dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 3. Brain MRI We developed a method to estimate the white matter pathology anatomically connected to a defined cortical region, using tractography from DTI images with the MRtrix3 package (http://www. mrtrix. org/) [20]  (Fig. 1 ), to obtain the FLAIRhyperintensity volume in the track. We normalized this volume by dividing it by the volume of the track itself.
Proteins exist for more than 3 billion years: proof of a sustainable design. They have mechanisms coping with internal perturbations (e. g. , amino acid mutations), which tie genetic backgrounds to diseases or drug therapy failure. One difficulty to grasp these mechanisms is the asymmetry of amino acid mutational impact: a mutation at position i in the sequence, which impact a position j does not imply that the mutation at position j impacts the position i. Thus, to distinguish the influence of the mutation of i on j from the influence of the mutation of j on i, position mutational influences must be represented with directions. Using the X ray structure of the third PDZ domain of PDS-95 (Protein Data Bank 1BE9) and in silico mutations, we build a directed network called GCAT that models position mutational influences.
From there, the issue is to distinguish changes tolerated by the structure which yield structural alternative and structurally robust solutions from changes not tolerated, which identify potential needs for "corrections" to accommodate the mutations. Likewise, this applies to dynamical and functional changes. Computational biology and learning methods, which can screen large dataset, offer several efficient tools and methods to analyze protein tolerance to mutations, more generally to analyze the mutational fate of amino acid positions  (Vuillon and Lesieur, 2015; Ponzoni and Bahar, 2018; Poelwijk et al. , 2019; Liang et al. , 2020; Pacini et al. , 2020; Demir et al. , 2021; Pacini et al. , 2021; Wingert et al. , 2021; Xavier et al. , 2021; Sen et al. , 2022). The features of amino acid positions (e.
Methods Case of study-The third PDZ domain of the synaptic protein PDS-95 (PSD95pdz3) is our case of study and we use its PDB 1BE9  (Doyle et al. , 1996). rASA-The relative Accessible Surface Area (rASA) of each amino acid of 1BE9 is computed using the DSSP method implemented in BioPython  (Cock et al. , 2009)  and amino acids are classified as surface exposed if rASA >0. 2 and buried otherwise. Over the 115 amino acids of PSD95pdz3, 64 are surface exposed (SE) and 51 are buried. Amino acid network (AAN)-Starting from the Protein Data Bank (PDB)  (Berman et al. , 2000)  data, protein structures are modeled using the Amino Acid Network (AAN)  (Dorantes-Gilardi et al. , 2018) , an established model in Computational Biology. The AAN is a graph G = (V; E), with V the set of the N nodes of the network and E the set of links of the network.
Nodes and links are also called vertices and edges of the graph, respectively. The edges constitute the pairs of the nodes (v, w) connected in the graph. The AAN is an undirected graph where the edges are unordered pairs of nodes. Nodes of the AAN. Each node in the AAN corresponds to one amino acid of the protein's sequence: V {i | i is an amino acid} Links of the AAN. A link is an atomic interaction defined by atomic proximity: two amino acids i and j are connected if there exists at least one couple of atoms, one belonging to i and one belonging to j, at a distance one from the other lower or equal to a given threshold. The threshold is fixed to 5 Å: E i, j i, j ∈ V with i ≠ j and ∃ atom i ∈ i, atom j ∈ j with dist atom i, atomj ≤ 5Å The AAN is built using Rodrigo Dorantes-Gilardi's implementation in the Biographs module available at https:// github.
Acknowledgments We would like to thank  Chang, Hong and Kang  for the open and useful discussions about their independent work [CHK22]. They analyzed Ascon and obtained results that have some overlap with ours. Jules Baudrin  is funded by  ANR (French National Research Agency)  grant  ANR-20-CE48-0017  (SELECT). The work of  Léo Perrin  is supported by the  European Research Council  ( ERC , grant agreement no. 101041545  " ReSCALE ").
