dataesr/scientific-paragraphs-categorization huggingface dataset:
Human adipocyte expression data Gene Expression Omnibus (GEO) analysis from the NCBI was performed using "adipocytes obese human" as keywords, filtered with "published in the last year" and sorted by the number of samples "high to low". The dataset GSE94753  [36]  was selected and analyzed using GEO2R. The platform GPL11532 was selected and groups were defined using the disease status (lean, WAT, control versus obese resistant, WAT OIR). Expression level data were extracted from "profile graph" using CDKN2A ID 8160441. 2. 12. Statistical analysis Data are presented as mean AE SEM. Statistical analyses were performed using unpaired two-tailed Student's t-test, one-way ANOVA with least significant difference Bonferroni post hoc test or two-way ANOVA with Bonferroni post hoc tests as appropriate using Graph-Pad Prism software.
Using the Human Protein Reference Database (HPRD)  [37]  to identify potential upstream kinases we identified several signaling pathways that may be affected in Cdkn2a-deficient adipocytes, including Protein Kinase A (PKA) and Protein Kinase G (PKG) signaling pathways. Interestingly, PKG is an important regulator of WAT browning  [38, 39]. For some phosphorylated peptides upstream kinases could not be identified using HPRD analysis (Figure  4B , NA or e ). When we analyzed the diseases and/or biological functions affected by the differences in peptide phosphorylation observed upon Cdkn2a genetic deficiency through Ingenuity Pathway Analysis (IPA), we found networks related to "Cancer," "Organismal Injury and Abnormalities," and "Developmental Disorders," but also to "Cardiovascular Disease" (Table  S2 ).
Cdkn2a modulates a regulatory gene network involved in brown adipocyte function We next carried out RNA-sequencing (RNA-seq) analysis in Cdkn2aþ/ þ and Cdkn2aÀ/À primary adipocytes at D8 of differentiation. We found 11,855 transcripts differentially expressed (Table  S3 ). IPA highlighted the presence of a network of 98 genes associated to Cdkn2a deletion which was involved in "energy production, lipid metabolism and small molecule biochemistry," including Ppargc1a and Prdm16 (Figure  S5A ). Subsequently, we analyzed the diseases and/or functions associated to Cdkn2a deficiency. The main differences between Cdkn2aþ/þ and Cdkn2aÀ/À primary adipocytes were included into "carbohydrate metabolism" (P ¼ 1. 67 Â 10 À3 ) or "cellular development, connective tissue development and function, tissues development" (P ¼ 4.
CDKN2A modulates adipose tissue browning in human adipocytes We next questioned whether deregulated expression of CDKN2A could be associated to obesity in humans. Interestingly, using global transcriptome profiling of adipocytes from lean and obese subjects (GEO: GSE94752; Ref. [36] ) we observed a significant increase of CDKN2A mRNA expression levels in obese insulin-resistant adipocytes compared to lean controls (Figure  5A ). We next wondered whether decreasing CDKN2A levels in human adipocytes could recapitulate some of the effects we observed in mice and induce browning in human cells. Recent reports demonstrated that human-induced pluripotent stem cells (hiPSCs) could be differentiated into brown/brite adipose progenitors (hiPSCs-BAP, Refs. [30, 31] ). To further analyze whether CDKN2A modulates UCP1 expression in human cells, we silenced CDKN2A expression during the exponential growth phase of hiPSCs and differentiated them into brown/brite adipose cells during 22 days as described previously  [30, 31].
ACKNOWLEDGMENTS We thank  Dr. Manuel Serrano  for providing us with the Cdkn2aÀ/À mice. We thank  Carine de Bettignies  and  Olivier Sand  from  UMR 8199  for helpful discussions and technical assistance. We thank  Estelle Le Borgne  for help with illustrations. We thank  Dr. David Blum  for sharing reagents. The authors thank the  Experimental Resources platform from Lille 2 University , especially  Yann Lepage ,  Ludovic Mercier ,  Kelly Timmerman ,  Mélanie Besegher , and  Delphine Taillieu  for animal care. We thank the Department of Histology from the  Lille Medicine Faculty , in particular M. H. Gevaert and  R. M. Siminski , for histological preparations. We are indebted to the  RHEM network (Réseau d'Histologie Expérimentale de Montpellier, IFR122, France)  for histology and, in particular, for tissue and slide preparations.
