Li-Yeh Chuang

Antioxidant Activity of Various Parts of Cinnamomum cassia Extracted with Different Extraction Methods

The aim of this study was to investigate the antioxidant activities of various parts (barks, buds, and leaves) of Cinnamomum cassia extracted with ethanol and supercritical fluid extraction (SFE). For the antioxidant activity comparison, IC50 values ofthe SFE and ethanol extracts in the DPPH scavenging assay were 0.562–10.090 mg/mL and 0.072–0.208 mg/mL, and the Trolox equivalent antioxidant capacity (TEAC) values were 6.789–58.335 mmole Trolox/g and 133.039–335.779 mmole Trolox/g, respectively. In addition, the total flavonoid contents were 0.031–1.916 g/ 100 g dry weight of materials (DW) and 2.030–3.348 g/ 100 g DW, and the total phenolic contents were 0.151–2.018 g/ 100 g DW and 6.313–9.534 g/ 100 g DW in the SFE and ethanol extracts, respectively. Based on the results, the ethanol extracts of Cinnamon barks have potential value as an antioxidant substitute and this study also provide a better technique to extract the natural antioxidant substances from C. cassia.

A hybrid feature selection method for DNA microarray data

Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. In cancer classification, available training data sets are generally of a fairly small sample size compared to the number of genes involved. Along with training data limitations, this constitutes a challenge to certain classification methods. Feature (gene) selection can be used to successfully extract those genes that directly influence classification accuracy and to eliminate genes which have no influence on it. This significantly improves calculation performance and classification accuracy. In this paper, correlation-based feature selection (CFS) and the Taguchi-genetic algorithm (TGA) method were combined into a hybrid method, and the K-nearest neighbor (KNN) with the leave-one-out cross-validation (LOOCV) method served as a classifier for eleven classification profiles to calculate the classification accuracy. Experimental results show that the proposed method reduced redundant features effectively and achieved superior classification accuracy. The classification accuracy obtained by the proposed method was higher in ten out of the eleven gene expression data set test problems when compared to other classification methods from the literature.

A Hybrid BPSO-CGA Approach for Gene Selection and Classification of Microarray Data

Microarray analysis promises to detect variations in gene expressions, and changes in the transcription rates of an entire genome in vivo. Microarray gene expression profiles indicate the relative abundance of mRNA corresponding to the genes. The selection of relevant genes from microarray data poses a formidable challenge to researchers due to the high-dimensionality of features, multiclass categories being involved, and the usually small sample size. A classification process is often employed which decreases the dimensionality of the microarray data. In order to correctly analyze microarray data, the goal is to find an optimal subset of features (genes) which adequately represents the original set of features. A hybrid method of binary particle swarm optimization (BPSO) and a combat genetic algorithm (CGA) is to perform the microarray data selection. The K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) served as a classifier. The proposed BPSO-CGA approach is compared to ten microarray data sets from the literature. The experimental results indicate that the proposed method not only effectively reduce the number of genes expression level, but also achieves a low classification error rate.

An improved particle swarm optimization with double-bottom chaotic maps for numerical optimization

Chaos theory studies the behavior of dynamical systems that are highly sensitive to their initial conditions. This effect is popularly referred to as the butterfly effect. Small differences in the initial conditions yield widely diverging outcomes for chaotic systems, rendering long-term prediction impossible in general. In mathematics, a chaotic map is a map (i.e., an evolution function) that exhibits some sort of chaotic behavior. Chaotic maps occur in the study of dynamical systems and often generate fractals. In this paper, an improved logistic map, namely a double-bottom map, with particle swarm optimization was applied to the test function. Simple PSO adopts a random sequence with a random starting point as a parameter, and relies on this parameter to update the positions and velocities of the particles. However, PSO often leads to premature convergence, especially in complex multi-peak search problems. In recent years, the use of chaotic sequences in optimization techniques rather than random sequences with random seeds has been growing steadily. Chaotic sequences, which are created by means of chaotic maps, have been proven easy and fast to generate and are more easily stored then random seed processes. They can improve the performance of PSO due to their unpredictability. Double-bottom maps are designed by the updating equation of PSO in order to balance the exploration and exploitation capability. We embedded many commonly used chaotic maps as well as our double-bottom map into PSO to improve performance, and compared these versions to each other to demonstrate the effectiveness of the PSO with the double-bottom map. We call this improved PSO method Double-Bottom Map PSO (DBMPSO). In the conducted experiments, PSO, DBMPSO and other chaotic PSOs were extensively compared on 22 benchmark test functions. The experimental results indicate that the performance of DBMPSO is significantly better than the performance of other PSOs tested.

Chemical Composition and Antibacterial Activities of Illicium verum Against Antibiotic-Resistant Pathogens

In recent years, human pathogenic microorganisms have developed multiple drug resistance and caused serious nosocomial infections. In this study, we identified four new antimicrobial compounds from the Chinese herbal medicine Illicium verum and assessed their antibacterial efficacies. The supercritical CO₂ and ethanol extracts of Illicium verum showed substantial antibacterial activity against 67 clinical drug-resistant isolates, including 27 Acinetobacter baumannii, 20 Pseudomonas aeruginosa, and 20 methicillin-resistant Staphylococcus aureus. The diethyl ether (EE) fraction obtained from partition extraction and supercritical CO₂ extracts revealed an antibacterial activity with a minimum inhibitory concentration value of 0.15-0.70u2009mg/mL and 0.11u2009mg/mL, respectively. The EE fraction of I. verum showed synergetic effects with some commercial antibiotics. The antimicrobial mechanism was investigated with killing curves and scanning electron microscopy observation. The chemical components of the extracts were analyzed by spectrophotometry; (E)-anethole, anisyl acetone, anisyl alcohol, and anisyl aldehyde exhibited antibacterial activity against different clinical isolates. These extracts from I. verum can be further developed into antibiotic medicines due to their proven antibacterial activity.

