Production and Characteristics of Raw-Potato-Starch-Digesting α-Amylase from Bacillus subtilis 65

A newly isolated bacterium, identified as Bacillus subtilis 65, was found to produce raw-starch-digesting α-amylase. The electrophoretically homogeneous preparation of enzyme (molecular weight, 68,000) digested and solubilized raw corn starch to glucose and maltose with small amounts of maltooligosaccharides ranging from maltotriose to maltoheptaose. This enzyme was different from other amylases and could digest raw potato starch almost as fast as it could corn starch, but it showed no adsorbability onto any kind of raw starch at any pH. The mixed preparation with Endomycopsis glucoamylase synergistically digested raw potato starch to glucose at 30°C. The raw-potato-starch-digesting α-amylase showed strong digestibility to small substrates, which hydrolyzed maltotriose to maltose and glucose, and hydrolyzed p-nitrophenyl maltoside to p-nitrophenol and maltose, which is different from the capability of bacterial liquefying α-amylase.     

Solution properties of synthetic polypeptides. 18. Helix-coil transition of poly-N5-(2-hydroxyethyl)L-glutamine

The helix–coil transition of poly-N⁵-(2-hydroxyethyl)L -glutamine (PHEG) in aqueous isopropanol was examined by means of optical rotatory dispersion (ORD) and intrinsic viscosity [η] measurements. The Zimm–Bragg parameters σ and s for the transition were determined from the ORD data as a function of molecular weight. It was found that the transition was characterized by a relatively low cooperativity; the values of \documentclass{article}\pagestyle{empty}\begin{document}$ \sqrt \sigma $\end{document} were in the range from 0.039 to 0.066, depending on the solvent composition. These σ values are much larger than those reported for other polypeptide–solvent systems. The transition enthalpy was negative and its magnitude varied with the solvent composition, with a maximum of 620 cal/mol at 40 wt% isopropanol. The curve of [η] versus helical content for a high-molecular-weight sample exhibited a very broad minimum, and this behavior was attributed to the low cooperativity of the transition.     

High concentrations of arachidonic acid induce platelet aggregation and serotonin release independent of prostagladin endoperoxides and thromboxane A2

We examined platelet aggregation and serotonin release, induced by less than 60 μM arachidonic acid, using washed platelet suspensions in the absense of albumin. The concentration of arachidonic acid use did not cause platelet lysis. Platelet responses induced by less than 20 μM arachidonic acid were inhibited by aspirin, whereas those induced by above 30 μM arachidonic acid were not inhibited, even by both aspirin and 5,8,11,14-eicosatetraynoic acid. Although phosphatidic acid and 1,2-diacylglcerol increased after the addition of arachidonic acid in aspirin-treated platelets, the amounts were not parallel to platelet aggregation. Oleic, linoleic and linolenic acids also induced platelet responses, while palmitic, stearic and arachidic acids did not. EDTA, dibutyryl cyclic AMP, apyrase and creatine phosphate / creatin phosphokinase brought about almost the same effects in platelet responses induced by the unsaturated fatty acids, other than arachodinic acid, as those induced by 40 μM arachodonic acid. These results suggest that the mechanism of the actions of more than 30 μM arachodinic acid on platelets is the same as that of the other unsaturated fatty acids and is independent of prostaglandin endoperoxides, thromboxane A₂ and, perhaps, phosphatidic acid and 1,2-diacylglycerol.     

Life cycle of CO2-emissions from electric vehicles and gasoline vehicles utilizing a process-relational model

This article aims at estimating life cycle CO₂ emissions from electric vehicles (EV) and gasoline vehicles (GV), although the estimation in this study is not an LCA according to ISO14040s. For this purpose, a mathematical tool called the Process-relational model was developed. The Process-relational model is used for establishing life cycle inventories. The model has a structure which improved the principle of input-output analysis in econometrics that only one product is generated by one process. This model enabled us to overcome difficulties of LCA in retracing complicated repercussions among production systems. Then, life cycle CO₂, emissions from electric vehicles (EV) and gasoline vehicles (GV) were estimated with this model. Estimated results indicated that the manufacture and driving of EV resulted in less CO₂ emissions than chose of GV. However, the difference between EV and GV dramatically changed depending on traffic situations. Namely, the difference became larger as the average velocity of the vehicles became lower. We also compared CO₂, emission from manufacturing EV with that from driving EV. The share of manufacture was shown to increase in total CO₂, emissions as the average velocity of the EV became higher. In conclusion, we clarified the direction of research and development of EV and GV for reducing the life cycle CO₂.