Search Results (10,218)

Herbicides are widely used in modern agriculture to control weeds and maintain crop productivity, but their persistence in soil raises concerns about unintended effects on non-target organisms. Pendimethalin, a dinitroaniline herbicide extensively applied to cereal and vegetable crops, is designed to target plant microtubules and is generally considered unlikely to pose genotoxic risks to animals. However, information on its sublethal effects on beneficial soil arthropods remains limited. In this study, we investigated the cytotoxic and histopathological effects of a commercial pendimethalin-based formulation on the ground beetle Pterostichus melas italicus, an ecologically relevant predatory species in agroecosystems. Adult males collected from an organic farm were exposed under laboratory conditions to soil treated at the recommended field dose and maintained for up to 7 days, corresponding to subchronic exposure. Individuals were sampled after 2 and 7 days, and the midgut and Malpighian tubules were analysed using histological and transmission electron microscopy. Exposure induced marked but non-lethal ultrastructural alterations, particularly in the Malpighian tubules, including reduction in the basal labyrinth, cytoplasmic vacuolisation, mitochondrial swelling, increased phagolysosome abundance, and nuclear karyorrhexis. These effects were transient under laboratory conditions and occurred without detectable impacts on survival, highlighting the Malpighian tubules as sensitive targets for the early detection of herbicide-induced physiological disturbances. However, the observed recovery may reflect compensatory physiological processes that could entail energetic costs and, under field conditions characterized by multiple concurrent stressors, potentially compromise physiological performance and predatory efficiency. Consequently, this study underscores the necessity of integrating sublethal ultrastructural biomarkers into environmental risk assessment frameworks for non-target beneficial insects. Full article

Heavy-duty freight transport remains one of the most difficult sectors to decarbonize due to its high energy demand, long-distance operation, and strong dependence on diesel fuel. In Chile, more than 90% of heavy trucks operate with diesel engines, contributing significantly to greenhouse gas emissions and local air pollutants. At the same time, Chile has favorable conditions for the development of green hydrogen due to its world-class solar and wind resources. This study presents a technical and economic assessment of green hydrogen fuel cell trucks recently introduced in Chile, comparing their operational performance with conventional diesel freight trucks. A techno-economic framework based on total cost of ownership, fuel consumption, operational range, fleet utilization, and hydrogen price scenarios was developed using information reported in public studies and official Chilean strategic documents. The results indicate that hydrogen trucks are technically suitable for long-haul and intensive-duty operations due to their rapid refueling capability and high operational autonomy. However, economic competitiveness remains strongly dependent on hydrogen price, fleet scale, infrastructure utilization, and vehicle capital cost. Under current market conditions, diesel trucks preserve cost advantages, while hydrogen trucks become increasingly competitive as hydrogen prices approach long-term target values and annual mileage increases. Chile’s renewable resource base positions the country as a strategic candidate for early adoption in mining, logistics corridors, and captive fleets. The study concludes that hydrogen freight transport can become a realistic decarbonization pathway if accompanied by targeted public policies, infrastructure deployment, and industrial scale-up. Full article

Cutaneous leishmaniosis (CL) remains a major neglected tropical disease, with current therapies constrained by toxicity, high cost, and variable efficacy. Here, we evaluated an immunochemotherapy strategy combining topical amphotericin B (AmB) with the therapeutic DNA vaccine HisAK70 in a murine model of Leishmania major infection. BALB/c mice were subcutaneously infected and treated with topical AmB cream alone, AmB plus HisAK70, or paromomycin (PM) as a reference therapy. Therapeutic efficacy was assessed through lesion progression, parasite burden in draining lymph nodes and spleen, and immunological markers associated with parasite control. Both PM and the combined AmB + HisAK70 treatment significantly reduced lesion progression and markedly decreased parasite burden compared with infected controls, demonstrating effective control of local infection and systemic dissemination. Importantly, the combination therapy enhanced the efficacy of AmB alone, supporting the beneficial contribution of vaccine-driven immune modulation to therapeutic outcome. Therapeutic efficacy was associated with reduced arginase activity in infected tissues and an increased IFN-γ/IL-4 ratio, indicative of a protective Th1-oriented immune response. Together, these findings highlight immunochemotherapy as a promising strategy for CL treatment, integrating localized topical drug delivery with targeted immune activation to improve therapeutic efficacy while potentially reducing systemic toxicity. Full article

