Search Results (1,829)

Knee osteoarthritis (KOA) is a leading cause of disability in older adults, characterized by persistent pain and reduced physical function. Beyond localized joint pathology, many individuals with knee osteoarthritis experience multisite pain and live with multiple comorbidities, reflecting a heterogeneous and multifactorial pain condition. Prognostic models based primarily on biomedical variables have shown limited ability to explain long-term outcomes, partly due to insufficient integration of pain chronicity, comorbidity count and psychosocial determinants such as treatment expectations and pain self-efficacy. While exercise and education are commonly recommended as primary non-surgical treatments, people often respond to them very differently. This study protocol describes a secondary longitudinal observational analysis of data from the EPIPHA-KNEE two-arm, multicentre randomized controlled trial. The primary outcomes will be knee OA pain intensity and physical function, assessed using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) questionnaire at baseline, 3, 6 and 12 months. Baseline prognostic factors will include pain duration, pain distribution, comorbidity count and patient expectations, including treatment expectations and pain self-efficacy. Linear mixed-effects models will be used to examine longitudinal associations between these predictors and pain and function trajectories, with particular emphasis on predictor-by-time interactions to characterize differential patterns of change over time. The planned analyses aim to improve understanding of how clinical characteristics and expectancy-related factors jointly shape 12-month pain and physical function trajectories in older adults with knee osteoarthritis receiving education and exercise-based care, thereby informing prognostic stratification within non-surgical management. Full article

Background and Objective: Colorectal cancer (CRC) liver metastasis (CRLM) represents a major clinical challenge, and acquired resistance to radiotherapy (RT) significantly limits therapeutic efficacy. A deep and comprehensive understanding of the cellular and molecular mechanisms driving RT resistance is urgently required to develop effective combination strategies. Here, we aimed to dissect the dynamic cellular landscape of the tumor microenvironment (TME) and identify key epigenetic regulators mediating radioresistance in CRLM by integrating cutting-edge single-cell and spatial omics technologies. Methods and Results: We performed integrated single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) on matched pre- and post-radiotherapy tumor tissues collected from three distinct CRLM patients. Employing a robust machine-learning framework on the multi-omics data, we successfully identified MORF4L1 (Mortality Factor 4 Like 1), an epigenetic reader, as a critical epigenetic mediator of acquired radioresistance. High-resolution scRNA-seq analysis of the tumor cell compartment revealed that the MORF4L1-high subpopulation exhibited significant enrichment in DNA damage repair (DDR) pathways, heightened activity of multiple pro-survival metabolic pathways, and robust signatures of immune evasion. Pseudotime trajectory analysis further confirmed that RT exposure drives tumor cells toward a highly resistant state, marked by a distinct increase in MORF4L1 expression. Furthermore, cell–cell communication inference demonstrated a pronounced, systemic upregulation of various immunosuppressive signaling axes within the TME following RT. Crucially, high-resolution ST confirmed these molecular and cellular interactions in their native context, revealing a significant spatial co-localization of MORF4L1-expressing tumor foci with multiple immunosuppressive immune cell types, including regulatory T cells (Tregs) and tumor-associated macrophages (TAMs), thereby underscoring its role in TME-mediated resistance. Conclusions: Our comprehensive spatial and single-cell profiling establishes MORF4L1 as a pivotal epigenetic regulator underlying acquired radioresistance in CRLM. These findings provide a compelling mechanistic rationale for combining radiotherapy with the targeted inhibition of MORF4L1, presenting a promising new therapeutic avenue to overcome treatment failure and improve patient outcomes in CRLM. Full article

Sleep plays a central role in shaping emotional development during childhood and adolescence, yet increasing evidence indicates that these processes unfold differently in boys and girls. This narrative review synthesizes current findings on sex-specific associations between sleep patterns, neurodevelopmental trajectories, and emotional regulation across pediatric populations. It examines how biological factors, including pubertal timing, sex hormones, circadian physiology, and maturation of fronto-limbic circuits, interact with environmental influences to generate distinct vulnerabilities to anxiety, depression, and behavioral dysregulation. Growing data suggest that girls exhibit greater sensitivity to sleep disturbances, particularly during the pubertal transition, with stronger links to internalizing symptoms such as anxiety and mood disorders. In contrast, boys appear more prone to externalizing behaviors and show differential responses to circadian misalignment and short sleep duration. Emerging evidence on sex-specific sleep architecture, REM-related emotional processing, and the bidirectional pathways through which sleep quality affects affective functioning are explored. Finally, clinical implications for early detection, personalized prevention, and targeted interventions tailored by sex and developmental stage are discussed. Understanding sex-based differences in sleep–emotion interactions offers a critical opportunity to refine pediatric mental health strategies and improve outcomes across developmental trajectories. Full article

