Stable carbon isotope ratios in Asian elephant collagen: implications for dietary studies

Stable carbon isotope ratios in bone collagen have been used in a variety of dietary studies in modern and fossil animals, including humans. Inherent in the stable isotope technique is the assumption that the isotopic signature is a reflection of the diet and is persistent in collagen because this is a relatively inert protein. Carbon isotope analyses of bones from a southern Indian population of Asian elephant (Elephas maximus), a long-lived mammal that alternates seasonally between a predominantly C3 (browse) and C4 (grass) plant diet, showed two patterns that have important implications for dietary interpretation based on isotopic studies. Relative to the quantity of the two plant types consumed on average, the ?13C signal in collagen indicated that more carbon was incorporated from C3 plants, possibly due to their higher protein contribution. There was a much greater variance in ?13C values of collagen in sub-adult (range -10.5� to-22.7�, variance=14.51) compared to adult animals (range -16.0� to -20.3�, variance=1.85) pointing to high collagen turnover rates and non-persistent isotopic signatures in younger, growing animals. It thus seems important to correct for any significant relative differences in nutritive value of food types and also consider the age of an animal before drawing definite conclusions about its diet from isotope ratios.

Direction of arrival estimation in the presence of distributed noise sources: Cumulant based approach

In the past few years there have been attempts to develop subspace methods for DoA (direction of arrival) estimation using a fourth?order cumulant which is known to de?emphasize Gaussian background noise. To gauge the relative performance of the cumulant MUSIC (MUltiple SIgnal Classification) (c?MUSIC) and the standard MUSIC, based on the covariance function, an extensive numerical study has been carried out, where a narrow?band signal source has been considered and Gaussian noise sources, which produce a spatially correlated background noise, have been distributed. These simulations indicate that, even though the cumulant approach is capable of de?emphasizing the Gaussian noise, both bias and variance of the DoA estimates are higher than those for MUSIC. To achieve comparable results the cumulant approach requires much larger data, three to ten times that for MUSIC, depending upon the number of sources and how close they are. This is attributed to the fact that in the estimation of the cumulant, an average of a product of four random variables is needed to make an evaluation. Therefore, compared to those in the evaluation of the covariance function, there are more cross terms which do not go to zero unless the data length is very large. It is felt that these cross terms contribute to the large bias and variance observed in c?MUSIC. However, the ability to de?emphasize Gaussian noise, white or colored, is of great significance since the standard MUSIC fails when there is colored background noise. Through simulation it is shown that c?MUSIC does yield good results, but only at the cost of more data.

Male-biased adult sex ratios and their significance for cooperative breeding in dhole, Cuon alpinus, packs

Two free-ranging packs of dholes (Asiatic wild dog, Cuon alpinus) were monitored for a period of 6 yr (Sep. 1990-Sep. 1996) in the Mudumalai sanctuary, southern India. Demographic data on age structure, litter-size, sex ratio and age and sex specific dispersal were collected. Behavioural data on social interactions and reproductive behaviour among pack members were obtained to determine the frequencies of dominant and subordinate behaviours shown by malt: and female pack members and a measure of each male's reproductive access to females. Behaviours displayed by pack members at dens were recorded to determine whether any age- or sex-specific role specialization existed during pup care. Tenures for dominant males and females within the pack were calculated to ascertain the rate of breeding vacancies occurring within packs. Approximate levels of genetic relatedness within packs were determined by studying pedigrees. In most years one study pack had a male-biased adult sex ratio. This was caused by almost twofold higher dispersal of adult females over adult males. A considerable variance existed in the percentage of sub-adults dispersing from the two packs. Differences existed in the frequencies of dominant and subordinate behaviours shown by males. For males, dominance ranks and ranks based on submissive behaviours were not correlated with frequencies of reproductive behaviours. Subordinate males also displayed reproductive behaviours. In packs, dominant males had lower tenures than dominant females indicating that among males breeding vacancies arose more quickly. The litter size was found to be negatively correlated with the age of the breeding female. There were no significant differences across individuals of varying age or sex classes in the display of pup care behaviours. Significant differences did exist among individual adults. Genetic relatedness among packs tended to vary temporally as a consequence of possible mating by subordinate animals and immigration of new males into the pack. In conclusion, it appears that males delay dispersal and cooperate within their natal packs because of the variety of reproductive strategies they could pursue within. A combination of ecological constraints and the difficulties of achieving breeding status within non-natal packs may make early dispersal and independent breeding less beneficial.

