Identification of protein-ligand binding site using multi-clustering and support vector machine

© 2016 IEEE. Multi-clustering has been widely used. It acts as a pre-training process for identifying protein-ligand binding in structure-based drug design. Then, the Support Vector Machine (SVM) is employed to classify the sites most likely for binding ligands. Three types of attributes are used, namely geometry-based, energy-based, and sequence conservation. Comparison is made on 198 drug-target protein complexes with LIGSITECSC, SURFNET, Fpocket, Q-SiteFinder, ConCavity, and MetaPocket. The results show an improved success rate of up to 86%

Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection

Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity. © 2011 Biomedical Engineering Society

Impact of Reactive Obstacle on Molecular Communication between Nanomachines

© 2018 IEEE. Molecular communication is an emerging technology for communication between bio-nanomachines in an aqueous environment. In this paper, we examine the effect of a reactive obstacle, which is placed in the diffusive molecular communication channel, on the expected number of the received molecules at the receiver. We develop a particle-based simulator that can predict the number of the received molecules for both passive and absorptive receivers by considering the impact of the reactive obstacle within the communication channel. The impacts of the reaction probability and radius of the obstacle on the received signal are examined and compared with the case of absence of the obstacle. The results show significant impact for the obstacle on the received signal, particularly, for obstacle with high reaction probability and large size

Comparison of reception mechanisms for molecular communication via diffusion

© 2018 IEEE. Molecular communication paradigm enables nanomachines or biological cells at nano/micro scales to communicate using chemical molecules. In this paper, we study different reception mechanisms in an unbounded 3-D biological medium for diffusion-based molecular communication system and compare their performances. The number of received molecules (i.e., number of activated receptors) is first analytically evaluated and then validated using a particle-based simulator developed by us. We address various receiver models, viz., passive, irreversible partially or fully absorptive, and a more general reversible receivers. The peak amplitude and peak time for passive and fully absorptive receivers are evaluated. The impact of various parameters, e.g., diffusion coefficient, separation distance, forward/backward reaction rates, on the received signal are examined

Singly-fed shaped planar inverted-F antenna for circular polarization

© 2016 IEEE. We propose a singly-fed planar inverted-F antenna (PIFA), with double-shorting planes and a tapered patch for radiating circular polarization. FEKO® simulations are employed to optimize the geometry of the antenna. The antenna prototype resonates at 2.52GHz and has a measured impedance bandwidth of around 240MHz. The calculated 3dB axial ratio bandwidth falls within the impedance bandwidth, demonstrating satisfactory circular polarization characteristics

Identification of hypoglycemic states for patients with T1DM using various parameters derived from EEG signals

For patients with Type 1 Diabetes Mellitus (T1DM), hypoglycemia is a very common but dangerous complication which can lead to unconsciousness, coma and even death. The variety of hypoglycemia symptoms is originated from the inadequate supply of glucose to the brain. In this study, we explore the connection between hypoglycemic episodes and the electrical activity of neurons within the brain or electroencephalogram (EEG) signals. By analyzing EEG signals from a clinical study of five children with T1DM, associated with hypoglycemia at night, we find that some EEG parameters change significantly under hypoglycemia condition. Based on these parameters, a method of detecting hypoglycemic episodes using EEG signals with a feed-forward multi-layer neural network is proposed. In our application, the classification results are 72% sensitivity and 55% specificity when the EEG signals are acquired from 2 electrodes C3 and O2. Furthermore, signals from different channels are also analyzed to observe the contributions of each channel to the performance of hypoglycemia classification. © 2011 IEEE

Techniques for RF localization of wireless capsule endoscopy

© 2016 IEEE. Location estimation of a wireless capsule endoscope at 400 MHz MICS band is implemented here using both RSSI and TOA-based techniques and their performance investigated. To improve the RSSI-based location estimation, a maximum likelihood (ML) estimation method is employed. For the TOA-based localization, FDTD coupled with continuous wavelet transform (CWT) is used to estimate the time of arrival and localization is performed using multilateration. The performances of the proposed localization algorithms are evaluated using a computational heterogeneous biological tissue phantom in the 402MHz-405MHz MICS band. Our investigations reveal that the accuracy obtained by TOA based method is superior to RSSI based estimates. It has been observed that the ML method substantially improves the accuracy of the RSSI-based location estimation

Characterization of volatile organic compounds at a roadside environment in Hong Kong: An investigation of influences after air pollution control strategies

Vehicular emission is one of the important anthropogenic pollution sources for volatile organic compounds (VOCs). Four characterization campaigns were conducted at a representative urban roadside environment in Hong Kong between May 2011 and February 2012. Carbon monoxide (CO) and VOCs including methane (CH4), non-methane hydrocarbons (NMHCs), halocarbons, and alkyl nitrates were quantified. Both mixing ratios and compositions of the target VOCs show ignorable seasonal variations. Except CO, liquefied petroleum gas (LPG) tracers of propane, i-butane and n-butane are the three most abundant VOCs, which increased significantly as compared with the data measured at the same location in 2003. Meanwhile, the mixing ratios of diesel- and gasoline tracers such as ethyne, alkenes, aromatics, halogenated, and nitrated hydrocarbons decreased by at least of 37%. The application of advanced multivariate receptor modeling technique of positive matrix factorization (PMF) evidenced that the LPG fuel consumption is the largest pollution source, accounting for 60 ± 5% of the total quantified VOCs at the roadside location. The sum of ozone formation potential (OFP) for the target VOCs was 300.9 μg-O3 m-3, which was 47% lower than the value of 567.3 μg-O3 m-3 measured in 2003. The utilization of LPG as fuel in public transport (i.e., taxis and mini-buses) contributed 51% of the sum of OFP, significantly higher than the contributions from gasoline- (16%) and diesel-fueled (12%) engine emissions. Our results demonstrated the effectiveness of the switch from diesel to LPG-fueled engine for taxis and mini-buses implemented by the Hong Kong Special Administrative Region (HKSAR) Government between the recent ten years, in additional to the execution of substitution to LPG-fueled engine and restrictions of the vehicular emissions in compliance with the updated European emission standards