dataesr/scientific-paragraphs-categorization huggingface dataset:
Architecture A system has been designed in order to link the ProstateAnalyzer application to a database of DICOM images and a XML server, which stores the annotated files of the DICOM images. This architecture is supported by a Zend Server in order to allocate the ProstateAnalyzer application and a XML database provided by eXist-db  (40). This type of architecture has been used in previous works for supporting other modalities of medical imaging databases such as PACS  (42). The ProstateAnalyzer application (Figure  2 ) communicates via socket TCP/IP with the Apache Server provided by Zend Server. This server is connected to a DICOM database, containing DICOM prostate images and with the eXist-db database, containing the XML files. We opted for the creation of the XML annotation files in the database in order to preserve the integrity of the original DICOM database (usually stored in a PACS system).
XML database The ProstateAnalyzer application is related to the eXist-db manager, responsible for the XML database, incorporating its own server for access and management via web interface. We also opted for the creation of the XML Web-based management of prostate cancer imaging annotation files in the database. Again, all XML files are related to their corresponding DICOM images and contain the records provided by experts. Figure  3  shows a scenario of the use of the eXist-db XML database. The database is configured at different levels in order to store XML files associated with studies, collections, status and images. First, the ProstateAnalyzer application connects with the eXist-db to access a study, where each study contains a set of XML collections corresponding to 3DT2, DWI (as ADC map), and DCE techniques.
Consequently, for each dataset, all the XML files concerning prostate images are allocated. It is important to note that each XML file contains the annotations provided by all the experts. In this way, the main structure of the XML file is the same for the images of the same study, thus enabling a faster access to the annotated information. Moreover, at the status level, for each study, a XML file associated with each user is saved. Consequently, once the user is logged into the ProstateAnalyzer, the corresponding XML file is obtained.
ProstateAnalyzer engine The search engine is the front-end for users once ProstateAnalyzer is accessed. There are two types of patient searches: search for all the patients and an advanced search. In the first option, the search engine returns a list with all the patients recorded in the database. In the second case, the system returns just the patients who share the same regular expression used as a query. To perform a search, it is necessary to create a data set that contains information concerning the four MR modalities described before. For each item, the tool shows different attributes (patient name, comments, and status) and the number of images provided by the concerned modality (anatomy, diffusion, perfusion, and spectroscopy). Moreover, each case is represented by a different color depending on the status of the study.
A table shows a list of color-coded overlays, specifying the username and the tissue type (indicated by the user). It also allows us to visualize or to delete each overlay, although this latter option is only available to the expert who created it. Selecting an overlay of the table allows displaying its related information (the annotations filled in a pop-up window). Finally, after pressing the confirmation button of the pop-up window, the overlay is added to the table, displayed on the image and stored in the XML database.
Reporting Once the study is analyzed, the findings need to be reported, if possible in a standardized way. ProstateAnalyzer includes the PI-RADS structured reporting scheme, according to ESUR prostate MR guidelines in 2012  (10) , which is being established as the common protocol in European countries  (21, 22). The use of a standardized graphic reporting scheme facilitates the communication with referring colleagues, and it increases the quality and diagnostic value of prostate analyses. The inclusion of the PI-RADS is carried out using a form with a drop-down list that includes the different answers of the PI-RADS question. Selecting the option, the corresponding number is assigned. The report is saved in an independent XML file and loaded into the XML database. The use of an independent XML file allows a faster search when looking just for similar cases in terms of PI-RADS scores.
Design effort benchmarks ProstateAnalyzer is currently being used in the MR department of the University Hospital of Dijon (France). The present working database is composed of more than 1600 patient datasets  64 anatomical images, 14 ADC images, 640 perfusion images, and a set of spectroscopic data containing up to 1000 files. It is important to note that the number of spectroscopic data files is very variable and depends on the prostate size. The average of the time consuming is around 60-70 s to load and display a typical prostate study with the data presented previously. The search engine provided by ProstateAnalyzer is tested in order to obtain the complete study. The timing response to retrieve a prostate study was around 1 min including the corresponding XML-associated files. The time necessary to record annotations on the database is around 3-5 s for anatomical and diffusion images and around 12 s for perfusion images.