Aerosol microphysical and optical properties play a crucial role in cloud microphysics, precipitation physics, and flood formation over areas characterized by complex monsoon regimes. This research presents a multi-source data integration approach to analyzing the spatio-temporal interaction between precipitation, aerosols, and flooding in the state of Kerala, incorporating an air mass trajectory analysis to examine its potential contribution to flooding. The results show that the Aerosol Optical Depth (AOD) values were high in the coastal districts (>0.8) in the La Niña year (2021) but low in the El Niño year (2015). On the precipitation side, 2018 and 2021 were both years with a high degree of anomalies, resulting in heavy rainfall that led to widespread flooding in the Thrissur district, among others. The trajectory analysis revealed that the Indian Ocean controls the precipitation during the southwest monsoon and the pre-monsoon. The post-monsoon precipitation is mainly sourced from the Arabian Peninsula and Arabian Sea, transferring marine aerosols along with desert aerosols. The overall study shows that the variability in aerosols and precipitation is more subject to change by the meteorological dynamics, as well as influenced by the regional changes in land use and land cover, causing fluxes in the land–atmosphere interactions. In conclusion, the present study highlights the possible interactive functions of atmospheric dynamics and anthropogenic land use modifications in generating a flood hazard. It provides essential information for land management policies and disaster risk reduction. Full article

FLASH radiotherapy requires real-time, non-invasive beam monitoring systems capable of operating under ultra-high dose rate (UHDR) conditions without perturbing the therapeutic beam. In this work, we characterized the extraction system of Supersonic Gas Curtain-based Ionization Profile Monitor (SGC-IPM) for its capabilities as a transverse beam profile and position monitor for FLASH protons. The monitor utilizes a tilted gas curtain intersected by the incident beam, leading to the generation of ions that are extracted through a tailored electrostatic field, and detected using a two stage microchannel plate (MCP) coupled to a phosphor screen and CMOS camera. CST Studio Suite was employed to conduct electrostatic and particle tracking simulations evaluating the ability of the extraction system to measure both beam profile and position. The ion interface, at the interaction region of proton beam and gas curtain, was modeled with realistic proton beam parameters and uniform gas curtain density distributions. The ion trajectory was tracked to evaluate the performance across multiple beam sizes. The simulations suggest that the extraction system can reconstruct transverse beam profiles for different proton beam sizes. Simulations also supported the system’s capability as a beam position monitor within the boundary defined by the beam size, the dimensions of the extraction system, and the height of the gas curtain. Some simulation results were benchmarked against experimental data of 28 MeV proton beam with 70 nA average beam current. This study will further help to optimize the design of the extraction system to facilitate the integration of SGC-IPM in medical accelerators. Full article

Bt Cry toxins remain the cornerstone of transgenic crop protection against Lepidopteran pests, yet field-evolved resistance, particularly in invasive species such as Spodoptera frugiperda and Helicoverpa armigera, can threaten their long-term efficacy. This review presents a comprehensive and unified mechanistic framework that synthesizes current understanding of Bt Cry toxin modes of action and the complex, multilayered regulatory mechanisms of field-evolved resistance. Beyond the classical pore-formation model, emerging evidence highlights signal transduction cascades, immune evasion via suppression of Toll/IMD pathways, and tripartite toxin–host–microbiota interactions that can dynamically modulate protoxin activation and receptor accessibility. Resistance arises from target-site alterations (e.g., ABCC2/ABCC3, Cadherin mutations), altered midgut protease profiles, enhanced immune regeneration, and microbiota-mediated detoxification, orchestrated by transcription factor networks (GATA, FoxA, FTZ-F1), constitutive MAPK hyperactivation (especially MAP4K4-driven cascades), along with preliminary emerging findings on non-coding RNA involvement. Countermeasures now integrate synergistic Cry/Vip pyramiding, CRISPR/Cas9-validated receptor knockouts revealing functional redundancy, Domain III chimerization (e.g., Cry1A.105), phage-assisted continuous evolution (PACE), and the emerging application of AlphaFold3 for structure-guided rational redesign of resistance-breaking variants. Future sustainability hinges on system-level integration of single-cell transcriptomics, midgut-specific CRISPR screens, microbiome engineering, and AI-accelerated protein design to preempt resistance trajectories and secure Bt biotechnology within integrated resistance and pest management frameworks. Full article