Neonatal encephalopathy (NE) with suspected hypoxic ischaemic encephalopathy (HIE) (NESHIE) is a complex syndrome occurring in newborns, characterised by altered neurological function. It has been suggested that genetic variants may influence NESHIE susceptibility and outcomes. Unlike NESHIE, for which a limited number of genetic studies have been performed, many studies have identified genetic variants associated with cerebral palsy (CP), which can develop from severe NESHIE. Identifying variants in patients with CP, as a consequence of NESHIE, may provide a starting point for the identification of genetic variants associated with NESHIE outcomes. We have constructed NCGR (NESHIE and CP Genetics Resource), a database of genes and variants reported in patients with NESHIE and CP (where relevant to NESHIE), for the purpose of collating and comparing genetic findings between the two conditions.
To date, genes involved in inflammatory processes (IL1B and IL6) and blood flow regulation (the NOS family) have been significantly associated with both NESHIE and CP  [26, 31, [56] [57] [58] [59] [60] [61]. However, there may be a greater overlap of genes associated with both conditions. Consolidating genetic data on both CP (where relevant to NESHIE) and NESHIE into a single resource can aid in the identification of novel genes that may be associated with NESHIE. Additionally, as research progresses on whether a genetic predisposition to NESHIE might exist, the importance of collecting data on the genes and variants associated with NESHIE becomes important. With the aforementioned in mind, we have created The NESHIE and CP Genetics Resource (NCGR), a database for collating gene and variant data on NESHIE and CP (where relevant to NESHIE) to enable the comparison of genotypic and phenotypic data between the disorders.
NCGR provides the most comprehensive collection to date of data and metadata on genes and short-length variants with potential clinical relevance to NESHIE. Furthermore, NCGR integrates complex search and filter functionality to aid in the comparison and prioritisation of genes and variants that may be involved in a genetic predisposition to NESHIE. Here we describe the construction of NCGR and demonstrate an example of how NCGR can be used to prioritise genes and variants that may influence susceptibility to/severity of NESHIE.
Construction and content NCGR (available at: http://ncgr. bi. up. ac. za/) was compiled to provide an overview of publicly-available data on genes and variants with potential clinical relevance, that may underlie a genetic predisposition to NESHIE. NCGR was developed using the Django web-development framework (Django Software Foundation, USA)  [62]  and Djangobased content management system, Wagtail (Torchbox, USA)  [63]. Updates to the database will be performed on a biannual basis and researchers are encouraged to contribute their data and suggestions to NCGR.
1. Firstly, relevant publications were identified by performing a literature search in the National Centre for Biotechnology Information (NCBI) PubMed database (Fig. 1A ). The search terms: (gene AND hypoxic ischemic encephalopathy) OR (variant AND hypoxic ischemic encephalopathy) OR (gene AND cerebral palsy) OR (variant AND cerebral palsy) were used, and articles published between January 2002 and May 2022 were retrieved. Retrieved article titles and abstracts were pre-screened using the following inclusion criteria: (i) condition: patients with NESHIE and/or CP; (ii) language: English; (iii) study type: peer-reviewed case-control, cohort, cross-sectional, case, meta-analyses, genome-wide association, or family-based heritability study; and (iv) study outcome: association of short-length variant [i.
Data annotation and nomenclature standardisation Data captured from the publications included the variant and gene coordinates, variant reference and alternate alleles, study population size and demographics, data acquisition methods, and the statistical significance of the study findings. To ensure consistent reporting, international standards were used for the naming of variants and genes. Human Genome Organisation (HUGO) Gene Nomenclature Committee (HGNC) official symbols were used to classify genes  [64] , while variants were classified according to Human Genome Variation Society (HGVS) nomenclature  [65]  or NCBI Single Nucleotide Polymorphism Database (dbSNP) reference sequence identifiers (RSIDs)  [66]. If the HGVS nomenclature for a variant was not provided in a publication, the canonical transcript-based HGVS nomenclature was retrieved using the Ensembl Variant Effect Prediction (VEP) toolset with the reference genome and corresponding genomic coordinates of the variant as input  [67].
