Chinese Medical Question Answer Matching Based on Interactive Sentence Representation Learning

Chinese medical question-answer matching is more challenging than the open-domain question answer matching in English. Even though the deep learning method has performed well in improving the performance of question answer matching, these methods only focus on the semantic information inside sentences, while ignoring the semantic association between questions and answers, thus resulting in performance deficits. In this paper, we design a series of interactive sentence representation learning models to tackle this problem. To better adapt to Chinese medical question-answer matching and take the advantages of different neural network structures, we propose the Crossed BERT network to extract the deep semantic information inside the sentence and the semantic association between question and answer, and then combine with the multi-scale CNNs network or BiGRU network to take the advantage of different structure of neural networks to learn more semantic features into the sentence representation. The experiments on the cMedQA V2.0 and cMedQA V1.0 dataset show that our model significantly outperforms all the existing state-of-the-art models of Chinese medical question answer matching

Cloning and biochemical characterization of a novel lipolytic gene from activated sludge metagenome, and its gene product

In this study, a putative esterase, designated EstMY, was isolated from an activated sludge metagenomic library. The lipolytic gene was subcloned and expressed in Escherichia coli BL21 using the pET expression system. The gene estMY contained a 1,083 bp open reading frame (ORF) encoding a polypeptide of 360 amino acids with a molecular mass of 38 kDa. Sequence analysis indicated that it showed 71% and 52% amino acid identity to esterase/lipase from marine metagenome (ACL67845) and Burkholderia ubonensis Bu (ZP_02382719), respectively; and several conserved regions were identified, including the putative active site, GDSAG, a catalytic triad (Ser203, Asp301, and His327) and a HGGG conserved motif (starting from His133). The EstMY was determined to hydrolyse p-nitrophenyl (NP) esters of fatty acids with short chain lengths (≤C8). This EstMY exhibited the highest activity at 35°C and pH 8.5 respectively, by hydrolysis of p-NP caprylate. It also exhibited the same level of activity over wide temperature and pH spectra and in the presence of metal ions or detergents. The high level of stability of esterase EstMY with unique substrate specificities makes it highly valuable for downstream biotechnological applications

The preparation of 3,5-dihydroxy-4-isopropylstilbene nanoemulsion and in vitro release

We have reported a novel procedure to prepare 3,5-dihydroxy-4-isopropylstilbene (DHPS) nanoemulsion, using a low-energy emulsification method. Based on the phase diagram, the optimum prescription of nanoemulsion preparation was screened. With polyoxyethylenated castor oil (EL-40) as the surfactant, ethanol as the co-surfactant, and isopropyl myristate (IPM) as the oil phase, the DHPS nanoemulsion was obtained with a transparent appearance, little viscosity, and spherically uniform distribution verified by transmission electron microscopy and laser scattering analyzer. The nanoemulsion was also determined by FT-Raman spectroscopy. The DHPS nanoemulsion demonstrated good stability and stable physical and chemical properties. The nanoemulsion dramatically improved the transdermal release of DHPS (from 8.02 μg · cm−2 to 273.15 μg · cm−2) and could become a favorable new dosage form for DHPS

A Novel Method for ECG Signal Classification Via One-Dimensional Convolutional Neural Network

This paper develops an end-to-end ECG signal classification algorithm based on a novel segmentation strategy and 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals and alleviate the workload of physicians. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) is used for segmenting the original ECG data into segments to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments for diagnosis. The performance of the proposed end to end ECG signal classification algorithm was verified with the ECG signals from 48 records in the MIT-BIH arrhythmia database. When the heartbeat types were divided into the five classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, and paced beat, the classification accuracy, the area under the curve (AUC), the sensitivity, and the F1-score achieved by the proposed model were 0.9924, 0.9994, 0.99 and 0.99, respectively. When the heartbeat types were divided into six classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, paced beat and other beats, the beat classification accuracy, the AUC, the sensitivity, and the F1-score achieved by the model reached 0.9702, 0.9966, 0.97, and 0.97, respectively. When the heartbeat types were divided into five classes recommended by the Association for Advancement of Medical Instrumentation (AAMI), i.e., normal beat, supraventricular ectopic beat, ventricular ectopic beat, fusion beat, and unknown beat, the beat classification accuracy, the sensitivity, and the F1-score were 0.9745, 0.97, and 0.97, respectively. Experimental results show that the proposed method achieves better performance than the state-of-the-art methods

IMPLEMENTING TEMPORAL LOGIC IN HOL AND APPLICATIONS

Higher-Order Logic (HOL) system has been proved to be very powerful for hardware verification. Many of the largest proofs completed to date have been constructed using HOL system. It seems to be that the HOL system is more suitable for dealing with static object though we can define functions with parameter of time for handling dynamic objects in HOL system. On the other hand, Temporal logic which includes time in its semantics has the potential of handling timing problems. In order to mechanize the deducing process of temporal logic and explore the possibilities of applying temporal logic to hardware verification, we have tried to implement the linear timing temporal logic on the top level of HOL system by expressing the temporal logic concepts in high-order logic and proving the axioms and inference rules of a formal temporal logic system. This paper describes the ideas and results of this work, and gives some examples of applying temporal logic to hardware specification and verification. We have proved that there exists a static hazard in the combinational circuit considered under certain conditions, and the hazard is eliminated in the improved circuit. We have also specified and verified the properties of a S-R latch by the combined power of temporal logic and HOL system.We are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at [email protected]

THE IMPLEMENTATION AND VERIFICATION OF A CONDITIONAL SUM ADDER

In this paper we first formulate the Conditional Sum Addition (CSA) algorithm, then design an area-time efficient Conditional Sum Adder in CMOS. We also design a Binary Look-ahead Carry adder and a fast ripple carry adder in the same technology for the comparison of their performances. Finally we formally prove that the CMOS implementation of the CSA adder is correct (i.e. the implementation meets the specification of the intended behavior) by using Mike Gordon's Higher Order Logic (HOL) system.We are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at [email protected]