Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

In this paper. we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% front support vector machine. We observed that the classification rate is high for a Support vector machine classifier compared to self-organizing map-based approach.

Analyzing information flow in brain networks with nonparametric Granger causality

Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furnish this capability. The estimation of Granger causality currently requires autoregressive modeling of neural data. Here, we propose a nonparametric approach based on widely used Fourier and wavelet transforms to estimate both pairwise and conditional measures of Granger causality, eliminating the need of explicit autoregressive data modeling. We demonstrate the effectiveness of this approach by applying it to synthetic data generated by network models with known connectivity and to local field potentials recorded from monkeys performing a sensorimotor task.

Glioblastoma-Specific Protein Interaction Network Identifies PP1A and CSK21 as Connecting Molecules between Cell Cycle-Associated Genes

Glioblastoma (GBM; grade IV astrocytoma) is a very aggressive form of brain cancer with a poor survival and few qualified predictive markers. This study integrates experimentally validated genes that showed specific upregulation in GBM along with their protein-protein interaction information. A system level analysis was used to construct GBM-specific network. Computation of topological parameters of networks showed scale-free pattern and hierarchical organization. From the large network involving 1,447 proteins, we synthesized subnetworks and annotated them with highly enriched biological processes. A careful dissection of the functional modules, important nodes, and their connections identified two novel intermediary molecules CSK21 and protein phosphatase 1 alpha (PP1A) connecting the two subnetworks CDC2-PTEN-TOP2A-CAV1-P53 and CDC2-CAV1-RB-P53-PTEN, respectively. Real-time quantitative reverse transcription-PCR analysis revealed CSK21 to be moderately upregulated and PP1A to be overexpressed by 20-fold in GBM tumor samples. Immunohistochemical staining revealed nuclear expression of PP1A only in GBM samples. Thus, CSK21 and PP1A, whose functions are intimately associated with cell cycle regulation, might play key role in gliomagenesis. Cancer Res; 70(16); 6437-47. (C)2010 AACR.

Network Rhythms Influence the Relationship between Spike-Triggered Local Field Potential and Functional Connectivity

Characterizing the functional connectivity between neurons is key for understanding brain function. We recorded spikes and local field potentials (LFPs) from multielectrode arrays implanted in monkey visual cortex to test the hypotheses that spikes generated outward-traveling LFP waves and the strength of functional connectivity depended on stimulus contrast, as described recently. These hypotheses were proposed based on the observation that the latency of the peak negativity of the spike-triggered LFP average (STA) increased with distance between the spike and LFP electrodes, and the magnitude of the STA negativity and the distance over which it was observed decreased with increasing stimulus contrast. Detailed analysis of the shape of the STA, however, revealed contributions from two distinct sources-a transient negativity in the LFP locked to the spike (similar to 0 ms) that attenuated rapidly with distance, and a low-frequency rhythm with peak negativity similar to 25 ms after the spike that attenuated slowly with distance. The overall negative peak of the LFP, which combined both these components, shifted from similar to 0 to similar to 25 ms going from electrodes near the spike to electrodes far from the spike, giving an impression of a traveling wave, although the shift was fully explained by changing contributions from the two fixed components. The low-frequency rhythm was attenuated during stimulus presentations, decreasing the overall magnitude of the STA. These results highlight the importance of accounting for the network activity while using STAs to determine functional connectivity.

A new load distribution strategy for linear network with communication delays

In this paper, we propose a new load distribution strategy called `send-and-receive' for scheduling divisible loads, in a linear network of processors with communication delay. This strategy is designed to optimally utilize the network resources and thereby minimizes the processing time of entire processing load. A closed-form expression for optimal size of load fractions and processing time are derived when the processing load originates at processor located in boundary and interior of the network. A condition on processor and link speed is also derived to ensure that the processors are continuously engaged in load distributions. This paper also presents a parallel implementation of `digital watermarking problem' on a personal computer-based Pentium Linear Network (PLN) topology. Experiments are carried out to study the performance of the proposed strategy and results are compared with other strategies found in literature.