Background: Pancreatic ductal adenocarcinoma (PDAC) carries a dismal prognosis, and KRAS and TP53 mutational status is increasingly recognized as both prognostic and therapeutically actionable. Because next-generation sequencing has long turnaround times, high costs, and substantial tissue requirements, we aimed to develop and externally validate a deep-learning framework for inferring KRAS and TP53 mutation status directly from routine hematoxylin-and-eosin (H&E) whole-slide images (WSIs) of PDAC. Methods: A training cohort was assembled from TCGA-PAAD (n = 206) and CPTAC-PAAD (n = 147), and an independent external validation cohort (n = 86) was obtained from Osaka Metropolitan University (OMU) Hospital. We benchmarked 28 model configurations per gene, comprising three pathology foundation models (CONCH-v1.5, CTransPath, and Prov-GigaPath) crossed with nine multiple-instance-learning (MIL) aggregators (ABMIL, CLAM-SB, CLAM-MB, DSMIL, TransMIL, MeanMIL, MaxMIL, AEM, and MIL-Dropout), plus a ResNet-50 + MeanMIL baseline. Performance was evaluated by patient-level five-fold cross-validation (AUC, accuracy, precision, sensitivity, and specificity) and external AUC; attention heatmaps were generated for interpretability. Results: For KRAS, CONCH-v1.5 + MeanMIL achieved the best internal AUC of 0.717 and CONCH-v1.5 + ABMIL the best external AUC of 0.705. For TP53, CTransPath + DSMIL achieved the best internal AUC of 0.668 and CTransPath + MeanMIL the best external AUC of 0.744. Conclusions: H&E-based deep learning can infer KRAS and TP53 mutation status in PDAC with moderate but reproducible discrimination, supporting its potential as a low-cost upstream prescreening tool that triages candidates for confirmatory molecular sequencing and genotype-directed targeted therapy. Full article

Idiopathic pulmonary fibrosis (IPF) is one of the most severe forms of idiopathic interstitial pneumonia. Increasing evidence indicates that the gradual accumulation of senescent fibroblasts and alveolar epithelial cells contributes significantly to IPF pathogenesis, suggesting senescence as a potentially targetable process. Recurrent injury to the alveolar epithelium promotes senescence in epithelial cells, impairing their regenerative capacity and thereby predisposing the tissue to fibrotic degeneration. Although epithelial senescence is strongly implicated in the initiation and progression of lung fibrosis, the mechanisms through which it drives IPF remain challenging, partially due to the lack of physiologically relevant in vitro models capable of recapitulating lung architecture under both normal and pathological conditions. The objective of the present study was to develop a reproducible alveolosphere model in healthy and senescent conditions as a preliminary approach to investigate epithelial features that may be relevant to aspects of the IPF microenvironment. An alveolosphere system was generated by culturing alveolar epithelial cells with or without basement membrane components in combination with alveolar/epithelial optimized medium. Cultures were maintained for 3, 6, and 8 days, and cell viability together with morphological assessment confirmed the absence of cytotoxicity. The expression of keratin 8/18 and AQP5 was consistent with the maintenance of epithelial and alveolar-associated features. Cellular senescence was induced by exposing alveolospheres to doxorubicin for 24 h. Subsequent analyses of viability, along with the expression of senescent and pro-fibrotic markers, inflammatory mediators, and tissue remodeling factors, such as MMPs, were carried out in senescent 3D structures. The results demonstrated robust cell viability at all time points, supported by morphological observations. Marker expression suggested preservation of key epithelial characteristics, while senescence-inducing conditions were associated with an increase in senescence-associated, pro-fibrotic, inflammatory, and matrix-modulating markers. Collectively, these findings describe the preliminary establishment of a cost-effective and reproducible alveolosphere platform that may represent a useful starting point for studying epithelial senescence and its potential association with pro-fibrotic signaling relevant to aspects of IPF pathogenesis. Furthermore, this model may provide a basis for the preliminary evaluation of senotherapeutic compounds aimed at delaying or preventing the onset of cellular senescence. Full article

This research presents the development and feasibility testing of a novel closed-loop neuromodulation system targeting the prefrontal cortex based on real-time EEG analysis. Performance in executive-demanding tasks, such as mental arithmetic and emotion regulation, is monitored via electroencephalography (EEG), while transcranial direct current stimulation (tDCS) parameters are dynamically adapted based on the participant’s neural responses. To overcome the accessibility barriers posed by proprietary software development kits, we implemented a custom Robotic Process Automation (RPA) framework in Python 3.10 and Computer Vision to autonomously control the Neuroelectrics Starstim 32 hardware. The system isolates alpha (8–13 Hz) frequency band, extracted primarily from prefrontal channels (e.g., F3, F4, Fp1, Fp2), to establish individualized stimulation thresholds. Results from a pilot session confirm the system’s ability to maintain stable real-time acquisition and automate stimulation triggers without manual intervention. This architecture demonstrates a cost-effective and reliable approach for state-dependent interventions, providing a personalized framework for enhancing cognitive control and supporting clinical neuromodulation. Full article