GNSS Signal Detection Under Noise Uncertainty

High sensitivity detection techniques are required for indoor navigation using Global Navigation Satellite System (GNSS) receivers, and typically, a combination of coherent and non- coherent integration is used as the test statistic for detection. The coherent integration exploits the deterministic part of the signal and is limited due to the residual frequency error, navigation data bits and user dynamics, which are not known apriori. So, non- coherent integration, which involves squaring of the coherent integration output, is used to improve the detection sensitivity. Due to this squaring, it is robust against the artifacts introduced due to data bits and/or frequency error. However, it is susceptible to uncertainty in the noise variance, and this can lead to fundamental sensitivity limits in detecting weak signals. In this work, the performance of the conventional non-coherent integration-based GNSS signal detection is studied in the presence of noise uncertainty. It is shown that the performance of the current state of the art GNSS receivers is close to the theoretical SNR limit for reliable detection at moderate levels of noise uncertainty. Alternate robust post-coherent detectors are also analyzed, and are shown to alleviate the noise uncertainty problem. Monte-Carlo simulations are used to confirm the theoretical predictions.

Assessment of conditional moment closure models of turbulent autoignition using DNS data

A systematic assessment of the submodels of conditional moment closure (CMC) formalism for the autoignition problem is carried out using direct numerical simulation (DNS) data. An initially non-premixed, n-heptane/air system, subjected to a three-dimensional, homogeneous, isotropic, and decaying turbulence, is considered. Two kinetic schemes, (1) a one-step and (2) a reduced four-step reaction mechanism, are considered for chemistry An alternative formulation is developed for closure of the mean chemical source term , based on the condition that the instantaneous fluctuation of excess temperature is small. With this model, it is shown that the CMC equations describe the autoignition process all the way up to near the equilibrium limit. The effect of second-order terms (namely, conditional variance of temperature excess sigma(2) and conditional correlations of species q(ij)) in modeling is examined. Comparison with DNS data shows that sigma(2) has little effect on the predicted conditional mean temperature evolution, if the average conditional scalar dissipation rate is properly modeled. Using DNS data, a correction factor is introduced in the modeling of nonlinear terms to include the effect of species fluctuations. Computations including such a correction factor show that the species conditional correlations q(ij) have little effect on model predictions with a one-step reaction, but those q(ij) involving intermediate species are found to be crucial when four-step reduced kinetics is considered. The "most reactive mixture fraction" is found to vary with time when a four-step kinetics is considered. First-order CMC results are found to be qualitatively wrong if the conditional mean scalar dissipation rate is not modeled properly. The autoignition delay time predicted by the CMC model compares excellently with DNS results and shows a trend similar to experimental data over a range of initial temperatures.

Geochemistry of water and ground water in the Nhecolandia, Pantanal of Mato Grosso, Brazil: Variability and associated processes

A distinctive feature of the Nhecolandia, a sub-region of the Pantanal wetland in Brazil, is the presence of both saline and freshwater lakes. Saline lakes used to be attributed to a past and phase during the Pleistocene. However, recent studies have shown that saline and fresh water lakes are linked by a continuous water table, indicating that saline water could come from a contemporary concentration process. This concentration process could also be responsible for the large chemical variability of the waters observed in the area. A regional water sampling has been conducted in surface and sub-surface water and the water table, and the results of the geochemical and statistical analysis are presented. Based on sodium contents, the concentration shows a 1: 4443 ratio. All the samples belong to the same chemical family and evolve in a sodic alkaline manner. Calcite or magnesian calcite precipitates very early in the process of concentration, probably followed by the precipitation of magnesian silicates. The most concentrated solutions remain under-saturated with respect to the sodium carbonate salt, even if this equilibrium is likely reached around the saline lakes. Apparently, significant amounts of sulfate and chloride are lost simultaneously from the solutions, and this cannot be explained solely by evaporative concentration. This could be attributed to the sorption on reduced minerals in a green sub-surface horizon in the "cordilhieira" areas. In the saline lakes, low potassium, phosphate, magnesium, and sulfate are attributed to algal blooms. Under the influence of evaporation, the concentration of solutions and associated chemical precipitations are identified as the main factors responsible for the geochemical variability in this environment (about 92 % of the variance). Therefore, the saline lakes of Nhecolandia have to be managed as landscape units in equilibrium with the present water flows and not inherited from a past and phase. In order to elaborate hydrochemical tracers for a quantitative estimation of water flows, three points have to be investigated more precisely: (1) the quantification of magnesium involved in the Mg-calcite precipitation; (2) the identification of the precise stoichiometry of the Mg-silicate; and (3) the verification of the loss of chloride and sulfate by sorption onto labile iron minerals.

