Resource Ontology Research Articles

Lifestyle factors in the biomedical literature: an ontology and comprehensive resources for named entity recognition.

Despite lifestyle factors (LSFs) being increasingly acknowledged in shaping individual health trajectories, particularly in chronic diseases, they have still not been systematically described in the biomedical literature. This is in part because no named entity recognition (NER) system exists, which can comprehensively detect all types of LSFs in text. The task is challenging due to their inherent diversity, lack of a comprehensive LSF classification for dictionary-based NER, and lack of a corpus for deep learning-based NER. We present a novel lifestyle factor ontology (LSFO), which we used to develop a dictionary-based system for recognition and normalization of LSFs. Additionally, we introduce a manually annotated corpus for LSFs (LSF200) suitable for training and evaluation of NER systems, and use it to train a transformer-based system. Evaluating the performance of both NER systems on the corpus revealed an F-score of 64% for the dictionary-based system and 76% for the transformer-based system. Large-scale application of these systems on PubMed abstracts and PMC Open Access articles identified over 300 million mentions of LSF in the biomedical literature. LSFO, the annotated LSF200 corpus, and the detected LSFs in PubMed and PMC-OA articles using both NER systems, are available under open licenses via the following GitHub repository: https://github.com/EsmaeilNourani/LSFO-expansion. This repository contains links to two associated GitHub repositories and a Zenodo project related to the study. LSFO is also available at BioPortal: https://bioportal.bioontology.org/ontologies/LSFO.

Research on the Management Model of University Teaching Materials based on Knowledge Graph

Colleges and universities will produce a large number of teaching materials in their teaching work. It is of great significance to collect and manage teaching materials. In this study, knowledge graph technology is applied to the management of teaching materials in colleges and universities, and an ontology model of teaching materials in colleges and universities based on knowledge graph is proposed. Based on the relevant norms of teaching resources ontology in China, the ontology model of teaching materials in colleges and universities is constructed by seven-step method, and it is transformed into the attribute graph model of teaching materials in colleges and universities, which is stored in the Neo4 j graph database. The model can define the entity type and the relationship between entities, and construct the subject knowledge framework.

Endothelial cells and fibroblasts mediate the microenvironmental regulatory network of carotid body paraganglioma

Objective:To explore the gene expression characteristics of endothelial cells and fibroblasts in the microenvironment of SDHD-mutated carotid body tumors（SDHD-CBT）, to fine the functional enrichment of each subcluster, and to further explore the network of cell-cell interactions in the microenvironment of SDHD-CBT. Methods:The bioinformatics analysis was used to download and reanalyze the single-nuclear RNA sequencing data of SDHD-CBT, SDHB mutated thoracic and abdominal paraganglioma（SDHB-ATPGL）, SDHB-CBT, and normal adrenal medulla（NAM）, to clarify the information of cell populations of the samples. We focused on exploring the gene expression profiles of endothelial cells and fibroblasts subclusters, and performed functional enrichment analysis based on Gene Ontology（GO） resources. CellChat was used to compare the cell-cell interactions networks of different clinical samples and predict significant signaling pathways in SDHD-CBT. Results:A total of 7 cell populations were profiled. The main subtypes of endothelial cells in SDHD-CBT are arterial and venous endothelial cells, and the main subtypes of fibroblasts are myofibroblasts and pericytes. Compared to NAM, SDHB-CBT and SDHB-ATPGL, cell communication involving endothelial cells and fibroblasts in SDHD-CBT is more abundant, with significant enrichment in pathways such as FGF, PTN, WNT, PROS, PERIOSTIN, and TGFb. Conclusion:Endothelial cells and fibroblasts in SDHD-CBT are heterogeneous and involved in important cellular interactionprocesses, in which the discovery of FGF，PTN，WNT，PROS，PERIOSTIN and TGFb signals may play an important role in the regulation of microenvironment of SDHD-CBT.

