Stress concentration and load diffusion in rectangular panels with constant stress flanges

The stress concentration that occurs when load is diffused from a constant stress member into thin sheet is an important problem in the design of light weight structures. By using solutions in biharmonic polar-trigonometric series, the stress concentration can be effectively isolated so that highly accurate information necessary for design can be obtained. A method of analysis yielding high accuracy with limited effort is presented for rectangular panels with transverse edges free or supported by inextensional end ribs. Numerical data are given for panels with length twice the width.

Part I: Enzyme replacement versus neurotrophic factor viral vector-mediated gene therapy of Parkinson’s disease, Part II: The effects of orally dosed tolcapone and entacapone on dopamine metabolism in the dorsal striatum and nucleus accumbens in rats

Part I: Parkinson’s disease is a slowly progressive neurodegenerative disorder in which particularly the dopaminergic neurons of the substantia nigra pars compacta degenerate and die. Current conventional treatment is based on restraining symptoms but it has no effect on the progression of the disease. Gene therapy research has focused on the possibility of restoring the lost brain function by at least two means: substitution of critical enzymes needed for the synthesis of dopamine and slowing down the progression of the disease by supporting the functions of the remaining nigral dopaminergic neurons by neurotrophic factors. The striatal levels of enzymes such as tyrosine hydroxylase, dopadecarboxylase and GTP-CH1 are decreased as the disease progresses. By replacing one or all of the enzymes, dopamine levels in the striatum may be restored to normal and behavioral impairments caused by the disease may be ameliorated especially in the later stages of the disease. The neurotrophic factors glial cell derived neurotrophic factor (GDNF) and neurturin have shown to protect and restore functions of dopaminergic cell somas and terminals as well as improve behavior in animal lesion models. This therapy may be best suited at the early stages of the disease when there are more dopaminergic neurons for neurotrophic factors to reach. Viral vector-mediated gene transfer provides a tool to deliver proteins with complex structures into specific brain locations and provides long-term protein over-expression. Part II: The aim of our study was to investigate the effects of two orally dosed COMT inhibitors entacapone (10 and 30 mg/kg) and tolcapone (10 and 30 mg/kg) with a subsequent administration of a peripheral dopadecarboxylase inhibitor carbidopa (30 mg/kg) and L- dopa (30 mg/kg) on dopamine and its metabolite levels in the dorsal striatum and nucleus accumbens of freely moving rats using dual-probe in vivo microdialysis. Earlier similarly designed studies have only been conducted in the dorsal striatum. We also confirmed the result of earlier ex vivo studies regarding the effects of intraperitoneally dosed tolcapone (30 mg/kg) and entacapone (30 mg/kg) on striatal and hepatic COMT activity. The results obtained from the dorsal striatum were generally in line with earlier studies, where tolcapone tended to increase dopamine and DOPAC levels and decrease HVA levels. Entacapone tended to keep striatal dopamine and HVA levels elevated longer than in controls and also tended to elevate the levels of DOPAC. Surprisingly in the nucleus accumbens, dopamine levels after either dose of entacapone or tolcapone were not elevated. Accumbal DOPAC levels, especially in the tolcapone 30 mg/kg group, were elevated nearly to the same extent as measured in the dorsal striatum. Entacapone 10 mg/kg elevated accumbal HVA levels more than the dose of 30 mg/kg and the effect was more pronounced in the nucleus accumbens than in the dorsal striatum. This suggests that entacapone 30 mg/kg has minor central effects. Also our ex vivo study results obtained from the dorsal striatum suggest that entacapone 30 mg/kg has minor and transient central effects, even though central HVA levels were not suppressed below those of the control group in either brain area in the microdialysis study. Both entacapone and tolcapone suppressed hepatic COMT activity more than striatal COMT activity. Tolcapone was more effective than entacapone in the dorsal striatum. The differences between dopamine and its metabolite levels in the dorsal striatum and nucleus accumbens may be due to different properties of the two brain areas.

