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.
Since lots of miRNA-disease associations have been verified, it is meaningful to discover more miRNAdisease associations for serving disease diagnosis and prevention of human complex diseases. However, it is not practical to identify potential associations using traditional biological experimental methods since the process is expensive and time consuming. Therefore, it is necessary to develop efficient computational methods to accomplish this task. In this work, we introduced a matrix completion model with dual Laplacian regularization (DLRMC) to infer unknown miRNA-disease associations in heterogeneous omics data. Specifically, DLRMC transformed the task of miRNAdisease association prediction into a matrix completion problem, in which the potential missing entries of the miRNA-disease association matrix were calculated, the missing association can be obtained based on the prediction scores after the completion procedure. Meanwhile, the miRNA functional similarity and the disease semantic similarity were fully exploited to serve the miRNAdisease association matrix completion by using a dual Laplacian regularization term. In the experiments, we conducted global and local Leave-One-Out Cross Validation (LOOCV) and case studies to evaluate the efficacy of DLRMC on the Human miRNA-disease associations dataset obtained from the HMDDv2.0 database. As a result, the AUCs of DLRMC is 0.9174 and 0.8289 in global LOOCV and local LOOCV, respectively, which significantly outperform a variety of previous methods. In addition, in the case studies on four significant diseases related to human health including Colon Neoplasms, Kidney neoplasms, Lymphoma and Prostate neoplasms, 90%, 92%, 92% and 94% out of the top 50 predicted miRNAs has been confirmed, respectively.
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