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.

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.

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.

Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

Microstructure and texture evolution during symmetric and asymmetric rolling of a martensitic Ti-6Al-4V alloy

In the current study, the effect of deformation mode (i.e., symmetric vs asymmetric rolling) on the extent of grain refinement and texture development in Ti-6Al-4V was examined through warm rolling of a martensitic starting microstructure. During rolling, the initial martensitic lath structure was progressively fragmented, primarily through continuous dynamic recrystallization. This eventually led to an ultrafine-grained (UFG) microstructure composed of equiaxed grains with a mean size of 180 to 230 nm, mostly surrounded by high-angle grain boundaries. Depending on the rolling reduction and deformation mode (symmetric and asymmetric), the rolled specimens displayed different layer morphologies throughout the specimen thickness: a fully UFG surface layer, a partial UFG transition layer, and a partially fragmented lath interior layer. Due to a higher level of effective strain and continuous rotation of the principle axis, asymmetric rolling resulted in a greater extent of grain refinement compared with symmetric rolling at a given thermomechanical condition. A bulk UFG structure was successfully obtained using 70 pct asymmetric rolling. In addition, the rolling texture exhibited various characteristics throughout the thickness due to a different combination of shear and compressive strains. Principally, the basal texture component was displaced from the normal toward rolling direction during asymmetric rolling, differing from the symmetric rolling textures.

Simulation of oleuropein structural conformation in vacuum, water and triolein-water systems using molecular dynamics

Oleuropein, the main phenolic compound of olive leaves, exhibits a unique blend of biological activities and has been shown to locate itself at the oil-water (O/W) interface. This behavior could influence the physico-chemical properties of dispersed systems such as emulsions. In this work, we study the effect of the microenvironment (vacuum, water, and triolein-water) on the conformational preferences of oleuropein using molecular dynamics (MD) simulations at 300K for at least 30ns. The seven torsions that describe the flexible skeleton of oleuropein were monitored together with the distance between the glucose (Glu) and hydroxytyrosol (Hyd) moieties (dglu-hyd) of the molecule. The obtained trajectories demonstrated that oleuropein adopts different conformations that depend on the environment. The preferential conformers in each system were analyzed for their molecular geometry and internal energy. In vacuum, the oleuropein preferential conformation is tight with the glucose moiety in close proximity with the hydroxytyrosol moiety. In water, oleuropein preferential conformers presented large differences in their structural properties, varying from a close like U form, and a semi-opened form, to an opened form characterized by high fluctuations in dglu-hyd values. In a triolein-water system, oleuropein tends to adopt a more open form where the glucose moiety could be approximately aligned with the hydroxytyrosol and elenolic acid moieties. Based on a calculation at the HF/6-31G* level, these flexibilities of oleuropein required energy of 19.14kcal/mol in order to adopt the conformation between water and triolein-water system. A radial distribution function (RDF) analysis showed that specific hydroxyl groups of Hyd and Glu interact with water molecules, enabling us to understand the amphiphilic character of oleuropein at the triolein-water interface. MD calculations together with interfacial tension measurements revealed that the oleuropein binding at O/W interface is an enthalpy driven mechanism.

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.