How Big Data and High-performance Computing Drive Brain Science

Brain science accelerates the study of intelligence and behavior, contributes fundamental insights into human cognition, and offers prospective treatments for brain disease. Faced with the challenges posed by imaging technologies and deep learning computational models, big data and high-performance computing (HPC) play essential roles in studying brain function, brain diseases, and large-scale brain models or connectomes. We review the driving forces behind big data and HPC methods applied to brain science, including deep learning, powerful data analysis capabilities, and computational performance solutions, each of which can be used to improve diagnostic accuracy and research output. This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible, by improving data standardization and sharing, and by providing new neuromorphic insights.     

Programming the Grid: Distributed Software Components, P2P and Grid Web Services for Scientific Applications

Computational Grids [17,25] have become an important asset in large-scale scientific and engineering research. By providing a set of services that allow a widely distributed collection of resources to be tied together into a relatively seamless computing framework, teams of researchers can collaborate to solve problems that they could not have attempted before. Unfortunately the task of building Grid applications remains extremely difficult because there are few tools available to support developers. To build reliable and re-usable Grid applications, programmers must be equipped with a programming framework that hides the details of most Grid services and allows the developer a consistent, non-complex model in which applications can be composed from well tested, reliable sub-units. This paper describes experiences with using a software component framework for building Grid applications. The framework, which is based on the DOE Common Component Architecture (CCA) [1,2,3,8], allows individual components to export function/service interfaces that can be remotely invoked by other components. The framework also provides a simple messaging/event system for asynchronous notification between application components. The paper also describes how the emerging Web-services [52] model fits with a component-oriented application design philosophy. To illustrate the connection between Web services and Grid application programming we describe a simple design pattern for application factory services which can be used to simplify the task of building reliable Grid programs. Finally we address several issues of Grid programming that better understood from the perspective of Peer-to-Peer (P2P) systems. In particular we describe how models for collaboration and resource sharing fit well with many Grid application scenarios.     

Amino acid sequence of the carboxy-terminal end of human erythrocyte glucose-6-phosphate dehydrogenase

Human erythrocyte glucose-6-phosphate dehydrogenase was purified to homogeneity by a simplified procedure, consisting of 2',5'-ADP-Sepharose affinity chromatography, followed by Sephadex G-100 gel filtration. The carboxy-terminal region of the protein was identified by carboxypeptidase digestion: the sequence -Lys-Leu-COOH was found instead of the reported -Gly-COOH, thus showing identity with the carboxy-terminal sequence of glucose-6-phosphate dehydrogenase from human leukocytes and platelets. In addition, the carboxyl-terminal peptide was isolated from a tryptic digest of the protein and sequenced. The sequence is: Trp-Val-Asp-Pro-His-Lys-Leu.     

Survey on granularity clustering

With the rapid development of uncertain artificial intelligent and the arrival of big data era, conventional clustering analysis and granular computing fail to satisfy the requirements of intelligent information processing in this new case. There is the essential relationship between granular computing and clustering analysis, so some researchers try to combine granular computing with clustering analysis. In the idea of granularity, the researchers expand the researches in clustering analysis and look for the best clustering results with the help of the basic theories and methods of granular computing. Granularity clustering method which is proposed and studied has attracted more and more attention. This paper firstly summarizes the background of granularity clustering and the intrinsic connection between granular computing and clustering analysis, and then mainly reviews the research status and various methods of granularity clustering. Finally, we analyze existing problem and propose further research.     

Analysis of active rotenone concentration during treatment of the Rondegat River,Cape Floristic Region,South Africa

Most endemic freshwater fish species of the Cape Floristic Region are listed as threatened, due mainly to the impacts of invasive alien fish species. The piscicide rotenone has been identified as a potential tool to aid the conservation of indigenous species through the removal of invasive fish. Rotenone was used in the Rondegat River, Cederberg, where smallmouth bass Micropterus dolomieu had extirpated the indigenous fish. An initial rotenone treatment in March 2012 was followed by another in March 2013. Due to concerns following the first treatment about possible build-up of rotenone between treatment stations, the second treatment included monitoring of rotenone concentrations during the treatment. Measured concentrations were consistently below the selected treatment concentration of 37.5 µg l^?1 and dropped to below the tested effective piscicidal concentration of 12.5 µg l^?1 at some sampling points. There was no build-up of rotenone within the treatment zones, but rotenone took longer than expected to clear out of the treatment area. The rotenone was effectively neutralised when the neutralisation station was operational, but was still detectable after neutralisation was terminated.     