Self-regularized pseudo time-marching schemes for structural system identification with static measurements

We explore the application of pseudo time marching schemes, involving either deterministic integration or stochastic filtering, to solve the inverse problem of parameter identification of large dimensional structural systems from partial and noisy measurements of strictly static response. Solutions of such non-linear inverse problems could provide useful local stiffness variations and do not have to confront modeling uncertainties in damping, an important, yet inadequately understood, aspect in dynamic system identification problems. The usual method of least-square solution is through a regularized Gauss-Newton method (GNM) whose results are known to be sensitively dependent on the regularization parameter and data noise intensity. Finite time,recursive integration of the pseudo-dynamical GNM (PD-GNM) update equation addresses the major numerical difficulty associated with the near-zero singular values of the linearized operator and gives results that are not sensitive to the time step of integration. Therefore, we also propose a pseudo-dynamic stochastic filtering approach for the same problem using a parsimonious representation of states and specifically solve the linearized filtering equations through a pseudo-dynamic ensemble Kalman filter (PD-EnKF). For multiple sets of measurements involving various load cases, we expedite the speed of thePD-EnKF by proposing an inner iteration within every time step. Results using the pseudo-dynamic strategy obtained through PD-EnKF and recursive integration are compared with those from the conventional GNM, which prove that the PD-EnKF is the best performer showing little sensitivity to process noise covariance and yielding reconstructions with less artifacts even when the ensemble size is small.

Self-regularized pseudo time-marching schemes for structural system identification with static measurements

We explore the application of pseudo time marching schemes, involving either deterministic integration or stochastic filtering, to solve the inverse problem of parameter identification of large dimensional structural systems from partial and noisy measurements of strictly static response. Solutions of such non-linear inverse problems could provide useful local stiffness variations and do not have to confront modeling uncertainties in damping, an important, yet inadequately understood, aspect in dynamic system identification problems. The usual method of least-square solution is through a regularized Gauss-Newton method (GNM) whose results are known to be sensitively dependent on the regularization parameter and data noise intensity. Finite time, recursive integration of the pseudo-dynamical GNM (PD-GNM) update equation addresses the major numerical difficulty associated with the near-zero singular values of the linearized operator and gives results that are not sensitive to the time step of integration. Therefore, we also propose a pseudo-dynamic stochastic filtering approach for the same problem using a parsimonious representation of states and specifically solve the linearized filtering equations through apseudo-dynamic ensemble Kalman filter (PD-EnKF). For multiple sets ofmeasurements involving various load cases, we expedite the speed of the PD-EnKF by proposing an inner iteration within every time step. Results using the pseudo-dynamic strategy obtained through PD-EnKF and recursive integration are compared with those from the conventional GNM, which prove that the PD-EnKF is the best performer showing little sensitivity to process noise covariance and yielding reconstructions with less artifacts even when the ensemble size is small. Copyright (C) 2009 John Wiley & Sons, Ltd.

Transcription factor GATA4 and apoptotic TRAIL pathway in granulosa cell function and tumors

