Search Results (13,239)

Tornadoes pose growing threats to both communities and the built environment, yet few studies have quantified how spatial characteristics of the built environment interact with social and economic factors while influencing tornado impacts. This paper introduces an integrated metric that combines tornado risk and exposure to evaluate localized disaster impact. Focusing on Florida’s Panhandle, we examine how housing density and affordability, network connectivity, and urban form efficiency, together with demographic and socioeconomic attributes, shape tornado impacts at the U.S. census block group (CBG) level. To address spatial autocorrelation and non-stationarity, five statistical models were compared, including both global and local spatial regressions. The findings indicate that multiscale geographically weighted regression (MGWR) most effectively captures the spatial heterogeneity of tornado impacts. Built-environment and affordability factors show clear spatial heterogeneity— smart location indexand housing cost burden (h_ami) are positively associated with tornado impact in CBGs near Tallahassee and parts of Pensacola—suggesting amplified impacts in location-efficient urban areas where exposure is concentrated and affordability stress may limit preparedness and recovery. In contrast, network density is negatively associated with the impact of key clusters, consistent with the idea that denser, more redundant road networks can reduce canopy-weighted disruption by providing alternative routes for emergency access and restoration. Overall, these findings can inform our understanding of how the built environment influences tornado exposure, offering critical insights for planners and policymakers seeking to strengthen communities against tornadoes. Full article

Neuromorphic computing seeks to mimic structure and function of biological neural systems to enable energy-efficient, adaptive information processing. A critical component of this paradigm is neural encoding—the translation of analog or digital input data into spike-based representations suitable for spiking neural networks (SNNs). This paper provides a comprehensive overview of major neural encoding schemes used in neuromorphic systems, including rate and temporal encoding, as well as latency, interspike interval, phase, and multiplexed encoding. The purpose of this paper is to explore the use of encoding techniques for deep learning applications. We discussed the underlying principles of spike encoding approaches, their biological inspiration, computational efficiency, power consumption, integrated circuit design and implementation, and suitability for various neuromorphic applications. We also presented our research on a hardware-and-software co-design platform for different encoding schemes and demonstrated their performance. By comparing their strengths, limitations, and implementation challenges, we aim to provide insights that will guide the development of more efficient and application-specific neuromorphic systems. We also performed an encoder performance analysis via Python 3.12 simulations to compare classification accuracies across these spike encoders on three popular image and video datasets. The performance of neural encoders working with both deep neural networks (DNNs) and SNNs is analyzed. Our performance data is largely consistent with the benchmark data on image classification from other papers, while limited performance data on the University of Central Florida’s 101 (UCF-101) video dataset were found in comparable studies on spike encoders. Based on our encoder performance data, the Interspike Interval (ISI) encoder performs well across all three datasets, preserving continuous, detailed spike timing and richer temporal information for standard classification tasks. Further, for image classification, multiplexing encoders outperform other spike encoders as they simplify timing patterns by enforcing phase locking and improve stability and robustness to noise. Within the SNN testbenches, the ISI-Phase encoder achieved the highest accuracy on the Modified National Institute of Standards and Technology (MNIST) dataset, surpassing the Time-To-First Spike (TTFS) encoder by 1.9%. On the Canadian Institute For Advanced Research (CIFAR-10) dataset, the ISI encoder achieved the highest accuracy. This ISI encoder had 22.7% higher accuracy than the TTFS encoder on the CIFAR-10 dataset. The ISI encoder performed best on the UCF-101 dataset, achieving 12.7% better performance than the TTFS encoder. Full article

Background: Hypermobile Ehlers–Danlos syndrome (hEDS) can present as a complex interplay of widespread symptomatology and multisystem involvement, posing diagnostic and treatment challenges. Objective characterization of cervical spine and neurovascular findings in hEDS has been limited. Previous studies have emphasized upper cervical spine complications in hEDS, yet the relevance and mechanisms underlying associated symptomatology have not been elucidated. This study examined objective test findings in patients with hEDS at an outpatient neck clinic to explore cervical spine and neurovascular pathology that could contribute to further understanding the clinical profile of a subset of patients with hEDS. Methods: This single-center, retrospective observational study included patients with hEDS aged 20–50 years from 1 January 2022–31 December 2024, at an outpatient neck center. It excluded previous neck surgery, traumatic events, or related injury. Demographic, clinical, and diagnostic data were collected through a retrospective chart review, including measurements from standard clinical diagnostic protocols: digital motion X-ray (videofluoroscopy), cone beam CT, Doppler ultrasound, and tonometry. Results: More than 71% of patients reported ≥29 symptoms. Nearly all patients exhibited co-occurring forward head, decreased depth of curve, ligamentous cervical instability, and decreased internal jugular vein (IJV) and vagus nerve cross-sectional area (CSA). Vagus nerve CSA was found to be significantly smaller than the comparative healthy/normal population. IJV CSA was significantly smaller at C1 than at C4–C5, suggesting evidence of carotid sheath compression at C1. Conclusions: This study offers novel evidence that cervical spine pathology, IJV compression, and vagus nerve degeneration are uniformly prevalent in hEDS, which may contribute to, or be an etiological basis for, the multisystem involvement in a subset of patients with this disorder. These findings provide hypothesis-generating data to inform future mechanistic and therapeutic studies, including exploration of new diagnostic and treatment targets. Full article