Additional information from several publicly-available databases was integrated into NCGR to provide researchers with context on the disease-causing potential of the catalogued variants and genes. Variant allele frequencies, variant consequences, and VEP scores were calculated using several algorithms including Functional Analysis through Hidden Markov Models (FATHMM), Sorting Intolerant from Tolerant for genomes (SIFT 4G), Polymorphism Phenotyping v2 (PolyPhen-2) and Combined Annotation Dependent Depletion (CADD), and Ensembl's VEP database. Additionally, MutationTaster VEP scores, which were not available for all variants on Ensembl's VEP database, were retrieved from the MutationTaster database (2021 release)  [68]. For catalogued genes, gene Residual Variation Intolerance Scores (RVIS), which provide a measure of genic variation intolerance and consequential disease susceptibility, were included  [69].
Database design and schema The PostgreSQL relational database was used to capture study-, variant-and gene-level information which were used as the principal guiding variables of interest during the design of the database structured query language (SQL) schema. The relationship between studies and variants was found to be more accurately described using a graph-theory approach, where the connection between two nodes in a graph may also possess its own unique properties. To represent this, a custom many-tomany through model was used to capture the relationship. The remaining relationships were adequately represented using one-to-many and many-to-many relationships where applicable. The "Study details" table contains information on NESHIE and CPrelated scientific publications, including the publication Digital Object Identifier (DOI) provided by means of a hyperlink to the online publication, study methods and study population size and demographics.
By selecting a particular study, users can navigate to the results of that study. Additional information on variants identified in the studies, including the reference genome, genomic location, reference, and alternate alleles, are included in a "Variant details" table. By selecting a variant in this table, users can retrieve a tabulated list of pre-computed variant consequences, effects, and allele frequencies. Lastly, the "Gene details" table provides access to metadata on each candidate gene including the gene symbol, genomic cytoband position, variation intolerance scores, and associated disease phenotype terms. Selection of a gene in this table provides users with all the disease phenotype terms associated with the gene. Fig. 2  provides a schematic overview of the relational structure of the database.
Utility 2. 2. 1. Search and filter functions Due to the complex nature of biological data and the need for crosssectional inquiry methods, complex query construction was integrated into NCGR to provide users with advanced data-filtering capabilities. This functionality was achieved using the JQuery QueryBuilder library (https://querybuilder. js. org/), which allows for the creation of complex queries through an intuitive graphical user interface. We utilised QueryBuilder to construct advanced AND/OR expression queries with filters based on disease type, statistical significance, variant consequences, pre-computed variant effect predictions, gene RVIS scores and gene HPO terms. These filters are available on the "Filter and Search" page of NCGR. The code used for construction of the complex filters is available at https://gist.
github. com/G-kodes/3103a8bbdeb3c80 317313f2ef279c685. In addition to the integration of advanced data filters, all data tables within NCGR were constructed with sortable column headers for categorising results alphabetically and a local search bar whereby users can filter the results of a specified table based on search criteria. If instead a user would like to query the NCGR resource in its entirety for a specific term relating to a gene, variant or its associated metadata, a global search bar accessible at the top of each page can be used.
Demonstration of database functionality To demonstrate the functionality of NCGR, we performed a complex query to generate a list of candidate genes likely to play a role in predisposing individuals to NESHIE (Supplementary Fig. 1 ). Genes associated with NESHIE or CP (as a result of NESHIE/birth asphyxia) were selected. These genes were further filtered for those with nonsynonymous variants that had (i) statistically significant associations (p-value <0. 05); or were (ii) associated with HPO terms similar to that of NESHIE, namely, "cerebral ischemia" or "tissue ischemia". Known disease genes often cause diseases with similar phenotypes, thus by selecting for genes associated with HPO terms similar to that of NESHIE, we can identify genes that may not previously have had significant associations with NESHIE but may make good candidates for future genetic studies.
