Liquid metal/metal oxide frameworks with incorporated Ga2O3 for photocatalysis

Solvothermally synthesized Ga2O3 nanoparticles are incorporated into liquid metal/metal oxide (LM/MO) frameworks in order to form enhanced photocatalytic systems. The LM/MO frameworks, both with and without incorporated Ga2O3 nanoparticles, show photocatalytic activitydue to a plasmonic effect where performance is related to the loading of Ga2O3 nanoparticles. Optimum photocatalytic efficiency is obtained with 1 wt% incorporation of Ga2O3 nanoparticles. This can be attributed to the sub-bandgap states of LM/MO frameworks, contributing to pseudo-ohmic contacts which reduce the free carrier injection barrier to Ga2O3.

Crystal structures and hydrogen bonding in the proton transfer salts of nicotine with 3,5-dinitrosalicylic acid and 5-sulfosalicylic acid

The structures of the 1:1 anhydrous salts of nicotine (NIC) with 3,5-dinitrosalicylic acid (DNSA) and 5-sulfosalicylic acid (5-SSA), namely (1R,2S)-1-methyl-2-(3-pyridyl)-1H-pyrrolidin-1-ium 2-carboxy-4,6-dinitrophenolate, C10H15N2+ C7H3N2O7-, (I) and (1R,2S)-1-methyl-2-(3-pyridyl)-1H-pyrrolidin-1-ium 3-carboxy-4-hydroxybenzenesulfonate, C10H15N2+ C7H5O6S-, (II) are reported. The asymmetric units of both (I) and (II) comprise two independent nicotinium cations (C and D) and either two DNSA or two 5-SSA anions (A and B), respectively. One of the DNSA anions shows a 25% rotational disorder in the benzene ring system. In the crystal of (I), inter-unit pyrrolidinium N-H...N(pyridine) hydrogen bonds generate zigzag NIC cation chains which extend along a while the DNSA anions are not involved in any formal inter-species hydrogen bonding but instead form pi--pi associated stacks which parallel the NIC chains along a [ring centroid separation, 3.857(2)A]. Weak C-H...O interactions between chain substructures give an overall three-dimensional structure. With (II), A and B anions form independent zigzag chains with C and D cations, respectively, through carboxylic acid O-H...N(pyridine) hydrogen bonds. These chains, which extend along b are pseudo-centrosymmetrically related and give pi--pi interactions between the benzene rings of anions A and B and the pyridine rings of the NIC cations C and D, respectively [ring centroid separations, 3.6422(19) and 3.7117(19)A]. Present also are weak intermolecular C-H...O hydrogen-bonding interactions between the chains, giving an overall three-dimensional structure.

Bisphenol A sorption by organo-montmorillonite: Implications for the removal of organic contaminants from water

Remediation of bisphenol A (BPA) from aqueous solutions by adsorption using organoclays synthesized from montmorillonite (MMT) with different types of organic surfactant molecules was demonstrated. High adsorption capacities of the organoclays for the uptake of BPA were observed and these demonstrated their potential application as strong adsorbents for noxious organic water contaminants. The adsorption of BPA was significantly influenced by pH, with increased adsorption of BPA in acidic pH range. However, the organoclays intercalated with highly loaded surfactants and/or large surfactant molecules were less influenced by the pH of the environment and this was thought to be due to the shielding the negative charge from surfactant molecules and the development of more positive charge on the clay surface, which leads to the attraction of anionic BPA even at alkaline pH. The hydrophobic phase created by loaded surfactant molecules contributed to a partitioning phase, interacting with BPA molecules strongly through hydrophobic interaction. Pseudo-second order kinetic model and Langmuir isotherm provided the best fit for the adsorption of BPA onto the organoclays. In addition, the adsorption process was spontaneous and exothermic with lower temperature facilitating the adsorption of BPA onto the organoclays. The described process provides a potential pathway for the removal of BPA from contaminated waters.

Alive SMC^2: Bayesian model selection for low-count time series models with intractable likelihoods

In this paper we present a new method for performing Bayesian parameter inference and model choice for low count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel pseudo-marginal algorithm, which we refer to as alive SMC^2. The advantages of this approach over competing approaches is that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series and the cumulative number of poison disease cases in mule deer.

An efficient DSE using conditional multivariate complex Gaussian distribution

This paper presents an efficient noniterative method for distribution state estimation using conditional multivariate complex Gaussian distribution (CMCGD). In the proposed method, the mean and standard deviation (SD) of the state variables is obtained in one step considering load uncertainties, measurement errors, and load correlations. In this method, first the bus voltages, branch currents, and injection currents are represented by MCGD using direct load flow and a linear transformation. Then, the mean and SD of bus voltages, or other states, are calculated using CMCGD and estimation of variance method. The mean and SD of pseudo measurements, as well as spatial correlations between pseudo measurements, are modeled based on the historical data for different levels of load duration curve. The proposed method can handle load uncertainties without using time-consuming approaches such as Monte Carlo. Simulation results of two case studies, six-bus, and a realistic 747-bus distribution network show the effectiveness of the proposed method in terms of speed, accuracy, and quality against the conventional approach.

