Development of partial least squares regression with discriminant analysis for software bug prediction.

Many prediction models and approaches have been introduced during the past decades that try to forecast bugged code elements based on static source code metrics, change and history metrics, or both. However, there is still no universal best solution to this problem, as most suitable features and models vary from dataset to dataset and depend on the context in which we use them. Therefore, novel approaches and further studies on this topic are highly necessary.

Predicting the uniqueness of single non-negative profiles estimated by multivariate curve resolution methods.

In many kinds of chemical data, one or more species are unknown and the only efficient way to identify and/or quantify them is by mathematical resolution of the mixture spectra. The major problem with such mathematical decompositions is the possibility of obtaining a range of feasible solutions instead of a unique solution due to insufficient prior information about the system under study. However, even with the minimal non-negativity assumptions, there may be some levels of uniqueness, i.

Effects of the Pentapeptide P33 on Memory and Synaptic Plasticity in APP/PS1 Transgenic Mice: A Novel Mechanism Presenting the Protein Fe65 as a Target.

Regulated intramembrane proteolysis (RIP) of the amyloid precursor protein (APP) leads to the formation of fragments, among which the intracellular domain of APP (AICD) was also identified to be a causative of early pathological events. AICD-counteracting proteins, such as Fe65, may serve as alternative therapeutic targets of Alzheimer's disease (AD). The detection of elevated levels of Fe65 in the brains of both human patients and APP transgenic mice may further strengthen the hypothesis that influencing the interaction between Fe65 and APP may have a beneficial effect on the course of AD.

Duality based direct resolution of unique profiles using zero concentration region information.

Self Modeling Curve Resolution (SMCR) is a class of techniques concerned with estimating pure profiles underlying a set of measurements on chemical systems. In general, the estimated profiles are ambiguous (non-unique) except if some special conditions fulfilled. Implementing the adequate information can reduce the so-called rotational ambiguity effectively, and in the most desirable cases lead to the unique solution.

Local Rank Deficiency Caused Problems in Analyzing Chemical Data.

Multivariate curve resolution (MCR) is a powerful methodology for analyzing chemical data in different application fields such as pharmaceutical analysis, agriculture, food chemistry, environment, and industrial and clinical chemistry. However, MCR results are often complicated by rotational ambiguity, meaning that there is a range of feasible solutions that fulfill the constraints and explain equally well the observed experimental data. Constraints determine the properties of resolved profiles in MCR methods by enforcing different assumptions on data.

Analytical solution and meaning of feasible regions in two-component three-way arrays.

Although many efforts have been directed to the development of approximation methods for determining the extent of feasible regions in two- and three-way data sets; analytical determination (i.e. using only finite-step direct calculation(s) instead of the less exact numerical ones) of feasible regions in three-way arrays has remained unexplored.

A review of recent methods for the determination of ranges of feasible solutions resulting from soft modelling analyses of multivariate data.

Soft modelling or multivariate curve resolution (MCR) are well-known methodologies for the analysis of multivariate data in many different application fields. Results obtained by soft modelling methods are very likely impaired by rotational and scaling ambiguities, i.e.

Fitting-free curve resolution of spectroscopic data: Chemometric and physical chemical viewpoints.

In this paper the authors have investigated spectroscopic data analysis according to a recent development, i.e. the Direct Inversion in the Spectral Subspace (DISS) procedure.

Definition and detection of data-based uniqueness in evaluating bilinear (two-way) chemical measurements.

Multivariate curve resolution methods, frequently used in analyzing bilinear data sets, result in ambiguous decomposition in general. Implementing the adequate constraints may lead to reduce the so-called rotational ambiguity drastically, and in the most favorable cases to the unique solution. However, in some special cases, non-negativity constraint as minimal information of the system is a sufficient condition to resolve profiles uniquely.

Newer developments on self-modeling curve resolution implementing equality and unimodality constraints.

Analytical self-modeling curve resolution (SMCR) methods resolve data sets to a range of feasible solutions using only non-negative constraints. The Lawton-Sylvestre method was the first direct method to analyze a two-component system. It was generalized as a Borgen plot for determining the feasible regions in three-component systems.

Investigation of the equality constraint effect on the reduction of the rotational ambiguity in three-component system using a novel grid search method.

The obtained results by soft modeling multivariate curve resolution methods often are not unique and are questionable because of rotational ambiguity. It means a range of feasible solutions equally fit experimental data and fulfill the constraints. Regarding to chemometric literature, a survey of useful constraints for the reduction of the rotational ambiguity is a big challenge for chemometrician.

Functional changes in transcriptomes of the prefrontal cortex and hippocampus in a mouse model of anxiety.

Anxiety is a multi-etiology disorder influenced by both genetic background and environment. To study the impact of a genetic predisposition, we developed a novel mouse model of anxiety using a combination of crossbreeding and behavioral selection. Comparison of the transcriptomes from the prefrontal cortex and hippocampus of anxious and control mice revealed that the numbers of significantly up- and down-regulated genes were modest, comprising approximately 2% of the tested genes.

Protein array based interactome analysis of amyloid-β indicates an inhibition of protein translation.

