Using Hybrid Angle/Distance Information for Distributed Topology Control in Vehicular Sensor Networks

In a vehicular sensor network (VSN), the key design issue is how to organize vehicles effectively, such that the local network topology can be stabilized quickly. In this work, each vehicle with on-board sensors can be considered as a local controller associated with a group of communication members. In order to balance the load among the nodes and govern the local topology change, a group formation scheme using localized criteria is implemented. The proposed distributed topology control method focuses on reducing the rate of group member change and avoiding the unnecessary information exchange. Two major phases are sequentially applied to choose the group members of each vehicle using hybrid angle/distance information. The operation of Phase I is based on the concept of the cone-based method, which can select the desired vehicles quickly. Afterwards, the proposed time-slot method is further applied to stabilize the network topology. Given the network structure in Phase I, a routing scheme is presented in Phase II. The network behaviors are explored through simulation and analysis in a variety of scenarios. The results show that the proposed mechanism is a scalable and effective control framework for VSNs

TCN AA: A Wi Fi based Temporal Convolution Network for Human to Human Interaction Recognition with Augmentation and Attention

The utilization of Wi-Fi-based human activity recognition (HAR) has gained considerable interest in recent times, primarily owing to its applications in various domains such as healthcare for monitoring breath and heart rate, security, elderly care, and others. These Wi-Fi-based methods exhibit several advantages over conventional state-of-the-art techniques that rely on cameras and sensors, including lower costs and ease of deployment. However, a significant challenge associated with Wi-Fi-based HAR is the significant decline in performance when the scene or subject changes. To mitigate this issue, it is imperative to train the model using an extensive dataset. In recent studies, the utilization of CNN-based models or sequence-to-sequence models such as LSTM, GRU, or Transformer has become prevalent. While sequence-to-sequence models can be more precise, they are also more computationally intensive and require a larger amount of training data. To tackle these limitations, we propose a novel approach that leverages a temporal convolution network with augmentations and attention, referred to as TCN-AA. Our proposed method is computationally efficient and exhibits improved accuracy even when the data size is increased threefold through our augmentation techniques. Our experiments on a publicly available dataset indicate that our approach outperforms existing state-of-the-art methods, with a final accuracy of 99.42%.Comment: Published to IEEE Internet of things Journal but haven't been accepted yet (under review

CR3 and Dectin-1 Collaborate in Macrophage Cytokine Response through Association on Lipid Rafts and Activation of Syk-JNK-AP-1 Pathway

Copyright: © 2015 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Acknowledgments We are grateful to the Second Core Laboratory of Research Core Facility at the National Taiwan University Hospital for confocal microscopy service and providing ultracentrifuge. We thank Dr. William E. Goldman (University of North Carolina, Chapel Hill, NC) for kindly providing WT and ags1-null mutant of H. capsulatum G186A. Funding: This work is supported by research grants 101-2320-B-002-030-MY3 from the Ministry of Science and Technology (http://www.most.gov.tw) and AS-101-TP-B06-3 from Academia Sinica (http://www.sinica.edu.tw) to BAWH. GDB is funded by research grant 102705 from Welcome Trust (http://www.wellcome.ac.uk). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Cucurbitacin E inhibits the Yes‑associated protein signaling pathway and suppresses brain metastasis of human non‑small cell lung cancer in a murine model.

Human non‑small cell lung cancer (NSCLC) is associated with an extremely poor prognosis especially for the 40% of patients who develop brain metastasis, and few treatment strategies exist. Cucurbitacin E (CuE), an oxygenated tetracyclic triterpenoid isolated from plants particularly of the family Cucurbitaceae, has shown anti‑tumorigenic properties in several types of cancer, yet the mechanism remains unclear. Yes‑associated protein (YAP), a main mediator of the Hippo signaling pathway, promotes tumorigenesis, drug resistance and metastasis in human NSCLC. The present study was designed to ascertain whether CuE inhibits YAP and its downstream gene expression in the human NSCLC cell lines H2030‑BrM3 (K‑rasG12C mutation) and PC9‑BrM3 (EGFRΔexon19 mutation), which have high potential for brain metastasis. The efficacy of CuE in suppressing brain metastasis of H2030‑BrM3 cells in a murine model was also investigated. It was found that after CuE treatment in H2030‑BrM3 and PC9‑BrM3 cells, YAP protein expression was decreased, and YAP signaling GTIIC reporter activity and expression of the downstream genes CTGF and CYR61 were significantly (P<0.01) decreased. CuE treatment also reduced the migration and invasion abilities of the H2030‑BrM3 and PC9‑BrM3 cells. Finally, our in vivo study showed that CuE treatment (0.2 mg/kg) suppressed H2030‑BrM3 cell brain metastasis and that mice treated with CuE survived longer than the control mice treated with 10% DMSO (P=0.02). The present study is the first to demonstrate that CuE treatment inhibits YAP and the signaling downstream gene expression in human NSCLC in vitro, and suppresses brain metastasis of NSCLC in a murine model. More studies to verify the promising efficacy of CuE in inhibiting brain metastasis of NSCLC and various other cancers may be warranted

Ankle-Brachial Index Is a Powerful Predictor of Renal Outcome and Cardiovascular Events in Patients with Chronic Kidney Disease

Ankle-brachial index (ABI) is an accurate tool to diagnose peripheral arterial disease. The aim of this study was to evaluate whether ABI is also a good predictor of renal outcome and cardiovascular events in patients with chronic kidney disease (CKD). We enrolled 436 patients with stage 3–5 CKD who had not been undergoing dialysis. Patients were stratified into two groups according to the ABI value with a cut point of 0.9. The composite renal outcome, including doubling of serum creatinine level and commencement of dialysis, and the incidence of cardiovascular events were compared between the two groups. After a median follow-up period of 13 months, the lower ABI group had a poorer composite renal outcome (OR = 2.719, P = 0.015) and a higher incidence of cardiovascular events (OR = 3.260, P = 0.001). Our findings illustrated that ABI is a powerful predictor of cardiovascular events and renal outcome in patients with CKD