Source and target enzyme signature in serine protease inhibitor active site sequences

Amino acid sequences of proteinaceous proteinase inhibitors have been extensively analysed for deriving information regarding the molecular evolution and functional relationship of these proteins. These sequences have been grouped into several well defined families. It was found that the phylogeny constructed with the sequences corresponding to the exposed loop responsible for inhibition has several branches that resemble those obtained from comparisons using the entire sequence. The major branches of the unrooted tree corresponded to the families to which the inhibitors belonged. Further branching is related to the enzyme specificity of the inhibitor. Examination of the active site loop sequences of trypsin inhibitors revealed that there are strong preferences for specific amino acids at different positions of the loop. These preferences are inhibitor class specific. Inhibitors active against more than one enzyme occur within a class and confirm to class specific sequence in their loops. Hence, only a few positions in the loop seem to determine the specificity. The ability to inhibit the same enzyme by inhibitors that belong to different classes appears to be a result of convergent evolution

A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma

Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n = 222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio HR] = 2.4; 95% CI = 1.4-3.8; p < 0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR = 1.7; 95% CI = 1.1-2.8; p = 0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR = 2.0; 95% CI = 1.4-2.8; p < 0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR = 1.120; 95% CI = 1.04-1.20; p = 0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy.

Vibration Signature Analysis Using Artificial Neural Networks

Damage detection by measuring and analyzing vibration signals in a machine component is an established procedure in mechanical and aerospace engineering. This paper presents vibration signature analysis of steel bridge structures in a nonconventional way using artificial neural networks (ANN). Multilayer perceptrons have been adopted using the back-propagation algorithm for network training. The training patterns in terms of vibration signature are generated analytically for a moving load traveling on a trussed bridge structure at a constant speed to simulate the inspection vehicle. Using the finite-element technique, the moving forces are converted into stationary time-dependent force functions in order to generate vibration signals in the structure and the same is used to train the network. The performance of the trained networks is examined for their capability to detect damage from unknown signatures taken independently at one, three, and five nodes. It has been observed that the prediction using the trained network with single-node signature measurement at a suitability chosen location is even better than that of three-node and five-node measurement data.

Optical studies of SN 2009jf: a Type Ib supernova with an extremely slow decline and aspherical signature

Optical UBVRI photometry and medium-resolution spectroscopy of the Type Ib supernova SN 2009jf, during the period from similar to -15 to +250 d, with respect to the B maximum are reported. The light curves are broad, with an extremely slow decline. The early post-maximum decline rate in the V band is similar to SN 2008D; however, the late-phase decline rate is slower than other Type Ib supernovae studied. With an absolute magnitude of M-V = -17.96 +/- 0.19 at peak, SN 2009jf is a normally bright supernova. The peak bolometric luminosity and the energy deposition rate via the 56Ni -> 56Co chain indicate that similar to 0.17+0.03(-0.03) M-circle dot of 56Ni was ejected during the explosion. The He i 5876 A line is clearly identified in the first spectrum of day similar to -15, at a velocity of similar to 16 000 km s-1. The O i] 6300-6364 A line seen in the nebular spectrum has a multipeaked and asymmetric emission profile, with the blue peak being stronger. The estimated flux in this line implies that greater than or similar to 1.34 M-circle dot oxygen was ejected. The slow evolution of the light curves of SN 2009jf indicates the presence of a massive ejecta. The high expansion velocity in the early phase and broader emission lines during the nebular phase suggest it to be an explosion with a large kinetic energy. A simple qualitative estimate leads to the ejecta mass of M-ej = 4-9 M-circle dot and kinetic energy E-K = 3-8 x 1051 erg. The ejected mass estimate is indicative of an initial main-sequence mass of greater than or similar to 20-25 M-circle dot.

Off-line signature verification using genetically optimized weighted features

This paper is concerned with off-line signature verification. Four different types of pattern representation schemes have been implemented, viz., geometric features, moment-based representations, envelope characteristics and tree-structured Wavelet features. The individual feature components in a representation are weighed by their pattern characterization capability using Genetic Algorithms. The conclusions of the four subsystems teach depending on a representation scheme) are combined to form a final decision on the validity of signature. Threshold-based classifiers (including the traditional confidence-interval classifier), neighbourhood classifiers and their combinations were studied. Benefits of using forged signatures for training purposes have been assessed. Experimental results show that combination of the Feature-based classifiers increases verification accuracy. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

Spin Probe Signature of Freezing in Water Slow Cooling Experiment: Our Observation is Global or Local?

