Sarcopenia-related Traits, Body Mass Index and Ovarian Cancer Risk: Investigation of Causal Relationships Through Multivariable Mendelian Randomization Analyses

Objective: This study was aimed at exploring the causal relationships of four sarcopenia-related traits (appendicular lean mass, usual walking pace, right hand grip strength, and levels of moderate to vigorous physical activity) with body mass index (BMI) and ovarian cancer risk, by using univariable and multivariable Mendelian randomization (MR) methods. Materials and Methods: Univariable and multivariable MR was performed to estimate causal relationships among sarcopenia-related traits, BMI, and ovarian cancer risk, in aggregated genome-wide association study (GWAS) data from the UK Biobank. Genetic variants associated with each variable (P < 5 × 10−8) were identified as instrumental variables. Three methods—inverse variance weighted (IVW) analysis, weighted median analysis, and MR-Egger regression—were used. Results: Univariable MR analyses revealed positive causal effects of high appendicular lean mass (P = 0.02) and high BMI (P = 0.001) on ovarian cancer occurrence. In contrast, a genetically predicted faster usual walking pace was associated with lower risk of ovarian cancer (P = 0.03). No evidence was found supporting roles of right hand grip strength and levels of moderate to vigorous physical activity in ovarian cancer development (P = 0.56 and P = 0.22, respectively). In multivariable MR analyses, the association between a genetically predicted faster usual walking pace and lower ovarian cancer risk remained significant (P = 0.047). Conclusions: Our study highlights a role of slower usual walking pace in the development of ovarian cancer. Further studies are required to validate our findings and understand the underlying mechanisms

EEG-Based Auditory Attention Detection via Frequency and Channel Neural Attention

10.1109/thms.2021.3125283IEEE Transactions on Human-Machine System

A Neural-Inspired Architecture for EEG-Based Auditory Attention Detection

10.1109/THMS.2022.317621

Real-Time Detection of Slug Flow in Subsea Pipelines by Embedding a Yolo Object Detection Algorithm into Jetson Nano

In the multiple-phase pipelines in terms of the subsea oil and gas industry, the occurrence of slug flow would cause damage to the pipelines and related equipment. Therefore, it is very necessary to develop a real-time and high-precision slug flow identification technology. In this study, the Yolo object detection algorithm and embedded deployment are applied initially to slug flow identification. The annotated slug flow images are used to train seven models in Yolov5 and Yolov3. The high-precision detection of the gas slug and dense bubbles in the slug flow image in the vertical pipe is realized, and the issue that the gas slug cannot be fully detected due to being blocked by dense bubbles is solved. After model performance analysis, Yolov5n is verified to have the strongest comprehensive detection performance, during which, mAP0.5 is 93.5%, mAP0.5:0.95 is 65.1%, and comprehensive mAP (cmAP) is 67.94%; meanwhile, the volume of parameters and Flops are only 1,761,871 and 4.1 G. Then, the applicability of Yolov5n under different environmental conditions, such as different brightness and adding random obstructions, is analyzed. Finally, the trained Yolov5n is deployed to the Jetson Nano embedded device (NVIDIA, Santa Clara, CA, USA), and TensorRT is used to accelerate the inference process of the model. The inference speed of the slug flow image is about five times of the original, and the FPS has increased from 16.7 to 83.3

Single‐cell third‐generation sequencing‐based multi‐omics uncovers gene expression changes governed by ecDNA and structural variants in cancer cells

Abstract Background Cancer cells often exhibit large‐scale genomic variations, such as circular extrachromosomal DNA (ecDNA) and structural variants (SVs), which have been highly correlated with the initiation and progression of cancer. Currently, no adequate method exists to unveil how these variations regulate gene expression in heterogeneous cancer cell populations at a single‐cell resolution. Methods Here, we developed a single‐cell multi‐omics sequencing method, scGTP‐seq, to analyse ecDNA and SVs using long‐read sequencing technologies. Results and Conclusions We demonstrated that our method can efficiently detect ecDNA and SVs and illustrated how these variations affect transcriptomic changes in various cell lines. Finally, we applied and validated this method in a clinical sample of hepatocellular carcinoma (HCC), demonstrating a feasible way to monitor the evolution of ecDNA and SVs during cancer progression

The dynamic catalysis of Ga/ZSM-5 catalysts for propane-CO 2 coupling conversion to aromatics and syngas

International audienc