We have made the first observation of superconductivity in TlNi2Se2 at T(C)=3.7 K, and it appears to involve heavy electrons with an effective mass m*=(14-20)m(b), as inferred from the normal-state electronic specific heat and the upper critical field, H(C2)(T). We found that the zero-field electronic specific-heat data, C(es)(T) (0.5 K≤T<3.7 K) in the superconducting state can be fitted with a two-gap BCS model, indicating that TlNi2Se2 seems to be a multiband superconductor, which is consistent with the band calculation for the isostructural KNi2S2. It is also found that the electronic specific-heat coefficient in the mixed state γN(H) exhibits a H(1/2) behavior, which is considered as a common feature of the d-wave superconductors. TlNi2Se2, as a d-electron system with heavy electron superconductivity, may be a bridge between cuprate- or iron-based and conventional heavy-fermion superconductors.
High-quality superconducting K x Fe y Se 2 single crystals were synthesized using an easy one-step method.Detailed annealing studies were performed to make clear the phase formation process in K x Fe y Se 2 .Compatible observations were found in temperature-dependent X-ray diffraction patterns, back-scattered electron images and corresponding electromagnetic properties, which proved that good superconductivity performance was close related to the microstructure of superconducting component.Analysis based on the scaling behavior of flux pinning force indicated that the dominant pinning mechanism was ∆T c pinning and independent of connectivity. The annealing dynamics studies were also performed, which manifested that the humps in temperature-dependent resistance (RT) curves were induced by competition between the metallic/superconducting and the semiconducting/insulating phases.
Predicted interactions are a valuable complement to experimentally reported interactions in molecular mechanism studies, particularly for higher organisms, for which reported experimental interactions represent only a small fraction of their total interactomes. With careful engineering consideration of the lessons from previous efforts, the Predicted Arabidopsis Interactome Resource (PAIR; http://www.cls.zju.edu.cn/pair/) presents 149,900 potential molecular interactions, which are expected to cover ;24% of the entire interactome with ;40% precision. This study demonstrates that, although PAIR still has limited coverage, it is rich enough to capture many significant functional linkages within and between higher-order biological systems, such as pathways and biological processes. These inferred interactions can nicely power several network topology-based systems biology analyses, such as gene set linkage analysis, protein function prediction, and identification of regulatory genes demonstrating insignificant expression changes. The drastically expanded molecular network in PAIR has considerably improved the capability of these analyses to integrate existing knowledge and suggest novel insights into the function and coordination of genes and gene networks.
Small molecule drugs are rarely selective enough to interact solely with their designated targets. Unintended "off-target" interactions often lead to side effects, but also serendipitously lead to new therapeutic uses. Identification of the off-targets of a compound is therefore of significant value to the evaluation of its developmental potential. In computational biology, the strategy of "reverse docking" has been introduced to predict the targets of a compound, which uses a compound to virtually screen a library of proteins, reversing the bait and prey in "normal" docking screenings. The present study shows that, in reverse docking, additional optimization of the scoring function may help to improve the target prediction accuracy. In a case study with the Glide scores, we found that only 57% of the ligand-protein relationships could be correctly identified in a library of 58 complexes whose crystal binding conformations were all able to be accurately reproduced. This was likely a result of the constant over- or under-estimation of the scores for specific proteins. In other words, there were interprotein noises in the Glide scores. Introducing a correction term based on protein characteristics improved the target-prediction accuracy by 27% (57-72%). It is our hope that this focused discussion on the Glide scores would invite further efforts to characterize and normalize this type of interprotein noises in all docking scores, so that better target prediction accuracy can be achieved with the strategy of reverse docking.
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