MOTIVATION: Skeletal muscle cells (skMCs) combine together to create long, multi-nucleated structures called myotubes. By studying the size, length, and number of nuclei in these myotubes, we can gain a deeper understanding of skeletal muscle development. However, human experimenters may often derive unreliable results owing to the unusual shape of the myotube, which causes significant measurement variability. RESULTS: We propose a new method for quantitative analysis of the dexamethasone side effect on human-derived young and aged skeletal muscle by simultaneous myotube and nuclei segmentation using deep learning combined with post-processing techniques. The deep learning model outputs myotube semantic segmentation, nuclei semantic segmentation, and nuclei center, and post-processing applies a watershed algorithm to accurately distinguish overlapped nuclei and identify myotube branches through skeletonization. To evaluate the performance of the model, the myotube diameter and the number of nuclei were calculated from the generated segmented images and compared with the results calculated by human experimenters. In particular, the proposed model produced outstanding outcomes when comparing human-derived primary young and aged skMCs treated with dexamethasone. The proposed standardized and consistent automated image segmentation system for myotubes is expected to help streamline the drug-development process for skeletal muscle diseases. AVAILABILITY AND IMPLEMENTATION: The code and the data are available at https://github.com/tdn02007/QA-skMCs-Seg.

To realize intelligent network management for future 6G-mobile edge computing (MEC) systems, mobile traffic prediction is crucial. Most of the previous machine learning-driven prediction approaches adopt traditional centralized training paradigm wherein mobile traffic data should be transferred to a central server. To exploit the distributed and parallel processing nature of MEC servers for training mobile traffic prediction models in a fast and secure manner, we propose a novel federated learning (FL) framework wherein locally trained prediction models over MEC servers are aggregated into a global model with joint optimization of MEC server selection and data set management for FL participation. From mathematical investigations of the influence of MEC server participation and data set utilization on the global model accuracy and training costs, including both training latency and energy consumption in the FL process, we first formulate an optimization problem for balancing the accuracy-cost tradeoff by considering a linear accuracy estimation model. Here, the optimization problem is designed using mixed-integer nonlinear programming, which is generally known as NP-hard. We then leverage a number of relaxation techniques to develop near-optimal yet the plausible algorithm based on linear programming. Furthermore, for practical concern, the proposed problem is extended by considering a concave accuracy estimation model; a genetic-based heuristic approach to the extension is proposed for determining the suboptimal solution. The numerical and simulation results suggest that our proposed framework can be effective for building mobile traffic prediction models in a more cost-efficient manner while maintaining competitive prediction accuracy.