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Effectiveness of voluntary isocapnic hyperpnoea for mitigating hypoxemia and acute mountain sickness in normobaric hypoxia: a randomized crossover pilot trial
Impact of self-efficacy on food waste reduction and its consequences in the solid waste economy using Monte Carlo simulation
Super-resolution reconstruction of industrial PET images using a prior-knowledge-based generative adversarial network
Abstract Positron tomography technology (PET) can adapt to complex on-site environments, enabling industrial non-destructive testing without disturbance or damage. PET super-resolution reconstruction aims to reduce detection costs and improve accuracy, making it highly valuable for research. In this study, we propose a generative adversarial network (GAN)-based super-resolution model for industrial PET images that incorporates prior knowledge to address issues such as detail loss and artifact distortion in existing algorithms. We design a texture enhancement network to extract detailed features and employ a connection network to fuse texture and super-resolution features, enhancing texture details. Additionally, we introduce texture loss and super-resolution loss to further improve the model’s performance. Experimental results demonstrate that the proposed method enhances super-resolution image quality in both visual and objective evaluation metrics and has been validated in practical industrial detection.
RhoA accelerates atherosclerosis progression by interacting with Hspa5
Designing network based intervention strategies for epidemics of infectious diseases from edge based infection probability
Abstract Epidemics underscore the critical role of human contact networks in shaping the spread of infectious diseases. Transmission varies depending on a range of factors, including virus characteristics, the type and duration of contact, and whether it occurs indoors or outdoors. However, not only does the probability of transmission differ, but the impact of each transmission event depends on the ability of a single event to spread the virus to new, previously unaffected, socially segmented groups in society. Effective policymaking should be guided by a nuanced understanding of how infections spread, ensuring that interventions are proportional to the risks they aim to address. In this study, we conducted a series of theoretical experiments on generated networks that are structurally similar to real social contact networks. Using models that distinguish between regular, repeated contacts and occasional, random, or transient contacts, we simulated fictitious epidemics on different sample graphs with varying contact restrictions and then compared their trajectories. Based on the observed differences, we identified the contact types whose restriction can effectively curb the epidemic. We find that it is particularly important to focus on relationships that form a bridge between clusters or communities and on contacts with particularly high transmission probability. By doing so, public health efforts can more effectively balance the dual goals of minimizing transmission and maintaining social and economic stability.
Trait-like individual signatures dominate sleep EEG over insomnia-specific features
Abstract Insomnia-specific features of sleep EEG activity have remained elusive, with existing findings being inconsistent and often weak in effect. Using machine learning, we analyzed two independent electroencephalogram (EEG) datasets spanning two nights (N subjects/nights =198/396), comprising individuals with insomnia disorder (ID) (mild to moderate/severe) and good sleeper controls (GSCs). The findings demonstrated that sleep EEG spectral features differentiated ID from GSC only when using identical participants for training and testing, indicating that model performance was driven by individual EEG signatures instead of ID-related patterns. Analyses with unsupervised learning, similarity matrices, and periodicity assessments further confirmed that brain activity during sleep is characterized by robust, individual-specific EEG signatures with trait-like stability over two nights. We also show that the individual sleep EEG signatures are driven by high frequency cortical activity, previously associated with cortical arousal during sleep. The results then demonstrate that high frequency cortical activity is not specific to ID, but the key to characterizing individual sleep EEG signatures. While ID may be characterized by EEG features beyond spectral power, our findings underscore the importance of a precision brain health framework that prioritizes deviations from an individual’s own neural baseline rather than relying solely on group-level comparisons.
TS-YOLO: a small traffic sign detection algorithm for various harsh driving conditions in bad weather
Solubility of Glibenclamide in supercritical solvent versus pressure and temperature via development of machine learning and rain optimization algorithm
Comparison of human metabolome changes identified in a placebo-controlled amphetamine administration study versus those using forensic toxicology routine data
Health risk assessment of combined exposure to heavy metals diazinon and mycotoxins in Iranian rice
AI-generated artwork detection using self-distilled transformers with global–local feature learning and Grad-CAM interpretability
Physics-informed neural network with weighted loss and hard constraints for hyperbolic conservation laws
Exploring the relative contribution of genetic and external exposomic risk scores to allergies in elderly women
NiO/CuO@Graphene oxide-modified electrode for sensitive detection of an antidiabetic drug
Abstract A new, simple, and cost-effective electrochemical sensor was developed for the determination of Sitagliptin phosphate monohydrate (SP) in powder, pharmaceutical tablet form and biological fluids as spiked plasma. Electrochemical sensor used a chemically modified electrode (CME) based on a glassy carbon electrode (GCE) modified with graphene oxide (GO) and nickel/copper oxide nanoparticles (NiO/CuO NPs). The crystalline nature of GO/ (NiO/CuO NPs), the particle size of (NiO/CuO NPs) and the surface topography of the prepared surface were investigated using surface analysis techniques such as X-ray diffraction (XRD), transmission electron microscopy (TEM) and scanning electron microscopy (SEM) combined with energy dispersive X-ray analysis (EDXA) and elemental mapping. Surface characterization revealed that NiO/CuO NPs had an average size of 27.5 ± 2 nm and were uniformly distributed homogenously on the surface of GO. This distribution increased the effective surface area and enhanced electron transfer, confirming the structural and electrochemical advantages of the modified electrode material. The sensor efficiency was validated by different electrochemical techniques such as differential pulse voltammetry (DPV), cyclic voltammetry (CV), and electrical impedance (EIS). The Voltammetric response of SP powder form toward sensor was observed at − 0.1 V in DPV and − 0.055 V in CV, using 0.1 M phosphate-buffered solution (PBS, pH 7.4) as the electrolyte. The linear concentration range for SP in powder form was 0.05–1.071 mM, with a limit of detection (LOD) of 0.0223 mM and a limit of quantification (LOQ) of 0.0677 mM while, for powder in spiked plasma linear range was 0.0295–0.2715 mM, with LOD of 0.0061 mM and LOQ of 0.0185 mM. The diffusion coefficient (D) of 0.614 mM SP in PBS (pH 7.4, 0.1 mol/L) was evaluated using CV at various scan rates and found to be 2.41 × 10⁻⁶ cm²/s, based on the Randles–Sevcik equation. The method was validated by detecting SP in both powder and tablet forms using an Agilent 1200 HPLC system equipped with a UV detector at 266 nm. Statistical calculations such as recovery and precision were carried out to validate the accuracy and reliability of the method. The recovery values for both the electrochemical and HPLC-UV methods ranged from 99% to 101%, while the repeatability and reproducibility of the electrochemical method showed RSD values below 1.5%. The sensor demonstrated good sensitivity and selectivity toward SP, even in the presence of potential interferents such as glucose, ascorbic acid, and metal ions.