This work was supported by grants from  «European Genomic Institute for Diabetes» (EGID ,  ANR-10-LABX-46  to PF and J-SA),  Agence Nationale pour la Recherche  ( BETAPLASTICITY ,  ANR-17-CE14-0034  to PF and  J-SA ;  Equipex 2010 ANR-10-EQPX-07-01 ; ' LIGAN-PM' Genomics platform ),  European Foundation for the Study of Diabetes (EFSD , to J-SA),  European Commission, European Research Council  ( GEPIDIAB 294785  to PF),  Institut National de la Santé et de la Recherche Médicale ,  Centre National de la Recherche Scientifique ,  Association pour la Recherche sur le Diabète  (to J-SA),  Lille 2 University  (to NR, XG, ES, and  J-SA ),  Conseil Régional Hauts de France  and  Métropole Européenne de Lille  (to NR, XG, and J-SA),  FEDER  ( Fonds Européen de Développement Régional , to NR, PF, J-SA) and  Société Francophone du Diabète/ Servier  (to SAH and J-SA).
Background: Skeletal muscle has the capacity to adapt to environmental changes and regenerate upon injury. To study these processes, most experimental methods use quantification of parameters obtained from images of immunostained skeletal muscle. Muscle cross-sectional area, fiber typing, localization of nuclei within the muscle fiber, the number of vessels, and fiber-associated stem cells are used to assess muscle physiology. Manual quantification of these parameters is time consuming and only poorly reproducible. While current state-of-the-art software tools are unable to analyze all these parameters simultaneously, we have developed MuscleJ, a new bioinformatics tool to do so. Methods: Running on the popular open source Fiji software platform, MuscleJ simultaneously analyzes parameters from immunofluorescent staining, imaged by different acquisition systems in a completely automated manner.
This is distinguished from undamaged fibers, which, in mice, display peripheral nuclei. Consequently, the number of centronuclei per fiber and CSA of regenerated fibers are readouts of skeletal muscle regeneration efficiency. In addition, the number of fiber-associated satellite cells informs on their potential to perform successive rounds of degeneration/regeneration. These experiments and analyses are time consuming, and different semi-automatic and automatic tools have been developed over the years based on different software and strategies  [6] [7] [8] [9] [10]. Among them, TREAT-NMD SOP DMD_M. 1. 2. 001 (  2008 ) is an automated image analysis method running on the commercial CellR Software (Olympus) that allows determination of fiber diameter and detection of centronuclei in segmented fibers.
Another application, Semi-automatic Muscle Analysis using Segmentation of Histology (SMASH), is developed in the MATLAB environment. It provides fiber properties and typing, centronucleated fiber detection as well as quantification of inter-fiber objects (e. g. , capillaries), after semi-automatic fiber segmentation on immunofluorescent images, which requires manual adjustments  [8]. Finally, the recently developed open source MyoVision software provides the number of fibers per section, CSA, minimum Feret diameter, fiber type, and number of myonuclei  [7]. However, none of these methods are designed to provide either the number of nuclei in centronucleated fibers, the CSA of different types of fibers (e. g. , centronucleated fibers vs normal fibers or type I fibers vs type IIA fibers), nor the number of fiber-associated satellite cells.
In addition, the diversity of the available software makes it time-consuming to master each of them for studying different parameters in one study. Consequently, while many tools have been developed to automate histological analysis of skeletal muscle, most quantification is still performed manually using the well-known and open source Fiji (Fiji is just ImageJ) platform  [11]. In this study, we present a new, complete high-content skeletal muscle analysis tool developed on this software that we named MuscleJ. It presents a number of advantages over existing methods as it groups most parameters frequently studied in fluorescence by the skeletal muscle community. The tool we developed uses images coming from acquisition of immunofluorescent staining. It can perform segmentation of skeletal muscle fibers and quantification of centronucleated fibers and the number of centronuclei per fiber.
