Formation of Coenzyme Q11 by Pseudomonas M16, a Mutant

Pseudomonas M16 is the mutant derived from a facultative methylotroph, Pseudomonas N842, which is the potent producer of coenzyme Q₁₀ (CoQ₁₀). This mutant with elevated productivity of CoQ₁₀ was observed to accumulate the significant amount of another CoQ homolog, which could not be detected in the parent strain. This CoQ homolog was extracted from the intact cells of the mutant and purified to crystaline state. The chemical properties and the results of UV, NMR and mass spectrometries revealed that this CoQ homolog was CoQ₁₁.     

Studies on Flavor Components in Soybean

Ethanol (1:1) extract of defatted soybean flour was fractionated systematically and the resulting phonolic acid fraction was investigated. This fraction had strong phenol-like flavor and contained at least seven phenolic acids including syringic, vanillic, ferulic, gentisic, salicylic, p-coumaric, and p-hydroxybenzoic acids. The main component among these was syringic acid, which was isolated as 3,5-dinitrobenzoate.

In addition, two isomers of chlorogenic acids, presumably isochlorogenic and chlorogenic acids approximately in a ratio of 1 : 10, were found in this extract. These substances have sour, bitter and astringent flavors.     

Growth-Promoting Factors for Lactobacillus bifidus var. pennsylvanicus Produced by the Amino-Carbonyl Reaction of l-Lysine and d-Glucose

l-Lysine monohydrochloride and d-glucose were allowed to react in a bicarbonate buffer of pH 8.8 under refluxing. The reaction mixture was then chromatographed on a thin-layer plate of Kiesel Gel using n-propanol ethyl-acetate water 25% aqueous ammonia (6: 1: 2: 1 v/v) as a developing agent. Elson-Morgan-reactive spots on the chromatogram were eluted individually, and each of the eluates was incubated with L. bifidus var. pennsylvanicus in a defined medium. A certain fraction on the chromatogram showed remarkably promoting effect on both the acid productivity and the growth of the organism. And such effect of the fraction was much stronger than that of N-acetyl-d-glucosamine which had been known as a “Bifidus Factor”     

DOPA Production with Enterobacter cloacae NB 320 by Transamination Reaction

Taxonomical investigation was performed on the bacterium, strain NB 320 isolated from soil, and it was identified as Enterobacter cloacae. This bacterium produced the enzyme which catalyzed the transamination reaction between 3,4-dihydroxyphenyl pyruvate and an amino acid to form l-Dopa.

The optimum culture conditions for the enzyme production were studied along with the characteristics of the enzyme. The enzyme of the strain was different in some properties from that of Alcaligenes faecalis IAM 1015 which had been already studied. The former utilized glutamate as an amino donor best among the amino acids tested for transamination and was induced by the addition of glutamine and asparagine. Intact cells of the strain did not catalyze the reaction unless they were treated with sonication or with a detergent.     

Studies on DOPA Transaminase of Alcaligenes faecalis

Some enzymatic properties were examined on the transaminase (DOPA transaminase) which catalyzes the reaction between 3,4-dihydroxy phenyl pyruvate (DOPP) and certain amino acids to form 3,4-dihydroxyphenyl-L-alanine (DOPA). The cell-free extract from Alcaligenes faecalis IAM 1015 was used as the DOPA transaminase. L-Aspartate, L-glutamate, and L-phenylalanine were utilized efficiently as amino donor. The occurrence of three kinds of transaminase—aspartate-DOPP transaminase (ADT), glutamate-DOPP transaminase (GDT), and phenylalanine-DOPP transaminase (PDT)—was postulated.

The pH optima of these enzymes were observed in the alkaline pH range. The enzymes were unstable in the acidic range and inactivated above 60°C. Ca²⁺, Mg²⁺, and Mn²⁺ protected PDT from heat denaturation. Fe²⁺, Cu²⁺, and Al³⁺ remarkably inhibited the enzyme reaction.     

Development of preparative and analytical methods of the hop bitter acid oxide fraction and chemical properties of its components

The bitter acids in hops (Humulus lupulus L.) and beer, such as α-, β-, and iso-α-acids, are known to affect beer quality and display various physiological effects. However, these compounds readily oxidize, and the effect of the oxides on the properties of beer or their potential health benefits are not well understood. In this study, we developed a simple preparative method for the bitter acid oxide fraction derived from hops and designated the constituents as matured hop bitter acids (MHBA). HPLC-PDA-ESI/HRMS and MS² revealed that MHBA are primarily composed of α-acid-derived oxides, which possess a common β-tricarbonyl moiety in their structures similar to α-, β-, and iso-α-acids. We also developed a quantitative analytical method of whole MHBA by HPLC, which showed high precision and reproducibility. Using our newly developed method, the concentration of whole MHBA in several commercial beers was evaluated. Our results will promote the study of bitter acid oxides.     

Application of the backstepping method to the prediction of increase or decrease of infected population

Background

In mathematical epidemiology, age-structured epidemic models have usually been formulated as the boundary-value problems of the partial differential equations. On the other hand, in engineering, the backstepping method has recently been developed and widely studied by many authors.

Methods

Using the backstepping method, we obtained a boundary feedback control which plays the role of the threshold criteria for the prediction of increase or decrease of newly infected population. Under an assumption that the period of infectiousness is same for all infected individuals (that is, the recovery rate is given by the Dirac delta function multiplied by a sufficiently large positive constant), the prediction method is simplified to the comparison of the numbers of reported cases at the current and previous time steps.

Results

Our prediction method was applied to the reported cases per sentinel of influenza in Japan from 2006 to 2015 and its accuracy was 0.81 (404 correct predictions to the total 500 predictions). It was higher than that of the ARIMA models with different orders of the autoregressive part, differencing and moving-average process. In addition, a proposed method for the estimation of the number of reported cases, which is consistent with our prediction method, was better than that of the best-fitted ARIMA model ARIMA(1,1,0) in the sense of mean square error.

Conclusions

Our prediction method based on the backstepping method can be simplified to the comparison of the numbers of reported cases of the current and previous time steps. In spite of its simplicity, it can provide a good prediction for the spread of influenza in Japan.