dataesr/scientific-paragraphs-categorization huggingface dataset:
Input data SISAQUA offers access to various data like vector data represented by points, lines, or polygons as well as raster data (images). They are either stored and saved locally on Ifremer's own servers or harvested from other devices through remote services (e. g. WMS). Data management/administration is based on a set of norms and standards widely used by other web-based mapping tools. The description of the data (metadata) follows the European Directive INSPIRE as well as the ISO19115 and ISO19139 standards. Regarding data access, the tool relies on the standardized protocols of the Open Geospatial Consortium (OGC) such as the WMS for visualization, the Web Coverage Service (WCS), WFS for downloading and the standard Web Processing Service (WPS) for online processing. Locally, administration rights of the tool are attributed to a list of users managing the website, data catalogue and web services as well as the API.
Geoprocessing and output products Geoprocessing for computing SISAQUA indicators, is made through a linkage between Qgis software and PyWPS, which is an implementation of the WPS standard. PyWPS is written in Python. In the case of SISAQUA, original treatments implemented in Sextant for one layer were adapted to manage several layers at the same time and then combine several data. The dialog box for the treatment can be customized in terms of titles, addition of help, input locking, or default values. The output product is a map that can be displayed directly in the viewer but also downloaded. The tool is upgradeable and could integrate other treatments or other outputs (graphs, reports, etc. ).
Tool benefits and limits A key strength of SISAQUA is that it relies on Sextant SDI, which ensures a continuous maintenance under operational condition. Presently, about thirty other data access portals use the Sextant API. Among these portals, one finds the Odatis ocean Pole giving access to oceanographic data and mainly designed for the French scientific community ( https://www. odatis-ocean. fr/ ); the Information System for the Marine Environment gathering various public information and depending on the French state ( https://www. milieumarinfrance. fr ); the JERICO Research Infrastructure representing the European gateway for coastal observation ( https://www. jerico-ri. eu/ ). Sextant is described through numerous guides (in French), available on the Sextant website ( https://sextant.
ifremer. fr/eng/ Ressources/Guides-and-supports ). At this stage, there are still ways to improve SISAQUA, such as the implementation of metadata on treatments themselves. The biggest limitation concerns the access to external services. Regular monitoring is required to ensure that remote data are still being served by the data providers. This type of problem has been noted and corrected on several occasions since the implementation of the tool. There is also space for the development of indicators and to generate automatic reports to help decision-making.
Reproducibility and application in other cases SISAQUA is based on a map viewer which makes available approximately 120 local, regional or national data sources. To reproduce the SISAQUA application in another geographical area or with a slightly different aquaculture issue, the main work would consist of a complete inventory of the data to be published online. Data should comply to data standards (WMS, WFS, CSW), whatever the source of data -e. g. data produced by Ifremer or distributed by another organization. As for SISAQUA, it would be possible to integrate data created specifically for this new application by writing a metadata and creating the associated distribution services. Besides, the tool would require very few adjustments: -the technical base would be the same and only a few minor configurations would need to be made (XML for the map context, API code to be deployed in a website) -the metadata catalog should be filled in to provide a standardized description of each data.
Tool technical features APDSS is an ArcGIS-based spatial planning tool (system), which adopts C/S (Client/Server) + B/S (Browser/Server) mixed mode for development, by giving full play to the advantages of the two modes. The tool has also applied a set of free software that ArcGIS is compatible with, such as Python. This tool contains two parts including a desktop part and a web-based part, which have differentiated functions ( Fig. 3  ). The desktop system provides data storage, updating and processing, model simulation and analyses; while both systems can accomplish a range of tasks, such as, data query and browsing, display of model simulated results and map layers, and map retrieval. (1) Architecture of the desktop-based APDSS: The map viewer of the desktop system adopts userfriendly WinForms embedded with ArcEngine Object components, such as Map Control component and TOC (Tool of Control) component.
