dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to find such "hidden treasures", we performed thorough chemical analysis of the drug discovery-oriented commercial chemical space, featuring (after standardization and duplicate removal) 800M ZINC compounds, versus 1. 6M molecules that have already attracted the attention of medicinal chemists and were therefore captured in the ChEMBL database together with their observed biological activities. Both ZINC and ChEMBL compounds were split into four groups depending on the type of biological tests and selected drug design strategy, resulting in fragment-like  18  , lead-like  19  , drug-like  20  and PPI-like  21  subfamilies. The purchasability of ZINC molecules was also assessed: they were further split into ZINC-Real -in-stock compounds directly available for purchase, and ZINC-Tangible -compounds that can be synthesized upon request.
It was constructed in a way to be able to predict biological activities present in ChEMBL database. Being multitarget-oriented, this map can be considered as a generalized framework for biologiacally-biased chemical space visualization. It is based on one of the ISIDA fragment descriptors -atom sequences with a length from 2 to atoms labeled by CVFF Force Field types and Formal Charges labels  37. See more details about the construction of the Universal map #1 in Supporting Information. First, each of the abovementioned ZINC subsets were projected onto the universal map. Density landscapes of the subsets were built in order to obtain a general overview of the structural features of the purchasable chemical space (Figure  2 ). Interestingly, the commercial compounds are distributed in a highly imbalanced manner: the major part of the map area is rather sparsely populated (gray zones), by contrast to a few outstanding density peaks (multicolored regions).
Thus, in order to dispel remaining doubts, additional analysis of the key substructures as a potential source of the highly potent hits was performed. The ultimate pertinence of herein highlighted ZINC-specific MCSs for biological exploration of the chemical space will only be completely validated by actual experimental screening of those compounds, by MedChem groups pursuing specific drug discovery projects. This path is beyond the present work, which limits itself to present some indirect hints of the usefulness of these compounds, notably by (i) investigating whether those types of compounds have been tested already, without being reported yet in ChEMBL database or (ii) predicting biological properties of the compounds of interest using the same universal map-based property landscapes -a fast, robust and intuitive approach directly emerging from the chemographic concept.
Not being present in ChEMBL is not yet synonymous with being "off the beaten path". ChEMBL focuses mostly on the higher-level (dose-response) biological data, but some of the ZINC-specific MCS might have served in HTS campaigns reported elsewhere. PubChem, the largest collection of structure-activity data including high-throughput screening (HTS) reports, has been chosen in this study as an alternative external subset. 101M compounds, 80M of which are unique structures (stereoisomers were considered duplicates) were collected after analysis of ChEMBL-and ZINC-specific maximum common substructures was finished (December 2019). 3. 1M of those compounds have been tested in at least one biological assay, while only 1. 1M compounds were labeled as "active" by PubChem. In a search for the potential drug candidates out of ZINC-specific subspace, around 24K of lead-like ZINC-Real unique MCS (absent not only in lead-like subset but also in the unfiltered version of ChEMBL) were used as queries against 3.
Criteria for discontinuing or modifying allocated interventions {11b} There will be no special criteria for discontinuing or modifying allocated interventions. Patients will inform at the time of inclusion that they will not be able to choose between IST or GPACTS and that they will not be able to change therapy during the course of the study. They will be also informed that they could stop the therapy at any time. In this case, the patient is re-oriented towards routine care, according to patient needs (as evaluated by the medical teams involved). Statistical analyses however will evaluate data according to initial randomized arm, i. e. on an intent-to-treat basis. Follow-up is continued as described in this protocol (i. e. the stopping of the experimental intervention does not result in exclusion from the study).
Sequence generation {16a} Patients will be randomized to either study arm in a 1:1 ratio. Randomization lists consisting of randomly sized blocks will be established per centre. These lists are the responsibility of the study methodologist at the Department of Biostatistics, Epidemiology, Public Health and Medical Information at the Nîmes University Hospital (BESPIM). A specifically designed SAS program (Cary, NC, USA) will be used to carry out randomization. The number of subjects per block will be known only to the methodologist.
Data management {19} Data management will be performed in line with The International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) requirements. The related documents will be stored on the BESPIM server. eCRF data eCRF fields are formatted so as to enforce homogenous value types and require confirmation for less probable values and/or out-of-expected-range values: data entries, modifications, who made them and when are fully traceable (complete audit trail). An electronic signature by the investigator engages his/her responsibility for the data in an eCRF. The eCRF will represent only raw data values as opposed to calculated values, unless the latter are necessary for further eCRF input.
eCRF data security The software used to create eCRFs is hosted on a website within the Nîmes University Hospital (NUH). Access to this software is secured via a password. The data collected through generated eCRFs are subject to daily backup on a secure network. The network is connected to the Internet and the access is protected by a firewall. Clinical study data will be stored in a specific directory on the server. Only network administrators and authorized persons from the BESPIM may have access to this directory. The following measures were taken to implement confidentiality: -The required information technology is located in the BESPIM; access is controlled and secure. -Data are stored on a server hosted in a secure room at the NUH. To cope with any hardware or software problem that could occur involving the storage unit of virtual machines, another storage unit that is larger in terms of space, but less swift in terms of throughput, is deployed.
