Explore the vast range of titles available.

Pilots are crucial to the safety of the airline industry; as a result, their sleep and biorhythm, which are closely related to fatigue, play an important role. During the COVID-19 pandemic, the Civil Aviation Administration of China exempted flight crews from mandatory layovers and imposed limitations on duty period and flight times. Given that the effect of this policy on their sleep and biorhythm is poorly understood, this study explores the key factors affecting the sleep and biorhythm of pilots on intercontinental flights and compares the rest status on and after flying days on exempt and non-exempt flights. Eighty pilots flying from China to five countries wore a body movement recorder, which has been validated for estimating total sleep time, sleep efficiency, and interdaily stability. The results of the K-means clustering analysis showed differences in sleep and biorhythm on flying days between departures during the day and at night, west–east and north–south flights, and exempt and non-exempt flights. ANOVA was performed based on the categorization in which each indicator contributed significantly to the clustering ( p  = 0.000). This study contributes to the literature by validating a new intercontinental flight operation model under the COVID-19 pandemic conditions and proposes critical points for the future management of pilot fatigue on long-haul flights.

High-power ultrasound has been widely used to regulate the anti-digestibility of starch-based products, including the formation of resistant starch (RS-V) in amylopectin assemblies. This can contribute to the attenuation of postprandial hyperglycemia. However, the mechanisms by which high-power ultrasound modulates RS-V remain to be elucidated. Therefore, nano-scale Euryale ferox amylopectin (EFA) ternary assemblies were constructed under high-power ultrasound. All EFA assemblies exhibited ternary self-assembly peaks and V-type crystallinity. Combined chemometric analyses revealed that, with increasing ultrasound power, the rising self-assembly sites within B2 and C chains promoted the increase in self-assembly index but decreased semicrystalline lamellae thickness and structural fractal dimension. Consequently, a compact and ordered molecular cross-linking network was formed, contributing to increases in residual crystallinity, molecular weight, short-range order, and molecular density. This resulted in the shrinkage of digestion channel structures and optimization of the molecular gel network. As a result, the reduction in hydrolysis sites with increasing ultrasound power led to increased RS-V content (22.66–60.17%), causing a decline in the estimated glycemic index. The EFA–lauric acid–lactoglobulin assemblies prepared under 600 W ultrasound were the optimal composition and exhibited enhanced anti-digestibility relative to amylopectin assemblies derived from staple crops such as white waxy maize. The present investigation not only serves as a valuable supplement for studying the precise regulation mechanisms of nano-scale amylopectin RS-V, but also provides critical theoretical guidance for the development of foods aimed at preventing hyperglycemia.

Objective Cognitive dysfunction is one of the main clinical features in patients with cerebral small vessel disease (CSVD). Over time, cognitive decline related to CSVD might evolve into dementia. This study aims to explore the changes in brain functional networks of CSVD patients with cognitive impairment through microstate analysis of resting-state electroencephalogram, and to investigate the association between these changes and cognitive dysfunction. Methods A cohort of 42 CSVD patients from the Department of Neurology at the Third People's Hospital of Mianyang City was recruited between September 2023 and March 2024, constituting the CSVD group. Concurrently, 40 individuals who underwent health checkups served as the control group (HC group). Both groups underwent neuropsychological assessments and 19-channel video EEG recordings. EEG data were analyzed using the MICROSTATELAB 1.0 toolbox to identify microstates. Four microstate prototypes (Ms) were derived, and their temporal characteristics were compared across groups. Moreover, correlations between these characteristics and neuropsychological scale scores were examined. Results 1. Neuropsychological assessments indicated that the CSVD group scored significantly lower than the HC group in general cognitive level, memory, visuospatial ability, executive function, and language function ( p  < 0.05). 2. In resting-state EEG microstate analysis, the CSVD group exhibited significantly lower appearance rates for MsA, MsB, MsC, and MsD compared to the HC group ( p  = 0.014, p  = 0.047, p  = 0.014, p  = 0.02). Durations of MsB, MsC, and MsD were prolonged in the CSVD group ( p  = 0.014, p  = 0.037, p  = 0.036). Furthermore, the MsC to MsA transition rate was significantly reduced in the CSVD group ( p  = 0.036). 3. Correlation analysis within the CSVD group revealed several significant relationships. The MsD to MsC transition rate was negatively correlated with the AD8 score ( p  = 0.049, r  =  − 0.309). The MsD occurrence rate was negatively correlated with the FAQ score ( p  = 0.039, r  =  − 0.324). The MsA to MsC transition rate was positively correlated with the FAQ score ( p  = 0.038, r  = 0.326). The MsD occurrence rate was positively correlated with MMSE ( p  = 0.027, r  = 0.345). The MsA to MsB transition rate was negatively correlated with MMSE ( p  = 0.041, r  =  − 0.321). The MsB duration was negatively correlated with DST scores ( p  = 0.027, r  =  − 0.345). The MsD occurrence was positively correlated with BNT scores ( p  = 0.031, r  = 0.338). MsA coverage, MsA to MsB transition rate, and MsB to MsA transition rate were negatively correlated with Stroop C scores [( p  = 0.019, r  =  − 0.364), ( p  = 0.01, r  =  − 0.399), ( p  = 0.022, r  =  − 0.358)]. The MsC to MsD transition rate and MsD to MsC transition rate were positively correlated with the Stroop C score [( p  = 0.014, r  = 0.381), ( p  = 0.01, r  = 0.397)]. Conclusion The CSVD group demonstrated increased stability in brain functional networks but reduced inter-network interaction capabilities. Network activities related to MsA and MsB were enhanced, while those related to MsC were diminished. Abnormal changes in microstates were correlated with cognitive deficits in CSVD patients, suggesting that microstate alterations could serve as potential biomarkers for CSVD-related cognitive impairment.