Five independent experts with strong experience in skeletal muscle biology from two independent labs received a random set of images. Using Fiji software, and following specific instructions, they manually quantified fiber size, number of centronucleated fibers, fiber typing, and number of vessels and satellite cells. These results were compared to those obtained from MuscleJ. A second validation was performed by two independent experts to manually track individual fibers and determine their CSA, as well as the number of nuclei, in each centronucleated fiber on Fiji.
Up to four channels can be treated simultaneously. MuscleJ adapts the process to these different parameters automatically, leading to better recognition of muscle fiber outlines and fluorescent signals of interest. MuscleJ organigram (Fig. 1b ) The first step of the program is preprocessing. In this stage, all muscle fibers of the section are segmented. A ratio comparing the area of the entire muscle section with the sum of all segmented fiber areas is automatically established during this step and serves to determine the quality of images. The percentage of accepted artefact is indicated by the user in the initial dialog box. MuscleJ then controls the decision to continue the analysis or exclude the image as an artifact, as illustrated in the Additional file 1: Figure  S1A. This function, which works as a quality control, avoids the analysis of a partial or non-representative part of the section.
b Following option selection in a, a second dialog box appears and should be informed on the channels order in original file format. c Data are saved in global tables where the requested information is filled in, as well as details for each muscle fiber. Selected cartographies are also saved at this step images of co-staining with laminin, DAPI, and the specific marker of satellite cells, Pax7. Results are given per fiber, and a pink scale cartography was implemented for this parameter in order to directly distinguish on skeletal muscle sections, the fibers which are not associated with satellite cells (in white), from those containing one (in light pink) or more than one satellite cells (in dark pink). Finally, MuscleJ was also designed to quantify up to three intrafiber stainings. Here we demonstrate fiber typing as an example.
Discussion Histological characterization is of central interest in most studies of fundamental and clinical aspects of skeletal muscle pathophysiology. For these analyses, a variety of different tools have been developed using different software, but to date, there is no single tool capable of automatically analyzing all parameters simultaneously. As a result, each laboratory often develops its own strategy to quantify parameters of interest. Manual quantifications, mainly performed with Fiji, are often described in scientific articles. However, manual approaches are subject to technical and observer bias, questioning the reproducibility of results obtained by different laboratories and different image acquisition systems. In this study, we propose a high-content analysis tool developed on the free, open-source Fiji software, which we named MuscleJ.
It allows quantification of many important skeletal muscle parameters, based on immunofluorescent staining of skeletal muscle sections and can be applied to all high-quality images obtained from a variety of microscopes. As other available software, e. g. , SMASH and Myovision, MuscleJ allows to analyze fiber morphology by measuring the number of skeletal muscle fibers, their CSA, and minimum and maximum Feret diameter after fiber segmentation based on laminin staining (Additional file 1: Table  S3 ). In addition to laminin, up to three different intrafiber stainings can be quantified. Here, we highlight fiber typing based on myosin type I, type IIA and type IIB immunostainings. However, the use of other antibodies against specific proteins located within the fiber can also be analyzed in terms of number of positive fibers and intensity of the signal.
In addition, the capacity of MuscleJ to quickly analyze all these parameters makes it possible to increase the amount of muscle sections to be analyzed and consequently improve statistical power. Another original feature of MuscleJ is the optional in situ cartographies. Five different cartographies have been designed. The first one represents the number of centronuclei per fiber by a color code from white to red, where white fibers are normal fibers and red fibers correspond to those containing more than three nuclei in a central position. There are many advantages of this option, including the capability to directly visualize the extent of an injury induced by myotoxin injection or comparison of the degenerative state of different skeletal muscles (Additional file 1: Figure  S2 ). The second cartography depicts the CSA of muscle fibers by a gradual color code ranging from pale to dark green.
Conclusions MuscleJ has been designed to allow characterization of many parameters currently analyzed in fundamental and clinical studies in the skeletal muscle field. We have developed this tool on the free publicly accessible software Fiji, in order to offer the capacity to the muscle community to use it freely on high quality images obtained from a range of different microscopes. Using MuscleJ significantly reduces the time of analysis and renders possible the comparison of experiments performed at different times and in different laboratories around the world by providing highly reproducible analyses. The easy-to-use interface is highly intuitive and facilitates its usage. We expect that MuscleJ will become the tool of reference for all skeletal muscle histological analysis in the future.
