Regression based D-optimality experimental design for sparse kernel density estimation

This paper derives an efficient algorithm for constructing sparse kernel density (SKD) estimates. The algorithm first selects a very small subset of significant kernels using an orthogonal forward regression (OFR) procedure based on the D-optimality experimental design criterion. The weights of the resulting sparse kernel model are then calculated using a modified multiplicative nonnegative quadratic programming algorithm. Unlike most of the SKD estimators, the proposed D-optimality regression approach is an unsupervised construction algorithm and it does not require an empirical desired response for the kernel selection task. The strength of the D-optimality OFR is owing to the fact that the algorithm automatically selects a small subset of the most significant kernels related to the largest eigenvalues of the kernel design matrix, which counts for the most energy of the kernel training data, and this also guarantees the most accurate kernel weight estimate. The proposed method is also computationally attractive, in comparison with many existing SKD construction algorithms. Extensive numerical investigation demonstrates the ability of this regression-based approach to efficiently construct a very sparse kernel density estimate with excellent test accuracy, and our results show that the proposed method compares favourably with other existing sparse methods, in terms of test accuracy, model sparsity and complexity, for constructing kernel density estimates.

Nonlinear model structure detection using optimum experimental design and orthogonal least squares

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the subset selection cost function includes an A-optimality design criterion to minimize the variance of the parameter estimates that ensures the adequacy and parsimony of the final model. An illustrative example is included to demonstrate the effectiveness of the new approach.

High-resolution global climate simulations with the ECMWF model in Project Athena: experimental design, model climate, and seasonal forecast skill

The sensitivity to the horizontal resolution of the climate, anthropogenic climate change, and seasonal predictive skill of the ECMWF model has been studied as part of Project Athena—an international collaboration formed to test the hypothesis that substantial progress in simulating and predicting climate can be achieved if mesoscale and subsynoptic atmospheric phenomena are more realistically represented in climate models. In this study the experiments carried out with the ECMWF model (atmosphere only) are described in detail. Here, the focus is on the tropics and the Northern Hemisphere extratropics during boreal winter. The resolutions considered in Project Athena for the ECMWF model are T159 (126 km), T511 (39 km), T1279 (16 km), and T2047 (10 km). It was found that increasing horizontal resolution improves the tropical precipitation, the tropical atmospheric circulation, the frequency of occurrence of Euro-Atlantic blocking, and the representation of extratropical cyclones in large parts of the Northern Hemisphere extratropics. All of these improvements come from the increase in resolution from T159 to T511 with relatively small changes for further resolution increases to T1279 and T2047, although it should be noted that results from this very highest resolution are from a previously untested model version. Problems in simulating the Madden–Julian oscillation remain unchanged for all resolutions tested. There is some evidence that increasing horizontal resolution to T1279 leads to moderate increases in seasonal forecast skill during boreal winter in the tropics and Northern Hemisphere extratropics. Sensitivity experiments are discussed, which helps to foster a better understanding of some of the resolution dependence found for the ECMWF model in Project Athena

A slugfest of intuitions: contextualism and experimental design

This paper considers ways that experimental design can affect judgments about informally presented context shifting experiments. Reasons are given to think that judgments about informal context shifting experiments are affected by an exclusive reliance on binary truth value judgments and by experimenter bias. Exclusive reliance on binary truth value judgments may produce experimental artifacts by obscuring important differences of degree between the phenomena being investigated. Experimenter bias is an effect generated when, for example, experimenters disclose (even unconsciously) their own beliefs about the outcome of an experiment. Eliminating experimenter bias from context shifting experiments makes it far less obvious what the “intuitive” responses to those experiments are. After it is shown how those different kinds of bias can affect judgments about informal context shifting experiments, those experiments are revised to control for those forms of bias. The upshot of these investigations is that participants in the contextualist debate who employ informal experiments should pay just as much attention to the design of their experiments as those who employ more formal experimental techniques if they want to avoid obscuring the phenomena they aim to uncover

Use of Experimental Design for the Optimization of the Production of New Secondary Metabolites by Two Penicillium Species

A fractional factorial design approach has been used to enhance secondary metabolite production by two Penicillium strains. The method was initially used to improve the production of bioactive extracts as a whole and subsequently to optimize the production of particular bioactive metabolites. Enhancements of over 500% in secondary metabolite production were observed for both P. oxalicum and P. citrinum. Two new alkaloids, citrinalins A (5) and B (6), were isolated and identified from P. citrinum cultures optimized for production of minor metabolites.

