dataesr/scientific-paragraphs-categorization huggingface dataset:
Proposed Dataset An important result of this work is the generation of a diverse and updated data set containing binding sites, ligands, and docking scores. The complete data set of 1. 2M molecular docking values can be downloaded at the following 2. We first establish our docking procedure and compare our candidate docking programs using self and cross docking experiments. In self-docking experiments, we evaluate programs' ability to predict the binding modes inside the native macromolecule by comparing the generated ligand poses to the crystal structure. In cross docking experiments, we evaluate the ability of docking tools to retrieve actives among decoys. Based on both experiments, we choose to use rDock as our docking program. An in-depth explanation of these experiments is available in section A.
2. 1. We then turn to the analysis of our dataset of binding sites and ligands. In particular, we provide an analysis of the redundancy of our binding sites using RMScore  [45]  as a distance. We show that our pockets cannot be clustered and thus display a low redundancy (section A. 2. 3). We show that our ligands are diverse with a low pairwise Tanimoto score. Moreover, we show that the RNA ligands found in the PDB do not necessarily display drug-like features and often obtain worse docking scores than compounds derived from ChEMBL. This motivates our database and the integration of multiple data sources (section A. 2. 4).
Fingerprint Prediction As a first validation, we want to compare the original RNAmigos paper to our fp setting that resembles the most theirs. The enhancements we propose are training on the updated data set, representing binding sites with directed graphs and updating the model architecture. For fairness, in this section, we follow the original validation protocol, except for removing ligands based on our filters. We use the DecoyFinder and PDB ligands sets as decoys sets and emphasize that at this step, we ignore the ChEMBL compounds. For each pocket in the test set, we perform a virtual screening and aim to retrieve the native binder among decoys. We use the MAR metric introduced above as a metric and present the results in Table  1.
DecoyFinder PDB Ligands RNAmigos 0. 609± 0. 294 0. 627± 0. 284 fp 0. 821± 0. 272 0. 856± 0. 219 fp + pre 0. 858± 0. 253 0. 872± 0. 210 Table  1 : Mean Active Rank (MAR) values computed on DecoyFinder and PDB decoy sets. We compare those values for the original RNAmigos results and our models in the fp setting, with and without pretraining. On this more challenging filtered data set, we reproduce that RNAmigos is able to retrieve a binding affinity signal. Moreover, we see that the enhancements yield a significant performance gain of over 22% MAR on the DecoyFinder decoy set, achieving a new state-of-the-art on this task. We observe that as expected, pretraining our RNA encoder boosts the performance of our method.
Virtual Screening Validation We now turn to an evaluation of our results using our full dataset. For each binding site, we now use a decoy set containing ChEMBL compounds similar to the native ligand, a scenario closer to an actual drug design validation. On this new data, we train models in our three settings, and report the MAR metric value. We include rDock performance for comparison and present results in Figure  4. The evaluation set in this experiment is a held-out set of 128 pockets which do not appear anywhere in the training or validation data.
Conclusion We address the timely question of in-silico RNA drug design. In contrast to its protein counterpart, the reduced amount of structural data limits the direct applicability of machine learning based approaches. Dedicated methods are needed as the RNA drug design field becomes more established. In this work, we built a dataset of RNA binding sites structures, along with their ligands, and a curated set of drug-like compounds. To make up for the scarce data, we use RNA docking as a data augmentation strategy and rely on unsupervised pre-training techniques. We start by showing that this method outperforms existing machine learning models by a margin of over 22%. Then, we compare different models with docking and show that we obtain comparable enrichment factors, in a fraction of docking runtime.
When scoring compounds with an average of docking scores and our models, we are able to halve the docking error. Finally, we propose a strategy to get the best of both worlds by using our tool as a preprocessing step for docking and show that this strategy reduces the computational costs by a factor twelve at no performance cost on average, and with a positive impact on all binding sites in our test set. This paper is a step towards establishing machine learning on the structure of RNAs, and unlocking its power for RNA drug design. By publicly releasing all our datasets, source code and model weights, we hope others will propose more efficient methods and spark a community effort in this important direction. Moreover, emerging techniques such as generative models of ligands are drastically increasing the size of the libraries of potential drugs.