The computational cost is higher for the latter because of the need to calculate the signal-time curve. In order to obtain a spectrum, the cost is around 5 s to display it in the graphical window. Once the analysis is finished by the user, ProstateAnalyzer takes around 4 s to save the XML file into the database. With the new architecture, ProstateAnalyzer can also be accessed using regular external network connections outside the intranet connection of the hospital. We tested the connection from Girona in Spain (several hundreds of kilometers from Dijon). Experiments were carried out by loading a large study consisting of a set of 64 anatomical images, 15 ADC images, 720 perfusion images, and a set of spectroscopic data containing up to 1200 files (corresponding to around 150 MB of transferred data).
DISCUSSION ProstateAnalyzer is a web-based application for the analysis of prostate images using four MR modalities: T2WI, DWI, DCE, and MRS. One of the most evident advantages of ProstateAnalyzer is that it allows simultaneous analysis of a prostate study using the different modalities. It also provides an interaction among them and MR spectroscopy. Since the application has been designed to work with DICOM files, any equipment that operates under such image standard can potentially be used with ProstateAnalyzer. The principal problem encountered in the diagnosis of a prostate study is the localization of a ROI-containing tumor tissue. Normally, experts use different tools to validate the diagnoses using different software and make many annotations in different files. This is not a practical solution to managing abundant medical data.
Finally, our tool offers the possibility to work remotely via the web and represents an improvement in the data management. The access to the ProstateAnalyzer should be provided to multiple users in order to make easier both local and external connections. In order to facilitate this task, the application is implemented as a JAVA applet tool. Thus, it is not necessary to install any program on computers to run our interface unless users have a browser that supports Java technology. Moreover, applets can be executed from any operating system (Windows, Linux, and MAC) because they are running in a Web browser. ProstateAnalyzer can be integrated in a server to manage medical images stored in a prostate database. ProstateAnalyzer still presents some limitations which will be addressed in order to enrich the tool.
For instance, in perfusion analysis, the tool uses a mean DCE signal-time curve to display the signal enhancement during the arrival of the contrast agent into the tissue. However, providing quantitative values related to microvascular permeability, K trans or the diffusion space, e could also be helpful. With regard to the spectroscopy section, the possibility of displaying 2D and 3D metabolite maps will be addressed. For this task, specialized spatial and spectral data processing methods, for which sources are not commonly available, are needed (e. g. morphological analysis and spectral characteristics of the observed metabolites). We are also trying to improve the efficiency of the computational cost. One solution could be to load single frames only, on the basis of their spatial correspondence, instead of the whole dataset.
This would also allow the possibility of automatically displaying the four sequences simultaneously. Thus, when scrolling the slices, the information will change simultaneously on the T2WI, DWI, DCE, and MRSI displays. Taking into account how rapidly clinical databases are growing, ProstateAnalyzer should be an important contribution to the management of large databases. Indeed, the implementation of ProstateAnalyzer offers the possibility to extend and adapt to other MR modalities. As a proof of concept, this project is an adaptation from previous work based on mammography  (42)  and it demonstrates that our approach is easily portable to other sources of medical images. Finally, as ProstateAnalyzer allows downloading reports in PDF format containing the annotations and graphical results (mean signaltime curve and signal spectra), it should also be useful as a pre-report.
CONCLUSION In this paper, we have presented ProstateAnalyzer, a new medical tool that allows the evaluation of the prostate cancer in an effective way. ProstateAnalyzer visualizes the different MRI techniques (anatomy, diffusion, and perfusion) together with MR spectroscopy, and automatically places annotations, made in one of the images, onto the others. In addition, ProstateAnalyzer also includes the PI-RADS reporting protocol, thereby offering the possibility to fully report the prostate study in a standardized way. It has been implemented as an interactive JAVA applet application with the purpose of facilitating the inclusion of medical findings on existing prostate images, using the combination of MRI techniques and MR spectroscopic analysis. Furthermore, a new architecture is presented to store the medical records in a XML database which stores a set of annotated files.