Florida (FL)’s agriculture sector is undergoing rapid transformation due to biological shocks, environmental stressors, import competition, and accelerating urbanization. Citrus greening, laurel wilt, and hurricane-related damage have sharply reduced yields and acreage, while rising imports from Mexico and Brazil erode market share and depress prices. Urban development and recreational land-use expansion are accelerating land-value increases, which in turn drives farmland loss and abandonment. This policy-oriented review synthesizes these pressures and evaluates state policy responses. Our findings highlight the need for integrated strategies that improve resilience, strengthen land conservation, and enhance the long-term competitiveness of FL’s agricultural sector. Full article

Loss of skeletal muscle mass in cancer cachexia is associated with poorer survival, reduced treatment tolerance, and diminished quality of life. Routine oncology computed tomography (CT) can yield skeletal muscle area (SMA) and skeletal muscle index (SMI) for early cachexia assessment and prognostication, but manual annotation is labor intensive and existing automated tools often show inconsistent reliability. We developed SMAART-AI (Skeletal Muscle Assessment—Automated and Reliable Tool based on AI), a fully automated pipeline that localizes the third lumbar (L3) vertebral level, segments skeletal muscle, and quantifies prediction uncertainty to flag potentially unreliable outputs. Performance and reliability were evaluated across gastroesophageal, pancreatic, colorectal, and ovarian cancer cohorts, benchmarking against expert annotations and existing tools. SMAART-AI achieved a Dice score of 97.80% ± 0.93% in gastroesophageal cancer and a median SMA deviation of 2.48% from expert annotations across pancreatic, colorectal, and ovarian cohorts. Uncertainty scores correlated strongly with prediction error, enabling identification of high-error cases to support trustworthy deployment. Integrating the SMA/SMI with clinical features and body mass index (BMI) improved survival prediction (concordance index was +2.19% for colorectal, +9.82% for pancreatic, and +2.58% for ovarian cancer) and supported cachexia detection (70.00% accuracy; F1 80.00%). Overall, SMAART-AI provides an uncertainty-aware, clinically translatable framework for scalable CT-based muscle assessment and improved oncologic prognostication. Full article

Access to quality seed is a critical driver of smallholder productivity and household food security in South Africa, yet rural communities in the Eastern Cape continue to rely heavily on informal seed systems. Limited seed literacy among farmers and vendors is widely recognized as a constraint to the effective selection and use of high-quality seed. The purpose of this study is to assess seed literacy levels among smallholder farmers in KwaMkhiva village and evaluate how knowledge gaps shape farmers’ seed sourcing patterns and access to quality seed. The study hypothesizes that low seed literacy significantly increases reliance on informal seed systems and reduces adoption of certified or improved varieties. A quantitative, cross-sectional survey design was used to collect data from 50 smallholder farmers and 12 informal seedling vendors, complemented by semi-structured interviews with three extension officers. Descriptive statistics, chi-square tests, correlation analysis, and a composite Seed Literacy Index (SLI) were employed to assess literacy dimensions and their association with seed choices. Findings show that 49% of farmers rely on local markets and 40% use farm-saved seed, with 75% assessing quality visually rather than through germination or varietal indicators. Only 10% had received any seed-related training, and awareness of seed adaptability and crop rotation was below 20%. Higher SLI scores were positively associated with adoption of certified seed (r = 0.42, p < 0.01) and crop diversification. The study concludes that seed literacy is a critical yet underserved capability that shapes smallholder seed access within dual seed economies. Strengthening farmer-centred seed literacy programmes, revitalising extension services, and supporting community seed banks could enhance access to quality seed and improve smallholder resilience. Full article

