dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions Gigwa v2 is a user-friendly, species-agnostic web application for managing and exploring high-density genotyping data. The software can be installed on a local computer or deployed as a data portal. It supports various standard import and export formats and provides advanced filtering options as well as means to visualize density charts or push selected data into various stand-alone or online tools. It implements 2 standard REST APIs: GA4GH, which is health-oriented, and BrAPI, which is breedingoriented, thus offering wide possibilities of interaction with other systems. Once installed, which is done by simply decompressing a zip archive for "stand-alone" users, its management interface obviates the need for any particular computer skills for users to administer, publish, or share their data.
Since its original version, Gigwa's data structure and query syntax have been optimized to a point where its speed performance is comparable to that of state-of-the-art command-line tools when running on production hardware. For instance, this version is able to deal with datasets as large as the 3000 Rice Genomes CoreSNP (genotypes for 3,024 individuals on 4,817,964 SNPs). Current live instances listed at  [29]  provide access to a range of diverse public datasets  [26, [30] [31] [32] [33]  as well as video demonstrations to facilitate use and adoption. Gigwa v2 allows for anonymous users to import their own data into temporary databases, thus allowing anyone to test the system on the mentioned live instances, for a limited duration. Its filtering functionalities are advanced enough to rapidly ob-tain an overview of variants discriminating 2 groups of individuals.
1. 16) when simultaneously querying on variant-level (indexed in Gigwa) and genotype-level (unindexed in Gigwa) fields. (ii) Estimate the benefit of migrating to high-performance hardware by monitoring differences in response times between tools Run on configuration 2 using dataset 1 without its derivatives, sub-sampling now being performed on the fly by restricting the search to a varying list of chromosomes. The query was the same MAF range query as above3(i) Test Gigwa v2's suitability for working on very large datasets. (ii) Compare trends with those observed in a small dataset (Test 2).
Acknowledgments The authors thank the  South Green Platform team  for technical support. We are also grateful to  Manuel Ruiz ,  St éphanie Sidibe-Bocs ,  Benjamin Penaud , and  Ga ëtan Droc  for promoting the software and providing new feature ideas,  Iain Milne  and  Gordon Stephen  for sharing their  Java BrAPI  client code, and  Jean-Marc Mienville  for careful reading that helped improve the manuscript. Finally, we render thanks to  Bioversity International, UMR Diade, UMR AGAP , and  UMR BGPI  for investing in high-performance servers used for hosting public Gigwa instances.
We used an interest/impact diagram to classify local stakeholders according to their willingness and their potential impact on AET  (Therond et al. 2010). A third dimension taken into account in our analysis was stakeholders' position regarding AET (pro or con) (cf. Fig. 4 ). This diagram was used as a decision support tool with local PETR partners to build a stakeholders database for the participatory process. In order to ensure the representativeness of the participatory process outcomes and thus to ensure its relevance, all diagram stakeholder categories were contacted: favourable, unfavour- able and neutral stakeholders, with low, medium or high interest or impact. We finally gathered 57 heterogeneous workshop participants in total for both territories: • Farmers (organic, conservation agriculture, conventional) • Farmer groups (conventional and organic) • Civil society (consumers or environmental associations) • Farming advisory groups (conventional and organic) • Territorial planning institutions • Supply chain and retailers: conventional large chains (cooperative, suppliers), certification labels, short chains.
While the 2016-library was sequenced at the GeT-PlaGe platform at Toulouse (France) using a single lane on an Illumina HiSeq system (2 × 150 bp sequencing), the 2018 library was sequenced by Genewiz (Leipzig, Germany) using a single lane on an Illumina HiSeq system (2 × 150 bp sequencing). The raw sequence read data from these two Illumina HiSeq runs are available from the NCBI Sequence Read Archive, under the study accession number: PRJNA573048.