This study examines the occurrence of the artificial sweetener aspartame (E951) in foods and beverages sampled by food control authorities in Germany between 2000 and 2022. The dataset was obtained through the Consumer Information Act. Out of 53,116 samples analyzed, aspartame was present in 7331 samples (14%), of which 5703 samples (11%) in nine major food groups were further evaluated. The results showed that aspartame was most commonly found in powdered drink bases (84%), flavored milk drinks (78%), chewing gum (77%), and diet soft drinks (72%). In the solid food groups, the highest mean aspartame content was detected in chewing gum (1543 mg/kg, n = 241), followed by sports foods (1453 mg/kg, n = 125), fiber supplements (1248 mg/kg, n = 11), powdered drink bases (1068 mg/kg, n = 162), and candies (437 mg/kg, n = 339).
Data Request under the Consumer Information Act The official food control laboratories in Germany collected and analyzed the samples in this dataset between 2000 and 2022. Analytical methods were validated and externally accredited as part of government food control efforts according to the guidelines of ISO/IEC 17025  [15]. The methodology for sample selection in the study is a combination of convenience sampling and systematic testing, such as annual food monitoring projects. Samples were collected using a risk-based sampling approach, which means that samples are not selected at random, but based on a perceived risk of contamination or non-compliance  [16]. Samples were collected in all German states from a variety of sources, including retailers, importers, and manufacturers, which can introduce some bias into the results, such as the focus of food control on the "bottleneck", i.
This includes information on unauthorized deviations from requirements, as well as measures and decisions taken in response to deviations and potential health and safety risks posed by products. The act also covers monitoring activities and measures for consumer protection  [18]. Access to the complete dataset, as well as the original consumer request, can be found on the internet portal FragDenStaat. de, which is managed by the non-profit association Open Knowledge Foundation Deutschland e. V. (located in Berlin, Germany)  [17].
Data Description and Analysis Methods The available data include information such as the year and type of store where the sample was collected, the country of origin, the product group and matrix of the sample, the aspartame content, the analytical methods, and the limit of detection (LOD) and the limit of quantification (LOQ) of the method used. In total, this dataset contained 53,116 analytical results for aspartame in different food products. The dataset included separate tables with qualitative data (n = 720) and quantitative data (n = 52,396). In this study, only the quantitative data were further evaluated. The analytical methods used to determine the aspartame content in the major food groups were primarily test methods according to the official collection of paragraph 64 of the German Food, Commodities and Feed Code (Lebensmittel-, Bedarfsgegenständeund Futtermittelgesetzbuch) for food control authorities and testing institutions.
This ensures consistent quality of testing and comparability of results  [19]. Generally, the highperformance liquid chromatography (HPLC) method BVL L 00. 00-28 is used, which is an adoption of the DIN EN 12856 standard with identical wording  [20]. Some laboratories have used other validated and accredited methods, based on HPLC coupled with different detectors, or based on nuclear magnetic resonance (NMR) spectroscopy (e. g. ,  [21] ). The method used for each analytical result is indicated in the raw data Excel table called "Anlage 5" of the dataset  [17].
Data Analysis and Selection of Food Items for Inclusion: Regrouping and Prioritizing Foods Analysis of the dataset presented in this study, including all statistical calculations, was performed using Microsoft Excel version 2016 (Microsoft, Redmond, WA, USA). During the evaluation of the data, it quickly became apparent that most samples (86% of the total sample, 87% of the quantitatively analyzed samples) were negative for aspartame, i. e. , the food was analyzed but aspartame was not detected. This finding can be explained by the fact that multiparameter methods, which analyze several sweeteners with the same assay, are commonly used for food control. This means that aspartame could have been measured even if the intention was to measure another sweetener. Therefore, it is important to note that the dataset is heavily biased toward foods that were suspected to contain aspartame or other compounds that were measured with the same assay.
