Investigation of mechanical properties of chromium, chromium-rhenium, and derived alloys twenty-second quarterly progress report, jul. 1 - sep. 30, 1965

Quenching experiment for determination of effect of rhenium on vacancy clustering in molybdenu

Applications of sparse approximation in communications

Sparse approximation problems abound in many scientific, mathematical, and engineering applications. These problems are defined by two competing notions: we approximate a signal vector as a linear combination of elementary atoms and we require that the approximation be both as accurate and as concise as possible. We introduce two natural and direct applications of these problems and algorithmic solutions in communications. We do so by constructing enhanced codebooks from base codebooks. We show that we can decode these enhanced codebooks in the presence of Gaussian noise. For MIMO wireless communication channels, we construct simultaneous sparse approximation problems and demonstrate that our algorithms can both decode the transmitted signals and estimate the channel parameters

Understanding grapevine-microbiome interactions: implications for viticulture industry.

Until recently, the analysis of complex communities such as that of the grapevine-microbe holobiont has been limited by the fact that most microbes are not culturable under laboratory conditions (less than 1%). However, metagenomics, the study of the genetic material recovered directly from environmental samples without the need for enrichment or of culturing, has led to open an unprecedented era in the field of microbiology. Importantly, this technological advance has now become so pervasive that it is being regularly applied to explore soils and plants of agricultural interest. Interestingly, many large companies are taking notice, with significant financial investment being used to exploring ways to manipulate the productivity, disease resistance and stress tolerance for crops by influencing the microbiome. To understand which microbes one needs to manipulate to influence this valuable characteristics, we need to sequence the microbiome and capture the genetic and hence functional metabolic information contained therein. For viticulture and other agricultural fields where the crop is also associated to particular flavor properties that may also be manipulated, understanding how the bacteria, fungi and viruses influence the development and hence chemical makeup of the crop is essential

The role of HiPPI switches in mass storage systems: A five year prospective

New standards are evolving which provide the foundation for multi-gigabit per second data communication structures. The lowest layer protocols are so generalized that they encourage a wide range of application. Specifically, the ANSI High Performance Parallel Interface (HiPPI) is being applied to computer peripheral attachment as well as general data communication networks. The HiPPI Standards suite and technology products which incorporate the standards are introduced. The use of simple HiPPI crosspoint switches to build potentially complex extended 'fabrics' is discussed in detail. Several near term applications of the HiPPI technology are briefly described with additional attention to storage systems. Finally, some related standards are mentioned which may further expand the concepts above

An empirical comparison of supervised machine learning techniques in bioinformatics

Research in bioinformatics is driven by the experimental data. Current biological databases are populated by vast amounts of experimental data. Machine learning has been widely applied to bioinformatics and has gained a lot of success in this research area. At present, with various learning algorithms available in the literature, researchers are facing difficulties in choosing the best method that can apply to their data. We performed an empirical study on 7 individual learning systems and 9 different combined methods on 4 different biological data sets, and provide some suggested issues to be considered when answering the following questions: (i) How does one choose which algorithm is best suitable for their data set? (ii) Are combined methods better than a single approach? (iii) How does one compare the effectiveness of a particular algorithm to the others

Prediction of protein-protein interactions using one-class classification methods and integrating diverse data

This research addresses the problem of prediction of protein-protein interactions (PPI) when integrating diverse kinds of biological information. This task has been commonly viewed as a binary classification problem (whether any two proteins do or do not interact) and several different machine learning techniques have been employed to solve this task. However the nature of the data creates two major problems which can affect results. These are firstly imbalanced class problems due to the number of positive examples (pairs of proteins which really interact) being much smaller than the number of negative ones. Secondly the selection of negative examples can be based on some unreliable assumptions which could introduce some bias in the classification results. Here we propose the use of one-class classification (OCC) methods to deal with the task of prediction of PPI. OCC methods utilise examples of just one class to generate a predictive model which consequently is independent of the kind of negative examples selected; additionally these approaches are known to cope with imbalanced class problems. We have designed and carried out a performance evaluation study of several OCC methods for this task, and have found that the Parzen density estimation approach outperforms the rest. We also undertook a comparative performance evaluation between the Parzen OCC method and several conventional learning techniques, considering different scenarios, for example varying the number of negative examples used for training purposes. We found that the Parzen OCC method in general performs competitively with traditional approaches and in many situations outperforms them. Finally we evaluated the ability of the Parzen OCC approach to predict new potential PPI targets, and validated these results by searching for biological evidence in the literature

Development of an optical fiber interferometer for detection of surface flaws in aluminum

The main objective was to demonstrate the potential of using an optical fiber interferometer (OFI) to detect surface flaws in aluminum samples. Standard ultrasonic excitation was used to generate Rayleigh surface waves. After the waves interacted with a defect, the modified responses were detected using the OFI and the results were analyzed for time-of-flight and frequency content to predict the size and location of the flaws

The role of pyridoxine as a countermeasure for in-flight loss of lean body mass

Ground based and in flight research has shown that humans, under conditions of microgravity, sustain a loss of lean body tissue (protein) and changes in several biological processes including, reductions in red blood cell mass, and neurotransmitters. The maintenance of muscle mass, the major component of lean body mass, is required to meet the needs of space station EVAs. Central to the biosynthesis of amino acids, the building blocks of protein, is pyridoxine (vitamin B-6). Muscle mass integrity requires the availability of vitamin B-6 for protein metabolism and neurotransmitter synthesis. Furthermore, the formation of red blood cells require pyridoxine as a cofactor in the biosynthesis of hemoglobin, a protein that carries oxygen to tissues. In its active form, pyridoxal-5'-phosphate (PLP), vitamin B-6 serves as a link between amino acid and carbohydrate metabolism through intermediates of glycolysis and the tricarboxylic acid cycle. In addition to its role in energy metabolism, PLP is involved in the biosynthesis of hemoglobin and neurotransmitter which are necessary for neurological functions. Alterations in pyridoxine metabolism may affect countermeasures designed to overcome some of these biochemical changes. The focus of this research is to determine the effects of microgravity on the metabolic utilization of vitamin B-6, integrating nutrition as an integral component of the countermeasure (exercise) to maintain lean body mass and muscle strength. The objectives are: 1) to determine whether microgravity effects the metabolic utilization of pyridoxine and 2) to quantitate changes in B-6 vitamer distribution in tissue and excreta relative to loss of lean body tissue. The rationale for this study encompasses the unique challenge to control biochemical mechanisms effected during space travel and the significance of pyridoxine to maintain and counter muscle integrity for EVA activities. This experiment will begin to elucidate the importance of biochemical interactions between micronutrients and the homeostasis condition of biological processes in the space environment. To address this research topic a simulated microgravity model has been developed. The experiment uses radioisotopically labelled pyridoxine administered as an oral dose to rats which are maintained by tail suspension to simulate a microgravity environment. At the termination of the study, liver, muscle, blood and urine are collected and analyzed by reverse phase high pressure liquid chromatography to determine the quantity and distribution of the B-6 vitamers in tissue and excreta relative to lean body tissue loss. Earlier studies, published by this investigator, have shown that differences in vitamer distribution among samples from experimental versus control subjects indicate changes in metabolic utilization and storage of vitamin B-6

Rapid design of LCC current-output resonant converters with reduced electrical stresses

The paper presents and validates a straightforward design methodology for realising LCC current-output resonant converters, with the aim of reducing tank currents, and hence, electrical stresses on resonant components. The scheme is ideally suited for inclusion in a rapid iterative design environment e.g. part of a graphical user interfac