dataesr/scientific-paragraphs-categorization huggingface dataset:
This functional level is implemented as a RESTful web service, using standard web protocol HTTP operations and data encoding (JSON) to perform all the necessary operations  (11). RESTful web services are very popular having the advantage of being lightweight, efficient and simple be use and to be implemented in different programming languages. The API for NaviCell Web Service is implemented in Python and R, and Java API is in development. These APIs will facilitate the integration of Navi-Cell Web Service in other applications: among them, there is ongoing work on providing the data visualization services through the Garuda Alliance web platform, which aims to be a one-stop service for bioinformatics and systems biology  (12)  and GeneSpring software  (13). Figure  1  summarizes the NaviCell Web Service architecture and information flow.
Input data types The pathway maps which can be used for NaviCell Web Service data visualization can be prepared using CellDesigner tool  (14) , which is widely used in systems biology community and based on the Systems Biology Graphical Notation standard  (15)  for representing biological networks. These pathway maps are converted into interactive Google Mapsbased web-interfaces via NaviCell tool  (10). Other pathway map formats can be used in NaviCell through dedicated converters implemented in BiNoM Cytoscape plugin  (16, 17). They extend the functionalities of NaviCell to practically any network that can be imported into Cytoscape environment, though careful and sometimes time-consuming design of the network layout is required in order to obtain meaningful and interpretable network support for data visualization.
NaviCell data visualization web service is able to process several types of 'omics' data, see tations that determine what methods of data visualization can be applied. For instance, a mRNA expression data matrix is associated to a 'continuous' numerical internal representation. Thus, if the user chooses to display this data with a heatmap, mapping to a color gradient will be applied by default, with a possibility to modify the default settings. On the other hand, when a matrix with discrete copy-number data is loaded, it is associated with a 'discrete ordered' internal representation for which a specific color palette is applied for visualization, with a distinct color associated to each discrete copy-number state. The input format for data sets is standard tab-delimited text files, with rows representing genes, their products or metabolites and columns representing samples (or experiments, or time points).
Graphical data representations We have included in the NaviCell Web Service several methods for graphical representation of molecular data, both broadly used in molecular biology (heatmaps and barplots) and original ones such as map staining that have not been previously employed in the context of network-based data visualization. r Simple markers are pictograms (similar to the ones used in Google Maps to indicate the geographical locations) drawn on the pathway map at the location of a given molecular species (protein, gene, complex, phenotype). They are used to display the results of a search performed by the user or to display a list of gene names mapped onto the biological network (including species that are part of a complex, or different forms of a given protein). r Heatmaps are often used in molecular biology, in partic- ular for displaying expression data by color gradients.
In NaviCell Web Service, heatmaps can be used to visualize continuous data types such as expression values, or discrete data such as copy-number or mutation values. The user can arrange the visualization to show several samples or groups of samples from several data sets (for example, for showing simultaneously gene promoter methylation and expression values). r Barplots are charts with rectangular bars whose height is proportional to the values they represent. Barplots can be efficient to visually distinguish numerical values between two or more conditions (e. g. disease versus control as in Figure  3B-D ). r Glyphs are graphical representations using basic geomet- rical shapes (triangle, square, circle, etc. ) and their size and color to visually combine different types of data. For example, the shape of the glyphs can be assigned to the copy-number values, while the expression data can be used to define the color of the glyph.
Performing statistical analyses from within NaviCell Web Service GUI Data visualization using biological networks is a powerful method to gain insights into functioning of biological mechanisms. However, the intrinsic capability of human brain to detect complex patterns in distributions of colors, points, etc. , can lead to overfitting and poorly supported conclusions. This is to say that all hypotheses formulated from visual data inspection must be supported by rigorous statistical analysis. We provide two examples of such follow-up analyses in Supplementary Materials. In order to quickly verify 'impressions' from data visualization, NaviCell GUI provides a possibility of simple statistical analysis of the uploaded data ('Functional analysis' button). For example, the standard hypergeometric test applied to any uploaded gene list allows computing the enrichment P-values for all modules of the map, and, therefore, selecting the most interesting module or map for further data   visualization and analysis.
