A versatile precision diffractometer for nuclear Bragg scattering was designed and constructed at the Photon Factory. Standardized component goniometers can be arranged to address various requirements of incident beam conditioning for nuclear Bragg scattering, including a high-energy resolution monochromator with wide angular acceptance as well as a circularly polarized incident beam.

The soil microbial community is strongly influenced by a wide variety of factors, such as soil characteristics and field management systems. In order to use biological indicators based on microbial community structure, it is very important to know whether or not these factors can be controlled. The present study aimed to determine whether soil type or fertilization has a greater influence on the soil microbial community based on denaturing gradient gel electrophoresis (DGGE) analysis of 12 experimental field plots containing four different soil types, Cumulic Andosol, Low-humic Andosol, Yellow Soil and Gray Lowland Soil, kept under three different fertilizer management systems since 2001 (the application of chemical fertilizer, the application of rice husk and cow manure, and the application of pig manure). Bacterial DGGE analysis using 16S rRNA genes and fungal DGGE analysis using 18S rRNA genes revealed that the bacterial community was related to the soil type more than the fertilization; however, the fungal community was related to the fertilization more than the soil type. These results might suggest that the fungal community is easier to control by fertilization than the bacterial community. Thus, we propose that indicators based on the fungal community might be more suitable as microbial indicators for soil quality.

The number and distribution of branches in soybean plants influence seed yield through effects on the efficiency of light utilization as well as on tolerance to lodging. We have developed recombinant inbred lines (RILs) from a cross between two experimental determinant lines, which differ in branching number. The 172 RILs were divided into four maturity groups according to their alleles for two maturity loci, E1 and E3, and were planted separately to avoid confounding effects of competition. The late-maturity RIL groups with the E1 genotype were grown in two different locations, whereas the early-maturity RIL groups with the e1 genotype were planted at one location. Analysis of all lines resulted in the identification of five quantitative trait loci (QTLs) for branching number, designated qBr1 to qBr5. Among these QTLs, qBr1 and qBr2 were mapped to the proximal regions of the E1 and E3 loci, respectively. The other three QTLs were mapped to regions distant from any known maturity loci and were detected only in the presence of the E1 genotype, indicating that they interact with qBr1. Our results suggest that branching number might be controlled genetically by the identified QTLs, even though the maturity loci substantially affect branching phenotype.

This research presents a microfermentor integrated into an optical fiber sensor based on quasi-elastic light scattering (QELS) to monitor and swiftly identify cellular growth kinetic parameters. The system uses a 1310 nm laser light that is guided through single-mode silica optical fibers to the interior of perfusion chambers, which are separated by polycarbonate membranes (470 nm pores) from microchannels, where a culture medium flows in a constant concentration. The system contains four layers, a superior and an inferior layer made of glass, and two intermediate poly(dimethylsiloxane) layers that contain the microchannels and the perfusion chambers, forming a reversible microfluidic device that requires only the sealing of the fibers to the inferior glass cover. The QELS autocorrelation decay rates of the optical signals were correlated to the cells counting in a microscope, and the application of this microsystem to the monitoring of alcoholic fermentation of Saccharomyces cerevisiae resulted in the kinetic parameters of KM = 4.1 g/L and μm = 0.49 h−1. These results agree with both the data reported in the literature and with the control batch test, showing that it is a reliable and efficient biological monitoring system.

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