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Identification of low-energy kaons in the ProtoDUNE-SP detector
No full textThe Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment with a rich physics program that includes searches for the hypothetical phenomenon of proton decay. Utilizing liquid-argon time-projection chamber technology, DUNE is expected to achieve world-leading sensitivity in the proton decay channels that involve charged kaons in their final states. The first DUNE demonstrator, ProtoDUNE Single-Phase, was a 0.77 kt detector that operated from 2018 to 2020 at the CERN Neutrino Platform, exposed to a mixed hadron and electron test-beam with momenta ranging from 0.3 to 7 GeV/c. We present a selection of low-energy kaons among the secondary particles produced in hadronic reactions, using data from the 6 and 7 GeV/c beam runs. The selection efficiency is 1\% and the sample purity 92\%. The initial energies of the selected kaon candidates encompass the expected energy range of kaons originating from proton decay events in DUNE (below 200 MeV). In addition, we demonstrate the capability of this detector technology to discriminate between kaons and other particles such as protons and muons, and provide a comprehensive description of their energy loss in liquid argon, which shows good agreement with the simulation. These results pave the way for future proton decay searches at DUNE
AI AND MYTHS : Principles for Business
No full textWhat do Frankenstein and Icarus have in common with Artificial Intelligence? What can the stories of Cassandra and Prometheus tell us about how businesses use the technology? Lee Francis looks at the links between myth, science fiction, and AI
Benzo[a]pyrene exacerbates atherosclerosis by upregulating SPP1 to promote macrophage inflammation and lipid dysregulation : An integrated network toxicology, RNA-seq, and experimental validation study
No full textBenzo[a]pyrene (BaP), a pervasive environmental pollutant, has been implicated in cardiovascular injury, yet its mechanistic contribution to atherosclerosis remains unclear. Here, we combined network toxicology, RNA-seq profiling, molecular simulations, and cellular validation to elucidate BaP-driven vascular effects. Integration of BaP-associated targets with atherosclerosis gene sets identified SPP1 as a key hub. Transcriptomic analysis of aortas from BaP-treated ApoE−/− mice revealed differential expression enriched in inflammatory responses, cytokine signaling, xenobiotic metabolism, and lipid-handling pathways. STRING-based protein interaction networks and Reactome analysis further supported coordinated activation of innate immunity and metabolic dysfunction. Molecular docking and 100-ns MD simulations demonstrated stable, energetically favorable binding between BaP and SPP1. In THP-1 macrophages, BaP enhanced oxLDL-induced SPP1 expression, reduced cell viability, and promoted a foam-cell-like phenotype characterized by suppressed ABCA1 and increased CD36 and PLIN2. Silencing SPP1 partially rescued BaP-induced cytotoxicity and lipid dysregulation, confirming SPP1's functional involvement. Collectively, these findings indicate that BaP aggravates atherosclerosis through SPP1-mediated macrophage inflammation and impaired lipid metabolism, highlighting SPP1 as a potential mechanistic link and therapeutic target for pollution-exacerbated vascular disease
Designing an mHealth App to Encourage Uptake of Muscle-Strengthening Exercise in Older Adults : Co-Design Focus Group Study
No full textBackground: Sarcopenia, the age-related decline in muscle mass and strength, poses a significant threat to functional independence in older adults. Despite strong evidence supporting resistance training as a preventive and therapeutic strategy, adherence to muscle-strengthening guidelines remains low. Mobile health (mHealth) technologies offer a promising avenue to bridge this gap; however, few apps are tailored to older adults or designed with their input. Objective: This study aimed to identify key features that a muscle-strengthening exercise app should include to enhance engagement and uptake among older adults. Secondary aims were to explore perceived barriers and facilitators to app use and to inform the development of an evidence-based, co-designed mHealth intervention. Methods: We used a qualitative co-design approach, involving 4 focus groups with 18 older adults (aged 60-83 years); each group comprised 3 to 6 older adults, stratified by experience with mHealth apps. Sessions were conducted online via Microsoft Teams and guided by a semistructured protocol informed by prior mHealth research and behavior change theory. Transcripts were analyzed using deductive thematic analysis, underpinned by the Technology Acceptance Model, focusing on perceived usefulness and perceived ease of use. Results: A total of 4 overarching themes and 10 subthemes were identified. Theme 1, mHealth as a tool for supporting health and well-being, highlighted participants’ recognition of digital tools in promoting activity and overcoming accessibility barriers. Theme 2, motivation and engagement through app features, revealed the importance of reminders, progress tracking, and feedback, although views on gamification were mixed. Theme 3, drawbacks of current mobile apps, captured concerns around complexity, poor usability, and lack of age-appropriate content, with skepticism regarding safety and evidence base. Theme 4, desired app elements and features, emphasized the need for customizable reminders, clear instructional videos, adaptable exercise options, and optional social features. Participants stressed the importance of simplicity, personalization, and relatable content to foster trust and sustained engagement. Conclusions: Older adults are receptive to mHealth interventions for muscle-strengthening when design is user centered and grounded in their lived experiences. This study provides a framework for future app development, highlighting the need for intuitive interfaces, personalized features, and credible educational content. By aligning design with Technology Acceptance Model constructs and co-design principles, mHealth apps can better support healthy aging and sarcopenia prevention. These findings offer actionable guidance for developers and researchers aiming to enhance digital health equity and effectiveness in older populations
Mediation roles of oxidative stress, inflammation, and insulin resistance biomarkers in the sitting time-depression association among U.S. adults