Genomic testing is a key component of precision oncology; however, Black patients receive genomic testing at lower rates. The purpose of this qualitative study was to identify individual and health system drivers of genomic testing disparities at a National Cancer Institute-designated comprehensive cancer center. We conducted interviews with 15 oncology providers and 11 Black cancer patients between September 2023 and October 2024. These patients were eligible for genomic testing based on National Comprehensive Cancer Network (NCCN) guidelines, being diagnosed within last 10 years (2014–2023), at least 18 years old, and English-speaking. Providers included oncologists and oncology patient navigators. Topics included motivators, barriers, and knowledge of genomic testing and factors influencing decision-making. The Penchansky and Thomas theoretical framework of healthcare access (e.g., availability, accessibility, accommodation, affordability, and acceptability) guided thematic analysis. Among patients eligible for genomic testing, most participants (n = 7) received genomic testing as part of their cancer treatment based on EMRs, however many patients (n = 7) could not recall discussing genomic testing with their oncologist. Most patients and all providers highlighted affordability as a challenge: patients were concerned about unexpected costs associated with testing, while providers were concerned about costs of matched molecular targeted therapy. Both patients and providers highlighted patient-centered communication to mitigate mistrust and promote patient engagement in care. Despite limited awareness, Black patients view genomic testing positively. Addressing multiple dimensions of access is key to improving system-level processes and ensuring that more patients benefit from lifesaving targeted therapy. Full article

This study conducts a preliminary FE simulation of screw piercing and screw rolling processes for producing bimetallic pipes with variable inner and outer positioning and thickness of the corrosion-resistant steel CL (13Cr and 18Cr10Ni grades) as a rational first step before experimental testing. The results demonstrate that a favorable stress–strain state is formed in both processes under the selected deformation parameters (there are no high tensile stresses in the area of high strains and low temperatures). Shape change analysis confirmed that the pipe geometric dimensions according to simulation are sufficiently close to the target values, with only minor deviations in wall thickness and ovality. The change in CL thickness during piercing ranges from 34% to 51% and increases with the elongation ratio. In the rolling process, it reaches approximately 55–56%. The CL position, its thickness and the material choice significantly influence the deformation heating intensity within the bonding of base and clad materials, as well as the magnitude of the forces acting on the tool in contact with the CL. The obtained results can serve as a methodology that lays the groundwork for experimental verification and the further technology implementation, while minimizing risks and costs. Full article

In planetary radar observations, accurately scaled inverse synthetic aperture radar (ISAR) images are essential for near-Earth asteroid (NEA) shape reconstruction, rotational-state estimation, and impact risk assessment. However, conventional ISAR cross-range scaling methods typically rely on time–frequency analysis or multidimensional parameter searches, resulting in high computational complexity and limited robustness under the low signal-to-noise ratio (SNR) conditions commonly encountered in NEA observations. To address this challenge, this paper proposes a joint ISAR phase autofocus and cross-range scaling framework based on adaptive phase tracking (APT). The method employs a multi-scale Laplacian of Gaussian (LoG) detector to extract isolated scatterers and reformulates Doppler chirp-rate estimation as a recursive state estimation problem. By adaptively tracking the cross-range Doppler phase of these scatterers, the target’s effective rotation velocity is directly estimated without exhaustive parameter searches. A unified phase compensation function is then constructed to simultaneously achieve range-dependent autofocus and cross-range scaling. Simulation results based on a three-dimensional NEA model with realistic planetary radar imaging parameters demonstrate the effectiveness of the proposed method in near-Earth asteroid imaging scenarios. Experimental results from a UAV turntable setup further verify its capability for cross-range scaling and phase autofocus in a controlled near-field ISAR imaging scenario. These results show that the proposed framework achieves high estimation accuracy and robustness with reduced computational cost, making it an efficient and reliable solution for practical ISAR imaging in challenging radar environments. Full article

