Two new atom centered fragment descriptors and scoring function enhance classification of antibacterial activity

Classification of pharmacologic activity of a chemical compound is an essential step in any drug discovery process. We develop two new atom-centered fragment descriptors (vertex indices) - one based solely on topological considerations without discriminating atomor bond types, and another based on topological and electronic features. We also assess their usefulness by devising a method to rank and classify molecules with regard to their antibacterial activity. Classification performances of our method are found to be superior compared to two previous studies on large heterogeneous data sets for hit finding and hit-to-lead studies even though we use much fewer parameters. It is found that for hit finding studies topological features (simple graph) alone provide significant discriminating power, and for hit-to-lead process small but consistent improvement can be made by additionally including electronic features (colored graph). Our approach is simple, interpretable, and suitable for design of molecules as we do not use any physicochemical properties. The singular use of vertex index as descriptor, novel range based feature extraction, and rigorous statistical validation are the key elements of this study.

Spatial variation of seismicity parameters across India and adjoining areas

An attempt has been made to quantify the variability in the seismic activity rate across the whole of India and adjoining areas (0–45°N and 60–105°E) using earthquake database compiled from various sources. Both historical and instrumental data were compiled and the complete catalog of Indian earthquakes till 2010 has been prepared. Region-specific earthquake magnitude scaling relations correlating different magnitude scales were achieved to develop a homogenous earthquake catalog for the region in unified moment magnitude scale. The dependent events (75.3%) in the raw catalog have been removed and the effect of aftershocks on the variation of b value has been quantified. The study area was divided into 2,025 grid points (1°91°) and the spatial variation of the seismicity across the region have been analyzed considering all the events within 300 km radius from each grid point. A significant decrease in seismic b value was seen when declustered catalog was used which illustrates that a larger proportion of dependent events in the earthquake catalog are related to lower magnitude events. A list of 203,448 earth- quakes (including aftershocks and foreshocks) occurred in the region covering the period from 250 B.C. to 2010 A.D. with all available details is uploaded in the website http://www.civil.iisc.ernet.in/*sreevals/resource.htm.

Smoothed Functional Algorithms for Stochastic Optimization Using q-Gaussian Distributions

Smoothed functional (SF) schemes for gradient estimation are known to be efficient in stochastic optimization algorithms, especially when the objective is to improve the performance of a stochastic system However, the performance of these methods depends on several parameters, such as the choice of a suitable smoothing kernel. Different kernels have been studied in the literature, which include Gaussian, Cauchy, and uniform distributions, among others. This article studies a new class of kernels based on the q-Gaussian distribution, which has gained popularity in statistical physics over the last decade. Though the importance of this family of distributions is attributed to its ability to generalize the Gaussian distribution, we observe that this class encompasses almost all existing smoothing kernels. This motivates us to study SF schemes for gradient estimation using the q-Gaussian distribution. Using the derived gradient estimates, we propose two-timescale algorithms for optimization of a stochastic objective function in a constrained setting with a projected gradient search approach. We prove the convergence of our algorithms to the set of stationary points of an associated ODE. We also demonstrate their performance numerically through simulations on a queuing model.

Super-Resolution Reconstruction in Frequency-Domain Optical-Coherence Tomography Using the Finite-Rate-of-Innovation Principle

The standard approach to signal reconstruction in frequency-domain optical-coherence tomography (FDOCT) is to apply the inverse Fourier transform to the measurements. This technique offers limited resolution (due to Heisenberg's uncertainty principle). We propose a new super-resolution reconstruction method based on a parametric representation. We consider multilayer specimens, wherein each layer has a constant refractive index and show that the backscattered signal from such a specimen fits accurately in to the framework of finite-rate-of-innovation (FRI) signal model and is represented by a finite number of free parameters. We deploy the high-resolution Prony method and show that high-quality, super-resolved reconstruction is possible with fewer measurements (about one-fourth of the number required for the standard Fourier technique). To further improve robustness to noise in practical scenarios, we take advantage of an iterated singular-value decomposition algorithm (Cadzow denoiser). We present results of Monte Carlo analyses, and assess statistical efficiency of the reconstruction techniques by comparing their performance against the Cramer-Rao bound. Reconstruction results on experimental data obtained from technical as well as biological specimens show a distinct improvement in resolution and signal-to-reconstruction noise offered by the proposed method in comparison with the standard approach.

