dataesr/scientific-paragraphs-categorization huggingface dataset:
The Drugs-lib, FOOD-lib and NP-lib were constructed to be implemented in the MTiOpenScreen service such as to be used in drug discovery structure-based virtual protocols. As a consequence, we filtered our initial sets of compounds according to three criteria: (1) the compounds should be suitable for docking computations (which excludes highly flexible compounds and very small or large molecules); (2) the compounds structures should not include major documented toxicophores; and (3) the compounds should be purchasable to enable experimental validation of the docking predictions. Our filtration protocol associates both automatic filtration steps using FAF-Drugs4 and babel, and a visual inspection step to validate the selection of compounds that should be included in the datasets. Finally, the Drugs-lib includes 7173 stereoisomers corresponding to 4574 single isomer drugs, the FOOD-lib gathers 10997 stereoisomers corresponding to 3015 single isomer food constituents and the NP-lib is formed of 1228 stereoisomers corresponding to 653 single isomer natural products.
Compounds libraries diversity. Structural dissimilarity of compounds, which is used to measure the chemical space covered by a dataset, is one of the key points to ensure success of virtual screening protocols. Among the different approaches that can be used for its measure (see  [57]  for a review), we chose in this study, to use atom pair fingerprints to measure distances between compounds within each library and FragFp fingerprint descriptors to visualize the chemical space covered by the three libraries. Results show that, despite their relative limited size, the three libraries include compounds that are structurally diverse. The FOOD-lib and NP-lib chemical space coverage are largely overlapping, which is logical since the food constituents listed in the FooDB are chemical compounds present in unprocessed foods and are thus also natural products.
e. co-crystallized with a ligand) that had the best resolution with no missing residues in the binding site areas. It is to note that for GP130, only apo structures (i. e. structures in unbound conformations) were available and then were used for this study. For the other oncotargets presented here, suitable apo structures were available for CDK2 (PDB ID: 5IF1) and cereblon (PDB ID: 6BN8) and the screening of the Drugs-lib using these structures led to results comparable to those obtained with the holo structures (data not shown). Yet, it is in general recommended to use holo structures as starting point for virtual screening. We are aware that the resolution is not the perfect metric to guide protein structure selection for virtual screening, but it presents the advantage of being directly available in the PDB file and can be easily followed by researchers that are not familiar with structure-based methods and protein structure handling.
realize the docking of the database into multiple and diverse conformations of the receptor) can be used. The set of receptor conformations can be obtained by selecting different PDB structures  [68]  or generated using molecular dynamics simulations and normal mode analysis  [69]. The results obtained with such approaches can thus be interpreted not only by considering the scores and ranks of the compounds in each structure of the considered target but by selecting the best AutoDock Vina score in the ensemble of PDB structures for each compound. Using the ensemble docking approach that makes use of several PDB files, the final ranks of fluspirilene, mebendazole, raloxifene, sulindac and thalidomide were respectively 636, 409, 300, 1164 and 53 (and the corresponding AutoDock Vina scores were respectively -12.
Ligand collection The compounds used to generate the three datasets, Drugs-lib, Food-lib and NP-lib, were extracted from six different freely available databases. To construct the Drugs-lib, four different drug databases were merged: (1) the "drug" subset of the ChEMBL database  [26]  included 11. 542 compounds (chemicals and biologics), (2) the "approved" subset of DrugBank  [27]  version 5. 0. 10 gathering 2141 compounds, (3) the DrugCentral online compendium  [28]  that contained 3965 compounds and (4) the "approved" SuperDrug2 database version 2. 0  [29]  that contained 3628 molecules. The Food-lib was constructed using as a starting point the FooDB (http://www. foodb. ca/) a large database on food constituents. The NP-lib was obtained by filtering a purchasable diverse library of natural products  [30].
