A Generative Probabilistic Oriented Wavelet Model for Texture Segmentation

This Letter addresses image segmentation via a generative model approach. A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images. The model is similar to a Hidden Markov model (HMM), but with non-stationary transitive conditional probability distributions. It is composed of discrete hidden variables and observable Gaussian outputs for wavelet coefficients. In particular, the Gabor wavelet transform is considered. The introduced model is compared with the simplest joint Gaussian probabilistic model for Gabor wavelet coefficients for several textures from the Brodatz album [1]. The comparison is based on cross-validation and includes probabilistic model ensembles instead of single models. In addition, the robustness of the models to cope with additive Gaussian noise is investigated. We further study the feasibility of the introduced generative model for image segmentation in the novelty detection framework [2]. Two examples are considered: (i) sea surface pollution detection from intensity images and (ii) image segmentation of the still images with varying illumination across the scene.     

Current Knowledge on Interactions of Plant Materials Traditionally Used in Skin Diseases in Poland and Ukraine with Human Skin Microbiota

Skin disorders of different etiology, such as dermatitis, atopic dermatitis, eczema, psoriasis, wounds, burns, and others, are widely spread in the population. In severe cases, they require the topical application of drugs, such as antibiotics, steroids, and calcineurin inhibitors. With milder symptoms, which do not require acute pharmacological interventions, medications, dietary supplements, and cosmetic products of plant material origin are gaining greater popularity among professionals and patients. They are applied in various pharmaceutical forms, such as raw infusions, tinctures, creams, and ointments. Although plant-based formulations have been used by humankind since ancient times, it is often unclear what the mechanisms of the observed beneficial effects are. Recent advances in the contribution of the skin microbiota in maintaining skin homeostasis can shed new light on understanding the activity of topically applied plant-based products. Although the influence of various plants on skin-related ailments are well documented in vivo and in vitro, little is known about the interaction with the network of the skin microbial ecosystem. The review aims to summarize the hitherto scientific data on plant-based topical preparations used in Poland and Ukraine and indicate future directions of the studies respecting recent developments in understanding the etiology of skin diseases. The current knowledge on investigations of interactions of plant materials/extracts with skin microbiome was reviewed for the first time.     

Charge Density Influences C1 Domain Ligand Affinity and Membrane Interactions

The C1 domain, which represents the recognition motif on protein kinase C for the lipophilic second messenger diacylglycerol and its ultrapotent analogues, the phorbol esters, has emerged as a promising therapeutic target for cancer and other indications. Potential target selectivity is markedly enhanced both because binding reflects ternary complex formation between the ligand, C1 domain, and phospholipid, and because binding drives membrane insertion of the C1 domain, permitting aspects of the C1 domain surface outside the binding site, per se, to influence binding energetics. Here, focusing on charged residues identified in atypical C1 domains which contribute to their loss of ligand binding activity, we showed that increasing charge along the rim of the binding cleft of the protein kinase C δ C1 b domain raises the requirement for anionic phospholipids. Correspondingly, it shifts the selectivity of C1 domain translocation to the plasma membrane, which is more negatively charged than internal membranes. This change in localization is most pronounced in the case of more hydrophilic ligands, which provide weaker membrane stabilization than do the more hydrophobic ligands and thus contributes an element to the structure–activity relations for C1 domain ligands. Coexpressing pairs of C1‐containing constructs with differing charges each expressing a distinct fluorescent tag provided a powerful tool to demonstrate the effect of increasing charge in the C1 domain.     

