Advancing mechanistic explanations through natural and artificial embodied cognitive systems.

Mougenot and Matheson propose that mechanistic models can explain behavior by describing the complex interactions among components of the brain, body, and environment as an integrated system, which aligns with embodied cognition. However, we suggest incorporating cognitive ontology theory and addressing degeneracy and neuronal reuse. We also recommend studying natural embodied cognition through artificial systems to develop a comprehensive mechanistic framework.

RNA-seq dataset of the chorioallantoic membrane of male and female chicken embryos, after 11 and 15 days of incubation.

The chicken chorioallantoic membrane (CAM) is an extraembryonic structure that exhibits many vital functions to support the development of the chicken embryo (gaseous exchange, innate defence, calcium transport from the eggshell to the embryo skeleton, homeostasis). Developing from day 6 of incubation, the CAM progressively differentiates into three functional layers (the chorionic epithelium in contact with the inner eggshell, the highly vascularized mesoderm, and the allantoic epithelium), between 11 and 15 days of incubation. This article describes the RNASeq dataset and the analyses performed on total CAMs collected from male and female embryos after 11 and 15 days of incubation. The datasets are available at the NCBI Gene Expression Omnibus (GEO) repository (http://www.ncbi.nlm.nih.gov/geo) using GSE199780 as the accession number. The statistical analysis of the data allowed identifying genes differentially expressed depending on the sex of the embryo at two time points of CAM differentiation. Knowing that the CAM is widely used as a model to study tumour growth, metastasis or wound healing, the resulting analysis highlights the necessity to include this sex variable in experimental assays to avoid any bias of interpretation. Indeed, the functional annotation of genes that are differentially expressed between male and female CAMs revealed an enrichment of activities and functions related to lipid metabolism, bone formation, and morphogenesis suggesting that the response of the CAM to external and experimental stimuli might be different depending on the sex of the embryo.

Analyzing the expression of the transcriptome in adipose tissue of fat- and thin-tailed sheep.

Significant efforts have been made to understand how fat deposition in sheep tail is regulated in genetic, transcriptomic, physiologic, biochemical, and metabolic levels in order to elucidate the complex mechanisms underlying the energy storage, lipid metabolism in adipose tissue, adaptability to harsh environments, and evolutionary domestication. Through RNA-seq data analysis, we are able to compare the gene expression of fat-tailed sheep versus thin-tailed sheep breeds in an acceptable resolution at transcriptome level. The purpose of this study was to compare the transcriptomes of Ghezel (fat-tailed) and Zel (thin-tailed) sheep. Total RNA from subcutaneous and tail tissue samples from healthy lambs was sequenced (150b PE) to identify differentially expressed genes (DEGs) between the two mentioned tissues and between the Ghezel and Zel sheep breeds. Further downstream pathway and network analyses were conducted afterwards. The results uncovered the association of the most important DEGs such as CAV1, ALB, and SOCS3 with cellular signaling pathways of lipids metabolism. It seems that the SOCS3 gene plays an important role in the differential deposition of lipid in the tails of two phenotypically different sheep breeds. Although the detail of gene expression in the tail and subcutaneous tissues of two morphologically different breeds was decoded here, to fully understand how differential expression of the SOCS3 gene affects the fat synthesis, further studies are needed.

GOTermViewer: Visualization of Gene Ontology Enrichment in Multiple Differential Gene Expression Analyses.

Gene ontology phrases are a widely used set of hierarchical terms that describe the biological properties of genes. These terms are then used to annotate individual genes, making it possible to determine the likely physiological properties of groups of genes such as a list of differentially expressed genes. Consequently, their ability to predict changes in biological features and functions based on alterations in gene expression has made gene ontology terms popular in the wide range of bioinformatic fields, such as differential gene expression and evolutionary biology. However, while they make the analysis easier, it is seldom easy to convey the results in a readily understandable manner. A number of applications have been developed to visualize gene ontology (GO) term enrichment; however, these solutions tend to focus on the display of aggregated results from a single analysis, making them unsuitable for the analysis of a series of experiments such as a time course or response to different drug treatments. As multiple pair wise comparisons are becoming a common feature of RNA profiling experiments, the absence of a mechanism to easily compare them is a significant problem. Consequently, to overcome this obstacle, we have developed GOTermViewer, an application that displays GO term enrichment data as determined by GOstats such that changes in physiological response across a number of individual analyses across a time course or range of drug treatments can be visualized.