CMCN: Chinese medical concept normalization using continual learning and knowledge-enhanced

Medical Concept Normalization (MCN) is a crucial process for deep information extraction and natural language processing tasks, which plays a vital role in biomedical research. Although MCN in English has achieved significant research achievements, Chinese medical concept normalization (CMCN) remains insufficiently explored due to its complex syntactic structure and the paucity of Chinese medical semantic and ontology resources. In recent years, deep learning has been extensively applied across numerous natural language processing tasks, owing to its robust learning capabilities, adaptability, and transferability. It has proven to be well suited for intricate and specialized knowledge discovery research in the biomedical field. In this study, we conduct research on CMCN through the lens of deep learning. Specifically, our research introduces a model that leverages polymorphic semantic information and knowledge enhanced through multi-task learning and retain more important medical features through continual learning. As the cornerstone of CMCN, disease names are the main focus of this research. We evaluated various methodologies on Chinese disease dataset built by ourselves, finally achieving 76.12 % on Accuracy@1, 87.20 % on Accuracy@5 and 90.02 % on Accuracy@10 with our best-performing model GCBM-BSCL. This research not only advances the fields of knowledge mining and medical concept normalization but also enhances the integration and application of artificial intelligence in the medical and health field. We have published the source code and results on https://github.com/BearLiX/CMCN.

Bioinformatics analysis of potential pathogenesis and risk genes of neuroinflammation-promoted brain injury in intracerebral hemorrhage

IntroductionSpontaneous (non-traumatic) intracerebral hemorrhage (ICH) is one of the major causes of global death. The purpose of our bioinformatics analysis was to detect viable pathophysiological targets and small-molecule drug candidates and to identify the precise secondary mechanisms of brain injury in ICH. Material and methodsThe GSE24265 dataset, consisting of data from four perihematomal brain tissues and seven contralateral brain tissues, was downloaded from the Gene Expression Omnibus (GEO) database and screened for differentially expressed genes (DEGs) in ICH. Online analysis tool GEO2R and Drug Susceptibility Assessment Module within the ACBI Bioinformation tool was used for data differential expression analysis. TargetScan, miRDB, and RNA22 were used to investigate the miRNAs regulating the DEGs. The functional annotation of DEGs was performed using Gene Ontology (GO) resources, and the cell signaling pathway analysis of DEGs was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG). DAVID is used to perform GO function enrichment analysis and KEGG pathway analysis of candidate target genes. Enrichment analysis was performed for delving the molecular mechanism of DEGs, and protein–protein interaction (PPI) networks and microRNA (miRNA)-messenger RNA (mRNA) networks were used to reveal the hub nodes and the related interaction relationships. Hub genes and miRNA-mRNA interaction of PPI network were identified by STRING version 12.0 online software and Cytoscape. Next, the DEGs were analyzed using the L1000CDS2 database to identify small-molecule compounds with potential therapeutic effects. ResultsA total of 325 upregulated genes and 103 downregulated genes associated with ICH were identified. The biological functions of DEGs associated with ICH are mainly involved in the inflammatory response, chemokine activity, and immune response. The KEGG analysis identified several pathways significantly associated with ICH, including but not limited to cytokine-cytokine receptor interaction and MAPK signaling pathway. A PPI network consisting of 188 nodes and 563 edges was constructed using STRING, and 27 hub genes were identified with Cytoscape software. The miRNA-mRNA network with high connectivity contained key 27 mRNAs (from C-C motif chemokine ligand 5 (CCL5), C-C motif chemokine ligand 8 (CCL8), …., to dishevelled-associated activator of morphogenesis 1 (DAAM1), and FRAT regulator of WNT signaling pathway 1 (FRAT1)) and 135 candidate miRNAs. These genes and miRNAs are closely related to secondary brain injury induced by ICH. In addition, a L1000CDS2 analysis of six small-molecule compounds revealed their therapeutic potential. ConclusionsOur study explores the pathogenesis of brain tissue injury promoted by neuroinflammation in ICH and extends the clinical utility of its key genes. At the same time, we constructed a miRNA-mRNA network which may play crucial roles in the pathogenesis of ICH. In addition, we obtained six small molecule compounds that will have anti-inflammatory effects on ICH, including Geldanamycin, Dasatinib, BMS-345541, Saracatinib, and Afatinib.