Thermodynamic analysis of enzyme catalysed reactions: new insights into the Michaelis-Menten equation

A simple thermodynamic analysis of the well-known Michaelis-Menten equation (MME) of enzyme catalysis is proposed that employs the chemical potential mu to follow the Gibbs free energy changes attending the formation of the enzyme-substrate complex and its turnover to the product. The main conclusion from the above analysis is that low values of the Michaelis constant KM and high values of the turnover number k(cat) are advantageous: this supports a simple algebraic analysis of the MME, although at variance with current thinking. Available data apparently support the above findings. It is argued that transition state stabilisation - rather than substrate distortion or proximity - is the key to enzyme catalysis.

An investigation into the feasibility of fetal lung maturity prediction using statistical textural features

Fetal lung and liver tissues were examined by ultrasound in 240 subjects during 24 to 38 weeks of gestational age in order to investigate the feasibility of predicting the maturity of the lung from the textural features of sonograms. A region of interest of 64 X 64 pixels is used for extracting textural features. Since the histological properties of the liver are claimed to remain constant with respect to gestational age, features obtained from the lung region are compared with those from liver. Though the mean values of some of the features show a specific trend with respect to gestation age, the variance is too high to guarantee definite prediction of the gestational age. Thus, we restricted our purview to an investigation into the feasibility of fetal lung maturity prediction using statistical textural features. Out of 64 features extracted, those features that are correlated with gestation age and less computationally intensive are selected. The results of our study show that the sonographic features hold some promise in determining whether the fetal lung is mature or immature.

Characterization of microstructure in laser-surface-alloyed layers of aluminum on nickel

In order to obtain basic understanding of microstructure evolution in laser-surface-alloyed layers, aluminum was surface alloyed on a pure nickel substrate using a CO2 laser. By varying the laser scanning speed, the composition of the surface layers can be systematically varied. The Ni content in the layer increases with increase in scanning speed. Detailed cross-sectional transmission electron microscopic study reveals complexities in solidification behavior with increased nickel content. It is shown that ordered B2 phase forms over a wide range of composition with subsequent precipitation of Ni2Al, an ordered omega phase in the B2 matrix, during solid-state cooling. For nickel-rich alloys associated with higher laser scan speed, the fcc gamma phase is invariably the first phase to grow from the liquid with solute trapping. The phase reorders in the solid state to yield gamma' Ni3Al. The phase competes with beta AlNi, which forms massively from the liquid. The beta AlNi transforms martensitically to a 3R structure during cooling in solid state. The results can be rationalized in terms of a metastable phase diagram proposed earlier. However, the results are at variance with earlier studies of laser processing of nickel-rich alloys.

Particle dynamics in a turbulent particle–gas suspension at high Stokes number. Part 2. The fluctuating-force model

A fluctuating-force model is developed for representing the effect of the turbulent fluid velocity fluctuations on the particle phase in a turbulent gas–solid suspension in the limit of high Stokes number, where the particle relaxation time is large compared with the correlation time for the fluid velocity fluctuations. In the model, a fluctuating force is incorporated in the equation of motion for the particles, and the force distribution is assumed to be an anisotropic Gaussian white noise. It is shown that this is equivalent to incorporating a diffusion term in the Boltzmann equation for the particle velocity distribution functions. The variance of the force distribution, or equivalently the diffusion coefficient in the Boltzmann equation, is related to the time correlation functions for the fluid velocity fluctuations. The fluctuating-force model is applied to the specific case of a Couette flow of a turbulent particle–gas suspension, for which both the fluid and particle velocity distributions were evaluated using direct numerical simulations by Goswami & Kumaran (2010). It is found that the fluctuating-force simulation is able to quantitatively predict the concentration, mean velocity profiles and the mean square velocities, both at relatively low volume fractions, where the viscous relaxation time is small compared with the time between collisions, and at higher volume fractions, where the time between collisions is small compared with the viscous relaxation time. The simulations are also able to predict the velocity distributions in the centre of the Couette, even in cases in which the velocity distribution is very different from a Gaussian distribution.

