Search Results (1,156)

Drawing from the philosophies of pragmatism and somaesthetics, as developed by Richard Shusterman, this inquiry argues that performance holds a unique ontological status, one that emphasizes participation, shared meaning making, and the aesthetic qualities of ordinary, lived experience. As a philosopher trained as a dancer, I share some insights from my own experience as a performer offering a first-person aesthetic experience as a tool for conceptual inquiry. This experience allows the inquiry to explore the distinction between “scene” and “spectacle”, positioning the scene as a space of co-creation, in contrast to the distant, objectifying gaze encouraged by spectacle. By examining participatory projects and firsthand artistic experiences, I try to illustrate how performance can dissolve the boundaries between art and life, proposing a model of art that is shared, embodied, deeply connected to the rhythms of the everyday and gives foundation to a political transformation. Full article

Construction safety requirements (SRs), which serve as critical information encapsulating a wide range of safety-related issues, constitute a fundamental basis for effective construction safety management. The constraints of the complex information characteristics and uncertainty of knowledge migration, however, lead to the failure to transform most of the requirement information into effective knowledge. This study proposes a multi-stage knowledge transformation framework for realizing the transformation of SRs from abstract information to canonical knowledge, and it accurately completes the knowledge transformation through document matching, knowledge extraction, and knowledge representation. Meanwhile, a semi-automated model was introduced into this study to develop a domain ontology knowledge base for SRs and to represent each type of knowledge through class definitions. The proposed framework was validated by testing project documents collected from two types of building projects, and the results show that the RD-based association rules can accurately match documents associated with SRs and adapt to match different types of sentiment attribute documents. Moreover, the improved TF-IDF algorithm improved by 20% in precision and recall, showing that the algorithm can extract tacit knowledge by combining knowledge points. Further, the domain ontology knowledge base facilitates normative documentation and representation for each type of knowledge in SRs. Full article

The extraction of disaster geospatial intelligence (DGI) from social media data with spatiotemporal attributes plays a crucial role in real-time disaster monitoring and emergency decision-making. However, conventional machine learning approaches struggle with semantic complexity and limited Chinese disaster corpus. Recent advancements in large language models (LLMs) offer new opportunities to overcome these challenges due to their enhanced semantic comprehension and multi-task learning capabilities. This study investigates the potential application of LLMs in disaster intelligence extraction and proposes an efficient, scalable method for multi-hazard DGI extraction. Building upon a unified ontological framework encompassing core natural disaster elements, this method employs parameter-efficient low-rank adaptation (LoRA) fine-tuning to optimize open-source Chinese LLMs using a meticulously curated instruction-tuning dataset. It achieves simultaneous identification of multi-hazard intelligence cues and extraction of disaster spatial entity attributes from unstructured Chinese social media texts through unified semantic parsing and structured knowledge mapping. Compared to pre-trained models such as BERT and ERNIE, the proposed method was shown to achieve state-of-the-art evaluation results, with the highest recognition accuracy (F1-score: 0.9714) and the best performance in structured information generation (BLEU-4 score: 92.9649). Furthermore, we developed and released DGI-Corpus, a Chinese instruction-tuning dataset covering various disaster types, to support the research and application of LLMs in this field. Lastly, the proposed method was applied to analyze the spatiotemporal evolution patterns of the Zhengzhou “7.20” flood disaster. This study enhances the efficiency of natural disaster monitoring and emergency management, offering technical support for disaster response and mitigation decision-making. Full article

Background/Objectives: Incomptine A (IA) has cytotoxic activity in non-Hodgkin lymphoma (NHL) cancer cell lines. Its effects on U-937 cells include induction of apoptosis, production of reactive oxygen species, and inhibition of glycolytic enzymes. We examined the altered protein levels present in the lymph nodes of an in vivo mouse model. Methods: We induced an in vivo model with Balb/c mice with U-937 cells and treated it with IA or methotrexate, as well as healthy mice. We determined expressed proteins by TMT based on the LC-MS/MS method (Data are available via ProteomeXchange with identifier PXD060392) and a molecular docking study targeting 15 deregulated proteins. We developed analyses through the KEGG, Reactome, and Gene Ontology databases. Results: A total of 2717 proteins from the axillary and inguinal lymph nodes were analyzed and compared with healthy mice. Of 412 differentially expressed proteins, 132 were overexpressed (FC ≥ 1.5) and 117 were underexpressed (FC ≤ 0.67). This altered expression was associated with 20 significantly enriched processes, including chromatin remodeling, transcription, translation, metabolic and energetic processes, oxidative phosphorylation, glycolysis/gluconeogenesis, cell proliferation, cytoskeletal organization, and with cell death with necroptosis. Conclusions: We confirmed the previously observed dose-dependent effect of IA as a secondary metabolite with important potential as an anticancer agent for the treatment of NHL, showing that the type of drug or the anatomical location influences the response to treatment. The IA promises to be a likely safer and more effective treatment to improve outcomes, reduce toxicities, and improve survival in patients with NHL, initially targeting histones and transcription factors that will affect cell death proteins. Full article

