dataesr/scientific-paragraphs-categorization huggingface dataset:
It is therefore essential that this is done in a playful way so that they are not discarded. The use of a graphical interface allowing modeling notions to be acquired, while putting aside the programming aspects, allows the different problems to be separated: modeling on the one hand and programming on the other hand. As soon as they wish to go further in the understanding of their subject, students very quickly perceive the limitations that a graphical interface can represent (options too limited, etc. ). In addition, use can quickly become daunting if tasks need to be repeated (for example, clicking a large number of times and in a welldefined order to reproduce the results on several datasets). Sometimes there is not enough time to learn programming, justifying the need to use simple tools.
This tool can contribute to UPH21 ("How can the (un)certainty in hydrological predictions be communicated to decision makers and the general public?") as it has already been used by several decision makers in hydrological training. airGRteaching can be seen as a gateway to mastering airGR and other airGRdependent packages, thus indirectly helping to solve other UPHs. This is notably the case for questions UPH22 ("What are the synergies and tradeoffs between societal goals related to water management (e. g. , water-environment-energyfood-health)?") and UPH23 ("What is the role of water in migration, urbanization, and the dynamics of human civilizations, and what are the implications for contemporary water management?"), linked to water usage, thanks to the airGRiwrm package  (Dorchies et al.
, 2022) , which allows water resources management to be integrated. This package could help to solve problems of spatial heterogeneity and change of scale, namely UPH5 ("What causes spatial heterogeneity and homogeneity in runoff, evaporation, subsurface water and material fluxes (carbon and other nutrients, sediments), and in their sensitivity to their controls (e. g. , snowfall regime, aridity, reaction coefficients)?") and UPH6 ("What are the hydrologic laws at the catchment scale and how do they change with scale?") because it simplifies the use of airGR in a semi-distributed mode. The airGRdatassim package, which enables data assimilation, could be linked to questions of prediction uncertainty, namely UPH20.
Conclusions Teaching hydrological modeling requires hands-on experience with rainfall-runoff models. Dedicated tools need to be adapted to the skills of the students and users and preferably developed in an open-source programming language to ensure the reproducibility of the results. In this context, the airGRteaching R package has been developed as an add-on to the airGR package, which gathers several lumped rainfall-runoff models widely used by hydrological researchers and practitioners. airGRteaching contains a graphical user interface and allows teachers and students to import their own data and create their own exercises. A specific dataset of 19 different catchments in France is included in the add-on airGRdatasets package. This dataset is composed of hydro-climatic time series (solid and liquid precipitation, potential evapotranspiration, air temperature, and streamflow time series).
Acknowledgements. The authors would like to thank  Météo-France  (https://www. data. gouv. fr/en/organizations/meteo-france/, last access: 30 December 2022) and the SCHAPI (https://hydro. eaufrance. fr/, last access: 30 December 2022) for providing the SAFRAN meteorological series and the streamflow series included in the airGRdatasets package. We would like to thank  Charles Perrin  and  Vazken Andréassian  for their useful remarks and for their suggestions that significantly improved the paper and the package vignettes. We thank  Léo Carriba Demange ,  Ayoub Chanoual , and  Antoine Gazull  for their student report on the evaluation of the modeling software available for teaching hydrological modeling. Finally, we thank  Matjaz Mikos  (the editor) and the anonymous referees for their comments that improved the paper.
O. Delaigue et al. :  airGRteaching: an open-source tool for teaching hydrological modeling with R Code and data availability. The code and data used in this paper are included in the airGRteaching and airGRdatasets packages that are available from the CRAN (https://CRAN. R-project. org/package=airGRteaching,  Delaigue et al. , 2023b).
Data Collection To achieve the aims of the study, three questionnaires were administered to the participants, which collected the following information: (1) basic demographics and past VR experience, (2) perception of continuance intention, with an instrument developed by Roca et al  [28] , and (3) the System Usability Scale (SUS) developed by Brooke  [29]. To maintain consistency, we explained how to use the head-mounted device and how to put it on to ensure it was in good working condition. Any device issues were rectified, and then the participants brought the IVR device home for 2 weeks of testing on their own time. Upon returning the device, they were invited to complete the questionnaires. The scale for perception of intention to continue was adapted from Roca et al  [28]  and had the following subscales: (1) usefulness, (2) cognitive absorption, (3) ease of use, (4) system quality, (5) confirmation, (6) satisfaction, and (7) continuance intention.