The Urinary Bladder is Rich in Glycosphingolipids Composed of Phytoceramides

Glycosphingolipids (GSLs) are composed of a polar glycan chain and a hydrophobic tail known as ceramide. Together with variation in the glycan chain, ceramides exhibit tissue-specific structural variation in the long-chain base (LCB) and N-acyl chain moieties in terms of carbon chain length, degree of desaturation, and hydroxylation. Here, we report the structural variation in GSLs in the urinary bladders of mice and humans. Using TLC, we showed that the major GSLs are hexosylceramide, lactosylceramide, globotriaosylceramide, globotetraosylceramide, Neu5Ac-Gal-Glc-Ceramide, and Neu5Ac-Neu5Ac-Gal-Glc-Ceramide. Our LC-MS analysis indicated that phytoceramide structures with a 20-carbon LCB (4-hydroxyeicosasphinganine) and 2-hydroxy fatty acids are abundant in hexosylceramide and Neu5Ac-Gal-Glc-Ceramide in mice and humans. In addition, quantitative PCR demonstrated that DES2 and FA2H, which are responsible for the generation of 4-hydroxysphinganine and 2-hydroxy fatty acid, respectively, and SPTLC3 and SPTSSB, which are responsible for the generation of 20-carbon LCBs, showed significant expressions in the epithelial layer than in the subepithelial layer. Immunohistochemically, dihydroceramide:sphinganine C4-hydroxylase (DES2) was expressed exclusively in urothelial cells of the urinary bladder. Our findings suggest that these ceramide structures have an impact on membrane properties of the stretching and shrinking in transitional urothelial cells.     

Navigating in the Storm: Using Astrolabe to Adaptively Configure Web Services and Their Clients

The dramatic growth of distributed computing applications is creating both an opportunity and a daunting challenge for users seeking to build applications that will play critical roles in their organization. Here, we discuss the use of a new system, Astrolabe, to automate self-configuration, monitoring, and to control adaptation. Astrolabe operates by creating a virtual system-wide hierarchical database, which evolves as the underlying information changes. Astrolabe is secure, robust under a wide range of failure and attack scenarios, and imposes low loads even under stress. To focus the discussion, we structure it around a hypothetical Web Services scenario. One of the major opportunities created by Astrolabe is to allow Web Services client systems to autonomically adapt when a data center becomes slow or unreachable. The authors were supported by Intel Corporation, DARPA/AFRL grant RADC F30602-99-1-0532, by AFOSR/MURI grant F49620-02-1-0233, Microsoft Research BARC and the Cornell/AFRL Information Assurance Institute.     

Reverse-phase high-performance liquid chromatography of Escherichia coli ribosomal small subunit proteins

Reverse-phase high-performance liquid chromatography has been explored as an approach for the separation of the proteins of the 30 S subunit of Escherichia coli ribosomes. The majority of these proteins are of similar molecular weight and isoelectric point, making separation by size exclusion or ion exchange difficult. With the use of an octadecasilyl silica column and a trifluoroacetic acid-acetonitrile solvent system, the 21 proteins of the 30 S subunit have been separated into 15 peaks. The yield of total protein recovered from the column was ≥85%. The proteins present in each peak have been identified by polyacrylamide gel electrophoretic analysis of the peaks as well as by comparison with the relative retention volumes of known purified 30 S proteins on the column. The results clearly show that this method is a powerful and rapid technique for the identification and purification of 30 S proteins. Analysis of [³H]puromycin-labeled 30 S subunit protein provides an illustrative example of its utility for affinity labeling studies.