Electrodeposition of copper on titanium wires: Taguchi experimental design approach

Electrodeposition of thin copper layer was carried out on titanium wires in acidic sulphate bath. The influence of titanium surface preparation, cathodic current density, copper sulphate and sulphuric acid concentrations, electrical charge density and stirring of the solution on the adhesion of the electrodeposits was studied using the Taguchi statistical method. A L(16) orthogonal array with the six factors of control at two levels each and three interactions was employed. The analysis of variance of the mean adhesion response and signal-to-noise ratio showed the great influence of cathodic current density on adhesion. on the contrary, the other factors as well as the three investigated interactions revealed low or no significant effect. From this study optimized electrolysis conditions were defined. The copper electrocoating improved the electrical conductivity of the titanium wire. This shows that copper electrocoated titanium wires could be employed for both electrical purpose and mechanical reinforcement in superconducting magnets. (C) 2008 Elsevier B.V. All rights reserved.

Molecular assay optimized by Taguchi experimental design method for venous thromboembolism investigation

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Keratinase production by new strain of Bacillus Amyloliquefaciens: Application of statistical experimental design for optimization of keratinase production

Feathers are rich in amino acids and can be employed as a dietary protein supplement for animal feed. Microbial degradation is an alternative technology for improving the nutritional value of feathers. Other potential applications of keratinase include use in the leather industry, detergents and medicine as well as the pharmaceutical for the treatment of acne, psoriasis and calluses. A new keratinolytic enzyme production bacterium was isolated from a poultry processing plant. To improve keratinase yield, statistically based experimental designs were applied to optimize three significant variables: temperature, substrate concentration (feathers) and agitation speed. Response surface methodology demonstrated an increase in keratinolytic activity at temperature, agitation speed and substrate concentration of 26.6°C, 150 rpm and 2%, respectively. Liquid chromatography revealed the release of amino acids in the Bacillus amyloliquefaciens culture broth, thereby demonstrating the potential of feather meal in the animal feed industry. © Global Science Publications.

UV filters in water samples: Experimental design on the SPE optimization followed by GC-MS/MS analysis

Esse estudo descreve o desenvolvimento e otimização de um método de extração em fase solida (SPE) para análise dos filtros ultravioletas (UV): benzofenona-3 (BP-3), etilhexil salicilato (ES), etilhexil metoxinamato (EHMC) e octocrileno (OC) em matrizes ambientais. Um planejamento fatorial fracionário (PFF) 25-1 foi empregado na avaliação das variáveis significativas do método de extração. As condições experimentais otimizadas da avaliação estatística foram: capacidade do cartucho de 500 mL, eluente acetato de etila, metanol como solvente de lavagem (10% em água, v/v) and volume do eluente de 3 × 2 mL e pH 3. Os parâmetros analíticos avaliados foram satisfatõrios, apresentando linearidade de 100 a 4000 ng L -1, recuperaç ões para os quatro níveis de fortificação (Limite de Quantificação do Método, 200, 1000 e 2000 ng L-1) entre 62 e 107% com desvio padrão relativo menor que 14%. Os limites de quantificação foram encontrados na faixa de ng L-1, variando entre 10 e 100 ng L-1. O método proposto foi aplicado para a determinação dos quatro filtros UV em amostras de águas naturais. This study describes the development and optimization of a solid-phase extraction (SPE) method for analysis of ultraviolet (UV) filters, benzophenone-3 (BP-3), ethylhexyl methoxycinnamate (EHMC), ethylhexyl salicylate (ES) and octocrylene (OC), in environmental matrices. A 25-1 fractional factorial design (FFD) was used to evaluate the significant variables for the extraction method. The optimized experimental conditions determined from the statistical evaluation were: breakthrough volume of 500 mL, eluent of ethyl acetate, wash solvent of methanol (10% in water, v/v), eluent volume of 3 × 2 mL and pH 3. The evaluated analytical parameters were satisfactory for the analytes and showed linearity between 100 and 4000 ng L-1, recoveries for four fortification levels (Method Quantification Limit, 200, 1000 and 2000 ng L-1) were between 62 and 107% with relative standard deviations less than 14%. Limits of quantification were in the ng L-1 range and were between 10 and 100 ng L-1. The proposed method was used to analyze four UV filters in natural water samples. ©2013 Sociedade Brasileira de Química.

Optimization of in situ derivatization SPME by experimental design for GC-MS multi-residue analysis of pharmaceutical drugs in wastewater

This paper presents the development of a procedure, which enables the analysis of nine pharmaceutical drugs in wastewater using gas chromatography-mass spectrometry (GC-MS) associated with solid-phase microextraction (SPME) for the sample preparation. Experimental design was applied to optimize the in situ derivatization and the SPME extraction conditions. Ethyl chloroformate (ECF) was employed as derivatizing agent and polydimethylsiloxane-divinylbenzene (PDMS-DVB) as the SPME fiber coating. A fractional factorial design was used to evaluate the main factors for the in situ derivatization and SPME extraction. Thereafter, a Doehlert matrix design was applied to find out the best experimental conditions. The method presented a linear range from 0.5 to 10 mu g/L, and the intraday and interday precision were lower than 16%. Applicability of the method was verified from real influent and effluent samples of a wastewater treatment plant, as well as from samples of an industry wastewater and a river.