Strengths and Limitations The strengths of this study included, first, that all interactive clinical scenarios were reviewed and refined by the various parties. Second, faculty and students conducted several rounds of user acceptability testing to refine the prototypes prior to this study. Finally, developing the clinical procedures was a collaborative effort among clinical, academic, and IT professionals. All comments received were valuable, and improvements suggested by the participants were incorporated into the future refinement of the IVR procedures. The limitations included the IT professionals using Unreal Engine software for the first time, which led to teething issues. As they gained expertise in using the program, the IVR procedures significantly improved. Although the sample size was small, considering that it was a usability study, 29 participants was considered a sufficient size, and data collection for reviewing the design and evaluating its usefulness reached saturation.
In each experiment three Petri dishes were used per strain per condition. The linear mixed model was used to analyze the effect of temperature on hyphal growth and sporulation as follows: Y = Xb + Zu + e (Equ: 1) where Y is the response vector (observed values of hyphal growth or spore production) X is the N x p design matrix for the p fixed factor including the grand mean of the trait (hyphal growth or spore production), temperatures and the different strains, and Z represents N x q j design matrix for the q random effects of experiments (defined as the combination of blocks within repeats, repeats and temperature). An analysis of variance (ANOVA) was performed using the lmer function of the lmerTest package  (Kuznetsova et al. , 2017)  of the R 4. 1. 0 statistical software (R Core Team, 2020) to determine variability for growth and sporulation among the V.
Genome-wide association mapping To fine map the genomic regions of M. truncatula with additive effects associated with AUDPC and MSS, TASSEL 5. 2. 50 was used  (Bradbury et al. , 2007). Single Nucleotide Polymorphism (SNP) data were obtained from the Medicago truncatula HapMap project (http://www. medicagohapmap. org/) and were filtered with a minimum allele frequency (MAF) of 5% and minimum count to 200, which resulted in 5,671,743 SNPs retained. A total of five GWAS statistical models were tested including General Linear Model (GLM) without any correction for population structure (naive model), GLM Q-Model with Qmatrix as correction for population structure and three Mixed Linear Models (K model, Q model and K + Q model) with Kmatrix and/or Q-matrix respectively as correction for kinship relationships and population structure.
The population structure (Q matrix) is based on population admixture proportions of M. truncatula individuals. The kinship matrix (K matrix) was computed from a 840K LD-pruned SNP dataset. Both matrices have been described earlier by  Gentzbittel et al. (2019). To reduce computing time, the Population Parameter Previously Determined (P3D) algorithm  (Zhang et al. , 2010)  was used to fit mixed linear models. For all models, the Q-Q plot was plotted to evaluate which was the best fitted model for this study. Manhattan plots were computed to illustrate the association of SNPs with AUDPC and MSS. For plotting the Manhattan graph the association score of SNPs was calculated through p-values of all SNPs as follows -log10 (p-values). To correct for multiple testing, Bonferroni and False Discovery Rate (FDR) corrections were used.
Gene expression studies 2. 3. 1 Selection of candidate genes and primer design The suggestive line which was proposed by the qqman R package (D. Turner, 2018)  was very close to the FDR, thus it was chosen as the main threshold for selecting the significant candidate SNPs. Hence, SNPs with association score values equal or greater than 5 were selected. Based on data on linkage disequilibrium and recombination rates described for M. truncatula in a previous study  (Branca et al. , 2011) , the regions 10 kb upstream and downstream of these SNPs were explored on the JBrowser site  (Buels et al. , 2016). Genes with functional annotation associated to defense pathways which were located within these areas were favoured as possible candidate genes. In addition, two genes encoding hypothetical proteins were included for further steps because of their high association score.
