Characterizing the switching transitions of an adsorbed peptide by mapping the potential energy surface

Peptide adsorption occurs across technology, medicine, and nature. The functions of adsorbed peptides are related to their conformation. In the past, molecular simulation methods such as molecular dynamics have been used to determine key conformations of adsorbed peptides. However, the transitions between these conformations often occur too slowly to be modeled reliably by such methods. This means such transitions are less well understood. In the study reported here, discrete path sampling is used for the first time to study the potential energy surface of an adsorbed peptide (polyalanine) and the transition pathways between various stable adsorbed conformations that have been identified in prior work by two of the authors [Mijajlovic, M.; Biggs, M. J. J. Phys. Chem. C 2007, 111, 15839−15847]. Mechanisms for the switching of adsorbed polyalanine between the stable conformations are elucidated along with the energetics of these switches

Explicit numerical simulation-based study of the hydrodynamics of micro-packed beds

Knowledge of the hydrodynamic character of micro-packed beds (μPBs) is critical to understanding pumping power requirements and their performance in various applications, including those where heat and mass transfer are involved. The report here details use of smoothed particle hydrodynamics (SPH) based simulation of fluid flow on models of μPBs derived from X-ray microtomography to predict the hydrodynamic character of the beds as a function of the bed-to-particle diameter ratio over the range 5.2≤≤15.1⁄. It is shown that the permeability of the μPBs decreases in a non-linear but monotonic manner with this ratio to a plateau beyond ⁄≈10 that corresponded to the value predicted by the Ergun equation. This permeability variation was best represented by the model of Reichelt (Chem. Ing. Technik, 44, 1068, 1972) and also reasonably well-represented by that of Foumeny (Intnl. J. Heat Mass Transfer, 36, 536, 1993), both of which were developed using macroscale packed beds of varying bed-to-particle diameter ratios. Four other similarly determined correlations did not match well the permeability variation predicted by SPH. The flow field within the μPBs varied in an oscillatory manner with radial position (i.e. channelling occurred at multiple radial positions) due to a similar variation in the porosity. This suggests that use of performance models (e.g. for heat and mass transfer) derived for macroscale beds may not be suitable for μPBs. The SPH-based approach here may well form a suitable basis for predicting such behaviour, however

Palladium-Catalyzed Negishi Coupling of α‑CF₃ Oxiranyl Zincate: Access to Chiral CF₃‑Substituted Benzylic Tertiary Alcohols

We report a Pd-catalyzed stereospecific α-arylation of optically pure 2,3-epoxy-1,1,1-trifluoropropane (TFPO). This method allows for the direct and reliable preparation of optically pure 2-CF₃-2-(hetero)­aryloxiranes, which are precursors to many CF₃-substituted tertiary alcohols. The use of continuous-flow methods has allowed the deprotonation of TFPO and subsequent zincation at higher temperature compared to that under traditional batch conditions

Development of stock correlation networks using mutual information and financial big data

<div><p>Stock correlation networks use stock price data to explore the relationship between different stocks listed in the stock market. Currently this relationship is dominantly measured by the Pearson correlation coefficient. However, financial data suggest that nonlinear relationships may exist in the stock prices of different shares. To address this issue, this work uses mutual information to characterize the nonlinear relationship between stocks. Using 280 stocks traded at the Shanghai Stocks Exchange in China during the period of 2014-2016, we first compare the effectiveness of the correlation coefficient and mutual information for measuring stock relationships. Based on these two measures, we then develop two stock networks using the Minimum Spanning Tree method and study the topological properties of these networks, including degree, path length and the power-law distribution. The relationship network based on mutual information has a better distribution of the degree and larger value of the power-law distribution than those using the correlation coefficient. Numerical results show that mutual information is a more effective approach than the correlation coefficient to measure the stock relationship in a stock market that may undergo large fluctuations of stock prices.</p></div

Stocks network based on normalized mutual information using stock price data from 04/01/2014 to 30/12/2016.

<p>Stocks network based on normalized mutual information using stock price data from 04/01/2014 to 30/12/2016.</p

Comparison of STD and Fano factor values for the prices of stock pairs using the correlation coefficient and mutual information.

<p>Comparison of STD and Fano factor values for the prices of stock pairs using the correlation coefficient and mutual information.</p

Comparison study of the correlation coefficient and normalized mutual information.

<p>(A) Frequency distribution of the correlation coefficient values. (B) Frequency distribution of normalized mutual information values. (C) Percentage of stock pairs that are the top pairs when different numbers of top stock pairs in both the correlation coefficient and mutual information measures when different numbers of top stock pairs are considered. (D) Normalized mutual information values against the corresponding values of the correlation coefficient.</p

Degree distributions based on normalized mutual information and the correlation coefficient.

<p>(A,C,E) Degree distributions based on normalized mutual information with the whole dataset, data of stage 1 and data of stage 2, respectively. (B,D,F) Degree distributions based on the correlation coefficient with the whole dataset, data of stage 1 and data of stage 2, respectively.</p

Daily closing prices of six stocks pairs.

<p>(A,B) Stock pairs having large values of both the correlation coefficient and mutual information; (C,D) Stock pairs having large value of mutual information but relatively small value of correlation coefficients; (E,F) Stock pairs having large values of correlation coefficients but relatively small values of mutual information.</p

Network topology properties of six networks including networks in Figs 4 and 5 and four networks in S2–S5 Figs.

<p>Network topology properties of six networks including networks in Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195941#pone.0195941.g004" target="_blank">4</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195941#pone.0195941.g005" target="_blank">5</a> and four networks in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195941#pone.0195941.s002" target="_blank">S2</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195941#pone.0195941.s005" target="_blank">S5</a> Figs.</p