The flavin-containing monooxygenase 2 gene (FMO2) of humans, but not of other primates, encodes a truncated, nonfunctional protein

Flavin-containing monooxygenases (FMOs) are NADPH-dependent flavoenzymes that catalyze the oxidation of heteroatom centers in numerous drugs and xenobiotics. FMO2, or "pulmonary" FMO, one of five forms of the enzyme identified in mammals, is expressed predominantly in lung and differs from other FMOs in that it can catalyze the N-oxidation of certain primary alkylamines. We describe here the isolation and characterization of cDNAs for human FMO2. Analysis of the sequence of the cDNAs and of a section of the corresponding gene revealed that the major FMO2 allele of humans encodes a polypeptide that, compared with the orthologous protein of other mammals, lacks 64 amino acid residues from its C terminus. Heterologous expression of the cDNA revealed that the truncated polypeptide was catalytically inactive. The nonsense mutation that gave rise to the truncated polypeptide, a C --> T transition in codon 472, is not present in the FMO2 gene of closely related primates, including gorilla and chimpanzee, and must therefore have arisen in the human lineage after the divergence of the Homo and Pan clades. Possible mechanisms for the fixation of the mutation in the human population and the potential significance of the loss of functional FMO2 in humans are discussed.     

Purification and properties of beta-N-Acetylhexosaminidase from cabbage

beta-N-Acetylhexosaminidase was purified from the extract of cabbage by sequential steps of ammonium sulfate fractionation, chromatofocusing, DEAE-Sepharose CL-6B ion exchange chromatography and Sephacryl S-200 HR gel filtration. By these steps, the purity of the enzyme increased by 256 fold with a recovery of 8%. The purified enzyme was homogeneous as examined by native PAGE. It showed an optimal pH of 4, an optimal temperature of 60 degrees C and a Km of 0.94 mM for hydrolysis of pNp-beta-GlcNAc. The molecular mass of the enzyme determined from filtration through Sephacryl S-200 was 150 kDa. Three subunits with molecular mass of 64, 57 and 51 kDa were observed as determined by SDS-PAGE. NBS (0.025 mM), DEPC (3 mM) and WRK (30 mM) significantly inhibited the activity of the enzyme. The enzyme also showed activity toward pNp-beta-GalNAc, N,N'-diacetylchitobiose, N,N',N"-triacetylchitotriose and N,N',N",N"'-tetraacetyl chitotetraose but showed no activity toward pNp-alpha-GlcNAc, chitin and ethylene glycol chitin.     

Simulation of solid thermal explosion and liquid thermal explosion of dicumyl peroxide using calorimetric technique

Dicumyl peroxide (DCPO), is produced by cumene hydroperoxide (CHP) process, is utilized as an initiator for polymerization, a prevailing source of free radicals, a hardener, and a linking agent. DCPO has caused several thermal explosion and runaway reaction accidents in reaction and storage zone in Taiwan because of its unstable reactive property. Differential scanning calorimetry (DSC) was used to determine thermokinetic parameters including 700 J g^–1 of heat of decomposition (ΔH_d), 110 °C of exothermic onset temperature (T₀), 130 kJ mol^–1 of activation energy (E_a), etc., and to analyze the runaway behavior of DCPO in a reaction and storage zone. To evaluate thermal explosion of DCPO with storage equipment, solid thermal explosion (STE) and liquid thermal explosion (LTE) of thermal safety software (TSS) were applied to simulate storage tank under various environmental temperatures (T_e). T_e exceeding the T₀ of DCPO can be discovered as a liquid thermal explosion situation. DCPO was stored under room temperature without sunshine and was prohibited exceeding 67 °C of self-accelerating decomposition temperature (SADT) for a tank (radius = 1 m and height = 2 m). SADT of DCPO in a box (width, length and height = 1 m, respectively) was determined to be 60 °C. The TSS was employed to simulate the fundamental thermal explosion behavior in a large tank or a drum. Results from curve fitting demonstrated that, even at the earlier stage of the reaction in the experiments, ambient temperature could elicit exothermic reactions of DCPO. To curtail the extent of the risk, relevant hazard information is quite significant and must be provided in the manufacturing process.     

