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Garland chrysanthemum (Glebionis coronaria L.) is an antioxidant-rich leafy vegetable. We found that garland chrysanthemum consumption ameliorated age-related hearing loss (AHL) in C57BL/6J mice, an early onset model. We also found that AHL progression was significantly ameliorated by three of ten products. Metabolome analysis of the 10 products using nuclear magnetic resonance (NMR) spectroscopy indicated that phytosterols may be involved in the amelioration of AHL. However, the direct inhibitory effect of phytosterol mixture on mouse AHL progression was not identified. These results suggest that garland chrysanthemum consumption delays AHL development in mice and its efficiency varies depending on the source of the product. Our findings also suggest that phytosterol content in garland chrysanthemum functions as an evaluation marker for the efficiency. Furthermore, to accelerate the search for foods that prevent AHL, we have used these data to develop an automatic threshold determination method for auditory brainstem response using machine learning.

Background Salting is a traditional procedure for producing pickled vegetables. Salting can be used as a pretreatment, for safe lactic acid fermentation and for salt stock preparation. This study aimed to provide valuable knowledge to improve pickle production by investigating the dynamics of microbiota and metabolites during the pretreatment and salt stock preparation processes, which have previously been overlooked. The differences in these process conditions would be expected to change the microbiota and consequently influence the content of metabolites in pickles. Methods Samples, collected from eight commercial pickle manufacturers in Japan, consisted of the initial raw materials, pickled vegetables and used brine. The microbiota were analyzed by 16S rRNA sequencing and the metabolites quantified by liquid chromatograph-mass spectrometry. Statistical analyses helped to identify any significant differences between samples from the initial raw materials, pretreatment process and salt stock preparation process groups. Results Under pretreatment conditions, aerobic and facultative anaerobic bacteria were predominant, including Vibrio, a potentially undesirable genus for pickle production. Under salt stock preparation conditions, the presence of halophilic bacteria, Halanaerobium, suggested their involvement in the increase in pyruvate derivatives such as branched-chain amino acids (BCAA). PICRUSt analysis indicated that the enhanced production of BCAA in salt stock was caused not by quantitative but by qualitative differences in the biosynthetic pathway of BCAA in the microbiota. Conclusion The differences in the microbiota between pretreatment and previously studied lactic acid fermentation processes emphasized the importance of anaerobic conditions and low pH under moderate salinity conditions for assuring safe pickle production. The results from the salt stock preparation process suggested that the Halanaerobium present may provide a key enzyme in the BCAA biosynthetic pathway which prefers NADH as a coenzyme. This feature can enhance BCAA production under anaerobic conditions where NADH is in excess. The effects shown in this study will be important for adjusting pickling conditions by changing the abundance of bacteria to improve the quality of pickled vegetables.

Black Aspergillus luchuensis and its white albino mutant are essential fungi for making alcoholic beverages in Japan. A large number of industrial strains have been created using novel isolation or gene/genome mutation techniques. Such mutations influence metabolic and phenotypic characteristics in industrial strains, but few comparative studies of inter-strain mutation have been conducted. We carried out comparative genome analyses of 8 industrial strains of A. luchuensis and A. kawachii IFO 4308, the latter being the first albino strain to be isolated. Phylogenetic analysis based on 8938 concatenated genes exposed the diversity of black koji strains and uniformity among albino industrial strains, suggesting that passaged industrial albino strains have more genetic mutations compared with strain IFO 4308 and black koji strains. Comparative analysis showed that the albino strains had mutations in genes not only for conidial pigmentation but also in those that encode N-terminal acetyltransferase A and annexin XIV-like protein. The results also suggest that some mutations may have emerged through subculturing of albino strains. For example, mutations in the genes for isocitrate lyase and sugar transporters were observed only in industrial albino strains. This implies that selective pressure for increasing enzyme activity or secondary metabolites may have influenced the mutation of genes associated with environmental stress responses in A. luchuensis albino strains. Our study clarifies hitherto unknown genetic and metabolic characteristics of A. luchuensis industrial strains and provides potential applications for comparative genome analysis for breeding koji strains.

International audience; Since the discovery of the giant mimivirus, evolutionarily related viruses have been isolated or identified from various environments. Phylogenetic analyses of this group of viruses, tentatively referred to as the family "Megaviridae", suggest that it has an ancient origin that may predate the emergence of major eukaryotic lineages. Environmental genomics has since revealed that Megaviridae represents one of the most abundant and diverse groups of viruses in the ocean. In the present study, we compared the taxon richness and phylogenetic diversity of Megaviridae, Bacteria, and Archaea using DNA-dependent RNA polymerase as a common marker gene. By leveraging existing microbial metagenomic data, we found higher richness and phylogenetic diversity in this single viral family than in the two prokaryotic domains. We also obtained results showing that the evolutionary rate alone cannot account for the observed high diversity of Megaviridae lineages. These results suggest that the Megaviridae family has a deep co-evolutionary history with diverse marine protists since the early "Big-Bang" radiation of the eukaryotic tree of life.

