Dynamic changes in mitogen-activated protein kinase (MAPK) activities in the corpus luteum of the bonnet monkey (Macaca radiata) during development, induced luteolysis, and simulated early pregnancy: A role for p38 MAPK in the regulation of luteal function

Changes in MAPK activities were examined in the corpus luteum (CL) during luteolysis and pregnancy, employing GnRH antagonist (Cetrorelix)-induced luteolysis, stages of CL, and hCG treatment to mimic early pregnancy as model systems in the bonnet monkey. We hypothesized that MAPKs could serve to phosphorylate critical phosphoproteins to regulate luteal function. Analysis of several indices for structural (caspase-3 activity and DNA fragmentation) and functional (progesterone and steroidogenic acute regulatory protein expression) changes in the CL revealed that the decreased luteal function observed during Cetrorelix treatment and late luteal phase was associated with increased caspase-3 activity and DNA fragmentation. As expected, human chorionic gonadotropin treatment dramatically increased luteal function, but the indices for structural changes were only partially attenuated. All three MAPKs appeared to be constitutively active in the mid-luteal-phase CL, and activities of ERK-1/2 and p38-MAPK (p38), but not Jun N-terminal kinase (JNK)-1/2, decreased significantly (P < 0.05) within 12 - 24 h after Cetrorelix treatment. During the late luteal phase, in contrast to decreased ERK-1/2 and p38 activities, JNK-1/2 activities increased significantly (P < 0.05). Although human chorionic gonadotropin treatment increased ERK-1/2 and p38 activities, it decreased JNK-1/2 activities. The activation status of p38 was correlated with the phosphorylation status of an upstream activator, MAPK kinase-3/6 and the expression of MAPK activated protein kinase-3, a downstream target. Intraluteal administration of p38 kinase inhibitor (SB203580), but not MAPK kinase-1/2 inhibitor (PD98059), decreased the luteal function. Together, these data suggest an important role for p38 in the regulation of CL function in primates.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

A New Structure Preserving Energy Function Incorporating Transmission Line Resistances

A novel method to account for the transmission line resistances in structure preserving energy functions (SPEF) is presented in this paper. The method exploits the equivalence of a lossy network having the same conductance to susceptance ratio for all its elements to a lossless network with a new set of power injections. The system equations and the energy function are developed using centre of inertia (COI) variables and the loads are modelled as arbitrary functions of respective bus voltages. The application of SPEF to direct transient stability evaluation is presented considering a realistic power system example.

A soluble random-matrix model for relaxation in quantum systems

We study the relaxation of a degenerate two-level system interacting with a heat bath, assuming a random-matrix model for the system-bath interaction. For times larger than the duration of a collision and smaller than the Poincaré recurrence time, the survival probability of still finding the system at timet in the same state in which it was prepared att=0 is exactly calculated.

Experimental investigation of the dependence of barrier height on metal work function for metal---SiO2---p---Si (MIS) Schottky-barrier diodes in the presence of inversion

The dependence of barrier height on the metal work function of metal-SiO2-p-Si Schottky barrier diodes was investigated and nonlinearity was found. This is explained by the theoretical model proposed recently by Chattopadhyay and Daw. The values of interface trap density and fixed charge density of the insulating layer of the diodes were calculated using this model and found to be appreciably different from those estimated by the usual method.

Random matrices: Universality of ESDs and the circular law

Given an n x n complex matrix A, let mu(A)(x, y) := 1/n vertical bar{1 <= i <= n, Re lambda(i) <= x, Im lambda(i) <= y}vertical bar be the empirical spectral distribution (ESD) of its eigenvalues lambda(i) is an element of C, i = l, ... , n. We consider the limiting distribution (both in probability and in the almost sure convergence sense) of the normalized ESD mu(1/root n An) of a random matrix A(n) = (a(ij))(1 <= i, j <= n), where the random variables a(ij) - E(a(ij)) are i.i.d. copies of a fixed random variable x with unit variance. We prove a universality principle for such ensembles, namely, that the limit distribution in question is independent of the actual choice of x. In particular, in order to compute this distribution, one can assume that x is real or complex Gaussian. As a related result, we show how laws for this ESD follow from laws for the singular value distribution of 1/root n A(n) - zI for complex z. As a corollary, we establish the circular law conjecture (both almost surely and in probability), which asserts that mu(1/root n An) converges to the uniform measure on the unit disc when the a(ij) have zero mean.

Hot gaseous coronae of early-type galaxies and their radio luminosity function

Recent X-ray observations have revealed that early-type galaxies (which usually produce extended double radio sources) generally have hot gaseous haloes extending up to approx102kpc1,2. Moreover, much of the cosmic X-ray background radiation is probably due to a hotter, but extremely tenuous, intergalactic medium (IGM)3. We have presented4–7 an analytical model for the propagation of relativistic beams from galactic nuclei, in which the beams' crossing of the pressure-matched interface between the IGM and the gaseous halo, plays an important role. The hotspots at the ends of the beams fade quickly when their advance becomes subsonic with respect to the IGM. This model has successfully predicted (for typical double radio sources) the observed8 current mean linear-size (approx2Dsime350 kpc)4,5, the observed8–11 decrease in linear-size with cosmological redshift4–6 and the slope of the linear-size versus radio luminosity10,12–14 relation6. We have also been able to predict the redshift-dependence of observed numbers and radio luminosities of giant radio galaxies7,15. Here, we extend this model to include the propagation of somewhat weaker beams. We show that the observed flattening of the local radio luminosity function (LRLF)16–20 for radio luminosity Papproximately 1024 W Hz-1 at 1 GHz can be explained without invoking ad hoc a corresponding break in the beam power function Phi(Lb), because the heads of the beams with Lb < 1025 W Hz-1 are decelerated to sonic velocity within the halo itself, which leads to a rapid decay of radio luminosity and a reduced contribution of these intrinsically weaker sources to the observed LRLF.

