Biomedical subjects

Shigeo Abe

Publications and source records attributed to Shigeo Abe.

4 recordsLinked to original sources

PubMed2004

Accumulation of allo-isocitric acid by a Penicillium strain.

The occurrence of L(S)-allo-isocitric acid in nature, as a new fungal product, was first found in the culture medium of Pen. purpurogenum Stoll var. rubri-sclerotium Thom. No. 1148. The acid was obtained as its gamma-lactone in a crystalline form. Some chemical and physical characters were described. The acid was the only one detectable acid accumulated by its fungus in the given condition, and the yield of the acid by weight exceeded about 70% of the consumed glucose.

Fermentation↗

PubMed2003

n-Hexane sensitivity of Escherichia coli due to low expression of imp/ostA encoding an 87 kDa minor protein associated with the outer membrane.

Most Escherichia coli strains are resistant to n-hexane. E. coli OST4251 is a n-hexane-sensitive strain that was constructed by transferring the n-hexane-sensitive phenotype from a n-hexane-sensitive strain by P1 transduction. OST4251 is resistant to diphenyl ether, which is less harmful than n-hexane to micro-organisms. The genetic determinant responsible for this subtle difference in the solvent resistance is mapped at 1.2 min on the E. coli chromosome. Nucleotide sequence analysis showed that IS2 and IS5 had integrated upstream of the imp/ostA structural gene in OST4251. The integration of IS2 decreased the activity of the imp/ostA promoter. A product of the gene was identified immunologically as an 87 kDa minor protein associated with the outer membrane. Upon transformation with plasmids containing the imp/ostA gene, OST4251 produced a high level of the gene product in the membrane and acquired n-hexane resistance. Thus, the low level of promoter activity resulted in low Imp production and the n-hexane-sensitivity phenotype. It is likely that the gene product contributes to n-hexane resistance by reducing the influx of n-hexane.

Bacterial Outer Membrane Proteins↗

PubMedDate not supplied

Incremental learning of feature space and classifier for face recognition.

We have proposed a new approach to pattern recognition in which not only a classifier but also a feature space of input variables is learned incrementally. In this paper, an extended version of Incremental Principal Component Analysis (IPCA) and Resource Allocating Network with Long-Term Memory (RAN-LTM) are effectively combined to implement this idea. Since IPCA updates a feature space incrementally by rotating the eigen-axes and increasing the dimensions, the inputs of a neural classifier must also change in their values and the number of input variables. To solve this problem, we derive an approximation of the update formula for memory items, which correspond to representative training samples stored in the long-term memory of RAN-LTM. With these memory items, RAN-LTM is efficiently reconstructed and retrained to adapt to the evolution of the feature space. This function is incorporated into our face recognition system. In the experiments, the proposed incremental learning model is evaluated over a self-compiled video clip of 24 persons. The experimental results show that the incremental learning of a feature space is very effective to enhance the generalization performance of a neural classifier in a realistic face recognition task.

Algorithms↗

PubMedDate not supplied

Fuzzy least squares support vector machines for multiclass problems.

In least squares support vector machines (LS-SVMs), the optimal separating hyperplane is obtained by solving a set of linear equations instead of solving a quadratic programming problem. But since SVMs and LS-SVMs are formulated for two-class problems, unclassifiable regions exist when they are extended to multiclass problems. In this paper, we discuss fuzzy LS-SVMs that resolve unclassifiable regions for multiclass problems. We define a membership function in the direction perpendicular to the optimal separating hyperplane that separates a pair of classes. Using the minimum or average operation for these membership functions, we define a membership function for each class. Using some benchmark data sets, we show that recognition performance of fuzzy LS-SVMs with the minimum operator is comparable to that of fuzzy SVMs, but fuzzy LS-SVMs with the average operator showed inferior performance.

Fuzzy Logic↗