dataesr/scientific-paragraphs-categorization huggingface dataset:
89 0. 82 0. 82 0. 86 0. 95 0. 95 0. 96 0. 95 0. 96 0. 96 0. 96 0. 89 0. 97 0. 91 0. 95 0. 97 0. 97 Mm 0. 97 0. 96 0. 96 0. 96 0. 94 0. 94 0. 94 0. 93 0. 95 0. 95 0. 96 0. 96 0. 96 0. 96 0. 96 0. 93 0. 97 0. 95 0. 96 0. 97 0. 97 Temporally inconsistent Literature dataset Wh Dr Rv 0. 92 0. 92 0. 96 0. 91 0. 92 0. 96 0. 91 0. 92 0. 96 0. 91 0. 91 0. 96 0. 68 0. 44 0. 9 0. 3 0. 14 0. 92 0. 48 0. 31 0. 9 0. 83 0. 81 0. 86 0. 71 0. 48 0. 94 0. 88 0. 87 0. 94 0. 89 0. 89 0. 95 0. 89 0. 9 0. 95 0. 89 0. 88 0. 95 0. 89 0. 89 0. 95 0. 89 0. 89 0. 95 0. 78 0. 83 0. 94 0. 91 0. 92 0. 97 0. 86 0. 83 0. 92 0. 86 0. 86 0. 97 0. 94 0. 96 0. 98 0. 9 0. 92 0. 98 (continued) MajorVote AvgProba AccWProba F1WProba Reveal MinProba MaMaF MaxProba MaMaP ProdProba MalD MalH MalK MalCl MalA MalCo LinearSVC RF KNN AdaBoost Bagging GradBoosting Table 4 RQ4 RQ3.
95 0. 95 0. 96 0. 94 0. 95 0. 95 0. 96 0. 94 0. 95 0. 95 0. 95 0. 93 0. 94 0. 94 0. 96 0. 97 0. 97 0. 97 0. 94 0. 91 0. 92 0. 93 0. 97 0. 98 0. 97 0. 98 0. 98 0. 98 0. 98 0. 98 0. 98 0. 98 0. 98 0. 98 Temporally-consistent R P F 1 A 0. 92 0. 82 0. 87 0. 99 0. 27 0. 79 0. 41 0. 96 0. 19 1 0. 32 0. 96 0. 08 0. 9 0. 14 0. 96 0. 87 0. 58 0. 7 0. 97 0. 95 0. 56 0. 7 0. 96 0. 88 0. 57 0. 69 0. 96 0. 87 0. 69 0. 77 0. 98 0. 95 0. 56 0. 71 0. 96 0. 95 0. 56 0. 7 0. 96 0. 97 0. 6 0. 73 0. 97 0. 75 1 0. 85 0. 99 0. 79 0. 73 0. 76 0. 98 0. 88 0. 94 0. 91 0. 99 0. 77 0. 95 0. 85 0. 99 0. 71 0. 98 0. 82 0. 99 Literature datasets Literature DREBIN dataset Temporally-inconsistent R P F 1 A RQ1 DREBIN 0. 93 0. 96 0. 94 0. 99 Reveal 0. 29 0. 87 0. 44 0. 97 MaMaF 0. 18 1 0. 31 0. 96 MaMaP 0. 31 1 0. 48 0. 97 MalD 0.
