As an important field of emerging technology, wireless sensor networks (WSN) offer many new possibilities for applications such as target tracking and environmental surveillance by allowing the observer to move around freely. However, disseminating sensing data to the mobile observer raises significant design challenges for the routing scheme. In addition, WSN often operate under certain energy constraints, and therefore reducing energy dissipation in order to prolong the lifetime of the WSN is another challenge that must be faced. Most proposed routing protocols focus on achieving effective data dissemination and energy efficiency at the same time as working to satisfy the requirements of the mobile observer. However, almost all of these methods use frequent rerouting as a way of handling the mobility issue. Such rerouting increases both overheads and energy consumption, resulting in a trade-off between the need for rerouting to optimize network operations and that of maximizing network lifetime. This paper presents the Energy-aware Grid-based Routing Scheme (EAGER) for WSN with mobile observers, which is an approach that seeks to save more energy in the context of dynamic topology. In this paper, EAGER is compared to other proposed grid-based schemes by using extensive simulations. These simulations clearly show that EAGER outperforms other grid-based schemes in terms of both energy efficiency and routing performance.

Thermostability is a protein property that impacts many types of studies, including protein activity enhancement, protein structure determination, and drug development. However, most computational tools designed to predict protein thermostability require tertiary structure data as input. The few tools that are dependent only on the primary structure of a protein to predict its thermostability have one or more of the following problems: a slow execution speed, an inability to make large-scale mutation predictions, and the absence of temperature and pH as input parameters. Therefore, we developed a computational tool, named KStable, that is sequence-based, computationally rapid, and includes temperature and pH values to predict changes in the thermostability of a protein upon the introduction of a mutation at a single site. KStable was trained using basis features and minimal redundancy–maximal relevance (mRMR) features, and 58 classifiers were subsequently tested. To find the representative features, a regular-mRMR method was developed. When KStable was evaluated with an independent test set, it achieved an accuracy of 0.708.

Protein quaternary structure complex is also known as a multimer, which plays an important role in a cell. The dimer structure of transcription factors is involved in gene regulation, but the trimer structure of virus-infection-associated glycoproteins is related to the human immunodeficiency virus. The classification of the protein quaternary structure complex for the post-genome era of proteomics research will be of great help. Classification systems among protein quaternary structures have not been widely developed. Therefore, we designed the architecture of a two-layer machine learning technique in this study, and developed the classification system PClass. The protein quaternary structure of the complex is divided into five categories, namely, monomer, dimer, trimer, tetramer, and other subunit classes. In the framework of the bootstrap method with a support vector machine, we propose a new model selection method. Each type of complex is classified based on sequences, entropy, and accessible surface area, thereby generating a plurality of feature modules. Subsequently, the optimal model of effectiveness is selected as each kind of complex feature module. In this stage, the optimal performance can reach as high as 70% of Matthews correlation coefficient (MCC). The second layer of construction combines the first-layer module to integrate mechanisms and the use of six machine learning methods to improve the prediction performance. This system can be improved over 10% in MCC. Finally, we analyzed the performance of our classification system using transcription factors in dimer structure and virus-infection-associated glycoprotein in trimer structure. PClass is available via a web interface at http://predictor.nchu.edu.tw/PClass/.

Wireless sensor networks have been proposed as a promising technology employed for tracking target and observing environment. Since the targets and sinks could move anywhere at any time, how to efficiently trace the positions of targets and sinks and route the sensing data from the targets to the sinks poses significant design challenges of a well-behavior routing scheme in WSNs. Moreover, sensor nodes are often operated under energy-constrained situation, thus, reducing energy consumption of each node to extend network lifetime is also an important issue. In this paper, we present a novel routing scheme, named TRENS, to solve the above problem. TRENS is based on the visual-grid structure and uses tracking technology to assist the routing procedure for routing traffic efficiently while reducing power dissipation. Additionally, TRENS introduces a shortcutting scheme to optimize the routing paths for decreasing communication cost and latency. We compare the performance of TRENS with previous schemes by performing extensive simulation. The simulation results show that the proposed architecture conserves energy efficiently and achieves much better performance.