Approaches to automating processes of working with ontological resources

Ontological models are extensively used in information support systems that offer resources and services for solving management, design, and scientific and technical problems. Specifically, domain ontologies are commonly utilized in decision support systems. In the ontological modeling of complex systems, there arises a need to automate the process-es of handling ontological resources. This work discusses the main software systems and methodologies for ontological modeling, approaches to automating the processes of creating, populating, and using ontological models, and considers the temporal aspect of the ontological representation of objects. The aim of the work is to explore methods for auto-mating the life cycle of ontological resources and to analyze the extent of their adaptation in applied ontologies. The work highlights a relatively high degree of automation in the process of populating ontologies and the use of large lan-guage models in this process. However, it points out the lack of methods for automating the conversion of information from tables and diagrams into ontological models, as well as for validating and processing the content of the model. Promising directions for automating work with ontological resources are also indicated.

Representation of child and youth participation within the Unified Medical Language System (UMLS)

Purpose To examine (1) how much participation is represented in the benchmark Unified Medical Language System (UMLS) resource, and (2) to what extent that representation reflects the definition of child and youth participation and/or its related constructs per the family of Participation-Related Constructs framework. Materials and methods We searched and analysed UMLS concepts related to the term “participation.” Identified UMLS concepts were rated according to their representation of participation (i.e., attendance, involvement, both) as well as participation-related constructs using deductive content analysis. Results 363 UMLS concepts were identified. Of those, 68 had at least one English definition, resulting in 81 definitions that were further analysed. Results revealed 2 definitions (2/81; 3%; 2/68 UMLS concepts) representing participation “attendance” and 18 definitions (18/81; 22%; 14/68 UMLS concepts) representing participation “involvement.” No UMLS concept definition represented both attendance and involvement (i.e., participation). Most of the definitions (11/20; 55%; 9/16 UMLS concepts) representing attendance or involvement also represent a participation-related construct. Conclusion(s) The representation of participation within the UMLS is limited and poorly aligned with the contemporary definition of child and youth participation. Expanding ontological resources to represent child and youth participation is needed to enable better data analytics that reflect contemporary paediatric rehabilitation practice.

What Powers Trastuzumab's Cardiotoxicity? Decoding Mitochondrial-Related Gene Expression Through Integrative Review and Meta-Analysis in Cardiomyocytes.

Trastuzumab is a monoclonal antibody used in oncotherapy for HER2-positive tumors. However, as an adverse effect, trastuzumab elevates the risk of heart failure, implying the involvement of energy production and mitochondrial processes. Past studies with transcriptome analysis have offered insights on pathways related to trastuzumab safety and toxicity but limited study sizes hinder conclusive findings. Therefore, we meta-analyzed mitochondria-related gene expression data in trastuzumab-treated cardiomyocytes. We searched the transcriptome databases for trastuzumab-treated cardiomyocytes in the ArrayExpress, DDBJ Omics Archive, Gene Expression Omnibus, Google Scholar, PubMed, and Web of Science repositories. A subset of 1270 genes related to mitochondrial functions (biogenesis, organization, mitophagy, and autophagy) was selected from the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology Resource databases to conduct the present meta-analysis using the Metagen package (Study register at PROSPERO: CRD42021270645). Three datasets met the inclusion criteria and 1243 genes were meta-analyzed. We observed 69 upregulated genes after trastuzumab treatment which were related mainly to autophagy (28 genes) and mitochondrial organization (28 genes). We also found 37 downregulated genes which were related mainly to mitochondrial biogenesis (11 genes) and mitochondrial organization (24 genes). The present meta-analysis indicates that trastuzumab therapy causes an unbalance in mitochondrial functions, which could, in part, help explain the development of heart failure and yields a list of potential molecular targets. These findings contribute to our understanding of the molecular mechanisms underlying the cardiotoxic effects of trastuzumab and may have implications for the development of targeted therapies to mitigate such effects.