This implies that the sample of foods in Germany is not representative and that the mean values should be interpreted with caution. The original BVL grouping of the raw data was less informative (see the Excel table called "Anlage 7" of the original dataset  [17] ) because only some subgroups of the major food groups appeared to contain aspartame. For example, in the dairy category, aspartame was found only in certain subgroups such as ready-to-drink buttermilk beverages. It was decided to re-analyze the data and categorize them into more meaningful groups using the standardized FoodEx2 food classification and description system of the European Food Safety Authority (EFSA)  [22]. Only food groups in which at least 20 samples were analyzed and at least 40% of the samples were found to contain aspartame were generally retained in the analysis.
Application of Artificial Intelligence Tools Manuscript editing, data textualization and summarization, and German-English translation were performed using ChatGPT, an artificial intelligence (AI) language model developed by OpenAI (https://chat. openai. com/). ChatGPT provided helpful insights and suggestions throughout the writing process, and its advanced language processing capabilities improved the clarity and accuracy of the manuscript. Specifically, the literature data in the first part of the discussion section, which were only available as tabulated data in governmental reports, were textualized using ChatGPT. The final manuscript was edited using DeepL Write (https://www. deepl. com/write) and Trinka (https://www. trinka. ai/) to improve the English language and grammar. These tools utilize AI technology to suggest text changes, suggest synonyms, and improve overall writing quality.
Results Out of a total of 53,116 samples included in the dataset, 7331 samples (14%) contained aspartame. Based on the selection criteria of minimum detection frequency and number of samples, 5703 samples (11%) in nine major food groups were selected for further evaluation. Samples not belonging to the selected food groups, 1628 samples (3%), were excluded. Food items were grouped according to their respective FoodEx2 product groups (flavored milk drinks, soft drinks, diet soft drinks, mixed beer drinks, candies, chewing gum, powdered drink bases, sports foods, and fiber supplements). The product groups and the corresponding individual products included in each product group are listed in Table  A1  in Appendix A. Table  1  shows these food groups with the calculated percentage of positive samples.
Discussion According to market data published by the German Federal Institute for Risk Assessment, between 2015 and 2018, 989 foods and 1055 beverages with added non-nutritive sweeteners (NNS) were launched in Germany, of which 16% contained aspartame  [24]. Despite this common use, a lack of occurrence and exposure data was noted in Germany. The last major European assessment by EFSA in 2013  [25]  did not include data from Germany. The only comprehensive study of aspartame exposure in Germany was published by Bär and Biermann in 1992  [26]. In this study, the occurrence data of aspartame in different food and beverage products were evaluated by different methods. First, the researchers collected data from food manufacturers by requesting information on the sweetener content of their products.
Second, package labels were examined to determine the presence and amount of aspartame. Finally, chemical analysis was used in cases where information was not readily available. These multiple complementary approaches provided a comprehensive understanding of the prevalence of aspartame in the marketplace and allowed for a comprehensive exposure assessment when the data were combined with food frequency questionnaires, but the authors did not publish the occurrence data so no comparison with this survey is possible. Currently, quantitative labeling of aspartame is not required, so only the qualitative presence of the compound is indicated on the ingredient list. Therefore, it is not possible to assess quantitative occurrence by evaluating package labels. Some data from previous surveys have been published, mostly in government reports.