Effect of constitutive material models on seismic response of two-story reinforced concrete frame

This paper focuses on the finite element (FE) response sensitivity and reliability analyses considering smooth constitutive material models. A reinforced concrete frame is modeled for FE sensitivity analysis followed by direct differentiation method under both static and dynamic load cases. Later, the reliability analysis is performed to predict the seismic behavior of the frame. Displacement sensitivity discontinuities are observed along the pseudo-time axis using non-smooth concrete and reinforcing steel model under quasi-static loading. However, the smooth materials show continuity in response sensitivity at elastic to plastic transition points. The normalized sensitivity results are also used to measure the relative importance of the material parameters on the structural responses. In FE reliability analysis, the influence of smoothness behavior of reinforcing steel is carefully noticed. More efficient and reasonable reliability estimation can be achieved by using smooth material model compare with bilinear material constitutive model.

Stochastic analysis of a radial-inflow turbine in the presence of parametric uncertainties

This paper presents an uncertainty quantification study of the performance analysis of the high pressure ratio single stage radial-inflow turbine used in the Sundstrand Power Systems T-100 Multi-purpose Small Power Unit. A deterministic 3D volume-averaged Computational Fluid Dynamics (CFD) solver is coupled with a non-statistical generalized Polynomial Chaos (gPC) representation based on a pseudo-spectral projection method. One of the advantages of this approach is that it does not require any modification of the CFD code for the propagation of random disturbances in the aerodynamic and geometric fields. The stochastic results highlight the importance of the blade thickness and trailing edge tip radius on the total-to-static efficiency of the turbine compared to the angular velocity and trailing edge tip length. From a theoretical point of view, the use of the gPC representation on an arbitrary grid also allows the investigation of the sensitivity of the blade thickness profiles on the turbine efficiency. The gPC approach is also applied to coupled random parameters. The results show that the most influential coupled random variables are trailing edge tip radius coupled with the angular velocity.

Scalable Bayesian Computation for intractable likelihoods in image analysis

The inverse temperature hyperparameter of the hidden Potts model governs the strength of spatial cohesion and therefore has a substantial influence over the resulting model fit. The difficulty arises from the dependence of an intractable normalising constant on the value of the inverse temperature, thus there is no closed form solution for sampling from the distribution directly. We review three computational approaches for addressing this issue, namely pseudolikelihood, path sampling, and the approximate exchange algorithm. We compare the accuracy and scalability of these methods using a simulation study.

A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC

Membrane proteins play important roles in many biochemical processes and are also attractive targets of drug discovery for various diseases. The elucidation of membrane protein types provides clues for understanding the structure and function of proteins. Recently we developed a novel system for predicting protein subnuclear localizations. In this paper, we propose a simplified version of our system for predicting membrane protein types directly from primary protein structures, which incorporates amino acid classifications and physicochemical properties into a general form of pseudo-amino acid composition. In this simplified system, we will design a two-stage multi-class support vector machine combined with a two-step optimal feature selection process, which proves very effective in our experiments. The performance of the present method is evaluated on two benchmark datasets consisting of five types of membrane proteins. The overall accuracies of prediction for five types are 93.25% and 96.61% via the jackknife test and independent dataset test, respectively. These results indicate that our method is effective and valuable for predicting membrane protein types. A web server for the proposed method is available at http://www.juemengt.com/jcc/memty_page.php

Development and characterisation of a new bioink for additive tissue manufacturing

Additive manufacturing forms a potential route towards economically viable production of cellular constructs for tissue engineering. Hydrogels are a suitable class of materials for cell delivery and 3D culture, but are generally unsuitable as construction materials. Gelatine-methacrylamide is an example of such a hydrogel system widely used in the field of tissue engineering, e.g. for cartilage and cardiovascular applications. Here we show that by the addition of gellan gum to gelatine-methacrylamide and tailoring salt concentrations, rheological properties such as pseudo-plasticity and yield stress can be optimised towards gel dispensing for additive manufacturing processes. In the hydrogel formulation, salt is partly substituted by mannose to obtain isotonicity and prevent a reduction in cell viability. With this, the potential of this new bioink for additive tissue manufacturing purposes is demonstrated.

Non-Newtonian mixed convection flow from a horizontal circular cylinder with uniform surface heat flux

Mixed convection laminar two-dimensional boundary-layer flow of non-Newtonian pseudo-plastic fluids is investigated from a horizontal circular cylinder with uniform surface heat flux using a modified power-law viscosity model, that contains no unrealistic limits of zero or infinite viscosity; consequently, no irremovable singularities are introduced into boundary-layer formulations for such fluids. The governing boundary layer equations are transformed into a non-dimensional form and the resulting nonlinear systems of partial differential equations are solved numerically applying marching order implicit finite difference method with double sweep technique. Numerical results are presented for the case of shear-thinning fluids in terms of the fluid temperature distributions, rate of heat transfer in terms of the local Nusselt number.