To investigate the interaction effects of prolonged working periods and different task loads on response lapses, focusing on the mechanisms of delayed responses and error lapses. Professionals such as pilots, truck drivers, and nurses often face extended work hours and fluctuating task loads. While these factors individually affect performance, their interaction and its impact on response lapses remain unclear. Twenty participants completed the Uchida–Kraepelin (U–K) Psychological Test and a dual-task version with functional near-infrared spectroscopy. Independent variables were time-on-task and task load. Dependent variables included measures of fatigue, arousal, workload, task performance (delayed and error rates), and brain functional connectivity. Both time-on-task and task load significantly affected cerebral connectivity, response lapses, workload (frustration level), fatigue, and arousal. Arousal levels significantly decreased and reaction times increased after 60 min of work. Cognitive resource regulation became challenging after 90 min under high task load levels. A decline in the connection between the prefrontal and occipital cortex during high-load tasks was observed. The findings provide insight into the mechanisms of response lapses under different task load levels and can inform strategies to mitigate these lapses during extended work periods. This study’s findings can be applied to improve work schedules and fatigue management in industries like aviation, transportation, and healthcare, helping reduce response lapses and errors during extended work periods under high task load conditions.

Objective: To introduce contrast-enhanced 3D-STIR-VISTA sequence that would improve the image quality for the brachial plexus imaging and enhance the contrast between the brachial plexus and surrounding tissues.Methods: Thirty subjects (average age, 47.33 ± 15.15 years; 22 males and 8 females) were enrolled, including 7 patients with brachial plexus injuries, 4 patients with schwannomas, 1 patient with neurofibroma, 1 patient with thoracic outlet syndrome, 1 patient with metastasis, 1 patient with brachial plexus neuritis, and 15 patients without abnormal findings. Scores of unenhanced and contrast-enhanced 3D-STIR-VISTA images using a 5-point scale were compared by Wilcoxon’s signed-rank test. The sigl intensity (SI), sigl to noise ratio (SNR), contrast to noise ratio (CNR) and contrast ratio (CR) between 3D-STIR-VISTA images without and with contrast agent were compared by the paired Student t-test.Results: The SNRs of the brachial plexus between 3D-STIR-VISTA without and with contrast agent were not significantly different, while SNRs of surrounding tissues were significantly decreased with contrast agent. The CNRs of 3D-STIR-VISTA images with contrast agent were significantly higher than that without contrast agent. The 3D-STIR-VISTA sequence with contrast agent exhibited a statistically higher CR than that without contrast agent. The average score for 3D-STIR-VISTA images with contrast agent was significantly higher than that without contrast agent.Conclusion: The 3D-STIR-VISTA sequence with contrast agent is qualitatively and quantitatively superior to that without a contrast agent. The contrast-enhanced 3D-STIR-VISTA sequence can provide distinct visualization of the brachial plexus and enhance the contrast between the brachial plexus and surrounding tissues.