With in situ imaging systems becoming more common, precise, and economically viable, use of these systems has grown dramatically, including both automated classification and biomass estimations. However, a rather large and overlooked portion of these efforts is reliable detection and classification of these organisms as they pass through the imaging device. This paper focuses on the development of an end-to-end classification CNN-based algorithm for marine zooplankton using the in situ Ichthyoplankton Imaging System (ISIIS-DPI) from Bellamare (La Jolla, CA, USA). Our novel approach considers many issues with automated segmentation and classification, including over-segmentation, noise segmentation, and organism size input. This allows for classifications in diverse water types, demonstrated by the comparison of three datasets created in conjunction with this project, each with very different water properties and zooplankton communities (Florida Gulf coast; Trondheimsfjord, Norway; Sargasso Sea). Our segmented image dataset contains 70,624 regions of interest (ROIs) across four organism classes—Chaetognath, Crustacean, Gelatinous, and Larvacean—with two classes dedicated to detritus. Four common network architectures—Resnet, Xception, GoogleNet, and Darknet—are trained on this dataset, with final test accuracies in the range of 95.94–96.09%. Following this initial training, a secondary level of classification is introduced. The base Gelatinous class is further divided into six groups. The same four CNN architectures are used once again, with final accuracies in the range of 86.12–90.40%, showing the ability to taxonomically classify down to the order level. The present work introduces a versatile, adaptable, scalable and autonomous segmentation and classification algorithm using niched networks mirroring taxonomy, and is fully contained in a publicly available MATLAB R2025a custom graphical user interface. Full article

There is no approved drug therapy for schwannomas associated with NF2-related schwannomatosis (NF2-SWN). Neither life-saving surgical resection or radiation are curative and can compound the debilitating neurological effects of the schwannomas. We previously identified fimepinostat, a dual histone deacetylase (HDAC)/phosphoinositide-3 kinase (PI3K) inhibitor, as a promising drug candidate with pro-apoptotic effects on NF2-related schwannomas. This preclinical study used the pharmaceutical formulation of fimepinostat to confirm its efficacy in schwannomas and identify pro-apoptotic signaling pathways. Fimepinostat was tested in human schwannoma model cells, patient-derived primary vestibular and non-vestibular schwannoma cells, and in a sciatic nerve allograft model. The signaling pathways leading to caspase-3-dependent apoptosis were elucidated using immune assays, flow cytometry, imaging, proteome, and acetylome analysis. Acute exposure to fimepinostat led to p21-dependent cell cycle inhibition, upregulation of tumor necrosis factor-related apoptosis-inducing ligand receptor 2 (TRAIL R2), and downregulation of tumor necrosis factor receptor 1 (TNFR1), Yes-associated protein (YAP), and inhibitors of apoptosis. Moreover, fimepinostat downregulated cytokine and chemokine secretion increased by merlin loss in schwannoma cells. Fimepinostat is a promising new drug intervention for NF2-SWN patients with the potential to promote tumor regression. Full article

Artificial intelligence (AI) is at the vanguard of transforming radiology in several ways, including augmenting diagnoses, improving workflows, and increasing operational efficiency. Several integration challenges, including concerns over privacy, clinical usability, and workflow compatibility, still remain. This review discusses the foundations and current trends of clinical AI in radiology to provide essential context for ongoing developments. To illustrate translational potential, we describe representative applications, including: (1) local deployment of large language models (LLMs) for restructuring and streamlining radiology reports, improving clarity and consistency without relying on external resources; (2) multimodal AI frameworks combining CT images, clinical data, laboratory biomarkers, and LLM-extracted features from clinical notes for early detection of cachexia in pancreatic cancer; (3) privacy-preserving federated learning (FL) infrastructure enabling collaborative AI model development across institutions without sharing raw patient data; and (4) an uncertainty-aware de-identification pipeline for removing Protected Health Information (PHI) from radiology images and clinical reports to support secure data analysis and sharing. We further discuss emerging opportunities for tumor board decision support, clinical trial matching, radiology report quality assurance, and the development of an imaging complexity index. Collectively, these applications highlight the importance of local deployment, multimodal reasoning, privacy preservation, and human-in-the-loop oversight in translating AI models from research to oncology radiology practice. Full article