All these functions are developed using C # language in the. NET Framework environment. (2) Architecture of the web-based APDSS: The map viewer is running based on ArcGIS API for JavaScript and ArcGIS Server, where the base map layer is loaded by satellite raster tiles from Esri ArcGIS Image Server, and in contrast, the other feature layer such as raster layer or shape file are published online from local ArcGIS server. The Web-based APDSS is developed in Asp. Net-Microsoft web platform. The languages adopted in web-APDSS involved JavaScript, php, html, CSS, and PHP for server-side processing. The system is deployed to the client's environment where actual users can run the system. All data and components are stored locally. System administrators can make necessary enhancements, corrections, and changes for maintenance to ensure the system works normally.
Input data There are several icons in the toolbar above the top of the map viewer of the desktop APDSS tool. Each of these icons launches a widget. Data and associated metadata in four types of formats, such as vector (shapefile), raster and NetCDF data, and non-spatial data in excel format, are imported and processed into the system by these widgets. In the APDSS map viewer, users can manage and browse data in the catalog. A pop-up is also available in the catalog pane that shows metadata (name of data, type, date, and location). In addition, environmental data storage is one of the main functions of the desktop APDSS. The input data are all stored locally before they are published on ArcGIS for server by server manager. Afterwards, the clients of web service can receive data from the database server.
Results VirPy, an all-in-one RNA-Seq-based tool. The VirPy pipeline performs a multitude of computational procedures to generate a complete, comprehensive analysis of both the patient's genetic mutation and virome. The pipeline was developed using the Python programming language employing third-party tools, including STAR  (12) , HISAT2  (13) , SAMtools  (14) , eXpress  (15) , Subread featureCounts  (16) , FreeBayes (http:// arxiv. org/abs/1207. 3907), Salmon  (17) , and SnpEff packages  (18). RNA extraction and whole-transcriptome sequencing were performed as previously described  (19). In this process, the quality of RNA-Seq data is assessed by FastQC, and according to the results, trimming is considered. The high-quality reads in the FASTQ files are used as inputs for the pipeline. FASTQ paired-end reads are aligned to the human genome reference (GRCh37) using the STAR RNA-Seq aligner.
Unaligned reads or reads only partially aligned to the human genome are assumed to be nonhuman in origin and represent genetic material from microorganism species. The nonhuman paired mates are extracted and realigned using the HISAT2 aligner for bulk RNA-Seq and STAR/BWA mem for single-cell RNA-Seq (scRNA-Seq) to a compiled viral genome reference containing 926 unique viral species obtained from the National Center for Biotechnology Information (NCBI), including over 400 known types of HPV  (7). The precompiled viral genome reference database can be updated and modified with new entries for future usage, allowing for flexibility depending on the types of analyses or viral species of interest. The virus-containing SAM file is filtered to contain only concordant pairs. Reads aligned to the viral genome reference are formatted into the aligned, sorted, and indexed BAM files, which can be visualized in Integrated Genome Viewer (IGV) for bulk RNA-Seq (Figure  1  and ref.
Reads that did not align concordantly or had additional secondary alignments were filtered out of the BAM file to avoid incorrectly attributing genome segments that were common between viruses. In scRNA-Seq, the viral copy number was measured by using SAMtools idxstats, and the read counts were  We integrated the previously developed pipeline of whole-transcriptome analysis for pathogenic sequence variants by RNA-Seq with our virome detection pipeline into a single-run experiment  (9). In our previously published pipeline, we used genome and transcriptome references concurrently for alignment mapping to identify genomic mutations with an overall yield of over 85%. The details of the detection, quantification, and variant calling for the human transcriptome within the VirPy module are described elsewhere  (19, 22).
We have now expanded the VirPy tool for simultaneous detection of both virome repertoire and the underlying causal mutations in the host genome from bulk and scRNA-Seq FASTQ files. The VirPy workflow was developed using the Python programming language employing third-party tools and is publicly available for free use at https://github. com/Vahidnezhad-Lab/VirPy (commit ID 8e67ee7244e23d-21a198180206a5d9dee2570b5f). We have also deployed a publicly available web-based application for VirPy, available at https://www. VirPy. org. JCI Validation and utility of VirPy. In the current study, all data sets (in-house and public) were examined experimentally, and the viral infection was confirmed with other standard methods such as reverse transcription PCR, real-time PCR, and histopathology (i. e. , presence of koilocytes).
