Dirichlet Priors for MAP Inference of Protein Conformation Abundances from SAXS

Estimation of mixture coefficients of protein conformations in solution find applications in understanding protein behavior. We describe a method for maximum a posteriori (MAP) estimation of the mixture coefficients of ensemble of conformations in a protein mixture solution using measured small angle X-ray scattering (SAXS) intensities. The proposed method builds upon a model for the measurements of crystallographically determined conformations. Assuming that a priori information on the protein mixture is available, and that priori information follows a Dirichlet distribution, we develop a method to estimate the relative abundances with MAP estimator. The Dirichlet distribution depends on concentration parameters which may not be known in practice and thus need to be estimated. To estimate these unknown concentration parameters we developed an expectation-maximization (EM) method. Adenylate kinase (ADK) protein was selected as the test bed due to its known conformations Beckstein et al. (Journal of Molecular Biology, 394(1), 160 1). Known conformations are assumed to form the full vector bases that span the measurement space. In Monte Carlo simulations, mixture coefficient estimation performances of MAP and maximum likelihood (ML) (which assumes a uniform prior on the mixture coefficients) estimators are compared. MAP estimators using known and unknown concentration parameters are also compared in terms of estimation performances. The results show that prior knowledge improves estimation accuracy, but performance is sensitive to perturbations in the Dirichlet distribution’s concentration parameters. Moreover, the estimation method based on EM algorithm shows comparable results to approximately known prior parameters.     

Optimised deep learning features for improved melanoma detection

In this article, we are addressing the question of effective usage of the feature set extracted from deep learning models pre-trained on ImageNet. Exploring this option will offer very fast and attractive alternative to transfer learning strategies. The traditional task of skin lesion recognition consists of several stages, where the automated system is typically trained on preprocessed images with known diagnosis, which allows classification of new samples to predefined categories. For this task, we are proposing here an improved melanoma detection method based on the combination of linear discriminant analysis (LDA) and the features extracted from the deep learning approach. We are examining the usage of the LDA approach on activation of the fully-connected layer of deep learning in order to increase the classification accuracy and at the same time to reduce the feature space dimensionality. We tested our method on five different classifiers and evaluated results using various metrics. The presented comparison demonstrates the very high effectiveness of the suggested feature reduction, which leads not only to the significant lowering of employed features but also to the increasing performance of all tested classifiers in almost all measured characteristics.

     

Electrochemical and optical properties of dicyclohexylmethyl substituted poly(3,4‐propylenedioxythiophene) analogue

An analogue of disubstituted 3,4‐propylenedioxythiophenes, namely 3,3‐bis(cyclohexylmethyl)‐3,4‐dihydro‐2H‐thieno[3,4‐b][1,4]dioxepin (ProDOT‐CycHex₂), was synthesized and its electrochemical polymerization was carried out successfully in an electrolyte solution of 0.1 M tetrabutylammonium hexafluorophosphate dissolved in a mixture of acetonitrile and dichloromethane (3/1: v/v). The corresponding polymer called PProDOT‐CycHex₂ has a reduced band gap of 1.85 eV and an electrochromic property: blue/violet when neutralized and highly transparent when oxidized. Also, PProDOT‐CycHex₂ film exhibited faster response time (0.7 s) and higher coloration efficiency (769 cm²/C) during oxidation when compared to its benzyl substituted analogue. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46214.     

A novel UV‐cured interpenetrating organic–inorganic hybrid polymer network based phase change materials (IPN‐PCM)

The purpose of this paper is to introduce a novel UV‐cured interpenetrating polymer networked phase change materials (IPN‐PCMs), on which no article has been found in the so far published research. Maleated castor oil (MCO) was synthesized via maleinization reaction of castor oil with maleic anhydride. Organic–inorganic hybrid interpenetrating polymer networked (IPN) materials containing both cationic and radical sections and IPN‐PCMs containing tetradecanol, hexadecanol, and octadecanol were prepared. The chemical structure of MCO and organic–inorganic hybrid IPN‐PCMs were determined by using Fourier Transform Infrared Spectroscopy (FT‐IR). Differential scanning calorimetry (DSC) was used for examining the phase‐change behaviors of the materials. Thermal stability was investigated by thermogravimetric analysis (TGA). Moreover, the surface formation of the specimen was investigated by scanning electron microscopy (SEM). In conclusion, our study proved that because of their high latent heat storage scope and high thermal stability, the obtained organic–inorganic hybrid IPN‐PCMs could be used as thermal energy storage materials. POLYM. ENG. SCI., 58:870–875, 2018. © 2017 Society of Plastics Engineers     