This study presents a comprehensive evaluation of sorbent injection to mitigate sodium emissions in a 250 MW_e cyclone-fired boiler using lignite coal. Using historical boiler operational data, a computational fluid dynamics (CFD) model was validated and simulations were subsequently conducted to identify optimum sorbent injection locations for maximizing dispersion within the boiler cross-section and limiting sorbent temperatures to avoid deactivation. Data from the literature were used to guide sorbent injection rates and target sorbent particle sizes. Subsequent field demonstrations with the injection of a commercially available sorbent achieved a 60–80% reduction in the gas phase sodium, which was visually corroborated by reduced deposition on heat exchanger probes placed inside the boiler as well as by data on ash composition as a function of size. Furthermore, a diffusion-kinetic model, incorporating alkali vapor (NaOH) capture and subsequent sorbent deactivation, was developed and integrated into the CFD simulations as a post-processing tool and tested against the field demonstration data. Additional bench-scale testing was conducted with a range of sorbents as part of tool development for selecting from locally available sorbent sources. These bench-scale tests indicated a definite shift in the aerosol particle size distribution (PSD) toward a coarser range and depletion in the ultra-fine sizes, confirming the capture of vapor phase sodium species by the sorbents. Notably, in these tests, the sorbents remained effective even when they became molten, suggesting the potential for more convenient and cost-effective injection strategies. Full article

Background: Adolescents and young adults (AYAs; aged 15–39) diagnosed with cancer face distinct challenges that are poorly addressed within traditional cancer care models. This qualitative study explored AYAs’ unmet supportive cancer care needs in Eastern Ontario (Canada) to inform the development of a tailored multidisciplinary program. Methods: As part of a larger mixed-methods study, AYAs receiving/post-cancer treatment in the Champlain region of Eastern Ontario were purposively recruited to complete a survey and a semi-structured interview. Demographic and interview data were analyzed descriptively and via thematic analysis, respectively. Results: Sixteen AYAs (M_age = 32.2 years [range: 19–42]; 56.3% female) were interviewed virtually using a co-designed, semi-structured guide between October 2024 and February 2025. Analysis revealed five themes (i.e., major care gaps) and 12 sub-themes, including: (1) lack of standardized fertility counselling, (2) neglected psycho-emotional impact, (3) limited sexual health education and support, (4) difficulty navigating the healthcare system, and (5) financial toxicity and the cost of being sick young. Conclusions: AYAs in Eastern Ontario face persistent gaps in supportive cancer care that undermine their quality of life. Our findings underscore the need for targeted system-level improvements and offer a foundation for co-designing an evidence-based regional AYA care model that better addresses the holistic needs of this growing population. Full article

Graphene-based nanosheets (GNS), including graphene, graphene oxide (GO), reduced graphene oxide (rGO), and graphene nanoplatelets (GNPs), have attracted increasing attention for developing high-performance and sustainable cementitious composites. Compared with conventional dispersion strategies, graphene-based coating strategies enable the targeted localization of GNS at critical interfacial transition zones (ITZs), thereby maximizing their reinforcing efficiency while mitigating agglomeration issues. This review systematically summarizes recent advances in GNS coating technologies for cementitious composites, including physical adsorption, chemical assembly, electrophoretic deposition, and in situ growth. The effects of GNS coatings on interfacial engineering, mechanical performance, durability enhancement, and smart functionalities are critically discussed. Existing studies indicate that GNS coatings can improve strength, crack resistance, impermeability, and resistance to chloride ingress, freeze–thaw cycles, and other degradation processes mainly through ITZ densification and microstructure refinement. However, these benefits are strongly dependent on the coating method, substrate type, and stability of the graphene–substrate interface in calcium-rich alkaline pore solutions. In particular, physically adsorbed GO coatings may suffer from desorption or Ca²⁺-induced aggregation, chemically assembled coatings require further validation beyond laboratory-scale systems, and electrophoretic deposition is mainly applicable to electrically conductive substrates. In addition, localized conductive networks created by GNS coatings facilitate multifunctional properties such as self-sensing, electromagnetic shielding, and electrothermal performance. From a carbon-neutrality perspective, the improvements in mechanical properties and durability provide opportunities to reduce material consumption, extend service life, and lower life-cycle carbon emissions. Nevertheless, their carbon-neutral contribution should be verified through quantitative life-cycle assessment rather than inferred directly from strength or durability enhancement alone. Finally, the remaining challenges associated with large-scale implementation, long-term stability, cost-effectiveness, and field-scale validation are discussed. Particular attention is given to the fact that most existing evidence is derived from laboratory-scale specimens rather than real structural elements exposed to service environments. Full article