Identification of diagnostic model in heart failure with myocardial fibrosis and conduction block by integrated gene co-expression network analysis

BackgroundDespite the advancements in heart failure(HF) research, the early diagnosis of HF continues to be a challenging issue in clinical practice. This study aims to investigate the genes related to myocardial fibrosis and conduction block, with the goal of developing a diagnostic model for early treatment of HF in patients.MethodThe gene expression profiles of GSE57345, GSE16499, and GSE9128 were obtained from the Gene Expression Omnibus (GEO) database. After merging the expression profile data and adjusting for batch effects, differentially expressed genes (DEGs) associated with conduction block and myocardial fibrosis were identified. Gene Ontology (GO) resources, Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, and gene set enrichment analysis (GSEA) were utilized for functional enrichment analysis. A protein-protein interaction network (PPI) was constructed using a string database. Potential key genes were selected based on the bioinformatics information mentioned above. SVM and LASSO were employed to identify hub genes and construct the module associated with HF. The mRNA levels of TAC mice and external datasets (GSE141910 and GSE59867) are utilized for validating the diagnostic model. Additionally, the study explores the relationship between the diagnostic model and immune cell infiltration.ResultsA total of 395 genes exhibiting differential expression were identified. Functional enrichment analysis revealed that these specific genes primarily participate in biological processes and pathways associated with the constituents of the extracellular matrix (ECM), immune system processes, and inflammatory responses. We identified a diagnostic model consisting of 16 hub genes, and its predictive performance was validated using external data sets and a transverse aortic coarctation (TAC) mouse model. In addition, we observed significant differences in mRNA expression of 7 genes in the TAC mouse model. Interestingly, our study also unveiled a correlation between these model genes and immune cell infiltration.ConclusionsWe identified sixteen key genes associated with myocardial fibrosis and conduction block, as well as diagnostic models for heart failure. Our findings have significant implications for the intensive management of individuals with potential genetic variants associated with heart failure, especially in the context of advancing cell-targeted therapy for myocardial fibrosis.

An An Ideological Land Reform Policy Shift of State Custodianship and ‘Transforming the Rural Land Economy’

In customary landholding practices land is regarded as more than an asset of economic value to be owned. African people’s rights in land and other natural resources exceed the idea of mere possession. To African rural land holders, land is not only a material resource for agricultural production, nor is the commercialisation and commoditisation of land the primary driving force for most customary landholding communities’ even though the land is productively used. This broader conceptualisation of the social function of land beyond the economic, aligns with modern property law on the proprietary function of property. However, excessive state limitations imposed on customary land tenure can hinder economically valuable property rights, but also other rights to social, cultural, and ontological resources embodied in the spirituality of African communities.
 Security of tenure for land reform beneficiaries cannot be secondary to commercialisation and commoditisation of the rural land economy in the national interest. Commercialisation and commoditisation are the by-products of recognised, protected, and enforceable property rights and not the “pre-requisites” or “qualifiers” for secure land tenure. Thus, the state custodial approach reflected in land reform law and policy that places an overemphasis on private property capitalism, above the constitutional mandate of security of land tenure is critically discussed. It is argued that if not carefully formulated transformative land reform law and policy can be conduits for a state custodianship approach that inadvertently reproduces apartheid-like ideology and preoccupations.
 Key words: Secure tenure, state custodianship, National Development Plan 2030, transformative land reform law and policy.

Research on Knowledge Graph Multiple Environment-Aware Recommendation Algorithm for Teaching Course Recommendation

Abstract With the rapid development of education informatization and intelligence, personalized teaching and course recommendations have gradually become an important research direction in the field of education technology. A knowledge graph, as a structured knowledge representation and organization, provides rich semantic information and a relationship network for teaching course recommendations. Based on this research background, this paper focuses on the research of knowledge graph multivariate environment-aware recommendation algorithms for teaching courses. The article firstly constructs the ontology of course resources, researches and analyzes the methods and steps of acquiring and processing data in order to build the knowledge graph, and then constructs the framework of the recommendation model combining multivariate perception in order to further analyze and explore the user’s course-aware representation learning and the user’s course-aware embedded learning. Embedded Learning, and finally, the teaching course of a school as an empirical study, it is shown that the performance of Ripple_mlp+ and Ripple_mlp in this article are higher than the benchmark algorithms through the experimental class and the control class of the pre and post-test scores have been sorted out and counted. The experimental students’ scores are steadily increasing, and the overall scores are better than the control class. In the experimental class, the significance value is 0.013, indicating that there is a significant difference between the midterm and final grades of the experimental class. Through the paired samples t-test on the midterm and final grades of the experimental class, after analyzing and comparing the midterm and final grades of students at different levels, it can be concluded that the t-value of the superior students is −3.326. The value of significance is 0.049, indicating that there is a significant difference between the midterm and final grades of the exceptional students. The T-value for intermediate students is −0.134, and the value of significance is 0.045, which indicates that there is a significant difference in the midterm final grades of intermediate students. The T-value of −1.623 and the value of significance is 0.534 for advanced students indicates that the difference in midterm final grades of advanced students is not significant.