A biomechanical approach to iris normalization

The richness of the iris texture and its variability across individuals make it a useful biometric trait for personal authentication. One of the key stages in classical iris recognition is the normalization process, where the annular iris region is mapped to a dimensionless pseudo-polar coordinate system. This process results in a rectangular structure that can be used to compensate for differences in scale and variations in pupil size. Most iris recognition methods in the literature adopt linear sampling in the radial and angular directions when performing iris normalization. In this paper, a biomechanical model of the iris is used to define a novel nonlinear normalization scheme that improves iris recognition accuracy under different degrees of pupil dilation. The proposed biomechanical model is used to predict the radial displacement of any point in the iris at a given dilation level, and this information is incorporated in the normalization process. Experimental results on the WVU pupil light reflex database (WVU-PLR) indicate the efficacy of the proposed technique, especially when matching iris images with large differences in pupil size.

Analysis of differential gene expression based on Bayesian estimation of variance

Gene expression is arguably the most important indicator of biological function. Thus identifying differentially expressed genes is one of the main aims of high throughout studies that use microarray and RNAseq platforms to study deregulated cellular pathways. There are many tools for analysing differentia gene expression from transciptomic datasets. The major challenge of this topic is to estimate gene expression variance due to the high amount of ‘background noise’ that is generated from biological equipment and the lack of biological replicates. Bayesian inference has been widely used in the bioinformatics field. In this work, we reveal that the prior knowledge employed in the Bayesian framework also helps to improve the accuracy of differential gene expression analysis when using a small number of replicates. We have developed a differential analysis tool that uses Bayesian estimation of the variance of gene expression for use with small numbers of biological replicates. Our method is more consistent when compared to the widely used cyber-t tool that successfully introduced the Bayesian framework to differential analysis. We also provide a user-friendly web based Graphic User Interface for biologists to use with microarray and RNAseq data. Bayesian inference can compensate for the instability of variance caused when using a small number of biological replicates by using pseudo replicates as prior knowledge. We also show that our new strategy to select pseudo replicates will improve the performance of the analysis. - See more at: http://www.eurekaselect.com/node/138761/article#sthash.VeK9xl5k.dpuf

Synthesis, reactivity and structural characterisation of bicyclic tetraphosphapentazanes

The diphenoxy bicyclic tetraphosphapentazane derivatives (EtN)(5)P-4(OPh)(2) 2 and its monoxide (EtN)(5)P-4(O)(OPh)(2) 3 have been prepared. Both 2 and 3 exist as a mixture of two isomers. One isomer of (EtN)(5)P-4(O)(OPh)(2) 3a has been isolated and its reaction with tetrachloro-1,2-benzoquinone yielded (EtN)(5)P-4(O)(OPh)(2)(O2C6Cl4) 5 in which the junction phosphorus atom becomes five-co-ordinated. Treatment of 2 or 3a with [Mo(CO)(4)(nbd)] (nbd = norbornadiene, bicyclo[2.2.1]hepta-2,5-diene), on the other hand, yielded the chelate complex [Mo(CO)(4){(EtN)(5)P-4(O)(n)(OPh)(2)}] (n = 0 or 1; 6 or 7) in which the peripheral phosphorus atoms are bonded to the metal. The structures of 3a and 5-7 have been confirmed by single-crystal X-ray diffraction studies. The two P3N3 rings in 3a and 5 adopt twist/twist and irregular/twist conformations respectively; the phenoxy substituents occupy the 'pseudo axial' positions. However, an ideal chair conformation is observed for the P3N3 rings in 6 and 7 with the phenoxy substituents taking up the 'pseudo equatorial' positions. The NMR spectroscopic data for the compounds are discussed.

Adenosine-5'-carboxylic Acid

The two molecules in the asymmetric unit of adenosine-5'-carboxylic acid, C10H11N5O5, exist as zwitterions with N1 protonated and the carboxyl groups ionized. Both molecules are in an anti conformation with glycosyl torsion angles of -161.4(3) and -155.5(3)degrees. The ribose moieties adopt a C3-endo-C2-exo twist conformation. The pseudo-rotation parameters are P = 0.01(1) and 6.58(1)degrees, and tau(m) = 36.2(2) and 34.6(2)degrees, for molecules A and B, respectively. The carboxyl groups of A and B are not in the standard g(+), g(-) or t conformations. Both Watson-Crick sites, N1 and N6, of the adenine bases are involved in a pair of hydrogen bonds with the dissociated carboxyl groups, forming a cyclic tetramer. The adenine base of molecule A stacks on the ribose O4' atom of a symmetry-related B molecule at a distance of 2.88 Angstrom; the adenine base of B stacks in an analogous way at a distance of 2.91 Angstrom.