Analysis of Rice Genotyping-by-Sequencing Data Read processing, read mapping and single nucleotide polymorphism (SNP) calling were carried out using bioinformatic pipelines designed using Toggle (https://github. com/SouthGreenPlatform/ TOGGLE). Reads were aligned to the Nipponbare rice reference genome (https://plants. ensembl. org/ Oryza_sativa/Info/Index) using Burrows Wheeler alignment (BWA)  [17] , and SNPs were called using UnifiedGenotyper in GATK v. 3. 8  [18]. Only positions in coding sequences, with DP > 3 and MQ > 20, and with less than 50% missing data were retained. The final dataset used for genotyping consisted of 12,112 SNPs. Accessions Plot-22-P3 and YYT10-A were removed from the data as proportions of missing data in these exceeded 95%. We constructed a neighbor-network using SplitsTree 4.
13  [19] , to visualize evolutionary relationships between the indica rice genotypes while taking the possibility of recombination or incomplete lineage sorting into account. We also used the program sNMF (http://membres-timc. imag. fr/Olivier. Francois/snmf/index. htm) to infer population subdivision by partitioning genotypes into K ancestral populations and estimating individual ancestry coefficients in the K populations. We ran sNMF for K ranging from 1 to 20, and for each K value 10 replications were performed. We used the Greedy algorithm in CLUMPP 1. 1. 2 (http://web. stanford. edu/group/rosenberglab/clumpp. html) to identify runs belonging to the same mode (i. e. , representing the same clustering solution) and we randomly selected one representative of the major mode for graphical representation as a stacked barplot using the Matplotlib package in Python.
SRBSDV Genetic Diversity The degree of genetic diversity within the amplified HHRTS SRBSDV genome segment 8 sequences was compared to that of 29 other publicly available SRBSDV segment 8 sequences collected since 2003 from several regions of China and Vietnam. The 26 fragments (624 bp long after alignment and trimming of the 5 -and 3 -ends of the sequences; GenBank accession numbers: MN244953-MN244978) from the HHRTS share between 98. 2% and 100% pairwise identity with one another and between 94. 1% and 100% pairwise identity with the 29 publicly available SRBSDV segment 8 sequences. Interestingly, all SRBSDV sequences from rice plants are genetically highly homogeneous (sharing between 98. 2% and 100% pairwise identity with one another) whereas they share slightly lower identities with SRBSDV isolates sampled from maize (between 94.
However, properly testing this hypothesis will require both the genomic sequence characterization and analysis of many more SRBSDV isolates from rice and maize plants throughout the entire current geographical range of the virus, and an accurate estimate of the SRBSDV nucleotide substitution rate. With this data in hand it should be possible to determine the most likely time when, and place where, the SRBSDV isolates that are responsible for rice black-streaked dwarf disease first arose. Although all of the SRBSDV isolates from the HHRTS clustered within the SRBSDV "rice clade" (Figure  2 ) there was no evidence to indicate that they may have all shared a more recent common ancestor with one another than with other SRBSDV isolates from China and Vietnam. This suggests that there have likely been multiple (and possibly ongoing) transmissions of SRBSDV into the HHRTS.
Chaste is an open-source simulation package that includes tissue modelling. As such, it supports distinct cell-based modelling approaches  11, 12. The second tool, named PhysiBoSS  13  , combines intra-cellular Boolean signalling networks as handled by MaBoSS 14 and multi-cellular agent based modelling as defined in PhysiCell, an open source agent-based simulator  15. While these tools involve explicit representations of intra-cellular networks in the form of Boolean models, and the consideration of biophysical processes governing multi-cellular behaviours, they do not allow explicit specification of cell-cell communication, a special feature of EpiLog.
REVISED Developers can freely access the code repository at http:// github. com/epilog-tool/epilog, clone it and extend the code. For dependency management of external libraries, EpiLog relies on Apache Maven. A particular library is the bioLQM Java toolkit (Logical Qualitative Models of biological regulatory networks), for the representation and manipulation of logical, cellular models, available at https://github. com/colomoto/bioLQM  16.
Summary To the best of our knowledge, EpiLog is the first software tool for the definition, simulation and visualisation of qualitative, logical (Boolean and multivalued) models over hexagonal grids. It provides a graphical user interface and tools to conveniently support the study of epithelial pattern formation, relying on a logical framework. EpiLog is free, open source and implemented in Java for operating system independence. It relies on the SBML qual standard format to import cellular models. 1.