Library curation for structure-based virtual screening computations The three libraries were prepared using the same protocol (Figure  1 ), with only one additional step of databases merging and duplicates removing to prepare Drugs-lib. The ChEMBL "drug" (https://www. ebi. ac. uk/ chembl/drugstore, downloaded 28/11/2017), DrugBank "approved" (https://www. drugbank. ca/releases/latest, downloaded the 14/11/21017) and SuperDrug2 "approved" (http://cheminfo. charite. de/superdrug2/downloads. html, downloaded 13/12/2017) subsets were downloaded in SDF format from their respective website. The DrugCentral subset was downloaded in smiles format (http://drugcentral. org/download, downloaded 04/01/2018). The FooDB database was downloaded (http://foodb. ca/downloads, downloaded 04/01/2018) in sql format and all compounds for which a smiles string was provided were extracted in smiles format.
The DrugCentral and FooDB subsets were converted from smiles format to SDF format using Babel  [70]. The MolPort IDs of the diverse library of natural products were retrieved from the supplementary data of ref  [30]  and the corresponding compounds were downloaded in SDF format from the MolPort website (https://www. molport. com/shop/index). Some MolPort compounds with outlier structures were removed. For each of the six initial compound datasets, we used FAF-Drugs4 (Free ADME-Tox Filtering)  [24]  to perform a primary filter that involved the removal of salts, mixtures, inorganics, isotopes and duplicates. Furthermore, we applied soft physico-chemical filters to remove compounds with a molecular weight inferior to 100 or superior to 1000, more than 20 rotatable bonds and less than 5 heavy atoms.
After this first step, and for the Drugs-lib only, we merged the FAF-Drugs4 "Accepted" compounds of the ChEMBL "drug", DrugBank "approved", DrugCentral and SuperDrug2 "approved" into a unique dataset. The duplicates compounds were removed using both the Babel --unique /nostereo/nochg option (version 2. 3. 2, for more information see http://openbabel. org/wiki/Main_Page) and FAF-Drugs4 duplicate tool that compares the canonical smiles of the molecules. This collection containing unique drugs was then used as input for the next library preparation steps. A second filtering step of the three compound collections was achieved by visual inspection of their 2D structures in DataWarrior  [31]  such as to remove molecules with structures not suitable for docking studies (e. g. , drugs with long aliphatic flexible chains, some sugars, some sub-structures that could not be generated in 3D nor docked with the currently available approaches…).
e. compounds with no structural alerts), "Intermediate" (i. e. compounds with low-risk structural alerts and/or for which the number of occurrences are below the defined threshold) and "Rejected" (i. e. compounds that include a high-risk structural alert and/or exceed the threshold of occurrence of low-risk structural alerts) (for more information, see http://fafdrugs4. mti. univ-paris-diderot. fr/groups. html). The FAF-Drugs4 "Accepted" and "Intermediate" compounds were downloaded in SDF format and converted in smiles format using Babel. To investigate whether or not the selected compounds could be purchased, we searched the ZINC15 database  [25]  with our list of smiles as query input. The compounds tagged as 'for-sale', i. e. in-stock compounds (compounds purchased from manufacturers and ready for sale), on-demand compounds (all substances that are for sale) and boutique compounds (compounds often made to order), were kept and their corresponding purchasable stereoisomers were downloaded from the ZINC15 database  [25]  in SDF format.
MTi OpenScreen The MTiOpenScreen web service provides users with a blind docking (full surface) service for a small number of molecules or for extensive docking in a user defined binding pocket (this service is called MTiAutoDock and the computations are carried out with AutoDock4. 2 [74] ) and a screening service named MTiOpenScreen  [23]. The screening service implements the docking program AutoDock Vina  [75]  to perform virtual screening computations and two in-house prepared drug-like chemical libraries (a Diverse-library and library expected to be enriched in inhibitors of protein-protein interaction, the iPPI-library) are available. It is also possible to upload a library of up to 5000 molecules generated by the user. AutoDock Vina associates a gradient-based conformational search approach and an empirical scoring function.
