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Abstract Human microbiota mainly resides on the skin and in the gut. Human gut microbiota can produce a variety of short chain fatty acids (SCFAs) that affect many physiological functions and most importantly modulate brain functions through the bidirectional gut-brain axis. Similarly, skin microorganisms also have identical metabolites of SCFAs reported to be involved in maintaining skin homeostasis. However, it remains unclear whether these SCFAs produced by skin bacteria can affect brain cognitive functions. In this study, we hypothesize that the brain’s functional activities are associated with the skin bacterial population and examine the influence of local skin-bacterial growth on event-related potentials (ERPs) during an oddball task using EEG. Additionally, five machine learning (ML) methods were employed to discern the relationship between skin microbiota and cognitive functions. Twenty healthy subjects underwent three rounds of tests under different conditions—alcohol, glycerol, and water. Statistical tests confirmed a significant increase in bacterial population under water and glycerol conditions when compared to the alcohol condition. The metabolites of bacteria can turn phenol red from red–orange to yellow, confirming an increase in acidity. P3 amplitudes were significantly enhanced in response to only oddball stimulus at four channels (Fz, FCz, and Cz) and were observed after the removal of bacteria when compared with that under the water and glycerol manipulations. By using machine learning methods, we demonstrated that EEG features could be separated with a good accuracy (> 88%) after experimental manipulations. Our results suggest a relationship between skin microbiota and brain functions. We hope our findings motivate further study into the underlying mechanism. Ultimately, an understanding of the relationship between skin microbiota and brain functions can contribute to the treatment and intervention of diseases that link with this pathway.

Climate change has exacerbated severe rainfall events, leading to rapid and unpredictable fluctuations in river water levels. This environment necessitates the development of real-time, automated systems for water level detection. Due to degradation, traditional methods relying on physical river gauges are becoming progressively unreliable. This paper presents an innovative methodology that leverages ResNet-50, a Convolutional Neural Network (CNN) model, to identify distinct water level features in Closed-Circuit Television (CCTV) river imagery of the Chengmei Bridge on the Keelung River in Neihu District, Taiwan, under various weather conditions. This methodology creates a virtual water gauge system for the precise and timely detection of water levels, thereby eliminating the need for dependable physical gauges. Our study utilized image data from 1 March 2022 to 28 February 2023. This river, crucial to the ecosystems and economies of numerous cities, could instigate a range of consequences due to rapid increases in water levels. The proposed system integrates grid-based methods with infrastructure like CCTV cameras and Raspberry Pi devices for data processing. This integration facilitates real-time water level monitoring, even without physical gauges, thus reducing deployment costs. Preliminary results indicate an accuracy range of 83.6% to 96%, with clear days providing the highest accuracy and heavy rainfall the lowest. Future work will refine the model to boost accuracy during rainy conditions. This research introduces a promising real-time river water level monitoring solution, significantly contributing to flood control and disaster management strategies.

IntroductionSarcopenia and frailty are well-known public health problems in middle-aged and older people. Calf circumference (CC) is a representative anthropometric index that may be useful for screening sarcopenia. Physical performance, assessed by hand grip strength and gait speed, measures sarcopenia and frailty. This community-based, cross-sectional study was conducted in Guishan District, Taoyuan City, between April and October 2017 to investigate the relationship between CC and physical performance among community-dwelling middle-aged, older people in Taiwan and to evaluate potential sex differences. CC tends to be an efficient predictor of physical performance in community health screenings and outpatient clinics for community health examinations, where there is limited time for surveys.MethodsA total of 1,308 volunteers aged 50–85 were recruited. Volunteers who declined to participate, those with recent cardiovascular disease, and those with an inability to complete an interview, physical performance examinations, and body composition measurements were excluded from the study. A total of 828 participants were enrolled in this study (237 men and 591 women). The statistical methods applied in this study were the Mann–Whitney U-test, independent two-sample t-test, Chi-square test, and multivariate logistic regression models.Result and discussionSignificant differences were observed in age, waist circumference, appendicular skeletal mass index, calf circumference, hand grip strength, and income between men and women. No significant differences were observed between the men and women regarding body mass index, gait speed, exercise habits, or underlying disorders of diabetes mellitus, hypertension, or hyperlipidemia. Comparing across three different CC tertiles, we discovered significant differences in age, body mass index, waist circumference, appendicular skeletal muscle index, gait speed, and hand grip strength in both men and women. On multivariate logistic regression, after adjusting for age, appendicular skeletal mass index, body mass index, exercise habits, income levels, and CC were positively correlated with physical performance as measured by both gait speed (β = 0.15, p = 0.01) and hand grip strength (β = 0.25, p < 0.001) in women, compared to only hand grip strength (β = 0.41, p < 0.001) in men. Lower calf circumference is an independent risk factor for poor physical performance, especially among women.

The “blue shark”, Prionace glauca (class: Chondrichthyes), is a pelagic shark species commonly found in tropical and temperate oceans. This shark is mainly sold in Asian countries as food and as traditional Chinese medicine. According to the Red List of the International Union for the Conservation of Nature, P. glauca is classified as low-risk to near endangered. P. glauca cartilage contains collagen type II, which makes it suitable as a bioactive ingredient in cosmeceutical products. This study evaluated the effects of a gel containing various concentrations (0.125–5%) of lyophilized hydrolyzed P. glauca cartilage on the human inner wrist skin compared to a placebo (base). A skin properties evaluation test was conducted before and after applying various concentrations (0.125–5%) of the P. glauca cartilage gel for 10 and 20 min on the inner wrists of participants using a skin analyzer that determined the moisture level, oil level, texture level, complexion level, and the 3D level. Adding lyophilized hydrolyzed shark cartilage (LHSC) significantly improved the moisture, texture, and complexion of the skin while controlling oil and providing a wrinkle-smoothing effect. The result indicated that LHSC formulations were prepared at different concentrations, and they had significantly enhanced effects on skin hydration and elasticity (texture) and the smoothing of wrinkles (3D level). The LHSC also effectively controlled oil secretion and the complexion.

The layered coding structure of scalable video coding (SVC) with adaptive inter-layer prediction causes noticeable computational complexity increments when compared to existing video coding standards. To lighten the computational complexity of SVC, we present a fast algorithm to speed up the inter-mode decision process. The proposed algorithm terminates inter-mode decision early in the enhancement layers by estimating the ratedistortion (RD) cost from the macroblocks of the base layer and the enhancement layer in temporal, spatial, and inter-layer directions. Moreover, a search range decision algorithm is also proposed in this paper to further increase the motion estimation speed by using the motion vector information from temporal, spatial, or inter-layer domains. Simulation results show that the proposed algorithm can determine the best mode and provide more efficient total coding time saving with very slight RD performance degradation for spatial and quality scalabilities. [ABSTRACT FROM AUTHOR]