Resistance Training Using VAriable Resistance Suit (VARS) Increased Isometric and Isokinetic Muscle Strength.

While resistance training promotes muscle hypertrophy and strength, accessibility of equipment is a barrier. This study evaluated a wearable VAriable Resistance Suit (VARS) as a novel and alternative method to achieve muscle strength improvement. It was hypothesized that by providing adjustable, bi-directional and speed dependent resistance, VARS can target specific muscles to improve muscle strength via an accessible and portable device. Twelve untrained healthy male adults (22.08 ± 4.1 years old) participated in an 8-week long resistance training using VARS to strengthen four muscles (biceps brachii, triceps brachii, biceps femoris, rectus femoris) of their non-dominant arm and leg using VARS. The results showed significant improvements in the muscle strength measured by isokinetic dynamometer - 49.9±9.6% increase in isokinetic force and 30.6±7.6% increase in isometric force. Muscle size and body composition were also assessed using ultrasound imaging and bioelectrical impedance analysis, which did not show significant changes. The study demonstrates the efficacy and feasibility of VARS as a resistance training tool to achieve muscle strength improvement and its potential extension to clinical populations.

Myoelectric Control Systems for Upper Limb Wearable Robotic Exoskeletons and Exosuits-A Systematic Review.

In recent years, myoelectric control systems have emerged for upper limb wearable robotic exoskeletons to provide movement assistance and/or to restore motor functions in people with motor disabilities and to augment human performance in able-bodied individuals. In myoelectric control, electromyographic (EMG) signals from muscles are utilized to implement control strategies in exoskeletons and exosuits, improving adaptability and human-robot interactions during various motion tasks. This paper reviews the state-of-the-art myoelectric control systems designed for upper-limb wearable robotic exoskeletons and exosuits, and highlights the key focus areas for future research directions. Here, different modalities of existing myoelectric control systems were described in detail, and their advantages and disadvantages were summarized. Furthermore, key design aspects (i.e., supported degrees of freedom, portability, and intended application scenario) and the type of experiments conducted to validate the efficacy of the proposed myoelectric controllers were also discussed. Finally, the challenges and limitations of current myoelectric control systems were analyzed, and future research directions were suggested.

Hair Follicle Classification and Hair Loss Severity Estimation Using Mask R-CNN.

Early and accurate detection of scalp hair loss is imperative to provide timely and effective treatment plans to halt further progression and save medical costs. Many techniques have been developed leveraging deep learning to automate the hair loss detection process. However, the accuracy and robustness of assessing hair loss severity still remain a challenge and barrier for transitioning such a technique into practice. The presented work proposes an efficient and accurate algorithm to classify hair follicles and estimate hair loss severity, which was implemented and validated using a multitask deep learning method via a Mask R-CNN framework. A microscopic image of the scalp was resized, augmented, then processed through pre-trained ResNet models for feature extraction. The key features considered in this study concerning hair loss severity include the number of hair follicles, the thickness of the hair, and the number of hairs in each hair follicle. Based on these key features, labeling of hair follicles (healthy, normal, and severe) were performed on the images collected from 10 men in varying stages of hair loss. More specifically, Mask R-CNN was applied for instance segmentation of the hair follicle region and to classify the hair follicle state into three categories, following the labeling convention (healthy, normal and severe). Based on the state of each hair follicle captured from a single image, an estimation of hair loss severity was determined for that particular region of the scalp, namely local hair loss severity index (P), and by combining P of multiple images taken and processed from different parts of the scalp, we constructed the hair loss severity estimation (Pavg) and visualized in a heatmap to illustrate the overall hair loss type and condition. The proposed hair follicle classification and hair loss severity estimation using Mask R-CNN demonstrated a more efficient and accurate algorithm compared to other methods previously used, enhancing the classification accuracy by 4 to 15%. This performance supports its potential for use in clinical settings to enhance the accuracy and efficiency of current hair loss diagnosis and prognosis techniques.

The complete mitochondrial genome of Cerithidea sinensis (Philippi, 1848).

Cerithidea sinensis is a common and important component of mangrove ecosystem. In this study, the mitochondrial genome of C. sinensis was determined for the first time using next-generation sequencing; the overall base components of mitogenome consisting of 15633 bp was 31.14% for A, 35.70% for T, 16.65% for G, 16.51% for C, and its GC content was 33.16%. The mitochondrial circular genome was composed of 13 protein-coding genes, 22 tranfer RNAs, and 2 ribosomal RNAs. Polygenetic analysis showed that the C. sinensis was more closed to Semisulcospira libertina than Turritella bacillum and Tylomelania sarasinorum. We may speculate that the C. sinensis is evolved from freshwater species.