The rapid growth of Unmanned Aircraft Systems (UASs) and Advanced Air Mobility (AAM) presents significant integration and safety challenges for the National Airspace System (NAS), often relying on disconnected Air Traffic Management (ATM) and Unmanned Aircraft System Traffic Management (UTM) practices that contribute to airspace incidents. This study evaluates Geographic Information Systems (GISs) as a unified, data-driven framework to enhance shared airspace safety and efficiency. A comprehensive, multi-phase methodology was developed using GIS (specifically Esri ArcGIS Pro) to integrate heterogeneous aviation data, including FAA aeronautical data, Automatic Dependent Surveillance–Broadcast (ADS-B) for crewed aircraft, and UAS Flight Records, necessitating detailed spatial–temporal data preprocessing for harmonization. The effectiveness of this GIS-based approach was demonstrated through a case study analyzing a critical interaction between a University UAS (Da-Jiang Innovations (DJI) M300) and a crewed Piper PA-28-181 near Purdue University Airport (KLAF). The resulting two-dimensional (2D) and three-dimensional (3D) models successfully enabled the visualization, quantitative measurement, and analysis of aircraft trajectories, confirming a minimum separation of approximately 459 feet laterally and 339 feet vertically. The findings confirm that a GIS offers a centralized, scalable platform for collating, analyzing, modeling, and visualizing air traffic operations, directly addressing ATM/UTM integration deficiencies. This GIS framework, especially when combined with advancements in sensor technologies and Artificial Intelligence (AI) for anomaly detection, is critical for modernizing NAS oversight, improving situational awareness, and establishing a foundation for real-time risk prediction and dynamic airspace management. Full article

Traffic state estimation (TSE) is a core task in intelligent transportation systems (ITSs) that seeks to infer key operational parameters—such as speed, flow, and density—from limited observational data. Existing methods often face challenges in practical deployment, including limited estimation accuracy, insufficient physical consistency, and weak generalization capability. To address these issues, this paper proposes a hybrid estimation framework that integrates multi-agent reinforcement learning (MARL) with the Lighthill–Whitham–Richards (LWR) traffic flow model. In this framework, each roadside detector is modeled as an agent that adaptively learns fundamental diagram (FD) parameters—the free-flow speed and jam density—by fusing local detector measurements with global CAV trajectory sequences via an interactive attention mechanism. The learned parameters are then passed to an LWR solver to perform sequential (rolling) prediction of traffic states across the entire road segment. We design a reward function that jointly penalizes estimation error and violations of physical constraints, enabling the agents to learn accurate and physically consistent dynamic traffic state estimates through interaction with the physics-based LWR environment. Experiments on simulated and real-world datasets demonstrate that the proposed method outperforms existing models in estimation accuracy, real-time performance, and cross-scenario generalization. It faithfully reproduces dynamic traffic phenomena, such as shockwaves and queue waves, demonstrating robustness and practical potential for deployment in complex traffic environments. Full article