The Development of a Water Resource Monitoring Ontology as a Research Tool for Sustainable Regional Development

The development of knowledge graphs about water resources as a tool for studying the sustainable development of a region is currently an urgent task, because the growing deterioration of the state of water bodies affects the ecology, economy, and health of the population of the region. This study presents a new ontological approach to water resource monitoring in Kazakhstan, providing data integration from heterogeneous sources, semantic analysis, decision support, and querying and searching and presenting new knowledge in the field of water monitoring. The contribution of this work is the integration of table extraction and understanding, semantic web rule language, semantic sensor network, time ontology methods, and the inclusion of a module of socioeconomic indicators that reveal the impact of water quality on the quality of life of the population. Using machine learning methods, the study derived six ontological rules to establish new knowledge about water resource monitoring. The results of the queries demonstrate the effectiveness of the proposed method, demonstrating its potential to improve water monitoring practices, promote sustainable resource management, and support decision-making processes in Kazakhstan, and can also be integrated into the ontology of water resources at the scale of Central Asia.

Exploring occurrences of verb phrase ellipsis in journalistic discourse and in a sample of modern American fiction

Verb Phrase Ellipsis (VPE) is a prevailing syntactic phenomenon, yet only a few studies have focused on its occurrences and most of these have been concerned with only a single genre. This study compares two genres: journalistic discourse and modern American fiction, in a sample size of approximately 260,000 words, in terms of their uses of VPE. On the assumption that there is such a thing as differential typological and ontological resources of syntax, these two genres are expected to be constituted by sufficiently distinct syntactic resources. VPE was found to occur three times more frequently in modern American fiction than in journalistic discourse. This suggests that VPE in modern American fiction is driven by such factors as the heightening of emotion, engaging readers and challenging readers’ minds. However, VPE is less common in journalistic discourse, the ellipsis being considered bad form for journalists. Nonetheless, this marginal use can still also elicit emotional responses from readers and be used as a deterrent to face-threatening behavior.

Broiler Heart Muscle Monoaminergic Receptors Alteration in Response to Chronic Heat Stress: Based on Transcription Analysis.

Chronic heat stress affects numerous physiological and behavioral mechanisms. Epigenetic changes following prolonged cyclic heat stress, creating new opportunities for molecular biology research. One of these changes involves monoamines, such as serotonin, epinephrine, norepinephrine, dopamine, and their transmission. Broiler chickens are highly susceptible to heat stress, and their hearts become insufficient during the growth phase, leading to hypertrophy of the left heart. RNA-seq data were obtained from NCBI with accession number SRP082125. The expression level of genes was determined with DESeq2 packages. Gene Ontology qualification, including biological processes, cellular components, and molecular role (MF), was performed from the Gene Ontology Resource. Cyclic heat stress in broilers significantly altered monoamine receptor expression. Twenty-nine genes of the monoamine pathway changed their expression in the left heart. Significant downregulation of expression was statistically associated with the ADRB1, HTR2A, and PNMT genes and upregulation of the MAOA gene (P<0.01). STRING database was used to construct the protein-protein interaction network; based on network analysis, the HTR2C, HTR2A, and HTR5A genes were identified as the major nodal genes in the network followed by MAOA, DRD2, DRD5, HTR1B, DRD1, DRD3, and HTR2B genes occupying the second important place in the network module. In conclusion, heat stress treatment prevented cardiac hypertrophy and altered the expression of monoamine genes. This would imply that monoamine transmission plays an important role in the development of cardiac hypertrophy, and that cyclic-chronic heat treatment modulates the cardiac monoaminergic system. These molecular biomarkers could be useful for screening, diagnosis, and treatment of cardiac hypertrophy.

