Biomedical relation extraction:from binary to complex

Biomedical relation extraction aims to uncover high-quality relations from life science literature with high accuracy and efficiency. Early biomedical relation extraction tasks focused on capturing binary relations, such as protein-protein interactions, which are crucial for virtually every process in a living cell. Information about these interactions provides the foundations for new therapeutic approaches. In recent years, more interests have been shifted to the extraction of complex relations such as biomolecular events. While complex relations go beyond binary relations and involve more than two arguments, they might also take another relation as an argument. In the paper, we conduct a thorough survey on the research in biomedical relation extraction. We first present a general framework for biomedical relation extraction and then discuss the approaches proposed for binary and complex relation extraction with focus on the latter since it is a much more difficult task compared to binary relation extraction. Finally, we discuss challenges that we are facing with complex relation extraction and outline possible solutions and future directions

Event trigger identification for biomedical events extraction using domain knowledge

Motivation: In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. Results: In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier. Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework. The source code for the proposed framework is freely available and can be downloaded at http://cse.seu.edu.cn/people/zhoudeyu/ETI_Sourcecode.zip

Thermal stability, mechanical properties, and tribological performance of TiAlXN coatings: Understanding the effects of alloying additions

In tribological applications, the degradation of metallic coatings due to oxidation and thermal softening at high temperatures is an issue of increasing concern. Recently, researchers have focused on the development of durable hard coatings that can perform well under elevated temperatures. The alloying of ternary TiAlN coatings with various elements has received considerable attention due to its ability to improve coating properties at high temperatures by solid solution hardening, grain refinement, formation of new phases, diffusion barriers, and self-lubricious tribo-oxides. This paper reviews the microstructure, thermal stability, oxidation behaviour, and mechanical and tribological properties of resultant quaternary TiAlXN coatings (X = Si, Cr, V, Ta and B). The effects of the deposition parameters, chemical composition, high-temperature annealing, and coating architecture on the coating properties are discussed in depth. The properties of quinary TiAlCrSiN coatings are also reviewed to provide a better understanding of the synergistic effects of Si and Cr additions to TiAlN. The maximum hardness and plastic deformation resistance (H/E and H3/E2) of TiAlXN coatings produced by various deposition techniques are compared. This paper provides useful insights into the challenges and future research perspectives of the reviewed coatings

General and Practical Potassium Methoxide/Disilane-Mediated Dehalogenative Deuteration of (Hetero)Arylhalides

Herein we describe a general, mild and scalable method for deuterium incorporation by potassium methoxide/hexamethyldisilane-mediated dehalogenation of arylhalides. With CD3CN as a deuterium source, a wide array of heteroarenes prevalent in pharmaceuticals and bearing diverse functional groups are labeled with excellent deuterium incorporation (>60 examples). The ipso-selectivity of this method provides precise access to libraries of deuterated indoles and quinolines. The synthetic utility of our method has been demonstrated by the incorporation of deuterium into complex natural and drug-like compounds