Secondary water pore formation for proton transport in a ClC exchanger revealed by an atomistic molecular dynamics simulation

Several prokaryotic ClC proteins have been demonstrated to function as exchangers that transport both chloride ions and protons simultaneously in opposite directions. However, the path of the proton through the ClC exchanger and how the protein brings about the coupled movement of both ions are still unknown. In the present work, we demonstrate that a previously unknown secondary water pore is formed inside a ClC exchanger by using an atomistic molecular dynamics (MD) simulation. From the systematic simulations, it was determined that the glutamate residue exposed to the intracellular solution, E203, plays an important role as a trigger for the formation of the secondary water pore. Based on our simulation results, we conclude that protons in the ClC exchanger are conducted via a water network through the secondary water pore and we propose a new mechanism for the coupled transport of chloride ions and protons

The influence of communication, empowerment and trust on organizational ethical climates

In this study, communication, empowerment and trust were examined to determine their influence on an organization’s ethical climate. A total of 150 questionnaires completed by managers and executives based in the Klang Valley, Malaysia were analysed. The results demonstrated that empowerment was positively related to a benevolent-local climate while trust was positively related to both benevolent-local and principled-local climates. However, communication did not have a significant influence on all three ethical climate types. We discuss our results and the implications for both future academic research and practice

Tail-scope: Using friends to estimate heavy tails of degree distributions in large-scale complex networks

Many complex networks in natural and social phenomena have often been characterized by heavy-tailed degree distributions. However, due to rapidly growing size of network data and concerns on privacy issues about using these data, it becomes more difficult to analyze complete data sets. Thus, it is crucial to devise effective and efficient estimation methods for heavy tails of degree distributions in large-scale networks only using local information of a small fraction of sampled nodes. Here we propose a tail-scope method based on local observational bias of the friendship paradox. We show that the tail-scope method outperforms the uniform node sampling for estimating heavy tails of degree distributions, while the opposite tendency is observed in the range of small degrees. In order to take advantages of both sampling methods, we devise the hybrid method that successfully recovers the whole range of degree distributions. Our tail-scope method shows how structural heterogeneities of large-scale complex networks can be used to effectively reveal the network structure only with limited local information.Comment: 9 pages, 6 figure