Background/Objective: Lifestyle behaviors evolve with age and are driven by biological requirements (e.g., growth and development) and environmental changes (e.g., living and working situations), and they interact bidirectionally with health. Few studies have tracked these behaviors from emerging adulthood into later adulthood. This study examines changes in lifestyle behavior patterns from precollege to adulthood and their association with weight trajectories. Methods: Between 1998 and 2007, 4783 incoming undergraduate students at a northeastern US university completed a health survey. In 2018, 970 completed a follow-up alumni survey. Latent class analysis (LCA) was used to categorize respondents into five lifestyle patterns: stable healthy, stable moderately healthy, stable minimally healthy, worsened, or improved. BMI trajectories were similarly classified into five weight status patterns. Associations between LCA lifestyle patterns and weight were examined using ANCOVA. Results: The most common lifestyle pattern was stable moderately healthy (36.7%). Over 11–20 years, 31.7% of respondents experienced a decline in lifestyle behaviors, and 18.6% improved. During this period, the prevalence of overweight more than doubled (12% to 26%), and obesity quadrupled (2% to 8%). Transitioning to a higher BMI category was noted in 34.9% of those with a stable minimally healthy lifestyle compared with 15.9% among those with a stable healthy lifestyle. Conclusions: Early lifestyle behaviors have long-term implications for weight status. Initiatives that promote the adoption and maintenance of healthy behaviors from precollege through adulthood might reduce obesity risk. Full article

A four-compartment tumor–immune interaction model is studied in a belief-based uncertain framework. The deterministic dynamics for tumor cells, natural killer cells, dendritic cells, and cytotoxic CD8${}^{+}$ T lymphocytes are extended to an uncertain differential system driven by a canonical Liu process, with therapeutic effects represented through treatment-related parameters acting on the respective populations. The analysis establishes well-posedness in the biologically relevant positive orthant under structural conditions compatible with the model nonlinearities, and it characterizes stability properties in the sense appropriate to uncertain dynamical systems. Sufficient conditions are derived for the existence of a global attracting set describing the long-time behavior of trajectories. The analytical results are complemented by numerical experiments based on $\alpha$-path dynamics to illustrate uncertainty-aware therapeutic scenarios and to connect the qualitative theory with observable system behavior. Full article

Geothermal energy is a crucial component of climate adaptation and sustainability transitions, as it provides a dependable, low-carbon source of baseload power that can accelerate sustainable energy transitions and enhance climate resilience. Yet, in East Africa—one of the world’s most promising geothermal regions, with the East African Rift—a unique climate-energy opportunity zone—the harnessing of geothermal power remains slow and uneven. This study examines the contextual conditions that facilitate the successful and sustainable development of geothermal power in the region. Drawing on semi-structured interviews with 17 experienced professionals who have worked extensively on geothermal projects across East Africa, the analysis identifies how technical, institutional, managerial, and relational circumstances interact to shape outcomes. The findings indicate an interdependent configuration of success conditions, with structural, institutional, managerial, and meta-conditions jointly influencing project trajectories rather than operating in isolation. The most frequently emphasised enablers were resource confirmation and technical design, leadership and team competence, long-term stakeholder commitment, professional project management and control, and collaboration across institutions and communities. A co-occurrence analysis reinforces these insights by showing strong patterns of overlap between core domains—particularly between structural and managerial factors and between managerial and meta-conditions, highlighting the mediating role of managerial capability in translating contextual conditions into operational performance. Together, these interrelated circumstances form a system in which structural and institutional foundations create the enabling context, managerial capabilities operationalise this context under uncertainty, and meta-conditions sustain cooperation, learning, and adaptation over time. The study contributes to sustainability research by providing a context-sensitive interpretation of how project success conditions manifest in geothermal development under climate transition pressures, and it offers practical guidance for policymakers and partners working to advance SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), and SDG 13 (Climate Action) in Africa. Full article

Carbon sequestration and oxygen release (CSOR) are core regulating functions of terrestrial ecosystems. However, regional assessments often fail to (i) separate scale-driven high supply from per-area efficiency, (ii) detect structural instability and degradation risk from long-term trajectories, and (iii) provide evidence that is comparable across units for management prioritization. Using Minnesota, USA, we integrated satellite-derived net primary productivity (NPP; 1998–2021) with a Quantity–Intensity–Structure (Q–I–S) framework to quantify CSOR, detect trends and change points (Mann–Kendall and Pettitt tests), map spatial clustering and degradation risk (Exploratory Spatial Data Analysis, ESDA), and attribute natural and human drivers (principal component regression and GeoDetector). CSOR increased overall from 1998 to 2021, with a marked shift around 2013 from a slight, variable decline to sustained recovery. Spatially, CSOR showed a persistent north–south gradient, with higher and improving services in northern Minnesota and lower, more degraded services in the south; persistent degradation was concentrated in a central high-risk belt. The Q–I–S framework also revealed inconsistencies between total supply and condition, identifying high-supply yet degrading areas and low-supply areas with recovery potential that are not evident from the totals alone. Climate variables primarily controlled CSOR quantity and structure, whereas human factors more strongly influenced intensity; the interactions of the two further shaped observed patterns. These results provide an interpretable and transferable basis for diagnosing degradation and prioritizing restoration under long-term environmental change. Full article