65 0. 85 0. 95 0. 9 0. 84 0. 78 0. 95 0. 86 0. 79 0. 85 0. 95 0. 9 0. 85 Literature datasets Literature MaMaDroid dataset Temporally-inconsistent R P F 1 A RQ1 DREBIN 0. 98 0. 98 0. 98 0. 96 Reveal 0. 97 0. 92 0. 94 0. 91 MaMaF 0. 98 0. 9 0. 94 0. 89 MaMaP 0. 98 0. 92 0. 95 0. 92 MalD 0. 96 0. 95 0. 95 0. 93 MalH 0. 96 0. 96 0. 96 0. 93 MalK 0. 96 0. 95 0. 96 0. 93 MalCl 0. 96 0. 95 0. 96 0. 93 MalA 0. 96 0. 96 0. 96 0. 94 MalCo 0. 96 0. 96 0. 96 0. 94 RQ3 LinearSVC 0. 92 0. 94 0. 93 0. 89 RF 0. 97 0. 96 0. 97 0. 95 KNN 0. 96 0. 93 0. 95 0. 92 AdaBoost 0. 96 0. 96 0. 96 0. 94 Bagging 0. 97 0. 97 0. 97 0. 96 GradBoosting 0. 97 0. 96 0. 97 0. 95.
Introduction In the past decade, the pan-genome concept has been largely employed to investigate the bacterial comparative genomics and evolution analyses  (Hyun et al. , 2022; Jonkheer et al. , 2021; Vernikos, 2020). Many programs have been developed for this purpose such as Roary  (Page et al. , 2015)  or more recently PanACoTA  (Perrin et al. , 2021) , and a need is still present for the efficient storage, exploitation and visualization of data derived from pan-genome analyses. To address this question, we developed a web-based application, PanExplorer, which performs online pan-genome analysis and displays resulting information as a comprehensive and easy-touse solution, through several modules facilitating the exploration of gene clusters and interpretation of data. Several web applications have been recently published for this purpose.
PanX  (Ding et al. , 2018)  is a sophisticated web application for browsing among clusters after pre-computed pan-genome analyses, but it does not allow matrix visualization, synteny exploration nor genome import. On the other hand, PGAweb  (Chen et al. , 2018) , PGAPX  (Zhao et al. , 2018)  and PanWeb  (Pantoja et al. , 2017)  are web servers for running online the PGAP software  (Zhao et al. , 2012)  on users' Genbank files, but output files and images cannot be manipulated and browsed interactively. Thus, in contrast to the existing webbased tools for exploring pan-genomes, PanExplorer brings together in a single web application a whole set of data representation modules that allow to view pan-genomic information from different angles and offers the possibility to submit online a customized selection of bacteria strains if completely assembled and annotated.
The PanExplorer application The application takes a list of GenBank identifiers as input and the server will execute successively: (i) retrieval of the corresponding published complete genome sequences and their annotations, (ii) gene clustering and pan-genome analysis using PGAP  (Zhao et al. , 2012) , Roary  (Page et al. , 2015)  or PanACoTA  (Perrin et al. , 2021) , (iii) attribution of the Clusters of Orthologous Groups (COG) functional categories using RPSblast against the COG database  (Tatusov et al. , 2000)  and (iv) GC content calculation using SkewIT  (Lu et al. , 2020)  (Fig. 1A ). The PanExplorer application is developed in Perl CGI, web interactivity is allowed by external JavaScript libraries: hotmap. js (https://github. com/nconrad/hotmap), MauveViewer (https://github. com/PATRIC3/mauve-viewer), CircosJS (https:// github.
Pan-genome visualization As a presence/absence variation (PAV) matrix (Fig. 1B ) using the hotmap. js javascript library. This overview allows to easily identify V C The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals. permissions@oup. com 1 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons. org/licenses/by/4. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. and distinguish core-genes (present in all strains), dispensable genes (genes from the accessory genome) and strain-specific genes. Furthermore, this section allows a particular cluster of interest to be selected for further investigation and displayed in detail (Fig.
Conclusions PanExplorer offers broad-spectrum of tools for easily exploring pangenomes for scientists without programing skills (automatization of process, no installation is required). Thanks to a connection to NCBI ftp server, the application guarantees an up-to-date availability of public genomes, to be analyzed on-the-fly, and represents a versatile tool for genome exploration filling a need for bacteriologist After data processing, the application provides an overview of results, this includes PAV matrix, distribution of core and accessory genome and global phylogeny. Exploratory analysis consists of Circos representations of core-genes and strain-specific genes, synteny investigation or visual inspection of clusters of interest community. Among perspectives and further development, new functionalities might be implemented shortly such as online pan-Genome Wide Association Studies (pan-GWAS) based on Scoary software  (Brynildsrud et al.
