dataesr/scientific-paragraphs-categorization huggingface dataset:
As a prototype, TBS-EOS does not include a soft-tissue compensation system, and the TBS-EOS values are not designed to be in the same range as TBS ® values computed on DXA systems. TBS ® was calculated on postero-anterior view of the first four lumbar vertebrae. It was computed on a rectangular projected area, the largest fitting in between upper and lower vertebral endplates (Figure  1 ). This area was retrieved from the plain projection of the 3D reconstruction of the spine using sterEOS TM software. Vertebrae were excluded from the analysis when vertical vertebral rotation exceeded 22°. This threshold was fixed as the limit allowing to have an area of at least 33% of the maximal projected surface area of the vertebra (figure  2 ). This 33% threshold was set arbitrary in order to get an area representative of the whole vertebra.
Acknowledgement: We acknowledge the assistance of  Mr Franck Michelet ,  Ms Doris Tran  and  Mr Christophe Lelong  from  Medimaps, Mérignac, France  for their technical help and the analysis of TBS on DXA scans and EOS images. Sami Kolta ,  Adrien Etcheto ,  Jacques Fechtenbaum ,  Antoine Feydy ,  Christian Roux  and  Karine Briot  declare that they have no conflict of interest.
Mitochondria participate in metabolism and signaling. They adapt to the requirements of various cell types. Publicly available expression data permit to study expression dynamics of genes with mitochondrial function (mito-genes) in various cell types, conditions and organisms. Yet, we lack an easy way of extracting these data for mito-genes. Here, we introduce the visual data mining platform mitoXplorer, which integrates expression and mutation data of mito-genes with a manually curated mitochondrial interactome containing ∼1200 genes grouped in 38 mitochondrial processes. User-friendly analysis and visualization tools allow to mine mitochondrial expression dynamics and mutations across various datasets from four model species including human.
INTRODUCTION Enormous amounts of transcriptomic data are publicly available for exploration. This richness of data gives us the unique opportunity to explore the behavior of individual genes or groups of genes within a vast variety of different cell types, developmental or disease conditions or in different species. By integrating these data in a sophisticated way, we may be capable to discover new dependencies between genes or processes. Specific databases are available for mining and exploring disease-associated data, such as The Cancer Genome Atlas (TCGA, https://portal. gdc. cancer. gov/) (1) , or the International Cancer Consortium Data Portal (ICGC, https://dcc. icgc. org/) (2). Especially cancer data portals allow users to perform deeper exploration of expression changes of individual genes or gene groups in different tumor types ((1-3); for a review on available cancer data portals, see (  4 )).
Expression Atlas (https://www. ebi. ac. uk/gxa/home) on the other hand provides pre-processed data from a large variety of different studies in numerous species  (5). Indeed, the majority of transcriptomic datasets are not related to cancer and are stored in public repositories such as Gene Expression Omnibus (GEO, https://www. ncbi. nlm. nih. gov/geo/) (6) , DDBJ Omics Archive (https://www. ddbj. nig. ac. jp/gea/ index-e. html) (7) , or ArrayExpress (https://www. ebi. ac. uk/ arrayexpress/)  (8). Currently, it is not straightforward to integrate data from these repositories without at least basic programming knowledge. Next to extracting reliable information from -omics datasets, it is equally important to support interactive data visualization. This is a key element for a user-guided exploration and interpretation of complex data, facilitating the generation of biologically relevant hypotheses--a process referred to as visual data mining (VDM, reviewed e.
Moreover, these lists are not integrated with any available data analysis tool to explore mitochondrial expression dynamics under varying conditions or in different tissues or species. In this study, we present mitoXplorer, a web-based, highly interactive visual data mining (VDM) platform designed to specifically mine the dynamics of a manually curated gene set with mitochondrial functions in transcriptome, proteome, as well as mutation-based data. To achieve this, mitoXplorer integrates -omics data with our handcurated mitochondrial interactomes for currently four different model species. With mitoXplorer, we can explore the expression dynamics, as well as mutations of mito-genes and their associated mitochondrial processes (mito-processes) across a large variety of different -omics datasets without the need of programming knowledge.