RESULTS We show that NaviCell Web Service can be used for visualization of different types of high-throughput data in two case studies: r comparing two prostate cancer cell lines using the cell cy- cle map, focusing on transcriptomics and mutation data. r using the map of molecular interactions involved in Alzheimer's disease (AD)  (19)  for visualizing the transcriptome data collected for different brain areas  (20). Furthermore, several examples of using NaviCell Web Service for data visualization can be found in NaviCell Web Service user guide and the case studies provided at http://navicell. curie. fr/pages/nav web service. html. In Supplementary Materials, we provide two case studies demonstrating visualization of ovary cancer data obtained from The Cancer Genome Atlas  (21)  on the large map of Atlas of Cancer Signalling Network  (22)  and an example of using the non-CellDesigner network map of the Ewing's sarcoma signalling network  (23)  for visualizing transcriptomic time series data.
DISCUSSION AND CONCLUSION NaviCell Web Service shall contribute to the growing set of highly demanded tools for molecular biology allowing visualization of 'omics' data in the context of biological network maps. We have compared the features of the NaviCell Web Service for data visualization with the web-based tools providing similar functionalities  (3) (4) (5) (26) (27) , i. e. easy pathway browsing functions and interactive data visualization capabilities. We have focused on the features related to advanced map navigation, molecular data types, graphical representations and programmatic access. The results of this comparison show that the NaviCell Web Service's strengths are: multiple possibilities for graphical representations, support of multiple data types and extended capabilities for programmatic access from different computer languages (Table  2 ).
Students learn to create VEs by generating the geometries of their projects using conventional 3D modeling applications. The best rendering standard is achieved following a straightforward process: the models are assigned standard materials and the scene becomes realistic when adding daylight systems and/or artificial lights; these materials and lights are baked into the models and exported to Vizard. Vizard 4 is a python-based application that allows, among many other functions, for the addition of avatars, animated backgrounds, and also adjustments for the seamless playback of the VE in our immersive environment. See illustration 1. The CAP VR Environment provides effective immersion; the level of presence that the users experience tends to be very high mainly because the navigation episodes are for the most part free of any cyber-sickness.
Data Analysis & Results The Likert Scale format was used in all questionnaires. The average rating method was applied to establish the comparison between subjects and projects. The results of Questionnaire #1 in the section describing the experience of using the CAP VR Environment is described in Illustration 7. This illustration shows that the students regarded the use of the VE as a good or very good experience (60%). The questions posed to the students referred to the students' sense of orientation within the VE, whether they felt confused or disoriented within the VE, and how convinced they were in the space while using the simulation.
MEG and EEG were measured in magnetically shielded room using Elekta Neuromag Vectorview 306 system (102 magnetometers, 204 planar gradiometers), a 70 easy channel Easycap EEG cap was used to record the EEG data simultaneously. Stimuli were presented in six 7. 5 min runs at 1100 Hz sampling rate. The face stimuli contains two sets of 300 grayscale photographs, half from unfamiliar people (unknown to the subjects) and the remaining from famous people. For the third condition, 150 photographs of scrambled faces are obtained from either famous or unfamiliar people. Across subjects, between 880 and 889 epochs were extracted (around 295 per condition i. e. Famous (Fs), Unfamiliar (Ur), Scrambled (Sd))  [8, 9]. For each of the 11 participants and conditions, we averaged the EEG and MEG measurements.
Expression data analysis Statistical filtration and classification were performed for each pairwise comparison of the samples: mock GRMD versus healthy dogs, GRMD MuStem versus healthy dogs and GRMD MuStem versus mock GRMD dogs. The detectable probes (22783/22841/22457) yielding signals !7. 32 (median of the normalized expression dataset) were first identified in each of the three respective comparisons. A total of 1074 (605/940/99) probes were further selected displaying a high expression variation (fold-change, FC !2. 0). A LIMMA statistical test  [35]  was then performed to identify 608 (430/567/41) probes that were significantly differentially expressed (F-value adjusted with the False Discovery Rate, p 0. 05). For each of the three comparisons, the selected probes were finally classified into two groups (based on the fold-change), named "up" or "down" according to their expression profiles.
