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Branched-chain fatty acids (BCFAs) are natural components with a variety of biological activities. However, the regulation of lipid metabolism by BCFAs is unknown. It was dedicated to examining the impacts of BCFAs inferred from yak ghee on the expression of qualities related to lipid metabolism, natural pathways, and intestinal microbiota in mice. The treatment group (purified BCFAs from yak ghee) exhibited a decrease in cholesterol levels; a decrease in HMGCR levels; downregulation of FADS1, FADS2, ACC-α, FAS, GAPT4, GPAM, ACSL1, THRSP, A-FABP, and PPARα gene expression; and upregulation of SCD1, ACSS1, FABP1, CPT1, and DGAT-1 gene expression. Gut microbiota 16S rDNA sequencing analysis showed that the treatment group improved the gut microbiota by increasing the relative abundances and increasing the short-chain fatty acid levels produced by the genera Akkermansia, Clostridium, Lachnospiraceae, Lactobacillus, Anaerotaenia, and Prevotella. After adding BCFAs to cultured breast cancer cells, pathways that were downregulated were found to be related to fatty acid degradation and fatty acid metabolism, while 20 other pathways were upregulated. Our results suggest that BCFAs reduce body fat in mice by modulating intestinal flora and lipid metabolism and modulating fatty acid metabolism in breast cancer cells. [ABSTRACT FROM AUTHOR]

Detecting objects, particularly naval mines, on the seafloor is a complex task. In naval mine countermeasures (MCM) operations, sidescan or synthetic aperture sonars have been used to search large areas. However, a single sensor cannot meet the requirements of high-precision autonomous navigation. Based on the ORB-SLAM3-VI framework, we propose ORB-SLAM3-VIP, which integrates a depth sensor, an IMU sensor and an optical sensor. This method integrates the measurements of depth sensors and an IMU sensor into the visual SLAM algorithm through tight coupling, and establishes a multi-sensor fusion SLAM model. Depth constraints are introduced into the process of initialization, scale fine-tuning, tracking and mapping to constrain the position of the sensor in the z-axis and improve the accuracy of pose estimation and map scale estimate. The test on seven sets of underwater multi-sensor sequence data in the AQUALOC dataset shows that, compared with ORB-SLAM3-VI, the ORB-SLAM3-VIP system proposed in this paper reduces the scale error in all sequences by up to 41.2%, and reduces the trajectory error by up to 41.2%. The square root has also been reduced by up to 41.6%. [ABSTRACT FROM AUTHOR]

The Wasserstein distance, especially among symmetric positive-definite matrices, has broad and deep influences on the development of artificial intelligence (AI) and other branches of computer science. In this paper, by involving the Wasserstein metric on S P D (n) , we obtain computationally feasible expressions for some geometric quantities, including geodesics, exponential maps, the Riemannian connection, Jacobi fields and curvatures, particularly the scalar curvature. Furthermore, we discuss the behavior of geodesics and prove that the manifold is globally geodesic convex. Finally, we design algorithms for point cloud denoising and edge detecting of a polluted image based on the Wasserstein curvature on S P D (n) . The experimental results show the efficiency and robustness of our curvature-based methods. [ABSTRACT FROM AUTHOR]

Electrocardiograms (ECG) analysis is one of the most important ways to diagnose heart disease. This paper proposes an efficient ECG classification method based on Wasserstein scalar curvature to comprehend the connection between heart disease and the mathematical characteristics of ECG. The newly proposed method converts an ECG into a point cloud on the family of Gaussian distribution, where the pathological characteristics of ECG will be extracted by the Wasserstein geometric structure of the statistical manifold. Technically, this paper defines the histogram dispersion of Wasserstein scalar curvature, which can accurately describe the divergence between different heart diseases. By combining medical experience with mathematical ideas from geometry and data science, this paper provides a feasible algorithm for the new method, and the theoretical analysis of the algorithm is carried out. Digital experiments on the classical database with large samples show the new algorithm's accuracy and efficiency when dealing with the classification of heart disease. [ABSTRACT FROM AUTHOR]