Normal growth and development require the precise control of gene expression. Transcription factors are proteins that regulate gene expression by binding specific sequences of DNA. Abnormalities in transcription are implicated in a variety of human diseases, including cancer, endocrine disorders and birth defects. Transcription factor GATA4 has emerged as an important regulator of normal development and function in a variety of endoderm- and mesoderm- derived tissues, including gut, heart and several endocrine organs, such as gonads. Mice harboring a null mutation of Gata4 gene die during embryogenesis due to failure in heart formation, complicating the study of functional role of GATA4 in other organs. However, the expression pattern of GATA4 suggests it may play a role in the regulation of ovarian granulosa cell development, function and apoptosis. This premise is supported by in vitro studies showing that GATA4 regulates several steroidogenic enzymes as well as auto-, para- and endocrine signaling molecules important for granulosa cell function. This study assessed the in vivo role of GATA4 for granulosa cell function by utilizing two genetically modified mouse strains. The findings in the GATA4 deficient mice included delayed puberty, impaired fertility and signs of diminished estrogen production. At the molecular level, the GATA4 deficiency leads to attenuated expression of central steroidogenic genes, Steroidogenic acute regulatory protein (StAR), Side-chain cleavage (SCC), and aromatase as a response to stimulations with exogenous gonadotropins. Taken together, these suggest GATA4 is necessary for the normal ovarian function and female fertility. Programmed cell death, apoptosis, is a crucial part of normal ovarian development and function. In addition, disturbances in apoptosis have been implicated to pathogenesis of human granulosa cell tumors (GCTs). Apoptosis is controlled by extrinsic and intrinsic pathways. The intrinsic pathway is regulated by members of Bcl-2 family, and its founding member, the anti-apoptotic Bcl-2, is known to be important for granulosa cell survival. This study showed that the expression levels of GATA4 and Bcl-2 correlate in the human GCTs and that GATA4 regulates Bcl-2 expression, presumably by directly binding to its promoter. In addition, disturbing GATA4 function was sufficient to induce apoptosis in cultured GCT- derived cell line. Taken together, these results suggest GATA4 functions as an anti-apoptotic factor in GCTs. The extrinsic apoptotic pathway is controlled by the members of tumor necrosis factor (TNF) superfamily. An interesting ligand of this family is TNF-related apoptosis-inducing ligand (TRAIL), possessing a unique ability to selectively induce apoptosis in malignant cells. This study characterized the previously unknown expression of TRAIL and its receptors in both developing and adult human ovary, as well as in malignant granulosa cell tumors. TRAIL pathway was shown to be active in GCTs suggesting it may be a useful tool in treating these malignancies. However, more studies are required to assess the function of TRAIL pathway in normal ovaries. In addition to its ability to induce apoptosis in GCTs, this study revealed that GATA4 protects these malignancies from TRAIL-induced apoptosis. GATA4 presumably exerts this effect by regulating the expression of anti-apoptotic Bcl-2. This is of particular interest as high expression of GATA4 is known to correlate to aggressive GCT behavior. Thus, GATA4 seems to protect GCTs from endogenous TRAIL by upregulating anti-apoptotic factors such as Bcl-2.

Optimal non-linear reinforcement schemes for stochastic automata

Two optimal non-linear reinforcement schemes—the Reward-Inaction and the Penalty-Inaction—for the two-state automaton functioning in a stationary random environment are considered. Very simple conditions of symmetry of the non-linear function figuring in the reinforcement scheme are shown to be necessary and sufficient for optimality. General expressions for the variance and rate of learning are derived. These schemes are compared with the already existing optimal linear schemes in the light of average variance and average rate of learning.

Complement factor H dysfunction in atypical hemolytic uremic syndrome

Alternative pathway (AP) of complement can be activated on any surface, self or non-self. In atypical hemolytic uremic syndrome (aHUS) the AP regulation on self surfaces is insufficient and leads to complement attack against self-cells resulting usually in end-stage renal disease. Factor H (FH) is one of the key regulators of AP activation on the self surfaces. The domains 19 and 20 (FH19-20) are critical for the ability of FH to discriminate between C3b-opsonized self and non-self surfaces and are a hot-spot for mutations that have been described from aHUS patients. FH19-20 contains binding sites for both the C3d part of C3b and self surface polyanions that are needed for efficient C3b inactivation. To study the dysfunction of FH19-20, crystallographic structures of FH19-20 and FH19-20 in complex with C3d (FH19-20:C3d) were solved and aHUS-associated and structurally interesting point mutations were induced to FH19-20. Functional defects caused by these mutations were studied by analyzing binding of the FH19-20 mutant proteins to C3d, C3b, heparin, and mouse glomerular endothelial cells (mGEnCs). The results revealed two independent binding interfaces between FH19-20 and C3d - the FH19 site and the FH20 site. Superimposition of the FH19-20:C3d complex on the previously published C3b and FH1-4:C3b structures showed that the FH20 site on C3d is partially occluded, but the FH19 site is fully available. Furthermore, binding of FH19-20 via the FH19 site to C3b did not block binding of the functionally important FH1-4 domains and kept the FH20 site free to bind heparin or an additional C3d. Binding assays were used to show that FH20 domain can bind to heparin while FH19-20 is bound to C3b via the FH19 site, and that both the FH19 site and FH20 are necessary for recognition of non-activator surfaces. Simultaneous binding of FH19 site to C3b and FH20 to anionic self structures are the key interactions in self-surface recognition by FH and thereby enhanced avidity of FH explains how AP discriminates between self and non-self. The aHUS-associated mutations on FH19-20 were found to disrupt binding of the FH19 or FH20 site to C3d/C3b, or to disrupt binding of FH20 to heparin or mGEnC. Any of these dysfunctions leads to loss of FH avidity to C3b bearing self surfaces explaining the molecular pathogenesis of the aHUS-cases where mutations are found within FH19-20.