In this study, we recruited 9 distinct families including 10 patients with cutaneous viral infections who were diagnosed with IEI of unknown genetic and virome etiology. In addition, we investigated the possibility of simultaneous detection of single-gene susceptibilities and virome in patients with various viral infections, including HPV, HIV, EBV, CMV, and human T-lymphotropic virus type 1, by applying VirPy on bulk RNA-Seq skin biopsy data from 10 patients as explained in detail below. Elaborative case reports of all 9 families are available in Supplemental Tables  1  and 2  and the Supplemental Methods section. HPV detection in patients with IEI affected by epidermodysplasia verruciformis. Epidermodysplasia verruciformis (EV) is an autosomal recessive disorder characterized by widespread flat warts, associated with the propensity to develop squamous cell carcinomas  (24).
Control samples from the healthy appearing skin of the patients, their family members, and unrelated individuals were used to establish the baseline viral expression for each virus detected. In each case, clinically normal skin as a control showed low or absent levels of virus, while in the skin from healthy family members and unrelated controls, no virus was detected. To account for similarities in viral genetic material, such as in closely related types of HPV, the pipeline excludes reads that have primary and secondary alignments to more than 1 viral genome from the quantification step. Interestingly, in several lesions, multiple HPV types were detected, though in most cases one of them was predominant with the highest detected copy numbers. The VirPy pipeline was also designed to be readily accessible for investigating the virome in clinically derived RNA-Seq data by noncomputational biologists through a web-app-based platform to carry out investigations regardless of access to high-level infrastructure and software knowledge.
Implementation of the STAR RNA-Seq aligner with parallelization reduced the total computational time by more than 20-fold compared with methods using single-threaded aligners for human alignment. Secondary alignment of unaligned reads to the viral genome reference was done with HISAT2, as there was no advantage computationally using STAR, with similar output results. Additionally, our pipeline accepts compressed, gun-zipped FASTQ files as input, providing users improved flexibility with space efficiency in storing sample data. The multitude of data output files generated for each step in the workflow also allows user flexibility in the analysis using this tool. Using RNA-Seq as a first-tier diagnostic method has several advantages, such as reclassifications of intronic variants with unknown significance and exonic variants with effect on splicing by alteration of ESS and ESE regions.
RNA-Seq provides high-quality transcription profiles that enable reliable genotyping with subtyping of HPVs and quantification of the viral gene expression. Other advantages of RNA-over DNA-based detection of HPVs include improved specificity, greater predictability of pathogenicity, and precise correlation with histopathology. Although still new, we believe VirPy has potential clinical utility. For example, our diagnostic pathology center at Thomas Jefferson University reported 1 patient to be HPV negative; however, VirPy was able to detect HPV33 in this patient. The patient was subsequently enrolled in a clinical trial for HPV-positive H&N cancer treatment; without this pipeline, a potentially life-saving intervention may have been delayed. The primary drawback of VirPy is that it requires a database of known viruses.
Although the viral reference database can be expanded upon, the tool cannot detect the presence of new viruses. As of now, we have integrated 926 distinct viruses in the database and will update our database as new viruses are characterized. Another limitation is the lack of detection of retroviral proviruses or endogenous retroviruses and endogenous viral elements using this method. However, the primary focus of this study was only on viruses associated with IEI, especially HPVs. In this study, because patient samples were obtained only from the skin of diagnosed cutaneous lesions, in some cases we were limited in the detecting the expression of viruses that may have low viral load in the normal looking skin. With expanding application of NGS in clinical settings, there is an increasing demand for bioinformatics tools to process and interpret these generated data.
The total mRNA capture and library preparation at 4 nmol/L concentration, including initial barcoding and sequencing on an Illumina NextSeq 500 machine, were performed according to routine procedures at the Thomas Jefferson University genomic facility. With 150 bp paired-end chemistry, a sequencing depth of 50 to 100 million paired reads per sample was achieved. Variant calling from RNA-Seq data. We first used genome mappers, such as BWA or Bowtie2, for alignment of RNA-Seq data to the reference transcriptome (GRCh37/hg19). Next, variant calling of the aligned reads was done by Picard and SAMtools. We identified the final variants through the filtering steps previously described  (19) , and for families with evidence of consanguinity, overlapping variants with the ROHs were identified. Filtration, annotation, and variant prioritization.
