With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92% and 96%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective.
Sesquiterpenes are common small-molecule natural products with a wide range of promising applications and are biosynthesized by sesquiterpene synthase (STS). Basidiomycetes are valuable and important biological resources. To date, hundreds of related sesquiterpenoids have been discovered in basidiomycetes, and the biosynthetic pathways of some of these compounds have been elucidated. This review summarizes 122 STSs and 2 fusion enzymes STSs identified from 26 species of basidiomycetes over the past 20 years. The biological functions of enzymes and compound structures are described, and related research is discussed.
Thermally conductive and electrically insulating thermal interface materials (TIMs) are highly desired for electronic cooling. To improve heat transfer efficiency, thermally conductive fillers with a high loading content have been incorporated into the polymer-based TIMs. However, this is usually at the expense of the interfacial thermal resistance reduction and reliability. In this study, vertically aligned boron nitride nanosheet films (VBNFs) have been prepared by a scalable microfluidic spinning process and template-assisted chemical vapor deposition conversion method. A further high-temperature annealing was applied to achieve high crystallinity. VBNFs have been applied as fillers to fabricate TIMs and achieve a superior through-plane thermal conductivity of 6.4 W m −1 K −1 and low modulus of 2.2 MPa at low BN loading of 9.85 vol %, benefitting from the well-aligned vertical sheet structure and high crystallinity. In addition, the fabricated TIMs present high-volume resistivity and breakdown strength, satisfying the electrical insulation demands. The high thermal conductivity and low modulus contribute an outstanding cooling performance to the TIMs in the heat dissipation application for high-power LEDs. This template-assisted conversion technology for the fabrication of orientated BN nanosheets structure and the prepared high-performance TIMs pave the way for efficient thermal management of high-power electronics.
Background Medical information has rapidly increased on the internet and has become one of the main targets of search engine use. However, medical information on the internet is subject to the problems of quality and accessibility, so ordinary users are unable to obtain answers to their medical questions conveniently. As a solution, researchers build medical question answering (QA) systems. However, research on medical QA in the Chinese language lags behind work on English-based systems. This lag is mainly due to the difficulty of constructing a high-quality knowledge base and the underutilization of medical corpora in the Chinese language. Results This study developed an end-to-end solution to implement a medical QA system for the Chinese language with low cost and time. First, we created a high-quality medical knowledge graph from hospital data (electronic health/medical records) in a nearly automatic manner that trained a supervised model based on data labeled using bootstrapping techniques. Then, we designed a QA system based on a memory-based neural network and attention mechanism. Finally, we trained the system to generate answers from the knowledge base and a QA corpus on the internet. Conclusions Bootstrapping and deep neural network techniques can construct a knowledge graph from electronic health/medical records with satisfactory precision and coverage. Our proposed context bridge mechanisms perform training with a variety of language features. Our QA system can achieve state-of-the-art quality in answering medical questions with constrained topics. As we evaluated, complex Chinese language processing techniques, such as segmentation and parsing, were not necessary for practice and complex architectures were not necessary to build the QA system. Lastly, we created an application using our method for internet QA usage.
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