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Orthogonal experimental-based thermal management design and simulation optimization of a liquid-cooled battery module
Inter-observer agreement of ultrasound shear wave elastography measurements of renal cortical stiffness and morphometry in diabetic patients
Three-dimensional ultrastructural characterization of Drosophila melanogaster hygrosensilla across humidity conditions
Understanding how organisms detect environmental humidity remains a fundamental problem in sensory biology. While specialised sensory neurons in insect antennae can detect changes in humidity, the mechanism underlying this ability is not fully understood. Here, we present an integrated approach combining precise humidity control, rapid cryo-preservation, and serial block-face scanning electron microscopy (SBF-SEM) to investigate the ultrastructure of hygrosensilla in the vinegar fly Drosophila melanogaster. We developed a deep learning-based segmentation pipeline to analyse three-dimensional structural features of sensilla exposed to different humidity conditions at stable temperature. Our analysis reveals consistent differences in sensilla width between high and low humidity conditions across all chambers of the sacculus. Additionally, we identified chamber-specific patterns in sensilla tapering, indicating specialised structural adaptations across different sensilla populations. The observed structural differences suggest a potential role for mechanical transduction in humidity sensing. This study establishes a technical framework for high-resolution analysis of sensory organs while providing new insights into the structural basis of humidity detection. Our findings advance our understanding of how specialised sensory organs might transduce environmental signals into neural responses.
Spatiotemporal graph neural networks for analyzing the influence mechanisms of river hydrodynamics on microplastic transport processes
Salivary hormonal analysis as a tool for skeletal maturity assessment
Emerging horizons: A Rainbow Model for the sustainable implementation of Rain Classroom in vocational nursing education
Background The COVID-19 pandemic has accelerated several social changes, particularly the shift in education towards online environments. Digital technologies are being extensively used in educational practices, but current research mostly focuses on describing digital teaching practices, with few studies summarising practical experiences and extracting universal principles and patterns. Objective To summarise the practical experience of using Rain Classroom in information technology teaching tools, and to extract universal principles and patterns. Method From July 1 to December 1 2023, eight semi-structured qualitative interviews were conducted with teachers in Hunan, China, who used Rain Classroom for digital teaching. Through multi-level coding, key themes were progressively distilled from interview data, combined with cross-level mapping from the multidimensional perspectives of the Rainbow Framework, ultimately abstracting theoretical principles. Results The convenience, interactivity and diversified learning functions of Rain Classroom’s digital teaching are highlighted in this study. Through the summarisation of practical experience, a theoretical model was established, focusing on Resilience, Adaptability, Inclusivity, Nurturing Continuous Improvement, Balanced Evaluations, Optimal User Experience and Weathering Challenges with Comprehensive Enablers.
Tribological and performance assessment of two wheeler brake pads using dynamometer testing
Abstract Braking systems are critical for vehicle safety, with brake pads influencing performance, durability, and environmental impact. This study compares organic and sintered brake pads, analyzing tribological properties, thermal stability, and wear resistance. Organic pads, made from synthetic fibers and resins, offer low noise and environmental benefits but degrade faster under heat. Sintered pads, composed of fused powdered metals, exhibit superior heat resistance, durability, and braking consistency under extreme conditions. Testing included dynamometer evaluations, measuring stopping distance, deceleration, braking pressure, and temperature variations. Sintered pads maintained a stable coefficient of friction, lower stopping distances, and consistent deceleration over time, requiring less hydraulic pressure for equivalent braking force. Organic pads wore faster and showed increased stopping times. The findings highlight sintered pads as ideal for high-performance applications, while organic pads remain suitable for cost-sensitive, noise-conscious environments, contributing to advancements in automotive safety and sustainable braking solutions.