We applied the following criteria to enhance the confidence in the accuracy of the called variants: read depth below 10, genotype quality below 20, and variant quality below 50 in vcf files were filtered out. The variant calling from mapped reads to the reference genome was performed by GATK4 (https:// gatk. broadinstitute. org/hc/en-us/articles/360036194592-Getting-started-with-GATK4). We particularly included Filter_reads_with_N_cigar to gain higher variant calling sensitivity. The variant calling region was set to the exons of all transcripts and surrounding 10 bp flanking intronic regions. We used Annovar for determination of the genomic location and functional consequence of called variants  (31). Deleteriousness of the variants was predicted using multiple prediction tools, such as FATHMM (http://fathmm.
biocompute. org. uk), MutationAssessor (http://mutationassessor. org/r3/), M-Cap (http://bejerano. stanford. edu/ mcap/), SIFT (https://sift. bii. a-star. edu. sg), Polyphen2 (http://genetics. bwh. harvard. edu/pph2/), and CADD v 1. 4 scores, as shown in Figures  3 4 5 6 7 8. Variants were checked to be absent from publicly available healthy population databases, such as gnomAD and Bravo. Final variants were identified by superimposing the surviving variants with the ROHs, in families with evidence of consanguinity. Ultimate prioritization of variants employed a 2-level strategy following the latest American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) and Sherloc classification criteria  (32). First, we included variants that were classified by ACMG/AMP as a VUS, as likely pathogenic, and as pathogenic.
Second, these variants were refined by Sherloc using a semiquantitative 0 to 5 scoring system. Variants classified as 3-U, VUS, and above were included in the final variant list. The surviving variants were those matched with phenotype and segregate within the family. Final variants were assessed at Genoox Franklin's website for clinical variant interpretation and annotation  (33). VirPy installation instructions and necessary packages can be found within the GitHub page (https:// github. com/Vahidnezhad-Lab/VirPy; commit ID 8e67ee7244e23d21a198180206a5d9dee2570b5f). After installation, the pipeline can be run from command line using "python VirPy. py -1 Fastq1 -2 Fastq2 -o outputDir -index human_reference/ -index_vir viruses_reference/". Running the VirPy pipeline creates directories containing measurements of viral quantification, as well as elucidation of viral mutations.
The eXpress directory contains the outputs of the viral quantification module, including the main "results. xprs" file. This results file can be opened using a spreadsheet viewer, such as Microsoft Excel. Data elements within the file include viral ID, total mapped read count, unique mapped read count, fragments per kilobase of transcript per million mapped reads, and transcripts per million. Manual analysis of viral quantification and comparison of relative viral abundance within the sample can be done by sorting the unique mapped read count measurement in descending order, with the most abundant viral species appearing at the top of the list. The featureCounts directory contains the outputs of the per virus gene quantification module. Viral gene quantification requires user input of a valid GTF format annotation file per virus.
Missing GTF annotation files will be noted for user within the "missingAnnots. txt" file for reference. Each output file within the featureCounts directory contains a unique, delimited text file quantifying gene products per virus detected within the sample. A short list of the detected viruses can be found in the "viruses_detected. txt" file. In addition, the VariantCalling directory contains outputs from the viral mutation analysis module of the VirPy pipeline. An annotated variant call file is created, detailing variants within detected viral species compared to reference genomes obtained from NCBI Virus. Last, viral variants are annotated using SnpEff with predicted effects based on the functional impact on their gene products, found within the "snpEff_genes. txt" file. The final indexed BAM file, which contains the detected viruses, can be visualized, and MAEC can also be obtained from the Sashimi plot feature using IGV.
