Suite of tools for statistical N-gram language modeling for pattern mining in whole genome sequences

Genome sequences contain a number of patterns that have biomedical significance. Repetitive sequences of various kinds are a primary component of most of the genomic sequence patterns. We extended the suffix-array based Biological Language Modeling Toolkit to compute n-gram frequencies as well as n-gram language-model based perplexity in windows over the whole genome sequence to find biologically relevant patterns. We present the suite of tools and their application for analysis on whole human genome sequence.

Prior image-constrained l(1)-norm-based reconstruction method for effective usage of structural information in diffuse optical tomography

A novel approach that can more effectively use the structural information provided by the traditional imaging modalities in multimodal diffuse optical tomographic imaging is introduced. This approach is based on a prior image-constrained-l(1) minimization scheme and has been motivated by the recent progress in the sparse image reconstruction techniques. It is shown that the proposed framework is more effective in terms of localizing the tumor region and recovering the optical property values both in numerical and gelatin phantom cases compared to the traditional methods that use structural information. (C) 2012 Optical Society of America

Minimal residual method provides optimal regularization parameter for diffuse optical tomography

The inverse problem in the diffuse optical tomography is known to be nonlinear, ill-posed, and sometimes under-determined, requiring regularization to obtain meaningful results, with Tikhonov-type regularization being the most popular one. The choice of this regularization parameter dictates the reconstructed optical image quality and is typically chosen empirically or based on prior experience. An automated method for optimal selection of regularization parameter that is based on regularized minimal residual method (MRM) is proposed and is compared with the traditional generalized cross-validation method. The results obtained using numerical and gelatin phantom data indicate that the MRM-based method is capable of providing the optimal regularization parameter. (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). DOI: 10.1117/1.JBO.17.10.106015]

Optical frequency metrology with an Rb-stabilized ring-cavity resonator-study of cavity-dispersion errors

We have developed a technique to measure the absolute frequencies of optical transitions by using an evacuated Rb-stabilized ring-cavity resonator as a transfer cavity. The absolute frequency of the Rb D-2 line (at 780 nm) used to stabilize the cavity is known and allows us to determine the absolute value of the unknown frequency. We study wavelength-dependent errors due to dispersion at the cavity mirrors by measuring the frequency of the same transition in the Cs D-2 line (at 852 nm) at three cavity lengths. The spread in the values shows that dispersion errors are below 30 kHz, corresponding to a relative precision of 10(-10). We give an explanation for reduced dispersion errors in the ring-cavity geometry by calculating errors due to the lateral shift and the phase shift at the mirrors, and show that they are roughly equal but occur with opposite signs. We have earlier shown that diffraction errors (due to Guoy phase) are negligible in the ring-cavity geometry compared to a linear cavity; the reduced dispersion error is another advantage. Our values are consistent with measurements of the same transition using the more expensive frequency-comb technique. Our simpler method is ideally suited for measuring hyperfine structure, fine structure, and isotope shifts, up to several hundreds of gigahertz.

Effective contrast recovery in rapid dynamic near-infrared diffuse optical tomography using l(1)-norm-based linear image reconstruction method

Traditional image reconstruction methods in rapid dynamic diffuse optical tomography employ l(2)-norm-based regularization, which is known to remove the high-frequency components in the reconstructed images and make them appear smooth. The contrast recovery in these type of methods is typically dependent on the iterative nature of method employed, where the nonlinear iterative technique is known to perform better in comparison to linear techniques (noniterative) with a caveat that nonlinear techniques are computationally complex. Assuming that there is a linear dependency of solution between successive frames resulted in a linear inverse problem. This new framework with the combination of l(1)-norm based regularization can provide better robustness to noise and provide better contrast recovery compared to conventional l(2)-based techniques. Moreover, it is shown that the proposed l(1)-based technique is computationally efficient compared to its counterpart (l(2)-based one). The proposed framework requires a reasonably close estimate of the actual solution for the initial frame, and any suboptimal estimate leads to erroneous reconstruction results for the subsequent frames.

As(3)p: A Fast Algorithm to Search Structurally Similar Protiens

Protein structure comparison is essential for understanding various aspects of protein structure, function and evolution. It can be used to explore the structural diversity and evolutionary patterns of protein families. In view of the above, a new algorithm is proposed which performs faster protein structure comparison using the peptide backbone torsional angles. It is fast, robust, computationally less expensive and efficient in finding structural similarities between two different protein structures and is also capable of identifying structural repeats within the same protein molecule.