Canine gene and probe annotation To obtain the most complete annotation possible, we considered the dog annotation based on the Entrez Gene IDs (NCBI) provided in the AMEN suite of tools  [33] , and then supplemented with the Ensembl annotation. The microarray probes were subsequently mapped on the can-Fam3 genome using the BLAT program  [36]. To filter the alignments, we considered a percentage of identity of !90% over the whole size of the probes (60 bps). The probes were further associated with canine genes based on their genome coordinates and the overlap with exonic regions of annotated loci. It is noteworthy that a given probe can be assigned to several genes, while several probes can be associated with a single gene.
Comparison with transcriptomic studies of individuals with DMD The group of genes significantly deregulated in muscle of individuals with DMD was extracted from four studies  [8, 9, 11, 12]. The conversion of the Affymetrix human probe set identifiers into dog NCBI Entrez Gene identifiers was performed with the AMEN suite of tools via the HomoloGene database  [38]  and the array annotations provided by the AILUN annotation platform  [39]. A group of human genes is taken as significantly associated with a group of dog genes if the associated p-value is 0. 005 (Hypergeometric test).
Quantitative RT-PCR for validating Agilent dataset The RT-qPCR validations were done on the three healthy dogs (#1H to #3H), on two mock GRMD dogs (#4G and #5G), on two GRMD MuStem dogs (7G Mu and 8G Mu ). Additional RT-qPCR experiments were also performed on muscle total RNA from two non-immunosuppressed GRMD dogs (referred to as GRMD nonIS ) kindly provided by collaborators. A subset of genes was selected to validate the data obtained by microarray analysis. Reverse transcription reactions were carried out on 0. 5 μg of total RNA using the GoScript reverse transcriptase (Promega, Madison, USA) in a total volume of 20 μL. All cDNA amplifications were performed, in duplicate or triplicate, using 1/20 th of the reverse transcription products and the MESA BLUE qPCR kit (Eurogentec, Seraing, Belgium).
Quantitative PCR was run on the Thermocycler CFX96 (Biorad, California, USA) with the following parameters: 5 min at 95°C for the initial denaturation step, then 15 sec at 95°C, 1 min at 60°C per cycle, for a total of 40 cycles. The specific amplification was checked using a melting curve. Gene-specific oligonucleotide primers were designed using Oligo Primer Analysis Software v. 7 (Molecular Biology Insights Inc. , Cascade, USA) and synthesized by Eurofins MWG Operon (Ebersberg, Germany). The primers used are listed in Table  2. RPS18 was selected as an endogenous control. The relative expression levels were calculated by the 2 -ΔΔ Ct method.
As reported in Table  3 , among the genes that are common between our dataset and the human transcriptomic studies, we can identify significant enrichments of genes that are up-regulated or down-regulated in both canine and human studies. The GO terms significantly enriched in the group of genes differentially expressed between GRMD dog and healthy dog muscle are determined  [41]  (Fig 4 and S2 File). Among the genes up-regulated in GRMD dog as compared to healthy dog, we find enrichment for processes such as immune system process (n = 49, p<2x10 -5 ), inflammatory response (n = 17, p<2x10 -4 ), response to stimulus (n = 126, p<7x10 -6 ), response to stress (n = 75, p<2x10 -4 ), response to lipid (n = 28, p<4x10 -4 ), regeneration (n = 11, p<2x10 -3 ) and cell adhesion (n = 27, p<2x10 -4 ).
