Distinct metabolites for photoreactive L-phenylalanine derivatives in Klebsiella sp. CK6 isolated from rhizosphere of a wild dipterocarp sapling.

Photoaffinity labeling is a reliable analytical method for biological functional analysis. Three major photophores--aryl azide, benzophenone and trifluoromethyldiazirine--are utilized in analysis. Photophore-bearing L-phenylalanine derivatives, which are used for biological functional analysis, were inoculated into a Klebsiella sp. isolated from the rhizosphere of a wild dipterocarp sapling in Central Kalimantan, Indonesia, under nitrogen-limiting conditions. The proportions of metabolites were quite distinct for each photophore. These results indicated that photophores affected substrate recognition in rhizobacterial metabolic pathways, and differential photoaffinity labeling could be achieved using different photophore-containing L-phenylalanine derivatives.

Effective Friedel-Crafts acylation of biotin acid chloride in trifluoromethanesulfonic acid.

Biotin is one of the most useful tags in (bio)analytical science due to its specific interaction with avidin, but is not easy to convert because of its low solubility in most solvents. Friedel-Crafts acylation of biotin acid chloride in triflic acid was examined, and the synthesized derivatives had stronger affinity to avidin than biotin in a binding assay using 2-(4'-hydroxyphenylazo)benzoic acid.

Estimation of force direction from functional near-infrared spectroscopy signals using sparse logistic regression.

The brain-machine interface (BMI) has been used as a communication tool for a person who has lost body function. Extracting functional information from brain signals is important for controlling a BMI in a realistic and natural way. For a BMI, a pattern classification algorithm, such as linear discriminant analysis (LDA) and support vector machine (SVM), has commonly been used. However, the classifier using brain signals tends to suffer from overfitting because there are too many obtained features compared with the number of samples. On the other hand, sparse logistic regression (SLR), which has been proposed as a new pattern classification method for brain signals, can select small number of features to classify and interpret brain functions. Thus, overfitting can be prevented using SLR. In this study, we measured functional near-infrared spectroscopy (fNIRS) signals during isometric arm movements in four directions and performed direction classification. The features to classify force direction were selected from obtained data sets using SLR and were used in a SVM. We compared the types of fNIRS signals (OxyHb and DeoxyHb) and feature selection methods. As a result, the classification accuracy was highest when both OxyHb and DeoxyHb were used as the features and both time and channel were selected. The peak time of the signal, when the task ends, and a few seconds after the task ends, were particularly well selected.

Temporal representation of arm force direction using fNIRS signals.

We investigated the possibility of creating a temporal representation of brain activity from fNIRS signals. In an experiment, subjects performed isometric arm movements in four directions, and fNIRS signals were measured over the primary motor area in the left hemisphere of their brain. We estimated the direction of the arm force from the fNIRS signals by using two classifiers: sparse linear regression (SLR) and support vector machine(SVM). Classification accuracy was approximately 70% with SLR. The temporal distribution of the features selected with SLR was the same as those selected with SVM. The results indicated that the fNIRS signals possibly included information about arm force direction in 4-6 [s] after stimulus onset and offset.