dataesr/scientific-paragraphs-categorization huggingface dataset:
The enzymeencoding genes whose protein products participate in the metabolic reaction of a given metabolite are referred to here as metabolic genes. Next, for each of the metabolic genes, we used the multiMiR package in R to build a comprehensive list of all the putative DEmiRNA regulators. In particular, we found a total of 11 differentially expressed metabolites (Additional file 10) that were putatively regulated by a total of 263 differentially expressed genes. Interestingly, we discovered that the spatiotemporal expression of those 263 metabolic genes could be extensively regulated at the transcription and post-transcription level through a total of 79 DEmiRNAs. For instance, let-7b-5p, miR-16-5p, miR-21-5p, miR-92a-3p, and miR-192-5p regulated more than ten metabolic genes each (Fig. 2 ).
This independent cohort included 13 horses sampled only at T0, and 18 other horses sampled only after completing the endurance competition (T1). At T0 (basal time), the MFA superimposed plot of each type of data and its barycenter showed particularly high levels of variability in all "omic" layers (Fig. 4a ). However, the MFA projection plot of the partial representations for each "omic" layer (metabolome, transcriptome and miRNome) onto PC1 was tightly clustered (RV-coefficient = 0. 51; Fig. 4b ). The two metabolites showing the highest correlation (|r 2 | > 0. 80) on the first axis were glutamine and glucose, whereas the genes most strongly correlating with PC1 (|r 2 | > 0. 80) were RNA polymerase 21-kDA subunit (POLR2I), torsin 3A (TOR3A), vesicle-associated membrane protein 5 (VAMP5), histone binding protein 5 (COPR5), N1acetyltransferase family member 2 (SAT2), AT-hook containing transcription factor 1(AHCTF1) and various ribosomal proteins (Fig.
Animals and samples The data sets used for metabolome, transcriptome, and miRNome analysis have already been published by Le Moyec et al. [19]  and Mach et al. [8]. Le Moyec et al. [19]  evaluated the effects of long endurance exercise on the plasma metabolomic profiles, whereas Mach et al. [8]  identified the whole blood miRNA-mRNA relationships specifically regulated by endurance exercise in the same horses. Altogether, we selected samples from a total of 41 pure-breed or halfbreed Arabian horses (13 females and 28 geldings; mean ± SEM age: 9. 7 ± 1. 5) participating in a 160 km endurance competition. All horses were recruited on a volunteer basis in three different French competitions (see Additional file 1). For each of these 41 horses, the combined metabolome, transcriptome, and miRNome profiles were available.
H NMR data acquisition and statistical analysis The 1 H NMR spectra were acquired with a Bruker® Advance II spectrometer (Bruker BioSpin, Wissembourg, France) operating at 500 MHz and using a standard water-suppressed 1D spectrum (NOESY1D) sequence for the preservation of lipid signal intensities. The methods for sample preparation, data acquisition, data quality control, spectroscopic data-pre-processing, and data pre-processing including peak alignment, scaling and normalization are broadly explained elsewhere  [19]. In contrast to Le Moyec et al. [19] , where in silico metabolite identification and statistics were done by the MATLAB commercial program, we used different opensource R packages, allowing automated qualitative and quantitative metabolite characterization, calculation of spatial significance and, importantly, metabolite pathway analysis.
Peaks with just one non-zero intensity (single mass events) were removed from the normalized matrix. Additionally, a metabolite peak was considered to be detectable only if it was expressed in at least 50% of the experimental samples. The metabolite identification was then performed by using structure message of metabolites acquired from other available biochemical databases, such as human metabolome database (HMD), http://www. hmdb. ca; KEGG, http://www. genome. jp/kegg/; METLIN, http://metlin. scripps. edu/; Chemical Entities of Biological Interest (http://www. ebi. ac. uk/Databases/); and Lipidmaps (http:// www. lipidmaps. org/). Metabolite assignment of each peak was considered when chemical shifts of peaks in the samples were the same as in the publicly available reference databases (with a shift tolerance level of ± 0.