Influence of temperature, light and nutrients on the growth rates of the macroalga Gracilaria domingensis in synthetic seawater using experimental design

In the present study, the daily relative growth rates (DRGR, in percent per day) of the red macroalga Gracilaria domingensis in synthetic seawater was investigated for the combined influence of five factors, i.e., light (L), temperature (T), nitrate (N), phosphate (P), and molybdate (M), using a statistical design method. The ranges of the experimental cultivation conditions were T, 18-26A degrees C; L, 74-162 mu mol photons m(-2) s(-1); N, 40-80 mu mol L-1; P, 8-16 mu mol L-1; and M, 1-5 nmol L-1. The optimal conditions, which resulted in a maximum growth rate of a parts per thousand yen6.4% d(-1) from 7 to 10 days of cultivation, were determined by analysis of variance (ANOVA) multivariate factorial analysis (with a 2(5) full factorial design) to be L, 74 mu mol photons m(-2) s(-1); T, 26A degrees C; N, 80 mu mol L-1; P, 8 mu mol L-1; and M, 1 nmol L-1. In additional, these growth rate values are close to the growth rate values in natural medium (von Stosch medium), i.e., 6.5-7.0% d(-1). The results analyzed by the ANOVA indicate that the factors N and T are highly significant linear terms, X (L), (alpha = 0.05). On the other hand, the only significant quadratic term (X (Q)) was that for L. Statistically significant interactions between two different factors were found between T vs. L and N vs. T. Finally, a two-way (linear/quadratic interaction) model provided a quite reasonable correlation between the experimental and predicted DRGR values (R (adjusted) (2) = 0.9540).

Determination of Opiates in Whole Blood and Vitreous Humor: A Study of the Matrix Effect and an Experimental Design to Optimize Conditions for the Enzymatic Hydrolysis of Glucuronides

Faculty of Medicine University of Sao Paulo

Study of Candidate Genes for production and carcass traits in Italian Heavy Pigs: identification of a new DNA Markers, Expression Studies and Association Analysis using Different Experimental Design

Heavy pig breeding in Italy is mainly oriented for the production of high quality processed products. Of particular importance is the dry cured ham production, which is strictly regulated and requires specific carcass characteristics correlated with green leg characteristics. Furthermore, as pigs are slaughtered at about 160 kg live weight, the Italian pig breeding sector faces severe problems of production efficiency that are related to all biological aspects linked to growth, feed conversion, fat deposition and so on. It is well known that production and carcass traits are in part genetically determined. Therefore, as a first step to understand genetic basis of traits that could have a direct or indirect impact on dry cured ham production, a candidate gene approach can be used to identify DNA markers associated with parameters of economic importance. In this thesis, we investigated three candidate genes for carcass and production traits (TRIB3, PCSK1, MUC4) in pig breeds used for dry cured ham production, using different experimental approaches in order to find molecular markers associated with these parameters.

Social cognition as a mediator variable between neurocognition and functional outcome in schizophrenia: empirical review and new results by structural equation modeling

Cognitive impairments are currently regarded as important determinants of functional domains and are promising treatment goals in schizophrenia. Nevertheless, the exact nature of the interdependent relationship between neurocognition and social cognition as well as the relative contribution of each of these factors to adequate functioning remains unclear. The purpose of this article is to systematically review the findings and methodology of studies that have investigated social cognition as a mediator variable between neurocognitive performance and functional outcome in schizophrenia. Moreover, we carried out a study to evaluate this mediation hypothesis by the means of structural equation modeling in a large sample of 148 schizophrenia patients. The review comprised 15 studies. All but one study provided evidence for the mediating role of social cognition both in cross-sectional and in longitudinal designs. Other variables like motivation and social competence additionally mediated the relationship between social cognition and functional outcome. The mean effect size of the indirect effect was 0.20. However, social cognitive domains were differentially effective mediators. On average, 25% of the variance in functional outcome could be explained in the mediation model. The results of our own statistical analysis are in line with these conclusions: Social cognition mediated a significant indirect relationship between neurocognition and functional outcome. These results suggest that research should focus on differential mediation pathways. Future studies should also consider the interaction with other prognostic factors, additional mediators, and moderators in order to increase the predictive power and to target those factors relevant for optimizing therapy effects.