To assess the quality of qRT-PCR reactions the melting curve was implemented at the end of the reaction (95°C/15 sec, 60°C/15 sec, 95°C/15 sec). In order select the primers for gene expression studies we checked primer efficiency with a dilution series of an equimolar mix of cDNA from mock-inoculated and inoculated roots of the susceptible and resistant plants. The C T values were extracted through Design & Analysis software (V. 2. 4. 3). Relative gene expression levels were analyzed by the comparative C T method  (Schmittgen and Livak, 2008). The Medtr2g008050 (Actin) and Medtr4g097170 (H3L) housekeeping genes were used for normalization. DC T values were normalized against the harmonic mean of the two housekeeping genes.
truncatula roots, have homologs in alfalfa (https:// medicago. legumeinfo. org/tools/sequenceserver/), with more than 90% identity, though the coverage was low for three among them. In order to validate the loci detected by GWAS, we selected genes in the areas 10 kb upstream and downstream of all significant SNPs. This distance is based on data on linkage disequilibrium and recombination rates described for M. truncatula in a previous study  (Branca et al. , 2011). Chromosome 8 shows the highest number of genes putatively involved in resistance towards AF1 with 31 and 17 genes for AUDPC and MSS respectively. One gene (Medtr1g042160) on chromosome 1 which was detected through AUDPC was common to the previous study with the French strain at 20°C  (Mazurier, 2018). This gene encodes a MATH domain and coiled-coil domain-containing protein homologous to At3g58250.
Acknowledgments This work has been supported by the  Center for International Scientific Studies & Collaboration (CISSC)  and the  French Embassy in Iran. The authors also thank the staff at  GeT-PlaGe  for assistance and use of the QuantStudio ™ 6 Flex Real-Time PCR System, and  Genotoul Bioinformatics platform Toulouse Occitanie  ( Bioinfo Genotoul , doi:  10. 15454/1. 5572369328961167E12 )for providing hardware infrastructure, help, computing and storage resources.
Participants were then able to fill out the questionnaire online at their convenience. Additionally, we also utilized WhatsApp to share the questionnaire with contacts identified within the relevant companies. This multi-channel approach allowed us to maximize the chances of participation and obtain representative responses. The data collection period extended over four months, from February 1, 2023, to the end of May 2023, ensuring ample participation and ensuring the relevance of the obtained results. The data collected from the respondents were subjected to statistical analysis using Microsoft Excel software. The results will be presented in a clear and concise manner, utilizing graphs and tables to facilitate the understanding and interpretation of the findings. The questionnaire was administered to a variety of players within companies, with respondents including senior executives, information systems security managers, information systems directors, as well as key employees involved in cybersecurity operations, such as IT security engineers and IT technicians.
Results: We present a program, 3DVizSNP, that enables the rapid visualization of nonsynonymous missense mutations extracted from a variant caller format file using the web-based iCn3D visualization platform. The program, written in Python, leverages REST APIs and can be run locally without installing any other software or databases, or from a webserver hosted by the National Cancer Institute. It automatically selects the appropriate experimental structure from the Protein Data Bank, if available, or the predicted structure from the AlphaFold database, enabling users to rapidly screen SNPs based on their local structural environment. 3DVizSNP leverages iCn3D annotations and its structural analysis functions to assess changes in structural contacts associated with mutations. Conclusions: This tool enables researchers to efficiently make use of 3D structural information to prioritize mutations for further computational and experimental impact assessment.
Background High-throughput sequencing experiments generate large numbers of variant calls. Though there are many types of variants, here we focus on nonsynonymous missense mutations affecting protein coding regions. There are databases with annotations of known SNPs, such as dbSNP  [1] , ClinVar  [2] , and COSMIC  [3] , but the volume of data generated by ongoing experiments means that researchers need tools to evaluate the effects of SNPs that, for most, are not present in these databases. The computational challenge is in predicting which variants may impact biological function  [4] [5] [6] [7] [8] [9]. There are numerous tools available that predict the possible deleterious impact of a mutation, most of which use one-dimensional sequence only  [8, 10, 11]. The three-dimensional structural context of variants provides critical information for assessing the potential impact of an amino acid mutation.