005 ppm), in order to counter-act the effects of measurements and pre-processing variability introduced by factors such as pH values and solvents. A manual curation for identified compounds was done by an expert in horse metabolomics  [19, 20]. Afterwards, the relative abundance of each metabolite was calculated as the area of the peak  [50]. Exploratory analysis of the metabolites signals were first performed by PCA, which displays the internal structure of data sets in an unbiased manner and reduces data dimensionality through linear combinations of the original variables. A PCA score plot was used to reveal the presence of outliers outside the 95% significance region of ellipse (i. e. , strong outliers). Interclass PCA of plasma metabolites with pre and post-endurance competition samples as an instrumental variable was also performed, based on a Monte Carlo test with 999 replicates.
Unsupervised and supervised methods were performed with a toolbox to explore NMR metabolomic data sets in the R environment  [51]. Then, we performed supervised projections to latent structures-Discriminant Analysis (PLS-DA) and the OPLS analysis, which integrates an orthogonal signal correction (OSC) to identify and characterize metabolic changes induced by endurance exercise. The OSC-correction approach was conducted using DeviumWeb R package; (https:// github. com/dgrapov/DeviumWeb) [52]. By extracting variation from its computed partial least squares (PLS) components that is uncorrelated (orthogonal) to the responses, OPLS produces a more interpretable regression model compared to PLS  [53]. Loading plots combining the reliability and correlation from the OPLS models were used to identify differential metabolites among the pre and post-endurance competition samples.
The firmness of the model was evaluated with R 2 outcomes and the precision of the prediction (Q 2 ). Complementary to the multivariate statistical methods to identify metabolites that were statistically different between T0 and T1 samples, analysis of variance (ANOVA) was performed to delineate whether there was a significant difference between the average values of metabolites normalized intensities between pre and post-endurance competition samples. Because of the multiple testing issues, Bonferroni corrected P-values were calculated. A significance level of corrected p < 0. 05 was accepted. This data is available at the NIH Common Fund's Data Repository and Coordinating Center (supported by NIH grant, U01-DK097430) website, http://www. meta bolomicsworkbench. org), where it has been assigned a Metabolomics Workbench Project ID: (UrqK1489).
RNA isolation, microarray experiments and mRNA and miRNA statistical analysis Transcriptome and miRNome profiling were performed using Agilent microarrays as described earlier  [8]. All the transcriptome and miRNome pre-processing, normalization and statistical analysis steps were carried out as described elsewhere  [8]. In contrast to the Mach et al. [8]  study, the p-values were corrected for multiple testing using the Benjamini and Hochberg method with a threshold of adjusted p < 0. 05, as a compromise between the unadjusted analysis and the Bonferroni procedures. The data sets supporting the conclusions of this article are available in the Gene Expression Omnibus (GEO) repository, [GSE73104; (http://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE73104].
Regulatory network of metabolites, metabolic genes and miRNAs: enrichment analysis The underlying assumption behind the enrichment analysis is that by combining the evidence based on changes in both gene expression and metabolite concentrations, one is more likely to pinpoint the metabolites and pathways involved in the underlying biological processes during endurance exercise. The power of this approach is that such models can provide non-intuitive metabolic and physiological hypothesis  [55]. First of all, we converted the equine Ensembl gene IDs to their orthologous associated Homo sapiens gene IDs through the Biomart retrieval tool (Ensembl release 83; http://www. ensembl. org/biomart/). Then after, for each metabolite with a loading different from zero along any OPLS principal component axis, we identified all the metabolic pathways that the given metabolite participates in through the KEGGREST package in R (Homo sapiens, [hsa] organism).
