Masayuki Murata

Bio: Masayuki Murata is an academic researcher from Kyushu University. The author has contributed to research in topics: Network packet & Network topology. The author has an hindex of 51, co-authored 1163 publications receiving 14719 citations. Previous affiliations of Masayuki Murata include Tokyo Metropolitan University & Tokyo Institute of Technology.

Vip21/caveolin is a cholesterol-binding protein

Abstract: VIP21/caveolin is localized to both caveolae and apical transport vesicles and presumably cycles between the cell surface and the Golgi complex. We have studied the lipid interactions of this protein by reconstituting Escherichia coli-expressed VIP21/caveolin into liposomes. Surprisingly, the protein reconstituted only with cholesterol-containing lipid mixtures. We demonstrated that the protein binds at least 1 mol of cholesterol per mole of protein and that this binding promotes formation of protein oligomers. These findings suggest that VIP21/caveolin, through its cholesterol-binding properties, serves a specific function in microdomain formation during membrane trafficking.

Indoor Localization System using RSSI Measurement of Wireless Sensor Network based on ZigBee Standard.

Abstract: To verify the validity of our previously reported autonomous indoor localization system in an actual environment, we implemented it in a wireless sensor network based on the ZigBee standard The system automatically estimates the distance between sensor nodes by measuring the RSSI (received signal strength indicator) at an appropriate number of sensor nodes Through experiments, we clarified the validity of our data collection and position estimation techniques The results show that when the deployment density of sensor nodes was set to 027 nodes/ , the position estimation error was reduced to 15-2 m

Performance of Alternate Routing Methods in All-Optical Switching Networks

Abstract: We study routing methods in all-optical switching networks. In all-optical switching networks, the connection with more hops encounters more call blocking, and it is especially true in optical networks with no wavelength conversions. We therefore consider an alternate routing method with limited trunk reservation in which connections with more hops are prepared more alternate routes. Through developing an approximate analytic approach, we show that our method keeps good performance when compared with the existing alternate routing methods, and also that the fairness among connections can be improved. Further performance improvement is investigated by introducing a wavelength assignment policy and a dynamic routing method. An effectiveness of the proposed method is investigated through simulation.

Expression of the peroxisome proliferator activated receptor γ gene is repressed by DNA methylation in visceral adipose tissue of mouse models of diabetes

Abstract: Adipose tissues serve not only as a store for energy in the form of lipid, but also as endocrine tissues that regulates metabolic activities of the organism by secreting various kinds of hormones. Peroxisome proliferator activated receptor γ (PPARγ) is a key regulator of adipocyte differentiation that induces the expression of adipocyte-specific genes in preadipocytes and mediates their differentiation into adipocytes. Furthermore, PPARγ has an important role to maintain the physiological function of mature adipocyte by controlling expressions of various genes properly. Therefore, any reduction in amount and activity of PPARγ is linked to the pathogenesis of metabolic syndrome. In this study, we investigated the contribution of epigenetic transcriptional regulatory mechanisms, such as DNA methylation, to the expression of the PPARγ gene, and further evaluated the contribution of such epigenetic regulatory mechanisms to the pathogenesis of metabolic syndrome. In 3T3-L1 preadipocytes, the promoter of the PPARγ2 gene was hypermethylated, but was progressively demethylated upon induction of differentiation, which was accompanied by an increase of mRNA expression. Moreover, treatment of cells with 5'-aza-cytideine, an inhibitor of DNA methylation, increased expression of the PPARγ gene in a dose-dependent manner. Methylation in vitro of a PPARγ promoter-driven reporter construct also repressed the transcription of a downstream reporter gene. These results suggest that the expression of the PPARγ gene is inhibited by methylation of its promoter. We next compared the methylation status of the PPARγ promoters in adipocytes from wild-type (WT) mice with those from two diabetic mouse models: +Lepr db /+Lepr db and diet-induced obesity mice. Interestingly, we found increased methylation of the PPARγ promoter in visceral adipose tissues (VAT) of the mouse models of diabetes, compared to that observed in wild-type mice. We observed a concomitant decrease in the level of PPARγ mRNA in the diabetic mice compared to the WT mice. We conclude that the expression of PPARγ gene is regulated by DNA methylation of its promoter region and propose that reduced expression of PPARγ owing to DNA methylation in adipocytes of the VAT may contribute to the pathogenesis of metabolic syndrome.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Functional rafts in cell membranes

Abstract: A new aspect of cell membrane structure is presented, based on the dynamic clustering of sphingolipids and cholesterol to form rafts that move within the fluid bilayer. It is proposed that these rafts function as platforms for the attachment of proteins when membranes are moved around inside the cell and during signal transduction.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.