Implementation 3DVizSNP is a Python program that, combined with the iCn3D structure viewing and analysis platform  [18, 19] , allows the user to quickly process a VCF file and produce a table with Ensembl Gene IDs, Gene symbols, UniProt IDs, PDB IDs (if applicable), SIFT  [20]  and PolyPhen  [21]  predictions and scores, and a link that can open iCn3D highlighting the mutations mapped in 3D on either the relevant PDB structure or the AlphaFold predicted structure. The user can quickly go through tens to hundreds of nonsynonymous mutations identified in high-throughput experiments and visualize the structure and structural contacts to inform and prioritize further studies, without requiring extensive structural biology expertise or heavy computational resources, and without installing a standalone 3D structure viewer or modeling software.
It leverages REST APIs including that of iCn3D, which enables the rapid retrieval of Fig. 1  Illustration depicting the role 3DVizSNP fills in the process of evaluating missense mutations. Many programs provide impact predictions for large numbers of SNPs based on sequence (purple box), and there are algorithms that assess the impact of individual mutations using 3D structure (blue box), but 3DVizSNP allows the user to easily visualize a moderate number of mutations in 3D to facilitate prioritization of mutations for further study experimental structures from the PDB or predictions from the AlphaFold database, and display of contacts in 1D/2D/3D formats, all through a saveable and shareable URL  [18]. 3DVizSNP workflow 3DVizSNP reads in a bgzipped VCF file with an associated tabix index and extracts the variants, with the option to only select variants from an input list of HGNC gene symbols.
It then submits the variants to the VEP server  [11]  via a REST API and gets back the Ensembl Gene IDs, Gene symbols, SwissProt IDs, amino acid mutation, and SIFT and PolyPhen predictions and scores. It uses the PDB REST API to identify the PDB ID of the X-ray crystal or electron micrograph with the highest resolution, or the NMR structure in the absence of X-ray or EM structures, for each amino acid mutation (correcting for mismatches between UniProt and PDB numbering). It also avoids structures with engineered mutations at the mutation site. If an experimental structure is not available, it loads the AlphaFold prediction (for sequences under 2700 amino acids in length) color-coded by prediction confidence. The output is an HTML table and a comma separated value (csv) file, which includes iCn3D links for each mutation.
(Fig. 2 ) The links can be clicked to open the PDB structure or Alpha-Fold predicted structure in iCn3D, with the mutations added as separate tracks in the 1D viewer for SIFT and PolyPhen predictions. The iCn3D 'scap interactions' command is used to highlight the wild-type and mutant amino acid side chains (which can be toggled) in iCn3D. iCn3D has the capability to add multiple annotations to the sequence viewer, enabling the user to quickly determine if the variant is in a known functional domain, matches known mutations in dbSNP or ClinVar, etc. The csv file can be loaded into a spreadsheet program, enabling the user to record notes about variants, sort them, store the results, etc. In addition to the python script, we have provided a website where the user can upload a VCF file and view the sortable, filterable output table along with an embedded version of iCn3D (Fig.
Known KRAS mutations To illustrate the ability to find and visualize known mutations in the KRAS oncogene, we ran 3DVizSNP against a colorectal adenocarcinoma (COAD) vcf file from a single patient from the TCGA database with the flag '-g KRAS' to select only variants in the KRAS gene. This produced an output table with two mutations, the well-known G12V mutation and a 2nd mutation, D154N. Figure  4  depicts these mutations in iCn3D. The G12V mutation is predicted to be 'deleterious_low_confidence' by SIFT (with a score of 0) and is not called in this instance by (Note that the PDB ID 7rov has the lowest resolution structure, so it is used for the D154N mutation, but it is an engineered G12D mutant, so 3DVizSNP discards it in favor of 7vvb for the G12V mutation. ) The impact of the G12V mutant is not obvious upon initial inspection, but it is adjacent to the GTP binding site and involves replacing a glycine in a turn with a valine, which is likely to cause steric hindrances.