Protein receptor preparation MTiOpenScreen service allows the user to upload the protein receptor with two different formats (mol2 and pdb). Users can thus download the protein structures from the PDB and use them directly without any preparation step in the MTiOpenScreen service. However, as some PDB structures can be problematic (missing side chains, alternate side chains conformations, presence of crystallographic adjuvant molecules in the binding site, …), we recommend to prepare the protein structures for docking. One very simple two-step protein preparation protocol is available within the Chimera package, a free to academic and nonprofit users and user-friendly visualization tool  [76]. The procedure is described in the Supplementary Data (Supplementary Figure  16 ).
Test cases protein structure selection and grid calculation parameters Fluspirilene. 368 holo X-ray crystallographic structures of human CDK2 are available in the Protein Data Bank  [77]. We used as test dataset of CDK2 proteins the 44 CDK2 proteins used for the ensemble docking study of ref  [35]  (Supplementary Table  1 ). In order to use the same AutoDock Vina grid center for all the proteins of the test dataset, the 44 proteins were aligned using the 4EK4 structure as the reference. The x, y, z AutoDock Vina grid center coordinates used are -1. 560, -7. 963, 27. 836 and the size of the search space was set to 20 Å x 20 Å x 20 Å. Mebendazole. 36 holo X-ray crystallographic structures of human VEGFR2 are available in the PDB  [77]. 23 holo VEGFR2 structures presenting missing crystallized residues in the binding site area were excluded and the 13 remaining structures were used for.
FUNDING Supports from the  Inserm Institute , the  University Paris Diderot  and the  ANR  grant  Patho-Methylome  [ ANR-15-CE12-0020-04 ] and  ANR-IA-2011-IFB [ANR-11-INBS-0013 ],  ANR   ToxME  [ ANR-16-CE18-0025 ].
RAS is one of the earliest and most popular forms of Neurological Music Therapy  [13] , and provides a promising therapy for gait impairments in PD  [14, 15]. Auditory cues, in comparison to other cueing methods (such as tactile or visual) exhibit the strongest beneficial effects for gait training  [7, 16]. In the BeatHealth project a system has been developed to deliver a training session for Persons with PD (PwPD) by exploiting RAS in the form of musical stimuli. The system is formed by: 1. Two sensors, worn on the leg shanks, for recording a user's gait kinematic data. 2. A mobile application running on a smartphone which receives the information from the sensors and is capable of adapting the music rhythm and synchronizing it with the user's gait. 3. A cloud platform assessed through a website where relevant information associated with and data generated by the application is safely stored and can be checked by the user and shared with health professionals.
The latter two components both involve a significant design effort 1. A website designed for health professionals to define the training program to be performed by the PwPD and later to track their progress through the program. 2. A mobile phone application designed for PwPD which loads the training program to be performed by the PwPD that has been designed by the health professional. 3. A website for PwPD to access their own progress data. This paper explores the following research questions: • Q1: Has the user centred design (UCD) methodology identified the appropriate functional requirements of the BeatHealth system? • Q2: How usable was the resulting BeatHealth system? The design process for the components of the BeatHealth system was iterative and followed a user centered design methodology which actively sought and incorporated the input of health professionals and end-users (both PwPD and caregivers).
Ethnographic observations. Ethnographic observation was the method used to gather data about the potential users of the proof-of-concept product and the effect that the symptoms of PD may have on their interactions with their environment in various contexts. This method was used to inform the design of the prototypes and the plan for the design process itself. Several observation sessions were conducted: two with PwPD and one with a healthy participant. Each session was intentionally designed around different contexts to gather understanding of a broader range of movement evaluation tools and techniques and their impact on participants. The first observation session was conducted by the research team at Centre Hospitalier Regional Universitaire de Montpellier (CHRU). One PwPD participated and was asked to carry out various tasks while wearing headphones to deliver varying rhythmic stimuli and monitoring equipment designed to capture movement and heart rate data.