Dedicated software (VRangerPro) provides a daily incremental backup of virtual machines (except Saturdays and Sundays). full backup of these machines is performed every Friday night for an instant return to N-15 days in case of problems. The hard drives of the virtual servers are installed "en raid", which ensures IT security in case of hardware failure of one of the disks. Clinical study data will be stored in a database server. Only network administrators and authorized BESPIM personnel can access this server.
A patient identification list will be maintained by the investigator (and only the investigator). The investigator will ensure that the anonymity of each person involved in the study is respected. No identifying information will be disclosed to third parties other than those statutorily entitled to hold this information (and who are bound by professional secrecy). Plans for collection, laboratory evaluation and storage of biological specimens for genetic or molecular analysis in this trial/future use {33} No biological data will be collected during this study.
Statistical methods for primary and secondary outcomes {20a} The statistical analysis will be performed by the Department of Biostatistics, Epidemiology, Public Health, and Health Economics of Nîmes Hospital Center using SAS statistical software, Carey, NC. A difference will be considered as statistically significant if the test gives a p value of 0. 05 or less. Description of the population included and main parameters studied Initial data analysis will describe the sample and the population per group. The Shapiro-Wilk test will be used to determine whether or not the quantitative variables show a normal distribution. Statistical results will be presented in the form of "mean ± standard deviation" for quantitative variables showing a normal distribution, and "median and interquartile intervals" for other variables.
Guess-the-group To control for the success of blinding, outcomes assessor responses to the "guess-the-group" question will be compared to true responses using the Kappa agreement coefficient. Methods used to manage data that are missing, unused or invalid For the primary analysis, the data are censored for the primary outcomes (SRFFP); it is not relevant to replace missing values. For the other outcomes, we do not have a replacement method for missing data. Choice of patients to be included in the analyses All patients included and randomized in the study will also be included in the analysis (intent-to-treat analysis). The conclusions of the study will be based on this analysis. Exploratory Per protocol analysis will be also performed. Methods for any additional analyses (e. g. , subgroup and adjusted analyses) {20b} Additional analyses are not required.
One or more visits will be carried out during the trial according to the rhythm of the inclusions and the duration of the study. The reasons for these visits are: -To verify that the protocol is being respected -To verify the consent forms -To verify serious adverse event reporting -To carry out quality control: to compare case report form data with source document data within each centre Those responsible for the quality control of this biomedical research trial and thereunto duly authorized by the sponsor have access to the individual data strictly necessary for quality control and are subject to professional secrecy. All monitoring visits are accompanied by a written monitoring report (visit traceability).
Acknowledgements The authors are grateful to  Dr. Marjan Ghahramanlou-Holloway , Associate Professor of Medical and Clinical Psychology and Psychiatry at  Uniformed Services University of the Health Sciences, Bethesda (Maryland, USA ) and Adjunct Faculty at the  Beck Institute for Cognitive Behavior Therapy, Philadelphia (Pennsylvania, USA) , for her collaboration and enthusiasm from the beginning of the project. We also wish to acknowledge the help of the research team at the  Psychiatry Department (UHN)  for the essential support they offer us in the development of this project and more specifically,  Ms. Leonie Gazel  ( Project Manager ),  Mr. Antoine Giron  (Research Nurse and Clinical Research Coordinator),  Mr. Rabah Tamimou  ( Clinical Research Associate ) and  Ms. Lorrie Lafuente  ( Clinical Research Associate ).
A DC microgrid with integrated V2G has also been examined in other papers  [28, 29]. The paper  [28]  proposes an optimal day-ahead operation planning procedure that aims to minimize microgrid operation daily costs, according to suitable load demand and source availability forecast without involving MATLAB/SIMULINK modeling. The review paper  [29]  focuses on the charging system components, architecture, operational modes, and control, including the interaction between the PV sources, public grid, energy storage unit, and power electronics, however, it does not take into account the management of multiple EVs. In addition, V2G was characterized as a comparatively advantageous means of peak load shaving  [10]. The paper  [24]  proposes an optimal logical control algorithm based on V2G to reduce the average charging cost of EVs, which is dedicated to the French energy billing system within the peak/base hour's contract.
