Sequence-dependent structure/function relationships of catalytic peptide-enabled gold nanoparticles generated under ambient synthetic conditions

Peptide-enabled nanoparticle (NP) synthesis routes can create and/or assemble functional nanomaterials under environmentally friendly conditions, with properties dictated by complex interactions at the biotic/abiotic interface. Manipulation of this interface through sequence modification can provide the capability for material properties to be tailored to create enhanced materials for energy, catalysis, and sensing applications. Fully realizing the potential of these materials requires a comprehensive understanding of sequence-dependent structure/function relationships that is presently lacking. In this work, the atomic-scale structures of a series of peptide-capped Au NPs are determined using a combination of atomic pair distribution function analysis of high-energy X-ray diffraction data and advanced molecular dynamics (MD) simulations. The Au NPs produced with different peptide sequences exhibit varying degrees of catalytic activity for the exemplar reaction 4-nitrophenol reduction. The experimentally derived atomic-scale NP configurations reveal sequence-dependent differences in structural order at the NP surface. Replica exchange with solute-tempering MD simulations are then used to predict the morphology of the peptide overlayer on these Au NPs and identify factors determining the structure/catalytic properties relationship. We show that the amount of exposed Au surface, the underlying surface structural disorder, and the interaction strength of the peptide with the Au surface all influence catalytic performance. A simplified computational prediction of catalytic performance is developed that can potentially serve as a screening tool for future studies. Our approach provides a platform for broadening the analysis of catalytic peptide-enabled metallic NP systems, potentially allowing for the development of rational design rules for property enhancement.

Effect of data length and bin numbers on distribution entropy (DistEn) measurement in analyzing healthy aging

Complexity analysis of a given time series is executed using various measures of irregularity, the most commonly used being Approximate entropy (ApEn), Sample entropy (SampEn) and Fuzzy entropy (FuzzyEn). However, the dependence of these measures on the critical parameter of tolerance `r' leads to precarious results, owing to random selections of r. Attempts to eliminate the use of r in entropy calculations introduced a new measure of entropy namely distribution entropy (DistEn) based on the empirical probability distribution function (ePDF). DistEn completely avoids the use of a variance dependent parameter like r and replaces it by a parameter M, which corresponds to the number of bins used in the histogram to calculate it. When tested for synthetic data, M has been observed to produce a minimal effect on DistEn as compared to the effect of r on other entropy measures. Also, DistEn is said to be relatively stable with data length (N) variations, as far as synthetic data is concerned. However, these claims have not been analyzed for physiological data. Our study evaluates the effect of data length N and bin number M on the performance of DistEn using both synthetic and physiologic time series data. Synthetic logistic data of `Periodic' and `Chaotic' levels of complexity and 40 RR interval time series belonging to two groups of healthy aging population (young and elderly) have been used for the analysis. The stability and consistency of DistEn as a complexity measure as well as a classifier have been studied. Experiments prove that the parameters N and M are more influential in deciding the efficacy of DistEn performance in the case of physiologic data than synthetic data. Therefore, a generalized random selection of M for a given data length N may not always be an appropriate combination to yield good performance of DistEn for physiologic data.

Process capability analysis in non normal linear regression profiles

When the distribution of a process characterized by a profile is non normal, process capability analysis using normal assumption often leads to erroneous interpretations of the process performance. Profile monitoring is a relatively new set of techniques in quality control that is used in situations where the state of product or process is represented by a function of two or more quality characteristics. Such profiles can be modeled using linear or nonlinear regression models. In some applications, it is assumed that the quality characteristics follow a normal distribution; however, in certain applications this assumption may fail to hold and may yield misleading results. In this article, we consider process capability analysis of non normal linear profiles. We investigate and compare five methods to estimate non normal process capability index (PCI) in profiles. In three of the methods, an estimation of the cumulative distribution function (cdf) of the process is required to analyze process capability in profiles. In order to estimate cdf of the process, we use a Burr XII distribution as well as empirical distributions. However, the resulted PCI with estimating cdf of the process is sometimes far from its true value. So, here we apply artificial neural network with supervised learning which allows the estimation of PCIs in profiles without the need to estimate cdf of the process. Box-Cox transformation technique is also developed to deal with non normal situations. Finally, a comparison study is performed through the simulation of Gamma, Weibull, Lognormal, Beta and student-t data.

The effects of various binders and moisture content on pellet stability of research diets for freshwater crayfish

Two experiments were conducted to assess the water stability of a practical research diet manufactured with various binders and differing levels of moisture. In the first experiment the binders – agar, gelatine, carrageenan, and carboxymethylcellulose (CMC) were included at both 3 and 5% of total ingredient weight. All binders were tested with equal ingredient weight to water volume, and additionally carrageenan was tested in a diet with double the water volume. The dry matter remaining following immersion for up to 180 min was calculated and the rate of pellet decay was modelled using the Weibull distribution. The analysis revealed that the rate of dry matter loss decreased with time, and that carrageenan and CMC binders were significantly better (P < 0.001) binders than the agar and gelatine. The 5% binder concentration slowed the decay rate by as much as 62% as compared with the 3% binder concentration. The second experiment compared the binding performance of carrageenan and sodium alginate in both 50% moisture and 10% moisture pellets. The same analysis revealed that 10% moisture alginate-bound pellets were more water stable than the others. A discussion of the use of moist diets for crayfish research is included.