To maintain privacy, the PwPD must give authorization for this feature to his/her physician in advance. A PwPD will be able to directly access their own recorded data using their credentials via the end-user website. Functional requirements for the end-user (PwPD) website and for the health professionals website can be found in S2 and S3 Files respectively. Given these requirements, a preliminary definition of the main system functionalities and actions was defined for the websites. Both websites need to access user data and present this data in the form of charts, tables, etc. Extensive data should be saved and must be accessible from different places depending on the final users (health professionals and PwPD). Therefore, in BeatHealth this data was stored in the cloud to make access possible for final users everywhere.
The key data recorded for each task included the task success (arriving at the end of the screen-flow for that particular task), the time to perform the task, the action flow within each task in relation to the expected flow, and any areas of confusion or errors. Post evaluation interviews and discussions with participants queried their thoughts about the system and the evaluation experience. The key topics covered in this discussion included the overall impression, any task or steps that may have caused confusion or errors, starting the application, placing the equipment on the body, charging the phone and sensors, connecting to WiFi, and any specific recommendations. The estimated duration of a participant's evaluation session was 60 minutes, however the actual time varied among participants.
This score can also be converted to a percentile rank of 70% which means that it has higher perceived usability than 70% of all products tested  [47]. Website evaluation. As detailed previously, the purpose of the website in BeatHealth is primarily to provide access to the data that is recorded and summarized from end-user (PwPD) walks using the BeatHealth mobile application. There are different websites functionalities for end-users (PwPD) and health professionals. The two different user types have access to different functionalities after log-in and therefore, different mock-ups were built for each type of user. The mock-ups were built using Flair-Builder  [49]  so that they were interactive and could be tested on a personal computer. Real underlying functionality was not implemented at this stage and the aesthetic design was also not a focus at this point.
Final implementation and evaluation After a two-year iterative design and implementation process, the final BeatHealth system was implemented. This system comprised custom wearable sensors that measure biomechanical data during gait training, a fully functional mobile application that was connected to the sensors to receive gait data and incorporated the BeatHealth technology and algorithms to perform real-time music adaptation, and two fully functional websites, one for end-users and one for health professionals. The user interface designs of the final mobile application and websites were both based upon the user interface mock-ups that had been evaluated during design and development, but nevertheless included some changes. There were a number of changes to the user interface design for the final Android mobile application.
Most of the responses were related to physical components or to the choice of music and not to the application itself. Among the participants, 12 reported that the BeatHealth system helped them with their gait or made them walk more, while five participants indicated that BeatHealth had not helped them. The presentation on the screen was clear. Lang, language could be French (F) or English (E). Questions are represented in French as they were designed and used, and in English, translated for the publication. The results show the number of participants and the corresponding percentage in parentheses for each answer. https://doi. org/10. 1371/journal. pone. 0207136. t005.
Discussion The BeatHealth system was developed based on the RAS technique to improve gait and reduce freezing episodes on PwPD. During the project design and development field trials were conducted to evaluate the effectiveness of the system. In this study, the subjective perception of the PwPD who used the system for three months prescribed by their physicians has been analyzed. Results show that only 32% of participants subjectively felt that the system helped them improve their gait. Additional understanding of this subjective result could be obtained by comparison with objective measurements of gait change during the trials. Such objective measurements require detailed processing and analysis of the recorded sensor and GPS data but this processing and analysis is outside the scope of the work presented in this paper.
This work was supported by the  BeatHealth  project within the  7th Framework Programme  of the European Union -Personalised health, active ageing, and independent living, contract Number  FP7-610633. See also: http:// www. euromov. eu/beathealth/homepage. This work has been also partially supported by the  Ministry of Economy and Competitiveness of the Spanish Conclusion which work best, and the extra care that has to be taken when evaluating non-functional mock-ups with end users. Government and by the  European Regional Development Fund  (projects  TIN2014-52665-C2-1-R  and  TIN2017-85409-P ), and by the  Department of Education, Universities and Research of the Basque Government  under grant  IT980-16.
e. univariate hypothesis testing  (Purcell et al. , 2007)  and with a state-of-the-art method, the logistic kernel machine method developed by  Wu et al. (2010)  on both synthetic and real datasets from the Wellcome Trust Case Control Consortium  (WTCCC, 2007)  and on ankylosing spondylitis data (International Genetics of Ankylosing Spondylitis Consortium  (IGAS) et al. , 2013).