Comparative Studies on the Bioremediation of Hexavalent and Trivalent Chromium using Citrobacter freundii: Part I-Effect of parameters controlling Biosorption

The potential of Citrobacter freundii, a Gram negative bacteria for the remediation of hexavalent chromium (Cr(VI)) and trivalent chromium (Cr(III))) from aqueous solutions was investigated. Bioremediation of Cr(VI) involved both biosorption and bioreduction processes, as compared to only biosorption process observed with respect to Cr(III) bioremediation. In the case of Cr(VI) bioremediation studies, about 59 % biosorption was achieved at an equilibrium time of 2 h, initial Cr(VI) concentration of 4 mg/L, pH 1 and a biomass loading of 5x10(11) cells/mL. The remainder, 41 %, was found to be in the form of Cr(111) ions owing to bioreduction of Cr(VI) by the bacteria resulting in the absence of Cr(VI) ions in the residue, there by meeting the USEPA specifications. Similar studies were carried out using Cr(III) solution for an equilibrium time of 2 h, Cr(III) concentration of 4 mg/L, pH 3 and a biomass loading of 6.3x10(11) cells/mL., wherein a maximum biosorption of about 30 % was achieved.

Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001-10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.

A bottom-up approach for optimization of friction stir processing parameters; a study on aluminium 2024-T3 alloy

Friction stir processing (FSP) is emerging as one of the most competent severe plastic deformation (SPD) method for producing bulk ultra-fine grained materials with improved properties. Optimizing the process parameters for a defect free process is one of the challenging aspects of FSP to mark its commercial use. For the commercial aluminium alloy 2024-T3 plate of 6 mm thickness, a bottom-up approach has been attempted to optimize major independent parameters of the process such as plunge depth, tool rotation speed and traverse speed. Tensile properties of the optimum friction stir processed sample were correlated with the microstructural characterization done using Scanning Electron Microscope (SEM) and Electron Back-Scattered Diffraction (EBSD). Optimum parameters from the bottom-up approach have led to a defect free FSP having a maximum strength of 93% the base material strength. Micro tensile testing of the samples taken from the center of processed zone has shown an increased strength of 1.3 times the base material. Measured maximum longitudinal residual stress on the processed surface was only 30 MPa which was attributed to the solid state nature of FSP. Microstructural observation reveals significant grain refinement with less variation in the grain size across the thickness and a large amount of grain boundary precipitation compared to the base metal. The proposed experimental bottom-up approach can be applied as an effective method for optimizing parameters during FSP of aluminium alloys, which is otherwise difficult through analytical methods due to the complex interactions between work-piece, tool and process parameters. Precipitation mechanisms during FSP were responsible for the fine grained microstructure in the nugget zone that provided better mechanical properties than the base metal. (C) 2014 Elsevier Ltd. All rights reserved.

MULTI-PITCH TRACKING USING GAUSSIAN MIXTURE MODEL WITH TIME VARYING PARAMETERS AND GRATING COMPRESSION TRANSFORM

Grating Compression Transform (GCT) is a two-dimensional analysis of speech signal which has been shown to be effective in multi-pitch tracking in speech mixtures. Multi-pitch tracking methods using GCT apply Kalman filter framework to obtain pitch tracks which requires training of the filter parameters using true pitch tracks. We propose an unsupervised method for obtaining multiple pitch tracks. In the proposed method, multiple pitch tracks are modeled using time-varying means of a Gaussian mixture model (GMM), referred to as TVGMM. The TVGMM parameters are estimated using multiple pitch values at each frame in a given utterance obtained from different patches of the spectrogram using GCT. We evaluate the performance of the proposed method on all voiced speech mixtures as well as random speech mixtures having well separated and close pitch tracks. TVGMM achieves multi-pitch tracking with 51% and 53% multi-pitch estimates having error <= 20% for random mixtures and all-voiced mixtures respectively. TVGMM also results in lower root mean squared error in pitch track estimation compared to that by Kalman filtering.

