Role of vascular endothelial growth factor and placental growth factors during retinal vascular development and hyaloid regression

PURPOSE. Vascular endothelial growth factor (VEGF)-A and placental growth factor (PIGF) are members of a large group of homologous peptides identified as the VEGF family. Although VEGF-A is known to act as a potent angiogenic peptide in the retina, the vasoactive function of PIGF in this tissue is less well defined. This study has sought to elucidate the expression patterns and modulatory role of these growth factors during retinal vascular development and hyaloid regression in the neonatal mouse. METHODS. C57BL6J mice were killed at postnatal days (P)1, P3, P5, P7, P9, and P11. The eyes were enucleated and processed for in situ hybridization and immunocytochemistry and the retinas extracted for total protein or RNA. Separate groups of neonatal mice were also injected intraperitoneally daily from P2 through P9 with either VEGF-neutralizing antibody, PIGF-neutralizing antibody, isotype immunoglobulin (Ig)-G, or phosphate-buffered saline (PBS). The mice were then perfused with fluorescein isothiocyanate (FITC)-dextran, and the eyes were subsequently embedded in paraffin wax or flat mounted. RESULTS. Quantitative (real-time) reverse transcription-polymerase chain reaction (RT-PCR) demonstrated similar expression patterns of VEGF-A and PIGF mRNA during neonatal retinal development, although the fluctuation between time periods was greater overall for VEGF-A. The localization of VEGF-A and PIGF in the retina, as revealed by in situ hybridization and immunohistochemistry, was also similar. Neutralization of VEGF-A caused a significant reduction in the hyaloid and retinal vasculature, whereas PIGF antibody treatment caused a marked persistence of the hyaloid without significantly affecting retinal vascular development. CONCLUSIONS. Although having similar expression patterns in the retina, these growth factors appear to have distinct modulatory influences during normal retinal vascular development and hyaloid regression.

Determinants of WTP for Prosecco wine: A latent class regression with attitudinal responses

Purpose – Under investigation is Prosecco wine, a sparkling white wine from North-East Italy.
Information collection on consumer perceptions is particularly relevant when developing market
strategies for wine, especially so when local production and certification of origin play an important
role in the wine market of a given district, as in the case at hand. Investigating and characterizing the
structure of preference heterogeneity become crucial steps in every successful marketing strategy. The
purpose of this paper is to investigate the sources of systematic differences in consumer preferences.
Design/methodology/approach – The paper explores the effect of inclusion of answers to
attitudinal questions in a latent class regression model of stated willingness to pay (WTP) for this
specialty wine. These additional variables were included in the membership equations to investigate
whether they could be of help in the identification of latent classes. The individual specific WTPs from
the sampled respondents were then derived from the best fitting model and examined for consistency.
Findings – The use of answers to attitudinal question in the latent class regression model is found to
improve model fit, thereby helping in the identification of latent classes. The best performing model
obtained makes use of both attitudinal scores and socio-economic covariates identifying five latent
classes. A reasonable pattern of differences in WTP for Prosecco between CDO and TGI types were
derived from this model.
Originality/value – The approach appears informative and promising: attitudes emerge as
important ancillary indicators of taste differences for specialty wines. This might be of interest per se
and of practical use in market segmentation. If future research shows that these variables can be of use
in other contexts, it is quite possible that more attitudinal questions will be routinely incorporated in
structural latent class hedonic models.

Pleural mesothelioma incidence in Europe:Evidence of some deceleration in the increasing trends

Objective: To summarize the geographical and temporal variations in incidence of pleural mesothelioma in Europe, using the extensive data available from European general cancer registries, and consider these in light of recent trends in asbestos extraction, use and import in European countries. Material and methods: The data were extracted from the European Cancer Incidence and Mortality database (EUROCIM). The inclusion criteria was acceptance in Volume VII of Cancer Incidence in Five Continents, Truncated age-standardized rates per 100,000 for the ages 40-74 were used to summarise recent geographical variations. Standardized rate ratios and 95% confidence intervals for the periods 1986-1990 and 1991-1995 were compared to assess geographical variations in risk. To investigate changes in the magnitude of most recent trends, regression models fitted to the latest available 10-year period (1988-1997) were compared with trends in the previous decade. Fitted rates in younger (40-64) and older adults (65-74) in the most recent period were also compared. Results: There was a great deal of geographical variation in the risk of mesothelioma, annual rates ranging from around 8 per 100,000 in Scotland, England and The Netherlands, to lower than 1 per 100,000 in Spain (0.96), Estonia (0.85), Poland (0.85) and Yugoslavia, Vojvodina (0.56) among men. The rank of the rates for women was similar to that observed for men, although rates were considerably lower. Between 1978 and 1987, rates in men significantly increased in all countries (excepting Denmark). In the following 10 years, there was a deceleration in trend, and a significant increase was detectable only in England and France. In addition, the magnitude of recent trends in younger men was generally lower than those estimated for older men, in both national and regional cancer registry settings. Conclusions: While mesothelioma incidence rates are still rising in Europe, a deceleration has started in some countries. A decrease may begin in the next few years in certain European populations considering the deceleration of observed trends in mesothelioma and asbestos exposure, as well as the recent ban on its use.

