55 resultados para Latent class model


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Background: Mycobacterium tuberculosis, a causative agent of chronic tuberculosis disease, is widespread among some animal species too. There is paucity of information on the distribution, prevalence and true disease status of tuberculosis in Asian elephants (Elephas maximus). The aim of this study was to estimate the sensitivity and specificity of serological tests to diagnose M. tuberculosis infection in captive elephants in southern India while simultaneously estimating sero-prevalence. Methodology/Principal Findings: Health assessment of 600 elephants was carried out and their sera screened with a commercially available rapid serum test. Trunk wash culture of select rapid serum test positive animals yielded no animal positive for M. tuberculosis isolation. Under Indian field conditions where the true disease status is unknown, we used a latent class model to estimate the diagnostic characteristics of an existing (rapid serum test) and new (four in-house ELISA) tests. One hundred and seventy nine sera were randomly selected for screening in the five tests. Diagnostic sensitivities of the four ELISAs were 91.3-97.6% (95% Credible Interval (CI): 74.8-99.9) and diagnostic specificity were 89.6-98.5% (95% CI: 79.4-99.9) based on the model we assumed. We estimate that 53.6% (95% CI: 44.6-62.8) of the samples tested were free from infection with M. tuberculosis and 15.9% (97.5% CI: 9.8 - to 24.0) tested positive on all five tests. Conclusions/Significance: Our results provide evidence for high prevalence of asymptomatic M. tuberculosis infection in Asian elephants in a captive Indian setting. Further validation of these tests would be important in formulating area-specific effective surveillance and control measures.

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Sub-pixel classification is essential for the successful description of many land cover (LC) features with spatial resolution less than the size of the image pixels. A commonly used approach for sub-pixel classification is linear mixture models (LMM). Even though, LMM have shown acceptable results, pragmatically, linear mixtures do not exist. A non-linear mixture model, therefore, may better describe the resultant mixture spectra for endmember (pure pixel) distribution. In this paper, we propose a new methodology for inferring LC fractions by a process called automatic linear-nonlinear mixture model (AL-NLMM). AL-NLMM is a three step process where the endmembers are first derived from an automated algorithm. These endmembers are used by the LMM in the second step that provides abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual proportions are fed to multi-layer perceptron (MLP) architecture as input to train the neurons which further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. AL-NLMM is validated on computer simulated hyperspectral data of 200 bands. Validation of the output showed overall RMSE of 0.0089±0.0022 with LMM and 0.0030±0.0001 with the MLP based AL-NLMM, when compared to actual class proportions indicating that individual class abundances obtained from AL-NLMM are very close to the real observations.

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A class of model reference adaptive control system which make use of an augmented error signal has been introduced by Monopoli. Convergence problems in this attractive class of systems have been investigated in this paper using concepts from hyperstability theory. It is shown that the condition on the linear part of the system has to be stronger than the one given earlier. A boundedness condition on the input to the linear part of the system has been taken into account in the analysis - this condition appears to have been missed in the previous applications of hyperstability theory. Sufficient conditions for the convergence of the adaptive gain to the desired value are also given.

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A pseudo-dynamical approach for a class of inverse problems involving static measurements is proposed and explored. Following linearization of the minimizing functional associated with the underlying optimization problem, the new strategy results in a system of linearized ordinary differential equations (ODEs) whose steady-state solutions yield the desired reconstruction. We consider some explicit and implicit schemes for integrating the ODEs and thus establish a deterministic reconstruction strategy without an explicit use of regularization. A stochastic reconstruction strategy is then developed making use of an ensemble Kalman filter wherein these ODEs serve as the measurement model. Finally, we assess the numerical efficacy of the developed tools against a few linear and nonlinear inverse problems of engineering interest.

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The minimal supergravity model predicts the polarization of the tau coming from the stau to bino decay in the co-annihilation region to +1. This can be exploited to extract this soft tau signal at LHC and also to measure the tiny mass differences between the stau and the bi lightest superparticle. Moreover, this strategy will be applicable for a wider class of bino lightest superparticle models, where the lighter stau has a right component at least of similar size as the left.

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Collections of non-Brownian particles suspended in a viscous fluid and subjected to oscillatory shear at very low Reynolds number have recently been shown to exhibit a remarkable dynamical phase transition separating reversible from irreversible behavior as the strain amplitude or volume fraction are increased. We present a simple model for this phenomenon, based on which we argue that this transition lies in the universality class of the conserved directed percolation models. This leads to predictions for the scaling behavior of a large number of experimental observables. Non-Brownian suspensions under oscillatory shear may thus constitute the first experimental realization of an inactive-active phase transition which is not in the universality class of conventional directed percolation.