To this end, we developed SSV-Seq, for next-generation sequencing (NGS) of single-stranded DNA viruses, together with ContaVect, a bioinformatic tool dedicated to QC of virus/ vector preparations from NGS datasets. As a proof of concept, we applied SSV-Seq to a singlestranded serotype 8 rAAV-expressing green fluorescent protein (GFP), manufactured by transient transfection in HEK-293 cells, followed by three different "state-of-the-art" GMP-compliant purification processes. Although our data were consistent with those from qPCR, we were able to exhaustively quantify DNA contaminants longer than 250 bp without the need for any indirect standard comparisons. Additionally, we identified unexpected contaminants and obtained a high-definition map of the rAAV vector genome. SSV-Seq could be applied as an in-process analytic to guide upstream and downstream process development towards rAAV preparations of higher potency, but one might also expect this method to improve knowledge of rAAV vector biology.
Distribution of DNA contaminants identified by NGS correlates with qPCR data Reads were assigned by ContaVect to one of the following reference sequences: (i) the rAAV 2/8 CMVp-eGFP-hygroTK-bGHpA genome; (ii) the bacterial backbone of the vector plasmid; (iii) the entire pDP8 helper plasmid; (iv) fragments of the Ad5 genome integrated into the HEK-293 packaging cell line genome; and (v) the human genome primary assembly GRCh38. To avoid the misattribution of reads originating from phage DNA, the reference sequences of phage λ (J02459. 1) and φ-X (J02482. 1) were also provided to Con-taVect. The parameters were optimized using an in silicogenerated artificial dataset mimicking the estimated sequence composition of an rAAV CMVp-eGFP-hygroTK-bGHpA vector preparation (Supplementary Results III, Supplementary Table  S4 ).
The optimized parameters, detailed in the configuration files (http://dx. doi. org/10. 5061/dryad. fs4cp), were used to obtain the raw distribution of reads in the references for the experimental and control samples (Supplementary Table  S5 ). Based on the number of reads obtained in the negative control, we defined a positivity threshold per run and per reference, to avoid false-positive detection due to environmental contamination. Although there were reads in the negative control for all of the references, the read count was always higher in the experimental samples, except for the Ad5 sequence, which was virtually undetectable. The raw data also indicated that the DNase treatment was effective because the read count for phage λ in DNase-positive (b) To reduce the amount of nonencapsidated DNA, eventually, a DNase digestion step can be performed, with a mix of highly efficient DNases (Baseline-ZERO and Plasmid-Safe DNases).
This observation is likely partially responsible for an over-estimation of the rAAV genome in our qPCR data. However, despite these limited variations, we obtained a significant correlation with the NGS results (Spearman's correlation coefficient = 0. 9938, P < 0. 0001) (Figure  2 ). Altogether, our NGS data obtained by SSV-Seq were consistent with the qPCR findings and yielded reproducible results.
Advanced analysis of the human genomic DNA contaminants SSV-Seq generated an enormous amount of data at singlenucleotide resolution, which opened up new possibilities for exploring the qualitative and quantitative features of rAAV preparations. Thus, we further analyzed the origin of reads spanning the human genome. The normalized densities of reads per chromosome suggested an overall random distribution for the rAAV samples, within a twofold range (Figure  3a ). Interestingly, we found two over-represented loci: (i) mitochondrial DNA (mtDNA) in rAAV preparations purified by CsCl; and (ii) specific DNA sequences from chromosome 15 in particles purified by AVB. The estimated relative quantities of rAAV genome and DNA contaminants are indicated in percentages of total sequences found with SSV-Seq or qPCR for each sample analyzed.
32 In addition, SSV-Seq could be used to identify specific targets for routine qPCR analyses based on their representativeness or to detect specific contaminants. Finally, given the concerns expressed by regulatory bodies regarding nucleic acid contaminants, NGS-based methods, such as SSV-Seq, should be included as an informative QC test for clinical-grade AAV preparations. In addition, we recommend that datasets be publicly released via an openaccess repository for external review and transparency.