MitoXplorer provides users with dynamic and interactive figures, which instantly display information on mitochondrial gene functions and protein-protein interactions. Users can analyze publicly available data stored in our mitoXlorer database or upload their own data for integration with our handcurated mitochondrial interactome. In order to demonstrate the analytical and predictive power of mitoXplorer and to experimentally verify mitoXplorer predictions, we generated transcriptome and proteome data from aneuploid cell lines, carrying trisomy 21 (T21), the most common chromosome abnormality in humans which is also known to cause substantial mitochondrial dysfunctions  (38). We used mitoXplorer to analyze and integrate our data with publicly available trisomy 21 data. MitoXplorer enabled us to predict respiratory failure in one of our T21 cell lines, which we experimentally confirmed, thus demonstrating the predictive power of mitoXplorer.
Implementation of mitoXplorer Web interface of mitoXplorer (front-end). The web interface of mitoXplorer at the front-end allows users to access, interact and visualize data from its database, including the interactome and expression/mutation data. The interactive elements and visualizations on mitoXplorer are all built with Javascript, a dynamic programming language that enables interactivity on webpages by manipulating elements through DOM (Document Object Model). DOM is a representation of document, such as HTML, in a tree structure, with each element as a node or an object. Through Javascript and its libraries, visualizations in mitoXplorer can react to users' action and dynamically change the properties (size, color, coordinates) of web elements and display interactivity. All the visualization components in mitoXplorer described below are modular by design and can be deployed individually or incorporated into web platforms easily.
Comparative plot (D3--transition and sorting). The comparative plot combines three interdependent visualizations (scatterplot, bar chart and heatmap) built upon D3. Apart from data-binding and selection, these visualizations exploit the functionality of D3 for transition and sorting through its API. In the scatterplot, genes are displayed as nodes, whose colors and positions again depend on the data (log2FC). When another mito-process is selected at the bar chart, D3 updates the data bound to the node and the properties of the nodes are changed. The transition (changes in color and position) is smooth and gives users the impression that the visualization is truly dynamic and interactive. D3 can manipulate not only the elements, but also the data bound to the elements. Upon clicking the dataset or gene names on the heatmap, the data can be sorted accordingly and an index is assigned to each element (tile on the heatmap) to indicate its position.
Hierarchical clustering (mpld3--visualization in Python implemented in D3 ). The heatmap displaying the results of hierarchical clustering is built with mpld3, a Python library that exports graphics made with Python's Matplotlib-based libraries to JSON objects that can be displayed on web browsers. Mpld3 benefits from D3's data-binding property and allows users to create a plugin that interacts with the data on the visualization. The advantage of using mpld3 is that analyses and visualizations made in Python can be directly translated to JSON and deployed in Javascript on webpages without re-programming. In the case of hierarchical clustering, since libraries for both clustering analysis and visualization of results in a heatmap with a dendrogram are available in Python (described below), it is exported to JSON with mpld3 and a Javascript tooltip plugin that allows users to select data or display information with D3.
Objects of different texture, geometry and color can now be added to and rendered on the scene. Finally, the scene with objects is attached to the DOM of a webpage to become visible. In the PCA visualization, each dataset is represented and rendered as a small sphere, with coordinates (x, y, z) depending on the values of its first three PCs, and colors on the grouping of that dataset. When users drag around on the canvas or zoom in or out, all objects are rerendered in such a way that the scene appears to be a 3dimensional space.
MitoXplorer database (back-end). A MySQL database hosted at the back-end of mitoXplorer contains the interactomes of mito-genes, including the mito-process, gene ontology and the interactions between gene products; and the expression and mutation data from public databases. Each entry of the expression and mutation data has a foreign link to the interactome and file directory (dataset table). This ensures that the expression and mutation data will be updated together with the interactome, or when a dataset is updated or deleted. Users can upload their own differential expression and/or mutation data, which will be processed and integrated with the interactome by extracting mito-genes, and stored in the mitoXplorer database for up to 7 days. Data analysis and communication between frontand backend.