Analysis of IL6 Signal Transduction Model using Reduced Rank Regression

This work presents the application of reduced rank regression to the field of systems biology. A computational approach is used to investigate the mechanisms of the janus-associated kinases/signal transducers and transcription factors (JAK/STAT) and mitogen activated protein kinases (MAPK) signal transduction pathways in hepatic cells stimulated by interleukin-6. The results obtained identify the contribution of individual reactions to the dynamics of the model. These findings are compared to previously available results from sensitivity analysis of the model which focused on the parameters involved and their effect. This application of reduced rank regression allows for an understanding of the individual reaction terms involved in the modelled signal transduction pathways and has the benefit of being computationally inexpensive. The obtained results complement existing findings and also confirm the importance of several protein complexes in the MAPK pathway which hints at benefits that can be achieved by further refining the model.

A dynamic sampling methodology for plasma etch processes using Gaussian process regression

Plasma etch is a key process in modern semiconductor manufacturing facilities as it offers process simplification and yet greater dimensional tolerances compared to wet chemical etch technology. The main challenge of operating plasma etchers is to maintain a consistent etch rate spatially and temporally for a given wafer and for successive wafers processed in the same etch tool. Etch rate measurements require expensive metrology steps and therefore in general only limited sampling is performed. Furthermore, the results of measurements are not accessible in real-time, limiting the options for run-to-run control. This paper investigates a Virtual Metrology (VM) enabled Dynamic Sampling (DS) methodology as an alternative paradigm for balancing the need to reduce costly metrology with the need to measure more frequently and in a timely fashion to enable wafer-to-wafer control. Using a Gaussian Process Regression (GPR) VM model for etch rate estimation of a plasma etch process, the proposed dynamic sampling methodology is demonstrated and evaluated for a number of different predictive dynamic sampling rules. © 2013 IEEE.

MSC-clustering and forward stepwise regression for virtual metrology in highly correlated input spaces

Increasingly semiconductor manufacturers are exploring opportunities for virtual metrology (VM) enabled process monitoring and control as a means of reducing non-value added metrology and achieving ever more demanding wafer fabrication tolerances. However, developing robust, reliable and interpretable VM models can be very challenging due to the highly correlated input space often associated with the underpinning data sets. A particularly pertinent example is etch rate prediction of plasma etch processes from multichannel optical emission spectroscopy data. This paper proposes a novel input-clustering based forward stepwise regression methodology for VM model building in such highly correlated input spaces. Max Separation Clustering (MSC) is employed as a pre-processing step to identify a reduced srt of well-conditioned, representative variables that can then be used as inputs to state-of-the-art model building techniques such as Forward Selection Regression (FSR), Ridge regression, LASSO and Forward Selection Ridge Regression (FCRR). The methodology is validated on a benchmark semiconductor plasma etch dataset and the results obtained are compared with those achieved when the state-of-art approaches are applied directly to the data without the MSC pre-processing step. Significant performance improvements are observed when MSC is combined with FSR (13%) and FSRR (8.5%), but not with Ridge Regression (-1%) or LASSO (-32%). The optimal VM results are obtained using the MSC-FSR and MSC-FSRR generated models. © 2012 IEEE.

Efficient Marginal Likelihood Computation for Gaussian Process Regression

In a Bayesian learning setting, the posterior distribution of a predictive model arises from a trade-off between its prior distribution and the conditional likelihood of observed data. Such distribution functions usually rely on additional hyperparameters which need to be tuned in order to achieve optimum predictive performance; this operation can be efficiently performed in an Empirical Bayes fashion by maximizing the posterior marginal likelihood of the observed data. Since the score function of this optimization problem is in general characterized by the presence of local optima, it is necessary to resort to global optimization strategies, which require a large number of function evaluations. Given that the evaluation is usually computationally intensive and badly scaled with respect to the dataset size, the maximum number of observations that can be treated simultaneously is quite limited. In this paper, we consider the case of hyperparameter tuning in Gaussian process regression. A straightforward implementation of the posterior log-likelihood for this model requires O(N^3) operations for every iteration of the optimization procedure, where N is the number of examples in the input dataset. We derive a novel set of identities that allow, after an initial overhead of O(N^3), the evaluation of the score function, as well as the Jacobian and Hessian matrices, in O(N) operations. We prove how the proposed identities, that follow from the eigendecomposition of the kernel matrix, yield a reduction of several orders of magnitude in the computation time for the hyperparameter optimization problem. Notably, the proposed solution provides computational advantages even with respect to state of the art approximations that rely on sparse kernel matrices.

On Regression Methods for Virtual Metrology in Semiconductor Manufacturing

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. The prediction models for VM can be from a large variety of linear and nonlinear regression methods and the selection of a proper regression method for a specific VM problem is not straightforward, especially when the candidate predictor set is of high dimension, correlated and noisy. Using process data from a benchmark semiconductor manufacturing process, this paper evaluates the performance of four typical regression methods for VM: multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), neural networks (NN) and Gaussian process regression (GPR). It is observed that GPR performs the best among the four methods and that, remarkably, the performance of linear regression approaches that of GPR as the subset of selected input variables is increased. The observed competitiveness of high-dimensional linear regression models, which does not hold true in general, is explained in the context of extreme learning machines and functional link neural networks.

Forward and backward least angle regression for nonlinear system identification

A forward and backward least angle regression (LAR) algorithm is proposed to construct the nonlinear autoregressive model with exogenous inputs (NARX) that is widely used to describe a large class of nonlinear dynamic systems. The main objective of this paper is to improve model sparsity and generalization performance of the original forward LAR algorithm. This is achieved by introducing a replacement scheme using an additional backward LAR stage. The backward stage replaces insignificant model terms selected by forward LAR with more significant ones, leading to an improved model in terms of the model compactness and performance. A numerical example to construct four types of NARX models, namely polynomials, radial basis function (RBF) networks, neuro fuzzy and wavelet networks, is presented to illustrate the effectiveness of the proposed technique in comparison with some popular methods.