Exploratory analysis of brain connectivity with ICA.

Covariance-based methods of exploration of functional connectivity of the brain from functional magnetic resonance imaging (fMRI) experiments, such as principal component analysis (PCA) and structural equation modeling (SEM), require a priori knowledge such as an anatomical model to infer functional connectivity. In this research, a hybrid method, combining independent component analysis (ICA) and SEM, which is capable of deriving functional connectivity in an exploratory manner without the need of a prior model is introduced. The spatial ICA (SICA) derives independent neural systems or sources involved in task-related brain activation, while an automated method based on the SEM finds the structure of the connectivity among the elements in independent neural systems. Unlike second-order approaches used in earlier studies, the task-related neural systems derived from the ICA provide brain connectivity in the complete statistical sense. The use and efficacy of this approach is illustrated on two fMRI datasets obtained from a visual task and a language reading task.     

Interaction of guanine nucleotides with the signal recognition particle from Escherichia coli

The bacterial signal recognition particle (SRP) is an RNA-protein complex. In Escherichia coli, the particle consists of a 114 nt RNA, a 4.5S RNA, and a 48 kDa GTP-binding protein, Ffh. GDP-GTP exchange on, and GTP hydrolysis by, Ffh are thought to regulate SRP function in membrane targeting of translating ribosomes. In the present paper, we report the equilibrium and kinetic constants of guanine nucleotide binding to Ffh in different functional complexes. The association and dissociation rate constants of GTP/GDP binding to Ffh were measured using a fluorescent analogue of GTP/GDP, mant-GTP/GDP. For both nucleotides, association and dissociation rate constants were about 10(6) M-1 s-1 and 10 s-1, respectively. The equilibrium constants of nonmodified GTP and GDP binding to Ffh alone and in SRP, and in the complex with the ribosomes were measured by competition with mant-GDP. In all cases, the same 1-2 microM affinity for GTP and GDP was observed. Binding of both GTP and GDP to Ffh was independent of Mg2+ ions. The data suggest that, at conditions in vivo, (i) there will be rapid spontaneous GDP-GTP exchange, and (ii) the GTP-bound form of Ffh, or of SRP, will be predominant.     

Multiple SVM-RFE for gene selection in cancer classification with expression data

This paper proposes a new feature selection method that uses a backward elimination procedure similar to that implemented in support vector machine recursive feature elimination (SVM-RFE). Unlike the SVM-RFE method, at each step, the proposed approach computes the feature ranking score from a statistical analysis of weight vectors of multiple linear SVMs trained on subsamples of the original training data. We tested the proposed method on four gene expression datasets for cancer classification. The results show that the proposed feature selection method selects better gene subsets than the original SVM-RFE and improves the classification accuracy. A Gene Ontology-based similarity assessment indicates that the selected subsets are functionally diverse, further validating our gene selection method. This investigation also suggests that, for gene expression-based cancer classification, average test error from multiple partitions of training and test sets can be recommended as a reference of performance quality.     

Grid-Enabled BLASTZ: Application to Comparative Genomics

BLASTZ is a sequence alignment tool designed mainly for aligning neutrally evolved bio-sequences and has been the choice for aligning noncoding sequences. However, its running time is impractical for high throughput alignment of long sequences, for example, for the alignment of human and mouse genomes. In order to improve the performance and efficiency for alignment at genome scale, BLASTZ was implemented using the GLOBUS toolkit on a computing grid. A dynamic load balancing technique was introduced to achieve enhanced performance on a grid which consists of sources of heterogeneous characteristics, such as resources of different computational powers. The robustness of the implementation to disturbances due to other processes on the grid is demonstrated.

Three-dimensional laser scanning two-photon fluorescence confocal microscopy of polymer materials using a new,efficient upconverting fluorophore

Three-dimensional confocal imaging of polymer samples was achieved by the use of two-photon excited fluorescence in both positive and negative contrast modes. The fluorophore was a new and highly efficient two-photon induced upconverter, resulting in improved signal strength at low pumping power. Because of the relatively long wavelength of the excitation source (798 nm from a mode-locked Ti:Sap-phire laser), this technique shows a larger penetration depth into the samples than provided by conventional single-photon fluorescence confocal microscopy. Single-photon and two-photon images of the same area of each sample show significant differences. The results suggest the possibility of using two-photon confocal microscopy, in conjunction with highly efficient fluorophores, as a tool to study the surface, interface, and fracture in material science applications.     