A Python application serves as a bridge between the front-and back-end of mitoXplorer. Upon the users' request to access the database or perform analysis at the web interface, an AJAX-asynchronous call directed to the Python application is made, so the request can be performed in the background and the webpage is updated without reloading. The Python application then processes the request by connecting to the MySQL database and analyzes the data retrieved from it. The application also handles the user uploads (e. g. data cleaning) before saving it to the MySQL database. The main libraries used by the Python application for analysis include: (i) Scikit-learn: a machine learning library that provides tools for PCA, to perform dimensionality reduction on the expression of all mito-genes and of each mito-process.
The first three principal components are extracted for each dataset. (ii) SciPy: a mathematical library that provides modules for hierarchical clustering, to calculate 2D distance matrices between genes and between datasets based on expression values, for each mito-process. (iii) Seaborn: a statistical visualization library built on top of SciPy to create heatmaps from the results. All the results are produced in JSON format, which are then sent via the HTTP protocol back to the front-end and visualized with Javascript. The usage of mitoXplorer does not require installation or programming knowledge. Documentation and tutorials are available online and on GitLab (https://gitlab. com/ habermannlab/mitox). MitoXplorer is also available for download and installation on a local server, if users wish to build their own gene list and apply the interactive features and database of mitoXplorer, which stores the available expression and mutation data for all genes.
Processing of public transcriptomic and proteomic data. Proteomic and transcriptomic data from K ühl  (40) , as well as Liu  (41) ,  Letourneau (42) , Sullivan  (43)  and Spletter  (44)  were uploaded as provided by the authors. Public NGS datasets downloaded from GEO which did not already contain differential expression data in the form of log2FC and P-value were analyzed according to available pre-analyzed data: datasets with available raw read counts were analyzed using DESeq2 (version 3. 9, (  45 )). These included data from Chowdhury  (46)  and Garipler  (47). Datasets for which only the normalized read counts were available, the log 2 FC was calculated for each sample, using the corresponding wild-type samples as control (or the mean of normal samples if there were no paired samples) following best-practice guidelines.
We applied this to data from TCGA (1), downloaded from the NCI GDC Data Portal (https://portal. gdc. cancer. gov/)), from Fleischer  (48)  and from Huang  (49). Finally, microarray timecourse data of yeast meisosis (GEO accession: GSE75257) were analyzed with GEO2R  (50). Metadata of the datasets (e. g. cell types, analysis pipeline and genome version used for mapping) were also downloaded and stored in the mitoXplorer database. The links to the experiments for each dataset are available at the DATABASE summary page of mitoXplorer. Transcriptomics and proteomics of aneuploid cell lines. The proteome analysis of the trisomic cell lines was previously described  (51, 52). The raw reads from RNA-sequencing were processed to remove low quality reads and adapter sequences (using TrimGalore v0. 4.
5 (https://www. bioinformatics. babraham. ac. uk/projects/trim galore/), which uses Cutadapt  (53) ) and FastQC (Andrew, S. (2010) FastQC: a quality control tool for high throughput sequence data. http:// www. bioinformatics. babraham. ac. uk/projects/fastqc)), and aligned to the human reference genome (version hg19) with TopHat2 (v2. 0. 11)  (54). Cuffdiff from the Cufflinks package (v2. 2. 1) (55) was used with standard parameters to calculate the expression difference between two samples (aneuploid versus diploid) of multiple replicates and test the statistical significance. Transcriptome and proteome information are available in public repositories: NGS data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO series accession number GSE102855 and GSE131249.
