dataesr/scientific-paragraphs-categorization huggingface dataset:
Notwithstanding these important discoveries, comprehensive understanding of growth strategies of differentiating chondrocytes and the relation between their morphology and growth plate structure and function is still lacking. One reason for that is that due to high density of cells and ECM, imaging the growth plate in its entirety has been a major challenge. Additionally, there is a lack of segmentation tools to accurately extract entire cells for analysis. In this work, we overcome these obstacles by developing a modular imaging and analysis pipeline called 3D MAPs, which enables to explore large datasets of hundreds of thousands of cells, accurately characterizing their morphology while preserving their tissue-level position. Using 3D MAPs, we discovered new cellular behaviors and patterns that contribute to growth plate structure and activity.
7A, B ). We established a database of hundreds of thousands of mutant cells, which we compared to the control database. Comparing proximal and distal growth plates of Gdf5 KO and control tibias revealed several significantly different features. Although mean cell volume in the resting and   and 3 ) was plotted for all three growth plates (proximal tibia, distal tibia, and distal ulna). In all cases, the growth plates were placed such that the RZ pointed upward and the HZ pointed downward. The colormap is normalized to each sample, representing the deviation in degrees from the average orientation of each growth plate. All three growth plates exhibited similar cell orientation behaviors. The long cell axis was aligned to the dorsal-ventral axis of the bone. The medium axis was highly variable in the RZ and HZ but in the PZ, it was aligned uniformly to the medial-lateral axis of the bone.
Discussion The tight connection between cell and organ morphogenesis  2, 3, 5, 6  highlights the need to characterize in detail the 3D morphology of cells in the spatial context of the tissue. In this work, we established a new pipeline called 3D MAPs, which enabled us to overcome these limitations and create complex multiresolution datasets of the 3D morphology of growth plate cells throughout their differentiation. By analyzing these datasets, we show that chondrocytes use both allometric and isometric growth strategies during differentiation in a cell axis-specific manner. Additionally, 3D MAPS revealed a striking conservation in the long cell axis orientation across all zones of the growth plate. Finally, we provide an explanation for the shortening of Gdf5 KO tibias by identifying a reduction in SMAD 1/5/9 activity together with multiple abnormalities in cell growth, shape and organization.
Slides were mounted with entellan and digitized with a Pannoramic scanner (3DHISTECH) and images were exported with QuPath software  92. For immunofluorescence staining for pSMAD1/5/9, 7 μm-thick paraffin sections of embryo limbs (n = 4) were deparaffinized and rehydrated in water and stained as described in Felsenthal and Zelzer  93. Slides were boiled in 0. 3% Triton X-100/PBS for 10 min. Endogenous peroxidase was quenched by incubation in 2% H 2 O 2 /PBS for 30 min at room temperature. Next, staining was performed using primary anti-pSMAD1/5/9 antibodies (1:200, CST-13820, Cell Signaling Technology) followed by biotin-conjugated secondary antibodies (1:100, 711-065-152, Jackson ImmunoResearch) and HRP-conjugated streptavidin (1:200, NEL750001EA, PerkinElmer). Finally, detection was performed using Cy5tyramide labeled fluorescent dyes, according to the instructions of the TSA Plus Fluorescent Systems Kit (PerkinElmer).
Nuclei were counterstained with DAPI and sections were imaged with LSM 800 confocal microscope (Zeiss). HCR smFISH  82  for Gdf5 was performed on cryosections after 3 h fixation in 4% PFA/PBS (DEPC) followed by demineralization and dehydration overnight at 4 ˚C in 0. 5 M EDTA/30% sucrose/PBS. The Gdf5 probe was designed and ordered from (www. molecularinstruments. com), based on mRNA accession number NM_008109. 3 [https://www. ncbi. nlm. nih. gov/nuccore/NM_008109. 3], and amplified with B4 647 nm amplifier overnight. Finally, slides were counterstained using DAPI (1:1000, D9542, Millipore Sigma). Sections were imaged with LSM 800 confocal microscope (Zeiss) at a resolution of 312 nm (x,y). The brightness of the in situ signal was enhanced in Photoshop. For quantification of Gdf5 transcripts per cell, amplification was performed for 1 h and z-stacks across the entire growth plate were acquired with a Zeiss LSM 800 confocal microscope at a resolution of 56 nm (x,y) and 380 nm (z).
