A Real-time clustering system for spatio-temporal signals from network of neurons

Over past few years, the studies of cultured neuronal networks have opened up avenues for understanding the ion channels, receptor molecules, and synaptic plasticity that may form the basis of learning and memory. The hippocampal neurons from rats are dissociated and cultured on a surface containing a grid of 64 electrodes. The signals from these 64 electrodes are acquired using a fast data acquisition system MED64 (Alpha MED Sciences, Japan) at a sampling rate of 20 K samples with a precision of 16-bits per sample. A few minutes of acquired data runs in to a few hundreds of Mega Bytes. The data processing for the neural analysis is highly compute-intensive because the volume of data is huge. The major processing requirements are noise removal, pattern recovery, pattern matching, clustering and so on. In order to interface a neuronal colony to a physical world, these computations need to be performed in real-time. A single processor such as a desk top computer may not be adequate to meet this computational requirements. Parallel computing is a method used to satisfy the real-time computational requirements of a neuronal system that interacts with an external world while increasing the flexibility and scalability of the application. In this work, we developed a parallel neuronal system using a multi-node Digital Signal processing system. With 8 processors, the system is able to compute and map incoming signals segmented over a period of 200 ms in to an action in a trained cluster system in real time.

Measurement and modeling of Alloy-Spinel-Corundum equilibrium in the Ni-Mn-Al-0 system at 1873 K

The oxygen content of liquid Ni-Mn alloy equilibrated with spinel solid solution, (Ni,Mn)O. (1 +x)A12O3, and α-Al2O3 has been measured by suction sampling and inert gas fusion analysis. The corresponding oxygen potential of the three-phase system has been determined with a solid state cell incorporating (Y2O3)ThO2 as the solid electrolyte and Cr + Cr2O3 as the reference electrode. The equilibrium composition of the spinel phase formed at the interface of the alloy and alumina crucible was obtained using EPMA. The experimental data are compared with a thermodynamic model based on the free energies of formation of end-member spinels, free energy of solution of oxygen in liquid nickel, interaction parameters, and the activities in liquid Ni-Mn alloy and spinel solid solution. Mixing properties of the spinel solid solution are derived from a cation distribution model. The computational results agree with the experimental data on oxygen concentration, potential, and composition of the spinel phase.

On numerical prediction of the acoustic source characteristics of an engine exhaust system

The contemporary methods for source characterization rely mainly on experiments. These methods produce inaccurate results in the low‐frequency band, where the characteristics are all the more important. Moreover, the experimental methods cannot be used at the design stage. Hence, a numerical technique to obtain the source characteristics is desirable. In this paper, the pressure‐time history and the mass‐flux‐time history obtained by means of the time‐domain analysis have been used, along with the two‐load method to compute the source characteristics. Two new computational methods for obtaining the source characteristics have been described. These are much simpler, and computationally more economical than the complete time‐domain simulation, which makes use of the method of characteristics.

Improvement of power-system transient stability by phase-shift insertion

Computational studies of the transient stability of a synchronous machine connected to an infinite busbar by a double-circuit transmission line are used to illustrate the effect of relative phase-shift insertion between the machine and its associated power system. This method of obtaining a change in the effective rotor-excitation angle, and thereby the power transfer, is described, together with an outline of possible methods of implementation by a phase-shifting transformer in a power system.

Computational studies on histidine kinase protein BaeS to target multidrug-resistant Salmonella

Multidrug-resistant Salmonella serovars have been a recent concern in curing infectious diseases like typhoid. Salmonella BaeS and BaeR are the two-component system (TCS) that signal transduction proteins found to play an important role in its multidrug resistance. A canonical TCS comprises a histidine kinase (HK) and its cognate partner response regulator (RR). The general approaches for therapeutic targeting are either the catalytic ATP-binding domain or the dimerization domain HisKA (DHp) of the HK, and in some cases, the receiver or the regulatory domain of the RR proteins. Earlier efforts of identifying novel drugs targeting the signal transduction protein have not been quite successful, as it shares similar ATP-binding domain with the key house keeping gene products of the mammalian GHL family. However, targeting the dimerization domain of HisKA through which the signals are received from the RR can be a better approach. In this article, we show stepwise procedure to specifically identify the key interacting residues involved in the dimerization with the RR along with effective targeting by ligands screened from the public database. We have found a few inhibitors which target effectively the important residues for the dimerization activity. Our results suggest a plausible de novo design of better DHp domain inhibitors.