Real-time quantitative PCR validation of array results To validate the related microarray data, we chose 15 of the 31 differentially expressed genes in the GRMD MuStem dog group compared to mock GRMD dog (Fig  5A ) to carry out real-time quantitative RT-PCR analyses. The RT-qPCR validations were performed on the same sample set as the microarray (#1H, #2H, #4G, #5G, #7G Mu , #8G Mu ) and also on the third healthy dog (#3H). To take account of the immunosuppressive context, we performed additional RT-qPCR experiments on muscle total RNA from two GRMD nonIS. Relative quantifications are calculated for healthy, GRMD nonIS and mock GRMD dogs (S1 Table ). GRMD dogs, whether or not immunosuppressed, show a similar variation of expression of the selected genes compared to healthy dogs. This reinforces the microarray data on the GRMD dog model.
Autophagy is required for cellular survival, and this new concept changes the current view that proteolysis is detrimental; the new vision is to consider transitory activation of the proteolytic systems to eliminate misfolded muscle proteins in myopathy  [69]. In human heart failure syndromes, recent evidence supports a role for protein damage and impaired clearance of damaged proteins in the pathology, and highlights the UPS as the primary effector of regulatory control  [70]. In the present study, we show that MuStem cell transplantation stimulates the expression of several genes involved in the protein degradation machinery via this system. Specifically, FBXO32 (Atrogin 1), TRIM63 (MURF1), ZFAND5 and MT2A encode proteins of the E3 ubiquitin ligase complex, which mediates the ubiquitination, and subsequent proteasomal degradation of target proteins.
This research was supported by grants from the  Association Française contre les Myopathies  ( AFM N°14379 ) and the  FEDER (Fonds Européen de Développement Régional   N°37085 ). It was carried out in the context of the  IHU-Cesti  project that received French government financial support managed by the  National Research Agency  via the investment of the future programme  ANR-10-IBHU-005. The  IHU-Cesti  project is also supported by  Nantes Metropole  and the  Pays de la Loire Region. of MuStem cells. received by  KR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Background: Papillary Thyroid Cancer (PTC) is the most prevalent type of endocrine cancer. Its incidence has rapidly increased in recent decades but little is known regarding its complete microRNA transcriptome (miRNome). In addition, there is a need for molecular biomarkers allowing improved PTC diagnosis. Methods: We performed small RNA deep-sequencing of 3 PTC, their matching normal tissues and lymph node metastases (LNM). We designed a new bioinformatics framework to handle each aspect of the miRNome: whole expression profiles, isomiRs distribution, non-templated additions distributions, RNA-editing or mutation. Results were validated experimentally by qRT-PCR on normal samples, tumors and LNM from 14 independent patients and in silico using the dataset from The Cancer Genome Atlas (small RNA deepsequencing of 59 normal samples, 495 PTC, and 8 LNM).
Results: We performed small RNA deep-sequencing of 3 PTC, their matching normal tissues and lymph node metastases (LNM). We designed a new bioinformatics framework to handle each aspect of the miRNome: whole expression profiles, isomiRs distribution, non-templated additions distributions, RNA-editing or mutation. Results were validated experimentally by qRT-PCR on normal samples, tumors and LNM from 14 independent patients and in silico using the dataset from The Cancer Genome Atlas (small RNA deep-sequencing of 59 normal samples, 495 PTC, and 8 LNM). We confirmed already described up-regulations of microRNAs in PTC, such as miR-146b-5p or miR-222-3p, but we also identified down-regulated microRNAs, such as miR-7-5p or miR-30c-2-3p. We showed that these down-regulations are linked to the tumorigenesis process of thyrocytes.
We selected the 14 most down-regulated microRNAs in PTC and we showed that they are potential biomarkers of PTC samples. Nevertheless, they can distinguish histological classical variants and follicular variants of PTC in the TCGA dataset. In addition, 12 of the 14 down-regulated microRNAs are significantly less expressed in aggressive PTC compared to non-aggressive PTC. We showed that the associated aggressive expression profile is mainly due to the presence of the BRAF V600E mutation. In general, primary tumors and LNM presented similar microRNA expression profiles but specific variations like the down-regulation of miR-7-2-3p and miR-30c-2-3p in LNM were observed. Investigations of the 5p-to-3p arm expression ratios, non-templated additions or isomiRs distributions revealed no major implication in PTC tumorigenesis process or LNM appearance.