Open Peer Review Current Referee Status: The paper describes a software used for qualitative simulations of logical models in a hexagonal grid, called EpiLog. The paper is well written, even though some parts could be slightly expanded for a better comprehension. The software is easy to use for people with logical modelling background and is a visual way of exploring populations of Boolean models and their effect on neighbouring cells. The software is freely available, cross-platform, can use open standards of logical models and its code can be studied and forked in GitHub. Nevertheless, the paper would benefit from some additions and clarifications: Major: Expansion of introduction: The last sentence of first paragraph could be completed with some of.
" Does cellular model mean one cell and epithelium model the whole set of cells? As the phrase starts with epithelium, the current wording is a bit confusing. Figure  1  legend: "Stable pattern" is a bit confusing, maybe it would be better to use "pattern of stable states". We thank you for the detailed comments and suggestions. We have revised the manuscript accordingly, and we briefly answer your points below. We have revised the introduction, noting that cellular automata are a natural modelling choice to define local rules governing the behaviours of cells over a lattice. We also clarified the link between the framework as implemented in EpiLog and the work presented in Mendes et al 2013. We have included a supplementary text illustrating and discussing the use of the alpha-asynchronous update, and varying the values of alpha.
The paper is well written, and the software is provided in a nice dedicated website, which also includes documentation and additional information. Furthermore, the source code is provided at GitHub. We have a few comments and suggestions regarding both the methodological approach and the software implementation: In regard to the method: 1) It is stated that the published method can only handle up to a dozen cells. However, the graphical interface seems to describe more. Maybe the authors could elaborate on the limitations of the tool, as well as on its performance for different types of problems. 2) It is not totally clear whether the approach used in this paper is exactly the same as the one published in (Mendes et al ). If it is not, and if there is no other published paper describing the method, an upgraded methods paper could be of interest.
On the other hand, if there is another publication describing the method, it should be cited in the paper. It is also unclear if the method has been previously validated in epithelium (hexagonal) case. The authors may want to clarify these points in a revised version. 3) We are aware that, being a software tool article, the present paper is probably not the best place for extended discussions about biological aspects. The following points may therefore be considered as 3) A real case study would make it easier to follow the pipeline. 3. 4) Some additional biological explanation or motivation of potential applications might make the paper more attractive. In regard to the software: 1) We tested EpiLog in Windows. We managed to load and simulate the Drosophila "Eggshell Patterning", but the software reported warnings/errors.
/doi. org/10. 5256/f1000research. 20225. r45550 Arnau Montagud Institut Curie, Inserm U900, Mines Paris Tech, PSL Research University, Paris, France Barcelona Supercomputing Center (BSC), Barcelona, Spain The authors have have done a good work in expanding and correcting all needed clarifications. I approve this improved version of the article. No competing interests were disclosed. Competing Interests: Reviewer Expertise: Systems Biology, Cancer, Boolean modelling, Multiscale modelling, Metabolic modelling, Data deconvolution I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. https://doi. org/10. 5256/f1000research. 17032. r37601 Arnau Montagud Institut Curie, Inserm U900, Mines Paris Tech, PSL Research University, Paris, France Barcelona Supercomputing Center (BSC), Barcelona, Spain.
No competing interests were disclosed. Competing Interests: 04 October 2018 Referee Report https://doi. org/10. 5256/f1000research. 17032. r38246 , Alejandro F. Villaverde David Henriques Bioprocess Engineering Group, IIM-CSIC, Vigo, Spain Bioprocess Engineering Group, IIM-CSIC, Vigo, Spain This paper describes a new software tool for modelling epithelial dynamics in 2D. The software, called EpiLog, uses a logical framework that assigns discrete values to the model variables. The approach used for specifying the logical rules was previously presented in a paper from the same senior author (Mendes et al ).
Characterization of Exosomes Isolated from Plasma of HR-NB Patients Exosomes were purified from peripheral blood samples obtained from 52 HR-NB patients whose clinical characteristics are summarized in Table  1. The majority of patients had a stage 4 disease (n = 47), 4 subjects had a stage 3 disease, and one patient had a 4S stage disease, according to INSS (International Neuroblastoma Staging System) classification system. MYCN oncogene was amplified in 22 patients, non-amplified in 25 cases, and not evaluable in 5 patients. According to the INRC (International Neuroblastoma Response Criteria) classification system, 6 patients reported a minor response (MR) to induction chemotherapy, 8 patients showed a very good partial response (VGPR), and 33 patients had a partial response (PR). For 5 patients, the data of response to treatment is missing because it was not updated in the SIOPEN database, even if the patients completed the induction chemotherapy.