Unsupervised pattern identification in spatial gene expression atlas reveals mouse brain regions beyond established ontology.

The rapid growth of large-scale spatial gene expression data demands efficient and reliable computational tools to extract major trends of gene expression in their native spatial context. Here, we used stability-driven unsupervised learning (i.e., staNMF) to identify principal patterns (PPs) of 3D gene expression profiles and understand spatial gene distribution and anatomical localization at the whole mouse brain level. Our subsequent spatial correlation analysis systematically compared the PPs to known anatomical regions and ontology from the Allen Mouse Brain Atlas using spatial neighborhoods. We demonstrate that our stable and spatially coherent PPs, whose linear combinations accurately approximate the spatial gene data, are highly correlated with combinations of expert-annotated brain regions. These PPs yield a brain ontology based purely on spatial gene expression. Our PP identification approach outperforms principal component analysis and typical clustering algorithms on the same task. Moreover, we show that the stable PPs reveal marked regional imbalance of brainwide genetic architecture, leading to region-specific marker genes and gene coexpression networks. Our findings highlight the advantages of stability-driven machine learning for plausible biological discovery from dense spatial gene expression data, streamlining tasks that are infeasible by conventional manual approaches.

simona: a comprehensive R package for semantic similarity analysis on bio-ontologies.

BACKGROUND: Bio-ontologies are keys in structuring complex biological information for effective data integration and knowledge representation. Semantic similarity analysis on bio-ontologies quantitatively assesses the degree of similarity between biological concepts based on the semantics encoded in ontologies. It plays an important role in structured and meaningful interpretations and integration of complex data from multiple biological domains. RESULTS: We present simona, a novel R package for semantic similarity analysis on general bio-ontologies. Simona implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. Moreover, it provides a robust toolbox supporting over 70 methods for semantic similarity analysis. With simona, we conducted a benchmark against current semantic similarity methods. The results demonstrate methods are clustered based on their mathematical methodologies, thus guiding researchers in the selection of appropriate methods. Additionally, we explored annotation-based versus topology-based methods, revealing that semantic similarities solely based on ontology topology can efficiently reveal semantic similarity structures, facilitating analysis on less-studied organisms and other ontologies. CONCLUSIONS: Simona offers a versatile interface and efficient implementation for processing, visualization, and semantic similarity analysis on bio-ontologies. We believe that simona will serve as a robust tool for uncovering relationships and enhancing the interoperability of biological knowledge systems.

An Ontology to Bridge the Clinical Management of Patients and Public Health Responses for Strengthening Infectious Disease Surveillance: Design Science Study.

BACKGROUND: Novel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved, enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semantic interoperability is ontology. OBJECTIVE: This study aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients, and disparate heterogeneous infectious disease surveillance. METHODS: The ontology modeling approach was chosen for its semantic richness in knowledge representation, flexibility, ease of extensibility, and capability for knowledge inference and reasoning. The IoT-MIDO was developed using the basic formal ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible to maximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency questions as requirements for the ontology to achieve the intended goals. RESULTS: We designed an ontology to integrate data from heterogeneous sources, including IoT-driven patient monitoring, clinical management of individual patients, and infectious disease surveillance systems. This integration aims to facilitate the collaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, and risk management; (2) clinical management of patients with infectious diseases; (3) epidemic risk analysis for timely response at the public health level; (4) infectious disease surveillance; and (5) transforming patient information into surveillance information. CONCLUSIONS: The development of the IoT-MIDO was driven by competency questions. Being able to answer all the formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management, infectious disease surveillance, and epidemic risk analysis. The novelty and uniqueness of the ontology lie in building a bridge to link IoT-based individual patient monitoring and early warning based on patient risk assessment to infectious disease epidemic surveillance at the public health level. The ontology can also serve as a starting point to enable potential decision support systems, providing actionable insights to support public health organizations and practitioners in making informed decisions in a timely manner.

The quantitative paradigm and the nature of the human mind. The replication crisis as an epistemological crisis of quantitative psychology in view of the ontic nature of the psyche.