Constrained Coding Regions (CCRs) in the human genome have been derived from DNA sequencing data of large cohorts of healthy control populations, available in the Genome Aggregation Database (gnomAD)  [1]. They identify regions depleted of protein-changing variants and thus identify segments of the genome that have been constrained during human evolution. By mapping these DNA-defined regions from genomic coordinates onto the corresponding protein positions and combining this information with protein annotations, we have explored the distribution of CCRs and compared their co-occurrence with different protein functional features, previously annotated at the amino acid level in public databases. As expected, our results reveal that functional amino acids involved in interactions with DNA/RNA, protein-protein contacts and catalytic sites are the protein features most likely to be highly constrained for variation in the control population.
These regions were identified in whole exome and genome sequencing data from large cohorts of healthy control populations from the Genome Aggregation Database (gnomAD2. 0. 1), which is currently the largest and most widely used publicly available collection of data on population variation from harmonised sequencing data  [2, 5]. The premise of  Havrilla et al. was that the data in gnomAD2. 0. 1 came from individuals who were either healthy or did not have early onset developmental abnormalities ('healthy control populations') and therefore their variant loci could be considered as 'tolerated'. In the CCRs model, each of the variant-depleted (constrained) regions is weighted based on i) its length in base pairs, and ii) the fraction of individuals (above 50% of total individuals) having at least a 10x sequencing coverage at each bp of the region.
Mapping the CCRs to amino acids Our aim was to explore how CCRs are distributed across the human canonical protein sequences as defined by UniProtKB/Swiss-Prot  [24]. For this purpose, first, we ran the CCRs model pipeline (available in the repository accompanying the work of Havrilla et al. , 2019: https://quinlan-lab. github. io/ccr/examples/updates) to obtain the genomic coordinates of the CCRs, but using the gnomAD3. 0 dataset of variants, which aggregates 76,156 whole genomes using coordinates from the human GRCh38 genome assembly. The resulting file with the genomic coordinates of the CCRs can be obtained from our GitHub repository (https://github. com/marciaah/CCRStoAAC/blob/main/data/rawCCRs/gnomad3_0/vep101/sor t_weightedresiduals-cpg-synonymous-novariant. txt. gz). Then, we mapped the genomic coordinates of the CCRs to amino acids in UniProtKB proteins, via the Ensmebl transcripts in the GENCODE basic set (see Figure  9  A for further details), the corresponding code of the pipeline is available in our repository https://github.
com/marciaah/CCRStoAAC, and the output of the mapping to amino acids can be found here https://github. com/marciaah/CCRStoAAC-output. The use of GRCh38 gives a more accurate cross-mapping of genes, transcripts and proteins in the Ensembl  [25]  and UniProtKB databases, while using the Swiss-Prot canonical set of proteins ensures the availability of functional annotations for further analysis. From a total of 18,583 human UniProtKB/Swiss-Prot canonical proteins that matched the Ensembl protein sequences, we were able to map CCRpct to at least one region in 17,366 of them (Figure  1 A ). 6,608 of the 17,366 proteins had partial coverage of CCRpct for their amino acid sites (residues) and 1,217 (from the expected 18,583) completely lacked CCRpct, as a consequence of low quality conflicted genomic regions (see Methods) that prevented the identification of CCRs.
