6703 research outputs found
Sort by
Nature\u27s Defense against Hurricanes
Full text linkBefore urban industrialism led to the widespread drainage and destruction of wetlands, many early cultures recognized their value in sustaining ecological balance rather than viewing them as “wastelands.” Wetlands provide natural flood control, absorb storm surges, and support biodiversity. Florida, the state with the most wetlands in the contiguous United States, contains approximately 11 million acres, with the Everglades—the largest tropical wetland in North America—located in the central region of the state. These wetlands play a crucial role in mitigating the impact of hurricanes and tropical storms by absorbing excess water and reducing flooding. This project examines how the presence of wetlands within a state contributes to controlling natural disasters. The focus of this research is on Florida as it is the most affected state during hurricane season due to its unique geography, which exposes both its Atlantic and Gulf coasts to powerful storms.https://orb.binghamton.edu/research_days_posters_2025/1197/thumbnail.jp
The Evolution of Global Gold and Copper Trade Networks
Full text linkGold and copper have emerged as two of the most vital commodities in global trade. Despite serving distinct purposes, their international trade networks reveal interconnected patterns, critical to understanding the dynamics of global economics. This paper studies these attributes and their evolution during the last 36 years for both metals and finds correlations between them. The first part of the research is focused on the sustainability of networks through efficiency and robustness indexes; the second part is dedicated to interconnectedness – the Louvain and Bayesian SBM algorithms, partition, and modularity instruments are used. Community detection algorithms provide valuable insights into how global trade is divided into separate communities. In the result, a comparison of trade networks is considered: how their trade structure influences the peculiarities and correlations between them. While the gold trade network demonstrates a gradual but steady trend toward resilience and interconnectedness, the copper trade network reveals vulnerabilities due to topology weaknesses. These findings emphasize the need for balanced topology development alongside rising trade volumes, which is essential to enhance the stability and adaptability of trade in a rapidly changing global landscape
Exploring the Potential of Large Language Models (LLMs) to Simulate Social Group Dynamics: A Case Study Using the Board Game Secret Hitler
Full text linkThis study explores the capacity of large language model-powered agents to simulate human-like behavior in multi-agent social systems. Using Secret Hitler — a hidden-role board game centered on trust, deception, and strategic communication — we evaluate how LLM agents navigate dynamic group interactions. Our findings show that agents exhibit human-like behaviors, including strategic temporal adaptation, contextual reasoning, and complex social cognition such as theory of mind and implicit coordination. Notably, 85% of agent decisions factored in at least two other players’ mental states, highlighting their capacity for multi-agent mental state inference. However, they struggled with key aspects of human gameplay, including more nuanced strategic deception, emotional subtlety, and fluid conversational dynamics. These insights contribute to computational social science, agent-based modeling, and game theory, advancing our understanding of the potential of LLMs to simulate complex social interaction
A Machine Learning-Driven Framework for Real Time Detection and Prevention of Replica Node Attacks in Wireless Sensor Networks
Full text linkMobile devices and wireless sensor networks (WSNs) are increasingly vulnerable to security threats such as unauthorized access and replica node attacks. Mobile devices face risks from replication and anomalous behavior, while attackers compromise WSNs by cloning legitimate nodes, thus threatening network integrity. Traditional security mechanisms often fall short in detecting such sophisticated threats, especially in resource-constrained environments. This research proposes a dual-component security system. A Machine Learning-Based Intrusion Detection System (IDS) for WSNs leverages Graph Neural Networks (GNNs) to detect replica nodes through structural network analysis and applies Federated Learning to preserve data privacy. The Sequential Probability Ratio Test (SPRT) enables swift anomaly detection, while lightweight algorithms improve energy efficiency.Additionally, the proposed History of Neighbor Node (HNN) technique enhances detection accuracy by recognizing replica nodes at both local and global levels, even under varying mobility constraints. On mobile devices, anomaly detection models such as Isolation Forest (99% accuracy), Support Vector Machine (90%), and Random Forest (86%) monitor user behavior to identify unauthorized access. A real-time alert mechanism notifies authorized personnel of potential threats. Overall, the system presents a scalable, energy-efficient, and high-accuracy approach to safeguard mobile devices and WSNs against dynamic replication-based threats