The in situ signal was segmented in CellProfiler  96  with a custom pipeline (S6 data) to produce heatmaps of the number of transcripts per cell. Tissue clearing. E16. 5 tibias and ulnas from mTmG:Col2a1-Cre:Gdf5-CreER control and mutant mice were cleared using the PACT-deCAL technique  29, 30. Shortly, decalcified samples were washed in PBS, then embedded into a hydrogel of 4% (wt/vol) acrylamide in 1x PBS with 0. 25% thermal initiator 2,2'-azobis[2-(2imidazolin-2-yl)propane]dihydrochloride (Wako, cat. No. VA-044). The hydrogel was allowed to polymerize at 37 °C for 3 h. The samples were removed from the hydrogel, washed in PBS, and moved to 10% SDS with 0. 01% sodium azide, shaking at 37 °C for 4 days, changing the SDS solution each day. Samples were washed four times with 1x PBST (PBS + 0.
5 resulting in x,y,z voxel sizes of 0. 091, 0. 091, 0. 387 µm. The DAPI and Col2Cre-mGFP channels were acquired with Ex' 405 nm Em'BP 420-470 and Ex' 488 nm Em' BP 505-545 lasers at 2. 4% and 3% laser powers, respectively. Light-sheet fusion of images was done in Zen software (Zeiss). Stitching of low-resolution images was done in ImarisStitcher. Growth plate segmentation. Prior to segmentation, images were down-sampled in the X,Y direction using the Downsample plugin in Fiji  94  resulting in x,y,z voxel size of 0. 194, 0. 194, 0. 387, and saved as TIFF files, which were then converted to VFF file format in MATLAB (S1 data). Non-cartilaginous regions were masked using Microview 2. 1. 2 (GE Healthcare). Shortly, spline contours were drawn around the growth plate throughout each z-stack, and the pixels outside the resulting 3D region of interest were reassigned an intensity value of 0 in Python (S2 data).
All image analysis pipelines were implemented using the open-source software tool XPIWIT  61  and executed on a Windows Server 2012 R2 64-bit workstation with 2 Intel(R) Xeon(R)CPU E5-2690 v3 processors, 256 GB RAM, 24 cores and 48 logical processors (S3 data). Cell segmentation. To segment fluorescently labeled cell membranes, we performed a manual contrast adjustment of the input images and extracted cell segments using a morphological watershed algorithm  99. Owing to intensity variation in different regions of the image, we processed individual tiles separately and manually tuned the watershed starting level to obtain a good segmentation, i. e. , all local minima below the specified level were used to initialize the catchment basins for the watershed regions. All image analysis pipelines were implemented using the open-source software tool XPIWIT  61  and executed on a Windows Server 2012 R2 64-bit workstation with 2 Intel(R) Xeon(R)CPU E5-2690 v3 processors, 256 GB RAM, 24 cores and 48 logical processors (S4 data).
PC1 is the largest, PC2 the second largest, and PC3 is the smallest principal components. To calculate the cell bounding box height, the growth plate samples were registered such that the proximal-distal axis aligned to the global z-axis. Next, each cell was fit with an ellipsoid and a bounding box was fitted to the ellipsoid. The height of the bounding box along the proximal-distal axis is reported. Feature extraction was done on an Intel® Xeon® CPU E5-1620 v4 @ 3. 50 GHz, 32. 0 GB RAM, Windows 10 64 bit, with MATLAB version 2017b (The MathWorks, Inc. , Natick, Massachusetts, USA). Growth plate registration. All growth plates (PT, DT, and DU) were registered based on the outer surface of the RZ. For this, the mean of nuclear centroids were fixed to the origin of each growth plate sample. To remove bias due to heterogeneous density of centroids, only centroids located on the surface boundary points were used.