Many suggestions for dealing with the so-called replication crisis in psychology revolve around the idea that better and more complex statistical-mathematical tools or stricter procedures are required in order to obtain reliable findings and prevent cheating or publication biases. While these aspects may play an exacerbating role, we interpret the replication crisis primarily as an epistemological crisis in psychology caused by an inadequate fit between the ontic nature of the psyche and the quantitative approach. On the basis of the philosophers of science Karl Popper, Thomas Kuhn, and Imre Lakatos we suggest that the replication crisis is therefore a symptom of a fundamental problem in psychology, but at the same time it is also an opportunity to advance psychology as a science. In a first step, against the background of Popper's Critical Rationalism, the replication crisis is interpreted as an opportunity to eliminate inaccurate theories from the pool of theories and to correct problematic developments. Continuing this line of thought, in an interpretation along the lines of Thomas Kuhn, the replication crisis might signify a model drift or even model crisis, thus possibly heralding a new paradigm in psychology. The reasons for this are located in the structure of academic psychology on the basis of Lakatos's assumption about how sciences operate. Accordingly, one hard core that lies at the very basis of psychology may be found in the assumption that the human psyche can and is to be understood in quantitative terms. For this to be possible, the ontic structure of the psyche, i.e., its very nature, must also in some way be quantitatively constituted. Hence, the replication crisis suggests that the ontic structure of the psyche in some way (also) contains a non-quantitative dimension that can only be grasped incompletely or fragmentarily using quantitative research methods. Fluctuating and inconsistent results in psychology could therefore also be the expression of a mismatch between the ontic level of the object of investigation and the epistemic level of the investigation.

Artificial Intelligence-Augmented Clinical Decision Support Systems for Pregnancy Care: Systematic Review.

BACKGROUND: Despite the emerging application of clinical decision support systems (CDSS) in pregnancy care and the proliferation of artificial intelligence (AI) over the last decade, it remains understudied regarding the role of AI in CDSS specialized for pregnancy care. OBJECTIVE: To identify and synthesize AI-augmented CDSS in pregnancy care, CDSS functionality, AI methodologies, and clinical implementation, we reported a systematic review based on empirical studies that examined AI-augmented CDSS in pregnancy care. METHODS: We retrieved studies that examined AI-augmented CDSS in pregnancy care using database queries involved with titles, abstracts, keywords, and MeSH (Medical Subject Headings) terms. Bibliographic records from their inception to 2022 were retrieved from PubMed/MEDLINE (n=206), Embase (n=101), and ACM Digital Library (n=377), followed by eligibility screening and literature review. The eligibility criteria include empirical studies that (1) developed or tested AI methods, (2) developed or tested CDSS or CDSS components, and (3) focused on pregnancy care. Data of studies used for review and appraisal include title, abstract, keywords, MeSH terms, full text, and supplements. Publications with ancillary information or overlapping outcomes were synthesized as one single study. Reviewers independently reviewed and assessed the quality of selected studies. RESULTS: We identified 30 distinct studies of 684 studies from their inception to 2022. Topics of clinical applications covered AI-augmented CDSS from prenatal, early pregnancy, obstetric care, and postpartum care. Topics of CDSS functions include diagnostic support, clinical prediction, therapeutics recommendation, and knowledge base. CONCLUSIONS: Our review acknowledged recent advances in CDSS studies including early diagnosis of prenatal abnormalities, cost-effective surveillance, prenatal ultrasound support, and ontology development. To recommend future directions, we also noted key gaps from existing studies, including (1) decision support in current childbirth deliveries without using observational data from consequential fetal or maternal outcomes in future pregnancies; (2) scarcity of studies in identifying several high-profile biases from CDSS, including social determinants of health highlighted by the American College of Obstetricians and Gynecologists; and (3) chasm between internally validated CDSS models, external validity, and clinical implementation.

Circulating Exosomal miRNA Profiles in Non-Small Cell Lung Cancers.

A growing number of studies have shown that microRNAs (miRNAs) can exert oncogenic or tumor suppressor activities in a variety of cancers, including lung cancer. Given their presence in exosome preparations, microRNA molecules may in fact participate in exosomal intercellular transfers and signaling. In the present study, we examined the profile of 25 circulating exosomal microRNAs in ostensibly healthy controls compared to patients with squamous cell lung cancers (SQCLC) or lung adenocarcinomas (LUAD). Eight miRNAs, namely, miR-21-5p, miR-126-3p, miR-210-3p, miR-221-3p, Let-7b-5p, miR-146a-5p, miR-222-3p, and miR-9-5p, were highly enriched in the cohort and selected for further analyses. All miRNAs were readily detected in non-small cell lung cancer (NSCLC) patients of both sexes at all cancer stages, and their levels in exosomes correlated with the clinicopathological characteristics of tumors. Thus, the presence of these miRNAs in circulating exosomes may contribute to the regulation of oncogenic activity in patients with NSCLC.