Protein features and CCRs percentiles Protein annotations from UniProtKB/Swiss-Prot  [24] , PDBe  [24, 29] , VarSite  [30] , M-CSA  [31] , BioLip  [32] , MobiDB  [33] , ELM  [34] , Ensembl  [35]  and ClinVar  [36]  databases were obtained and aggregated for each amino acid site in the human canonical and annotated proteins of UniProtKB. 9. 8 million sites were annotated in this way, assigning CCRpct and the 30 protein annotations listed in Figure  9  (Methods). In order to investigate in detail how different protein features are constrained across human populations, we calculated odds ratios (OR) to measure the enrichment of residues in CCRpct categorised in 7 groups for each of the 30 protein feature annotations, compared to a random distribution (see in Methods, Odds ratios tests for enrichment: I.
III. Disorder related features. The 1. 8M amino acids that were annotated in our dataset as intrinsically disordered (ID) or mobile and with CCRpct assigned (about 18. 5% from the total of 9. 8M residues) showed two contrasting tendencies. Although with weaker OR, these sites were more likely to coincide with unconstrained and very lowly constrained sites (percentiles [0,30)) and also with the top most highly constrained percentiles [99,100] (Figure  4 C ). ID regions/proteins tend to evolve faster than structured proteins at the sequence level  [37, 38] , this is reflected in the enrichment of lower mean interspecies amino acid conservation across all levels of constraint, even for the high CCRpct residues (Table  1 ). High percentiles of constraint ([95,100]) were strongly associated with residues in disorder to Residues in regions driving LLPS present the highest association we observed, with OR 9.
26 times more likely to be in the most highly constrained regions (percentiles [99,100]) and with the longest average length of 75 amino acids (Figure  3 ). Furthermore, 29 (53%) of the 54 LLPS proteins in our dataset have LLPS driving amino acid sites with percentiles in [95,100] (Supplementary Table  5 ). When considering gnomAD2. 1. 1 per gene variant intolerance metrics, 34 out of these 54 (63%) LLPS driving proteins have pLI >= 0. 9 (i. e. are 'essential' genes, extremely intolerant to pLoF variants in heterozygosity), while only 7 of the 54 are highly intolerant to missense changes (missense OEUF<=0. 35) (Supplementary Table  5 ).
Amino acid sites with clinically interpreted variants, their CCR percentiles and co-occurrence with protein features We next investigated how residues with different types of clinically interpreted variants correlate with the different percentiles of constraint. For this purpose, we employed variants from the ClinVar database (https://www. ncbi. nlm. nih. gov/clinvar), and classified the amino acid positions in our dataset as pathogenic (including pathogenic/likely_pathogenic), benign (including benign/likely_benign) and/or VUS/conflicting (including variants of uncertain significance or with conflicting interpretations of pathogenicity), and followed a similar methodology for performing OR tests (Supplementary Figures  5  and 7 ) Pathogenic missense variants account for only 28,398 protein sites, with the majority of missense variants in ClinVar corresponding to VUS/conflicting interpretations, affecting 194,508 residues.
The lollipops above the Pfam domains depict positions with de novo variants reported in bibliography as associated to developmental disorders (N12del, P138P, R149W, R150C, P157L, T252I and G265G with black circles)  [48] , acute myeloid leukaemia (N196K), colon adenocarcinoma and castration-resistant prostate carcinoma (G301D) (black triangles)  [49]. The unfilled triangle represents a variant of uncertain significance reported in ClinVar (G264E). By-residue ScoreCons conservation scores were obtained from VarSite. Lowcomplexity, mobility, disorder LLPS, and LIP annotations were obtained from the MobiDB database. PTMs and other interacting regions were obtained from UniProtKB. Interacting proteins in the PDB structures are shown in pale blue, with their interacting side chain shown in stick representation.
The key challenge for clinical genomics is interpreting the pathogenicity of rare variants. Identifying whether a rare variant lies within a defined constrained region of the protein facilitates consequence interpretation especially for novel variants absent from existing genomic databases. We emphasise that combining interspecies and intraspecies (human population) conservation can help to highlight regions of individual genes that have appeared more recently in evolution or confer some degree of uniqueness/specificity to an individual paralogue. This data has the potential to facilitate the discovery of new associations between genes/variants with previously unknown phenotypes. CCRs also highlight many highly constrained regions currently not linked to any Mendelian disease. This may indicate mutations in these regions are lethal to humans or are sufficiently rare that they have not yet been identified  [1].