RNA and DNA extraction Total RNA was extracted from tissues using Qiazol, followed by purification on miRNeasy columns (Qiagen) according to the manufacturer's recommendations. Genomic DNA was extracted from the remaining mix of phenol-chloroform using homemade protocol described in Additional file 2. Laser capture microdissection, qRT-PCR and other experimental procedures are described in Additional file 2. Human small RNA deep-sequencing: mapping strategy and downstream analyses Small RNA deep-sequencing of the 3 PTC and their matched samples were performed by the Beijing Genomics Institute (BGI) using the TruSeq Small RNA Sample Prep Kit (Illumina). 24 to 31 million of clean reads per sample were obtained. Illumina reads collapsed by sequence in fasta format are available from NCBI's Gene Expression Omnibus  [32]  and are accessible through GEO series accession number GSE57780.
With this strategy, up to 93 % of individual reads could be mapped during the first step when using the human genome hg19 as reference. microRNA differential expression profiles: after aligning reads to the reference genome, we counted the number of mapped reads for both annotated and novel mature microRNAs. Expression analysis was performed using edgeR  [34]  distributed as an R  [35]  package available at Bioconductor  [36]. As advised in the documentation, we first extracted all microRNAs with a normalized expression (in CPM: Count Per Million mapped reads) of 1 CPM in at least 3 samples, leading to a total of 398 microRNAs that were used as input for differential expression analysis. We used a generalized linear model and designed our analysis so that 3 replicates were available per condition while retaining the paired information for tissue types collected from the same patient.
) Fig. 3  Significant modulated microRNA expressions between samples types in the small RNA deep-sequencing data. Normal samples (N), primary tumors (T) and LNM (M) from 3 patients (M) were analyzed. The read counts are described for each sample. The fold change (FC) is the log 2 expression ratio in CPM. We used a generalized linear model and designed our analysis so that 3 replicates were available per condition while retaining the paired information for tissue types collected from the same patient (See Methods). a: comparison between tumors and normal samples. Up-regulated microRNA in tumors are in red and down-regulated microRNAs in tumors are in green. b: comparison between LNM and tumor samples. Up-regulated microRNAs in metastases are in red and down-regulated microRNAs in metastases samples are in green ▪ In silico validation on the TCGA small RNA deepsequencing dataset In order to further validate our results on the largest set of independent PTC samples, we collected the TCGA public small RNA deep-sequencing data processed by the Broad Institute with an independent bioinformatics approach (see Additional file 2 for a complete description of collected datasets).
Down-regulated microRNAs can distinguish the two principal histological variants of PTC To further analyze our results we compared the expression levels of the 14 down-regulated microRNAs between the normal samples (n = 59), the classical variants (n = 323) and the follicular variants of PTC (n = 99) in the TCGA dataset (Fig. 6a ). Less frequent variants and non-annotated samples were not taken into account. 11 microRNAs are down-regulated in both variant types and, except for miR-451a and miR-504, they are also significantly less expressed in classical variants. miR-7-5p, miR-204-5p and miR-139-5p are only significantly down-regulated in the classical variants. These results suggest that, in addition to their ability to discriminate PTC from normal samples, the studied down-regulated microRNAs are also able to distinguish tumor histological major subtypes.
Down-regulated microRNAs can distinguish aggressive from non-aggressive phenotype of PTC We obtained similar results when we compared the normal samples with the BRAF V600E positive PTC samples (n = 233) and the BRAF negative PTC samples (n = 168) in the TCGA dataset (Fig. 6b ). Again, nonannotated samples were not taken into account. The same 11 microRNAs plus miR-139-5p are downregulated in both tumor types but, except for miR-451a and miR-504, they are significantly less expressed in BRAF V600E positive samples. miR-7-5p and miR-204-5p are only significantly down-regulated in BRAF V600E positive tumors. These results were not unexpected taking into account that in the 191 BRAF V600E positive PTC samples annotated for variant type, 179 are classical and only 12 are follicular. In addition, in the 151 BRAF V600E negative samples annotated for variants, only 80 are classical and 71 are follicular.