Comment on "correlated noise in a logistic growth model"

We argue the results published by Bao-Quan Ai et al [Phys. Rev E 67, 022903 (2003)] on "correlated noise in a logistic growth model " are not correct. Their conclusion that for larger values of the correlation parameter, lambda, the cell population is peaked at x=0, which denotes the high extinction rate is also incorrect. We find the reverse behaviour corresponding to their results, that increasing lambda, promotes the stable growth of tumour cells. In particular, their results for steady-state probability, as a function of cell number, at different correlation strengths, presented in figures 1 and 2 show different behaviour than one would expect from the simple mathematical expression for the steady-state probability. Additionally, their interpretation at small values of cell number that the steady state probability increases as they increase the correlation parameter is also questionable. Another striking feature in their figures (1 and 3) is that for the same values of the parameter lambda and alpha, their simulation produces two different curves both qualitatively and quantitatively.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Extrinsic parameter extraction and RF modelling of CMOS

An analytical approach for CMOS parameter extraction which includes the effect of parasitic resistance is presented. The method is based on small-signal equivalent circuit valid in all region of operation to uniquely extract extrinsic resistances, which can be used to extend the industry standard BSIM3v3 MOSFET model for radio frequency applications. The verification of the model was carried out through frequency domain measurements of S-parameters and direct time domain measurement at 2.4 GHz in a large signal non-linear mode of operation. (C) 2003 Elsevier Ltd. All rights reserved.

Population-based local search for protein folding simulation in the MJ energy model and cubic lattices

We present experimental results on benchmark problems in 3D cubic lattice structures with the Miyazawa-Jernigan energy function for two local search procedures that utilise the pull-move set: (i) population-based local search (PLS) that traverses the energy landscape with greedy steps towards (potential) local minima followed by upward steps up to a certain level of the objective function; (ii) simulated annealing with a logarithmic cooling schedule (LSA). The parameter settings for PLS are derived from short LSA-runs executed in pre-processing and the procedure utilises tabu lists generated for each member of the population. In terms of the total number of energy function evaluations both methods perform equally well, however. PLS has the potential of being parallelised with an expected speed-up in the region of the population size. Furthermore, both methods require a significant smaller number of function evaluations when compared to Monte Carlo simulations with kink-jump moves. (C) 2009 Elsevier Ltd. All rights reserved.

Protein kinase B/Akt activity is involved in renal TGF-beta 1-driven epithelial-mesenchymal transition in vitro and in vivo

The molecular pathogenesis of diabetic nephropathy (DN), the leading cause of end-stage renal disease worldwide, is complex and not fully understood. Transforming growth factor-beta (TGF-beta1) plays a critical role in many fibrotic disorders, including DN. In this study, we report protein kinase B (PKB/Akt) activation as a downstream event contributing to the pathophysiology of DN. We investigated the potential of PKB/Akt to mediate the profibrotic bioactions of TGF-beta1 in kidney. Treatment of normal rat kidney epithelial cells (NRK52E) with TGF-beta1 resulted in activation of phosphatidylinositol 3-kinase (PI3K) and PKB/Akt as evidenced by increased Ser473 phosphorylation and GSK-3beta phosphorylation. TGF-beta1 also stimulated increased Smad3 phosphorylation in these cells, a response that was insensitive to inhibition of PI3K or PKB/Akt. NRK52E cells displayed a loss of zona occludins 1 and E-cadherin and a gain in vimentin and alpha-smooth muscle actin expression, consistent with the fibrotic actions of TGF-beta1. These effects were blocked with inhibitors of PI3K and PKB/Akt. Furthermore, overexpression of PTEN, the lipid phosphatase regulator of PKB/Akt activation, inhibited TGF-beta1-induced PKB/Akt activation. Interestingly, in the Goto-Kakizaki rat model of type 2 diabetes, we also detected increased phosphorylation of PKB/Akt and its downstream target, GSK-3beta, in the tubules, relative to that in control Wistar rats. Elevated Smad3 phosphorylation was also detected in kidney extracts from Goto-Kakizaki rats with chronic diabetes. Together, these data suggest that TGF-beta1-mediated PKB/Akt activation may be important in renal fibrosis during diabetic nephropathy.

Systematic Usage of Embedded Modelling Languages in Automated Model Transformation Chains

Annotation of programs using embedded Domain-Specific Languages (embedded DSLs), such as the program annotation facility for the Java programming language, is a well-known practice in computer science. In this paper we argue for and propose a specialized approach for the usage of embedded Domain-Specific Modelling Languages (embedded DSMLs) in Model-Driven Engineering (MDE) processes that in particular supports automated many-step model transformation chains. It can happen that information defined at some point, using an embedded DSML, is not required in the next immediate transformation step, but in a later one. We propose a new approach of model annotation enabling flexible many-step transformation chains. The approach utilizes a combination of embedded DSMLs, trace models and a megamodel. We demonstrate our approach based on an example MDE process and an industrial case study.