Non-AIDS-defining cancers (NADCs) have emerged as an increasingly prominent cause of non-AIDS-related morbidity and mortality among people living with HIV (PLWH). However, the scarcity of NADC clinical samples, compounded by privacy and security constraints, continues to present formidable obstacles to advancing pathological and clinical investigations. In this study, we adopted a joint analysis strategy and deeply integrated and analyzed transcriptomic data from 12,486 PLWH and cancer patients to systematically identify potential key regulators for 23 NADCs. This effort culminated in NADCdb—a database specifically engineered for NADC pathological exploration, structured around three mechanistic frameworks rooted in the interplay of immunosuppression, chronic inflammation, carcinogenic viral infections, and HIV-derived oncogenic pathways. The “rNADC” module performed risk assessment by prioritizing genes with aberrant expression trajectories, deploying bidirectional stepwise regression coupled with logistic modeling to stratify the risks for 21 NADCs. The “dNADC” module, synergized patients’ dysregulated genes with their regulatory networks, using Random Forest (RF) and Conditional Inference Trees (CITs) to identify pathogenic drivers of NADCs, with an accuracy exceeding 75% (in the external validation cohort, the prediction accuracy of the HIV-associated clear cell renal cell carcinoma model exceeded 90%). Meanwhile, “iPredict” identified 1905 key immune biomarkers for 16 NADCs based on the distinct immune statuses of patients. Importantly, we conducted multi-dimensional profiling of these key determinants, including in-depth functional annotations, phenotype correlations, protein–protein interaction (PPI) networks, TF-miRNA-target regulatory networks, and drug prediction, to deeply dissect their mechanistic roles in NADC pathogenesis. In summary, NADCdb serves as a novel, centralized resource that integrates data and provides analytical frameworks, offering fresh perspectives and a valuable platform for the scientific exploration of NADCs. Full article

NFT prices are shaped by heterogeneous signals including visual appearance, textual narratives, transaction trajectories, and on-chain interactions, yet existing studies often model these factors in isolation and rarely unify multimodal alignment, temporal non-stationarity, and heterogeneous relational dependencies in a leakage-safe forecasting setting. We propose MM-Temporal-Graph, a cross-modal temporal graph transformer framework for explainable NFT valuation and information-centric risk forecasting. The model encodes image, text, transaction time series, and blockchain behavioral features, constructs a heterogeneous NFT interaction graph (co-transaction, shared creator, wallet relation, and price co-movement), and jointly performs relation-aware graph attention and global temporal–structural transformer reasoning with an adaptive fusion gate. A contrastive multimodal alignment objective improves robustness under market drift, while a risk-aware regularizer and a multi-source risk index enable early warning and interpretable attribution across modalities, time segments, and relational neighborhoods. On MultiNFT-T, MM-Temporal-Graph improves MAE from 0.162 to 0.153 and R² from 0.823 to 0.841 over the strongest multimodal graph baseline, and achieves 87.4% early risk detection accuracy. These results support accurate, robust, and explainable NFT valuation and proactive risk monitoring in Web3 markets. Full article

The dynamics of two point masses interacting in a gravitational field has been the object of several scientific works. However, the complete explicit solution of the two-body problem is, to the best of our knowledge, not always available in the scientific literature. In this work, we describe the dynamics of a two-body system with that of an equivalent single-body with a reduced mass. Then, we solve the specific problems for elliptical, circular and parabolic trajectories, starting from different initial conditions. Through detailed analytical calculations, we write the Cartesian equations of the trajectories and the equations of motion both in the reference system of the centre of mass and in the original reference system. The proposed methodology is a simple but rigorous way to analyse the two-body dynamics under gravitational interactions, and can be applied also to more complex cases, such as the motion in a perturbed Newtonian potential and/or precession problems. The treatment presented in this work is particularly suitable to undergraduate students. Full article