Anisotropic, Intermediate Coupling Superconductivity in Cu0.03TaS2

The anisotropic superconducting state properties in Cu0.03TaS2 have been investigated by magnetization, magnetoresistance, and specific heat measurements. It clearly shows that Cu0.03TaS2 undergoes a superconducting transition at TC = 4.03 K. The obtained superconducting parameters demonstrate that Cu0.03TaS2 is an anisotropic type-II superconductor. Combining specific heat jump = 1.6(4), gap ratio 2/kBTC = 4.0(9) and the estimated electron-phonon coupling constant ~ 0.68, the superconductivity in Cu0.03TaS2 is explained within the intermediate coupling BCS scenario. First-principles electronic structure calculations suggest that copper intercalation of 2H-TaS2 causes a considerable increase of the Fermi surface volume and the carrier density, which suppresses the CDW fluctuation and favors the raise of TC.Comment: 16pages, 5figure

A Network-Based Approach to Investigate the Pattern of Syndrome in Depression

In Traditional Chinese Medicine theory, syndrome is essential to diagnose diseases and treat patients, and symptom is the foundation of syndrome differentiation. Thus the combination and interaction between symptoms represent the pattern of syndrome at phenotypic level, which can be modeled and analyzed using complex network. At first, we collected inquiry information of 364 depression patients from 2007 to 2009. Next, we learned classification models for 7 syndromes in depression using naïve Bayes, Bayes network, support vector machine (SVM), and C4.5. Among them, SVM achieves the highest accuracies larger than 0.9 except for Yin deficiency. Besides, Bayes network outperforms naïve Bayes for all 7 syndromes. Then key symptoms for each syndrome were selected using Fisher's score. Based on these key symptoms, symptom networks for 7 syndromes as well as a global network for depression were constructed through weighted mutual information. Finally, we employed permutation test to discover dynamic symptom interactions, in order to investigate the difference between syndromes from the perspective of symptom network. As a result, significant dynamic interactions were quite different for 7 syndromes. Therefore, symptom networks could facilitate our understanding of the pattern of syndrome and further the improvement of syndrome differentiation in depression

Co-pyrolysis of pine sawdust and lignite in a thermogravimetric analyzer and a fixed-bed reactor

The effects of pyrolysis temperature and blending ratio on the yield and composition of pyrolysis products (gas, tar, and char) were investigated. TGA experiments showed that pine sawdust decomposition took place at lower temperatures compared to lignite. With increasing the pine sawdust content in the blend, the DTG peaks shifted towards lower temperatures due to synergetic effect. In fixed-bed experiments, the synergetic effect increased the yield of volatile matter compared to the calculated values. The major gases released at low temperatures were CO₂ and CO. However, hydrogen was the primary gaseous product at higher temperatures. During co-pyrolysis, concentrations of benzene, naphthalene, and hydrocarbons in the tar decreased, accompanied by an increase in phenols and guaiacol concentrations. With increasing pyrolysis temperature, the OH, aliphatic CH, C=O, and CAO functional groups in char decomposed substantially

First-Principles Study of Atomic Diffusion by Vacancy Defect of the L1₂-Al₃M (M = Sc, Zr, Er, Y) Phase

Atomic diffusion by the vacancy defect of L12-Al3M (M = Sc, Zr, Er, Y) was investigated based on a first-principles calculation. The point defect formation energies were firstly evaluated. Then, the migration energy for different diffusion paths was obtained by the climbing-image nudged elastic band (CI-NEB) method. The results showed that Al atomic and M atomic diffusions through nearest-neighbor jump (NNJ) mediated by Al vacancy (VAl) were, respectively, the preferred diffusion paths in Al3M phases under both Al-rich and M-rich conditions. The other mechanisms, such as six-jump cycle (6JC) and next-nearest-neighbor jump (NNNJ), were energetically inhibited. The order of activation barriers for NNJ(Al-VAl) was Al3Zr 3Y 3Er 3Sc. The Al3Sc phase had high stability with a high self-diffusion activation barrier, while the Al3Zr and Al3Y phases were relatively unstable with a low self-diffusion activation energy. Moreover, the atomic-diffusion behavior between the core and shell layers of L12-Al3M was also further investigated. Zr atoms were prone to diffusion into the Al3Y core layer, resulting in no stable core-shelled Al3(Y,Zr), which well agreed with experimental observation

A Module Analysis Approach to Investigate Molecular Mechanism of TCM Formula: A Trial on Shu-feng-jie-du Formula