Integrated structure selection and parameter optimisation for eng-genes neural models

The eng-genes concept involves the use of fundamental known system functions as activation functions in a neural model to create a 'grey-box' neural network. One of the main issues in eng-genes modelling is to produce a parsimonious model given a model construction criterion. The challenges are that (1) the eng-genes model in most cases is a heterogenous network consisting of more than one type of nonlinear basis functions, and each basis function may have different set of parameters to be optimised; (2) the number of hidden nodes has to be chosen based on a model selection criterion. This is a mixed integer hard problem and this paper investigates the use of a forward selection algorithm to optimise both the network structure and the parameters of the system-derived activation functions. Results are included from case studies performed on a simulated continuously stirred tank reactor process, and using actual data from a pH neutralisation plant. The resulting eng-genes networks demonstrate superior simulation performance and transparency over a range of network sizes when compared to conventional neural models. (c) 2007 Elsevier B.V. All rights reserved.

Coherent X-ray production via pulse reflection from laser-driven dense electron sheets

A scheme to obtain brilliant x-ray sources by coherent reflection of a counter-propagating pulse from laser-driven dense electron sheets is theoretically and numerically investigated in a self-consistent manner. A radiation pressure acceleration model for the dynamics of the electron sheets blown out from laser-irradiated ultrathin foils is developed and verified by PIC simulations. The first multidimensional and integral demonstration of the scheme by 2D PIC simulations is presented. It is found that the reflected pulse undergoes Doppler-upshift by a factor 4?z2, where ?z = (1- vz2/c2)-1/2 is the effective Lorentz factor of the electron sheet al ong its normal direction. Meanwhile the pulse electric field is intensified by a factor depending on the electron density of the sheet in its moving frame ne/?, where ? is the full Lorentz factor.

Enhanced Laser-Driven Ion Acceleration in the Relativistic Transparency Regime

We report on the acceleration of ion beams from ultrathin diamondlike carbon foils of thickness 50, 30, and 10 nm irradiated by ultrahigh contrast laser pulses at intensities of similar to 7 X 10(19) W/cm(2). An unprecedented maximum energy of 185 MeV (15 MeV/u) for fully ionized carbon atoms is observed at the optimum thickness of 30 nm. The enhanced acceleration is attributed to self-induced transparency, leading to strong volumetric heating of the classically overdense electron population in the bulk of the target. Our experimental results are supported by both particle-in-cell (PIC) simulations and an analytical model.

Elevated expression of Runx2 as a key parameter in the etiology of osteosarcoma

To understand the molecular etiology of osteosarcoma, we isolated and characterized a human osteosarcoma cell line (OS1). OS1 cells have high osteogenic potential in differentiation induction media. Molecular analysis reveals OS1 cells express the pocket protein pRB and the runt-related transcription factor Runx2. Strikingly, Runx2 is expressed at higher levels in OS1 cells than in human fetal osteoblasts. Both pRB and Runx2 have growth suppressive potential in osteoblasts and are key factors controlling competency for osteoblast differentiation. The high levels of Runx2 clearly suggest osteosarcomas may form from committed osteoblasts that have bypassed growth restrictions normally imposed by Runx2. Interestingly, OS1 cells do not exhibit p53 expression and thus lack a functional p53/p21 DNA damage response pathway as has been observed for other osteosarcoma cell types. Absence of this pathway predicts genomic instability and/or vulnerability to secondary mutations that may counteract the anti-proliferative activity of Runx2 that is normally observed in osteoblasts. We conclude OS1 cells provide a valuable cell culture model to examine molecular events that are responsible for the pathologic conversion of phenotypically normal osteoblast precursors into osteosarcoma cells.

On how structural model variability influence transonic aeroelasticity stability.

This paper considers the ways in which structural model parameter variability can in?uence aeroelastic stability. Previous work on formulating the stability calculation (with the Euler equations providing the aerodynamic predictions) is exploited to use Monte Carlo, Interval and Perturbation calculations to allow this question to be investigated. Three routes are identi?ed. The ?rst involves variable normal mode frequencies only. The second involves normal mode frequencies and mode shapes. Finally, the third, in addition to normal mode frequencies and mode shapes, also includes their in?uence on the static equilibrium. Previous work has suggested only considering route 1, which allows signi?cant gains in computational e?ciency if reduced order models can be built for the aerodynamics. However, results in the current paper show that neglecting route 2 can give misleading results for the ?utter onset prediction.