The distribution of DNA fragment size was verified by the Agilent 2100 Bioanalyzer system using High sensitivity DNA chips (Agilent Technologies), according to the manufacturer's guidelines. NGS sequencing. Samples were quantified using KAPA Library Quantification Kits (Kapa Biosystems, Wilmington, MA) according to the manufacturer's instructions and were pooled in equimolar quantities. PhiX Control v3 DNA (1-5%; Illumina, San Diego, CA) was added to the libraries, which were subsequently sequenced with a HiSeq 1500 platform (Illumina) using rapid-run paired-end mode (2*101 bp) at the genomics and bioinformatics core facility of Nantes (INGB, Nantes, France). Bioinformatics. The reference sequences of the rAAV genome, vector plasmid backbone, helper plasmid and adenovirus 5 (Ad5) sequence are available in fasta format and annotated GenBank format from the Dryad Digital Repository: http://dx.
doi. org/10. 5061/dryad. fs4cp. The genome of the HEK-293 cells was approximated by the last build of the human genome (GRCh38 primary assembly) from Ensembl, while the genomes of bacteriophage λ (J02459. 1) and coliphage φ-X174 (J02482. 1) were retrieved from the European Nucleotide Archive. An in silico control was generated to mimic a real NGS library and to determine the prediction accuracy of the mapping software using Fastq Control Sampler, a custom C program. The open source code for the software is freely available with its documentation at https://github. com/a-slide/ fastq_control_sampler. Paired Fastq files were generated from the rAAV genome (14,000,000 reads), vector plasmid backbone (420,000 reads), helper plasmid (5,000 reads), Ad5 sequence (10 reads), human genome (29,524 reads) and a randomly generated sequence (200,000 reads), with the following parameters: size of the randomly generated reference sequence: 100,000; size of the reads: 101; lower sonication size of the fragments: 250; upper sonication size of the fragments: 450; maximal PHRED quality: 40; minimal PHRED quality: 30; frequency of errors in sequence strings: 0.
01; and maximum number of tries to generate a valid read pair: 100; pairs not selected in repeat regions. A detailed report of reads generation is supplied in the Supplementary Material. Raw BCL data for the samples were demultiplexed with CASAVA (Illumina, San Diego) according to their barcodes and were stored in independent files. Fastq files were analyzed with ContaVect using the configuration file. The program generated standard genomic files (Bam, Bed, and Bedgraph) as well as comprehensive text reports. The Fastq and raw output files are available from the Dryad Digital Repository: http://dx. doi. org/10. 5061/dryad. fs4cp. The program is still under active development, but the version used for the analyses performed in this study (v0. 2) is freely available with extensive user and developer documentation at https://github.
com/a-slide/ContaVect/tree/v0. 2. SNVs in the rAAV genome were retrieved from the BAM files containing reads aligned on rAAV with a custom program rather than with a classical program, such as samtoolsmpileup, because the read depth was far too great (> 200,000) for the classical (but more robust) callers. The source code for this java program is available on GitHub at https://github. com/lindenb/jvarkit/wiki/MiniCaller. Essentially, this program uses the java library for BAM (htsjdk) to load a set of BAM files and scans all of the bases in the reference genome from 5′ to 3′; for each position, it detects the proportion of bases in each sample and prints a summary in VCF format; no quality score is calculated. The VCF files were subsequently parsed and analyzed using an ipython notebook, available at http:// nbviewer.
ipython. org/github/a-slide/iPython-Notebook/blob/ master/Notebooks/VCF_analysis. ipynb. The VCF and CSV files are available from the Dryad Digital Repository: http:// dx. doi. org/10. 5061/dryad. fs4cp. Graphical representations and statistics. Graphs were generated using PRISM 5 software (GraphPad, La Jolla, CA), except for Figure  5 , which was created using Circos 0. 67 (http:// circos. ca/). The vector graphics pictures were postprocessed with Inkscape 0. 48 (https://inkscape. org) for aesthetic purposes (alignment, legends, fonts, etc. ). Raw tables containing the data are provided directly in the manuscript (Figure  2 ) or are available at the Dryad Digital Repository: http://dx. doi. org/10. 5061/dryad. fs4cp (Figures  3 4 5 ). When appropriate, statistical analyses were performed with PRISM 5 using nonparametric tests due to the limited number of samples.
