Thermal rectification effects of multiple semiconductor quantum dot junctions

Based on the multiple energy level Anderson model, this study theoretically examines the thermoelectric effects of semiconductor quantum dots (QDs) in the nonlinear response regime. The charge and heat currents in the sequential tunneling process are calculated by using the Keldysh Green's function technique. Results show that the thermal rectification effect can be observed in a multiple QD junction system, whereas the tunneling rate, size fluctuation, and location distribution of QD significantly influence the rectification efficiency.Comment: 5 pages, 8figure

Predicting Biological Functions of Compounds Based on Chemical-Chemical Interactions

Given a compound, how can we effectively predict its biological function? It is a fundamentally important problem because the information thus obtained may benefit the understanding of many basic biological processes and provide useful clues for drug design. In this study, based on the information of chemical-chemical interactions, a novel method was developed that can be used to identify which of the following eleven metabolic pathway classes a query compound may be involved with: (1) Carbohydrate Metabolism, (2) Energy Metabolism, (3) Lipid Metabolism, (4) Nucleotide Metabolism, (5) Amino Acid Metabolism, (6) Metabolism of Other Amino Acids, (7) Glycan Biosynthesis and Metabolism, (8) Metabolism of Cofactors and Vitamins, (9) Metabolism of Terpenoids and Polyketides, (10) Biosynthesis of Other Secondary Metabolites, (11) Xenobiotics Biodegradation and Metabolism. It was observed that the overall success rate obtained by the method via the 5-fold cross-validation test on a benchmark dataset consisting of 3,137 compounds was 77.97%, which is much higher than 10.45%, the corresponding success rate obtained by the random guesses. Besides, to deal with the situation that some compounds may be involved with more than one metabolic pathway class, the method presented here is featured by the capacity able to provide a series of potential metabolic pathway classes ranked according to the descending order of their likelihood for each of the query compounds concerned. Furthermore, our method was also applied to predict 5,549 compounds whose metabolic pathway classes are unknown. Interestingly, the results thus obtained are quite consistent with the deductions from the reports by other investigators. It is anticipated that, with the continuous increase of the chemical-chemical interaction data, the current method will be further enhanced in its power and accuracy, so as to become a useful complementary vehicle in annotating uncharacterized compounds for their biological functions

Prediction of Body Fluids where Proteins are Secreted into Based on Protein Interaction Network

Determining the body fluids where secreted proteins can be secreted into is important for protein function annotation and disease biomarker discovery. In this study, we developed a network-based method to predict which kind of body fluids human proteins can be secreted into. For a newly constructed benchmark dataset that consists of 529 human-secreted proteins, the prediction accuracy for the most possible body fluid location predicted by our method via the jackknife test was 79.02%, significantly higher than the success rate by a random guess (29.36%). The likelihood that the predicted body fluids of the first four orders contain all the true body fluids where the proteins can be secreted into is 62.94%. Our method was further demonstrated with two independent datasets: one contains 57 proteins that can be secreted into blood; while the other contains 61 proteins that can be secreted into plasma/serum and were possible biomarkers associated with various cancers. For the 57 proteins in first dataset, 55 were correctly predicted as blood-secrete proteins. For the 61 proteins in the second dataset, 58 were predicted to be most possible in plasma/serum. These encouraging results indicate that the network-based prediction method is quite promising. It is anticipated that the method will benefit the relevant areas for both basic research and drug development

The Electromechanical Behavior of a Micro-Ring Driven by Traveling Electrostatic Force

There is no literature mentioning the electromechanical behavior of micro structures driven by traveling electrostatic forces. This article is thus the first to present the dynamics and stabilities of a micro-ring subjected to a traveling electrostatic force. The traveling electrostatic force may be induced by sequentially actuated electrodes which are arranged around the flexible micro-ring. The analysis is based on a linearized distributed model considering the electromechanical coupling effects between electrostatic force and structure. The micro-ring will resonate when the traveling speeds of the electrostatic force approach some critical speeds. The critical speeds are equal to the ratio of the natural frequencies to the wave number of the correlative natural mode of the ring. Apart from resonance, the ring may be unstable at some unstable traveling speeds. The unstable regions appear not only near the critical speeds, but also near some fractions of some critical speeds differences. Furthermore the unstable regions expand with increasing driving voltage. This article may lead to a new research branch on electrostatic-driven micro devices

A Web-Based Recommendation System for Mobile Phone Selection

Mobile phones have become indispensable in our everyday life. The fierce market competition characterized by rapid expansion of advanced functionality and feature is making consumers’ mobile phone selections increasingly complex and challenging. In this study, we use Analytic Hierarchy Process (AHP), a multiple criteria decision method, to build a recommendation system for mobile phones selection. AHP provides a structural and easily comprehensible model for making product choices. We empirically evaluate our recommendation system by conducting a controlled experiment that involved 244 mobile phone users. Our analysis results indicate that the use of the proposed system results in higher satisfaction than that associated with the rank-based and equal-weight based benchmark systems

Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.

Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb-compound-target relationships. In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations. HTINet focuses on capturing the low-dimensional feature vectors for both herbs and proteins by network embedding, which incorporate the topological properties of nodes across multi-layered heterogeneous network, and then performs supervised learning based on these low-dimensional feature representations. HTINet obtains performance improvement over a well-established random walk based herb-target prediction method. Furthermore, we have manually validated several predicted herb-target interactions from independent literatures. These results indicate that HTINet can be used to integrate heterogeneous information to predict novel herb-target interactions

Coupled valence and spin state transition in (Pr0.7Sm0.3)0.7Ca0.3CoO3

The coupled valence and spin state transition (VSST) taking place in (Pr0.7Sm0.3)0.7Ca0.3CoO3 was investigated by soft x-ray absorption spectroscopy (XAS) experiments carried out at the Pr-M4,5, Co-L2,3, and O-1s edges. This VSST is found to be composed of a sharp Pr/Co valence and Co spin state transition centered at T*=89.3 K, followed by a smoother Co spin-state evolution at higher temperatures. At T < T*, we found that the praseodymium displays a mixed valence Pr3+/Pr4+ with about 0.13 Pr4+/f.u., while all the Co3+ is in the low-spin (LS) state. At T around T*, the sharp valence transition converts all the Pr4+ to Pr3+ with a corresponding Co3+ to Co4+ compensation. This is accompanied by an equally sharp spin state transition of the Co3+ from the low to an incoherent mixture of low and high spin (HS) states. An involvement of the intermediate spin (IS) state can be discarded for the Co3+. While above T* and at high temperatures the system shares rather similar properties as Sr-doped LaCoO3, at low temperatures it behaves much more like EuCoO3 with its highly stable LS configuration for the Co3+. Apparently, the mechanism responsible for the formation of Pr4+ at low temperatures also helps to stabilize the Co3+ in the LS configuration despite the presence of Co4+ ions. We also found out that that the Co4+ is in an IS state over the entire temperature range investigated in this study (10-290 K). The presence of Co3+ HS and Co4+ IS at elevated temperatures facilitates the conductivity of the material.Comment: 19 pages, 7 figures, Accepted in PR