Remote intervention assistance system for a person in difficulty based on probabilistic ontologies

The proposed theme of this paper is to develop a system for remote intervention assistance with a person in difficulty, which is becoming increasingly important as the world’s population ages. The goal is to provide effective long-term care to reduce hospitalizations of the elderly and alleviate the burden of care for hospitals and governments. The system requires careful consideration of costs, resources, and efficiency due to the complexity and heterogeneity of the data involved, making human analysis nearly impossible. To enable machine analysis and decision-making, the proposed system is based on an ontological approach that combines medical, social, temporary, and geographical factors, along with Semantic Web Rule Language (SWRL). The system is further enhanced by the integration of probabilistic reasoning through Probabilistic Web Ontology Language (PR-OWL), which allows for the use of ontological resources in a probabilistic environment. The paper also proposes two additional approaches in the form of Jena Application Programming Interfaces (API), including ontological enrichment based on Bayesian Network (BN) knowledge bases and structural/parametric learning based on ontological knowledge bases, as well as a probabilistic clause integrated into the ontological format. As results, the proposed approaches were tested in a simulation, and feedback was collected from users. The feedback was compared, and an increase in precision was observed from one approach to another with varying execution times. These results demonstrate the effectiveness of the proposed system for remote intervention assistance and provide a foundation for future research and development in this area.

MiR-383-5p Regulates Preadipocyte Proliferation and Differentiation by Targeting RAD51AP1.

Obesity has become a major health problem worldwide, and increasing evidence supports the importance of microRNAs (miRNAs) in its pathogenesis. Recently, we found that miR-383-5p_1 is highly expressed in the perirenal fat of high-fat-fed rabbits, but it is not yet known whether miR-383-5p is involved in lipid metabolism. Here, we used transcriptome sequencing technology to screen 1642 known differentially expressed genes between miR-383-5p mimic groups and miR-383-5p negative control groups. Gene Ontology Resource (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were enriched in the pathway related to lipid metabolism, and glycine biosynthesis, the NOD receptor signal pathway and nonalcoholic fatty liver were significantly enriched. Afterwards, our research results indicated that miR-383-5p can promote the proliferation and differentiation of rabbit preadipocytes, and there is a direct targeting relationship with RAD51AP1. Mechanistically, miR-383-5p directly interacts with the lipid metabolism and participates in adipogenesis and lipid accumulation by targeting RAD51AP1. In conclusion, our data highlight a physiological role for miRNA in lipid metabolism and suggest the miR-383-5p/RAD51AP1 axis may represent a potential mechanism for controlling lipid accumulation in obesity.

Identification of Key Biomarkers in Hepatocellular Carcinoma Induced by Non-alcoholic steatohepatitis or Metabolic Syndrome via Integrated Bioinformatics Analysis.

The burden of hepatocellular carcinoma (HCC) is steadily growing because obesity, type 2 diabetes, and nonalcoholic fatty liver disease (NAFLD) are replacing viral- and alcohol-related liver disease as major pathogenic promoters. The current study attempted to identify the key genes and pathways in the non-alcoholic steatohepatitis (NASH) induced development of HCC using integrated bioinformatics analyses. Two gene expression profiling datasets, GSE102079 and GSE164760 were downloaded. Differentially expressed genes (DEGs) from HCC and healthy control samples were screened. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource. Then protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Expression and survival analysis of hub genes, methylation and genetic mutation analysis were explored withGEPIA2, UALCAN, GSCA, and TIMER2.0 databases. We identified 158 overlapping genes from the 2 datasets. Up-regulated genes were mainly related to the proliferation, adhesion and metastasis of tumors, while down-regulated genes were mainly related to oxidative stress and energy metabolism. CDKN2A, SPP1, CYP2C9 and CYP4A11 were associated with prognostic performance and were considered the potential crucial genes, which SPP1, CYP2C9 and CYP4A11 were identified as the DNA methylation-driven genes. In different mutation statuses of HCC, gene expression of CDKN2A, SPP1, CYP2C9 and CYP4A11 showed significant differences. CDKN2A and SPP1 were identified as risk genes, while CYP2C9 and CYP4A11 were identified as protective genes, which may affect the transformation of NASH into HCC.

Low level of ARID1A contributes to adaptive immune resistance and sensitizes triple-negative breast cancer to immune checkpoint inhibitors.