The details of the statistical tests are indicated directly in the figure or table legend. grateful to Eduard Ayuso (VMD, PhD) for his critical reading and to Céline Vandamme for helping to maintain the blinding throughout the protocol. This work was supported by the "Association Française contre les Myopathies", the "région Pays-de-la-Loire" (Emergence collective 2012, AGTI-1), and the "Commissariat général à l'investissement" (ANR-PGT Investissements d'avenir).
Results We present MaGuS (map-guided scaffolding), a modular tool that uses a draft genome assembly, a genome map, and high-throughput paired-end sequencing data to estimate the quality and to enhance the continuity of an assembly. We generated several assemblies of the Arabidopsis genome using different scaffolding programs and applied MaGuS to select the best assembly using quality metrics. Then, we used MaGuS to perform map-guided scaffolding to increase continuity by creating new scaffold links in low-covered and highly repetitive regions where other commonly used scaffolding methods lack consistency.
Conclusions MaGuS is a powerful reference-free evaluator of assembly quality and a map-guided scaffolder that is freely available at https://github. com/institut-de-genomique/MaGuS. Its use can be extended to other high-throughput sequencing data (e. g. , long-read data) and also to other map data (e. g. , genetic maps) to improve the quality and the continuity of large and complex genome assemblies.
The ability of these methods to produce pseudomolecules also was tested, as reported in Popseq  [20]  and Allmaps  [21]. Allmaps infers the sizes of gaps using the relation between the local recombination rate and the physical distance between two adjacent genetic markers; however, the estimations can be inconsistent considering the inaccuracy of the recombination rate. Hybrid strategies, combining WGS and genome map data, are likely to help increase the quality of the assembled genome sequence. With this in mind, we developed MaGuS, a modular program that combines a genome map and WGS data. MaGuS can anchor a draft assembly onto a genome map for two applications: quality assessment of a draft assembly by calculating novel metrics, and improvement of the continuity of a draft assembly based on evidence provided by a genome map and highthroughput screening (HTS) data.
Arabidopsis thaliana genome assembly One 350-bp paired-end (PE) (ERX372154) and two 5. 35-kb mate-pair (MP) (ERX372148, ERX372150) Illumina sequence libraries from A. thaliana were downloaded from the European Nucleotide Archive (ENA). A total of 95. 3 Gb of data were obtained representing a coverage depth of 562X of PE and 170X of MP reads. Adapters and primers were removed from the reads, and low quality nucleotides were trimmed from both ends (quality values lower than 20). Reads were also trimmed from their second N to the end and reads longer than 30 nucleotides were kept. Reads that mapped onto run quality control sequences (i. e. , the PhiX genome that is used in Illumina sequencing as quality control) were removed. To decrease the number of sequencing errors present in the paired-end (PE) reads, we applied Musket v1.
1  [22]  with a k-mer size of 26 '-k 26'. We ran Kmergenie v1. 5692  [23]  on the PE reads to find the best k-mer size for the contig construction step and obtained an optimal k-mer size of 91 bp. For the SOAPdenovo2  [24]  assembly, a de Bruijn graph was constructed with parameters '-K 91 -R'. We selected contigs that were longer than 500 bp. We used the PE and MP reads in five different scaffolding programs: SOAPdenovo2, SSPACE  [25] , SGA  [26] , BESST  [27] , and OPERA-LG  [28]. For SOAPdenovo2, we ran the map command with parameter '-k 31', the scaf command with parameter '-L 500', and set the minimum number of links in the configuration file as 'pair_num_cutoff=5'. For SSPACE, we manually set the bowtie k-mer size '-l 31' and ran the program with parameter '-k 5'. For SGA and BESST, we first aligned the MP reads onto the contigs using BWA aln  [29]  with parameter '-l 31'.