Computational representations for multi state design tasks and enumeration of mechanical device behaviour

The role of a computer emerged from modeling and analyzing concepts (ideas) to generate concepts. Research into methods for supporting conceptual design using automated synthesis had attracted much attention in the past decades. To find out how designers synthesize solution concepts for multi-state mechanical devices, ten experimental studies were conducted. Observations from these empirical studies would be used as the basis to develop knowledge involved in the multi-state design synthesis process. In this paper, we propose a computational representation for expressing the multi-state design task and for enumerating multi-state behaviors of kinematic pairs and mechanisms. This computational representation would be used to formulate computational methods for the synthesis process to develop a system for supporting design synthesis of multiple state mechanical devices by generating a comprehensive variety of solution alternatives.

Information and Efficiency in the Nervous System-A Synthesis

In systems biology, questions concerning the molecular and cellular makeup of an organism are of utmost importance, especially when trying to understand how unreliable components-like genetic circuits, biochemical cascades, and ion channels, among others-enable reliable and adaptive behaviour. The repertoire and speed of biological computations are limited by thermodynamic or metabolic constraints: an example can be found in neurons, where fluctuations in biophysical states limit the information they can encode-with almost 20-60% of the total energy allocated for the brain used for signalling purposes, either via action potentials or by synaptic transmission. Here, we consider the imperatives for neurons to optimise computational and metabolic efficiency, wherein benefits and costs trade-off against each other in the context of self-organised and adaptive behaviour. In particular, we try to link information theoretic (variational) and thermodynamic (Helmholtz) free-energy formulations of neuronal processing and show how they are related in a fundamental way through a complexity minimisation lemma.

Computational framework to integrate the effect of antigen recognition on disease epidemiology outcome: multi-scale approach

Human Leukocyte Antigen (HLA) plays an important role, in presenting foreign pathogens to our immune system, there by eliciting early immune responses. HLA genes are highly polymorphic, giving rise to diverse antigen presentation capability. An important factor contributing to enormous variations in individual responses to diseases is differences in their HLA profiles. The heterogeneity in allele specific disease responses decides the overall disease epidemiological outcome. Here we propose an agent based computational framework, capable of incorporating allele specific information, to analyze disease epidemiology. This framework assumes a SIR model to estimate average disease transmission and recovery rate. Using epitope prediction tool, it performs sequence based epitope detection for a given the pathogenic genome and derives an allele specific disease susceptibility index depending on the epitope detection efficiency. The allele specific disease transmission rate, that follows, is then fed to the agent based epidemiology model, to analyze the disease outcome. The methodology presented here has a potential use in understanding how a disease spreads and effective measures to control the disease.

3-D GPU Based Real Time Diffuse Optical Tomographic System

3-Dimensional Diffuse Optical Tomographic (3-D DOT) image reconstruction algorithm is computationally complex and requires excessive matrix computations and thus hampers reconstruction in real time. In this paper, we present near real time 3D DOT image reconstruction that is based on Broyden approach for updating Jacobian matrix. The Broyden method simplifies the algorithm by avoiding re-computation of the Jacobian matrix in each iteration. We have developed CPU and heterogeneous CPU/GPU code for 3D DOT image reconstruction in C and MatLab programming platform. We have used Compute Unified Device Architecture (CUDA) programming framework and CUDA linear algebra library (CULA) to utilize the massively parallel computational power of GPUs (NVIDIA Tesla K20c). The computation time achieved for C program based implementation for a CPU/GPU system for 3 planes measurement and FEM mesh size of 19172 tetrahedral elements is 806 milliseconds for an iteration.

Crystal landscape in the orcinol:4,4 `-bipyridine system: synthon modularity, polymorphism and transferability of multipole charge density parameters

Polymorphism in the orcinol: 4,4'-bipyridine cocrystal system is analyzed in terms of a robust convergent modular phenol...pyridine supramolecular synthon. Employing the Synthon Based Fragments Approach (SBFA) to transfer the multipole charge density parameters, it is demonstrated that the crystal landscape can be quantified in terms of intermolecular interaction energies in the five crystal forms so far isolated in this complex system. There are five crystal forms. The first has an open, divergent O-H...N based structure with alternating orcinol and bipyridine molecules. The other four polymorphs have different three-dimensional packing but all of them are similar at an interaction level, and are based on a modular O-H...N mediated supramolecular synthon that consists of two orcinol and two bipyridine molecules in a closed, convergent structure. The SBFA method, which depends on the modularity of synthons, provides good agreement between experiment and theory because it takes into account the supramolecular contribution to charge density. The existence of five crystal forms in this system shows that polymorphism in cocrystals need not be considered to be an unusual phenomenon. Studies of the crystal landscape could lead to an understanding of the kinetic pathways that control the crystallization processes, in other words the valleys in the landscape. These pathways are traditionally not considered in exercises pertaining to computational crystal structure prediction, which rather monitors the thermodynamics of the various stable forms in the system, in other words the peaks in the landscape.