Phase evolutions, growth kinetics and diffusion parameters in the Co-Ni-Ta system

Diffusion couple experiments are conducted to study phase evolutions in the Co-rich part of the Co-Ni-Ta phase diagram. This helps to examine the available phase diagram and propose a correction on the stability of the Co2Ta phase based on the compositional measurements and X-ray analysis. The growth rate of this phase decreases with an increase in Ni content. The same is reflected on the estimated integrated interdiffusion coefficients of the components in this phase. The possible reasons for this change are discussed based on the discussions of defects, crystal structure and the driving forces for diffusion. Diffusion rate of Co in the Co2Ta phase at the Co-rich composition is higher because of more number of Co-Co bonds present compared to that of Ta-Ta bonds and the presence of Co antisites for the deviation from the stoichiometry. The decrease in the diffusion coefficients because of Ni addition indicates that Ni preferably replaces Co antisites to decrease the diffusion rate. (C) 2014 Elsevier B.V. All rights reserved.

Assignment of the C-13 NMR spectrum by correlation to dipolar coupled proton-pairs and estimation of order parameters of a thiophene based liquid crystal

Materials with widely varying molecular topologies and exhibiting liquid crystalline properties have attracted considerable attention in recent years. C-13 NMR spectroscopy is a convenient method for studying such novel systems. In this approach the assignment of the spectrum is the first step which is a non-trivial problem. Towards this end, we propose here a method that enables the carbon skeleton of the different sub-units of the molecule to be traced unambiguously. The proposed method uses a heteronuclear correlation experiment to detect pairs of nearby carbons with attached protons in the liquid crystalline core through correlation of the carbon chemical shifts to the double-quantum coherences of protons generated through the dipolar coupling between them. Supplemented by experiments that identify non-protonated carbons, the method leads to a complete assignment of the spectrum. We initially apply this method for assigning the C-13 spectrum of the liquid crystal 4-n-pentyl-4'-cyanobiphenyl oriented in the magnetic field. We then utilize the method to assign the aromatic carbon signals of a thiophene based liquid crystal thereby enabling the local order-parameters of the molecule to be estimated and the mutual orientation of the different sub-units to be obtained.

Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mgha(-1)) at spatial scales ranging from 5 to 250m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial ``dilution'' bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

Statistical significance of episodes with general partial orders

Frequent episode discovery is one of the methods used for temporal pattern discovery in sequential data. An episode is a partially ordered set of nodes with each node associated with an event type. For more than a decade, algorithms existed for episode discovery only when the associated partial order is total (serial episode) or trivial (parallel episode). Recently, the literature has seen algorithms for discovering episodes with general partial orders. In frequent pattern mining, the threshold beyond which a pattern is inferred to be interesting is typically user-defined and arbitrary. One way of addressing this issue in the pattern mining literature has been based on the framework of statistical hypothesis testing. This paper presents a method of assessing statistical significance of episode patterns with general partial orders. A method is proposed to calculate thresholds, on the non-overlapped frequency, beyond which an episode pattern would be inferred to be statistically significant. The method is first explained for the case of injective episodes with general partial orders. An injective episode is one where event-types are not allowed to repeat. Later it is pointed out how the method can be extended to the class of all episodes. The significance threshold calculations for general partial order episodes proposed here also generalize the existing significance results for serial episodes. Through simulations studies, the usefulness of these statistical thresholds in pruning uninteresting patterns is illustrated. (C) 2014 Elsevier Inc. All rights reserved.