Immune checkpoint inhibitors (ICIs) shed new light on triple-negative breast cancer (TNBC), but only a minority of patients demonstrate response. Therefore, adaptive immune resistance (AIR) needs to be further defined to guide the development of ICI regimens. Databases, including The Cancer Genome Atlas, Gene Ontology Resource, University of California Santa Cruz Genome Browser, and Pubmed, were used to screen epigenetic modulators, regulators for CD8+ T cells, and transcriptional regulators of programmed cell death-ligand 1 (PD-L1). Human peripheral blood mononuclear cell (Hu-PBMC) reconstruction mice were adopted for xenograft transplantation. Tumor specimens from a TNBC cohort and the clinical trial CTR20191353 were retrospectively analyzed. RNA-sequencing, Western blotting, qPCR and immunohistochemistry were used to assess gene expression. Coculture assays were performed to evaluate the regulation of TNBC cells on T cells. Chromatin immunoprecipitation and transposase-accessible chromatin sequencing were used to determine chromatin-binding and accessibility. The epigenetic modulator AT-rich interaction domain 1A (ARID1A) gene demonstrated the highest expression association with AIR relative to other epigenetic modulators in TNBC patients. Low ARID1A expression in TNBC, causing an immunosuppressive microenvironment, promoted AIR and inhibited CD8+ T cell infiltration and activity through upregulating PD-L1. However, ARID1A did not directly regulate PD-L1 expression. We found that ARID1A directly bound the promoter of nucleophosmin 1 (NPM1) and that low ARID1A expression increased NPM1 chromatin accessibility as well as gene expression, further activating PD-L1 transcription. In Hu-PBMC mice, atezolizumab demonstrated the potential to reverse ARID1A deficiency-induced AIR in TNBC by reducing tumor malignancy and activating anti-tumor immunity. In CTR20191353, ARID1A-low patients derived more benefit from pucotenlimab compared to ARID1A-high patients. In AIR epigenetics, low ARID1A expression in TNBC contributed to AIR via the ARID1A/NPM1/PD-L1 axis, leading to poor outcome but sensitivity to ICI treatment.

GOgetter: A pipeline for summarizing and visualizing GO slim annotations for plant genetic data.

The functional annotation of genes is a crucial component of genomic analyses. A common way to summarize functional annotations is with hierarchical gene ontologies, such as the Gene Ontology (GO) Resource. GO includes information about the cellular location, molecular function(s), and products/processes that genes produce or are involved in. For a set of genes, summarizing GO annotations using pre-defined, higher-order terms (GO slims) is often desirable in order to characterize the overall function of the data set, and it is impractical to do this manually. The GOgetter pipeline consists of bash and Python scripts. From an input FASTA file of nucleotide gene sequences, it outputs text and image files that list (1) the best hit for each input gene in a set of reference gene models, (2) all GO terms and annotations associated with those hits, and (3) a summary and visualization of GO slim categories for the data set. These output files can be queried further and analyzed statistically, depending on the downstream need(s). GO annotations are a widely used "universal language" for describing gene functions and products. GOgetter is a fast and easy-to-implement pipeline for obtaining, summarizing, and visualizing GO slim categories associated with a set of genes.

Application of the ontological approach in the problem of event data generation using simulation models

The article describes an application of the ontological approach to solving the problem of generating event data coming from the logs of simulation experiments. Currently, as part of the scientific direction Intelligent analysis of processes, methods and algorithms are being developed that allow solving machine learning problems in relation to event data. Simulation modeling can play an important role in the formation of training samples. However the experimental results of simulation in the form of logs of a certain structure must be brought to the form of event logs as they are understood in the intellectual analysis of processes. This paper provides a statement of the problem for forming an ontological resource that makes it possible to generate an event log based on the results of simulation experiments with a discrete-event model in which requests for processing are presented in the form of agents. A formal description of the domain ontology and an algorithm for its redefinition based on the log data of the simulation model are given. As an object of simulation the paper proposes to consider a hierarchical decision-making system, which receives tasks of varying complexity. The level of complexity of tasks is used for choosing the level of hierarchy at which this task needs to be solved. The architecture of the developed ontological system is given, as well as the structure of concepts with the corresponding semantic relations and sets of instances.