MTPSO algorithm for solving planar graph coloring problem

In this paper, we proposed a modified turbulent particle swarm optimization (named MTPSO) model for solving planar graph coloring problem based on particle swarm optimization. The proposed model is consisting of the walking one strategy, assessment strategy and turbulent strategy. The proposed MTPSO model can solve the planar graph coloring problem using four-colors more efficiently and accurately. Compared to the results shown in Cui et al. (2008), not only the experimental results of the proposed model can get smaller average iterations but can get higher correction coloring rate when the number of nodes is greater than 30.     

A novel intrusion detection system based on hierarchical clustering and support vector machines

This study proposed an SVM-based intrusion detection system, which combines a hierarchical clustering algorithm, a simple feature selection procedure, and the SVM technique. The hierarchical clustering algorithm provided the SVM with fewer, abstracted, and higher-qualified training instances that are derived from the KDD Cup 1999 training set. It was able to greatly shorten the training time, but also improve the performance of resultant SVM. The simple feature selection procedure was applied to eliminate unimportant features from the training set so the obtained SVM model could classify the network traffic data more accurately. The famous KDD Cup 1999 dataset was used to evaluate the proposed system. Compared with other intrusion detection systems that are based on the same dataset, this system showed better performance in the detection of DoS and Probe attacks, and the beset performance in overall accuracy.     

An expert system to classify microarray gene expression data using gene selection by decision tree

Gene selection can help the analysis of microarray gene expression data. However, it is very difficult to obtain a satisfactory classification result by machine learning techniques because of both the curse-of-dimensionality problem and the over-fitting problem. That is, the dimensions of the features are too large but the samples are too few. In this study, we designed an approach that attempts to avoid these two problems and then used it to select a small set of significant biomarker genes for diagnosis. Finally, we attempted to use these markers for the classification of cancer. This approach was tested the approach on a number of microarray datasets in order to demonstrate that it performs well and is both useful and reliable.     

A wavelet-tree-based watermarking method using distance vector of binary cluster

This paper proposes a wavelet-tree-based watermarking method using distance vector of binary cluster for copyright protection. In the proposed method, wavelet trees are classified into two clusters using the distance vector to denote binary watermark bits. The two smallest wavelet coefficients in a wavelet tree are used to reduce distortion of a watermarked image. The distance vector, which is obtained from the two smallest coefficients of a wavelet tree, is quantized to decrease image distortion. The trees are classified into two clusters so that they exhibit a sufficiently large statistical difference based on the distance vector, which difference is then used for subsequent watermark extraction. We compare the statistical difference and the distance vector of a wavelet tree to decide which watermark bit is embedded in the embedding process. The experimental results show that the watermarked image looks visually identical to the original and the watermark can be effectively extracted upon image processing attacks.     

A blind watermarking method using maximum wavelet coefficient quantization

This paper proposes a blind watermarking algorithm based on maximum wavelet coefficient quantization for copyright protection. The wavelet coefficients are grouped into different block size and blocks are randomly selected from different subbands. We add different energies to the maximum wavelet coefficient under the constraint that the maximum wavelet coefficient is always maximum in a block. The watermark is embedded the local maximum coefficient which can effectively resist attacks. Also, using the block-based watermarking, we can extract the watermark without using the original image or watermark. Experimental results show that the proposed method is quite robust under either non-geometry or geometry attacks.     

Multi-class support vector machine for classification of the ultrasonic images of supraspinatus

This article proposes an effort to apply the multi-class support vector machine classifiers to classify the supraspinatus image into different disease groups that are normal, tendon inflammation, calcific tendonitis and supraspinatus tear. The supraspinatus tendon is often involved in the above-mentioned disease groups. Four different texture analysis methods – texture feature coding method, gray-level co-occurrence matrix, fractal dimension evaluation and texture spectrum – are used to extract features of tissue characteristic in the ultrasonic supraspinatus images. The mutual information criterion is adopted to select the powerful features from ones generated from the above-mentioned four texture analysis methods in the training stage, meanwhile, the five implementations of multi-class support vector machine classifiers are also designed to discriminate each image into one of the four disease groups in the classification stage. In experiments, the most commonly used performance measures including sensitivity, specificity, classification accuracy and false-negative rate are applied to evaluate the classification of the five implantations of multi-class support vector machines. In addition, the receiver operating characteristics analysis is also used to analyze the classification capability. The present results demonstrate that the implementation of multi-class fuzzy support vector machine can achieve 90% classification accuracy, and performance measures of this implementation are significantly superior to the others.