Processing of ZrC–Mo Cermets for High-Temperature Applications, Part I: Chemical Interactions in the ZrC–Mo System

The chemical compatibility of ZrC and Mo was investigated in carburizing and carbon-free environments at temperatures from 1700° to 2200°C. Heating in the carburizing atmosphere resulted in the complete reaction of Mo with C, while the carbon-free atmosphere resulted in retained metallic phase with a maximum of 13.8 mol% Mo₂C formed. The presence of Mo₂C was not detected at 2100°C in the carbon-free atmosphere, confirming the existing phase equilibria in the Zr–Mo–C system. Heat treatments in the carbon-free atmosphere also showed liquid formation at 2200°C, as evident from microstructure analysis. Liquid formation was consistent with the interaction between Mo and Mo₂C. The liquid was found to comprise at least 7 vol% of the total component, based on a phase diagram for the Mo–C system. The formation of a liquid should allow for the processing of ZrC–Mo cermets by liquid-phase pressureless sintering.     

Generalized weighted likelihood density estimators with application to finite mixture of exponential family distributions

The family of weighted likelihood estimators largely overlaps with minimum divergence estimators. They are robust to data contaminations compared to MLE. We define the class of generalized weighted likelihood estimators (GWLE), provide its influence function and discuss the efficiency requirements. We introduce a new truncated cubic-inverse weight, which is both first and second order efficient and more robust than previously reported weights. We also discuss new ways of selecting the smoothing bandwidth and weighted starting values for the iterative algorithm. The advantage of the truncated cubic-inverse weight is illustrated in a simulation study of three-component normal mixtures model with large overlaps and heavy contaminations. A real data example is also provided.     

Nanostructural Evolution of Titania-Based Materials Using Modified Titanium Precursors

Titanium ethoxide [Ti(OEt)₄] was modified with aminobenzoic acid (AB) or aminosalicylic acid (AS) in order to control the hydrolysis and condensation rates, and to allow the preparation of organic–inorganic hybrid materials. A suite of complementary techniques, including Fourier transform infrared spectroscopy, NMR, SEM, thermogravimetric analysis, and X-ray diffraction, were used to elucidate the effects of incorporating an organic functional group into the precursor chemistry and its subsequent affect on the structure and morphology of the resultant hybrid material. The annealing behavior of the resulting hybrid titanium base materials was also investigated. Our studies show that both amino acid organic ligands, AB and AS, chemically bounded to the titanium complex, effect the precursor reactivity, specifically the hydrolysis and polycondensation reactions, which control the evolution and formation of the nanohybrid-based titania material. Following sol–gel processing, the nanohybrid materials are amorphous, due to the incorporation of the organic component. The phase transition (amorphous–anatase–rutile) observed during annealing from 25° to 800°C show subtle differences in the crystallization behavior, which are associated with the nature of the organic ligand.     

I2-doped and pyrrole ring-iodinated semi-conducting oligomers of N-vinyl-3-alkyl-2-phenylpyrroles

I₂-doped and pyrrole ring-iodinated semi-conducting oligomers of N-vinyl-3-alkyl-2-phenylpyrroles have been synthesized by free-radical polymerization of the above monomers (AIBN, 2–5 wt%, 60–80 °C) and further exposure of the oligomers obtained to I₂ vapor. The parent oligomers exhibit paramagnetic and fluorescent properties and stable up to 300–370 °C.     

Efficient generation of pareto‐optimal topologies for compliance optimization

In multi‐objective optimization, a design is defined to beit pareto‐optimal if no other design exists that is better with respect to one objective, and as good with respect to other objectives. In this paper, we first show that if a topology is pareto‐optimal, then it must satisfy certain properties associated with the topological sensitivity field, i.e. no further comparison is necessary. This, in turn, leads to a deterministic, i.e. non‐stochastic, method for efficiently generating pareto‐optimal topologies using the classic fixed‐point iteration scheme. The proposed method is illustrated, and compared against SIMP‐based methods, through numerical examples. In this paper, the proposed method of generating pareto‐optimal topologies is limited to bi‐objective optimization, namely compliance–volume and compliance–compliance. The future work will focus on extending the method to non‐compliance and higher dimensional pareto optimization. Copyright © 2011 John Wiley & Sons, Ltd.