Operating strategy for load service entities using flexible real-time pricing through stochastic dual dynamic programming
Statin effect on arrhythmogenic cardiomyopathy disease progression (SEARCH): Randomized clinical study protocol
Arrhythmogenic cardiomyopathy (ACM) is an inherited cardiac disorder that predisposes affected individuals, especially young patients, to malignant arrhythmias, sudden cardiac death, and heart failure. The disease is characterized by myocardial atrophy and fibro-fatty replacement, predominantly affecting the right ventricle. Current pharmacological treatments primarily aim to alleviate symptoms by addressing arrhythmias and heart failure. These approaches are often complemented by invasive interventions such as implantable cardioverter defibrillators (ICDs) and radiofrequency ablations. However, none of these strategies effectively halts disease progression, highlighting the urgent need for novel disease-modifying therapies. We recently demonstrated that elevated plasma levels of oxidized low-density lipoprotein (oxLDL) correlate with more advanced stages of ACM in patients. Moreover, treatment with atorvastatin, which reduces oxLDL levels, prevented disease manifestation in a mouse model of ACM. Based on these findings, we hypothesize that statins may attenuate disease progression in ACM patients not only through their lipid-lowering effects, but also via pleiotropic actions such as antioxidant, anti-inflammatory, and autonomic modulation. To test this hypothesis, we designed SEARCH (Statin Effect on ARrhythmogenic CardiomyopatHy), an investigator-initiated, multicenter, prospective, randomized, double-blind, placebo-controlled clinical trial, aimed at evaluating the efficacy of atorvastatin in preventing ACM progression (NCT06922994). A total of 102 patients meeting ACM diagnostic criteria will be enrolled and randomized in a 1:1 ratio to receive either atorvastatin 80 mg/die or placebo for 18 months. The primary outcome will be the change in right ventricular global longitudinal strain, a sensitive echocardiographic measure of ventricular function, from baseline to 18 months. Secondary outcomes will include changes in arrhythmic burden, electrocardiography parameters, additional structural and functional cardiac indices, and circulating biomarkers. Tertiary and exploratory outcomes include the validation of risk scores for ACM progression and the identification of variables predicting the best responders to atorvastatin. Participants will undergo a comprehensive evaluation at baseline, 9 months, and 18 months, including cardiology visits, echocardiography, electrocardiography, blood testing, ICD or loop recorder interrogation, and cardiac magnetic resonance imaging (at enrollment and at 18 months only). Additional safety assessments and telephone follow-ups will be conducted throughout the study to monitor treatment adherence and potential adverse events. The SEARCH trial is expected to generate the first clinical evidence on the efficacy of atorvastatin in slowing ACM progression, thereby addressing a major unmet therapeutic need. The findings will shape the design of future large-scale studies and may pave the way for a novel, disease-modifying treatment strategy to improve outcomes and quality of life for patients with ACM.
An approach for unplanned dilution assessment in open stope with consideration of the stope shape irregularity
Abstract This study aims to propose an assessment tool for unplanned dilution, considering the irregularity of the stope shapes due to the overbreak and underbreak of the stope walls. Data from the Ridder-Sokolny mine, an underground mine, located in East Kazakhstan, was used for the study. On the basis of the collected dilution data, the stopes were categorized into three types depending on the extent of the irregularity: simple, semi-complex, and complex shape. Numerical modelling was performed to illustrate the effect of the stope shape complexity on the overall instability, while the Rock Engineering System (RES) was employed to introduce a Dilution Index (DI) with the purpose of quantifying the stope shape irregularity effect. The results suggested that instabilities and rock failure are likely to occur with an increase in the stope shape complexity as tension increases while the strength factor decreases. Furthermore, the stope shape irregularity had the most significant effect on the DI system compared to other parameters affecting the dilution. The results indicated that the DI is a very good predictor of unplanned dilution and a fairly good predictor of stope loss, which indicates advantages over the conventional dilution graph method. It is concluded that the DI could be an alternative for unplanned stope dilution estimations depending on the stope shape complexity.
Revealing gait as a murine biomarker of injury, disease, and age with multivariate statistics and machine learning
Where does the carbon go? A new carbon balance method to assess what happens to plastics under solar exposure
Plastic pollution is a major and global threat to ecosystems and human health, resulting from the spreading and breakdown of plastic litter in the environment. In an aquatic environment, the first causes of this degradation are exposure to natural ultraviolet light and abrasion or collisions in the water. The extent of such degradation on a plastic object after a given time remains very difficult to quantify, especially regarding the relative production of microplastics, nanoplastics and soluble species along with volatile compounds. All of these degradation products may contribute differently to environmental pollution. Therefore, when evaluating the pollution caused by plastic objects, we should consider how much of each byproduct is generated. We propose a novel method based on conservation of the carbon mass during the degradation process. This approach is the first to enable quantification of carbon retrieved in each type of degradation product (Microplastics, Nanoplastics, Solubles, Volatile Compounds), as well as its evolution with exposure time. By applying this method to polypropylene granules, we demonstrate its effectiveness in tracking carbon footprint throughout the aging process. One of the unexpected results of this study is to show that the amount of carbon released in volatile form is far from negligible (17%) compared to MP (55%). The procedure we present is general enough to be applied to any type of polymer, and can be a valuable tool for assessing the amount of by-products of a given size released into the environment.