Then, a bounding volume of each growth plate was extracted using the MATLAB function alphaShape polygons. MATLAB functions AlphaTriangulation and freeBoundary were used to extract the vertices from the outer surface of the growth plates and eigenvectors were calculated from the covariance of the vertices. This eigenvector matrix was used for the growth plate registration. We checked manually that distal, proximal, medial, lateral, dorsal and ventral parts of the RZ matched between growth plates. Manual multiplication of 180-degree rotation matrix was used to correct the eigenvectors if needed. The coordinates of the registered growth plates were calculated by multiplying the eigenvectors with the centroids. 3D morphology maps of growth plates. To visualize the spatial distribution of cellular or nuclear morphology within each growth plate, data were first stitched back together in MATLAB using the stage coordinates and scaling of each image.
Since most growth plate cells at E16. 5 have only one nucleus, nuclear density was used to infer cell density as well. In addition, for each cube, we computed the occupation, defined as the sum of nuclear or cellular volumes divided by the volume of the cube, N/C ratio and all other features as shown in Supplementary Table  1. Then, using MATLAB's jet color map, we represented the characteristics of each cube on the grid by drawing spheres whose radii and colors are proportional to the computed values and whose centers correspond to the average position of the nuclei or cells within the bin. Orientation maps. The orientation of the three axes of a cell or a nucleus is described by the first, second, and third principal components of a PCA performed on the point cloud corresponding to the segmented shape of the cell or nucleus.
All the windows the bins of which had p ≤ 0. 05 were merged and the final p-value was recalculated on these merged bins, showing the significant regions in the graph. Linear regression of vol 2/3 /SA graphs. Four linear regressions were fit to data in RZ, PZ, PHZ, and HZ using the MATLAB function POLYFIT. The boundary between zones in the x-range was adjusted manually, such that each growth plate used the same range of data. The slope and intercept value of the linear regression are mentioned in the figure or legend (Figs. 3  and 6 ) and the fitted line was plotted using POLYVAL function. If actual and fitted data is y and yfit, respectively, then coefficient of determination R 2 is R 2 ¼ 1 À ∑ N i¼1 ðy i À yfit i Þ 2 ∑ N i¼1 ðy i À yÞ 2 ð2Þ and standard error of slope coefficient is SE ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi ∑ N i¼1 ðy i Àyfit i Þ 2 NÀ2 q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi ∑ N i¼1 ðx i À xÞ 2 q ð3Þ p-value of the slope was calculated using Student's t-test cumulative distribution function 2*tcdf ðabsðslope=SEÞ; dof ; 'upper'Þ ð 4Þ where the slope is the slope coefficient and dof is the degrees of freedom in the given data.
Acknowledgements We thank  Nitzan Konstantin  for editorial assistance and members of the  Zelzer lab  for their advice and encouragement throughout this project. We thank the de  Picciotto Cancer Cell Observatory in memory of Wolfgang  and  Ruth Lesser ,  Weizmann Institute of Science , for providing LSFM infrastructure,  Rada Massarwa  and  Jacob Hanna  from the  Department of Molecular Genetics, Weizmann Institute , for providing mTmg mice,  Ofra Golani  from the  MICC Cell Observatory Weizmann Institute , for support and discussions regarding segmentation analysis,  Ron Rotkopf  from the  Bioinformatics Unit  for consulting us on statistics,  Tali Wiesel  from the  Graphic Design Department  at the  Weizmann Institute of Science  for her help with graphics, and  Chinami Michaels  for her help creating illustrations of our findings.
Data availability The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Additionally, source data are provided within this manuscript and 3D MAPs sample data to test the codes are available on Figshare with the identifiers [https://doi. org/10. 6084/m9. figshare. 14903052. v1] and [https://doi. org/10. 6084/m9. figshare. 14932503. v1]. Source data are provided with this paper.
For facilitating the process of transcriptomics data, and to guarantee the reproducibility of our analyses, we developed AskoR, which is a R library for performing a suite of statistical analysis and graphical output from gene expression data obtained by sequencing (RNA-Seq). From raw counts, it makes it possible to filter and normalize the data, to check the consistency of the samples, and to carry out differential expression tests, GO terms enrichments, and clusters of co-expression, with a large number of figures in the output. AskoR can be downloaded and used in your favorite R environment or directly accessible through a Galaxy portal like the one which is hosted by the BioInformatics Platform for the Agroecosystems Arthropods (BIPAA).
