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Proton, Electron, and Hydrogen-Atom Transfer Thermodynamics of the Metal–Organic Framework, Ti-MIL-125, Are Intrinsically Correlated to the Structural Disorder
Burden and characteristics of inherited retinal diseases in China
It’s not wokeness — it’s human rights
Catalytic Consequences of Pore Structure, Nodal Identity, and Coordination Environment on Styrene Oxidation by Hydrogen Peroxide over Fe MOFs
Exploring associations between sleep duration and performance as well as heart rate variability in elite esports athletes
Abstract Sleep is essential for overall well-being, influencing physical, psychological, and mental health. While research shows that increasing sleep enhances athletic performance, data on the relationship between performance, sleep, and stress response in esports athletes remains limited. Our study aims to explore sleep counseling and its associations on neurocognitive function, gaming performance, and heart rate variability in esports. We enrolled competitive Valorant esports athletes. Participants maintained their regular sleep schedule for four to six weeks, followed by an attempted four-week sleep extension phase. Health data were collected via a wearable device. We evaluated individual performance through neurocognitive tests and by analyzing participants’ weekly match statistics. The study revealed that participants did not significantly increase sleep duration (p = 0.265). We observed improvements in neurocognitive test reaction times (p < 0.01) but not gaming performance. Sleep counseling was also associated with improved heart rate variability (p < 0.05), though the effect size was small. Overall, this study highlights the difficulty of implementing behavioral sleep interventions in elite esports athletes and the need for alternative or more targeted approaches. While the intervention did not achieve significant sleep extension and therefore constrained causal inference, the study demonstrates the feasibility of a rigorous methodological framework for investigating sleep and performance in esports athletes.
Isotropic Zero Thermal Expansion in Yb(Al,Mn)<sub>2</sub>: Achieving Continuous Shiftability over a Wide Temperature Range
Machine learning model optimization for flood susceptibility zonation over the Kosi megafan, Himalayan foreland basin, India
Macromolecular Diamidobenzimidazole Conjugates Can Activate Stimulator of Interferon Genes
In transfusion-dependent thalassemia, neuronal damage biomarkers are associated with affective and chronic fatigue symptoms
Arousal reframed as an organism‑wide dynamic system
The Divergent Reduction Chemistry of Ln(II) Bis(terphenylthiolate) Complexes, Ln(SAr<sup><i>i</i>Pr6</sup>)<sub>2</sub>, Leads to KLn(μ-SAr<sup><i>i</i>Pr6</sup>)<sub>2</sub>, C─H Bond Activation Products, and Two-Electron Reduction Reactivity
Diesel particulate matter-induced proteomic changes in three-dimensional spheroids derived from human primary cells of various tissue origins
Abstract This study investigated the effects of diesel particulate matter (DPM) on multiple human organs using 3D spheroids derived from eight human primary cell types. To assess the impact of DPM, we exposed these spheroids to varying concentrations of standardized DPM (Standard Reference Material, SRM 2975) and measured their viability, followed by proteomic analysis using tandem mass tag (TMT) labeling with liquid chromatography–tandem mass spectrometry (LC-MS/MS). A total of 9,707 proteins were identified, with 128 proteins exhibiting statistically significant changes (P-value < 0.05) in response to DPM exposure, as determined by two-way analysis of variance (ANOVA). Among these, five proteins, including apolipoprotein A-I (APOA1), significantly increased at higher DPM concentrations, while 36 proteins, primarily ribosomal proteins, showed notable decreases even at lower DPM levels. Canonical pathway analysis revealed activation of acute phase response signaling, liver X receptor/retinoid X receptor (LXR/RXR), and farnesoid X receptor (FXR)/RXR pathways across all spheroid types. APOA1 was identified as a potential biomarker for DPM exposure, with increased expression potentially linked to disruption in protein degradation pathways. This study provides valuable insights into the systemic toxicity of DPM, highlighting key proteomic changes across different tissue types and identifying potential biomarkers that could be used to assess exposure levels and health risks related to particulate matter.
Optimizing the Synthesis of Deuterated Isotopomers and Isotopologues of Cyclohexene using Molecular Rotational Resonance Spectroscopy
Integrated kinetic, thermodynamic, and statistical investigation of aniline blue dye removal using magnesium silicate nanoparticles
Abstract Magnesium silicate nanoparticles were found to be an effective adsorbent for aniline blue dye from wastewater. Using the sol-gel technique, magnesium silicate nanoparticles were synthesized and characterized using X-ray diffraction (XRD), transmission electron microscope (TEM), Brunauer-Emmett-Teller (BET) surface area, and fourier transform infrared spectroscopy (FTIR). Aniline blue removal was done at room temperature, pH 4, and a dosage of 3 g/L was about 99% in the first 30 min. Magnesium silicate can also be used for four cycles to adsorb Aniline blue dye without the need for disposal, which supports the principle of recycling. Response surface methodology was used for statistical analysis to investigate the impact of the factors. By studying the isotherms, kinetics, and thermodynamics, it became clear that the adsorption process involves a physical interaction that adheres to the Freundlich isotherm, follows pseudo-first-order kinetics, with the boundary layer (film) diffusion identified as the rate-determining step. The process is endothermic and spontaneous. Magnesium silicate nanoparticles were successfully used to remove dye contaminants from various actual water samples. The material’s reliability and potential for real-world environmental applications were demonstrated by the high efficiency and consistent adsorption results across multiple sample types.
pH-Responsive Self-Assembly of Renal-Clearable Nanoparticles in the Kidneys: One Assembly per Endosome
Searching for effective preprocessing method and CNN based architecture with efficient channel attention on speech emotion recognition
Abstract Recently, Speech emotion recognition (SER) performance has steadily increased as multiple deep learning architectures have adapted. Especially, convolutional neural network (CNN) models with spectrogram data preprocessing are the most popular approach in the SER. However, designing an effective and efficient preprocessing method and a CNN-based model for SER is still ambiguous. Therefore, it needs to search for more concrete preprocessing methods and a CNN-based model for SER. First, to search for a proper frequency-time resolution for SER, we prepare eight different datasets with preprocessing settings. Furthermore, to compensate for the lack of emotional feature resolution, we propose multiple short-term Fourier transform (STFT) preprocessing data augmentation that augments trainable data with all different sizes of windows. Next, because CNN’s channel filters are core to detecting hidden input features, we focus on the channel filters’ effectiveness on SER. To do so, we design several types of architecture that contain a 6-layer CNN model. Also, with efficient channel attention (ECA) that is well known to improve channel feature representation with only a few parameters, we find that it can more efficiently train the channel filters for SER. With two different SER datasets (Interactive Emotional Dyadic Motion Capture, Berlin Emotional Speech Database), increasing the frequency resolution in preprocessing emotional speech can improve emotion recognition performance. Consequently, the CNN-based model with only two ECA blocks can exceed the performance of previous SER models. Especially, with STFT data augmentation, our proposed model achieves the highest performance on SER.