First systematic spin probe ESR study of water freezing has been conducted using TEMPOL and TEMPO as the probes. The spin probe signature of the water freezing has been described in terms of the collapse of narrow triplet spectrum into a single broad line. This spin probe signature of freezing has been observed at an anomalously low temperature when a milimoler solution of TEMPOL is slowly cooled from room temperature. A systematic observation has revealed a spin probe concentration dependence of these freezing and respective melting points. These results can be explained in terms of localization of spin probe and liquid water,most probably in the interstices of ice grains, in an ice matrix. The lowering of spin probe freezing point, along with the secondary evidences, like spin probe concentration dependence of peak-to-peak width in frozen limit signal, indicates a possible size dependence of these localizations/entrapments with spin probe concentration. A weak concentration dependence of spin probe assisted freezing and melting points, which has been observed for TEMPO in comparison to TEMPOL, indicates different natures of interactions with water of these two probes. This view is also supported by the relaxation behavior of the two probes.

Calculation of the front part of the sonic boom signature for a Maneuvering aerofopil

The sonic boom at a large distance from its source consists of a leading shock, a trailing shock and a one parameter family of nonlinear wavefronts in between these shocks. A new ray theoretical method using a shock ray theory and a weakly nonlinear lay theory has been used to obtain the shock fronts and wavefronts respectively, for a maneuvering aerofoil in a homogeneous medium. This method introduces a one parameter family of Cauchy problems to calculate the shock and wave fronts emerging from the surface of the aerofoil. These problems are solved numerically to obtain the leading shock front and the nonlinear wavefronts emerging from the front portion of the aerofoil.

Signature of a silver phase percolation threshold in microscopically phase separated ternary Ge0.15Se0.85-xAgx (0≤x≤0.20) glasses

Temperature modulated alternating differential scanning calorimetric studies show that Se rich Ge0.15Se0.85−xAgx (0 x 0.20) glasses are microscopically phase separated, containing Ag2Se phases embedded in a Ge0.15Se0.85 backbone. With increasing silver concentration, Ag2Se phase percolates in the Ge–Se matrix, with a well-defined percolation threshold at x = 0.10. A signature of this percolation transition is shown up in the thermal behavior, as the appearance of two exothermic crystallization peaks. Density, molar volume, and microhardness measurements, undertaken in the present study, also strongly support this view of percolation transition. The superionic conduction observed earlier in these glasses at higher silver proportions is likely to be connected with the silver phase percolation.

Evolution of 1/f α noise during electromigration stressing of metal film: Spectral signature of electromigration process

In this paper we report a systematic study of low-frequency 1/fα resistance fluctuation in a metal film at different stages of electromigration. The resistance fluctuation (noise) measurement was carried out in presence of a dc electromigration stressing current. We observe that in addition to the increase in the spectral power SV(f), the frequency dependence of the spectral power changes as the electromigration process progresses and the exponent α starts to change from 1 to higher value closer to 1.5. We interpret this change in α as arising due to an additional contribution to the spectral power with a 1/f3/2 component, which starts to contribute as the electromigration process progresses. This additional component SV(f) ∼ 1/f3/2 has been suggested to originate from long range diffusion that would accompany any electromigration process. The experimental observation finds support in a model simulation, where we also find that the enhancement of noise during electromigration stressing is accompanied by a change in spectral power frequency dependence.

Covariance profiles: a signature representation for object sets

We consider the problem of extracting a signature representation of similar entities employing covariance descriptors. Covariance descriptors can efficiently represent objects and are robust to scale and pose changes. We posit that covariance descriptors corresponding to similar objects share a common geometrical structure which can be extracted through joint diagonalization. We term this diagonalizing matrix as the Covariance Profile (CP). CP can be used to measure the distance of a novel object to an object set through the diagonality measure. We demonstrate how CP can be employed on images as well as for videos, for applications such as face recognition and object-track clustering.

Fourteen gene GBM prognostic signature identifies association of immune response pathway and mesenchymal subtype with high risk group

Background: Recent research on glioblastoma (GBM) has focused on deducing gene signatures predicting prognosis. The present study evaluated the mRNA expression of selected genes and correlated with outcome to arrive at a prognostic gene signature. Methods: Patients with GBM (n = 123) were prospectively recruited, treated with a uniform protocol and followed up. Expression of 175 genes in GBM tissue was determined using qRT-PCR. A supervised principal component analysis followed by derivation of gene signature was performed. Independent validation of the signature was done using TCGA data. Gene Ontology and KEGG pathway analysis was carried out among patients from TCGA cohort. Results: A 14 gene signature was identified that predicted outcome in GBM. A weighted gene (WG) score was found to be an independent predictor of survival in multivariate analysis in the present cohort (HR = 2.507; B = 0.919; p < 0.001) and in TCGA cohort. Risk stratification by standardized WG score classified patients into low and high risk predicting survival both in our cohort (p = <0.001) and TCGA cohort (p = 0.001). Pathway analysis using the most differentially regulated genes (n = 76) between the low and high risk groups revealed association of activated inflammatory/immune response pathways and mesenchymal subtype in the high risk group. Conclusion: We have identified a 14 gene expression signature that can predict survival in GBM patients. A network analysis revealed activation of inflammatory response pathway specifically in high risk group. These findings may have implications in understanding of gliomagenesis, development of targeted therapies and selection of high risk cancer patients for alternate adjuvant therapies.

