated to display the frequency of food items; (1) the 100 most frequent single food and drink words (frequency range 118-6589) isolated from food item phrases (Fig. 2A , Supplemental Table  1 ), (  2 ) the 100 most frequently consumed food item phrases, as described in the nutrition databases (frequency range 73-3938) (Fig.
Comparison of nutrient output via acute dietary intake vs. habitual measures The agreement between two commonly employed dietary data assessment methods (EFR and FFQs) were assessed. Food frequency questionnaires are typically used to quantify habitual food intake via self-reported frequency of intake over a specified period, enabling calculation of an average day's nutrient output. Bland Altman plots were used to show the mean agreement and 95% confidence intervals between EFR and FFQs (n = 1224) for macronutrients as a percentage of energy (Fig. 3 ). Energy intake of protein, carbohydrate and total fat intake (Fig. 3A, B  and C ) are predominantly tightly spread with minimal variation, with the majority of the results clustered around the mean difference line. However, there is greater variation at with lower intakes.
habitual intakes). Portion size descriptions within the EFRs were limited with 45% containing incomplete information. Omission of foods and errors in portion size estimations have been associated with errors in coding during dietary data processing [7]. Kolar et al. evaluated 3-day self-administered diet diaries, where participants were reported to have missing portion size information for 3% of the food items described, with 8% of the portion sizes of foods incompletely described  [37]. However, study participants were given instructions for recording food intake together with a 12-page serving size booklet containing photographs and measurement tools to aid and prompt complete and accurate data recording. Interestingly, the researchers noted that there was a trend relating an increased number of missing portion sizes with a greater BMI  [37].