Oligomeric amyloid-β is currently of interest in amyloid-β mediated toxicity and the pathogenesis of Alzheimer's disease. Mapping the amyloid-β interaction partners could help to discover novel pathways in disease pathogenesis. To discover the amyloid-β interaction partners, we applied a protein array with more than 8100 unique recombinantly expressed human proteins.

Formulation optimization of sustained-release ammonio methacrylate copolymer microspheres. Effects of log p and concentration of polar cosolvents, and role of the drug/copolymer ratio.

The objectives of this work were the formulation optimization of the preparation process parameters and to evaluate spray-dried sustained-release microspheres using ammonio methacrylate copolymer (AMC) as a polymer matrix. The effects of log P and the concentrations of the cosolvents (acetone, methyl ethyl ketone and n-butyl acetate) and different drug/copolymer ratios as independent variables on the physicochemical parameters (the W1/O emulsion viscosity, the microsphere production yield, the average particle size, the encapsulation efficiency) and the cumulative in vitro drug release as dependent variables were studied. The optimization was carried out on the basis of the 33 factorial design study.

Additional knowledge for determining and interpreting feasible band boundaries in self-modeling/multivariate curve resolution of two-component systems.

Recently Tauler's mcrbands Matlab script and Maeder's grid method were used by Abdollahi et al. to calculate the elements of transformation matrix for obtaining feasible band boundaries in multivariate curve resolution of a two-component system. Neither method is analytical, instead they are iterative.

Quantum chemical characterization of Abraham solvation parameters for gas-liquid chromatographic stationary phases.

Quantum chemical based investigation is presented on the Abraham solvation parameters for 23 molecular (non-polymeric) GLC stationary phases. PM6 semiempirical calculations combined with conductor-like screening model (COSMO) have been utilized. Comprehensive search for an optimal model was carried out, based on best subset selection from 86 variables considered.

Self-modeling curve resolution method applied for the evaluation of dissolution testing data: a case study of meloxicam-mannitol binary systems.

This paper introduces some chemometric methods, i.e., self-modeling curve resolution (SMCR), multivariate curve resolution-alternating least squares (MCR-ALS) and parallel factor analysis (PARAFAC and PARAFAC2), which are used to evaluate in vitro dissolution testing data detected by a UV-vis spectrophotometer on meloxicam-mannitol binary systems.

Computation of the range (band boundaries) of feasible solutions and measure of the rotational ambiguity in self-modeling/multivariate curve resolution.

Nowadays self-modeling/multivariate curve resolution algorithms have become very popular in chemometrics, i.e. for evaluating analytical chemical measurements.

Theoretical characterization of gas-liquid chromatographic stationary phases with quantum chemical descriptors.

Quantitative structure-property relationship (QSPR) solvent model has been developed for the McReynolds constants (prototypical solutes) on 36 gas-liquid chromatographic stationary phases. PM6 semiempirical quantum chemical calculations combined with conductor-like screening model (COSMO) has been utilized. From 276 descriptors considered, forward stepwise variable selection, followed by best subset selection, yielded linear regression models containing six purely quantum chemical and two hybrid, topologically based descriptors.

Study of the effects of drugs on the structures of sucrose esters and the effects of solid-state interactions on drug release.

Sucrose esters (SEs) have a wide range of hydrophilic-lipophilic balance (HLB) values (1-16), and hence can be applied as surfactants, or as solubility or penetration enhancers. In general, SEs are used in hot-melt technology, because of their low melting points, but literature data are not available on the effects of active agents on the structures of SEs and the possible solid-state interactions. In this study, drug-SE products were prepared by melt technology and investigated by differential scanning calorimetry (DSC), X-ray powder diffraction (XRPD), rheological measurements and dissolution tests.

Preparation of a solid dispersion by a dropping method to improve the rate of dissolution of meloxicam.

Application of a solid dispersion system is one of the methods used to increase the bioavailability of poorly water-soluble drugs. Adaptation of the dropping method from the chemical industry as a formulation procedure may help the scaling-up process and simplify the formulation of poorly water-soluble compounds. Meloxicam (ME), a nonsteroidal anti-inflammatory drug that is poorly soluble in water, and polyethylene glycol (PEG) 4000, a water-soluble carrier, were formulated by using a dropping method in an attempt to improve the dissolution of ME.

Physicochemical characterization of meloxicam-mannitol binary systems.

The dissolution behaviour of drugs remains one of the most challenging aspects in formulation development. The anti-inflammatory drug, meloxicam (ME) has poor water solubility. The object of this experiment was to improve the rate of dissolution of meloxicam in capsule form.

Towards the solution of the eluent elimination problem in high-performance liquid chromatography-infrared spectroscopy measurements by chemometric methods.

The high-performance liquid chromatography-infrared spectroscopy (HPLC-IR) technique utilizing on-line flow through cell (FTC) detection has an inherent practical problem: strong absorption bands of the eluent may mask valuable analytical regions of the IR spectrum. The experimentalists' answer to this challenge is physical elimination of the chromatographic eluent before spectroscopic detection, which however results in off-line measurement of spectra. In the present work, the capabilities of some chemometric algorithms using iteratively applied multi-way methods such as parallel factor analysis (PARAFAC) and PARAFAC2, developed with the aim of overcoming the problems of eluent elimination are examined and evaluated.