The Particle of Haag's Local Quantum Physics: A Critical Assessment.

Rudolf Haag's Local Quantum Physics (LQP) is an alternative framework to conventional relativistic quantum field theory for combining special relativity and quantum theory based on first principles, making it of great interest for the purposes of conceptual analysis despite currently being relatively limited as a tool for making experimental predictions. In LQP, the elementary particles are defined as species of causal link between interaction events, together with which they comprise its most fundamental entities. This notion of particle has yet to be independently assessed as such. Here, it is captured via a set of propositions specifying particle characteristics and then compared to previous particle notions. Haag's particle differs decisively with respect to mechanical intuitions about particles by lacking, among other things, even an approximate independent space-time location. This notion is thus found to differ greatly even from those of relativistic quantum mechanics and quantum field theory, which have been applied to the known elementary particles.

MicroRNA Signature in an In Vitro Keratinocyte Model of Diabetic Wound Healing.

Treating diabetic wounds effectively remains a significant clinical challenge. Emerging studies suggest that microRNAs (miRNAs) play crucial roles in various physiological and pathological processes and hold promise as therapeutic tools. This study investigates the miRNA expression profile in keratinocytes using a cell model of diabetic wounds. Microarray analysis revealed that 43 miRNAs from wounded keratinocytes incubated under diabetic conditions (high glucose/hypoxia) exhibited a two-fold change in expression compared to those incubated under normal conditions (low glucose/normoxia). Quantitative RT-PCR confirmed significant differences in the expression of eight miRNAs, with miR-3138 and miR-3679-5p being further analyzed for their roles in keratinocyte migration. Transfection with a miR-3138 mimic and a miR-3679-5p inhibitor indicated that upregulation of miR-3138 and downregulation of miR-3679-5p enhance keratinocyte migration in both normal and diabetic wounds. Pathway and gene ontology (GO) analyses identified potential pathways and functional annotations associated with miR-3138 and miR-3679-5p in diabetic wound healing. Potential human gene targets of miR-3138 and miR-3679-5p were predicted using a three-way comparison of the TargetScan, miRDB, and DIANA databases. This study elucidates the miRNA expression signature of human keratinocytes in a diabetes-like environment, providing deeper insights into the pathogenesis of diabetic wounds.

Discovery of Novel Biomarkers with Extended Non-Coding RNA Interactor Networks from Genetic and Protein Biomarkers.

Curated online interaction databases and gene ontology tools have streamlined the analysis of highly complex gene/protein networks. However, understanding of disease pathogenesis has gradually shifted from a protein-based core to complex interactive networks where non-coding RNA (ncRNA) is thought to play an essential role. As current gene ontology is based predominantly on protein-level information, there is a growing need to analyze networks with ncRNA. In this study, we propose a gene ontology workflow integrating ncRNA using the NPInter V5.0 database. To validate the proposed workflow, we analyzed our previously published curated biomarker datasets for hidden disease susceptibility processes and pharmacogenomics. Our results show a novel involvement of melanogenesis in psoriasis response to biological drugs in general. Hyperpigmentation has been previously observed in psoriasis following treatment with currently indicated biological drugs, thus calling attention to melanogenesis research as a response biomarker in psoriasis. Moreover, our proposed workflow highlights the need to critically evaluate computed ncRNA interactions within databases and a demand for gene ontology analysis of large miRNA blocks.

Ontology of clinical practice guidelines for Integrated Traditional Chinese and Western Medicine.