The final list of prioritised genes was analysed for their potential roles in NESHIE pathogenesis by (i) identifying enriched gene ontology (GO) terms; and (ii) examining the interactions between the geneencoded proteins. To identify recurring processes, a GO analysis of the input list of genes was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics Resource  [72, 73]. Enriched biological process (BP), molecular function (MF) and cellular component (CC) ontologies were examined. The Search Tool for Retrieval of Interacting Genes (STRING) database tool v11. 5 was utilised to construct a protein-protein interaction network using the list of prioritised genes as input  [74]. To examine the interactions of our prioritised proteins with neighbouring proteins, a maximum of 10 additional directly-interacting proteins was included in the first shell of the network, and a maximum of 50 indirectlyinteracting proteins was included in the second shell.
Database overview An initial PubMed search yielded 1429 articles of potential relevance for inclusion in NCGR. Of these articles, 1331 were excluded on the basis of title and/or abstract content. The full content of the remaining articles was manually screened, and an additional 16 articles were excluded. Notably, a letter to the editor by Li et al. which stated a significant association of matrix metallopeptidase 3 (MMP3) rs2070744 with NESHIE was excluded at this stage as the authors did not provide information on the diagnostic criteria for NESHIE  [76]. Of the 82 remaining articles, contained variants of relevance according to specified criteria and were included in NCGR, along with their corresponding genes (Fig. 1 ). NCGR therefore contains data and metadata on 21 candidate genes and variants retrieved from 37 peer-reviewed studies.
Database functionality 3. 2. 1. Gene prioritisation To demonstrate the type of output that can be obtained from NCGR, the search and filter features were utilised to prioritise candidate genes and variants that were likely be associated with NESHIE. In total, 12 genes met the prioritisation criteria. Of the prioritised genes, the angiotensinogen (AGT), AP4B1, CARD8, CAT, IL6, IL10, IL1B, MTHFR, NOS3, OLIG2 and TNF genes had statistically significant associations with NESHIE or CP (as result of NESHIE). Although variants in COL4A1 have not been significantly associated with NESHIE or CP as a consequence of NESHIE, the COL4A1 gene was prioritised since (i) the gene has prior associations with cerebral ischemia and tissue ischemia disease phenotypes; and (ii) variants in the gene have been reported in patients with CP (as a result of birth asphyxia) as previously mentioned  [82].
Interestingly, TNF, MTHFR and CAT were also involved in the response to hypoxia (GO:0001666) albeit at a non-significant level. The generated GO enrichment results are provided in Supplementary Data B. A protein-protein interaction network was constructed using the STRING database to briefly examine the physical and functional interactions between the protein products of the prioritised genes and their interacting proteins. The network consisted of 72 nodes and 363 edges of which 70 nodes formed the main connected component (Supplementary Fig. 2 ). The NESHIE-associated proteins, IL6, IL1B, and TNF, were direct interactors within the network and were significantly enriched in GO inflammatory processes including the cytokine-mediated signalling pathway (GO:0019221, GO:0005125). TNF, a proinflammatory cytokine, had the highest degree of connectivity in comparison to other proteins in the network with 28 neighbouring proteins.
Discussion Recent studies suggest that a complex interplay of both environmental and genetic factors may influence NESHIE susceptibility and outcomes  [25] [26] [27] [28] [29] [30] [31] 33]. Moderate to severe NESHIE often results in CP  [37] , which is well-studied in terms of genetic susceptibility/predisposition. Identifying genetic variants underlying CP (as a result of NESHIE) may provide a starting point for identifying genetic variants that may predispose to NESHIE. To date, no resource exists collating genetic data on both NESHIE and CP (where relevant to NESHIE) to enable a comparison of findings between the conditions. Additionally, as the number of studies investigating the existence of a genetic predisposition to NESHIE increases, there is a need for a centralised database for the storage of genetic findings associated with this condition.
Esih and colleagues hypothesised that these inconsistencies may be caused by differences in the treatment regimens used for patients in the two groups. Additional studies are needed to confirm this. To demonstrate the search and filter functionality of NCGR, we performed a complex query of the database to prioritise candidate genes of potential clinical relevance. In total, 12 genes were identified of which 11 had a statistically significant association with NESHIE and/or CP as a result of NESHIE. Of these, IL1B, NOS3 and TNF were reported in at least three studies catalogued in NCGR and had negative RVIS scores indicative of intolerance to variation  [27, 28, 31, 33, 56, 57, 83, 92, [106] [107] [108] [109]. Genes with an intolerance to variation show high levels of evolutionary conservation and are thus most likely to incorporate pathogenic, disease causing variants  [110].