Power spectral based detection of brain activation from fMR images

An efficient method for detecting activation on single and multiple epoch functional MRI (fMRI) data based on power spectral density of time-series and hidden Markov model is presented. Conventional methods of analysis of fMRI data are generally based on time-domain correlation analysis concentrating mainly on the multiple epoch data and generally do not provide good results for single epoch data. The main focus of this study is the analysis of single epoch data, constrained by certain experiments such as pain response, sleep, administration of pharmacological agents, which can only have a single or very few stimulus cycles. Further, our method obviates the need to exclusively model the hemodynamic response function and correctly identifies the voxels with delayed activation. We demonstrate the efficacy of our method in detecting brain activation by using both synthetic and real fMRI data.     

Assembly of physalis mottle virus capsid protein in Escherichia coli and the role of amino and carboxy termini in the formation of the icosahedral particles

The coat protein gene of physalis mottle tymovirus (PhMV) was over expressed in Escherichia coli using pET-3d vector. The recombinant protein was found to self assemble into capsids in vivo. The purified recombinant capsids had an apparent s value of 56.5 S and a diameter of 29(+/-2) nm. In order to establish the role of amino and carboxy-terminal regions in capsid assembly, two amino-terminal deletions clones lacking the first 11 and 26 amino acid residues and two carboxy-terminal deletions lacking the last five and ten amino acid residues were constructed and overexpressed. The proteins lacking N-terminal 11 (PhCPN1) and 26 (PhCPN2) amino acid residues self assembled into T=3 capsids in vivo, as evident from electron microscopy, ultracentrifugation and agarose gel electrophoresis. The recombinant, PhCPN1 and PhCPN2 capsids were as stable as the empty capsids formed in vivo and encapsidated a small amount of mRNA. The monoclonal antibody PA3B2, which recognizes the epitope within region 22 to 36, failed to react with PhCPN2 capsids while it recognized the recombinant and PhCPN1 capsids. Disassembly of the capsids upon treatment with urea showed that PhCPN2 capsids were most stable. These results demonstrate that the N-terminal 26 amino acid residues are not essential for T=3 capsid assembly in PhMV. In contrast, both the proteins lacking the C-terminal five and ten amino acid residues were present only in the insoluble fraction and could not assemble into capsids, suggesting that these residues are crucial for folding and assembly of the particles.     

Securing Ownership in Aquaculture Development by Alternative Technology: A Case-study of the Saguling Reservoir,West Java

Development projects requiring displacement of people often result in their impoverishment; however, sustainable resettlement programmes can be devised with the introduction of alternative technologies that will enable the resettlers to exploit new productive opportunities. The majority of resettlers in the Saguling reservoir lacked the capital to invest in fisheries development and subsequently alternative technologies were introduced, enabling them to engage in aquaculture, but without success. This paper aims to examine the reasons why the introduction of alternative technology failed to establish in the Saguling reservoir, using data obtained through a literature review, interviews and a field survey. The analysis emphasizes seven factors that are crucial in the evaluation of the success of technology transfer and diffusion efforts in general.     

Robust and Efficient Feature Tracking for Indoor Navigation

Robust feature tracking is a requirement for many computer vision tasks such as indoor robot navigation. However, indoor scenes are characterized by poorly localizable features. As a result, indoor feature tracking without artificial markers is challenging and remains an attractive problem. We propose to solve this problem by constraining the locations of a large number of nondistinctive features by several planar homographies which are strategically computed using distinctive features. We experimentally show the need for multiple homographies and propose an illumination-invariant local-optimization scheme for motion refinement. The use of a large number of nondistinctive features within the constraints imposed by planar homographies allows us to gain robustness. Also, the lesser computation cost in estimating these nondistinctive features helps to maintain the efficiency of the proposed method. Our local-optimization scheme produces subpixel accurate feature motion. As a result, we are able to achieve robust and accurate feature tracking.     

Fuzzy inference system for software product family process evaluation

When developing multiple products within a common application domain, systematic use of a software product family process can yield increased productivity in cost, quality, effort and schedule. Such a process provides the means for the reuse of software assets which can considerably reduce the development time and the cost of software products. A comprehensive strategy for the evaluating the maturity of a software product family process is needed due to growing popularity of this concept in the software industry. In this paper, we propose a five-level maturity scale for software product family process. We also present a fuzzy inference system for evaluating maturity of software product family process using the proposed maturity scale. This research is aimed at establishing a comprehensive and unified strategy for process evaluation of a software product family. Such a process evaluation strategy will enable an organization to discover and monitor the strengths and weaknesses of the various activities performed during development of multiple products within a common application domain.