Evaluation of selective growth in a Fe-2.8% Si single crystal using Rodrigues-Frank space

A Fe-2.8%Si single crystal was scratched in order to randomise the texture in the neighbourhood of the notch. Annealing resulted in recrystallization and grain growth starting from the deformed zone. Misorientations between the single crystal matrix and the grown grains were gathered and were studied in order to investigate the possibility for selective growth based on a specific misorientation. However, instead of studying the misorientation angle or axis profiles separately in a 1D or 2D projection a full misorientation analysis was carried out in the 3-dimensional Rodrigues-Frank misorientation space, which offers an unambiguous interpretation of the data because no features are hidden or masked by a projection. It is concluded that the selective growth phenomenon following the <110>26.5deg misorientation relationship is strongly supported by the gathered orientation data, after appropriately normalizing these data with respect to a random misorientation distribution.

Effects of quenching rate on amorphous structures of Cu46Zr54 metallic glass

Metallic glass shows some superior properties different from crystalline, but the nature of amorphous structure and structural change during glass transition have not been completely understood yet. Molecular dynamics simulation provides intuitive insight into the microstructure and properties at atomistic level. Before probing into the microstructures of metallic glass with molecular dynamics (MD) simulation, it is important to obtain amorphous state first. In the current work, we reproduce the process of manufacturing metallic glass in laboratory including the melting, equilibrating and quenching procedure with molecular dynamics simulations. The structure changing at melting point and glass transition temperature are investigated with the different cooling processing. The partial radial distribution function (PRDF) is applied as a criterion to judge the final amorphous state obtained considering the quenching at different cooling rates and the effects of cooling rate on the formation of amorphous structures are further discussed.

Tensile behaviour of non-uniform fibres and fibrous composites

This work investigates the tensile behaviour of non-uniform fibres and fibrous composites. Wool fibres are used as an example of non-uniform fibres because they're physical, morphological and geometrical properties vary greatly not only between fibres but also within a fibre. The focus of this work is on the effect of both between-fibre and within-fibre diameter variations on fibre tensile behaviour. In addition, fit to the Weibull distribution by the non-brittle and non-uniform visco-elastic wool fibres is examined, and the Weibull model is developed further for non-uniform fibres with diameter variation along the fibre length. A novel model fibre composite is introduced to facilitate the investigation into the tensile behaviour of fibre-reinforced composites. This work first confirms that for processed wool, its coefficient of variation in break force can be predicted from that of minimum fibre diameters, and the prediction is better for longer fibres. This implies that even for processed wool, fibre breakage is closely associated with the occurrence of thin sections along a fibre, and damage to fibres during processing is not the main cause of fibre breakage. The effect of along-fibre diameter variation on fibre tensile behaviour of scoured wool and mohair is examined next. Only wet wool samples were examined in the past. The extensions of individual segments of single non-uniform fibres are measured at different strain levels. An important finding is the maximum extension (%) (Normally at the thinnest section) equals the average fibre extension (%) plus the diameter variation (CV %) among the fibre segments. This relationship has not been reported before. During a tensile test, it is only the average fibre extension that is measured. The third part of this work is on the applicability of Weibull distribution to the strength of non-uniform visco-elastic wool fibres. Little work has been done for wool fibres in this area, even though the Weibull model has been widely applied to many brittle fibres. An improved Weibull model incorporating within-fibre diameter variations has been developed for non-uniform fibres. This model predicts the gauge length effect more accurately than the conventional Weibull model. In studies of fibre-reinforced composites, ideal composite specimens are usually prepared and used in the experiments. Sample preparation has been a tedious process. A novel fibre reinforced composite is developed and used in this work to investigate the tensile behaviour of fibre-reinforced composites. The results obtained from the novel composite specimen are consistent with that obtained from the normal specimens.

Effects of quenching rate on amorphous structures of Cu46Zr54 metallic glass

Metallic glass shows some superior properties different from crystalline, but the nature of amorphous structure and structural change during glass transition have not been completely understood yet. Molecular dynamics simulation provides intuitive insight into the microstructure and properties at atomistic level. Before probing into the microstructures of metallic glass with molecular dynamics (MD) simulation, it is important to obtain amorphous state first. In the current work, we reproduce the process of manufacturing metallic glass in laboratory including the melting, equilibrating and quenching procedure with molecular dynamics simulations. The structure changing at melting point and glass transition temperature are investigated with the different cooling processing. The partial radial distribution function (PRDF) is applied as a criterion to judge the final amorphous state obtained considering the quenching at different cooling rates and the effects of cooling rate on the formation of amorphous structures are further discussed.