Microplastics and invasive crayfish: emerging interactions and ecological implications from three coexisting species in a subalpine lake
Abstract Microplastics (MPs) and invasive species are two of the most pressing threats to freshwater ecosystems, yet their interactions remain underexplored. This study presents the first comparative analysis of MP uptake among three coexisting invasive crayfish species (Faxonius limosus, Pacifastacus leniusculus, and Procambarus clarkii) from Lake Maggiore, a large subalpine lake subjected to intense anthropogenic pressure. A total of 90 individuals were analyzed for biometric traits and MP occurrence in intestinal content, with species identification confirmed via molecular analysis. No significant interspecific differences or correlations with biometric traits were found, though F. limosus showed the highest average concentration. Most MPs were < 1 mm polyester or polyacrylate fibers, suggesting a dominant domestic source. Additionally, this work provides the first evidence of MP uptake in F. limosus, filling a key knowledge gap. Beyond documenting MP ingestion, our findings support the potential of invasive crayfish as agents of MP removal, suggesting an ecological role with important implications for environmental monitoring and ecosystem management. These results also highlight the need for further research on trophic transfer and organ-level accumulation of MPs, especially in widely distributed invasive species that are increasingly relevant for environmental risk assessment.
Clinical significance of Ureaplasma species in bronchopulmonary dysplasia development in preterm infants
Iris color distribution in the United States of America
Objective To document the distribution of iris color in the United States of America. Design This original investigation is an epidemiologic assessment of eye color data from all 50 states’ Department of Motor Vehicles (DMV). Participants All driver’s license holders data nationwide (age 16 years and older, both genders) were requested from the Department of Motor Vehicles (DMVs) in each state. Driver’s license holders from states whose DMV did not participate in the study due to special state-specific regulations, did not compile iris color information, or did not respond were then excluded. Main Outcome Measures Self reported eye color information was obtained from the DMVs databases of each driver’s license applicant self-reported eye color information. Methods All 50 states’ Department of Motor Vehicles in the USA were contacted using various methods and the database of driver licenses eye color for current active licenses (without including any personal information) was requested. Any iris color beyond grey, blue, green, hazel, or brown/black was categorized as “others”. Results Iris color of 235,423,085 driver’s license holders (DLHs) from 31 states was collected. The data show that brown/black iris color was documented in 124,811,254 DLHs (53%), blue in 55,797,458 DLHs (23.7%), hazel in 24,152,854 DLHs (10.3%), green in 21,258,873 DLHs (9%), grey in 1,597,675 DLHs (0.7%), and other iris colors in 7,804,971 DLHs (3.3%). Conclusions Utilizing information from over 230 million driver’s license holders, this report is the largest study of iris color distribution representing the United States of America, thus providing a valuable source for future eye disease and other sociological research. The data show that the most prominent iris color in the United States of America is brown/black, then blue, hazel, green, other iris colors, and grey.
DOD-Boost: a temporal and distribution-optimized deep boosting framework for solar radiation modeling
Abstract This study proposes hybrid solar radiation temporal modeling approaches to support the design of clean energy systems using deep learning techniques and statistical distribution fitting. Solar radiation data are analyzed using a probability distribution to determine whether they follow a known statistical pattern, focusing on total solar radiation on a tilted surface (MJ/m2) ( $$\:{H}_{T}$$ ). Maximum likelihood estimation (MLE), whale optimization algorithm (WOA), and particle swarm optimization (PSO) are used to optimize the process of estimating probability distribution parameters. Subsequently, the cumulative distribution function (CDF) is constructed, and a particular distribution profile is applied to replace the inherent randomness in $$\:{H}_{T}$$ data during the preparation phase of estimation model inputs. In the next step, innovative hybrid $$\:{H}_{T}$$ temporal modeling approaches based on CDF are developed using long short-term memory networks (LSTMs), gated recurrent units (GRUs), and extreme gradient boosting (XGBoost) algorithms. Model results are evaluated through Jensen-Shannon divergence (JSD) analysis. Thus, the DOD-Boost framework is established. According to the findings from comprehensive analyses, DOD-Boost models that integrated a modeling approach for $$\:{H}_{T}$$ , optimization techniques, data preprocessing strategies, and temporal modeling achieved highly accurate predictions. Among all tested models, the Weibull (WOA) – LSTM – XGBoost model achieved the best distributional accuracy, with the lowest JSD value of 0.0084. The JSD metric was prioritized as it provides a more comprehensive assessment of performance by measuring the similarity of the predicted and actual data distributions, which is more informative than simple point predictions for energy planning. Consequently, this study provides a transferable hybrid model for PV-based energy planning that can also be used in developing countries.
