Multimodal Gait Abnormality Recognition Using a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) Network Based on Multi-Sensor Data Fusion.

Global aging leads to a surge in neurological diseases. Quantitative gait analysis for the early detection of neurological diseases can effectively reduce the impact of the diseases. Recently, extensive research has focused on gait-abnormality-recognition algorithms using a single type of portable sensor. However, these studies are limited by the sensor's type and the task specificity, constraining the widespread application of quantitative gait recognition. In this study, we propose a multimodal gait-abnormality-recognition framework based on a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) network. The as-established framework effectively addresses the challenges arising from smooth data interference and lengthy time series by employing an adaptive sliding window technique. Then, we convert the time series into time-frequency plots to capture the characteristic variations in different abnormality gaits and achieve a unified representation of the multiple data types. This makes our signal processing method adaptable to several types of sensors. Additionally, we use a pre-trained Deep Convolutional Neural Network (DCNN) for feature extraction, and the consequently established CNN-BiLSTM network can achieve high-accuracy recognition by fusing and classifying the multi-sensor input data. To validate the proposed method, we conducted diversified experiments to recognize the gait abnormalities caused by different neuropathic diseases, such as amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD). In the PDgait dataset, the framework achieved an accuracy of 98.89% in the classification of Parkinson's disease severity, surpassing DCLSTM's 96.71%. Moreover, the recognition accuracy of ALS, PD, and HD on the PDgait dataset was 100%, 96.97%, and 95.43% respectively, surpassing the majority of previously reported methods. These experimental results strongly demonstrate the potential of the proposed multimodal framework for gait abnormality identification. Due to the advantages of the framework, such as its suitability for different types of sensors and fewer training parameters, it is more suitable for gait monitoring in daily life and the customization of medical rehabilitation schedules, which will help more patients alleviate the harm caused by their diseases.

Ether lipids and a peroxisomal riddle in sperm.

Sperm are terminally differentiated cells that lack most of the membranous organelles, resulting in a high abundance of ether glycerolipids found across different species. Ether lipids include plasmalogens, platelet activating factor, GPI-anchors and seminolipid. These lipids play important roles in sperm function and performance, and thus are of special interest as potential fertility markers and therapeutic targets. In the present article, we first review the existing knowledge on the relevance of the different types of ether lipids for sperm production, maturation and function. To further understand ether-lipid metabolism in sperm, we then query available proteomic data from highly purified sperm, and produce a map of metabolic steps retained in these cells. Our analysis pinpoints the presence of a truncated ether lipid biosynthetic pathway that would be competent for the production of precursors through the initial peroxisomal core steps, but devoid of subsequent microsomal enzymes responsible for the final synthesis of all complex ether-lipids. Despite the widely accepted notion that sperm lack peroxisomes, the thorough analysis of published data conducted herein identifies nearly 70% of all known peroxisomal resident proteins as part of the sperm proteome. In view of this, we highlight open questions related to lipid metabolism and possible peroxisomal functions in sperm. We propose a repurposed role for the truncated peroxisomal ether-lipid pathway in detoxification of products from oxidative stress, which is known to critically influence sperm function. The likely presence of a peroxisomal-derived remnant compartment that could act as a sink for toxic fatty alcohols and fatty aldehydes generated by mitochondrial activity is discussed. With this perspective, our review provides a comprehensive metabolic map associated with ether-lipids and peroxisomal-related functions in sperm and offers new insights into potentially relevant antioxidant mechanisms that warrant further research.