OBJECTIVE: Clinical practice guidelines (CPGs) for Integrated Traditional Chinese and Western Medicine (TCM and WM) are important medical documents used to assist medical decision-making and are of great significance for standardizing clinical pathways. However, due to the constraints of text format, it is difficult for Integrated TCM and WM CPGs to play a real role in medical practice. In addition, how to standardize the structure and semantic relationships between Integrated TCM and WM CPG knowledge, and realize the construction of computable, sharable and reliable CPGs, remains an urgent issue to be addressed. Therefore, we are proposing an ontology of CPGs for Integrated TCM and WM. METHODS: We first initialized domain concepts and relationships to ensure the accuracy of the ontology knowledge structure. We then screened CPGs that meet the standards for Integrated TCM and WM, analyzed and classified the contents, and extracted the common structures. Based on the seven-step ontology construction method combined with inference-complement, referring to the representation methods and hierarchical relationships of terms and concepts in MeSH, ICD-10, SNOMED-CT, and other ontologies and terminology sets, we formed the concept structure and semantic relationship tables for the ontology. We also achieved the matching and mapping between the ontology and reference ontologies and term sets. Next, we defined the aspects and constraints of properties, selected multiple Integrated TCM and WM CPGs as instances to populate, and used ontology reasoning tools and formulated defined inference rules to reason and extend the ontology. Finally, we evaluated the performance of the ontology. RESULTS: The content of the Integrated TCM and WM CPGs is divided into nine parts: basic information, background, development method, clinical question, recommendation, evidence, conclusion, result, and reason for recommendations. The Integrated TCM and WM CPG ontology has 152 classes and defines 90 object properties and 114 data properties, with a maximum classification depth of 4 layers. The terms of disease, drug and examination item names in the ontology have been standardized. CONCLUSIONS: This study proposes an Integrated TCM and WM CPG ontology. The ontology adopts a modular design, which has both sharing and scaling ability, and can express rich guideline knowledge. It provides important support for the semantic processing and computational application of guideline documents.

Formononetin inhibits IgE by huPlasma/PBMCs and mast cells/basophil activation via JAK/STAT/PI3-Akt pathways.

Rationale: Food allergy is a prevalent disease in the U.S., affecting nearly 30 million people. The primary management strategy for this condition is food avoidance, as limited treatment options are available. The elevation of pathologic IgE and over-reactive mast cells/basophils is a central factor in food allergy anaphylaxis. This study aims to comprehensively evaluate the potential therapeutic mechanisms of a small molecule compound called formononetin in regulating IgE and mast cell activation. Methods: In this study, we determined the inhibitory effect of formononetin on the production of human IgE from peripheral blood mononuclear cells of food-allergic patients using ELISA. We also measured formononetin's effect on preventing mast cell degranulation in RBL-2H3 and KU812 cells using beta-hexosaminidase assay. To identify potential targets of formononetin in IgE-mediated diseases, mast cell disorders, and food allergies, we utilized computational modeling to analyze mechanistic targets of formononetin from various databases, including SEA, Swiss Target Prediction, PubChem, Gene Cards, and Mala Cards. We generated a KEGG pathway, Gene Ontology, and Compound Target Pathway Disease Network using these targets. Finally, we used qRT-PCR to measure the gene expression of selected targets in KU812 and U266 cell lines. Results: Formononetin significantly decreased IgE production in IgE-producing human myeloma cells and PBMCs from food-allergic patients in a dose-dependent manner without cytotoxicity. Formononetin decreased beta-hexosaminidase release in RBL-2H3 cells and KU812 cells. Formononetin regulates 25 targets in food allergy, 51 in IgE diseases, and 19 in mast cell diseases. KEGG pathway and gene ontology analysis of targets showed that formononetin regulated disease pathways, primary immunodeficiency, Epstein-Barr Virus, and pathways in cancer. The biological processes regulated by formononetin include B cell proliferation, differentiation, immune response, and activation processes. Compound target pathway disease network identified NFKB1, NFKBIA, STAT1, STAT3, CCND1, TP53, TYK2, and CASP8 as the top targets regulated at a high degree by formononetin. TP53, STAT3, PTPRC, IL2, and CD19 were identified as the proteins mostly targeted by formononetin. qPCR validated genes of Formononetin molecular targets of IgE regulation in U266 cells and KU812 cells. In U266 cells, formononetin was found to significantly increase the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R. In basophils KU812 cells, formononetin significantly increased the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK, TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R. Conclusion: These findings comprehensively present formononetin's mechanisms in regulating IgE production in plasma cells and degranulation in mast cells.

Comparative analysis of the transcriptomes from regenerated plants and root explants of endangered Oplopanax elatus.

