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Functional Modules (functional + module)

Distribution by Scientific Domains

Engineering	40%
Chemistry	40%

Selected Abstracts

Crystallization and preliminary crystallographic studies of Mycobacterium tuberculosis DNA gyrase B C-terminal domain, part of the enzyme reaction core

ACTA CRYSTALLOGRAPHICA SECTION F (ELECTRONIC), Issue 4 2009
Guangsen Fu
DNA gyrase subunit B C-terminal domain (GyrB-CTD) is a functional module of DNA gyrase which participates in forming the core of DNA gyrase and plays critical roles in G-segment binding and T-segment loading and passage. Here, the purification, crystallization and preliminary X-ray crystallographic studies of GyrB-CTD from Mycobacterium tuberculosis H37Rv are reported. Diffraction data were collected from crystals of native GyrB-CTD and its selenomethionine derivative to resolutions of 2.8 and 3.0,Å, respectively. These crystals belonged to space group P212121 with similar unit-cell parameters. The native protein crystals had unit-cell parameters a = 52.831, b = 52.763, c = 192.579,Å. [source]

Evolving modular networks with genetic algorithms: application to nonlinear time series

EXPERT SYSTEMS, Issue 4 2004
A.S. Cofiño
Abstract: A key problem of modular neural networks is finding the optimal aggregation of the different subtasks (or modules) of the problem at hand. Functional networks provide a partial solution to this problem, since the inter-module topology is obtained from domain knowledge (functional relationships and symmetries). However, the learning process may be too restrictive in some situations, since the resulting modules (functional units) are assumed to be linear combinations of selected families of functions. In this paper, we present a non-parametric learning approach for functional networks using feedforward neural networks for approximating the functional modules of the resulting architecture; we also introduce a genetic algorithm for finding the optimal intra-module topology (the appropriate balance of neurons for the different modules according to the complexity of their respective tasks). Some benchmark examples from nonlinear time-series prediction are used to illustrate the performance of the algorithm for finding optimal modular network architectures for specific problems. [source]

xBCI: A Generic Platform for Development of an Online BCI System

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 4 2010
I Putu Susila Non-member
Abstract A generic platform for realizing an online brain,computer interface (BCI) named xBCI was developed. The platform consists of several functional modules (components), such as data acquisition, storage, mathematical operations, signal processing, network communication, data visualization, experiment control, and real-time feedback presentation. Users can easily build their own BCI systems by combining the components on a graphical-user-interface (GUI) based diagram editor. They can also extend the platform by adding components as plug-ins or by creating components using a scripting language. The platform works on multiple operating systems and supports parallel (multi-threaded) data processing and data transfer to other PCs through a network transmission control protocol/internet protocol or user datagram protocol (TCP/IP or UDP). A BCI system based on motor imagery and a steady-state visual evoked potential (SSVEP) based BCI system were constructed and tested on the platform. The results show that the platform is able to process multichannel brain signals in real time. The platform provides users with an easy-to-use system development tool and reduces the time needed to develop a BCI system. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source]

Discovering functions and revealing mechanisms at molecular level from biological networks

PROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 16 2007
Shihua Zhang
Abstract With the increasingly accumulated data from high-throughput technologies, study on biomolecular networks has become one of key focuses in systems biology and bioinformatics. In particular, various types of molecular networks (e.g., protein,protein interaction (PPI) network; gene regulatory network (GRN); metabolic network (MN); gene coexpression network (GCEN)) have been extensively investigated, and those studies demonstrate great potentials to discover basic functions and to reveal essential mechanisms for various biological phenomena, by understanding biological systems not at individual component level but at a system-wide level. Recent studies on networks have created very prolific researches on many aspects of living organisms. In this paper, we aim to review the recent developments on topics related to molecular networks in a comprehensive manner, with the special emphasis on the computational aspect. The contents of the survey cover global topological properties and local structural characteristics, network motifs, network comparison and query, detection of functional modules and network motifs, function prediction from network analysis, inferring molecular networks from biological data as well as representative databases and software tools. [source]

AI-2 biosynthesis module in a magnetic nanofactory alters bacterial response via localized synthesis and delivery

BIOTECHNOLOGY & BIOENGINEERING, Issue 2 2009
Rohan Fernandes
Abstract Nanofactories are nano-dimensioned and comprised of modules serving various functions that alter the response of targeted cells when deployed by locally synthesizing and delivering cargo to the surfaces of the targeted cells. In its basic form, a nanofactory consists of a minimum of two functional modules: a cell capture module and a synthesis module. In this work, magnetic nanofactories that alter the response of targeted bacteria by the localized synthesis and delivery of the "universal" bacterial quorum sensing signal molecule autoinducer AI-2 are demonstrated. The magnetic nanofactories consist of a cell capture module (chitosan-mag nanoparticles) and an AI-2 biosynthesis module that contains both AI-2 biosynthetic enzymes Pfs and LuxS on a fusion protein (His-LuxS-Pfs-Tyr, HLPT) assembled together. HLPT is hypothesized to be more efficient than its constituent enzymes (used separately) at conversion of the substrate SAH to product AI-2 on account of the proximity of the two enzymes within the fusion protein. HLPT is demonstrated to be more active than the constituent enzymes, Pfs and LuxS, over a wide range of experimental conditions. The magnetic nanofactories (containing bound HLPT) are also demonstrated to be more active than free, unbound HLPT. They are also shown to elicit an increased response in targeted Escherichia coli cells, due to the localized synthesis and delivery of AI-2, when compared to the response produced by the addition of AI-2 directly to the cells. Studies investigating the universality of AI-2 and unraveling AI-2 based quorum sensing in bacteria using magnetic nanofactories are envisioned. The prospects of using such multi-modular nanofactories in developing the next generation of antimicrobials based on intercepting and interrupting quorum sensing based signaling are discussed. Biotechnol. Bioeng. 2009;102: 390,399. © 2008 Wiley Periodicals, Inc. [source]