Interestingly, the NFKB pathway is activated in response to hypoxia, whereby it initiates expression of pro-inflammatory genes, decreased apoptosis and increased angiogenesis  [125]. The NFKB pathway has been shown to have a dual role in the pathogenesis of NESHIE by contributing both to brain damage through induction of inflammation and neuroprotection through suppression of apoptosis  [126]. Although not on our list of prioritised genes, genes central to the NFKB pathway, such as NFKB1, may also make good targets in the future for studies investigating whether a genetic predisposition to NESHIE exists. One of the most crucial limitations of the research done to date is an under-representation of variant data from certain population groups. The catalogued studies in NCGR have predominantly used sequencing data of East Asian and European ancestry.
Conclusion and future directions To our knowledge, NCGR provides the largest compilation to date of publicly-accessible information on variants and genes reported in patients with NESHIE and CP (where relevant to NESHIE). Utilisation of NCGR allows researchers to identify patterns and gaps within research to date, including infrequently analysed population-groups, under-utilised study methodology, as well as replicable variants and genes. Furthermore, access to additional metadata on genes and variants, including gene-disease phenotype association data, genetic intolerance scores, and precomputed variant consequences and effect predictions, may aid researchers in the investigation of potential disease-causing variants and genes catalogued in NCGR. Through utilisation of filter and search features built into NCGR, researchers can prioritise genes and variants specific to their research purposes.
At present, NCGR houses data and metadata on genes and shortlength variants of potential clinical relevance. In the future, to enable a comparison of all findings, it may be important to expand the scope of the database to include all reported variants, including CNVs and haplotypes. To aid future research, it may also be important to include additional information on the study methods of articles catalogued in NCGR, such as potential confounding factors or methodology limitations that may influence association results. Additionally, as genetic studies on NESHIE and CP increase, NCGR may become more difficult to update and maintain. To counteract this challenge, new methods which utilise machine-learning and other computational approaches for the entry of data, will need to be implemented.
Acknowledgements We thank the members of the  South African NESHIE Study  for their ongoing contributions. Neonatal/Paediatrics Team:  Dr. Gugu Kali  ( Stellenbosch University ),  Assoc. Prof. Alan Horn  ( University of Cape Town ),  Dr. Natasha Rhoda  ( University of Cape Town ),  Dr. Shakti Pillay  ( University of Cape Town ),  Dr. Roopesh Bhoola  ( University of Kwa-Zulu Natal ),  Dr. Derisha Pillay  ( University of Pretoria ),  Dr. Khomotso Masemola  ( University of Pretoria ),  Dr. Marike Boersema  ( University of Pretoria ),  Dr. Melantha Coetzee  ( University of Pretoria ),  Dr. Thembi Mbowane  ( University of Pretoria ),  Dr. Rosine Tshehla  ( University of Pretoria ),  Prof. Robin Green  ( University of Pretoria ),  Dr. Firdose Nakwa  ( University of the Witwatersrand ),  Dr. Robin Saggers  ( University of the Witwatersrand ),  Dr.
Aims. The processes that determine the stellar initial mass function (IMF) and its origin are critical unsolved problems, with profound implications for many areas of astrophysics. The W43-MM2&MM3 mini-starburst ridge hosts a rich young protocluster, from which it is possible to test the current paradigm on the IMF origin. Methods. The ALMA-IMF Large Program observed the W43-MM2&MM3 ridge, whose 1. 3 mm and 3 mm ALMA 12 m array continuum images reach a ∼2500 au spatial resolution. We used both the best-sensitivity and the line-free ALMA-IMF images, reduced the noise with the multi-resolution segmentation technique MnGSeg, and derived the most complete and most robust core catalog possible. Using two different extraction software packages, getsf and GExt2D, we identified ∼200 compact sources, whose ∼100 common sources have, on average, fluxes consistent to within 30%.