TCGA VCF example To illustrate the utility of 3DVizSNP in screening mutations of unknown effect, we took a randomly selected VCF file from a Glioblastoma patient in the TCGA database, Fig. 3  The web interface to 3DVizSNP. The current row is highlighted in green and shown in the embedded iCn3D interface. Clicking on a different row loads that mutation in the iCn3D viewer. The table is sortable and filterable, and clicking on the 'View in iCn3D' button opens the mutation in a new full page iCn3D which had 27,526 variants in it. We submitted it to the OpenCRAVAT server  [10]  to filter the list down to a more manageable size, by using the filters coding = yes, polyphen-2 HumVar rank score 0. 8, and SIFT prediction 'damaging'. This produced a list of 901 variants, which we ran through 3DVizSNP.
B The D154N mutation (WT, left; D154N, right). The SIFT score is zero and the prediction is 'deleterious_low_confidence'. The red arrows highlight the hydrogen bond to a water molecule that is removed by the mutation free energy of folding of the mutant protein is predicted to be 0. 31 kcal/mol higher than that of the wild-type). The UniProt entry has a link to the DisGenNET database (entry 284541) which links mutations to diseases. DisGenNET lists liver carcinoma as a disease linked to CYP4A22, and links to PubMed ID 30069903  [26] , which indicates that higher CYP4A22 expression in hepatocellular carcinoma correlates with better disease prognosis. Obviously hepatocellular carcinoma differs from glioblastoma; the point is not to make a strong claim that this particular mutation contributes to glioblastoma progression, but rather to point out that there is evidence linking mutations that damage CYP4A22 function, as the L194R mutation is predicted to, to cancer progression, indicating further study is warranted.
This process illustrates that one can use tools such as OpenCRAVAT to filter a larger set of variants, then use 3DVizSNP to further sort and prioritize variants based on the available 3D structural information. One can then use other tools and databases such as Missense3D, SAAMBE, and UniProt to derive further support for the potential functional relevance of a given mutation. Continuing this process could winnow down the list of variants to around 50 for further in-depth computational assessment and experimental characterization.
Acknowledgements This project was initiated at a Hackathon organized by two of the authors (JW and PY) held at ISMB2022. MS was a team leader for the project during the Hackathon and MW was a team member and set up the initial GitHub repository for the project. The authors would like to thank the other team members  Bonface Onyango , and  Pranavathiyani G  for their help in getting the project started. The authors also would like to thank the other members of the CGBB for testing the script and the website, and for providing valuable feedback.
Availability and requirements Project name: 3DVizSNP. Project home page: https:// analy sisto ols. cancer. gov/ 3dviz snp. (Source code: https:// github. com/ CBIIT-CGBB/ 3DViz SNP). Operating system(s): Platform independent. Programming language: Python. Other requirements: Pysam v. 0. 19. 1, Pandas library. License: MIT License.
The detection of weld defects by using X-rays is an important task in the industry. It requires trained specialists with the expertise to conduct a timely inspection, which is costly and cumbersome. Moreover, the process can be erroneous due to fatigue and lack of concentration. In this context, this study proposes an automated approach to identify multi-class welding defects by processing the X-ray images. It is realized by an intelligent hybridization of the data augmentation techniques and convolutional neural network (CNN). The proposed data augmentation mainly performs random rotation, shearing, zooming, brightness adjustment, and horizontal flips on the intended images. This augmentation is beneficial for the realization of a generalized trained CNN model, which can process the multi-class dataset for the identification of welding defects.
Usually, this type of recommendation is founded on three stages: preliminary preparation, subdivision and characteristic extraction, and categorization. Normally, the characteristic extraction phase is frequently performed by experts and hence is a long and imprecise process. Certainly, this accuracy issue is mainly due to the variety of weld defect types. Further, the generated features are insufficient for the recognition quality to detect errors effectively  [9, 10]. Following, many studies have examined the general structure of machine learning applications and the algorithms used for feature extraction and ML. However, some artificial neural networks have simple applications, specialized classification, and low accuracy. In recent times, there has been a notable rise in the utilization of deep learning methodologies, driven by their ability to leverage extensive datasets and perform automated feature extraction.