Identification of miRNAs involved in the regulation of the identified metabolite-metabolic target genes regulatory network The multiMiR package in R was used to identify potential miRNAs regulating the metabolic target genes included in the regulatory network  [57]. For each miRNA expressed in our data set, we assembled a comprehensive list of all experimentally validated target genes using the multiMiR package in R. multiMiR is a comprehensive collection of predicted and validated miRNA-target interactions and their associations with diseases and drugs  [58]. It contains human and murine data from 14 external databases that are categorized into three components, including three validated miRNAtarget databases (miRecords, miRTarBase and TarBase), eight predicted miRNA-target databases (DIANA-microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA and TargetScan), and three disease-or drugrelated miRNA databases (miR2Disease, Pharmaco-miR and PhenomiR)  [58].
Availability of data and materials The transcriptome and miRNome data supporting the conclusions of this article are available in the Gene Expression Omnibus (GEO) repository, [GSE73104; (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE73104]. The metabolome data is available at the NIH Common Fund's Data Repository and Coordinating Center (supported by NIH grant, U01-DK097430) website, http://www. metabolomicsworkbench. org), where it has been assigned a Metabolomics Workbench Project ID: (UrqK1489). The data is directly accessible at: http://dev. metabolomicsworkbench. org:22222/data/DRCCMetadata. php?Mo-de=Study&DataMode=AllData&StudyID=ST000503&StudyType=NMR& ResultType=2&access=UrqK1489#DataTabs Additional data sets supporting the conclusions of this article are included within the article and its additional files.
We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats.
In many cases, these software tools come with their own data and hypnogram file formats, and this heterogeneity can represent a substantial obstacle for sharing of sleep data across laboratories or clinics. Some of the very few existing open sources graphical user interface (GUI) for reading and scoring sleep include Phypno 1  , written in Python, and the MATLAB-based toolboxes sleepSMG 2  or SpiSOP 3. With this in mind, we developed Sleep, an intuitive and efficient open-source GUI dedicated to the visualization of polysomnographic recordings and scoring of sleep stages. Sleep supports a range of data file formats and provides several scoring aid including the detection of essential features of NREM and REM sleep such as spindles, K-complexes, slow waves, and REM. Sleep was written in Python, an easy-to-learn high-level programming language widely used in the scientific community.
We developed Sleep on top of VisPy 4   (Campagnola et al. , 2015) , a Python scientific library based on OpenGL which offloads graphics rendering to the graphics processing unit (GPU) in order to provide fast and high-quality visualization, even under heavy loads as is the case with large dataset. Sleep therefore benefits from the high performances provided by VisPy alongside Python's inherent qualities such as its portability and ease of use.
METHODS Scientific visualization often consists of finding the best possible way to explore the data and to illustrate results in an intuitive and straightforward manner. The huge variety of neuroscientific data types and acquisition modalities naturally requires a wide range of specific visualization tools. Ideally, software packages needed for the various applications should be free and capable of handling several types of brain data recordings. In this context, we are currently developing a Python package we called Visbrain 5  distributed under a BSD license, which provides and centralizes a number of useful brain data visualization utilities. Given the lack of software solutions that wrap together a portable and userfriendly interface for polysomnographic data visualization and edition, we set out to develop an open-source module (included within the Visbrain package) and named Sleep.
The Choice of Python and the Project Vision The choice of the programming language naturally turned to Python as this high-level and open-source language benefits from many libraries, an extensive documentation and a dynamic community. From data analysis to the production of highdefinition paper figures, Python offers all the tools needed by scientists, with the comfort of a clean and easy to read syntax. Sleep is a pure Python software built on top of NumPy, VisPy, PyQt4 6  and uses a limited number of functions from SciPy and Matplotlib. Thanks to the Python portability, the software can be installed and used on any platform. One of the initial objectives of the project was to provide a user-friendly and intuitive interface capable of loading and displaying large sleep dataset. To this end, we paid a particular attention to avoid deep data copy and display only what is necessary.