A detailed schematic of the steps of the VirPy analysis workflow is shown in Figure  1. Validation and evaluation of the VirPy pipeline were done by analyzing 30 RNA-Seq samples from patients with H&N cancer from the Department of Otolaryngology at Thomas Jefferson University as discussed in detail previously, as well as publicly available data from bulk and scRNA-Seq. The raw FASTQ pairedend reads were obtained from the following experimental systems: human CD56Dim NK cells infected with HIV (Gene Expression Omnibus [GEO] GSE168212) (Figure  2A ), HNSCC_PBMC infected with HPV (GEO GSE139324) (Figure  2B ), D23_Parenchyma_ROI3_PanCK+ infected with SARS-CoV-2 (GEO GSE162911) (Figure  2C ), and primary B cells (EBV-infected) (GEO GSE158275) (Figure  2D ), for bulk and scRNA-Seq data.
Insight 2022;7(8):e156021 https://doi. org/10. 1172/jci. insight. 156021 quantified at the individual viral gene level by Salmon. Next steps for bulk RNA-Seq included a) quantification of the absolute count of viral features (i. e. , exons and transcripts) by the featureCounts tool in the Subread package, b) viral variant analysis by the variant caller FreeBayes, and c) annotation of the output variant call file with SnpEff. A consensus sequence is then generated from the variant call file using GATK (21) and Picard tools (https://broadinstitute. github. io/picard/).
Acknowledgments We thank  Masoomeh Faghankhani  and  Liaosadat Mirsafaei  for genetic counseling, case management, and drafting Supplemental Table 1. Carol Kelly assisted in manuscript preparation. We thank  Yelena Nemirovskaya  for administrative assistance. Funding was provided by  NIH  grant  R01AI143810  and  DEBRA International. Address correspondence to:  Hassan Vahidnezhad ,  Department of Dermatology and Cutaneous Biology, Sidney Kimmel Medical College at Thomas Jefferson University , 233 S. 10th Street, Suite 408 BLSB, Philadelphia, Pennsylvania 19107,  USA. Email: hassan. vahidnezhad@jefferson. edu.
R E S O U R C E A N D T E C H N I C A L A D V A N C E JCI Insight 2022;7(8):e156021 https://doi. org/10. 1172/jci. insight. 156021 gap by integrating various bioinformatics packages for analysis of RNA-Seq data, and its integration with our previously published pipeline for human transcriptome analysis makes human and virome analysis straightforward from RNA-Seq data. Data availability. Data sets related to this article can be found in National Center for Biotechnology Information's BioProject with submission identification PRJNA821903 (https://www. ncbi. nlm. nih. gov/ bioproject/PRJNA821903). Statistics. P values of less than 0. 05 were considered significant. Study approval. This study was approved by the Institutional Review Board of the Pasteur Institute of Iran, Tehran, Iran. Written informed consent was obtained from all adult patients and the parents or guardians of children to participate in research and publish their images.
At that point, neural activity is measured using single cell calcium fluorescence microscopy with a transduced calcium GCaMP biosensor. Our data show that: 1) astrocytes produce brainrelevant ECM under MMC conditions; 2) neurons co-cultured on MMC-treated astrocytes show enhanced synaptogenesis, complex neurites network and calcium activity; and 3) pharmacological responses to compounds targeting dopamine receptors in MMC-treated dopaminergic and astrocytes co-cultures emulate those expected from in vivo data.
Image analysis of Neural extensions and branching Image analysis and quantitation of immunofluorescence signal from neural coculture was done using the software Harmony v5 (Perkin-Elmer). Because we did not include a nuclear cell marker for live cells image analysis, we used the GCaMP6f/FITC signal to identify neuronal cells. and for the fixed cell image analysis, we used Hoechst 33342 fluorescence for identifying the nuclei. We used the 'Find nuclei: Method B' algorithm using an area of >30µm 2 with common threshold 0. 4, for locating nuclei. Used again GCaMP6f and TH primary antibody staining for live and fixed dopaminergic neuronal extensions identification, respectively. Then we applied the 'Find Neurites with CSIRO Neurites analysis 2' algorithm to mark the neurites with neurites segment from the preselected Nuclei.