Study Population and Blood Sample Collection Patients affected by HR-NB enrolled in the HR-NBL Study of SIOP-Europe (ClinicalTrials. gov identifier: NCT01704716) were considered for this study. Patients were recruited at enrollment into the HR-NBL SIOPEN study starting in 2016 without being preselected for the analysis. In this protocol, patients were randomized in the induction phase to receive one of two regimens, Rapid-COJEC (80 days treatment with two courses of carboplatin, etoposide, vincristine; four courses of cisplatin, vincristine; two courses of etoposide, and cyclophosphamide, 1 every 10 days) or modified-N7 regimen (105 days treatment with combinations of vincristine, doxorubicin, cyclophosphamide, cisplatin, and etoposide in 5 courses, 1 every 3 weeks). The procedures were carried out according to the proper guidelines and in adherence with the ethical principles of the Declaration of Helsinki.
Bioinformatic Procedures and Statistical Analysis We designed a new bioinformatic pipeline implemented in R, ExoPIPE, for the analysis of exo-miRNAs expression measured by quantitative RTqPCR technology. The pipeline uses functions implemented in the published R packages and R modules implemented in our laboratory. ExoPIPE is composed by data preprocessing, CT filtering, data normalization, data imputation, and feature selection. Any CT value higher than 32, lower than 14, or undergoing an experiment failure was categorized as unreliable, as recommended by the manufacturer guidelines (ViiA 7 Software, Thermo Fisher Scientific). Raw data were normalized by global mean method (GM)  [9]. The global mean value of each sample, calculated by considering only the exo-miRNAs expressed with a threshold cycle (CT) lower than 32, was subtracted from the raw CT of each miRNAs (∆CT).
This results in one CT unit value higher than the highest CT recorded for that miRNA in all samples [10]. The raw data used for the analysis are reported in the Supplementary Table  S1. Feature selection to identify relevant exo-miRNAs was performed with GLMNET  [13]  setting up the clinical response as the response variable, the exo-miRNAs as explanatory variables, elastic net mixing parameter at 0. 7 and the minimum mean square error as gamma value. Gamma value was assessed using the leave-one-out cross-validation technique. We assessed the presence of any batch effects using principal variance component analysis (PVCA)  [11]. We used the country of origin of the samples as batch variable and the age of patient, the INSS stage, and the MYCN status of the tumor as biological variables. PVCA provided a weighted average proportion variance (WAPV) for each variable and combination of variables.
Heat map visualization and unsupervised k-means clustering analysis were carried out using Morpheus: versatile matrix visualization and analysis software (Morpheus, Broad Institute, Cambridge Massachusetts, USA). Z test for proportions was used to estimate whether two patient groups differ significantly on one characteristic. Three-year event-free survival (EFS) curves were compared with the log-rank test using GraphPad Prism version 6. 0 for MAC, GraphPad Software, San Diego California USA. p values lower than 0. 05 were considered significant.
Study Approval Patients affected by HR-NB enrolled in the HR-NBL Study of SIOP-Europe (ClinicalTrials. gov identifier: NCT01704716) were considered for this study. The study was approved in 2016 by Comitato Etico Regionale (Verbale n. 11/2016). The procedures were carried out according to the proper guidelines and in adherence with the ethical principles of the Declaration of Helsinki. Written informed consent was provided from the parents or legal guardian of patients enrolled in this study.
Supplementary Materials: The following are available online at http://www. mdpi. com/2072-6694/11/10/1476/s1, Figure  S1 : Origin batch effect evaluation; Figure  S2 : Exo-miRNAs signature validation by real-time PCR (RT-PCR); Figure  S3 : Exo-miRNA signature in partial response patients and its efficacy in predicting event-free survival (EFS); Figure  S4 : Global Mean Normalization effectively reduces technical variability; Table  S1 : Dataset of normalized exo-miRNAs expression values; Table  S2.