Recently, deep drilling into the seafloor has revealed that there are vast sedimentary ecosystems of diverse microorganisms, particularly archaea, in subsurface areas. We investigated the β diversity patterns of archaeal communities in sediment layers under the seafloor and their determinants. This study was accomplished by analyzing large environmental samples of 16S ribosomal RNA gene sequences and various geochemical data collected from a sediment core of 365.3 m, obtained by drilling into the seafloor off the east coast of the Shimokita Peninsula. To extract the maximum amount of information from these environmental samples, we first developed a method for measuring β diversity using sequence data by applying probability theory on a set of strings developed by two of the authors in a previous publication. We introduced an index of β diversity between sequence populations from which the sequence data were sampled. We then constructed an estimator of the β diversity index based on the sequence data and demonstrated that it converges to the β diversity index between sequence populations with probability of 1 as the number of sampled sequences increases. Next, we applied this new method to quantify β diversities between archaeal sequence populations under the seafloor and constructed a quantitative model of the estimated β diversity patterns. Nearly 90% of the variation in the archaeal β diversity was explained by a model that included as variables the differences in the abundances of chlorine, iodine, and carbon between the sediment layers.

In this study, we developed a method of fractional parentage analysis using microsatellite markers. We propose a method for calculating parentage probability, which considers missing data and genotyping errors due to null alleles and other causes, by regarding observed alleles as realizations of random variables which take values in the set of alleles at the locus and developing a method for simultaneously estimating the true and null allele frequencies of all alleles at each locus. We then applied our proposed method to a large sample collected from a wild population of brown trout (Salmo trutta). On analyzing the data using our method, we found that the reproductive success of brown trout obeyed a power law, indicating that when the parent-offspring relationship is regarded as a link, the reproductive system of brown trout is a scale-free network. Characteristics of the reproductive network of brown trout include individuals with large bodies as hubs in the network and different power exponents of degree distributions between males and females.

Numbers and numerical vectors account for a large portion of data. However, recently the amount of string data generated has increased dramatically. Consequently, classifying string data is a common problem in many fields. The most widely used approach to this problem is to convert strings into numerical vectors using string kernels and subsequently apply a support vector machine that works in a numerical vector space. However, this non-one-to-one conversion involves a loss of information and makes it impossible to evaluate, using probability theory, the generalization error of a learning machine, considering that the given data to train and test the machine are strings generated according to probability laws. In this study, we approach this classification problem by constructing a classifier that works in a set of strings. To evaluate the generalization error of such a classifier theoretically, probability theory for strings is required. Therefore, we first extend a limit theorem on the asymptotic behavior of a consensus sequence of strings, which is the counterpart of the mean of numerical vectors, as demonstrated in the probability theory on a metric space of strings developed by one of the authors and his colleague in a previous study [18]. Using the obtained result, we then demonstrate that our learning machine classifies strings in an asymptotically optimal manner. Furthermore, we demonstrate the usefulness of our machine in practical data analysis by applying it to predicting protein--protein interactions using amino acid sequences., This manuscript has been withdrawn because the experiments in Section 6 are insufficient

The prediction problem in multivariate mixed linear models is considered. The balanced case is treated in Kubokawa and Srivastava (1999). The unbalanced case is dealt with in this article. The empirical Bayes estimators which dominate the sample mean are obtained.

nding of protein structures is important to find their functions. Many methods such as alignment, alignment-free similarity, and use of structural fragments have been developed for finding similar protein structures. In our previous study, we transformed protein structures into images each pixel of which represents the distance between the corre­ sponding C" atoms, and proposed similarity measures between two protein structures based on Kolmogorov complexity using image compression algorithms. In this paper, we examine efficient and effective image recognition techniques, SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Features), which are invariant to image scaling, translation, and rotation, and partially invariant to affine or three-dimensiona l projection. We propose similarity based on SIFT and SURF, and apply it to classification of several protein structures. The results suggest that the similarity based on SURF outperforms several existing similarity measures including the compression-base d similarity measures in our previous study, and that SIFT and SURF are useful for recognizing protein structures as well as objects in images.

We present a methodology for quantifying biodiversity at the sequence level by developing the probability theory on a set of strings. Further, we apply our methodology to the problem of quantifying the population diversity of microorganisms in several extreme environments and digestive organs and reveal the relation between microbial diversity and various environmental parameters.