Consequently, BIPAA is the home of several public reference databases including AphidBase, LepidoDB and ParWaspDB, each hosting multiple insect genomes. Altogether, 38 genomes are currently available online, and its infrastructure has evolved to support the load of numerous new genomes and to facilitate browsing and navigating. For each species, a collection of web applications allows users to explore reference genomes or transcriptome assemblies and annotations (e. g. genome browser, gene reports), to compare genomics regions (synteny viewer), to analyze these data with multiple tools (e. g. alignment of various sequences, annotation, SNP prediction etc. ) through a dedicated Galaxy server  [7]  or specific web applications (e. g. a blast form), or to correct or add information by curating the genome annotations within Apollo  [8] ).
Many of these analysis tools require programming skills in R or paid software subscriptions, such as OmicsBox  [24]  or CLC Workbenches  [25] , making it difficult for many researchers to implement reproducible workflows. On the other hand, some reproducible workflows have been designed  [26]  for managing RNA-Seq data from the mapping on a reference annotation (genome or transcriptome) but these workflows do not include a full set of supplementary analysis for statistically and graphically exploring data by classification or enrichment. Additionally, DEGs can often be integrated with other data types to improve the robustness of studies, such as QTL analysis, orthology studies, epigenomic landscapes, and proteomics or metabolomic studies. Thereupon, the semantic web technology (consisting in data modeling in structured format such as the Ressource Description Framework (RDF) allowing its querying with the SPARQL language), gives the opportunity to link various information from various sources into graphs of data.
Moreover, we are developing AskOmics  [27, 28]  which provides a web interface to upload and integrate heterogeneous data files (GFF, BED, and tabulated formats) into RDF and a visual SPARQL query builder software to allow the experts to compose and execute expressive and semantically-rich queries. Here, we report AskoR, a R pipeline for the analysis of gene expression data with a simple and reproducible script. It includes several steps (data filtering, normalization, sample validation, differential expression analysis, Gene Ontology enrichment and coexpression) and produces numerous files directly uploadable into an AskOmics instance in order to be linked with other data.
Installation and deployment AskoR can be directly installed from the git repository (https://github. com/askomics/ askoR). An AskoR Docker image, including a preconfigured RStudio instance, is maintained on GitHub (https://github. com/genouest/docker-galaxy-rstudio-askor). This image can be used in a Galaxy server as an interactive tool, allowing to use the preconfigured RStudio-AskoR environment directly from the Galaxy web interface. Instructions on configuring a Galaxy server to make use of this image are available on the GitHub repository.
AskoR implementation, usage and dependencies AskoR is a R package, it consists in a R library, which has to be loaded in a script. A user guide is available at the web site. As a template or example, we are providing a R script running sequentially each function which can be adapted to the needs of the user. AskoR has many parameters and flags (for example method of normalization, p-values or CPM thresholds) with default values which can be modified before or during the process. A complete list of parameters is available on the wiki. Because AskoR takes advantages of various R packages, it has many dependencies which have to be installed locally on the user R environment, or already available in the docker environment. The main dependencies are edgeR, limma, ggfortify, ggplot2, topGO, UpSetR and coseq.
The user can adjust several parameters, such as threshold_cpm and replicate_cpm, and visualize the results in density graphs to determine whether the low expressed genes have been successfully removed. From the matrix of CPM, AskoR produces Multi-dimensional Scaling (MDS) plot, displaying the coordinates of the samples on 3 axes, a heatmap of the correlation between the samples (with dendograms) and with their respective conditions encoded by a color, and a correlogram (Fig. 1A ). This result provides an overview of the samples and allows for the identification of outliers or inconsistencies in biological or technical replicates. The normalization and differential expression analyses are performed with the popular edgeR package  [16] , including the functions calcNormFactors for the normalization, estimateGLMCommonDisp, estimateGLMTrendedDisp, estimateGLMTrendedDisp or estimateDisp for the estimation of the dispersion and glmFit and glmLRT or glmQLFit, and glmQLFTest for the DE tests with the Generalized Linear Models (GLM).