Background: RNAseq is nowadays the method of choice for transcriptome analysis. In the last decades, a high number of statistical methods, and associated bioinformatics tools, for RNAseq analysis were developed. More recently, statistical studies realised neutral comparison studies using benchmark datasets, shedding light on the most appropriate approaches for RNAseq data analysis. Results: DiCoExpress is a script-based tool implemented in R that includes methods chosen based on their performance in neutral comparisons studies. DiCoExpress uses pre-existing R packages including FactoMineR, edgeR and coseq, to perform quality control, differential, and co-expression analysis of RNAseq data. Users can perform the full analysis, providing a mapped read expression data file and a file containing the information on the experimental design.
Following the quality control step, the user can move on to the differential expression analysis performed using generalized linear models thanks to the automated contrast writing function. A co-expression analysis is implemented using the coseq package. Lists of differentially expressed genes and identified co-expression clusters are automatically analyzed for enrichment of annotations provided by the user. We used DiCoExpress to analyze a publicly available RNAseq dataset on the transcriptional response of Brassica napus L. to silicon treatment in plant roots and mature leaves. This dataset, including two biological factors and three replicates for each condition, allowed us to demonstrate in a tutorial all the features of DiCoExpress.
Background During the last decades, Next-Generation Sequencing (NGS) technologies have developed at a fast pace with the improvement of data quality coupled with a reduction of experimental costs. Since the early years of NGS, the use of RNAseq to profile transcriptomes became the method of choice replacing in time microarraybased analyses  [1]. Plant biologists use RNAseq-based transcriptomic extensively, generating knowledge about transcriptional regulation in several biological processes  [2] [3] [4] [5]. Differential gene expression analysis across different experimental conditions is classically used to gain insight into gene regulation events and gene co-expression analysis to identify functional modules. A classical analysis workflow starts with a data normalization step to account for technical biases that affect the number of reads mapped to a gene.
Several methods are available, and among the most used, we can find RPKM (Reads Per Kilobase per Million mapped reads)  [6] , Upper quartile normalization  [7] , RLE (Relative Log Expression)  [8]  and TMM (Trimmed Mean of the M-values)  [9]. Multiple methods, based on different statistical modelling of data, are available to perform differential expression analysis. Negative binomial-based models with robust mean-variance modelling, have been used extensively at the beginning, and they are available in the R-packages edgeR  [10]  and DESeq  [11]. More recently, the linear models and their generalized extensions for negative binomial distributions (GLM) have been proposed to account for the versatility of multifactorial experiments. They are available in the R-package limma  [12]  for the linear models and in the R-packages edgeR  [10]  and DESeq2  [11]  for the generalized linear models.
Even with all these tools, a biologist is often facing a dilemma on how to analyse his dataset correctly. Indeed a characteristic shared by the majority of GUI tools developed up to date is to offer the user the possibility to choose among multiple statistical methods for each step of the analysis with no specific propositions. However, the RNAseq data specificities, such as heterogeneity of counts or overdispersion among biological replicates, represent a methodological challenge that has to be addressed by proper statistical modelling of the gene expression. It is worth noting that in the case of multifactorial experiments, if interaction terms are included in the modelling, the writing of the contrasts might become tricky, requiring a good understanding of some statistical concepts, not always mastered by a biologist.
Selection of the statistical methods DiCoExpress offers a validated set of methods, based on three neutral comparison studies  [17, 30, 31]. The idea of such studies is to design and implement a framework to generate realistic benchmark datasets with known truth to make an objective and reproducible performance assessment. Comparing normalisation methods, Dillies et al. [30]  showed that the RLE method implemented in the package DESeq2  [11]  and the TMM method implemented in the package edgeR  [10]  demonstrate satisfactory behaviour in the presence of highly expressed genes. Both these methods maintain a reasonable false-positive rate without loss of power. The choice of both methods was confirmed by Reddy et al. [32]  and Evans et al. [33]  even in experiments with slightly asymmetric differential expression or different amounts of mRNA/cell per condition.
[34] , when the proportion of differentially expressed genes is low, the results obtained with the method implemented in the edgeR package are more satisfying. We thus chose the statistical model implemented in the edgeR package as a method of choice for differential expression data analysis. Moreover, we propose automatic writing of a large number of contrasts in order to facilitate the comparisons between the biological conditions considered in the experimental design. This automatic writing is a real advantage because, in the available R-packages, most contrasts in GLM with interactions between two factors must be handwritten and require thus an excellent understanding of the statistical modelling. For the co-expression analysis, we preferred mixture models to correlation-based approaches.