Population genetic structure and conservation of Asian elephants (Elephas maximus) across India

This study examines the population genetic structure of Asian elephants (Elephas maximus) across India, which harbours over half the world's population of this endangered species. Mitochondrial DNA control region sequences and allele frequencies at six nuclear DNA microsatellite markers obtained from the dung of free-ranging elephants reveal low mtDNA and typical microsatellite diversity. Both known divergent clades of mtDNA haplotypes in the Asian elephant are present in India, with southern and central India exhibiting exclusively the β clade of Fernando et al. (2000), northern India exhibiting exclusively the α clade and northeastern India exhibiting both, but predominantly the α clade. A nested clade analysis revealed isolation by distance as the principal mechanism responsible for the observed haplotype distributions within the α and β clades. Analyses of molecular variance and pairwise population FST tests based on both mitochondrial and microsatellite DNA suggest that northern-northeastern India, central India, Nilgiris (in southern India) and Anamalai-Periyar (in southern India) are four demographically autonomous population units and should be managed separately. In addition, evidence for female philopatry, male-mediated gene flow and two possible historical biogeographical barriers is described.

Optimal local polynomial regression of noisy time varying signals

We address the problem of local-polynomial modeling of smooth time-varying signals with unknown functional form, in the presence of additive noise. The problem formulation is in the time domain and the polynomial coefficients are estimated in the pointwise minimum mean square error (PMMSE) sense. The choice of the window length for local modeling introduces a bias-variance tradeoff, which we solve optimally by using the intersection-of-confidence-intervals (ICI) technique. The combination of the local polynomial model and the ICI technique gives rise to an adaptive signal model equipped with a time-varying PMMSE-optimal window length whose performance is superior to that obtained by using a fixed window length. We also evaluate the sensitivity of the ICI technique with respect to the confidence interval width. Simulation results on electrocardiogram (ECG) signals show that at 0dB signal-to-noise ratio (SNR), one can achieve about 12dB improvement in SNR. Monte-Carlo performance analysis shows that the performance is comparable to the basic wavelet techniques. For 0 dB SNR, the adaptive window technique yields about 2-3dB higher SNR than wavelet regression techniques and for SNRs greater than 12dB, the wavelet techniques yield about 2dB higher SNR.

An analog MVUE for a wireless sensor network

An analog minimum-variance unbiased estimator(MVUE) over an asymmetric wireless sensor network is studied.Minimisation of variance is cast into a constrained non-convex optimisation problem. An explicit algorithm that solves the problem is provided. The solution is obtained by decomposing the original problem into a finite number of convex optimisation problems with explicit solutions. These solutions are then juxtaposed together by exploiting further structure in the objective function.

Correction for Bias in Downscaling GCM Simulations for Hydrologic Impact Assessment

In a statistical downscaling model, it is important to remove the bias of General Circulations Model (GCM) outputs resulting from various assumptions about the geophysical processes. One conventional method for correcting such bias is standardisation, which is used prior to statistical downscaling to reduce systematic bias in the mean and variances of GCM predictors relative to the observations or National Centre for Environmental Prediction/ National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data. A major drawback of standardisation is that it may reduce the bias in the mean and variance of the predictor variable but it is much harder to accommodate the bias in large-scale patterns of atmospheric circulation in GCMs (e.g. shifts in the dominant storm track relative to observed data) or unrealistic inter-variable relationships. While predicting hydrologic scenarios, such uncorrected bias should be taken care of; otherwise it will propagate in the computations for subsequent years. A statistical method based on equi-probability transformation is applied in this study after downscaling, to remove the bias from the predicted hydrologic variable relative to the observed hydrologic variable for a baseline period. The model is applied in prediction of monsoon stream flow of Mahanadi River in India, from GCM generated large scale climatological data.

Incremental natural-gradient actor-critic algorithms

We present four new reinforcement learning algorithms based on actor-critic and natural-gradient ideas, and provide their convergence proofs. Actor-critic rein- forcement learning methods are online approximations to policy iteration in which the value-function parameters are estimated using temporal difference learning and the policy parameters are updated by stochastic gradient descent. Methods based on policy gradients in this way are of special interest because of their com- patibility with function approximation methods, which are needed to handle large or infinite state spaces. The use of temporal difference learning in this way is of interest because in many applications it dramatically reduces the variance of the gradient estimates. The use of the natural gradient is of interest because it can produce better conditioned parameterizations and has been shown to further re- duce variance in some cases. Our results extend prior two-timescale convergence results for actor-critic methods by Konda and Tsitsiklis by using temporal differ- ence learning in the actor and by incorporating natural gradients, and they extend prior empirical studies of natural actor-critic methods by Peters, Vijayakumar and Schaal by providing the first convergence proofs and the first fully incremental algorithms.