Stringent constraints on the scalar K pi form factor from analyticity, unitarity and low-energy theorems

We investigate the scalar K pi form factor at low energies by the method of unitarity bounds adapted so as to include information on the phase and modulus along the elastic region of the unitarity cut. Using at input the values of the form factor at t = 0 and the Callan-Treiman point, we obtain stringent constraints on the slope and curvature parameters of the Taylor expansion at the origin. Also, we predict a quite narrow range for the higher-order ChPT corrections at the second Callan-Treiman point.

Performance analysis of a suprathreshold, stochastic resonance based nonlinear detector

In this paper we introduce a nonlinear detector based on the phenomenon of suprathreshold stochastic resonance (SSR). We first present a model (an array of 1-bit quantizers) that demonstrates the SSR phenomenon. We then use this as a pre-processor to the conventional matched filter. We employ the Neyman-Pearson(NP) detection strategy and compare the performances of the matched filter, the SSR-based detector and the optimal detector. Although the proposed detector is non-optimal, for non-Gaussian noises with heavy tails (leptokurtic) it shows better performance than the matched filter. In situations where the noise is known to be leptokurtic without the availability of the exact knowledge of its distribution, the proposed detector turns out to be a better choice than the matched filter.

A four-timescale algorithm for constrained stochastic optimization of RED

The overall performance of random early detection (RED) routers in the Internet is determined by the settings of their associated parameters. The non-availability of a functional relationship between the RED performance and its parameters makes it difficult to implement optimization techniques directly in order to optimize the RED parameters. In this paper, we formulate a generic optimization framework using a stochastically bounded delay metric to dynamically adapt the RED parameters. The constrained optimization problem thus formulated is solved using traditional nonlinear programming techniques. Here, we implement the barrier and penalty function approaches, respectively. We adopt a second-order nonlinear optimization framework and propose a novel four-timescale stochastic approximation algorithm to estimate the gradient and Hessian of the barrier and penalty objectives and update the RED parameters. A convergence analysis of the proposed algorithm is briefly sketched. We perform simulations to evaluate the performance of our algorithm with both barrier and penalty objectives and compare these with RED and a variant of it in the literature. We observe an improvement in performance using our proposed algorithm over RED, and the above variant of it.

Pricing Index Options with Stochastic Volatility

The objective of this paper is to investigate the pricing accuracy under stochastic volatility where the volatility follows a square root process. The theoretical prices are compared with market price data (the German DAX index options market) by using two different techniques of parameter estimation, the method of moments and implicit estimation by inversion. Standard Black & Scholes pricing is used as a benchmark. The results indicate that the stochastic volatility model with parameters estimated by inversion using the available prices on the preceding day, is the most accurate pricing method of the three in this study and can be considered satisfactory. However, as the same model with parameters estimated using a rolling window (the method of moments) proved to be inferior to the benchmark, the importance of stable and correct estimation of the parameters is evident.