Tabu Search and Binary Particle Swarm Optimization for Feature Selection Using Microarray Data

Gene expression profiles have great potential as a medical diagnosis tool because they represent the state of a cell at the molecular level. In the classification of cancer type research, available training datasets generally have a fairly small sample size compared to the number of genes involved. This fact poses an unprecedented challenge to some classification methodologies due to training data limitations. Therefore, a good selection method for genes relevant for sample classification is needed to improve the predictive accuracy, and to avoid incomprehensibility due to the large number of genes investigated. In this article, we propose to combine tabu search (TS) and binary particle swarm optimization (BPSO) for feature selection. BPSO acts as a local optimizer each time the TS has been run for a single generation. The K-nearest neighbor method with leave-one-out cross-validation and support vector machine with one-versus-rest serve as evaluators of the TS and BPSO. The proposed method is applied and compared to the 11 classification problems taken from the literature. Experimental results show that our method simplifies features effectively and either obtains higher classification accuracy or uses fewer features compared to other feature selection methods.

Gene selection and classification using Taguchi chaotic binary particle swarm optimization

The purpose of gene expression analysis is to discriminate between classes of samples, and to predict the relative importance of each gene for sample classification. Microarray data with reference to gene expression profiles have provided some valuable results related to a variety of problems and contributed to advances in clinical medicine. Microarray data characteristically have a high dimension and a small sample size. This makes it difficult for a general classification method to obtain correct data for classification. However, not every gene is potentially relevant for distinguishing the sample class. Thus, in order to analyze gene expression profiles correctly, feature (gene) selection is crucial for the classification process, and an effective gene extraction method is necessary for eliminating irrelevant genes and decreasing the classification error rate. In this paper, correlation-based feature selection (CFS) and the Taguchi chaotic binary particle swarm optimization (TCBPSO) were combined into a hybrid method. The K-nearest neighbor (K-NN) with leave-one-out cross-validation (LOOCV) method served as a classifier for ten gene expression profiles. Experimental results show that this hybrid method effectively simplifies features selection by reducing the number of features needed. The classification error rate obtained by the proposed method had the lowest classification error rate for all of the ten gene expression data set problems tested. For six of the gene expression profile data sets a classification error rate of zero could be reached. The introduced method outperformed five other methods from the literature in terms of classification error rate. It could thus constitute a valuable tool for gene expression analysis in future studies.

Morse code application for wireless environmental control systems for severely disabled individuals

Some physically-disabled people with neuromuscular diseases such as amyotrophic lateral sclerosis, multiple sclerosis, muscular dystrophy, or other conditions that hinder their ability to write, type, and speak, require an assistive tool for purposes of augmentative and alternative communication in their daily lives. In this paper, we designed and implemented a wireless environmental control system using Morse code as an adapted access communication tool. The proposed system includes four parts: input-control module; recognition module; wireless-control module; and electronic-equipment-control module. The signals are transmitted using adopted radio frequencies, which permits long distance transmission without space limitation. Experimental results revealed that three participants with physical handicaps were able to gain access to electronic facilities after two months practice with the new system.

Specific primer design for the polymerase chain reaction

The design of primers has a major impact on the success of PCR in relation to the specificity and yield of the amplified product. Here, we introduce the applications of PCR as well as the definition and characteristics for PCR primer design. Recent primer design tools based on Primer3, along with several computational intelligence-based primer design methods which have been applied in primer design, are also reviewed. In addition, characteristics of population-based methods used in primer design are discussed in detail.

Particle swarm optimization with reinforcement learning for the prediction of CpG islands in the human genome

Background Regions with abundant GC nucleotides, a high CpG number, and a length greater than 200 bp in a genome are often referred to as CpG islands. These islands are usually located in the 5′ end of genes. Recently, several algorithms for the prediction of CpG islands have been proposed. Methodology/Principal Findings We propose here a new method called CPSORL to predict CpG islands, which consists of a complement particle swarm optimization algorithm combined with reinforcement learning to predict CpG islands more reliably. Several CpG island prediction tools equipped with the sliding window technique have been developed previously. However, the quality of the results seems to rely too much on the choices that are made for the window sizes, and thus these methods leave room for improvement. Conclusions/Significance Experimental results indicate that CPSORL provides results of a higher sensitivity and a higher correlation coefficient in all selected experimental contigs than the other methods it was compared to (CpGIS, CpGcluster, CpGProd and CpGPlot). A higher number of CpG islands were identified in chromosomes 21 and 22 of the human genome than with the other methods from the literature. CPSORL also achieved the highest coverage rate (3.4%). CPSORL is an application for identifying promoter and TSS regions associated with CpG islands in entire human genomic. When compared to CpGcluster, the islands predicted by CPSORL covered a larger region in the TSS (12.2%) and promoter (26.1%) region. If Alu sequences are considered, the islands predicted by CPSORL (Alu) covered a larger TSS (40.5%) and promoter (67.8%) region than CpGIS. Furthermore, CPSORL was used to verify that the average methylation density was 5.33% for CpG islands in the entire human genome.