Dataset of exo-miRNAs fold change values of 52 patients. Author Contributions: Conceptualization, L. V. and R. L. ; methodology, L. V. , M. M. , D. C. , D. S. , F. R. , D. M. , and B. C. ; software, D. C. ; validation, M. M. and D. C. ; formal analysis, M. M. , D. C. , and L. V. ; investigation, M. M. , D. C. , D. S. , F. R. , L. V. , M. C. B. , and A. E. ; resources, A. C. , D. F. , V. V. , J. F. d. M. , Y. Y. , A. C. , G. S. , and S. B. ; data curation, M. M. , D. C. , M. C. B. , A. E. , L. V. , D. M. , R. L. , K. M. , A. R. S. , A. P. , M. C. , and A. G. ; writing-original draft preparation, M. M. , D. C. , M. C. B. , and A. E. ; writing-review and editing, D. F. , J. F. d. M. , G. S. , A. P. , S. B. , D. F. , M. V. C. , and A. G. ; visualization, M. M. , D. C. , M. C. B. , A. E. , and L.
: Dataset of exo-miRNAs fold change values of 52 patients. Author Contributions: Conceptualization, L. V. and R. L. ; methodology, L. V. , M. M. , D. C. , D. S. , F. R. , D. M. , and B. C. ; software, D. C. ; validation, M. M. and D. C. ; formal analysis, M. M. , D. C. , and L. V. ; investigation, M. M. , D. C. , D. S. , F. R. , L. V. , M. C. B. , and A. E. ; resources, A. C. , D. F. , V. V. , J. F. d. M. , Y. Y. , A. C. , G. S. , and S. B. ; data curation, M. M. , D. C. , M. C. B. , A. E. , L. V. , D. M. , R. L. , K. M. , A. R. S. , A. P. , M. C. , and A. G. ; writing-original draft preparation, M. M. , D. C. , M. C. B. , and A. E. ; writing-review and editing, D. F. , J. F. d. M. , G. S. , A. P. , S. B. , D. F. , M. V. C. , and A. G. ; visualization, M. M. , D. C. , M. C. B. , A. E. , and L.
Background: The phytopathogenic bacterium Xylella fastidiosa was thought to be restricted to the Americas where it infects and kills numerous hosts. Its detection worldwide has been blooming since 2013 in Europe and Asia. Genetically diverse, this species is divided into six subspecies but genetic traits governing this classification are poorly understood. Results: SkIf (Specific k-mers Identification) was designed and exploited for comparative genomics on a dataset of 46 X. fastidiosa genomes, including seven newly sequenced individuals. It was helpful to quickly check the synonymy between strains from different collections. SkIf identified specific SNPs within 16S rRNA sequences that can be employed for predicting the distribution of Xylella through data mining. Applied to inter-and intrasubspecies analyses, it identified specific k-mers in genes affiliated to differential gene ontologies.
The recent outbreaks and interception of imported, contaminated materials in Europe as well as investigations in South America also revealed the existence of previously unknown Sequence Types of several X. fastidiosa subspecies  [2, 9, 11, 17, 18]. Because management and regulations of X. fastidiosa outbreaks in France depends on the subspecies of X. fastidiosa, it is of major importance to precisely define these subspecies, understand the robustness of these groupings and their meaning in terms of specific or shared genetic material. One way to resolve such a series of interrogations is the achievement of comparative genomics to identify similarities and specificities between groups of individuals. Yet, exploring big datasets is not trivial and requires dedicated bioinformatic tools to be cost-and time-effective.
Various applications use k-mers mostly to analyze sequence reads to improve the quality of genome, transcriptome and metagenome assembly  [19] [20] [21] [22] [23] [24] [25] [26]. K-mer are all the possible substrings of length k that are contained in a nucleotide character string. K-mer-based methods can also be employed on whole genome sequences to taxonomically assign organisms  [27, 28]. Moreover, several tools were developed to calculate pairwise relationships, like the average nucleotide identities using blast (ANIb) or MUMmer (ANIm) algorithms, and the tetranucleotide frequency correlation coefficients (TETRA), which can be accessed online through JSpecies  [29]  or with workstation installation of python3 pyani module  [30]. Here, we developed SkIf (Specific k-mers Identification) and applied it to gain a better understanding of X.