X-Ray Studies On The Bilayer Structure Of Trypsin-Treated Rat-Brain Myelin

Trypsin-treated rat brain myelin was subjected to biochemical and X-ray studies. Untreated myelin gave rise to a pattern of three rings with a fundamental repeat period of 155 Angstrom consisting of two bilayers per repeat period, whereas myelin treated with trypsin showed a fundamental repeat period of 75 Angstrom with one bilayer per repeat period. The integrated raw intensity of the h=4 reflection with respect to the h=2 reflection is 0.38 for untreated myelin. The corresponding value reduced to 0.23, 0.18, 0.17 for myelin treated with 5, 10, 40 units of trypsin per mg of myelin, respectively, for 30 min at 30 degrees C. The decrease in relative raw intensity of the higher-order reflection relative to the lower-order reflection is suggestive of a disordering of the phosphate groups upon trypsin treatment or an increased mosaicity of the membrane or a combination of both these effects, However, trypsin treatment does not lead to a complete breakdown of the membrane, The integrated intensity of the h=1 reflection, though weak, is above the measurable threshold for untreated myelin, whereas the corresponding intensity is below the measurable threshold for trypsin-treated myelin, indicating a possible asymmetric to symmetric transition of the myelin bilayer structure about its centre after trypsin treatment.

A Network Model of Static Foam Drainage

On the basis of a more realistic tetrakaidecahedral structure of foam bubbles, a network model of static foam drainage has been developed. The model considers the foam to be made up of films and Plateau borders. The films drain into the adjacent Plateau borders, which in turn form a network through which the liquid moves from the foam to the liquid pool. From the structure, a unit flow cell was found, which constitutes the foam when stacked together both horizontally and vertically. Symmetry in the unit flow cell indicates that the flow analysis of a part of it can be employed to obtain the drainage for the whole foam. Material balance equations have been written for each segment of this subsection, ensuring connectivity, and solved with the appropriate boundary and initial conditions. The calculated rates of drainage, when compared with the available experimental results, indicate that the model predicts the experimental results well.

An Efficient Load Distribution Strategy for a Distributed Linear Network of Processors with Communication Delays

In this paper, we present an improved load distribution strategy, for arbitrarily divisible processing loads, to minimize the processing time in a distributed linear network of communicating processors by an efficient utilization of their front-ends. Closed-form solutions are derived, with the processing load originating at the boundary and at the interior of the network, under some important conditions on the arrangement of processors and links in the network. Asymptotic analysis is carried out to explore the ultimate performance limits of such networks. Two important theorems are stated regarding the optimal load sequence and the optimal load origination point. Comparative study of this new strategy with an earlier strategy is also presented.

Fault-tolerant characteristics and topological properties of a hierarchical network of hypercubes

We analyse the fault-tolerant parameters and topological properties of a hierarchical network of hypercubes. We take a close look at the Extended Hypercube (EH) and the Hyperweave (HW) architectures and also compare them with other popular architectures. These two architectures have low diameter and constant degree of connectivity making it possible to expand these networks without affecting the existing configuration. A scheme for incrementally expanding this network is also presented. We also look at the performance of the ASCEND/DESCEND class of algorithms on these architectures.

Characterization of an Evolutionarily Conserved Metallophosphoesterase That Is Expressed in the Fetal Brain and Associated with the WAGR Syndrome

Among the human diseases that result from chromosomal aberrations, a de novo deletion in chromosome 11p13 is clinically associated with a syndrome characterized by Wilms' tumor, aniridia, genitourinary anomalies, and mental retardation (WAGR). Not all genes in the deleted region have been characterized biochemically or functionally. We have recently identified the first Class III cyclic nucleotide phosphodiesterase, Rv0805, from Mycobacterium tuberculosis, which biochemically and structurally belongs to the superfamily of metallophosphoesterases. We performed a large scale bioinformatic analysis to identify orthologs of the Rv0805 protein and identified many eukaryotic genes that included the human 239FB gene present in the region deleted in the WAGR syndrome. We report here the first detailed biochemical characterization of the rat 239FB protein and show that it possesses metallophosphodiesterase activity. Extensive mutational analysis identified residues that are involved in metal interaction at the binuclear metal center. Generation of a rat 239FB protein with a mutation corresponding to a single nucleotide polymorphism seen in human 239FB led to complete inactivation of the protein. A close ortholog of 239FB is found in adult tissues, and biochemical characterization of the 239AB protein demonstrated significant hydrolytic activity against 2',3'-cAMP, thus representing the first evidence for a Class III cyclic nucleotide phosphodiesterase in mammals. Highly conserved orthologs of the 239FB protein are found in Caenorhabditis elegans and Drosophila and, coupled with available evidence suggesting that 239FB is a tumor suppressor, indicate the important role this protein must play in diverse cellular events.

