Energy-Aware Distributed Clustering Algorithm for Improving Network Performance in WSNs

Wireless sensor networks (WSNs) consist of a large number of sensor nodes equipped with a diverse number of small and low-cost devices with limited resources, such as a short communication range, a low bandwidth, a small memory, and a restricted energy. In particular, among these constraint factors, a sensor node's energy consumption is a very important factor in extending a network's lifetime. Many researchers are focused on the energy efficiency of wireless sensor networks. Many clustering algorithms have been proposed to improve energy efficiency. However, most protocols in previous literature have the problem of not considering the characteristics of real applications, for example, forest fire detection, intruder detection, target tracking, and the like. In this paper, we propose an energy-efficient clustering algorithm that can respond rapidly to unexpected events with increased energy efficiency, because each sensor node detects events individually and creates clusters using a regional competition scheme. Simulation results show improved performance when our algorithm is used

A Beacon Interval Shifting Scheme for Interference Mitigation in Body Area Networks

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Predictions for Three-Month Postoperative Vocal Recovery after Thyroid Surgery from Spectrograms with Deep Neural Network

Despite the lack of findings in laryngeal endoscopy, it is common for patients to undergo vocal problems after thyroid surgery. This study aimed to predict the recovery of the patient’s voice after 3 months from preoperative and postoperative voice spectrograms. We retrospectively collected voice and the GRBAS score from 114 patients undergoing surgery with thyroid cancer. The data for each patient were taken from three points in time: preoperative, and 2 weeks and 3 months postoperative. Using the pretrained model to predict GRBAS as the backbone, the preoperative and 2-weeks-postoperative voice spectrogram were trained for the EfficientNet architecture deep-learning model with long short-term memory (LSTM) to predict the voice at 3 months postoperation. The correlation analysis of the predicted results for the grade, breathiness, and asthenia scores were 0.741, 0.766, and 0.433, respectively. Based on the scaled prediction results, the area under the receiver operating characteristic curve for the binarized grade, breathiness, and asthenia were 0.894, 0.918, and 0.735, respectively. In the follow-up test results for 12 patients after 6 months, the average of the AUC values for the five scores was 0.822. This study showed the feasibility of predicting vocal recovery after 3 months using the spectrogram. We expect this model could be used to relieve patients’ psychological anxiety and encourage them to actively participate in speech rehabilitation

Upfront chemotherapy and involved-field radiotherapy results in more relapses than extended radiotherapy for intracranial germinomas: modification in radiotherapy volume might be needed

PURPOSE: To retrospectively compare the outcome of upfront chemotherapy plus radiotherapy (CRT) and the outcome of the use of extended radiotherapy (RT) only for intracranial germinoma. METHODS AND MATERIALS: Of 81 patients with tissue-confirmed intracranial germinoma, 42 underwent CRT and 39 underwent RT only. For CRT, one to five cycles of upfront chemotherapy was followed by involved-field or extended-field RT, for which the dose was dependent on the M stage. For RT only, all 39 patients underwent craniospinal RT alone. The median follow-up was 68 months. RESULTS: The 5- and 10-year overall survival rate was 100% and 92.5% for RT alone and 92.9% and 92.9% for CRT, respectively. The 5-year recurrence-free survival rate was 100.0% for RT and 88.1% for CRT (p = 0.0279). No recurrences developed in patients given RT, but four relapses developed in patients who had received CRT -- three in the brain and one in the spine. Only one patient achieved complete remission from salvage treatment. The proportion of patients requiring hormonal replacement was greater for patients who received RT than for those who had received CRT (p = 0.0106). CONCLUSIONS: The results of our study have shown that the better quality of life provided by CRT was compensated for by the greater rate of relapse. The possible benefit of including the ventricles in involved-field RT after upfront chemotherapy, specifically for patients with initial negative seeding, should be addressed in a prospective study

Diet-Regulating Microbiota and Host Immune System in Liver Disease

The gut microbiota has been known to modulate the immune responses in chronic liver diseases. Recent evidence suggests that effects of dietary foods on health care and human diseases are related to both the immune reaction and the microbiome. The gut-microbiome and intestinal immune system play a central role in the control of bacterial translocation-induced liver disease. Dysbiosis, small intestinal bacterial overgrowth, translocation, endotoxemia, and the direct effects of metabolites are the main events in the gut-liver axis, and immune responses act on every pathways of chronic liver disease. Microbiome-derived metabolites or bacteria themselves regulate immune cell functions such as recognition or activation of receptors, the control of gene expression by epigenetic change, activation of immune cells, and the integration of cellular metabolism. Here, we reviewed recent reports about the immunologic role of gut microbiotas in liver disease, highlighting the role of diet in chronic liver disease