Modeling non-stationarity in intensity, duration and frequency of extreme rainfall over India

Significant changes are reported in extreme rainfall characteristics over India in recent studies though there are disagreements on the spatial uniformity and causes of trends. Based on recent theoretical advancements in the Extreme Value Theory (EVT), we analyze changes in extreme rainfall characteristics over India using a high-resolution daily gridded (1 degrees latitude x 1 degrees longitude) dataset. Intensity, duration and frequency of excess rain over a high threshold in the summer monsoon season are modeled by non-stationary distributions whose parameters vary with physical covariates like the El-Nino Southern Oscillation index (ENSO-index) which is an indicator of large-scale natural variability, global average temperature which is an indicator of human-induced global warming and local mean temperatures which possibly indicate more localized changes. Each non-stationary model considers one physical covariate and the best chosen statistical model at each rainfall grid gives the most significant physical driver for each extreme rainfall characteristic at that grid. Intensity, duration and frequency of extreme rainfall exhibit non-stationarity due to different drivers and no spatially uniform pattern is observed in the changes in them across the country. At most of the locations, duration of extreme rainfall spells is found to be stationary, while non-stationary associations between intensity and frequency and local changes in temperature are detected at a large number of locations. This study presents the first application of nonstationary statistical modeling of intensity, duration and frequency of extreme rainfall over India. The developed models are further used for rainfall frequency analysis to show changes in the 100-year extreme rainfall event. Our findings indicate the varying nature of each extreme rainfall characteristic and their drivers and emphasize the necessity of a comprehensive framework to assess resulting risks of precipitation induced flooding. (C) 2014 Elsevier B.V. All rights reserved.

Wave propagation of phonon and phason displacement modes in quasicrystals: Determination of wave parameters

The paper presents the study of wave propagation in quasicrystals. Our interest is in the computation of the wavenumber (k(n)) and group speed (c(g)) of the phonon and phason displacement modes of one, two, and three dimensional quasicrystals. These wave parameter expressions are derived and computed using the elasto-hydrodynamic equations for quasicrystals. For the computation of the wavenumber and group speeds, we use Fourier transform approximation of the phonon and the phason displacement modes. The characteristic equations obtained are a polynomial equation of the wavenumber (k(n)), with frequency as a parameter. The corresponding group speeds (c(g)) for different frequencies are then computed from the wavenumber k(n). The variation of wavenumber and group speeds with frequency is plotted for the 1-D quasicrystal, 2-D decagonal Al-Ni-Co quasicrystals, and 3-D icosahedral Al-Pd-Mn and Zn-Mg-Sc quasicrystals. From the wavenumber and group speeds plots, we obtain the cut-off frequencies for different spatial wavenumber eta(m). The results show that for 1-D, 2-D, and 3-D quasicrystals, the phonon displacement modes are non-dispersive for low values of eta(m) and becomes dispersive for increasing values of eta(m). The cut-off frequencies are not observed for very low values of eta(m), whereas the cut-off frequency starts to appear with increasing eta(m). The group speeds of the phason displacement modes are orders of magnitude lower than that of the phonon displacement modes, showing that the phason modes do not propagate, and they are essentially the diffusive modes. The group speeds of the phason modes are also not influenced by eta(m). The group speeds for the 2-D quasicrystal at 35 kHz is also simulated numerically using Galerkin spectral finite element methods in frequency domain and is compared with the results obtained using wave propagation analysis. The effect of the phonon and phason elastic constants on the group speeds is studied using 3-D icosahedral Al-Pd-Mn and Zn-Mg-Sc quasicrystals. It is also shown that the phason elastic constants and the coupling coefficient do not affect the group speeds of the phonon displacement modes. (C) 2015 AIP Publishing LLC.

Structural, thermal and quantum chemical studies of p-coumaric and caffeic acids

Two hydroxycinnamic acids viz., p-coumaric, and caffeic acids have been extracted and purified from Parthenium hysterophorus, subsequently characterized by elemental analysis, FT-IR, NMR, single crystal X-ray crystallography. The optimized structures of these acids were calculated in terms of density functional theory by Gaussian 09. The validation of experimental and theoretically obtained data for structural parameters such as bond lengths and bond angles has have been carried out to analyze the statistical significance by curve fitting analysis and the values of correlation coefficient found to be 0.985, 0.992, and 0.984, 0.975 in p-coumaric, and caffeic acids, respectively. The calculated HOMO and LUMO energies show the eventual charge transfer interaction within the molecule. Thermal studies were also carried out by thermogravimetry (TG), differential thermogravimetric analysis (DTA), and derivative thermogravimetry (DTG). (C) 2014 Elsevier B.V. All rights reserved.