The wastewater generated during coffee processing contains high levels of acidity and organic matter, posing substantial environmental hazards, particularly in rural areas where traditional treatment methods are financially infeasible. This research assesses the field-scale effectiveness of Chrysopogon zizanioides (vetiver grass) in phytoremediation of coffee wastewater in Huánuco, Peru, with particular attention to how plant maturity affects treatment outcomes. A comparative analysis was performed on untreated and vetiver-filtered effluent from infiltration ponds at four growth stages (6, 8, 19, and 21 months), with measurements of pH, chemical oxygen demand (COD), biochemical oxygen demand (BOD₅), and suspended solids (TSS, SS) conducted according to standardized methods. The findings indicate notable improvements in water quality, as the pH rose from 4.07 ± 0.32 to 5.82 ± 0.40 (p < 0.001) and organic loads decreased by 39–41% (COD: 38,600 ± 12,100 to 23,000 ± 8500 mg L⁻¹ O₂; BOD₅: 27,700 ± 9400 to 16,500 ± 5600 mg L⁻¹ O₂). Total Suspended Solids (TSS) were reduced by 26%, while the settleable suspended solids fraction (SS) decreased by 69%, indicating strong particulate removal through combined filtration and sedimentation mechanisms. Mature vetiver stands (21 months old) showed better results, underscoring the importance of root development for effective phytoremediation. Strong correlations were observed between COD and BOD₅ (r = 0.92), while pH negatively correlated with organic and particulate parameters. The study presents empirical evidence supporting vetiver-based systems as an economical and sustainable approach to decentralized wastewater treatment in coffee-growing areas. Furthermore, it provides actionable insights for improving phytoremediation by focusing on plant maturity, which can be readily adapted for large-scale implementation in resource-constrained settings. The findings underscore the potential of nature-based technologies to address environmental challenges while supporting local economies dependent on coffee production. Full article

Hurricanes have intensified and become more persistent under a changing climate, increasing the risk of infrastructure damage and property loss in coastal regions, threatening their sustainability. This study examines how hurricane intensity and persistence influence infrastructure loss, contributing to a more comprehensive understanding of climate-related risks. Using data from the National Oceanic and Atmospheric Administration (NOAA) Storm Events Database from 1996 to 2024, we develop a series of machine learning models to predict property losses based on storm characteristics and contextual vulnerability factors. Narrative-based text analysis and time-series feature engineering were applied to extract meteorological and temporal attributes, while regression and ensemble models were used for predictive evaluation. Results show that storm intensity alone explains only a small portion of loss variance, with persistence influencing damage primarily through rainfall and hydrological effects. The findings highlight that vulnerability, exposure, and cumulative risk dynamics are essential for accurate long-term prediction and for assessing infrastructure sustainability. Overall, the study demonstrates that combining machine learning techniques with climate and vulnerability data can inform future research on infrastructure sustainability. The quantified vulnerability-versus-intensity breakdown presented here can support post-disaster resource allocation, insurance risk modeling, and the prioritization of infrastructure maintenance in hurricane-prone regions. Full article

Predicting the motion of ships and floating structures is essential for ensuring economical and environmentally friendly operations in the ocean. In this study, we propose a hybrid encoder–decoder Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to predict motions of a moored Floating Production Storage and Offloading (FPSO) vessel under varying sea conditions. The model integrates a CNN for spatial wave-field feature extraction and an LSTM encoder–decoder to capture temporal dependencies in vessel motion. Synthetic datasets were generated using mid-fidelity dynamics simulations of a coupled FPSO–mooring–riser system subjected to wave excitations. Five sea states ranging from calm to severe were considered to evaluate the model’s robustness. A key preprocessing step involved determining the optimal spatial domain for wave field input, and a wave field size of 600 m × 600 m was identified as the most cost-effective configuration while maintaining accuracy. The model was validated using the Root Mean Square Error (RMSE) or relative RMSE (RRMSE). Despite low RRMSE values in low sea states, predictions were noisier due to high-frequency, low-amplitude responses. In contrast, higher sea states yielded more stable predictions despite higher RRMSE values. The proposed method offers high-resolution motion forecasting capability, which can enhance operational safety and energy efficiency of offshore platforms, particularly when integrated with stereo camera-based wave monitoring systems. Full article