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Self-tuning is applied to the minimum variance control of non-linear multivariable systems which can be characterized by a ' multivariable Hammerstein model '. It is also shown that such systems are not amenable to self-tuning control if control costing is to be included in the performance criterion.

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This paper considers the on-line identification of a non-linear system in terms of a Hammerstein model, with a zero-memory non-linear gain followed by a linear system. The linear part is represented by a Laguerre expansion of its impulse response and the non-linear part by a polynomial. The identification procedure involves determination of the coefficients of the Laguerre expansion of correlation functions and an iterative adjustment of the parameters of the non-linear gain by gradient methods. The method is applicable to situations involving a wide class of input signals. Even in the presence of additive correlated noise, satisfactory performance is achieved with the variance of the error converging to a value close to the variance of the noise. Digital computer simulation establishes the practicability of the scheme in different situations.

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A mathematical model of social interaction in the form of two coupler! first-order non-linear differential equations, forms the topic of this study. This non-conservative model io representative of such varied social interaction problems as coexisting sub-populations of two different species, arms race between two rival countries and the like. Differential transformation techniques developed elsewhere in the literature are seen to be effective tools of dynamic analysis of this non-linear non-conservative mode! of social interaction process.

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Sufficient conditions for obtaining an equivalent linear model to classes of non-linear, bi-state, social interaction processes are derived. These parametric constraints, when satisfied, permit analytical determination of the dynamics of the non-linear process of social interaction.

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The scope of the differential transformation technique, developed earlier for the study of non-linear, time invariant systems, has been extended to the domain of time-varying systems by modifications to the differential transformation laws proposed therein. Equivalence of a class of second-order, non-linear, non-autonomous systems with a linear autonomous model of second order is established through these transformation laws. The feasibility of application of this technique in obtaining the response of such non-linear time-varying systems is discussed.

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Plants are sessile organisms that have evolved a variety of mechanisms to maintain their cellular homeostasis under stressful environmental conditions. Survival of plants under abiotic stress conditions requires specialized group of heat shock protein machinery, belonging to Hsp70:J-protein family. These heat shock proteins are most ubiquitous types of chaperone machineries involved in diverse cellular processes including protein folding, translocation across cell membranes, and protein degradation. They play a crucial role in maintaining the protein homeostasis by reestablishing functional native conformations under environmental stress conditions, thus providing protection to the cell. J-proteins are co-chaperones of Hsp70 machine, which play a critical role by stimulating Hsp70s ATPase activity, thereby stabilizing its interaction with client proteins. Using genome-wide analysis of Arabidopsis thaliana, here we have outlined identification and systematic classification of J-protein co-chaperones which are key regulators of Hsp70s function. In comparison with Saccharomyces cerevisiae model system, a comprehensive domain structural organization, cellular localization, and functional diversity of A. thaliana J-proteins have also been summarized. Electronic supplementary material The online version of this article (doi:10.1007/s10142-009-0132-0) contains supplementary material, which is available to authorized users.

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An Ocean General Circulation Model of the Indian Ocean with high horizontal (0.25 degrees x 0.25 degrees) and vertical (40 levels) resolutions is used to study the dynamics and thermodynamics of the Arabian Sea mini warm pool (ASMWP), the warmest region in the northern Indian Ocean during January-April. The model simulates the seasonal cycle of temperature, salinity and currents as well as the winter time temperature inversions in the southeastern Arabian Sea (SEAS) quite realistically with climatological forcing. An experiment which maintained uniform salinity of 35 psu over the entire model domain reproduces the ASMWP similar to the control run with realistic salinity and this is contrary to the existing theories that stratification caused by the intrusion of low-salinity water from the Bay of Bengal into the SEAS is crucial for the formation of ASMWP. The contribution from temperature inversions to the warming of the SEAS is found to be negligible. Experiments with modified atmospheric forcing over the SEAS show that the low latent heat loss over the SEAS compared to the surroundings, resulting from the low winds due to the orographic effect of Western Ghats, plays an important role in setting up the sea surface temperature (SST) distribution over the SEAS during November March. During March-May, the SEAS responds quickly to the air-sea fluxes and the peak SST during April-May is independent of the SST evolution during previous months. The SEAS behaves as a low wind, heat-dominated regime during November-May and, therefore, the formation and maintenance of the ASMWP is not dependent on the near surface stratification.

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We evaluate the mixed partition function for dyonic BPS black holes using the recently proposed degeneracy formula for the STU model. The result factorizes into the OSV mixed partition function times a proportionality factor. The latter is in agreement with the measure factor that was recently conjectured for a class of N = 2 black holes that contains the STU model.