Western Illinois University

Dengue virus is one of the most significant emerging viruses that cause dengue fever, dengue hemorrhagic disease and dengue shock syndrome, threatening one-third of the world’s population. There are currently no vaccinations or antiviral therapies available for this disease. Dengue virus protease (NS2B-NS3pro) is a therapeutic target since it is essential for viral processing and replication. The study aimed to describe an in-silico analysis to uncover efficient Dengue virus inhibitors. In this work, we used computer-assisted virtual screening, ADMET and molecular dynamics-based analysis focus using the NS2B-NS3 protease to find effective Dengue virus inhibitors. Through literature mining, forty-seven phytochemicals from Artocarpus heterophyllus (Jackfruit) were retrieved and screened against the targeted protein. According to their binding free energy in MM-GBSA, Oxidihydroartocarpesin (-36.19 kcal/mole), Cyanomaclurin (-34.09 kcal/mole) and Dihydromorin (-32.44 kcal/mole) were expected to be potent inhibitors of the NS2B-NS3 protease. These ligands showed several noncovalent interactions with the catalytic triad (His51-Asp75-Ser135), required for the target protein inhibition. Notably, the ligand-bound complexes exhibited lower RMSD values (≈ 0.18–0.25 nm) compared to the apo protein (≈ 0.30 nm), indicating enhanced structural stability upon ligand binding. RMSF analysis further demonstrated reduced flexibility around the catalytic residues His51, Asp75, and Ser135 in the presence of the selected phytochemicals, while stable radius of gyration and solvent-accessible surface area profiles confirmed compact and well maintained protein-ligand conformations throughout the simulation period. Additionally, the ligand-bound systems maintained a consistent radius of gyration (~ 1.85–1.90 nm) and sustained an average of 3–6 intermolecular hydrogen bonds throughout the simulation, further supporting the structural integrity and dynamic stability of the complexes relative to the apo form. As a consequence, our computational analysis may be useful in the future development of Dengue Virus inhibitors.

The plant microbiome is pivotal to sustainable agriculture and global food security, yet some challenges hinder fully harnessing it for field-scale impact. These challenges span measurement and integration, ecological predictability and translation across environments and seasons. Key obstacles include technical challenges, notably overcoming the limits of current sequencing for low-abundance taxa and whole-community coverage, integrating multi-omics data to uncover functional traits, addressing spatiotemporal variability in microbial dynamics, deciphering the interplay between plant genotypes and microbial communities, and enforcing standardized controls, metadata, depth targets and reproducible workflows. The rise of synthetic biology, omics tools, and artificial intelligence offers promising avenues for engineering plant-microbe interactions, yet their adoption requires regulatory, ethical, and scalability issues alongside clear economic viability for end-users and explicit accounting for evolutionary dynamics, including microbial adaptation and horizontal gene transfer to ensure durability. Furthermore, there is a need to translate research findings into field-ready applications that are validated across various soils, genotypes, and climates, while ensuring that advances benefit diverse regions through global, interdisciplinary collaboration, fair access, and benefit-sharing. Therefore, this review synthesizes current barriers and promising experimental and computational strategies to advance plant microbiome research. Consequently, a roadmap for fostering resilient, climate-smart, and resource-efficient agricultural systems focused on benchmarked, field-validated workflows is proposed.

Cloud computing has become a cornerstone of modern IT infrastructure, offering scalability and efficiency but also exposing organizations to evolving cyber threats such as data breaches, insider threats, and advanced persistent threats (APTs). Traditional security mechanisms struggle to address these dynamic challenges, necessitating the integration of AI-driven threat detection and prevention strategies. This conceptual paper explores the comparative effectiveness of supervised learning, unsupervised learning, reinforcement learning, and hybrid AI models in cloud security. Supervised learning excels in identifying known attack patterns, while unsupervised learning is crucial for detecting zero-day threats and anomalies. Reinforcement learning enables self-adaptive security measures, and hybrid models offer a comprehensive, multi-layered approach to cloud security. However, AI-driven cybersecurity faces significant challenges, including data privacy risks, bias in threat detection, adversarial AI attacks, and lack of model interpretability. Emerging AI trends such as federated learning, quantum security, and explainable AI (XAI) are shaping the future of cloud security, while regulatory frameworks like GDPR, NIST AI Risk Management, and the EU AI Act play a crucial role in standardizing ethical AI use. This study provides insights into the strengths, weaknesses, and future directions of AI-driven cloud security, offering recommendations for researchers, policymakers, and cybersecurity practitioners to enhance AI resilience against emerging threats.