Cells were fixed with 3% PFA in DMEM for 5 min at room temperature. After washing twice with 1xPBST, plates were stored with 1× PBS containing 0. 01% sodium azide. Plates were stored at 4 • C in the dark. Imaging was carried out on an inverted Zeiss Observer. Z1 microscope with a spinning disc and 473, 561 and 660 nm argon laser lines. Imaging devices were controlled, and images were captured, stored and processed with the SlideBook Software in Fiji  (56). The images were captured automatically on multiple focal planes (step size: 700 nm) with a 40× magnification air objective. Metabolic profiling of wild-type and T21 cell lines. RPE and HCT cells and their T21 derivatives were seeded at 25 000 or 36 000 cells/well respectively, on XF96 cell plates (Seahorse Bioscience, Agilent Technologies), 30 h before being assayed.
5 M oligomycin, CCCP and 2 M rotenone ± 4 M antimycin A or 20 mM Na-azide. The as-says in permeabilized cells were performed in poly-D-lysinecoated 96-well plates with at least five replicates per cell line. Normalization was performed with the CyQuant cell proliferation assay kit (Life Technologies) in the same plate used for the assay of intact cells; and in a parallel plate for the permeabilized cells. Data analysis was done according to  (57). The mitoMorph plugin for morphological characterization of mitochondria by image analysis. Classification and measurement of mitochondria were performed using the software ImageJ  (58) , complemented with all the default plugins provided by Fiji  (56)  and with the additional plugin Fea-tureJ. A set of functions were developed to assist the user in the preparation and analysis of the data, either in interactive or batch processing mode.
RESULTS The outline of the mitoXplorer web-platform is illustrated in Figure  1 : at the back-end, manually curated mitochondrial interactomes from human, mouse, Drosophila and budding yeast, as well as expression and mutation data from these four species are stored in a MySQL database (details on the implementation of the back-end are available in Materials and Methods, as well as Supplementary Figure  S1 ). The user interacts with the mitoXplorer web-platform via the front-end, which offers different visualization and analysis methods. Users can either browse stored public data or upload their own data.
The mitochondrial interactomes The main component of mitoXplorer is the mitochondrial interactome. Its accurate annotation and completeness are essential for performing a meaningful mitoXplorer-based analysis. To establish mitochondrial interactomes, we have assembled and manually curated lists of genes with annotated mitochondrial processes (mito-processes). Currently, the interactomes of four organisms are available on mi-toXplorer: Homo sapiens (human), Mus musculus (mouse), Drosophila melanogaster (fruit fly) and Saccharomyces cerevisiae (budding yeast). We started from published mitochondrial proteomic data  (27, 61)  for the selected species and manually cleaned the data, as well as supplemented missing mito-genes in the following way: we removed obvious false-positives from the datasets; these included mainly proteins for which there is compelling evidence in literature that they are not localized to mitochondria.
Next, we supplemented likely missing genes from the proteomic data using information from Mitocarta  (24) , KEGG (62) information of genes associated with mitochondrial pathways, as well as orthologs across species from the four proteomic datasets. To establish whether a protein in question is primarily localized to mitochondria or not, we relied on several sources: (i) evidence from the literature on a specific gene; (ii) information from the respective gene entry at NCBI  (63) , Flybase  (64)  or the Saccharomyces genome database (SGD) (65); (iii) information from the GeneCards database (66); (iv) information from UniProt  (67). We supplemented the lists with all non-coding genes present in the mitochondrial genomes, namely all mitochondrial rRNAs, as well as tRNAs. After manual curation, we obtained 1229 human, 1222 mouse, 1139 Drosophila and 988 budding yeast mitogenes.
We grouped the genes in mito-processes using controlled vocabulary. In addition to purely mitochondrial processes, we added cytosolic processes coupled to mitochondrial functions, including Glycolysis, the Pentose phosphate pathway, Apoptosis or the regulation of transcription of nuclear-encoded mitochondrial genes (Transcription (nuclear)). This resulted in a total of 38 mito-processes (Table 1). We selected the correct mito-process for each gene primarily using information from the same sources mentioned before (NCBI gene entry, Flybase, SGD, GeneCards, UniProt, KEGG). According to our current annotation, A manually curated, annotated mitochondrial interactome represents the central part of the mitoXplorer software, for which we have assembled 1229 mito-genes in human, 1222 mito-genes in mouse, 1139 mito-genes in fruit fly and 988 mito-genes in budding yeast in 38 mitochondrial processes (mito-processes).