Urban Heat Dynamics in Pune: The Influence of Land Cover and Local Climate
Full text linkUrban areas with high population density and extensive infrastructure development have been experiencing an increasing strain on the local heat budget, leading to a surge in heat-related illnesses and discomfort. This study examined the impact of climate and land use as heat islands in Pune, India, from 2012 to 2023 at six different locations representing varying degree of urbanization. Satellite land cover observations revealed that 55.17% of the total area was urbanized in the city itself, which was limited to 44.8% in 2012. This urbanization has significantly impacted the increasing tendency of maximum temperature (Tmax; 0.13℃ to 1.63℃ ) at almost each study site and minimum temperature (Tmin; 0.06℃ to 0.23℃ ) at a specific location during night. The mutual effect of land cover changes and meteorological conditions have evidenced the heat islands with varying intensities (2℃ to 8℃) at four of the six sites, with significantly intensifying rates from 0.05℃ to 0.39℃ . The estimation of dominating land cover type for the formation of heat islands demonstrated a significant simple determination (r2 = 0.001 to 0.013) and probability (P \u3c 7.91×10−13 to 2.33×10−5) with heat island temperature identifying urban land cover as the primary factor at two sites, while the other two were affected by mixed land covers influenced by local meteorological characteristics. The outcomes of this study offer valuable insights into the development of heat islands in Pune and could guide strategies for alleviating urban heat, ultimately improving climate resilience and thermal comfort citywide
Enhancing AAV-microdystrophin gene therapy after repeat dosing by blocking phagocytosis
Full text linkBackground: Inefficient transduction is a major limitation in achieving therapeutic levels of AAV-delivered microdystrophin capable of improving muscle function in patients with Duchenne muscular dystrophy. Additionally, some patients experience acute complications due to activation of innate immune pathways, such as complement. We propose that inhibiting complement receptor 1/2/3 (CR 1/2/3)-mediated phagocytosis and endosomal TLR 7/8/9 signaling pathways may decrease immune and inflammatory responses while simultaneously increasing the availability of AAV virus for muscle transduction. Methods: Mdx mice were randomly assigned to the following three experimental conditions (n=8-9/group): Group 1, mdx untreated; Group 2, mdx + rAAV9-microdystrophin; Group 3, mdx + rAAV9-microdystrophin + semiweekly dosing of TLR 7/8/9 antagonist + complement receptor antibodies (combination therapy). The rAAV9-microdystrophin was administered twice to 6- and 12-week-old mice. A separate group of 6-week-old mice received a single rAAV9-microdystrophin dose and no other treatment (Group 4). We assessed several immune and inflammatory responses and dystrophin expression in the muscle. Results: Viral load was significantly increased by 77-fold in white blood cells after two rAAV9-microdystrophin doses compared to mice receiving a single dose. Repeated gene therapy resulted in a lower viral load and microdystrophin expression in muscle compared to a single rAAV dose. 63% of mice treated with two rAAV9-microdystrophin doses produced antibodies to dystrophin, which was less in mice treated with two rAAV9-microdystrophin doses and combination therapy (25%). Likewise, AAV capsid specific antibody levels were reduced in mice receiving combination therapy. Microdystrophin expression in skeletal muscle evaluated by mass spectrometry, immunofluorescence, and western blotting showed significantly higher levels in combination-treated mice compared to rAAV9-microdystrophin alone. Conclusions: Our results demonstrate that combination treatment with complement receptor 1/2/3 antibodies and a TLR 7/8/9 antagonist enhances rAAV9-microdystrophin gene therapy in mdx mice by partially reducing inflammatory and immune responses and increasing microdystrophin expression in skeletal muscle. Furthermore, repeated gene therapy is associated with greater uptake by white blood cells and less microdystrophin expression in the skeletal muscle. This suggests that blocking complement receptors and/or TLR 7/8/9 pathways would be a promising strategy to enhance AAV-microdystrophin therapy
Utilizing panel discussions to facilitate interprofessional learning: Lessons from the rural and underserved service track
Full text linkIntroduction: Panel discussions are widely used in health professions education; however, little is known regarding best practices, learning outcomes, and their role in interprofessional education (IPE). The Rural and Underserved Service Track (TRUST) is a two-year co-curricular IPE track that regularly incorporates panel discussions into learning retreats. Objectives: This study aimed to describe student perceptions of the impact of panel discussions on IPE outcomes, determine factors of panel discussions which facilitate IPE, and analyze student learning outcomes in the setting of IPE. Methods: A qualitative descriptive study of student quotes (n = 4339) from 18 post-TRUST retreat assessments was conducted. A four-phased coding scheme was used to address each objective: (1) open-ended responses to the post-retreat assessments were deductively