This paper presents a comprehensive conceptual framework for the integration of AI-enabled Decision Support Systems (DSS) into infrastructure project management, with a focus on enhancing cost-efficiency, resource optimization, and multi-stakeholder coordination in U.S. public works. As infrastructure projects become increasingly complex and data-intensive, the adoption of intelligent systems capable of processing real-time information and generating actionable insights is crucial for timely and effective decision-making. The study explores the role of artificial intelligence, including machine learning, predictive analytics, and natural language processing, in conjunction with enterprise platforms such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Geographic Information Systems (GIS). Through a meta-analysis of 178 empirical studies and case evidence from state and federal infrastructure programs, the paper identifies critical enablers for successful implementation, including data interoperability, explainable AI interfaces, and integration with existing digital workflows. The proposed framework emphasizes dynamic scheduling, risk forecasting, lifecycle asset management, and compliance monitoring as core functional pillars of AI-DSS in infrastructure contexts. Furthermore, the study highlights institutional and governance considerations, such as change management, algorithmic accountability, and user adoption challenges, which significantly influence system performance. This contribution aligns with broader national goals of digital transformation, transparency, and sustainability in public sector infrastructure development.

ABSTRACT A steep north–south elevational gradient in Nepal supports a diverse freshwater fish fauna ranging from coldwater to tropical species. About 23% of the land area of Nepal is protected, but the conservation of water resources and aquatic species, which provides critical ecosystem services, has yet to be a primary goal. Threats to native fishes include habitat alteration, nonnative species, dams, unregulated exploitation, and climate change. Additionally, the fishes of Nepal are undersampled and inadequately known, with high levels of taxonomic uncertainty, potential cryptic species, and species that are likely unknown to science. Opportunities for effective conservation of native fish biodiversity are emerging, however, and center on the co-production of knowledge and co-development of conservation strategies with local communities. A multifaceted approach that integrates conservation with sustainable development is needed to protect Nepal's unique ichthyofauna and to promote a sustainable future for aquatic resources that are crucial to Nepal's ecology, economy, and culture.

Abstract Background Perceived household support during childhood may have long-term effects on mental and physical health across the life course. However, the specific associations between early supportive environments and adult health outcomes remain underexplored. Methods We conducted a cross-sectional analysis using data from the Behavioral Risk Factor Surveillance System (BRFSS) collected between 2016 and 2023. The study included 31,233 U.S. adults aged 18 years and older who provided complete responses regarding perceived childhood household support, depression diagnosis, and the number of poor mental and physical health days. The primary exposure was self-reported childhood support, categorized as: “Never,” “A Little of the Time,” “Some of the Time,” “Most of the Time,” or “All of the Time.” Outcomes included lifetime diagnosis of depression, average monthly poor mental health days, and poor physical health days. Analyses were adjusted using inverse probability weighting and controlled for sociodemographic factors, survey weights, and state, year, and month fixed effects. Results Among respondents (mean age 52.2 years; 63.4% female; 76.0% White), individuals who reported “Never” being supported during childhood were 19.4 percentage points more likely to report a depression diagnosis (95% CI: 11.6–27.2), experienced 5.33 more poor mental health days (95% CI: 3.64–7.03), and 2.77 more poor physical health days per month (95% CI: 1.23– 4.32), compared to those who reported being “Always” supported. A clear dose-response relationship was observed across all categories of household support. Conclusions Lower levels of perceived childhood household support are significantly associated with increased risk of adult depression and greater burden of poor mental and physical health. Interventions targeting early supportive environments may improve population health outcomes across the life span. Key Points Question Is perceived childhood household support associated with depression and self-reported mental and physical health outcomes in adulthood? Findings In this cross-sectional study of 31,233 U.S. adults, individuals reporting they were never supported during childhood had significantly higher depression risk and reported poorer mental and physical health days compared to those always supported, with results showing a consistent gradient across varying support levels. Meaning These findings suggest policies promoting consistent childhood household support may enhance lifelong mental and physical health outcomes.

ABSTRACT Faculty in higher education have a variety of role expectations, including research, service and teaching. Often the training they receive during their education provides little towards helping them develop their pedagogical skills. Faculty peer coaching is a practice that holds promise for effectively providing professional development aimed at improving faculty teaching to better meet the needs of adult learners. This case study at a small regional university outlines a process for peer coaching and the resulting faculty perceptions of its effectiveness to enhance teaching and usefulness of the model as a professional development tool. We conclude faculty may perceive peer coaching as an effective and satisfying professional development opportunity in a higher education setting.

Oncology care and research demand robust patient consent management to balance data sharing with privacy. This article explores a theoretical framework for implementing blockchain-based patient consent management within FHIR-compliant oncology platfor