The algorithm also makes use of the fact that the LD matrix can be modeled as block-diagonal by allowing only groups of variables that are adjacent on the genome to be merged, which significantly reduces the computation cost. This algorithm is available via the R package called adjclust on https://cran. r-project. org/web/packages/adjclust. Step 2. Dimension reduction function One way of addressing issues related to high-dimensional statistics (and in particular the multiple testing burden that we mentioned above) is to reduce the dimensionality of the predictor matrix X ∈ R N×J by creating a reduced matrix D with new covariates that nevertheless remain representative of the initial matrix. This means reducing the number of predictors J to G J, with row D i. the G-dimensional vector of new predictors for observation i.
We propose here a supervised validation set approach to find this optimum. Since this algorithm aims to identify phenotype-related SNPs clusters, it is necessary to split the dataset into two subsets to avoid an inflation of type I errors in the testing procedure. One subset, [Y sub1 , X sub1 ], is used to choose the optimal cut and the second one, [Y sub2 , X sub2 ], to perform the hypothesis testing in Step 4. First we apply the constrained-HAC on a subset X train sub1 ⊂ X sub1 , and for a given level of the hierarchy we apply the dimension reduction function defined above (Step 2) to each of the G clusters to construct the matrix D train sub1. We then fit a ridge regression model to estimate the coefficients of the predictors in D train sub1. Ridge regression is a penalized model which shrinks the estimated coefficients towards zero and is known to have a good stability in comparison to other penalized-regression models such as lasso regression  (Bousquet and Elisseeff, 2002).
NUMERICAL SIMULATIONS The performance evaluation described below was designed to assess the ability of our method to retrieve causal SNPs or causal clusters of SNPs under different simulation scenarios. For each scenario, we use a matrix X HAPGEN of SNPs generated by the HAPGEN2 software  (Su et al. , 2011)  with a sample size of 1000 individuals. This software allows to simulate an entire chromosome conditionally on a reference set of population haplotypes (from HapMap3) and an estimate of the fine-scale recombination rate across the region, so that the simulated data share similar patterns with the reference data. We generate the chromosome 1 (103 457 SNPs) using the haplotype structure of CEU population (Utah residents with Northern and Western European ancestry from the CEPH collection) as reference set.
The HAPGEN2 software allows to generate a controls-only matrix of SNPs (no disease allele). We filtered this matrix according to the minor allele frequency to only keep SNPs with a MAF greater than 5% thus reducing the size of X HAPGEN to 60 179 SNPs. We generate a posteriori the phenotype using the logit model with a given set of causal SNPs or cluster of SNPs. The main difference between the different scenarios is to be found in the way that the case-control phenotype y is simulated.
Odd ratio (β coefficients) of the causal predictors: For the SNPclus scenario we chose an equal OR of 2. 7 for each causal aggregated predictors, corresponding to a β coefficient equal to 1. For the singleSNP scenario we chose an equal OR of 1. 1 for each causal predictors, corresponding to a β coefficient equal to 0. 1. The rationale behind these coefficients arises from the hypothesis that the combined effect of several low-effect SNPs on the phenotype is stronger than the effects of each individual SNP. As previously mentionned, we generated the phenotype using causal SNPs simulated with the HAPGEN2 software. However, as commercial genechips such as Affymetrix and Illumina arrays do not genotype the full sequence of the genome, some SNPs are thereby unmapped and the marker density is in general lower than the HapMap marker density.