Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation

In this work, we propose a multi-view image translation framework, which can translate contrast-enhanced T1 (ceT1) MR imaging to high-resolution T2 (hrT2) MR imaging for unsupervised vestibular schwannoma and cochlea segmentation. We adopt two image translation models in parallel that use a pixel-level consistent constraint and a patch-level contrastive constraint, respectively. Thereby, we can augment pseudo-hrT2 images reflecting different perspectives, which eventually lead to a high-performing segmentation model. Our experimental results on the CrossMoDA challenge show that the proposed method achieved enhanced performance on the vestibular schwannoma and cochlea segmentation.Comment: 9 pages, 4 figure

DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework

Childhood and adolescent obesity rates are a global concern because obesity is associated with chronic diseases and long-term health risks. Artificial intelligence technology has emerged as a promising solution to accurately predict obesity rates and provide personalized feedback to adolescents. This study emphasizes the importance of early identification and prevention of obesity-related health issues. Factors such as height, weight, waist circumference, calorie intake, physical activity levels, and other relevant health information need to be considered for developing robust algorithms for obesity rate prediction and delivering personalized feedback. Hence, by collecting health datasets from 321 adolescents, we proposed an adolescent obesity prediction system that provides personalized predictions and assists individuals in making informed health decisions. Our proposed deep learning framework, DeepHealthNet, effectively trains the model using data augmentation techniques, even when daily health data are limited, resulting in improved prediction accuracy (acc: 0.8842). Additionally, the study revealed variations in the prediction of the obesity rate between boys (acc: 0.9320) and girls (acc: 0.9163), allowing the identification of disparities and the determination of the optimal time to provide feedback. The proposed system shows significant potential in effectively addressing childhood and adolescent obesity

The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets

IntroductionThe brain-computer interface (BCI) allows individuals to control external devices using their neural signals. One popular BCI paradigm is motor imagery (MI), which involves imagining movements to induce neural signals that can be decoded to control devices according to the user's intention. Electroencephalography (EEG) is frequently used for acquiring neural signals from the brain in the fields of MI-BCI due to its non-invasiveness and high temporal resolution. However, EEG signals can be affected by noise and artifacts, and patterns of EEG signals vary across different subjects. Therefore, selecting the most informative features is one of the essential processes to enhance classification performance in MI-BCI.MethodsIn this study, we design a layer-wise relevance propagation (LRP)-based feature selection method which can be easily integrated into deep learning (DL)-based models. We assess its effectiveness for reliable class-discriminative EEG feature selection on two different publicly available EEG datasets with various DL-based backbone models in the subject-dependent scenario.Results and discussionThe results show that LRP-based feature selection enhances the performance for MI classification on both datasets for all DL-based backbone models. Based on our analysis, we believe that it can broad its capability to different research domains

Farnesol prevents aging-related muscle weakness in mice through enhanced farnesylation of Parkin-interacting substrate

Peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) is a master regulator of mitochondrial biogenesis. Reduced PGC-1α abundance is linked to skeletal muscle weakness in aging or pathological conditions, such as neurodegenerative diseases and diabetes; thus, elevating PGC-1α abundance might be a promising strategy to treat muscle aging. Here, we performed high-throughput screening and identified a natural compound, farnesol, as a potent inducer of PGC-1α. Farnesol administration enhanced oxidative muscle capacity and muscle strength, leading to metabolic rejuvenation in aged mice. Moreover, farnesol treatment accelerated the recovery of muscle injury associated with enhanced muscle stem cell function. The protein expression of Parkin-interacting substrate (PARIS/Zfp746), a transcriptional repressor of PGC-1α, was elevated in aged muscles, likely contributing to PGC-1α reduction. The beneficial effect of farnesol on aged muscle was mediated through enhanced PARIS farnesylation, thereby relieving PARIS-mediated PGC-1α suppression. Furthermore, short-term exercise increased PARIS farnesylation in the muscles of young and aged mice, whereas long-term exercise decreased PARIS expression in the muscles of aged mice, leading to the elevation of PGC-1α. Collectively, the current study demonstrated that the PARIS-PGC-1α pathway is linked to muscle aging and that farnesol treatment can restore muscle functionality in aged mice through increased farnesylation of PARIS. © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.FALS

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

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