Studies that have sought to describe and account for pre-diagnosed depressive symptoms on BC treatment choice, delay in initiating treatment, and mortality have been inconsistent. The purpose of the study is to examine the association between pre-diagnosed depressive symptoms and their impact on breast cancer (BC) treatment, treatment delays, and mortality. We conducted a retrospective cohort study using the Surveillance, Epidemiology, and End Results–Medicare Health Outcomes Survey (SEER-MHOS) dataset among women aged 65 years and older diagnosed with BC. Among 3840 eligible patients, 28.1% had pre-diagnosed depressive symptoms. Patients with pre-diagnosed depressive symptoms who were diagnosed with early-stage BC were significantly more likely (OR = 1.52; 95% CI: 1.26–1.84) to undergo mastectomy or receive breast-conserving surgery (BCS) alone rather than BCS plus radiation therapy (RT) compared to patients who were not pre-diagnosed with depressive symptoms. Among patients with advanced-stage BC, pre-diagnosed depressive symptoms were not significantly associated with treatment type. Among Hispanic patients, pre-diagnosed depressive symptoms were associated with treatment delays. Overall, patients with pre-diagnosed depressive symptoms had a 16% increased adjusted risk of BC-related mortality compared to those who were not pre-diagnosed with depressive symptoms, and those with advanced-stage cancer had an 18% higher adjusted risk of death than early-stage BC. Conclusions: Overlooking depressive symptoms management prior to a breast cancer diagnosis may result in poorer survival outcomes. Early detection and consistent management of depression are critical for improving patient survival. Full article

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with a five-year survival rate of approximately 13%, partly because of limited treatment options and resistance to therapies. Although the recently discovered KRAS G12D inhibitor MRTX1133 has shown promising efficacy in preclinical models, its clinical efficacy as a single agent is expected to be limited, as is the case with KRAS G12C inhibitors. Therefore, in this study, we evaluated potential combination strategies to enhance the therapeutic effect of MRTX1133. We combined MRTX1133 with the BCL-xL proteolysis-targeting chimera (PROTAC) DT2216 and the mTOR inhibitor everolimus. Methods: The sensitization of MRTX1133 by the combination of DT2216 + everolimus was tested in KRAS G12D-mutant PDAC cell lines using colony formation and apoptosis assays. The effects of MRTX1133 and/or DT2216 + everolimus on KRAS signaling and BCL-2 family proteins were assessed by immunoblotting and/or RT-PCR. The functional roles of BIM/NOXA were elucidated via immunoprecipitation (IP) and siRNA knockdown. Triple combination efficacy was evaluated in AsPC1 parental and MRTX1133-resistant xenografts, with pharmacodynamic effects confirmed by immunoblotting and immunohistochemistry. Results: The triple combination leads to significantly greater colony growth inhibition and apoptosis induction as compared with single agents or two-drug combinations in multiple KRAS G12D-mutant PDAC cell lines. Mechanistically, MRTX1133 treatment increased BIM and decreased NOXA levels, and the combination of DT2216/everolimus simultaneously enhanced BIM release and stabilized NOXA. In vivo, DT2216/everolimus combination significantly potentiated the anti-tumor activity of MRTX1133 in the AsPC1 PDAC xenograft model. Furthermore, the triple combination effectively overcame acquired MRTX1133 resistance in vitro and in the AsPC1 xenograft model. Conclusions: Collectively, our findings suggest that the combination of DT2216/everolimus potentiates the anti-tumor efficacy of MRTX1133 associated with enhanced apoptosis induction and inhibition of compensatory survival signaling. Full article

Leucania loreyi (Duponchel, 1827) is a major lepidopteran pest that infests a wide range of crops worldwide. Effective mass production of insects for pest management programs depends on the availability of suitable artificial diets. Here, we evaluated 14 artificial diets (D1–D14) formulated from maize or cowpea seeds (19.5 g) plus standard diet components and supplemented with 1 g of pollen from different sources (rapeseed, date palm, maize, common hollyhock, saffron, and honey bee), along with control diets. We assessed their effects on demographic traits of L. loreyi. The maize seed–maize pollen diet (D3) and the cowpea seed–maize pollen diet (D10) produced the shortest developmental times (37.53 and 38.10 days, respectively), whereas the maize seed–saffron pollen (D5) and cowpea seed–saffron pollen (D12) diets resulted in the longest development (45.83 and 45.56 days, respectively). Diet also D3 yielded the shortest adult and total pre-oviposition periods (APOP and TROP), the greatest female longevity, and the highest fecundity and net reproductive rate (R₀) (801.69 and 88.69 offspring, respectively). In contrast, diet D12 produced the lowest fecundity and R₀ (339.73 and 68.15 offspring, respectively). Consistent with these patterns, D3 generated the highest intrinsic rate of increase (r) and finite rate of increase (λ), while diets D5 and D12 were associated with lower population growth rates. Cluster analysis further identified D3 as the most nutritionally favorable formulation under our experimental conditions, supporting its potential utility for large-scale L. loreyi rearing. Full article