A super-resolution network based on dual aggregate transformer for climate downscaling
Antiretroviral treatment outcomes and survival pattern of people living with HIV in Bauchi State, Nigeria
Background Antiretroviral therapy (ART) has greatly improved the survival and quality of life for individuals living with HIV. However, challenges in the prevention of HIV-related mortality and poor retention of patients in ART treatment pose threats to effective ART interventions. This study investigated the incidence, prevalence of ART outcomes, and survival pattern of persons living with HIV (PLHIV) on ART treatment in Bauchi state Nigeria. Methods A retrospective cohort study was conducted to investigate antiretroviral treatment outcomes in a sample of 5,608 HIV-positive persons from two clinics over 3 years from 1st January 2020–31st December 2022. Data was extracted from an electronic medical record (EMR) from treatment facilities, and analyzed to assess the incidence and survival function estimates for ART outcomes including interruption in treatment, lost-to-follow-up (LTFU), mortality, and viral load suppression. Patient baseline demographic characteristics, clinic, pharmacy, and laboratory data were also extracted to examine associations with ART survival outcomes. Descriptives statistics were used to summarize all variables. Meanwhile, to analyze the temporal-trend plot of incidence over the study years, the data were modeled using a Generalized Linear Model (GLM) with a Poisson distribution. Kaplan-Meier survival function plots were used to estimate the survival probability of treatment outcomes, while Cox proportional hazard was modeled to identify independent predictors of survival. Results The incidence of treatment interruption decreased steadily over the three years from 33.33 per 100 person-years (PY) in 2020 to 27.23 per 100PY in 2022. LTFU was shown to be low, decreasing significantly from 20.37 per 100PY in 2020 to 0.69 per 100PY in 2022. Incidence of mortality showed a reducing trend and ranged from 27.78 per 100PY in 2020 to 0.81 per 100PY in 2022. The high incidence observed for viral load suppression reduced slightly for the period of observation from 98.15 per 100PY to 89.62 per 100PY. Survival curves from survival analysis showed a generally decreasing survival probability trend on ART treatment outcomes, indicating a reduced risk of events over time. Additionally, participants with viral loads less than 1000 copies/ml had significantly reduced hazards of loss to follow-up (HR = 0.14, 95% CI: 0.06–0.33, p < 0.001) and death (HR = 0.26, 95% CI: 0.10–0.64, p = 0.003) compared to those with higher viral loads. Based on nutritional status of participants, overweight participants (HR = 0.07, 95% CI: 0.01–0.36, p = 0.001), as well as those with normal BMI (HR = 0.42, 95% CI: 0.18–0.98, p = 0.004) had a significantly reduced hazard of mortality compared with underweight participants. Conclusion This study demonstrated improvements in HIV treatment outcomes. However, health challenges which limit optimal ART outcomes require targeted interventions. The study highlights the importance of integrated care and support systems for optimal treatment and survival.
Exploratory study of non-ordinary states of consciousness during sleep show distinct electrophysiological features from wakefulness and canonical sleep stages
Abstract Consciousness does not always fade during sleep. Instead, it can re-emerge in the form of non-ordinary states of consciousness (NOSC), such as lucid dreams (LDs), sleep paralysis (SP), out-of-body experiences (OBEs), and false awakenings (FAs). While some of these states have been studied phenomenologically, their neurophysiological underpinnings remain unclear. In this exploratory study, we investigate their electrophysiological correlates and distinguish them from standard sleep stages. We conducted overnight polysomnography in frequent experiencers, capturing 10 episodes (3 LDs, 2 SP, 2 OBEs, 3 FAs). Eye movement markers identified periods of lucidity. Relative spectral power was analyzed using principal component analysis (PCA) and permutation-based multivariate analysis of variance (PERMANOVA). Our results indicate that these NOSC are distinct from wakefulness, yet share features with both stage 1 (S1) and rapid eye movement (REM) sleep.