We have connected gene products using protein-protein interactions from STRING  (69). Publicly available expression and mutation data from repositories such as TCGA or GEO are provided for data integration, analysis and visualization and are stored together with species interactomes in a MySQL database. Users can provide their own data, which are temporarily stored and only accessible to the user. A set of Python-based scripts at the back-end of the platform handle data formatting, integration and analysis (Supplementary Figure  S1 ). The user interacts with mitoXplorer via several visual interfaces to analyze, integrate and visualize his private, as well as public data. Four interactive visualization interfaces are offered: (i) the Interactome View allows at-a-glance visualization of the entire mitochondrial interactome of a single dataset (see Figure  2 ); (ii) comparative plots, consisting of a scatterplot and a sortable heatmap allows comparison of up to six datasets, whereby a single mito-process is analyzed at a time (see Figure  3 ); (iii) hierarchical clustering allows comparison of a large number of datasets which are clustered according to their expression values.
Hierarchical clustering plots are zoom-able and interactive (see Figure  4 ); (iv) principal component analysis displays PCA-analyzed datasets in 3D, providing filtering and grouping functions. There is in principle no limit to the number of datasets that can be analyzed using PCA (see Figure  5 ). one gene is part of only a single mito-process. We acknowledge that this annotation strategy has limitations, as a mitogene can be part of more than one mito-process: as an example, in KEGG, the human gene GPI is part of the pathways Glycolysis, as well as Pentose phosphate pathway. In GeneCards, GPI is primarily associated with Glycolysis, thus we assigned it to the mito-process Glycolysis. For other genes, the existing annotations were less clear and we had to decide on the primary process a gene should belong to.
The mitoXplorer expression and mutation database To foster the analysis of mitochondrial expression dynamics and mutations, mitoXplorer hosts expression and mutation data from public repositories in a MySQL database. Expression data encompass analyzed data of differentially expressed genes from RNA-seq studies and are available in the form of log 2 fold change (log2FC) and P-value. One differential dataset thus includes two experimental conditions with all replicates. Mutation data include analyzed data of identified SNPs of one sample against a publicly available reference genome or transcriptome. Pre-analyzed public data are taken as provided by the authors of the respective study: information on software and genome version used for read mapping, as well as soft-ware and settings used for differential expression analysis can generally be found at the GEO-link of the respective project listed on the DATABASE page.
We have ensured that only high-quality data with replicates, as well as a properly described analysis strategy are available in the mitoXplorer database. If only raw read counts were available, we analyzed the data using state-of-the-art software (DESeq2  (45) , for details see Methods). Finally, whenever only normalized read counts were available, which is typical for large population-based studies, we calculated log2FC according to  (70). It should be noted that due to the heterogeneity of the available formats of the provided data, the algorithms and their settings, as well as the genome version used for read mapping might differ for available projects in mitoXplorer. The largest public resource imported into mitoXplorer covers publicly available expression data of human cancers from The Cancer Genome Atlas (TCGA)  (1).
We have included all paired samples. This resulted in a total of 523 differential datasets from six different cancer types: kidney cancer (KIRK), breast cancer (BRCA), liver cancer (LIHC), thyroid cancer (THCA), lung cancer (LUAD) and prostate cancer (PRAD). Changes in mitochondrial metabolism have been described in many cancer types (for a review, see (  71 )). As mitoXplorer is thus far the only resource that allows a focused analysis of mito-genes across different cancer types or patient groups, this resource should be especially useful to shed light on the expression dynamics of mito-genes in cancer and to classify the mitochondrial metabolic profiles of tumor types and sub-types. Users can moreover integrate proprietary data with differential expression data from different tumor types and subtypes.