Therefore, even very large recordings with a consequent number of channels can be handled by Sleep on any modern laptop with snappy GUI response. From a programming perspective, we did our best to provide a clean, commented and high-quality code, with a NumPy style documentation and using static analysis tool, as recommended by PEP 8. Sleep is hosted on GitHub and we encourage Python programmers and sleep scientists to collaborate in order to collectively improve this software by extending its functionalities and data compatibilities.
Hardware Accelerated Graphics In addition to ergonomic considerations and providing a portable interface, a further important goal was to use a plotting library which would allow our Sleep module to support and process large sleep data. Using Matplotlib was an option we considered, but although it is particularly convenient to produce publication quality figures, it is not the best option when it comes to plotting and interacting in real-time. In contrast, VisPy is a scientific visualization library based on NumPy and OpenGL and was primarily designed to provide both high performances with realtime interactions and publication quality figures. VisPy provides a bridge between the intuitive Python syntax and modern shaderbased OpenGL pipeline allowing the graphical rendering cost to be offloaded to the GPU.
This package has been well-designed and is built on four levels, from a Matplotlib oriented one to the lowest-level (closer to OpenGL) which makes it more flexible and efficient at the cost of a potentially slower learning curve. Because all Sleep graphical elements are primitive 2D objects (line, points, and images) it was not a necessity to go down to the lowest level of VisPy (vispy. gloo). Indeed, all required objects were already implemented into the Visual library. Hence, any modern computer equipped with a GPU should see the benefits of the hardware accelerated graphics implemented in Sleep.
Portable GUI through Python Currently, among the major cross-platform GUI toolkits that interface with Python, wxWidgets 7  (wxPython), Tcl/Tk 8  (TkInter), and Qt 9  (PyQt/PySide) are probably the most known and used. We chose PyQt which is a python binding for the C++ Qt toolkit, and we used Qt Designer to design the GUI. Taken together, VisPy provides high-performance rendering graphics that are well-integrated in a portable, modular and responsive Qt GUI using Python PyQt package. The use of this library is therefore one of the major strengths of this open-source module, and is particularly important when it comes to handling large multi-dimensional brain data, such as full-night sleep EEG recordings.
Signal Processing Tools In addition to the automatic detections presented above, Sleep also provides a wide range of basic and advanced signal processing tools such as signal demeaning, detrending, and a filtering. The latter can be done either with Butterworth or Bessel filters and four filter designs are currently available: lowpass, highpass, bandpass, or bandstop. Importantly, further information can be extracted from the Morlet's wavelet complex decomposition  (Tallon-Baudry et al. , 1996)  such as time-resolved and band-specific amplitude, power or phase. Critically, each one of these signal processing tools are reversible and can therefore be activated and deactivated without altering the original data and without any memory-intensive data copy. Finally, loaded data can be re-referenced directly from the interface by either re-referencing to a selected single channel or common-average (frequently used for scalp EEG datasets) or by using bipolarization, which consists of subtracting neural activity from consecutives sites (classically used in intracranial EEG, see  Jerbi et al.
Documentation and Examples Sleep comes with a detailed step-by-step documentation, built with Sphinx 11 and hosted on GitHub 12. This documentation include a description of the graphical components and the main functionalities of the software. A PDF version of the documentation can also be downloaded from the "Help" contextual menu of the software. We also provide anonymized and free-to-use sample datasets, including the corresponding loading scripts. This will help users test the Sleep module and get familiar with its functionalities before trying it on their own data. Finally, we also implemented an interactive documentation using the tooltips provided by PyQt to describe each element of the interface.
Supported Electrophysiological and Hypnogram Data Formats Sleep natively supports several standard electrophysiological file formats, including European Data Format (EDF *. edf), Micromed ( *. trc), Brain Vision ( *. eeg), and Elan ( *. eeg). In addition, it is possible to load directly NumPy array or Matlab file using the command-line parameters. The hypnogram of the corresponding dataset can also be loaded and then edited directly from the GUI. Accepted hypnogram file formats are *. txt, *. csv, or *. hyp. There is a great heterogeneity among sleep laboratories with respect to hypnogram format. This represents an obvious barrier for data sharing. To overcome this problem, Sleep allows the user to specify the hypnogram format in a separate text file. This file should contain the names and integer values assigned to each sleep stage in the hypnogram file, as well as the number of values per second.