Resource Usage Monitoring in Clouds

Monitoring of infrastructural resources in clouds plays a crucial role in providing application guarantees like performance, availability, and security. Monitoring is crucial from two perspectives - the cloud-user and the service provider. The cloud user’s interest is in doing an analysis to arrive at appropriate Service-level agreement (SLA) demands and the cloud provider’s interest is to assess if the demand can be met. To support this, a monitoring framework is necessary particularly since cloud hosts are subject to varying load conditions. To illustrate the importance of such a framework, we choose the example of performance being the Quality of Service (QoS) requirement and show how inappropriate provisioning of resources may lead to unexpected performance bottlenecks. We evaluate existing monitoring frameworks to bring out the motivation for building much more powerful monitoring frameworks. We then propose a distributed monitoring framework, which enables fine grained monitoring for applications and demonstrate with a prototype system implementation for typical use cases.

Computing Reeb Graphs as a Union of Contour Trees

The Reeb graph of a scalar function tracks the evolution of the topology of its level sets. This paper describes a fast algorithm to compute the Reeb graph of a piecewise-linear (PL) function defined over manifolds and non-manifolds. The key idea in the proposed approach is to maximally leverage the efficient contour tree algorithm to compute the Reeb graph. The algorithm proceeds by dividing the input into a set of subvolumes that have loop-free Reeb graphs using the join tree of the scalar function and computes the Reeb graph by combining the contour trees of all the subvolumes. Since the key ingredient of this method is a series of union-find operations, the algorithm is fast in practice. Experimental results demonstrate that it outperforms current generic algorithms by a factor of up to two orders of magnitude, and has a performance on par with algorithms that are catered to restricted classes of input. The algorithm also extends to handle large data that do not fit in memory.

SOLVING VOLUME INTEGRAL EQUATION USING ScaLAPACK

In this article, we investigate the performance of a volume integral equation code on BlueGene/L system. Volume integral equation (VIE) is solved for homogeneous and inhomogeneous dielectric objects for radar cross section (RCS) calculation in a highly parallel environment. Pulse basis functions and point matching technique is used to convert the volume integral equation into a set of simultaneous linear equations and is solved using parallel numerical library ScaLAPACK on IBM's distributed-memory supercomputer BlueGene/L by different number of processors to compare the speed-up and test the scalability of the code.

RCS ENHANCEMENT DUE TO BRAGG SCATTERING

With the advances of techniques for RCS reduction, it has become practical to develop aircraft which are invisible to modern day radars. In order to detect such low visible targets it is necessary to explore other phenomenon that contributes to the scattering of incident electromagnetic wave. It is well known from the developments from the clear air scattering using RASS induced acoustic wave could be used to create dielectric constant fluctuation. The scattering from these fluctuations rather than from the aircraft have been observed to enhance the RCS of clear air, under the condition when the incident EM wave is half of the acoustic wave, the condition of Bragg scattering would be met and RCS would be enhanced. For detecting low visibility targets which are at significant distance away from the main radar, inducement of EM fluctuation from acoustic source collocated with the acoustic source is infeasible. However the flow past aircraft produces acoustic disturbances around the aircraft can be exploited to detect low visibility targets. In this paper numerical simulation for RCS enhancement due to acoustic disturbances is presented. In effect, this requires the solution of scattering from 3D inhomogeneous complex shaped bodies. In this volume surface integral equation (VSIE) is used to compute the RCS from fluctuation introduced through the acoustic disturbances. Though the technique developed can be used to study the scattering from radars of any shape and acoustic disturbances of any shape. For illustrative condition, enhancement due to the Bragg scattering are shown to improve the RCS by nearly 30dB, for air synthetic sinusoidal acoustic variation profile for a spherical scattering volume

HUMAN DETECTION USING SPARSE REPRESENTATION

The problem of human detection is challenging, more so, when faced with adverse conditions such as occlusion and background clutter. This paper addresses the problem of human detection by representing an extracted feature of an image using a sparse linear combination of chosen dictionary atoms. The detection along with the scale finding, is done by using the coefficients obtained from sparse representation. This is of particular interest as we address the problem of scale using a scale-embedded dictionary where the conventional methods detect the object by running the detection window at all scales.

