Broadband near-infrared luminescence in erbium ion single-doped tellurite glass for optical amplification.

This Letter proposes a simple approach for the realization of a broadband near-infrared (NIR) luminescence source in erbium ion single-doped tellurite glass, which is bent on tailoring the network structure. Under the collective action of multiple broadening mechanisms and fluorescence capture, broadband fluorescence with a full width at half maximum (FWHM) of 132 nm (1500-1632 nm) was achieved. To the best of our knowledge, this is the largest FWHM reported for erbium single-doping of tellurite glass materials. Meanwhile, this fiberglass exhibits excellent thermal stability and high visible to NIR transmittance. Furthermore, a novel equivalent five-level Stark splitting model is proposed that can effectively explain the spectrum broadening. This study is beneficial for the further development of broadband optical amplification.

Prediction of the Drug-Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks.

Drug combination therapies are a promising strategy to overcome drug resistance and improve the efficacy of monotherapy in cancer, and it has been shown to lead to a decrease in dose-related toxicities. Except the synergistic reaction between drugs, some antagonistic drug-drug interactions (DDIs) exist, which is the main cause of adverse drug events. Precisely predicting the type of DDI is important for both drug development and more effective drug combination therapy applications. Recently, numerous text mining- and machine learning-based methods have been developed for predicting DDIs. All these methods implicitly utilize the feature of drugs from diverse drug-related properties. However, how to integrate these features more efficiently and improve the accuracy of classification is still a challenge. In this paper, we proposed a novel method (called NMDADNN) to predict the DDI types by integrating five drug-related heterogeneous information sources to extract the unified drug mapping features. NMDADNN first constructs the similarity networks by using the Jaccard coefficient and then implements random walk with restart algorithm and positive pointwise mutual information for extracting the topological similarities. After that, five network-based similarities are unified by using a multimodel deep autoencoder. Finally, NMDADNN implements the deep neural network (DNN) on the unified drug feature to infer the types of DDIs. In comparison with other recent state-of-the-art DNN-based methods, NMDADNN achieves the best results in terms of accuracy, area under the precision-recall curve, area under the ROC curve, F1 score, precision and recall. In addition, many of the promising types of drug-drug pairs predicted by NMDADNN are also confirmed by using the interactions checker tool. These results demonstrate the effectiveness of our NMDADNN method, indicating that NMDADNN has the great potential for predicting DDI types.