Aggregation of protein features annotations and clinically interpreted variants We developed our own pipeline in R for fetching different protein features and functional annotations from multiple resources. For this purpose, we captured annotations and based our analysis only for the 9. 8 million protein sites in UniProtKB/SwissProt canonical sequences because such sequences are the main references for annotations in the databases we employed. An overview of the databases and features that we included are presented in Figure  9  B, and obtained as described more in detail in Supplementary Methods.
Generation of the CCRs based on gnomAD3 To obtain the CCRs we ran the pipeline developed by  [1]  (https://quinlanlab. github. io/ccr/examples/updates) but employing the dataset of gnomAD  [26]  version 3. 0 and the corresponding files for the coordinates of the human genome in version GRCh38  [65]  (https://www. ncbi. nlm. nih. gov/grc). We used the Variant Effect Predictor (VEP)  [66]  of Ensembl  [25]  version 101. We also followed the recommendations of the authors to only consider genetic variants from autosomes and chromosome X, and avoid those in conflicting genomic regions -i. e. where there are segmental duplications and/or high identity with other genomic regions (>=90% identity) or with low sequencing coverage. In the same line of recommendations, we ran the weighting of the regions for autosomes and X chromosome separately, but merged both output files into one for performing the mapping of the coordinates of the regions to protein amino acids.
Mapping the CCRs to protein amino acids We developed an in-house pipeline in R that uses the 'ensembldb' Bioconductor R package  [67]  to map the genomic coordinates of CCRs boundaries, and all the coding bases in between, to the Ensembl v101 transcripts which are part of the GENCODE  [68]  basic set version 35. This was to ensure we were including complete and well annotated relevant transcripts. For those amino acid sites where the corresponding codon had constrained and unconstrained bases, we assigned such amino acid sites as unconstrained. We then obtained the sequence identifiers that crosslink Ensembl transcripts and proteins and UniProtKB proteins  [24]  by querying the APIs (application programming interfaces) of both databases. Finally, the CCRpct were accurately transferred to the amino acids in UniProtKB sequences by downloading the corresponding protein sequences from Ensembl and UniProtKB and performing Blastp local alignments  [69]  requesting 100% sequence identity (perfect match).
The workflow is summarised in Figure  9  A, explained more in detail in Supplementary Methods and the corresponding scripts of the pipeline are available in this repository https://github. com/marciaah/CCRStoAAC. Wellcome Trust, the European Bioinformatics Institute (EMBL-EBI) and the Medical Research Council have also funded research infrastructure. We thank Prof. Raymond's and Prof. Thornton's group members for their valuable comments and suggestions. The code and data used for the present analysis is provided in GitHub repositories, as mentioned in the Methods section. The authors welcome requests for additional information regarding the material presented in this paper.
Neutron production in nuclear facilities can induce a SDDR (Shutdown Dose Rate) even after months. Different methods exist to simulate the SDDR of neutron activated materials. The most used method is the mesh-based R2S (Rigorous-Two-Steps). This method couples a Monte-Carlo code and an isotopic inventory code to compute the SDDR. The Monte-Carlo code computes the neutron flux on a user-defined mesh to take into account the neutron flux gradients in the cells. The aim of this article is to present RayActive, a prototype tool elaborated for computing the SDDR. RayActive is based on a modified R2S method with an almost exact identification of materials enclosed in each voxel of a superimposed mesh. The CAD based geometry definition for the model makes possible this identification. Moreover, RayActive is designed as an "all-in-one" package without any other code coupling.
