Understanding protein dynamical transition and protein-water interaction from dielectric relaxation calculations

Dielectric properties of an aqueous lysozyme solution were calculated from 2 ns long MD simulations in the temperature range of 150-300 K and an 4 ns long simulation at 300 K. Static and frequency dependent dielectric constants of the system were calculated from auto- and cross-correlations of its three components (protein, water, ions). Cole-Cole plots for protein, water and the total solution were obtained. Emergence of an intense protein-water interaction above the dynamical transition between 190 K and 210 K was evidenced by the presence of protein effects in the water components of the Cole-Cole plots and frequency dependent dielectric constants at and above 210 K. Backbone and side chain torsion angle trajectories for surface loop residues within this range of temperatures were calculated. Also, water molecules around side chains were labeled and monitored individually, and radial distribution functions of water around the side chains and in the bulk water were obtained. These data were used to support a model that accounts for the interaction between surface water and protein components, resulting in high mobility of the side chains at the transition temperature range. The water molecules in the vicinity of the protein surface are then propelled into the bulk for a much different electrostatic effect than is immediately expected of the known properties of water alone. The functional protein. therefore, exists as an integral part of a larger protein-water system that cannot be decoupled. The water molecules may even be thought of as information carriers that make other nearby biological molecules aware of the presence of the protein

A high-reproducibility and high-accuracy method for automated topic classification

Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent search, statistical characterization, and meaningful classification. Latent Dirichlet allocation (LDA) is the state-of-the-art in topic classification. Here, we perform a systematic theoretical and numerical analysis that demonstrates that current optimization techniques for LDA often yield results which are not accurate in inferring the most suitable model parameters. Adapting approaches for community detection in networks, we propose a new algorithm which displays high-reproducibility and high-accuracy, and also has high computational efficiency. We apply it to a large set of documents in the English Wikipedia and reveal its hierarchical structure. Our algorithm promises to make "big data" text analysis systems more reliable.Comment: 23 pages, 24 figure

Duality between time series and networks.

Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways

Microwave assisted processing of ceramics

Microwave heating can provide cost and time effective processing routes for ceramic systems. Microwave heating assisted drying of green compacts, low temperature sintering of certain oxides, and partial or complete nitridization, carburization or reduction of hemi-oxides have been attempted recently. Using a multi-mode microwave cavity, we were able to reduce MeOx (where Me = Cu and Ag) to their sub-oxides or base metal at close to room temperature within minutes. The product sub-oxides or metals were identified and characterized for their crystallinity and phase purity. The results suggest that the microwave heating can provide an alternative, cost effective route for alloying for certain metals. The criteria for effectiveness of microwave heating and the coupling between the materials and the electromagnetic wave are discussed

Statistical properties of the time series presented in <b>Figure 9</b>, generated from the <i>Arabidopsis thaliana</i> network and the USA Internet 1997.

<p>Note that the long-range correlations present in the metabolic network are well captured by the autocorrelation function and the corresponding power density spectrum, which displays a clear power-law scaling. On the other hand, the results in the USA Internet 1997 bear the footprint of the short-correlated signal generated by the Internet network. Note a power-law scaling with a less steep slope.</p

Different realizations of the inverse map in the real newtorks.

<p>We perform four realizations of to the <i>Arabidopsis thaliana</i> metabolic network ( nodes and 100,000 points), and USA Internet 1997 (1,589 nodes and 100,000 points). Note the clear similarity of these time series with the time series presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023378#pone-0023378-g009" target="_blank">Figure 9</a>, demonstrating the robustness of the proposed inverse map.</p

Illustration of the proposed forward map to the problem of detecting differences in the data structures of patients in different health conditions.

<p>We use 100-minute normalized heart rate time series from a healthy subject (upper panel) and a subject with severe congestive heart failure (lower panel) sampled every seconds ( = 10,000 points) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023378#pone.0023378-Physionet1" target="_blank">[36]</a>. We construct the networks using quantiles by applying from the corresponding time series. The resulting networks display clear differences in topology, which are especially apparent on the relatively separated cluster in the network associated with the unhealthy subject. These differences in topology are confirmed by generating networks with different number of nodes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023378#pone-0023378-g007" target="_blank">Fig. 7</a>) and using time series from different healthy and unhealthy subjects (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023378#pone-0023378-g008" target="_blank">Fig. 8</a>).</p

Comparison of statistical properties of first generation and second generation time series.

<p>We compare the means of these properties over 10 different realizations of first and second generation time series. Error bars denote standard deviation across realizations. For both the first and second generation time series, the autocorrelation function and the power spectrum reveal a distinct signal when the time series are periodic (), which disappears when the time series become random (). As expected from the toy model that has no biases toward particular values, both the first and second generation time series have values that are uniformly distributed between and for all values of .</p