BACKGROUND: Oplopanax elatus is a plant of therapeutic significance in oriental medicine; however, its mass cultivation is limited owing to the difficulties in propagating it from seeds. METHODS: In this study, we investigated the transcriptome profiles and transcriptional regulatory factors expressed during plantlet regeneration from root tissues of the endangered O. elatus. RESULTS: The RNA-seq results for the control and regenerated plants cultured in liquid medium for 8 weeks showed that the clean length of the control group was 11,901,667,912 and that of the 8-week sample was 10,115,155,171, indicating a clean value of 97% for both samples. The number of mapped paired-end reads was 63,922,480 for the control group and 54,146,902 for the 8-week sample. The number of genes for which at least one clean data point was mapped was 43,177 in the control group and 42,970 in the 8-week sample. The results of the differentially expressed gene analysis indicate that the number of upregulated genes in the 8-week sample was 158, and the number of downregulated genes was 424. Gene Ontology (GO) analysis of the upregulated genes revealed that GO terms were classified into 14 categories, and genes expressed in the biological process category occurred most frequently. GO terms of the downregulated genes were evenly distributed into two categories: biological process and molecular function. From the upregulated genes, eight reference genes with significant differences in expression were selected and analyzed using real-time PCR. The Oe38836 gene (late embryogenesis abundant protein M17-like isoform X1) showed the highest expression rate that was more than tenfold that of the control. Oe40610 (auxin-responsive protein SAUR21-like) and Oe07114 (glucose-1-phosphate adenyl transferase-like protein) genes showed expression levels that were increased eightfold relative to the control. CONCLUSIONS: The RNA sequencing (RNA-seq) results from the plants regenerated through liquid culture of O. elatus root tissue were confirmed using real-time PCR, indicating their reliability.

Ontology-based inference decision support system for emergency response in tunnel vehicle accidents.

Emergency response plans for tunnel vehicle accidents are crucial to ensure human safety, protect critical infrastructure, and maintain the smooth operation of transportation networks. However, many decision-support systems for emergency responses still rely significantly on predefined response strategies, which may not be sufficiently flexible to manage unexpected or complex incidents. Moreover, existing systems may lack the ability to effectively respond effectively to the impact different emergency scenarios and responses. In this study, semantic web technologies were used to construct a digital decision-support system for emergency responses to tunnel vehicle accidents. A basic digital framework was developed by analysing the knowledge system of the tunnel emergency response, examining its critical elements and intrinsic relationships, and mapping it to the ontology. In addition, the strategies of previous pre-plans are summarised and transformed into semantic rules. Finally, different accident scenarios were modelled to validate the effectiveness of the developed emergency response system.

Heterogeneous data integration: Challenges and opportunities.

Integrating multiple data source technologies is essential for organizations to respond to highly dynamic market needs. Although physical data integration systems have been considered to have better query processing systems, they pose higher implementation and maintenance costs. Meanwhile, virtual data integration has become an alternative topic that is increasingly attracting the attention of researchers in the current era of big data. Various data integration methodologies have been developed and used in various domains, processing heterogeneous data using various approaches. This review article aims to provide an overview of heterogeneous data integration research focusing on methodology and approaches. It surveys existing publications, highlighting key trends, challenges, and open research topics. The main findings are: (i) Research has been conducted in various domains. However, most focus on big data rather than specific study domains; (ii) researchers primarily focus on semantics challenges, and (iii) gaps still need to be addressed and related to integration issues involving semantics and unstructured data formats that must be thoroughly addressed. Furthermore, considering elements of cutting-edge technology, such as machine learning and data integration, about privacy concerns provides a chance for additional investigation. Finally, we provide insight into the potential for a broader review of integration challenges based on case studies.

Enrichment analysis of systemic lupus erythematosus susceptible loci identified by genome-wide association studies.

Systemic lupus erythematosus (SLE) is a systemic autoimmune disease. Genome-wide association studies (GWAS) have identified several loci associated with susceptibility to the disease. For the current analysis, enrichment analysis was performed using predicted SLE loci. SLE-associated loci were retrieved from the GWAS database. Gene ontology functional annotation and Reactome pathway analysis were performed using the Enrichr online server. A total of 293 loci encoding susceptible SLE proteins were identified. The analysis revealed that genes associated with SLE were primarily involved in cytokine signaling in the immune system, as demonstrated by the Reactome pathway analysis. In addition, gene ontology analysis suggested that SLE-associated genes were primarily involved in the immune system and its functions. Statistical analysis revealed that 9 segments of the human chromosome had a significantly higher number of SLE-associated genes. Specifically, the 6p21.31-p22.2 and 19p13.2-p13.3 contained 34 and 12 SLE-associated genes, respectively. This study showed that rheumatoid arthritis and multiple sclerosis share 33 and 42 genes, respectively, with SLE, and among the shared genes, four genes CD40, STAT4, TYK2, and IL2RA are involved in the cytokine signaling pathway in the immune system. It might suggest that positive regulation of cytokine production pathway is a common pathway among autoimmune diseases.