Mixture models aim at identifying an underlying structure in modelling the unknown distribution by a weighted sum of parametric distributions, each one representing a group of co-expressed genes. Gaussian mixture models were relevant for microarray data and were applied with success on several datasets  [35, 36]. For RNAseq data, which are discrete, Rau et al. [16]  first concluded that normalized expression profiles modelled with a Poisson mixture are relevant for co-expression analysis. However, in the Poisson mixture, the dependence structure between samples is not considered and can mislead the results. To tackle this problem, they proposed then a Gaussian mixture after a transformation of the normalized expression profiles  [17]. This model seems to be more suitable for RNAseq co-expression analysis by providing a proper identification of the groups of co-expressed genes because it accounts for per-cluster correlation structures among samples.
Architecture of DiCoExpress Using these neutral comparison studies, we combined the most suitable methods for each step of a standard RNAseq analysis. The tool DiCoExpress is a directory composed of a set of subdirectories that is to be installed on a computer for analysing RNAseq datasets (see Fig. 1 ). The directory Data stores all the projects, and the directory Results contains a subdirectory per project with all the results of the different steps. The directory Sources contains the R functions used by DiCoExpress. Finally, the directory Template_scripts contains an R script file for each project, allowing a semi-automated data analysis where the user is guided through all the steps from normalisation to co-expression analysis. With DiCoExpress, our objective is to automate the statistical analysis of gene expression Template_scripts directory is the directory where an analysis script for each project analyzed must be saved.
c Data directory is the directory where for each project, the input files (target and count tables) must be saved. If an annotation file used to describe biologically the different result tables as well as an reference file for the enrichment analysis are available, they must be also be saved in this directory. d Results directory contains all results obtained for each project according to the experimental design to ease the interpretation of the results in biological terms. To create DiCoExpress, we use the R programming language and several R-packages from CRAN and Bioconductor  [37, 38]. Each step of the analysis has a dedicated function available in the directory Sources. Seven functions compose the core of DiCoExpress (Fig. 2 ), and they are combined in a script, stored in the directory Tem-plate_scripts for each project to specify the steps of the analysis and the parameters to use.
csv. If it is inconsistent, then the columns of the Project_Name_ COUNTS. csv are reorganized according to the column of the target file. DiCoExpress performs analysis for a complete experimental design. If this condition is not verified, then an error message appears, and the script stops running. A filter option in Load_Data_Files is proposed to extract or remove a subset of samples, thus avoiding manual modifications of the expression file. The filtering rules are described according to the Project_Name_TAR-GET. csv (see section Results for an example). The (2) Quality_Control function produces a PDF file containing graphical outputs before and after normalisation: histograms of the library sizes, boxplots of the counts for each sample, heatmap, and principal component analysis (PCA), as shown in Additional file 2.
Differential expression analysis The differential analysis is based on a negative binomial GLM, where the log of the gene expression is modelled by all the factors describing the experiment. When the number of observations is two times greater than the number of parameters of the model, we advise to include interaction terms between the biological factors. Such terms in the gene expression definition might reveal meaningful interactions such as genotype-environment interaction and answer in a direct way to some biological questions  [39] [40] [41]. The (3) GLM_Contrasts function automatically writes a list of contrasts based on the model specified by the user. We focused on contrasts Fig. 2  Overview of DiCoExpress workflow. DiCoExpress is composed of seven functions written in R programming language and available in the directory Sources.
Running this function is a prerequisite to run the differential expression analysis. The (4) DiffAnalysis_edgeR function uses edgeR R-package to estimate the parameters of the GLM and then test every contrast chosen by the user. As proposed by Rigaill et al. [31] , the distribution of raw p-values of each contrast is inspected to assess the quality of the statistical modelling of the gene expression. Since the distribution of raw p-values is theoretically dominated by a uniform distribution, the fit between the statistical model and the data can be observed on these raw p-value histograms. If the raw p-value distribution is not satisfactory (see example in the section Results and Fig. 3 ), we advise repeating the analysis using a more stringent cut-off for the filtering step or another rule of filtering.
Co-expression analysis The (5) Venn_Intersection_Union function helps the user in the interpretation of the results by comparing different DEG lists. This function also generates the union and/or the intersections of these DEG lists to perform a co-expression analysis with the (6) Coexpression_coseq function. This latter function uses coseq R-package  [17]  to transform the raw data into normalised expression profiles. We kept the filter function of coseq removing the genes with low mean normalised counts. Those discarded genes are assigned in Cluster 0. A co-expression analysis is performed on the remaining genes using a Gaussian mixture after an arcsin transformation of the normalised expression profiles. Practically, multidimensional Gaussian mixtures of 5-30 subpopulations with unequal proportions and general covariance matrix are estimated.