At the molecular level, it is acknowledged that a TCM formula is often a complex system, which challenges researchers to fully understand its underlying pharmacological action. However, module detection technique developed from complex network provides new insight into systematic investigation of the mode of action of a TCM formula from the molecule perspective. We here proposed a computational approach integrating the module detection technique into a 2-class heterogeneous network (2-HN) which models the complex pharmacological system of a TCM formula. This approach takes three steps: construction of a 2-HN, identification of primary pharmacological units, and pathway analysis. We employed this approach to study Shu-feng-jie-du (SHU) formula, which aimed at discovering its molecular mechanism in defending against influenza infection. Actually, four primary pharmacological units were identified from the 2-HN for SHU formula and further analysis revealed numbers of biological pathways modulated by the four pharmacological units. 24 out of 40 enriched pathways that were ranked in top 10 corresponding to each of the four pharmacological units were found to be involved in the process of influenza infection. Therefore, this approach is capable of uncovering the mode of action underlying a TCM formula via module analysis

A Modularity-Based Method Reveals Mixed Modules from Chemical-Gene Heterogeneous Network

<div><p>For a multicomponent therapy, molecular network is essential to uncover its specific mode of action from a holistic perspective. The molecular system of a Traditional Chinese Medicine (TCM) formula can be represented by a 2-class heterogeneous network (2-HN), which typically includes chemical similarities, chemical-target interactions and gene interactions. An important premise of uncovering the molecular mechanism is to identify mixed modules from complex chemical-gene heterogeneous network of a TCM formula. We thus proposed a novel method (MixMod) based on mixed modularity to detect accurate mixed modules from 2-HNs. At first, we compared MixMod with Clauset-Newman-Moore algorithm (CNM), Markov Cluster algorithm (MCL), Infomap and Louvain on benchmark 2-HNs with known module structure. Results showed that MixMod was superior to other methods when 2-HNs had promiscuous module structure. Then these methods were tested on a real drug-target network, in which 88 disease clusters were regarded as real modules. MixMod could identify the most accurate mixed modules from the drug-target 2-HN (normalized mutual information 0.62 and classification accuracy 0.4524). In the end, MixMod was applied to the 2-HN of Buchang naoxintong capsule (BNC) and detected 49 mixed modules. By using enrichment analysis, we investigated five mixed modules that contained primary constituents of BNC intestinal absorption liquid. As a matter of fact, the findings of <i>in vitro</i> experiments using BNC intestinal absorption liquid were found to highly accord with previous analysis. Therefore, MixMod is an effective method to detect accurate mixed modules from chemical-gene heterogeneous networks and further uncover the molecular mechanism of multicomponent therapies, especially TCM formulae.</p></div

An illustration of a chemical-gene heterogeneous network.

<p>The blue nodes are chemical constituents and the red nodes represent potential gene targets. This network is an instance of 2-class heterogeneous network [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125585#pone.0125585.ref009" target="_blank">9</a>], which is more than a simple chemical-gene bipartite graph by including additional interactions between chemicals and between genes. Obviously, there are three mixed modules (1, 2, and 3) in this heterogeneous network. Each mixed module is a highly-interconnected unit in which chemicals directly or indirectly regulate the expression of corresponding genes. Additionally, module A and B are also considered as special cases of mixed module. Such modules may influence the final partition of module detection methods, but make little contribution to uncovering particular molecular mechanism.</p

Tests of five methods on benchmark 2-HNs with varying <i>μ</i>_A and <i>μ</i>_B.

<p>(<b>a</b>). Normalized Mutual Informations (NMIs) of five methods on benchmarks with <i>p</i> = 0.5 and <i>μ</i>_B = 0.2. (<b>b</b>). NMIs when <i>p</i> = 0.5 and <i>μ</i>_B = 0.8. (<b>c</b>). NMIs when <i>μ</i>_A = 0.2 and <i>p</i> = 0.5. (<b>d</b>). NMIs when <i>μ</i>_A = 0.8 and <i>p</i> = 0.5. (<b>e</b>)(<b>f</b>)(<b>g</b>)(<b>h</b>). CAs of five methods on 2-HNs with different parameters. In these figures, the variation curve of each method is marked by a unique color as shown in (<b>f</b>).</p

Tests of four methods on weighted benchmarks.

<p>(<b>a</b>). Normalized Mutual Informations (NMIs) of four methods on 2-HNs with different <i>μ</i>_A, <i>μ</i>_B and <i>p</i>. The subnetwork <i>G</i>_A of each 2-HN is weighted according to the weighting scheme of LFR benchmark. (<b>b</b>). NMIs of four methods on 2-HNs with weighted subnetwork <i>G</i>_Π. (<b>c</b>). NMIs of four methods on 2-HNs with weighted <i>G</i>_B.</p