Acknowledgements We thank  Dr. Meryem Tardivel  and  Antonino Bongiovanni  of the  BICeL-Campus HU Facility  for access to systems and technical advice. We thank  Dr. Mylène Fefeu  and  Emmanuel Parot  for performing manual expertise and for advice and comments on the manuscript and  Antoine Prémont  for critical testing of MuscleJ. We thank  Anthony Yasmann  for critical reading of the tutorial and  Dr. Joel Haas  for critical reading of the manuscript.
Funding This work was supported by research grants from the  Association Française contre les Myopathies AFM  (to AML, FC and DH),  Fédération Francophone de Recherche sur le Diabète FFRD , sponsored by  Fédération Française des Diabétiques (AFD) ,  AstraZeneca ,  Eli Lilly ,  Merck Sharp & Dohme (MSD) ,  Novo Nordisk & Sanofi , (to HD),  Fondation de France  (to HD), the  "European Genomic Institute for Diabetes" (EGID ,  ANR-10-LABX-46 ) (to HD and BS), the  Fondation des Gueules Cassées  (DH and FC), and an  ERC-Région Hauts de France funding  (to HD). BS is a holder of an  ERC  advanced grant no. 694717  "Bile acid, immune-metabolism, lipid and glucose homeostasis". The  PBI (AD, PR , and SLS) is part of the  France BioImaging infrastructure  supported by the  French National Research Agency  ( ANR-10-INSB-04-01 , "Investments for the future").
, 2008)  was a significant step forward in the sharing of clinically relevant data. More recently, the 2015 Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)  (Menze et al. , 2015)  has been a disruptive step forward, having allowed groups without access to high-quality data with delineations to contribute innovative new solutions for segmenting brain tumors  (Sauwen et al. , 2015; Banerjee et al. , 2016; Kamnitsas et al. , 2017). Thus in the spring of 2017, we invited colleagues from around the world to participate in a Cerebellum Parcellation Challenge as part of MICCAI 2017. As only eight groups responded to this call, it was decided that the workshop itself would not go forward due to lack of broad interest. Coming out of the discussions for this Cerebellum Parcellation Challenge (hereafter the Comparison), it was agreed that we would present the performance findings from seven of the research teams who participated in the Comparison (hereafter the Participants).
In Section 2, we outline the two cohorts of data that were provided to the Participants and the evaluation used in comparing the submitted results from each of the Participants. One of the Participants submitted two methods, however two of the Participants contributed no results for one of the cohorts. Thus, our first cohort has results from six algorithms, while our second cohort was processed by eight algorithms. Both cohorts are imaged using standard clinical protocols with an approximately 1 mm isotropic resolution, with complete details of the acquasition in Section 2. In our examination of these data and methods, we restrict our analyses to the Dice overlap; we outline our rationale behind this decision in Section 2. 2. Section 3 provides a complete description of the methods contributed by the Participants for the Comparison.
Data The Participants were given data from our Adult and Pediatric Cohorts, described below. The Participants were also encouraged to take advantage of other available data sets; in particular, they were made aware of data provided by Jörn Diedrichsen of the University of Western Ontario 1. The Diedrichsen data comprises 20 normal adult subjects, each of which have 30 labeled cerebellar components. Our Adult Cohort is an expertly labeled data set collected by the Image Analysis and Communications Laboratory (IACL) at Johns Hopkins University (PI: J. L. Prince)  (Bogovic et al. , 2013b). It contains 20 subjects, a mix of healthy controls and ataxia  patients, each with 28 labeled cerebellar components (complete demographic information is provided in Table  3 ; see Fig. 2  for an example image and corresponding manual labels).
For the training data, the defaced MR image, the WM peak skull stripped image, and the expert manual cerebellar parcellations were provided to the Participants. For the test subjects only the defaced MR image and the WM peak skull-stripped image were provided. All images in this cohort were acquired in an axial orientation. An example of both the defaced and WM peak skull stripped image for a data set are shown in Fig. 2  with the corresponding manual delineation. Our Pediatric Cohort comprises data collected at the Center for Neurodevelop-    4. Results of the methods on the same data are shown in Fig. 9. mental and Imaging Research at the Kennedy Krieger Institute (PI: S. H. Mostofsky). These 30 expertly labeled data sets, with 18 labeled cerebellar components, are from 8-12 year old boys and girls with a mix of healthy controls, ADHD and highfunctioning Autism (HFA) patients (complete demographic information is provided in Table  3 ).
