Spliceosome component Usp39 contributes to hepatic lipid homeostasis through the regulation of autophagy.

Regulation of alternative splicing (AS) enables a single transcript to yield multiple isoforms that increase transcriptome and proteome diversity. Here, we report that spliceosome component Usp39 plays a role in the regulation of hepatocyte lipid homeostasis. We demonstrate that Usp39 expression is downregulated in hepatic tissues of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) subjects. Hepatocyte-specific Usp39 deletion in mice leads to increased lipid accumulation, spontaneous steatosis and impaired autophagy. Combined analysis of RNA immunoprecipitation (RIP-seq) and bulk RNA sequencing (RNA-seq) data reveals that Usp39 regulates AS of several autophagy-related genes. In particular, deletion of Usp39 results in alternative 5' splice site selection of exon 6 in Heat shock transcription factor 1 (Hsf1) and consequently its reduced expression. Importantly, overexpression of Hsf1 could attenuate lipid accumulation caused by Usp39 deficiency. Taken together, our findings indicate that Usp39-mediated AS is required for sustaining autophagy and lipid homeostasis in the liver.

A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location.

In this paper, a cutting-edge video target tracking system is proposed, combining feature location and blockchain technology. The location method makes full use of feature registration and received trajectory correction signals to achieve high accuracy in tracking targets. The system leverages the power of blockchain technology to address the challenge of insufficient accuracy in tracking occluded targets, by organizing the video target tracking tasks in a secure and decentralized manner. To further enhance the accuracy of small target tracking, the system uses adaptive clustering to guide the target location process across different nodes. In addition, the paper also presents an unmentioned trajectory optimization post-processing approach, which is based on result stabilization, effectively reducing inter-frame jitter. This post-processing step plays a crucial role in maintaining a smooth and stable track of the target, even in challenging scenarios such as fast movements or significant occlusions. Experimental results on CarChase2 (TLP) and basketball stand advertisements (BSA) datasets show that the proposed feature location method is better than the existing methods, achieving a recall of 51% (27.96+) and a precision of 66.5% (40.04+) in the CarChase2 dataset and recall of 85.52 (11.75+)% and precision of 47.48 (39.2+)% in the BSA dataset. Moreover, the proposed video target tracking and correction model performs better than the existing tracking model, showing a recall of 97.1% and a precision of 92.6% in the CarChase2 dataset and an average recall of 75.9% and mAP of 82.87% in the BSA dataset, respectively. The proposed system presents a comprehensive solution for video target tracking, offering high accuracy, robustness, and stability. The combination of robust feature location, blockchain technology, and trajectory optimization post-processing makes it a promising approach for a wide range of video analytics applications, such as surveillance, autonomous driving, and sports analysis.

Filtering electrocardiographic signals using an unbiased and normalized adaptive noise reduction system.

We present a novel unbiased and normalized adaptive noise reduction (UNANR) system to suppress random noise in electrocardiographic (ECG) signals. The system contains procedures for the removal of baseline wander with a two-stage moving-average filter, comb filtering of power-line interference with an infinite impulse response (IIR) comb filter, an additive white noise generator to test the system's performance in terms of signal-to-noise ratio (SNR), and the UNANR model that is used to estimate the noise which is subtracted from the contaminated ECG signals. The UNANR model does not contain a bias unit, and the coefficients are adaptively updated by using the steepest-descent algorithm. The corresponding adaptation process is designed to minimize the instantaneous error between the estimated signal power and the desired noise-free signal power. The benchmark MIT-BIH arrhythmia database was used to evaluate the performance of the UNANR system with different levels of input noise. The results of adaptive filtering and a study on convergence of the UNANR learning rate demonstrate that the adaptive noise-reduction system that includes the UNANR model can effectively eliminate random noise in ambulatory ECG recordings, leading to a higher SNR improvement than that with the same system using the popular least-mean-square (LMS) filter. The SNR improvement provided by the proposed UNANR system was higher than that provided by the system with the LMS filter, with the input SNR in the range of 5-20 dB over the 48 ambulatory ECG recordings tested.