Sparse Modeling in Quantum Many-Body Problems

This review paper describes the basic concept and technical details of sparse modeling and its applications to quantum many-body problems. Sparse modeling refers to methodologies for finding a small number of relevant parameters that well explain a given dataset. This concept reminds us physics, where the goal is to find a small number of physical laws that are hidden behind complicated phenomena. Sparse modeling extends the target of physics from natural phenomena to data, and may be interpreted as “physics for data”. The first half of this review introduces sparse modeling for physicists. It is assumed that readers have physics background but no expertise in data science. The second half reviews applications. Matsubara Green’s function, which plays a central role in descriptions of correlated systems, has been found to be sparse, meaning that it contains little information. This leads to (i) a new method for solving the ill-conditioned inverse problem for analytical continuation, and (ii) a highly compact representation of Matsubara Green’s function, which enables efficient calculations for quantum many-body systems

Development of Information System for Bio-Chemical Study in Central Laboratory

MUMPS (Massachusetts General Hospital Utility Multi-Programing System) is a good system for medical information processing by computer. So, we have systemized the biochemical laboratory information system by using of DSM-11 (Digital Standard MUMPS) through PDP-11/34 mini-computer system in Hiroshima University Hospital. This system has some functions of transfering data from PDP11/34 to M-180 large computer and can transfer data form from tree model DBMS of DSM-11 on PDP11/34 to relational model DBMS of M-180 large computer. As HDM (relational model DBMS of M-180) has a function of using SPSS statistical programs package by DBMS, our designed system (DSM11/HDM system) can analyze a lot of laboratory data easily and statistically. We got much effortness by medical applications of this system

Discovery of a gene cluster for the biosynthesis of novel cyclic peptide compound, KK-1, in Curvularia clavata

KK-1, a cyclic depsipeptide with 10 residues produced by a filamentous fungus Curvularia clavata BAUA-2787, is a promising pesticide active compound with high activity against many plant pathogens, especially Botrytis cinerea. As a first step toward the future mass production of KK-1 through synthetic biological approaches, we aimed to identify the genes responsible for the KK-1 biosynthesis. To achieve this, we conducted whole genome sequencing and transcriptome analysis of C. clavata BAUA-2787 to predict the KK-1 biosynthetic gene cluster. We then generated the overexpression and deletion mutants for each cluster gene using our originally developed transformation system for this fungus, and analyzed the KK-1 production and the cluster gene expression levels to confirm their involvement in KK-1 biosynthesis. As a result of these, a region of approximately 71 kb was found, containing 10 open reading frames, which were co-induced during KK-1 production, as a biosynthetic gene cluster. These include kk1B, which encodes nonribosomal peptide synthetase with a domain structure that is consistent with the structural features of KK-1, and kk1F, which encodes a transcription factor. The overexpression of kk1F increased the expression of the entire cluster genes and, consequently, improved KK-1 production, whereas its deletion decreased the expression of the entire cluster genes and almost eliminated KK-1 production, demonstrating that the protein encoded by kk1F regulates the expressions of the other nine cluster genes cooperatively as the pathway-specific transcription factor. Furthermore, the deletion of each cluster gene caused a reduction in KK-1 productivity, indicating that each gene is involved in KK-1 production. The genes kk1A, kk1D, kk1H, and kk1I, which showed a significant decrease in KK-1 productivity due to deletion, were presumed to be directly involved in KK-1 structure formation, including the biosynthesis of the constituent residues. kk1C, kk1E, kk1G, and kk1J, which maintained a certain level of KK-1 productivity despite deletion, were possibly involved in promoting or assisting KK-1 production, such as extracellular transportation and the removal of aberrant units incorporated into the peptide chain

Identification of Cysteine Proteases and Screening of Cysteine Protease Inhibitors in Biological Samples by a Two-Dimensional Gel System of Zymography and Reverse Zymography

We have developed a two-dimensional (2D-) gel system of zymography and reverse zymography for the detection and characterization of proteases and protease inhibitors. Isoelectric focusing (IEF) agarose gels with pH gradients were employed for separation in the first-dimension and sodium dodecyl sulfate (SDS)-polyacrylamide gel copolymerized with gelatin used for the second dimension. Proteases and protease inhibitors separated by IEF gel were applied on the second gel without trichloroacetic acid (TCA) fixation. Protease activity in the 2D-gel was visualized as transparent spots where gelatin substrate was digested after commassie brilliant blue (CBB) staining. Some of the transparent spots from the skin mucus extract of rainbow trout were determined to be a cysteine protease through use of E-64 or CA-074. In the reverse zymography technique, the gel was incubated with papain solution at 37 ºC for 18 h. Cysteine protease inhibitors from broad bean seeds were detected as clear blue spots after CBB staining. The amino (N-) terminal sequences of four papain inhibitor spots thus detected were demonstrated to be identical to that of favin β chain, a broad bean lectin. Taken together, our system can be considered to be an efficient technique for discovering and characterizing new proteases and protease inhibitors in biological samples. This is the first report describing a 2D-gel system of zymography and reverse zymography