and retrospectively sorted, with panel-related quotes binned for analysis, (2) panel-related quotes were mapped to the 2023 Interprofessional Education Collaborative (IPEC) Competencies, (3) panel-related quotes were categorized to panel structure or panel outcomes, and (4) sub-themes for panel structure and panel outcomes were inductively identified using the techniques of thematic analysis. Multiple coders, multiple coding phases, and frequent research team meetings were used to increase rigor. Results: Panel discussions were ascribed to 834 (19.2%) quotes. At least one IPEC Competency was ascribed to 192 (23.0%) quotes. All competencies were represented in the analysis, with Values and Ethics (12.7%) and Roles and Responsibilities (12.0%) observed most often. Themes of panel structure and panel outcomes were ascribed to 299 (35.9%) and 259 (31.1%) quotes respectively, with six subthemes ascertained within each. Conclusion: This analysis of TRUST post-retreat assessments indicates that panel discussions can be utilized to facilitate the achievement of IPE competencies, although confirmation of study findings with prospective analyses is required. Sub-themes identified strategies for implementing effective panel discussions and promoting learning outcomes
Activity of ampicillin-sulbactam, sulbactam-durlobactam, and comparators against Acinetobacter baumannii-calcoaceticus complex strains isolated from respiratory and bloodstream sources: results from ACNBio study
Full text linkInfections caused by carbapenem-resistant Acinetobacter baumannii-calcoaceticus complex (ABC) are associated with high mortality rates and limited treatment options. This study aims to evaluate the in vitro activity of clinically utilized antimicrobials against a contemporary collection of ABC isolates with a predominant carbapenem-resistant phenotype. Geographically dispersed US medical centers (n =22) provided non-duplicate respiratory and bloodstream ABC isolates for surveillance testing. Antimicrobial susceptibility testing was conducted by broth microdilution and interpreted according to Clinical & Laboratory Standards Institute (CLSI) and Food and Drug Administration (FDA) breakpoints. ABC isolates (n = 523) from respiratory tract (74.4%) and blood (25.6%) sources were recovered from patients (2023–2024). Forty percent were obtained from intensive care unit patients. Carbapenem non-susceptibility was observed in 76.9% of isolates and was more common among respiratory tract cultures. The addition of durlobactam to sulbactam decreased the MIC90 by three-doubling dilutions from 32 to 4 μg/mL, increasing the susceptibility rate to 96.9% from 33.8%. Genome sequencing of sulbactam-durlobactam non-susceptible isolates (16/523; n = 3.1%) revealed MBL and non-enzymatic resistance mechanisms. Cefiderocol inhibited 93.5% and 76.1% of isolates at CLSI and FDA susceptible breakpoints, respectively. Minocycline susceptibility was \u3c 50%, while tigecycline and eravacycline MIC50/90 were 1/2 and 0.5/1 μg/mL, respectively. Sulbactam-durlobactam displayed high activity against sulbactam (95.4%), carbapenem (96.3%), and cefiderocol (95.2%) non-susceptible isolates. Susceptibility rates of clinically utilized antimicrobials against a US collection of ABC isolates ranged from 23% to 97%, with meropenem displaying the lowest rate and sulbactam-durlobactam demonstrating the highest overall rate. Sulbactam-durlobactam activity was preserved against sulbactam, carbapenem, and cefiderocol non-susceptible isolates among respiratory tract and bloodstream isolates
The Spacing Effect in List Learning: Role of Cognitive Load
No full textThe main file is the ReadMe file, with a dataset added in the additional file section
Circulating protein biomarkers identified in two independent clinical trial cohorts of glucocorticoid-naive Duchenne muscular dystrophy patients.
No full textBlood-accessible biomarkers offer promising insights into the pathogenesis of Duchenne muscular dystrophy (DMD) and other muscle diseases. Here, we quantified the relative abundance of 7,289 serum proteins using SomaScan proteomics in pre-treatment samples from 51 boys with DMD (aged 4 to \u3c 7) and 13 healthy controls from the VISION DMD (VBP15-004) trial. An independent validation cohort of untreated DMD boys (aged 4 to \u3c 8) from the FOR-DMD trial was also analyzed. Of the proteins screened, 26% and 15% were significantly elevated and decreased, respectively, in the serum of young DMD boys compared to controls (adjusted p-value \u3c 0.05). A high correlation (Spearman r = 0.85) in fold changes was observed between the two datasets. Many proteins with altered levels overlapped with known markers of muscle injury, inflammation, regeneration, and extracellular matrix remodeling. Selected biomarkers were queried in two published muscle mRNA and a muscle snRNAseq dataset in DMD biopsies. Novel factors involved in muscle regeneration and ECM remodeling were identified. This larger-scale, multi-clinical trial-based cohort study in untreated DMD boys substantially expands the catalog of circulating biomarkers, highlighting early-stage pathological processes. These findings can help identify new therapeutic targets and develop clinically actionable biomarkers to assess disease progression and response to therapies