Microglia are key immune cells in the central nervous system (CNS) and maintain hemostasis in physiological conditions. Microglial depletion leads to rapid repopulation, but the gene expression and signaling pathways related to repopulation remain unclear. Here, we used RNA sequencing (RNA-Seq) analysis to profile the transcriptome of microglia-depleted tissue by taking advantage of a conditional genetic microglial depletion model (CX3CR1CreER/+ system). Differential gene expression (DGE) sequencing analysis showed that 1226 genes were differentially up- and downregulated in both groups compared to control. Our data demonstrated that many microglial genes were highly regulated on day 3 after depletion but the numbers of differentially expressed genes were reduced by day 7. Gene ontology (GO) analysis categorized these differentially expressed genes on day 3 and day 7 to the specific biological processes, such as cell proliferation, cell activation, and cytokine and chemokine production. DGE analysis indicated that specific genes related to proliferation were regulated after depletion. Consistent with the changes in transcriptome, the histological analysis of transgenic mice revealed that the microglia after depletion undergo proliferation and activation from day 3 to day 7. Collectively, these results suggest that transcriptomic changes in microglial genes during depletion have a profound implication for the renewal and activation of microglia and may help to understand the regulatory mechanism of microglial activation in disease conditions. Full article

Background and Objectives: In previous studies, we reported that the assessment of the cumulative thermal dose in the crystalline lens, conducted through computational modeling utilizing a supercomputer and the biothermal transport equation, exhibited a significant association with the incidence of nuclear cataracts. In this study, we have investigated the types of proteins that expressed underlying 35.0 °C (normal-temp) and 37.5 °C (warming-temp) by using the shotgun liquid chromatography (LC) with tandem mass spectrometry (MS/MS)-based global proteomic approach. Materials and Methods: We have discussed the changes in protein expression in warmed iHLEC-NY2 cells using Gene Ontology analysis and a label-free semiquantitative method based on spectral counting. Results: In iHLEC-NY2, 615 proteins were detected, including 307 (49.9%) present in both lenses cultured at normal-temp and warming-temp, 130 (21.1%) unique to the lens cultured at normal-temp, and 178 (29.0%) unique to the lens cultured at warming-temp. Furthermore, LC–MS/MS analysis showed that warming decreased the expression of actin, alpha cardiac muscle 1, actin-related protein 2, putative tubulin-like protein alpha-4B, ubiquitin carboxyl-terminal hydrolase 17-like protein 1, ubiquitin-ribosomal protein eL40 fusion protein, ribosome biogenesis protein BMS1 homolog, histone H2B type 1-M, and histone H2A.J. in iHLEC-NY2. Conclusions: The decreases in the specific protein levels of actin, tubulin, ubiquitin, ribosomes, and histones may be related to cataract development under warming conditions. This investigation could provide a critical framework for understanding the correlation between temperature dynamics and the development of nuclear cataracts. Full article

The interpretability requirement is one of the largest obstacles when deploying machine learning models in various practical fields. Methods of eXplainable Artificial Intelligence (XAI) address those issues. However, the growing number of different solutions in this field creates a demand to assess the quality of explanations and compare them. In recent years, several attempts have been made to consolidate scattered XAI quality assessment methods into a single benchmark. Those attempts usually suffered from a focus on feature importance only, a lack of customization, and the absence of an evaluation framework. In this work, the eXplainable Artificial Intelligence Benchmark (XAIB) is proposed. Compared to existing benchmarks, XAIB is more universal, extensible, and has a complete evaluation ontology in the form of the Co-12 Framework. Due to its special modular design, it is easy to add new datasets, models, explainers, and quality metrics. Furthermore, an additional abstraction layer built with an inversion of control principle makes them easier to use. The benchmark will contribute to artificial intelligence research by providing a platform for evaluation experiments and, at the same time, will contribute to engineering by providing a way to compare explainers using custom datasets and machine learning models, which brings evaluation closer to practice. Full article