A participatory product design process with children with autism spectrum disorder

This study aims to explore the ways of involving children with autism in participatory product design processes. Due to the impaired skills of children with autism, a key aspect of the process is to gain an understanding of the nature of the disorder and how these children interact with their social and material surroundings as well as their daily life problems. Considering this, a case study was conducted with children with autism, their parents and teachers, and also industrial design students in a public special education centre in ?zmir, Turkey. The design task was to reconsider the conventional trampoline design with respect to the needs of the sample group and the special education centre as well as the benefits it provides. The task was based on the patterns of behaviours, actions and movement. Observations, interviews and questionnaires were carried out, as well as collaborative meetings and discussion meetings. Through the case study, the findings provided insights into conducting a participatory process with children with autism, the roles of the participants, and the interaction and communication among them. Furthermore, participants’ attitude towards participatory design, the potential benefits of the design process, and innovations to benefit children with autism were explained.     

Evaluation of various digestion procedures for trace element contents of some food materials

The levels of trace elements in different types of food material consumed in Turkey were determined by flame and graphite furnace atomic absorption spectrometry. Food samples were digested with dry ashing, wet ashing and microwave digestion procedures in this study. The microwave digestion procedure was chosen for the digestion of all the food samples because it required shorter time and made higher recovery (specially for Se). Fe, Cu, Mn, Zn, Al and Se were determined by flame and graphite furnace atomic absorption spectrometry, respectively. Relative standard deviations (RSD) were found below 10%. The accuracy of the procedure was confirmed by certified reference materials. Moreover, this procedure was easier to use when compared with dry and wet digestions.     

Transition metal cations extraction by ester and ketone derivatives of chromogenic azocalix[4]arenes

The molecule of azocalix[n]arene is a macrocyclic used effectively in the complexation of the heavy metal pollutants (like silver and mercury). In this work, our main aim is to prepare new chromogenic azocalix[n]arene molecules to elaborate an extractant with high extractant selectivity for metal ions able to detect this type of pollutant. The solvent extraction properties of four acetyls, four methyl ketones and four benzoyls derivatives from azocalix[4]arenes which were prepared by linking 4-ethyl, 4-n-butyl, 4-acetamid anilin and 2-aminothiazol to calix[4]arene through a diazo-coupling reaction, the alkaline earth (Sr2+) and the transition (Ag+, Hg2+, Co2+, Ni2+, Cu2+, Zn2+, Cd2+, Cr3+) metal cations have been determined by extraction studies with metal picrates. Both ketones are better extractants than esters, and show a strong preference for Ag+, while Cu2+ and Cr3+ are the most extracted cation with the esters. Both acetyl and benzoyl esters are good carriers for Ag+ and Hg2+.     

Single-molecule biophysics: at the interface of biology, physics and chemistry

Single-molecule methods have matured into powerful and popular tools to probe the complex behaviour of biological molecules, due to their unique abilities to probe molecular structure, dynamics and function, unhindered by the averaging inherent in ensemble experiments. This review presents an overview of the burgeoning field of single-molecule biophysics, discussing key highlights and selected examples from its genesis to our projections for its future. Following brief introductions to a few popular single-molecule fluorescence and manipulation methods, we discuss novel insights gained from single-molecule studies in key biological areas ranging from biological folding to experiments performed in vivo.     

Information Regularized Sensor Fusion: Application to Localization With Distributed Motion Sensors

We propose the information regularization principle for fusing information from sets of identical sensors observing a target phenomenon. The principle basically proposes an importance-weighting scheme for each sensor measurement based on the mutual information based pairwise statistical similarity matrix between sensors. The principle is applied to maximum likelihood estimation and particle filter based state estimation. A demonstration of the proposed regularization scheme in centralized data fusion of dense motion detector networks for target tracking is provided. Simulations confirm that the introduction of information regularization significantly improves localization accuracy of both maximum likelihood and particle filter approaches compared to their baseline implementations. Outlier detection and sensor failure detection capabilities, as well as possible extensions of the principle to decentralized sensor fusion with communication constraints are briefly discussed.