GYROSONICS: SIGNATURE ANALYSIS AND REDUCED-ORDER MODELS

In this paper, the authors study the structure of a novel binaural sound with a certain phase and amplitude modulation and the response to this excitation when it is applied to natural rewarding circuit of human brain through auditory neural pathways. This novel excitation, also referred to as gyrosonic excitation in this work, has been found to have interesting effects such as stabilization effects on the left and right hemispheric brain signaling as captured by Galvanic Skin Resistance (GSR) measurements, control of cardiac rhythms (observed from ECG signals), mitigation of psychosomatic syndrome, and mitigation of migraine pain. Experimental data collected from human subjects are presented, and these data are examined to categorize the extent of systems disorder and reinforcement reward due to the gyrosonic stimulus. A multi-path reduced-order model has been developed to analyze the GSR signals. The filtered results are indicative of complicated reinforcing reward patterns due to the gyrosonic stimulation when it is used as a control input for patients with psychosomatic and cardiac disorders.

Zero knowledge one time digital signature scheme

In many applications, when communicating with a host, we may or may not be concerned about the privacy of the data but are mainly concerned about the integrity of data being transmitted. This paper presents a simple algorithm based on zero knowledge proof by which the receiver can confirm the integrity of data without the sender having to send the digital signature of the message directly. Also, if the same document is sent across by the same user multiple times, this scheme results in different digital signature each time thus making it a practical one-time signature scheme.

A DNA methylation prognostic signature of glioblastoma: identification of NPTX2-PTEN-NF-kappa B nexus

Glioblastoma (GBM) is the most common, malignant adult primary tumor with dismal patient survival, yet the molecular determinants of patient survival are poorly characterized. Global methylation profile of GBM samples (our cohort; n = 44) using high-resolution methylation microarrays was carried out. Cox regression analysis identified a 9-gene methylation signature that predicted survival in GBM patients. A risk-score derived from methylation signature predicted survival in univariate analysis in our and The Cancer Genome Atlas (TCGA) cohort. Multivariate analysis identified methylation risk score as an independent survival predictor in TCGA cohort. Methylation risk score stratified the patients into low-risk and high-risk groups with significant survival difference. Network analysis revealed an activated NF-kappa B pathway association with high-risk group. NF-kappa B inhibition reversed glioma chemoresistance, and RNA interference studies identified interleukin-6 and intercellular adhesion molecule-1 as key NF-kappa B targets in imparting chemoresistance. Promoter hypermethylation of neuronal pentraxin II (NPTX2), a risky methylated gene, was confirmed by bisulfite sequencing in GBMs. GBMs and glioma cell lines had low levels of NPTX2 transcripts, which could be reversed upon methylation inhibitor treatment. NPTX2 overexpression induced apoptosis, inhibited proliferation and anchorage-independent growth, and rendered glioma cells chemosensitive. Furthermore, NPTX2 repressed NF-kappa B activity by inhibiting AKT through a p53-PTEN-dependent pathway, thus explaining the hypermethylation and downregulation of NPTX2 in NF-kappa B-activated high-risk GBMs. Taken together, a 9-gene methylation signature was identified as an independent GBM prognosticator and could be used for GBM risk stratification. Prosurvival NF-kappa B pathway activation characterized high-risk patients with poor prognosis, indicating it to be a therapeutic target. (C) 2013 AACR.

An early in-situ stress signature of the AlN-Si pre-growth interface for successful integration of nitrides with (111) Si

The integration of Metal Organic Chemical Vapor Deposition (MOCVD) grown group III-A nitride device stacks on Si (111) substrates is critically dependent on the quality of the first AlN buffer layer grown. A Si surface that is both oxide-free and smooth is a primary requirement for nucleating such layers. A single parameter, the AlN layer growth stress, is shown to be an early (within 50 nm), clear (<0.5 GPa versus > 1GPa), and fail-safe indicator of the pre-growth surface, and the AlN quality required for successful epitaxy. Grain coalescence model for stress generation is used to correlate growth stress, the AlN-Si interface, and crystal quality. (C) 2013 AIP Publishing LLC.