A comparative social network analysis of wasp colonies and classrooms: Linking network structure to functioning

A major question in current network science is how to understand the relationship between structure and functioning of real networks. Here we present a comparative network analysis of 48 wasp and 36 human social networks. We have compared the centralisation and small world character of these interaction networks and have studied how these properties change over time. We compared the interaction networks of (1) two congeneric wasp species (Ropalidia marginata and Ropalidia cyathiformis), (2) the queen-right (with the queen) and queen-less (without the queen) networks of wasps, (3) the four network types obtained by combining (1) and (2) above, and (4) wasp networks with the social networks of children in 36 classrooms. We have found perfect (100%) centralisation in a queen-less wasp colony and nearly perfect centralisation in several other queen-less wasp colonies. Note that the perfectly centralised interaction network is quite unique in the literature of real-world networks. Differences between the interaction networks of the two wasp species are smaller than differences between the networks describing their different colony conditions. Also, the differences between different colony conditions are larger than the differences between wasp and children networks. For example, the structure of queen-right R. marginata colonies is more similar to children social networks than to that of their queen-less colonies. We conclude that network architecture depends more on the functioning of the particular community than on taxonomic differences (either between two wasp species or between wasps and humans).

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

Latency, Power and Performance Trade-offs in Network-on-Chips by Link Microarchitecture Exploration

This paper presents a power, latency and throughput trade-off study on NoCs by varying microarchitectural (e.g. pipelining) and circuit level (e.g. frequency and voltage) parameters. We change pipelining depth, operating frequency and supply voltage for 3 example NoCs - 16 node 2D Torus, Tree network and Reduced 2D Torus. We use an in-house NoC exploration framework capable of topology generation and comparison using parameterized models of Routers and links developed in SystemC. The framework utilizes interconnect power and delay models from a low-level modelling tool called Intacte[1]1. We find that increased pipelining can actually reduce latency. We also find that there exists an optimal degree of pipelining which is the most energy efficient in terms of minimizing energy-delay product.

On-Chip Clock Network Skew Measurement using Sub-Sampling

We present a technique for an all-digital on-chip delay measurement system to measure the skews in a clock distribution network. It uses the principle of sub-sampling. Measurements from a prototype fabricated in a 65 nm industrial process, indicate the ability to measure delays with a resolution of 0.5ps and a DNL of 1.2 ps.

Cholinergic Muscarinic Receptors in Human Fetal Brain: Ontogeny of [3H]Quinuclidinyl Benzilate Binding Sites in Corpus Striatum, Brainstem, and Cerebellum

The ontogeny of muscarinic receptors was studied in human fetal striatum, brainstem, and cerebellum to investigate general principles of synaptogenesis as well as the physiological balance between various chemical synapses during development in a given region of the brain. [3H]Quinuclidinyl benzilate ([-'H]QNB) binding was assayed in total particulate fraction (TPF) from various parts of brain. In the corpus striatum, QNB binding sites are present at 16 weeks of gestation (average concentration 180 fmol/mg protein of TPF), slowly increase up to 24 weeks (average concentration 217 fmol/mg protein), and rapidly increase during the third trimester to 480 fmol/mg protein of TPF. In contrast, dopaminergic receptors exist as two subpopulations. one with low affinity and the other with high affinity up to the 24th week of gestation; all of them acquire the highaffinity characteristic during the third trimester. In brainstem, the muscarinic receptors show maximum concentration by 16 weeks of age (360 fmolimg protein of TPF). Subsequently the muscarinic receptor concentration shows a gradual decline in the brainstem. In cerebellum, except for a slight increase at 24 weeks (average concentration 90 fmol/mg protein of TPF), the receptor concentration remained nearly constant at about 60-70 fmolimg protein of TPF throughout fetal life. This study demonstrates that the ontogeny of muscarinic receptors varies among the different regions, and the patterns observed suggest that receptor formation occurs principally in the third trimester. Also noteworthy is the finding that the QNB binding sites decreased in all regions of the human brain during adult life. Key Words: Cholinergic muscarinic receptors-Quinuclidinyl benzilate-Corpus striaturn-Brainstem-Cerebellum. Ravikumar B. V. and Sastry P. S. Cholinergic muscarinic receptors in human fetal brain: Ontogeny of [3H]quinuclidinyl benzilate binding sites in corpus striatum, brainstem, and cerebellum. J. Neurochem. 45, 1948- 1950 (1985).