Cefquinome sulfate has a strong killing effect against Staphylococcus aureus (S. aureus), but bacterial resistance has become increasingly widespread. Experiments were conducted to investigate the pattern of adaptive resistance of S. aureus to cefquinome sulfate under different dosage regimens by using pharmacokinetic-pharmacodynamics (PK-PD) modeling, and the adaptive-resistant bacteria in different states were screened and subjected to transcriptomic sequencing. The results showed that the minimum inhibitory concentration of Staphylococcus aureus under the action of cefquinome sulfate was 0.5 μg/mL, the anti-mutation concentration was 1.6 μg/mL, and the mutation selection window range was 0.5~1.6 μg/mL. In the in vitro pharmacokinetic model to simulate different dosing regimens in the animal body, there are certain rules for the emergence of adaptive drug-resistant bacteria: the intensity of bacterial resistance gradually increased with culture time, and the order of emergence was tolerant bacteria (TO) followed by persistent bacteria (PE) and finally resistant bacteria (RE). The sequence reflected the evolution of adaptive drug resistance. Transcriptome Gene Ontology (GO) analysis revealed that differentially expressed genes were involved in cellular respiration, energy derivation by oxidation of organic compounds, and oxidation–reduction processes. The differentially expressed genes identified functioned in the synthesis of cell membranes, cytoplasm, and intracellular parts. A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis found that 65 genes were differentially expressed after cefquinome sulfate treatment, of which 35 genes were significantly upregulated and 30 genes were significantly downregulated. Five genes, sdhB, sdhA, pdhA, lpdA, and sucC, may be involved in network regulation. This study revealed the cross-regulation of multiple metabolic pathway networks and the targets of network regulation of S. aureus to produce adaptive drug resistance. The results will provide guidance for clinical drug use in animals infected with S. aureus. Full article

Any human action can be repeated; none can be repeated exactly. In fact, most human actions will be repeated—in its most basic sense, culture is the establishment of forms and standards of repeatability. The performing arts are based on this fact, they make it explicit and explore it, albeit in very different ways. In light of these differences, it becomes obvious that ontological questions in music and the performing arts have a cultural index—rather than asking about the nature of identity and repetition, we should ask what counts as identical under what circumstances. These circumstances differ widely across cultures as well as across disciplines, and they are subject to change. Drawing on examples from performance art and music, this paper will explore the different constellations of repeatability we find in these fields. This comparative perspective promises to reshape some gridlocked debates in music as well as in the theory of performance. In particular, Christopher Bedford’s idea of a “viral ontology” of performance and the displacement of the concept of the work by Bruno Nettl’s notion of the model prove fruitful. Full article

Research in STEAM education continues to develop, and yet the literature in this area is often theoretically ‘light’. Given the importance of theory to provide explanatory power, the aim of this paper is to offer a theoretical classification system to be used as a tool for researchers and practitioners in this area. Derived from literature searches on STEAM education within open science/schooling, secondary/tertiary education intersection and global challenges, 26 frameworks were identified from 26 academic sources and refined based on their coherence and strong theoretical heritage. A relational ontology was proposed as a recurring theme and is a foundational aspect of the final classification system, signifying a shift from traditional models of STEAM that trivialise the ‘arts’ as utility disciplines. The 26 individual frameworks are organised into four approaches: ‘experiential real-world interactions’, ‘human psychological and cognitive’, ‘social, spatial, and material interconnectivity’, and ‘cultural and equity’. The overall classification system provides a starting point for researchers and educators navigating theoretically driven yet diverse STEAM implementation and understanding its impact depending on, and in response to, their preferred methodology. This system is novel in its treatment of theoretical perspectives, and its dynamic nature allows for the inclusion of further frameworks in the future. Full article

White lupin (Lupinus albus) has become a model plant for understanding plant adaptations to phosphorus (P) and iron (Fe) deficiency, two major limiting factors for plant productivity. In response to both nutrient deficiencies, white lupin forms cluster roots, bottle-brush-like root structures that aid in P and Fe acquisition from soil. While the cluster root function is well-studied, not much is known about the signaling pathways involved in sensing and responding to a P and Fe deficiency. Sucrose has been identified as a long-distance signal sent in increased concentrations from shoot to root in response to both a P and Fe deficiency. Thus, sucrose plays a dual role both as a signal and as a major source of energy for the root. To unravel the responses to sucrose as a signal, we performed an Illumina paired-end cDNA sequencing of white lupin roots treated with sucrose for 20, 40 or 80 min, compared to untreated controls (0 min). We identified 634 up-regulated and 956 down-regulated genes in response to sucrose. Twenty minutes of sucrose treatment showed the most responses, with the ethylene-activated signaling pathway as the most enriched Gene Ontology (GO) category. The number of up-regulated genes decreased at 40 min and 80 min, and protein dephosphorylation became the most enriched category. Taken together, our findings indicate active responses to sucrose as a signal at 20 min after a sucrose addition, but fewer responses and a potential resetting of signal transduction pathways by the dephosphorylation of proteins at 40 and 80 min. Full article