Of a total of 353 samples, 185 contained aspartame. The mean amount of aspartame was 404 mg/kg, with a maximum of 1203 mg/kg. The 90th percentile value was 774 mg/kg. The maximum limit for aspartame of 1000 mg/kg was exceeded in one sample, which was an unfilled hard candy  [29]. During the German National Food Monitoring 2015, mineral waters, including raw water, were analyzed for selected sweeteners. Aspartame was not detected in any of the 19 samples  [30]. Van Vliet et al. reported aspartame analyses of seven soft drinks from Germany. Aspartame was detected in five samples with a mean of 119 mg/L (range 53-330 mg/L)  [  Recently, the German Federal Institute for Risk Assessment (BfR) published data from an analytical survey of 92 energy-reduced or sugar-free non-alcoholic beverages. For energy-reduced beverages, the study found that in three aspartame-positive samples, the average level of aspartame was 20 mg/kg, with a range of 0.
Second, the samples were only collected in Germany and may not be representative of other countries or regions. Studies reporting the occurrence of aspartame in foods and beverages in other countries are mainly from Europe and focus primarily on beverages. Comprehensive data on the occurrence of aspartame are available from only four European countries  [25]. Given this lack of comprehensive data, the main purpose of obtaining the data through the Consumer Information Act was to make the German data on aspartame publicly available so that the conditions of the IARC preamble are met, which require that the data be made publicly available to meet transparency requirements  [33]. It is hoped that the data can now be used in the work of the IARC and JECFA working groups. Since this study is the first to provide significant data on food groups such as dietary fiber supplements, certain dairy products, and powders used to make beverages, it is considered critical to providing a comprehensive understanding of aspartame exposure, despite the limitations noted above.
Conclusions These data on the occurrence and levels of aspartame in various foods in Germany provide new and updated information on the use of aspartame in the food industry that may help researchers better understand its prevalence and potential health effects. Based on the data provided, it appears that aspartame is present at varying levels in a variety of foods. In terms of the detected levels of aspartame, the data show that the highest concentrations were found in sports foods and chewing gum. The levels of aspartame found were generally within the legal limits set by the European Union. Consumers should read food labels carefully if they are concerned about their intake of this additive. Although the levels found in this dataset do not exceed the regulatory limits that are currently considered safe  [25] , this does not necessarily reflect the total aspartame intake of an individual who consumes several foods containing the sweetener.
Data Availability Statement: Publicly available datasets were analyzed in this study. These data can be found here: https://fragdenstaat. de/en/request/aspartam/ (accessed on 28 March 2023). The data were provided by the German authority Federal Office of Consumer Protection and Food Safety (BVL) in the area of food safety and consumer protection, survey on aspartame, 2000-2022. The own derivative calculations presented in this study are available in the Supplementary Materials.
Tourette Syndrome (TS) is a complex neurodevelopmental disorder characterized by vocal and motor tics lasting more than a year. It is highly polygenic in nature with both rare and common previously associated variants. Epidemiological studies have shown TS to be correlated with other phenotypes, but large-scale phenome wide analyses in biobank level data have not been performed to date. In this study, we used the summary statistics from the latest meta-analysis of TS to calculate the polygenic risk score (PRS) of individuals in the UK Biobank data and applied a Phenome Wide Association Study (PheWAS) approach to determine the association of disease risk with a wide range of phenotypes. A total of 57 traits were found to be significantly associated with TS polygenic risk, including multiple psychosocial factors and mental health conditions such as anxiety disorder and depression.
INTRODUCTION Tourette Syndrome (TS) is a complex neurodevelopmental disorder characterized by vocal and motor tics lasting more than a year  [1]. It is a childhood-onset condition with a prevalence of 0. 6-0. 8% in school age children and has a population heritability of 70% and SNP-based heritability estimates ranging from 0. 21 to 0. 58  [2] [3] [4] [5] [6]. TS is highly polygenic in nature and multiple genetic variants account for a substantial proportion of phenotypic variance of the condition  [6] [7] [8] [9] [10] [11]. Epidemiological studies have shown TS to be associated with various phenotypes and disorders, but large-scale phenome-wide analyses in biobank level data have not been performed to date  [12, 13]. This is likely due to the scarcity of individuals with TS in biobank-scale available datasets.
