Diurnal variation of plasma methyl sterols and cholesterol in the rat: Relation to hepatic cholesterol synthesis

Free cholesterol of plasma low density lipoproteins (LDL) and high density lipoproteins (HDL) of the rat was high and that of plasma very low density lipoproteins (VLDL) was low during the dark period of the diurnal cycle. Variations in the esterified plasma sterols were inconsistent. Free methyl sterols were high in all lipoproteins during the dark phase. Simultaneously, the incorporation of¹⁴C-acetate into nonsaponifiable sterols and the concentrations of free methyl sterols and cholesterol in the liver were elevated.     

Extracting Conditionally Heteroskedastic Components using Independent Component Analysis

In the independent component model, the multivariate data are assumed to be a mixture of mutually independent latent components. The independent component analysis (ICA) then aims at estimating these latent components. In this article, we study an ICA method which combines the use of linear and quadratic autocorrelations to enable efficient estimation of various kinds of stationary time series. Statistical properties of the estimator are studied by finding its limiting distribution under general conditions, and the asymptotic variances are derived in the case of ARMA-GARCH model. We use the asymptotic results and a finite sample simulation study to compare different choices of a weight coefficient. As it is often of interest to identify all those components which exhibit stochastic volatility features we suggest a test statistic for this problem. We also show that a slightly modified version of the principal volatility component analysis can be seen as an ICA method. Finally, we apply the estimators in analysing a data set which consists of time series of exchange rates of seven currencies to US dollar. Supporting information including proofs of the theorems is available online.     

Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives

A new bi-objective genetic programming (BioGP) technique has been developed for meta-modeling and applied in a chromatographic separation process using a simulated moving bed (SMB) process. The BioGP technique initially minimizes training error through a single objective optimization procedure and then a trade-off between complexity and accuracy is worked out through a genetic algorithm based bi-objective optimization strategy. A benefit of the BioGP approach is that an expert user or a decision maker (DM) can flexibly select the mathematical operations involved to construct a meta-model of desired complexity or accuracy. It is also designed to combat bloat – a perennial problem in genetic programming along with over fitting and under fitting problems. In this study the meta-models constructed for SMB reactors were compared with those obtained from an evolutionary neural network (EvoNN) developed earlier and also with a polynomial regression model. Both BioGP and EvoNN were compared for subsequent constrained bi-objective optimization studies for the SMB reactor involving four objectives. The results were also compared with the previous work in the literature. The BioGP technique produced acceptable results and is now ready for data-driven modeling and optimization studies at large.     

Clinical and genetic characteristics of type 2 diabetes with and without GAD antibodies

The aim of the study was 1) to establish the prevalence of GAD antibodies (GADab) in a population-based study of type 2 diabetes in western Finland, 2) to genetically and phenotypically characterize this subgroup, and 3) to provide a definition for latent autoimmune diabetes in adults (LADA). The prevalence of GADab was 9.3% among 1,122 type 2 diabetic patients, 3.6% among 558 impaired glucose tolerance (IGT) subjects, and 4.4% among 383 nondiabetic control subjects. Islet antigen 2 antibodies (IA2ab) or islet cell antibodies were detected in only 0.5% of the GADab- patients. The GADab+ patients had lower fasting C-peptide concentrations (median [interquartile range]: 0.46 [0.45] vs. 0.62 [0.44] nmol/l, P = 0.0002) and lower insulin response to oral glucose compared with GADab- patients. With respect to features of the metabolic syndrome, the GADab+ patients had lower systolic (140 [29.1] vs. 148 [26.0] mmHg, P = 0.009) and diastolic (79.2 [17.6] vs. 81.0 [13.1] mmHg, P = 0.030) blood pressure values, as well as lower triglyceride concentrations (1.40 [1.18] vs. 1.75 [1.25] mmol/l, P = 0.003). GADab+ men had a lower waist-to-hip ratio compared with GADab- patients. Compared with GADab- patients and control subjects, the GADab+ patients had an increased frequency HLA-DQB1*0201/0302 (13 vs. 4%; P = 0.002) and other genotypes containing the *0302 allele (22 vs. 12%; P = 0.010). However, the frequency of these high-risk genotypes was significantly lower in GADab+ type 2 patients than in type 1 diabetes of young or adult onset (0201/0302 or 0302/X: 36 vs. 66 vs. 64%, P < 0.001). The GADab+ type 2 group did not differ from control subjects with respect to genotypes containing the protective DQB1-alleles *0602 or *0603, nor with respect to the type 1 high-risk genotype in the IDDM1 (Hph1 +/+). We conclude that GADab+ patients differ from both GADab- type 2 diabetic patients and type 1 diabetic patients with respect to beta-cell function, features of the metabolic syndrome, and type 1 diabetes susceptibility genes. Further, we propose that LADA be defined as GADab positivity (>5 relative units) in patients older than 35 years at onset of type 2 diabetes.     

A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods

Computationally expensive multiobjective optimization problems arise, e.g. in many engineering applications, where several conflicting objectives are to be optimized simultaneously while satisfying constraints. In many cases, the lack of explicit mathematical formulas of the objectives and constraints may necessitate conducting computationally expensive and time-consuming experiments and/or simulations. As another challenge, these problems may have either convex or nonconvex or even disconnected Pareto frontier consisting of Pareto optimal solutions. Because of the existence of many such solutions, typically, a decision maker is required to select the most preferred one. In order to deal with the high computational cost, surrogate-based methods are commonly used in the literature. This paper surveys surrogate-based methods proposed in the literature, where the methods are independent of the underlying optimization algorithm and mitigate the computational burden to capture different types of Pareto frontiers. The methods considered are classified, discussed and then compared. These methods are divided into two frameworks: the sequential and the adaptive frameworks. Based on the comparison, we recommend the adaptive framework to tackle the aforementioned challenges.     

Soiling of plasticized poly(vinyl chloride)

The effects of three plasticizers and two plasticizer concentrations on the topography and soiling of poly (vinyl chloride) (PVC) were studied. Palmitic acid and triolein were chosen to represent solid and liquid soils. The feasibility of using infrared spectroscopy to quantify the amount of soil on PVC was examined. The structure of the solid model soil on plasticized PVC was studied with optical microscopy and atomic force microscopy. Palmitic acid formed two different structures on the PVC surface. Both the type and concentration of the plasticizer influenced the structure of the oily soil on plasticized PVC. The wetting of plasticized PVC with the liquid oily soil was compared to wetting with water through the measurement of the contact angles. Plasticized PVC was hydrophobic and oleophilic. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

The Discrete Basis Problem

Matrix decomposition methods represent a data matrix as a product of two factor matrices: one containing basis vectors that represent meaningful concepts in the data, and another describing how the observed data can be expressed as combinations of the basis vectors. Decomposition methods have been studied extensively, but many methods return real-valued matrices. Interpreting real-valued factor matrices is hard if the original data is Boolean. In this paper, we describe a matrix decomposition formulation for Boolean data, the Discrete Basis Problem. The problem seeks for a Boolean decomposition of a binary matrix, thus allowing the user to easily interpret the basis vectors. We also describe a variation of the problem, the Discrete Basis Partitioning Problem. We show that both problems are NP-hard. For the Discrete Basis Problem, we give a simple greedy algorithm for solving it; for the Discrete Basis Partitioning Problem we show how it can be solved using existing methods. We present experimental results for the greedy algorithm and compare it against other, well known methods. Our algorithm gives intuitive basis vectors, but its reconstruction error is usually larger than with the real-valued methods. We discuss about the reasons for this behavior.