Robust ODF smoothing for accurate estimation of fiber orientation

Q-ball imaging was presented as a model free, linear and multimodal diffusion sensitive approach to reconstruct diffusion orientation distribution function (ODF) using diffusion weighted MRI data. The ODFs are widely used to estimate the fiber orientations. However, the smoothness constraint was proposed to achieve a balance between the angular resolution and noise stability for ODF constructs. Different regularization methods were proposed for this purpose. However, these methods are not robust and quite sensitive to the global regularization parameter. Although, numerical methods such as L-curve test are used to define a globally appropriate regularization parameter, it cannot serve as a universal value suitable for all regions of interest. This may result in over smoothing and potentially end up in neglecting an existing fiber population. In this paper, we propose to include an interpolation step prior to the spherical harmonic decomposition. This interpolation based approach is based on Delaunay triangulation provides a reliable, robust and accurate smoothing approach. This method is easy to implement and does not require other numerical methods to define the required parameters. Also, the fiber orientations estimated using this approach are more accurate compared to other common approaches.

Maximum likelihood estimation for rician distributed data in analytical q-ball imaging

Analytical q-ball imaging is widely used for reconstruction of orientation distribution function (ODF) using diffusion weighted MRI data. Estimating the spherical harmonic coefficients is a critical step in this method. Least squares (LS) is widely used for this purpose assuming the noise to be additive Gaussian. However, Rician noise is considered as a more appropriate model to describe noise in MR signal. Therefore, the current estimation techniques are valid only for high SNRs with Gaussian distribution approximating the Rician distribution. The aim of this study is to present an estimation approach considering the actual distribution of the data to provide reliable results particularly for the case of low SNR values. Maximum likelihood (ML) is investigated as a more effective estimation method. However, no closed form estimator is presented as the estimator becomes nonlinear for the noise assumption of the Rician distribution. Consequently, the results of LS estimator is used as an initial guess and the more refined answer is achieved using iterative numerical methods. According to the results, the ODFs reconstructed from low SNR data are in close agreement with ODFs reconstructed from high SNRs when Rician distribution is considered. Also, the error between the estimated and actual fiber orientations was compared using ML and LS estimator. In low SNRs, ML estimator achieves less error compared to the LS estimator.

Evolution of cooperation in reputation system by group-based scheme

Reputation systems are very useful in large online communities in which users may frequently have the opportunity to interact with users with whom they have no prior experience. Recently, how to enhance the cooperative behaviors in the reputation system has become to one of the key open issues. Emerging schemes focused on developing efficient reward and punishment mechanisms or capturing the social or economic properties of participants. However, whether this kind of method can work widely or not has been hard to prove until now. Research in evolutionary game theory shows that group selection (or multilevel selection) can favor the cooperative behavior in the finite population. Furthermore, some recent works give fundamental conditions for the evolution of cooperation by group selection. In the paper, we extend the original group selection theory and propose a group-based scheme to enhance cooperation for online reputation systems. Related concepts are defined to capture the social structure and ties among participants in reputation system, e.g., group, assortativity, etc. Also, we use a Fermi distribution function to reflect the bounded rationality of participants and the existence of stochastic factors in evolutionary process. Extended simulations show that our scheme can enhance cooperation and improve the average performance of participants (e.g. payoff) in reputation system.

Maximum-likelihood based estimation for Q-ball imaging using diffusion-weighted MRI data

Q-ball imaging has been presented to reconstruct diffusion orientation distribution function using diffusion weighted MRI. In this thesiis, we present a novel and robust approach to satisfy the smoothness constraint required in Q-ball imaging. Moreover, we developed an improved estimator based on the actual distribution of the MR data.

Nature of crystalline particle assembly in ring shaped colloidal stains from concentrated dispersions

The drying of colloidal droplet suspensions is important in many realms of practical application and has sustained the interest of researchers over two decades. The arrangements of polystyrene and silica beads, both of diameter 1 μm, 10% by volume of solid deposited on normal glass (hydrophilic), and silicone (hydrophobic) surfaces evaporated from a suspension volume of 3 μL, were investigated. Doughnut shape depositions were found, imputing the influence of strong central circulation flows that resulted in three general regions. In the central region which had strong particle build-up, the top most layers of particle arrangement was confirmed to be disordered using power spectrum and radial distribution function analysis. On closer examination, this appeared more like frustrated attempts to crystallize into larger grains rather than beads arranging in a disordered fashion throughout the piling process. With an adapted micro-bulldozing operation to progressively remove layers of particles from the heap, we found that the later efforts to crystallize through lateral capillary inter-particle forces were liable to be undone once the particles contacted the disorganized particles underneath, which were formed out of the jamming of fast particles arriving at the surface. © 2014 Elsevier B.V.