Applying PRS based PheWAS methods across large datasets like the UK Biobank has led to the identification of novel phenotypes associated with genetic risk of psychiatric disorders that have been previously correlated with TS  [20, 21] , such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), schizophrenia and major depressive disorder  [22]. These associations have also shed light on the shared genotypic architecture of such related disorders. Here, using the summary statistics from the latest GWAS metaanalysis of TS  [11]  and genomic and phenomic data from the UK Biobank (UKB), we conducted a PRS-based PheWAS for TS, interrogating a wide range of phenotypes including physical and mental health, biochemical, and socio-demographic factors. Our goal was to uncover phenotypes that may be associated with TS genetic risk and compare to phenotypic associations with genetic risk of other disorders along the impulsivity-compulsivity spectrum that have previously been found to be genetically associated with TS  [20, 21].
Study population and quality control The UK Biobank dataset (Application Number 61553) which has data from ~500,000 individuals was used for the PheWAS analysis. All appropriate patient/participant consent has been obtained and the appropriate institutional forms have been archived. We removed individuals with non-white British ancestry based on the self-reported ancestry information available in the UK Biobank data. Next, we removed individuals with greater than third degree relatedness based on the kinship coefficient. Finally, we ran principal component analysis using TeraPCA  [23]  to remove individuals who did not overlap with European samples in the 1000 Genomes dataset (mean PC value of top 6 PCs ± 3 SD). We ran further quality controls on the genotype data to remove individuals with missingness >0.
PRS calculation PRScs software was used for the calculation of PRS  [24]. This method utilizes a Bayesian regression framework and places a continuous shrinkage prior on the variant effect sizes using GWAS summary data and an external linkage disequilibrium (LD) panel. We used the 1000 Genomes European LD reference with HapMap3 SNPs, to update effect sizes jointly for all the SNPs in LD. The output is a file with updated effect size estimates which we used to calculate PRS using the score function in Plink. The summary statistics file required for the estimation of risk scores was obtained from the latest GWAS meta-analysis of TS  [11]  with 6,133 cases and 13,565 controls to compute a weighted, mean score for each individual separately. The GWAS for other disorders were obtained from the Psychiatric Genomics Consortium  [25] [26] [27]  and the number of SNPs used for PRS calculation are mentioned in the Supplementary Table  1.
Phenome wide association study PHESANT package was used to run the PheWAS  [28]. This tool was designed specifically to study the associations in UKB data. It classifies each phenotype as one of four data types: continuous, binary, ordered categorical, or unordered categorical and then estimates the association between PRS scores and each phenotype using a regression model that fits the data type of the phenotype (linear, logistic-binary, ordered-logistic, or multinomial-logistic). We included 2242 phenotypes which were classified into 50 UKB categories and were manually grouped into five categories in our analysis (biochemical markers, cognition and mental health, disease diagnosis, health and medical history, socio-demographics). The phenotypes in the disease diagnosis category were obtained after mapping the 17,000 ICD-10 diagnosis codes of the UKB to 1430 phenotypic codes using the Natural Language Processing (NLP) method implemented in the R package Phewas  [29].
The number of phenotypes in each category are shown in Table  1  and the sub-categories are shown in Fig. 1  and Supplementary Table  2. Age, sex, and genotyping batch derived from the UKB data and top 10 PCs calculated using TeraPCA were used as covariates for the analysis. To account for multiple testing, we used a Bonferroni threshold of p < 2. 3 × 10 -5 (0. 05/2242) for the results of our main analyses. Given the possible correlation between various phenotypes, a Bonferroni threshold could be considered to be overly conservative, so we also used a false discovery rate of 0. 05 adjusted threshold (p < 0. 00495) to identify additional phenotypes associated with TS PRS in a secondary analysis. For the sex-specific PheWAS analysis, we split the data into two parts (179976 females and 153440 males) based on the sex data provided in UKB and ran the analyses independently for both datasets.