All images in this cohort were acquired in a coronal orientation. An example of both the Table  3 : Demographic details for the training and test data for both cohorts. The top line is the information of the entire data set, while subsequent lines within a section are specific to the patient diagnoses. N (M/F) denotes the number of patients and the male/female ratio, respectively. The Age column lists the mean, standard deviation, min, and max, in years, at scan time. The codes for the patient groups are: HC -Healthy controls; CB -Symptoms of cerebellar dysfunction without genetic diagnosis; SCA6 -Spinocerebellar ataxia type 6; ADHD -Attention-deficit and hyperactivity disorder; HFA -High-functioning Autism. defaced and WM peak skull stripped image for a training data set are shown in Fig.
Default SUIT v3. 2 (Carlos H. Castillo) Data analysis were performed using MATLAB R2015b (The Mathworks Inc. Natick, MA), SPM12  (Ashburner et al. , 2000) , and the spatially unbiased atlas template of the human cerebellum (SUIT) toolbox v3. 2  (Diedrichsen et al. , 2009). To achieve the best performance from SUIT, all anatomical images were first reoriented into LPI (Neurological) orientation and then the origin of each T1-w image was assigned to the manually selected anterior commissure. To ensure the correct normalization of the cerebellar cortex into the atlas template, SUIT first isolates the infra-tentorial structures from the rest of the brain. This is important because the occipital cortex has a similar intensity as the cerebellum and in most cases there is not a clearly visible separation between these two structures.
In the SUIT space, the 18 labels from the 19 subjects in the Pediatric Cohort were combined to create a customized parcellation using mri_concat from FreeSurfer. To apply the customized atlas to the remaining ten subjects in the Pediatric Cohort, C-SUIT first preprocesses the ten subjects using FreeSurfer to perform bias correction, intensity normalization, and skull stripping. Preprocessing the data in this manner provided a consistency between the volumetric measures based on the cerebellar parcellation and the existing cerebral measures derived from FreeSurfer. This feature was included to provide a supplementary cerebellar parcellation for projects with existing measures of the cerebrum derived from FreeSurfer, such as the ENIGMA Project 2. Using SUIT the preprocessed MP-RAGED is bounded, then ANTs estimates a symmetric diffeomorphic transformation into the customized atlas.
FS-SUIT FreeSurfer and SUIT in collaboration for Cerebellar Segmentation (Melanie Ganz & Vincent Beliveau) The approach considered in this work combines FreeSurfer (v5. 3)  (Fischl, 2012 ) and SUIT  (Diedrichsen et al. , 2009) , to overcome some of the limitations of these algorithms when used independently. Whole brain and cerebellar GM and WM segmentation of structural MRI data was performed with FreeSurfer. FreeSurfer processing included motion correction, removal of non-brain tissue, automated Talairach transformation, segmentation of subcortical WM and deep GM volumetric structures (including hippocampus, amygdala, caudate, putamen, ventricles)  (Fischl et al. , 2002 (Fischl et al. , , 2004)) , intensity normalization, and further cortical surface processing. FreeSurfer's cerebellum segmentation is driven by a probabilistic atlas segmentation  (Fischl et al.
Due to computation and memory limitations, the FCN network cannot apply dense training over the whole 3D input volume. Instead, FCN sub-samples this volume into S smaller sub-volumes, which are then fed into the network. In this way: 1) FCN avoids memory issues when the input is not down-sampled (as in this work), and 2) FCN has a high number of samples from each image, removing the need for data augmentation. A Parametric Rectified Linear Unit (PReLU)  (He et al. , 2015) , which applies an element-wise activation function, follows each convolutional layer. Let θ be the network trainable parameters, and L as the set of ground-truth labels such that L v s ∈ L represents the label of voxel v in the s th sampled sub-volume for all the predicted voxels V. The cost function is J(θ, L) = - 1 S V S s=1 V v=1 log p L v s (X v ) , where p L v s (X v ) is the output of the classification layer for voxel v in the segment s (i.