Pb-Zn resources are critical strategic assets for many nations. The Dian-Dongbei (northeastern Yunnan) region in Yunnan Province is a significant production area for these resources in China, boasting considerable prospecting potential. However, conventional exploration methods are increasingly inadequate, as they often fail to rapidly and effectively identify concealed mineralization information. To tackle this challenge, we propose a hybrid GAN-ResNet convolutional neural network methodology. This approach constructs a data-driven prospecting model for Pb-Zn deposits in the Dian-Dongbei region, utilizing multi-source geoscientific data encompassing geology, geophysics, geochemistry, and remote sensing (Geo-Phys-Chem-RS) to conduct quantitative mineral prospectivity mapping. A GAN model was introduced to augment the multi-source geoscientific data based on the concepts of random down-sampling and pseudo-window size. The quality of the generated synthetic samples was evaluated using the Peak Signal-to-Noise Ratio (PSNR) metric. The results show that the synthetic samples achieved an average PSNR value of 33.67 dB, effectively preserving the original features of the geoscientific data. This confirms the feasibility and quality of the data generated by this augmentation method. Furthermore, when applied to train the ResNet model, this augmented data effectively increased the prediction accuracy from 0.765 to 0.842. The results demonstrate that the integrated GAN-ResNet method produces prediction maps with higher accuracy. Moreover, it significantly refines and narrows down the target areas with high mineralization potential. This precision can substantially reduce exploration costs, representing a marked improvement in prediction efficacy. Full article

Electromyography (EMG)-controlled orthoses and wearable assistive systems require classifiers that combine accurate motion recognition, efficient embedded deployment, and support for local model formation. Long-term EMG deployment is affected by signal drift and inter-session variability, motivating architectures that can support post-deployment model updates. Local processing can reduce dependence on external computation and data transfer. To address these challenges, this work presents a hardware-targeted Hyperdimensional Computing (HDC) classifier for trainable EMG classification on the resource-constrained GateMate A1 FPGA from Cologne Chip. The proposed architecture performs on-device HDC model formation and inference directly on FPGA. Linear Discriminant Analysis (LDA) serves as a conventional offline-trained and FPGA-inferred baseline. The evaluation uses eight anonymized single-session EMG recordings from seven healthy participants and one participant with spinal cord injury with 32 channels and five motion classes and includes accuracy, robustness, and routed FPGA implementation analysis. Across all recordings, the per-recording best-case HDC configurations reach 95.20% classification accuracy, while the LDA baseline achieves 98.54% overall accuracy. Under controlled input perturbation with a standard deviation of $\sigma =0.10$, HDC retains 92.98% mean accuracy compared with 80.98% for LDA. A first board-level power measurement indicates an energy cost of approximately 0.40 mJ per inference sample and 0.87 mJ per training sample. The experiments demonstrate single-session on-device HDC model formation and inference, while longitudinal validation, fatigue robustness, electrode-shift robustness, and inter-session adaptation remain future work. The results indicate that HDC provides an architectural foundation for trainable wearable edge–AI systems with local model updates. Full article

We propose a decentralized strategy for coordinating the bidirectional charging and discharging of battery electric vehicles (BEVs) in renewable-powered parking lots. The framework combines mean-field games (MFGs) and model predictive control (MPC) to address the coupled stochastic dynamics induced by uncertain renewable generation and random vehicle arrivals and departures. Solar and wind power fluctuations are modeled using autoregressive moving-average (ARMA) processes, while the time-varying vehicle population is represented through finite Poisson processes. The coordination problem is formulated as a large-scale game, where an aggregator designs individual cost functions to maximize available energy utilization while promoting fairness through near-equal states of charge (SOCs) at departure. Scalability is achieved through MFG theory, ensuring convergence and stability even under highly volatile generation and fluctuating agent populations. Numerical simulations validate the proposed strategy against two straightforward algorithms: capacity-ordered saturation allocation (COSA) and capacity-ordered fair allocation (COFA). These centralized approaches achieve high target fulfillment in static, low-intensity environments, where available energy accommodates a stable fleet without exceeding power limits. However, their efficacy degrades significantly in dynamic, high-intensity environments, where the interplay of volatile generation, continuous fleet turnover, and strict power constraints strains the system. In contrast, the proposed MFG-MPC framework provides a decentralized response that elegantly navigates the trade-offs between energy availability, demand stochasticity, and power limits. Ultimately, this approach ensures robust energy utilization while safeguarding vehicle equity, confirming its strong suitability for real-time deployment. Full article