Integration of the detection inside the GUI is shown in Figure  6. To test how these detections performed on real datasets, we measured performances of the spindle and K-complex detection methods using visually-annotated EEG segments of N2 sleep collected from full-night polysomnographic recordings of 14 participants  (Eichenlaub et al. , 2012 (Eichenlaub et al. , , 2014;; Ruby P. et al. , 2013; Ruby P. M. et al. , 2013). Spindles and K-complexes were visually scored by an expert (JBE) as part of a previous work that focused specifically on the detection of these sleep features using machine-learning  (Lajnef et al. , 2015a). To perform the detection methods using Sleep algorithm, all N2-sleep EEG segments were concatenated into a single file of 210 min with a single channel (C3) and with a sampling rate of 100 Hz (native downsampling frequency of Sleep).
Sleep Class Inputs and Code Example From a programming point of view, the high-level interface with our software is provided by the Sleep class. This class can take into account a few input arguments. Hence, loading sleep data can be assessed in three ways adapted to a range of users, from non-programmers to advanced users. As shown in the Code Snippet 1, running Sleep without further input arguments will ask the user to specify the path to a supported sleep dataset ( *. eeg, *. edf, or *. trc). In addition, the user can either use an existing hypnogram or start a new one from scratch. Alternatively, instead of using the interface to select the files, they can be directly passed as input arguments (Code Snippet 2). In this example, we also demonstrate how to change the default order of the sleep stages in the hypnogram using a simple command-line option.
If this option is not specified, the default display of Sleep is as follows: Art, REM, Wake, N1, N2, N3. Finally, several others file formats such as EEGLab, Neuroscan, EGI, GDF, and BDF can be loaded using MNE Python package 15. We report in Code Snippet 3 a method to pass data to Sleep after loading them using MNE python. ([145. , 235. , 1045. ] ) # Onset of each event (sec) 15 https://martinos. org/mne/stable/manual/io. html#importing-eeg-data dur = np. array ([1. , 5. , 2. 5  Sleep comes with a GUI in which we embedded high-quality plots with graphical rendering offloaded to the GPU. As a result, plotting and user interactions can be processed in real-time. The software is capable of loading several widely-used sleep data files format, such as European Data Format and BrainVision, and to stream efficiently all of the polysomnographic channels, even on an average modern laptop.
Comparison with Other Solutions First, it is noteworthy that the scope and functionalities of the present module differs from a previous MATLAB tool we have released, called Spinky  (Lajnef et al. , 2017)  and which aims specifically to provide a joint spindle and Kcomplex detection framework using the tunable Q-factor wavelet transform (TQWT)  (Selesnick, 2011). In addition with being written entirely in Python, Sleep allows for a wide range of functionalities, such as sleep scoring, fast raw and spectral data visualization, edition and creation of hypnogram and annotation files, and automatic detection of several sleep features. Spinky and Sleep subserve distinct purposes and are thus highly complementary. Second, there are currently only a few freewares for human sleep scoring and analysis.
The Python package Phypno and MATLAB-based toolbox SpiSOP both provide a GUI for scoring sleep stages, and include several other command line features to perform automatic detections and compute sleep statistics. However, one of the advantages of Sleep in comparison with these two solutions is the dynamic integration of these features into the GUI, which we believe will allow our software to be understood and accessible by users with no or little programming knowledge. Finally, Sleep offers several advantages compared to the numerous existing commercial solutions, the most obvious one being that it is free and therefore more easily accessible to students or small sleep laboratories. Also, the fact that it is open-source allows more easily the community to contribute to its extension and development.