1B ). However, when making many contrasts, a complete graph including all gene-lists may be unreadable or meaningless, then with only two parameters (VD and compaVD or upset_list and upset_type) the user can select precisely the lists of genes to be displayed in the graphs. If gene ontology terms are provided, AskoR can perform enrichment analysis in lists of DEGs annotated and produce a table  (Table 1 ) with a statistical test from TopGO R package  [29]  and dedicated figure (Fig. 2 ). It is also often useful to group genes that show a similar expression in several conditions (expression profile), to identify co-regulated genes and to characterize genes with no function having similar expression to candidate genes. AskoR performs a clustering with the Coseq R package  [22]  that uses two classification algorithms (kmeans, and gaussian mixture) with respective transformations, tests for the best number of clusters (K), and produces statistics and graphics helping to check the quality and robustness of the chosen model.
Discussion AskoR is highly customizable, and includes many parameters with default values modifiable according to the needs of the analysis. Thus, it is straightforward to run an analysis with a single file and a set of data. The results of the analyses are then stored in multiple files under a named directory. Then, any set of results obtained with a particular set of parameters can be stored, compared and reproduced. AskoR is working as a standalone library and no web interface has been developed yet (with R Shiny for instance). Nevertheless, it can be run as a single script and easily modified directly on any laptop or server or integrate directly in workflows for an improved reproducibility. Furthermore, the docker image of AskoR can be integrated directly as an interactive environment in a Galaxy instance.
Acknowledgments: We are thankful to the GenOuest platform for the Galaxy environment, to  Emmanuelle Becker  and  Fanny Casse  for clustering and enrichment graphs, to  Andréa Rau  for the help while implanting the coseq package and to  Régine Delourme ,  Jean-Philippe Vernadet ,  Jérémy Gauthier ,  Florence Prunier ,  Ambre-Aurore Josselin ,  Mélanie Huguet  for testing and helping us to design the tool, and to  Olivier Dameron  and  Anne Siegel  for the discussion around Askomics.
Within TMG, the decrease in HbA 1c was greater in frequent users: -0. 23% (p = 0. 03) in the case of connections to telemonitoring synthesis above the median and -0. 21% (p = 0. 05) in the case of connections to tele-education software above the median compared to the CG. Significant weight loss was observed in the TMG but only in women (p = 0. 01). Findings: The EDUC@DOM telemonitoring and tele-education device did not highlight a significant decrease in HbA1c levels compared to routine management although a slight, albeit significant improvement in glycaemic control was observed in the frequent user subgroup as well as significant weight loss but only in women. A high level of satisfaction with the connected device was recorded amongst all participants. Trial Registration: This trial was registered in the Clinical Trials Database on September 27, 2013, under no.
Key Summary Points This work is one of the first to use a telemonitoring device which routinely gathers data on weight, physical activity and diet in addition to glycaemia control in order to improve the monitoring of type 2 diabetes patients The device used is connected to interactive lifestyle educational software based on artificial intelligence algorithms The telemonitoring and tele-education device failed to show a significant fall in HbA1c levels in individuals with type 2 diabetes (p = 0. 06) despite a slight, albeit significant improvement in the frequent user subgroup. A high level of satisfaction was recorded among all participants.
This randomised study was based on the hypothesis that tele-education and telemonitoring improve lifestyle and quality of glycaemic control in patients with T2D while rationalising the need for face-to-face consultations. The long-term goal is to reduce the incidence of diabetic complications and costs. We designed and developed Nutri-Educ, a new athome education software, in order to support changes in lifestyle. Nutri-Educ is the only software including artificial intelligence algorithms, which allow it to deliver personalised advice to correct errors in meal composition. This software has been combined with the use of connected objects to offer an integrated telemonitoring programme  [21]. Changes in HbA 1c , weight and waist circumference have been reviewed over a 1-year period in T2D subjects equipped with this innovative telemonitoring programme compared to standard care.
Interventions The home telemonitoring device combined biomedical data sensors (scale with impedancemetry, actimeter and blood glucose meter) with educational software  [22, 23]  available on tablets. Three tele-educational software programmes were given to participants in the TMG: Nutri-Kiosk-a set of self-learning quizzes for formative assessment based on nutritional knowledge; Acti-Kiosk-support in starting physical activity (assessment, information and videos); Nutri-Educ-a personalised nutritional educational software application that meets international clinical recommendations  [24]. It was developed using artificial intelligence algorithms and has already demonstrated its beneficial impact in obesity and T2D patients  [25] [26] [27]. Nutri-Educ was designed to help patients in the TMG group to improve their nutritional balance.