Persistent Inflammation Subverts Thrombospondin-1–Induced Regulation of Retinal Angiogenesis and Is Driven by CCR2 Ligation

Neovascular retinal disease is a leading cause of blindness orchestrated by inflammatory responses. Although noninfectious uveoretinitis is mediated by CD4(+) T cells, in the persistent phase of disease, angiogenic responses are observed, along with degeneration of the retina. Full clinical manifestation relies on myeloid-derived cells, which are phenotypically distinct from, but potentially sharing common effector responses to age-related macular degeneration. To interrogate inflammation-mediated angiogenesis, we investigated experimental autoimmune uveoretinitis, an animal model for human uveitis. After the initial acute phase of severe inflammation, the retina sustains a persistent low-grade inflammation with tissue-infiltrating leukocytes for over 4 months. During this persistent phase, angiogenesis is observed as retinal neovascular membranes that arise from inflamed venules and postcapillary venules, increase in size as the disease progresses, and are associated with infiltrating arginase-1(+) macrophages. In the absence of thrombospondin-1, retinal neovascular membranes are markedly increased and are associated with arginase-1(-) CD68(+) macrophages, whereas deletion of the chemokine receptor CCR2 resulted in reduced retinal neovascular membranes in association with a predominant neutrophil infiltrate. CCR2 is important for macrophage recruitment to the retina in experimental autoimmune uveoretinitis and promotes chronicity in the form of a persistent angiogenesis response, which in turn is regulated by constitutive expression of angiogenic inhibitors like thrombospondin-1. This model offers a new platform to dissect the molecular and cellular pathology of inflammation-induced ocular angiogenesis.

Determining the parametric effectiveness of a CAD model

The motivation for this paper is to present an approach for rating the quality of the parameters in a computer-aided design model for use as optimization variables. Parametric Effectiveness is computed as the ratio of change in performance achieved by perturbing the parameters in the optimum way, to the change in performance that would be achieved by allowing the boundary of the model to move without the constraint on shape change enforced by the CAD parameterization. The approach is applied in this paper to optimization based on adjoint shape sensitivity analyses. The derivation of parametric effectiveness is presented for optimization both with and without the constraint of constant volume. In both cases, the movement of the boundary is normalized with respect to a small root mean squared movement of the boundary. The approach can be used to select an initial search direction in parameter space, or to select sets of model parameters which have the greatest ability to improve model performance. The approach is applied to a number of example 2D and 3D FEA and CFD problems.

Development of the kinetic model of platinum catalyzed ammonia oxidation in a microreactor

The ammonia oxidation reaction on supported polycrystalline platinum catalyst was investigated in an aluminum-based microreactor. An extensive set of reactions was included in the chemical reactor modeling to facilitate the construction of a kinetic model capable of satisfactory predictions for a wide range of conditions (NH3 partial pressure, 0.01-0.12 atm; O-2 partial pressure, 0.10-0.88 atm; temperature, 523-673 K; contact time, 0.3-0.7 ms). The elementary surface reactions used in developing the mechanism were chosen based on the literature data concerning ammonia oxidation on a Pt catalyst. Parameter estimates for the kinetic model were obtained using multi-response least squares regression analysis using the isothermal plug-flow reactor approximation. To evaluate the model, the behavior of a microstructured reactor was simulated by means of a complete Navier-Stokes model accounting for the reactions on the catalyst surface and the effect of temperature on the physico-chemical properties of the reacting mixture. In this way, the effect of the catalytic wall temperature non-uniformity and the effect of a boundary layer on the ammonia conversion and selectivity were examined. After further optimization of appropriate kinetic parameters, the calculated selectivities and product yields agree very well with the values actually measured in the microreactor. (C) 2002 Elsevier Science B.V. All rights reserved.

How Structural Model Variability Influences Transonic Aeroelastic Stability

This paper considers the ways in which structural model parameter variability can influence aeroelastic stability. Previous work on formulating the stability calculation (with the Euler equations providing the aerodynamic predictions) is exploited to use Monte Carlo, interval, and perturbation calculations to allow this question to be investigated. Three routes are identified. The first involves variable normal-mode frequencies only. The second involves normal-mode frequencies and shapes. Finally, the third, in addition to normal-mode frequencies and shapes, also includes their influence on the static equilibrium. Previous work has suggested only considering the first route, which allows significant gains in computational efficiency if reduced-order models can be built for the aerodynamics. However, results in the current paper show that neglecting the mode-shape variation can give misleading results for the flutter-onset prediction, complicating the development of reduced aerodynamic models for variability analysis.