Data-resolution based optimal choice of minimum required measurements for image-guided diffuse optical tomography

Image-guided diffuse optical tomography has the advantage of reducing the total number of optical parameters being reconstructed to the number of distinct tissue types identified by the traditional imaging modality, converting the optical image-reconstruction problem from underdetermined in nature to overdetermined. In such cases, the minimum required measurements might be far less compared to those of the traditional diffuse optical imaging. An approach to choose these optimally based on a data-resolution matrix is proposed, and it is shown that such a choice does not compromise the reconstruction performance. (C) 2013 Optical Society of America

Structure of SAICAR synthetase from Pyrococcus horikoshii OT3: Insights into thermal stability

The first native crystal structure of Phosphoribosylaminoimidazole-succinocarboxamide synthetase (SAICAR synthetase) from a hyperthermophilic organism Pyrococcus horikoshii OT3 was determined in two space groups H3 (Type-1: Resolution 2.35 angstrom) and in C222(1) (Type-2: Resolution 1.9 angstrom). Both are dimeric but Type-1 structure exhibited hexameric arrangement due to the presence of cadmium ions. A comparison has been made on the sequence and structures of all SAICAR synthetases to better understand the differences between mesophilic, thermophilic and hyperthermophilic SAICAR synthetases. These SAICAR synthetases are reasonably similar in sequence and three-dimensional structure; however, differences were visible only in the subtler details of percentage composition of the sequences, salt bridge interactions and non-polar contact areas. (c) 2012 Elsevier B.V. All rights reserved.

Fast Likelihood Computation in Speech Recognition using Matrices

Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech processing problems. Computing likelihoods against a large set of Gaussians is required as a part of many speech processing systems and it is the computationally dominant phase for Large Vocabulary Continuous Speech Recognition (LVCSR) systems. We express the likelihood computation as a multiplication of matrices representing augmented feature vectors and Gaussian parameters. The computational gain of this approach over traditional methods is by exploiting the structure of these matrices and efficient implementation of their multiplication. In particular, we explore direct low-rank approximation of the Gaussian parameter matrix and indirect derivation of low-rank factors of the Gaussian parameter matrix by optimum approximation of the likelihood matrix. We show that both the methods lead to similar speedups but the latter leads to far lesser impact on the recognition accuracy. Experiments on 1,138 work vocabulary RM1 task and 6,224 word vocabulary TIMIT task using Sphinx 3.7 system show that, for a typical case the matrix multiplication based approach leads to overall speedup of 46 % on RM1 task and 115 % for TIMIT task. Our low-rank approximation methods provide a way for trading off recognition accuracy for a further increase in computational performance extending overall speedups up to 61 % for RM1 and 119 % for TIMIT for an increase of word error rate (WER) from 3.2 to 3.5 % for RM1 and for no increase in WER for TIMIT. We also express pairwise Euclidean distance computation phase in Dynamic Time Warping (DTW) in terms of matrix multiplication leading to saving of approximately of computational operations. In our experiments using efficient implementation of matrix multiplication, this leads to a speedup of 5.6 in computing the pairwise Euclidean distances and overall speedup up to 3.25 for DTW.

Cloning, expression, purification, crystallization and preliminary X-ray crystallographic study of thymidylate kinase (TTHA1607) from Thermus thermophilus HB8

Nucleotide biosynthesis plays a key role in cell survival and cell proliferation. Thymidylate kinase is an enzyme that catalyses the conversion of dTMP to dTDP using ATP-Mg2+ as a phosphoryl-donor group. This enzyme is present at the junction of the de novo and salvage pathways; thus, any inhibitor designed against it will result in cell death. This highlights the importance of this enzyme as a drug target. Thymidylate kinase from the extremely thermophilic organism Thermus thermophilus HB8 has been expressed, purified and crystallized using the microbatch method. The crystals diffracted to a resolution of 1.83 angstrom and belonged to the orthorhombic space group P2(1)2(1)2(1), with unit-cell parameters a = 39.50, b = 80.29, c = 122.55 angstrom. Preliminary studies revealed the presence of a dimer in the asymmetric unit with a Matthews coefficient (V-M) of 2.18 angstrom(3) Da(-1).