Objective Polycystic ovary syndrome (PCOS) is a multifactorial endocrine disorder characterized by reproductive and metabolic abnormalities. This study aimed to identify key immunometabolic regulators in endometrial tissue and construct a predictive model for PCOS using machine learning approaches. Methods Three endometrial transcriptomic datasets (GSE277906, GSE193123, GSE199225) were integrated and analyzed for differentially expressed genes (DEGs), immune cell infiltration, and metabolic pathway enrichment. Core genes were identified via protein–protein interaction networks and functional annotation. A predictive model was developed using SVM-RFE, XGBoost, and random forest algorithms and validated through qRT-PCR on granulosa cell samples. Results Five core metabolism-related genes were identified, among which ACO1 was consistently downregulated and negatively correlated with CD8⁺ T cell infiltration. High ACO1 expression was enriched in oxidative phosphorylation and mTOR signaling, while low expression was associated with immune activation. The random forest model incorporating ACO1, CHPF, and STOML1 achieved strong predictive performance (AUC = 0.800). Discussion ACO1 may function as an immunometabolic modulator by linking iron metabolism, oxidative stress, and T cell activity. Its downregulation may contribute to local immune suppression and endometrial dysfunction in PCOS. The tissue-level model demonstrated good diagnostic value and biological interpretability across cohorts. Conclusion This study highlights ACO1 as a key biomarker of immunometabolic dysregulation in PCOS and presents a robust predictive model for early diagnosis. The findings offer new insights into the molecular mechanisms underlying PCOS and suggest potential targets for precision treatment.

We propose a low-frequency magnetic sensing method using a magnetically modulated microcavity resonant mode. Our magnetically sensitive unit with periodically changing magnetic poles is formed by combining an AC excitation coil with a microcavity. The microcavity vibrates at the frequency of the AC amplitude-modulated signal and changes its resonant mode when the sensing unit interacts with a low-frequency magnetic field. Signal processing is performed on the resonant spectrum to obtain low-frequency magnetic signals. The results of the experiment show that the measured sensitivity to a 0.5 Hz magnetic field is 12.49 V/mT, and a bias instability noise of 16.71 nT is achieved. We have extended the measurable frequency range of the whispering gallery mode microcavity magnetometer and presented a development in microcavity magnetic sensing and optical readout.

This study examined how complexes formed between breadfruit starch, lauric acid (LA), and phenolic acids (gallic/GA, 3,4-dihydroxyphenylacetic/DOPAC, caffeic/CA) affect starch digestibility and properties. All complexes increased resistant starch (RS) content from 47.19% (native) to 49.12–70.14%, with the caffeic acid-starch binary complex showing the highest RS (70.14%) and lowest hydrolysis index (52.60). LA-containing samples formed V-type inclusion complexes, evidenced by a cooling-phase viscosity peak, while polyphenol-only samples did not. The formation of the complex raised the starch gelatinization peak temperature from 78.6 °C to 100.6–120.9 °C. Structural analysis indicated increased short-range order and crystallinity. Ternary complexes exhibited lower short-range order but higher crystallinity than binary complexes, suggesting LA primarily influences long-range order, while polyphenols affect both short- and long-range structure. These findings clarify interaction mechanisms in starch–lipid–polyphenol systems for designing low-digestibility foods.

In order to improve the accuracy of yield risk evaluation, an intelligent evaluation model of yield risk based on empirical probability distribution is constructed. The dimensionality reduction method of risk factor based on principal component analysis is adopted. After adjusting the multiple data dimensions of risk factors that affect the rate of return to a unified dimension, the cluster-based evaluation index screening method is used to build the evaluation index set that best reflects the risk of the rate of return; The index weight vector equation method based on entropy weight and information entropy is used to set the evaluation index weight. Through the comprehensive evaluation model based on the empirical probability distribution of risk indicators, the empirical probability distribution information of risk indicators at all levels is analyzed, and the risk level of yield is intelligently evaluated. The research structure shows that the model can effectively evaluate the level of return risk and provide an effective reference for preventing and controlling investment return risk.

Urban expansion occurs in horizontal and vertical directions, but the construction process of a traditional planar ecological network (EN) ignores the ecological protection of the vertical space. Birds, as representative species in urbanized areas, are important for expanding the vertical analysis dimension of ENs. In this study, urban parks with good habitat quality were extracted as ecological sources from the perspective of birds. Then, 2D and 3D ecological resistance surfaces were constructed on the basis of the perspective of influencing bird migration, and planar and 3D ecological corridors were extracted and compared for analysis. Finally, accessibility analysis was performed to identify the parks in the study area that need priority protection. Results show that building height is an important factor affecting bird migration, and the introduction of 3D resistance surfaces into EN construction is important for adapting to the trend of vertical urban expansion. The combination of accessibility and EN analyses can help identify ecological spaces having the dual role of “species conservation” and “human well-being enhancement.” This study can provide a reference for refining the service species of ENs, exploring the vertical dimension of ENs, and improving the health of urban ecosystems and the rationality of urban planning.