This paper explores the possibility of counteracting the crisis of culture and institutions by investing in the identity values of the user-actor within digital spaces built for the purpose. The strategy is applied to the analysis of three Catalan cloisters (Spain), with a focus on the representation of the cloister of Sant Cugat (Barcelona). Heuristic picklocks are found in the semantic richness proposed by Marius Schneider exclusively on the verbal level. The authors interpret the contents and transcribe them into graphic signs and digital denotations of sounds and colors. They organize proprietary ontologies, or syntagmatic lines, to be entrusted to the management of computer algorithms. The syncretic culture that characterized the medieval era allowed the ability to mediate science and faith to be entrusted to the mind of the praying monk alone in every canonical hour. The hypothesis that a careful direction has programmed the ways in which to orient souls to “navigate by sight” urges the authors to find the criteria that advanced statistics imitates to make automatic data processing “Intelligent”. In step with the times and in line with the most recent directions for the Safeguarding of Heritage, the musical, astral, and narrative rhythms feared by Schneider are used to inform representative models, to increase not only the visual perception of the user (XR Extended Reality) but also to solicit new analogies and illuminating associations. The results return a vision of the culture of the time suitable for shortening the distances between present and past, attracting the visitor and, with him, the resources necessary to protect and enhance the spaces of the Romanesque era. The methodology goes beyond the contingent aspect by encouraging the ‘remediation’ of contents with the help of machine learning. Full article

Software engineering occupies a prominent place in the theory and practice of simulation modeling, which necessitates scientific research in the field of methodological principles for forming software product quality. The problem of determining the optimal option for software development is one of the key ones in the field of information technology because it determines the quality of the final product and the efficiency of project management. The article considers the concept of developing an automated system, the basis of which is the software for assessing alternatives in the process of creating innovative IT projects. The main goal of the study is to model alternatives and select the optimal option for the process of creating an IT project using modern methodological approaches. For this purpose, the methods of ontological analysis, expert evaluation, multi-criteria optimization, pairwise comparisons and multi-factor selection of alternatives are applied. In the course of the research, a subset of Pareto factors is singled out and alternative development options are formed based on the method of linear convolution of criteria. The proposed methodology allows for assessing the importance of key factors and selecting the optimal option for the software development process. As a result, the developed approach contributes to strategic planning and increases the transparency of the decision-making process. The key result of the research is the created software product that allows one to automate the procedure for selecting the optimal solution for the IT project development process, providing reliable support for simulation modeling and increasing the efficiency of project management. The proposed methodology creates a new paradigm for making informed decisions regarding systems for creating complex software complexes. Full article

The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designed to harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KG augmentation empowers CAs with advanced reasoning, knowledge management, and context awareness abilities. We utilized a hybrid method integrating a participatory design collaborative methodology (CM) and Methontology to construct a domain-centric robust formal ontological model depicting and mapping information flow during peak hours in EDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamless interaction with end users and enhancing the quality of care within EDs. The authors leveraged semantic web rule language (SWRL) to enhance inferencing capabilities within the KG framework further, facilitating efficient information management for assisting healthcare practitioners and patients. This innovative assistive solution helps efficiently manage information flow and information provision during peak hours. It also leads to better care outcomes and streamlined workflows within EDs. Full article

Academic publications from the Web of Science Core Collection on “e-business” and “artificial intelligence” (AI) are investigated to reveal the role of AI, extract latent themes and identify potential research topics. The proposed methodology includes relevant graphical representations (trends, co-occurrence networks, Sankey diagrams), sentiment analyses and latent topics identification. A renewed interest in these publications is evident post-2018, with a sharp increase in publications around 2020 that can be attributed to the COVID-19 pandemic. Chinese institutions dominate the collaboration network in e-business and AI. Keywords such as “business transformation”, “business value” and “e-business strategy” are prominent, contributing significantly to areas like “Operations Research & Management Science”. Additionally, the keyword “e-agribusiness” recently appears connected to “Environmental Sciences & Ecology”, indicating the application of e-business principles in sustainable practices. Although three sentiment analysis methods broadly agree on key trends, such as the rise in positive sentiment over time and the dominance of neutral sentiment, they differ in detail and focus. Custom analysis reveals more pronounced fluctuations, whereas VADER and TextBlob present steadier and more subdued patterns. Four well-balanced topics are identified with a coherence score of 0.66 using Latent Dirichlet Allocation, which is a probabilistic generative model designed to uncover hidden topics in large text corpora: Topic 1 (29.8%) highlights data-driven decision-making in e-business, focusing on AI, information sharing and technology-enabled business processes. Topic 2 (28.1%) explores AI and Machine Learning (ML) in web-based business, emphasizing customer service, innovation and workflow optimization. Topic 3 (23.6%) focuses on analytical methods for decision-making, using data modeling to enhance strategies, processes and sustainability. Topic 4 (18.5%) examines the semantic web, leveraging ontologies and knowledge systems to improve intelligent systems and web platforms. New pathways such as voice assistance, augmented reality and dynamic marketplaces could further enhance e-business strategies. Full article