Markov random fields and spatial smoothing in improving of forest inventory estimates

Markov random fields (MRF) are popular in image processing applications to describe spatial dependencies between image units. Here, we take a look at the theory and the models of MRFs with an application to improve forest inventory estimates. Typically, autocorrelation between study units is a nuisance in statistical inference, but we take an advantage of the dependencies to smooth noisy measurements by borrowing information from the neighbouring units. We build a stochastic spatial model, which we estimate with a Markov chain Monte Carlo simulation method. The smooth values are validated against another data set increasing our confidence that the estimates are more accurate than the originals.

Medium access control schemes for local area networks with multiple priority function

This paper deals with the development and performance evaluation of three modified versions of a scheme proposed for medium access control in local area networks. The original scheme implements a collision-free and fair medium arbitration by using a control wire in conjunction with a data bus. The modifications suggested in this paper are intended to realize the multiple priority function in local area networks.

The function of NR3B and NR4A orphan nuclear receptors in osteoblasts

Nuclear receptors (NRs) comprise a large family of proteins that mediate the effects of small lipophilic molecules such as steroid hormones. In addition, there are a group of NRs which lack identified natural ligands and are referred as orphan NRs. In this thesis, the function of two such orphan NR families, the NR3B (ERRα, ERRβ and ERRγ) and the NR4A family (NGFI-B, Nurr1 and Nor1), was studied. NR3B and NR4A receptors regulate many biological processes such as energy metabolism and carcinogenesis. In addition, NR3B and NR4A receptors are expressed in bone. Therefore, the signaling and function of NR3B and NR4A orphan nuclear receptors was studied specifically in osteoblasts. NR4A receptors were found to be regulated by NR3B receptors and the Wnt/β-catenin signaling pathway as ERRα, ERRγ and β-catenin repressed the transcriptional activity of NR4A receptors in U2-OS cells. NGFI-B was found to repress the transcriptional activity of ERRγ in HeLa cells. The phytoestrogen equol was identified as a new agonist for ERRγ and ERRβ in PC-3, U2-OS, and SaOS-2 cells. Equol increased the transcriptional activity of ERRγ by increasing ERRγ co-activator binding and by inducing a conformational change in the ligand binding pocket of ERRγ. The growth inhibitory effect of equol on PC-3 prostate cancer cells was decreased by blocking ERRγ expression by siRNA. Therefore, ERRγ could mediate some of the beneficial health effects of equol. The Wnt/β-catenin signaling pathway is important for the differentiation and function of osteoblasts. NR3B and NR4A receptors were found to repress the transcriptional activity mediated by β-catenin in U2-OS cells. The mesenchymal stem cells (MSCs) isolated from ERRα knockout (KO) mice showed diminished proliferation and osteoblastic differentiation compared to the wild-type cells. The overexpression of ERRα in osteoblastic MC3T3-E1 cell line increased their mineralization. Bone sialoprotein (BSP) was shown to be a direct target gene for ERRα and ERRγ as the BSP promoter was activated by ERRα or ERRγ and PGC-1α in HeLa cells. The adipogenic differentiation of ERRα KO MSCs was also decreased and they expressed less adipogenic marker genes. In conclusion, the studies described in this thesis demonstrated that the transcriptional activity of NR3B and NR4A receptors can be regulated by other orphan NRs and signaling pathways in osteoblasts. NR3B receptors can also be regulated by ligands and a new agonist, equol, was identified for ERRβ and ERRγ. New roles for NR3B and NR4A were also identified as they were shown to converge with the Wnt signaling pathway in osteoblasts, ERRγ was shown to mediate the growth inhibitory effect of equol in prostate cancer cells, and ERRα was shown to regulate positively MSC proliferation, osteoblastic differentiation and adipogenesis.

Algorithms for Colouring Random k-colourable Graphs

The k-colouring problem is to colour a given k-colourable graph with k colours. This problem is known to be NP-hard even for fixed k greater than or equal to 3. The best known polynomial time approximation algorithms require n(delta) (for a positive constant delta depending on k) colours to colour an arbitrary k-colourable n-vertex graph. The situation is entirely different if we look at the average performance of an algorithm rather than its worst-case performance. It is well known that a k-colourable graph drawn from certain classes of distributions can be ii-coloured almost surely in polynomial time. In this paper, we present further results in this direction. We consider k-colourable graphs drawn from the random model in which each allowed edge is chosen independently with probability p(n) after initially partitioning the vertex set into ii colour classes. We present polynomial time algorithms of two different types. The first type of algorithm always runs in polynomial time and succeeds almost surely. Algorithms of this type have been proposed before, but our algorithms have provably exponentially small failure probabilities. The second type of algorithm always succeeds and has polynomial running time on average. Such algorithms are more useful and more difficult to obtain than the first type of algorithms. Our algorithms work as long as p(n) greater than or equal to n(-1+is an element of) where is an element of is a constant greater than 1/4.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.