No full textOBJECTIVE: This study aimed to investigate the mediating roles of biomarkers of oxidative stress, inflammation, and insulin resistance in the association between sitting time and depression, and to determine the threshold value of sitting time linked to elevated depression rate. METHODS: Nationally representative data from the United States were analyzed, including 22,410 adults. Sitting time was self-reported using a Global Physical Activity Questionnaire (GPAQ) based interview item. Depression was assessed with the Patient Health Questionnaire-9 (PHQ-9), with a score of ≥10 indicating depression. Mediators included biomarkers of oxidative stress (GGT, UA, HDL, UHR), inflammation (NLR, MLR, NMLR, HRR, RAR, SIRI, SII), and insulin resistance (TYG, TYG_BMI, TYG_WHTR, HOMA_IR, METS_IR). Associations and mediation effects were examined using logistic regression, linear regression, restricted cubic spline (RCS) analyzes, and Bayesian mediation models, adjusted for demographic and comorbidity confounders. RESULTS: Sitting time ≥ 8 h per day was significantly associated with increased rate of depression (OR = 1.39, 95% CI: 1.17-1.66). RCS analysis revealed a nonlinear J-shaped relationship between sitting time and depression (P for nonlinear =0.010), with the curve nadir located around 3.3 h (P = 0.004). Insulin resistance biomarkers showed the strongest mediation effects, with TYG_WHTR accounting for the largest proportion (11.45%), followed by METS_IR (9.25%), TYG_BMI (9.17%), and HOMA_IR (1.53%). Among inflammatory markers, RAR (5.03%) had the highest mediating effect, followed by SIRI (2.36%), NLR (1.26%), NMLR (1.22%), and SII (1.08%). For oxidative stress, HDL and UHR mediated 3.45% and 2.22% of the sitting time-depression association, respectively. CONCLUSION: Sitting time is associated with depression rate partly mediated by biomarkers of oxidative stress, inflammation, and, most notably, insulin resistance. These findings suggest that reducing sitting time is associated with a lower depression risk, and this association may be accompanied by improvements in related biological pathways such as insulin resistance
Global Analysis of Shallow Underwater Fish Observation Research : 70 Years of Progress, Persistent Geographic Biases and a Path Forward
Get PDFMarine ecosystems are increasingly threatened by overfishing, pollution, coastal development and climate change, underscoring the need for long‐term, representative information on key fish populations and habitats to inform management and policy. Underwater fish observation (UFObs) techniques, such as Underwater Visual Census (UVC), stereo‐Baited Remote Underwater Video (stereo‐BRUV) and Remotely Operated Vehicles (ROVs), play a key role in sustaining long‐term data collection. Despite technological advancements, gaps persist in understanding research focus, geographic distribution and methodological biases inherent in these methods. We conducted a scientometric analysis of 1443 peer‐reviewed publications (1953–2023), employing natural language processing and network analysis to map the research landscape. We identified 15 knowledge clusters, including marine protected areas, apex predator conservation and reef ecosystems. Our findings reveal increasing use of BRUVS and ROVs in studies of marine protected areas and subsea infrastructure, while UVC remains prevalent in shallow coral reef research. Geographic representation is skewed, with the field dominated by researchers based in Australia and the United States, and underrepresented in Africa and Southeast Asia. This imbalance highlights the need for more inclusive, globally coordinated monitoring and reporting. Our results underscore the urgency of standardising protocols within each observation method and developing interoperable reporting frameworks across techniques to maximise data comparability and foster international collaboration. Addressing these challenges will strengthen the field's capacity to inform global conservation strategies and support sustainable fisheries management
pFedBlock : A Blockchain-Enhanced Split Federated Learning Framework for Robust and Traceable Model Training
Get PDFPersonalized federated learning is a practical solution to provide personalized services for Internet of Things devices while protecting their privacy. However, recent research found that personalized federated learning is still vulnerable to attacks, and its privacy can still be compromised during the training process. With an increasing number of devices and more diverse data, information traceability is also much more complicated. To address these problems, this paper proposes pFedBlock, a blockchain-based split federated learning framework to alleviate privacy risks and enhance the traceability during model training. Through the application of blockchain decentralized and immutable characteristics, pFedBlock can save each model update in a secure way and preserve a trustworthy log for all training behaviors. Such design can be beneficial to protect the training process and minimize the possible attacks from adversarial behaviors. Meanwhile, we also design a hybrid aggregation strategy in the federated framework so that devices can perform model updates in a more secure way. Experimental analysis shows that compared with traditional personalized federated learning methods, pFedBlock can obtain better performance in both models performance and system security
Communities of Student-Led Dialogue : Promoting Empowerment and Advancing Changemaking Engagement through Reflective Educational Practices
Get PDFIn response to the growing emphasis on civic engagement and social responsibility within Canada’s post-secondary education landscape, this qualitative study explores the transformative potential of student-led dialogue in fostering leadership and sustained social action. Traditional pedagogy has primarily focused on educator-led models to promote student engagement. However, there has been less research on the effectiveness of student leadership roles in driving peer-to-peer social action, motivation and learning. This research investigates the role and experiences of thirteen student facilitators in a Compelling Conversation (CC) workshop at a Canadian Changemaker College. Through semi-structured interviews, analyzed thematically from a contextualist perspective, the study examines how leadership, empowerment, and critical reflection emerge within peer-led environments. Findings reveal that student facilitators, when supported through inclusive and dialogic spaces, successfully cultivate reflective peer engagement, build confidence, and develop leadership skills. Key conditions, such as openness, connectivity, and perspective sharing, were found to be instrumental in students expressing motivation to extend their social justice efforts beyond the workshop setting. The discussion situates these outcomes within broader discourses on participatory leadership and student subjectification, arguing for the integration of student-led pedagogical models that promote critical hope and inclusive social innovation. Ultimately, the study concludes indicating the empowerment of students as autonomous agents of change enhances their capacity to navigate complex societal challenges with agency, confidence, and sustained commitment to social justice