RayActive is designed to be as easy to use as possible and each part of the computation has been optimized to minimize the computational time. Verification and validation of RayActive has been done on shutdown dose rate benchmarks made with FLUKA code and classical R2S process coupling MCNP and FISPACT codes. Good agreement was found for these benchmarks even if some differences occur for the validation benchmark. These discrepancies show the impact on the results of the modified R2S method employed in RayActive.
Several methods exist for computing the SDDR. The Direct-1-Step (D1S) method  (Valenza et al. , 2001; Villari et al. , 2014)  computes the SDDR in a single Monte-Carlo transport Rubinstein and Kroese, 2016 by replacing the prompt photons by decay photons. A preliminary study is made to compute time correction coefficients. These coefficients are then used to compute the SDDR at the requested time. Another method implemented in FLUKA  (Ferrari et al. , 2011)  consists in simulating the decays by a Monte-Carlo process: for every radioactive nucleus produced, a decay time is sampled following the decay time probability. Then, the decay particle is transported to compute the SDDR. Finally, the Rigorous-Two-Step (R2S) method also allows to compute the SDDR. First, a neutron transport is made with a Monte-Carlo code such as MCNP  (Team, 2005).
Then, an inventory code as for example FISPACTII  (Sublet et al. , 2017)  or ACAB  (Sanz et al. , 2008)  computes the inventory at the decay time following the irradiation scenario. The last step is the decay photons transport made with a Monte-Carlo method. The R2S method implementation often couples different nuclear codes. As the R2S scheme follows the real physical processes, this scheme is used for the new tool. A lot of R2S method validations exist. Some of them are described in  (Petrizzi et al. , 2001; Chen and Fischer, 2002; Petrizzi et al. , 2003)  and show good agreement of the method with experimental data. More recently, new features were implemented for the R2S method to overcome some of its limitations. In  (Majerle et al. , 2012) , a fine mesh is used to reached converged neutron flux intensities in voxels and a coarse mesh to get the energy shape of the fluxes.
Geometry modelling RayActive relies on the features of RayXpert software. RayXpert is a unique code able to model a geometry by CAD means and to run a Monte-Carlo 3D transport with this model in the same interface. Moreover, RayXpert has a graphical user interface to create the model. So, in RayActive, the model is made with the CAD kernel of RayXpert which is Open Cascade technology Cascade, YYYY. The geometry it therefore in the boundary representation format (B-Rep). RayXpert could also import a STEP model file  (Kemmerer et al. , 1999)  directly. Monte-Carlo transport is handled by a Ray Tracing algorithm  Schmitt et al. , 1988. This allows the spaces between cells to be clearly identified and by defining an ambient material, particles could be transported without needing to construct cells of this material between the components of the geometry.
The solutions with both RayActive and FISPACTII code  (Sublet et al. , 2017)  (whose ODE solver is LSODE) have been compared. Coherent results were found between the two methods even if discrepancies appeared for some isotopes. The origin of these discrepancies have been identified and will be published in a future article. Changes have been made to adapt the new method to FISPACTII and the inventory benchmark presented here has been made again. For this benchmark, the initial number of nuclei of 56 Fe is 1. 075382 × 10 22 and the total neutron flux computed in the cube by the Monte-Carlo process is equal to 6. 13762 × 10 15 neutrons/cm 2 /s. A large enough number of source particles were emitted to ensure that the neutron flux uncertainties are below 10% for every group that contributes at least 0.
Verification of RayActive results To verify RayActive neutron induced activation analysis capacities, two simple benchmarks were realised with RayActive and by two other means. The first process used to compute the SDDR of the simple benchmarks was a classical R2S technique coupling MCNP6. 2  (Pelowitz, 2011)  and FISPACT codes. The second code used is FLUKA which is able to compute neutron induced activation and SDDR map in the same run. The activation method used in FLUKA can process the activation by two different methods. The first method is a Monte-Carlo process that computes randomly the decay time of any radioactive nucleus produced during neutron transport. The second method computes the production rates of each isotope then solves analytically the inventory at the requested time instead of randomly sampling the decay time.