Development of real-time recombinase polymerase amplification (RPA) and RPA combined with lateral flow dipstick (LFD) assays for the rapid and sensitive detection of cyprinid herpesvirus 3.

In this issue, we established rapid, cost-effective, and simple detection methods including recombines polymerase amplification with lateral flow dipstick (RPA-LFD) and real-time RPA for cyprinid herpesvirus 3(CyHV-3), and evaluated their sensitivity, specificity, and applicability, the real-time RPA method could achieve sensitive diagnosis of CyHV-3 within 1.3 copies per reaction, respectively. The real-time RPA method is 10-fold more sensitive than RPA-LFD method. The exact number of CyHV-3 can be calculated in each sample by real-time RPA. The sera from koi also can be tested in these methods. In addition, no cross-reaction was observed with other related pathogens, including carp oedema virus (CEV), spring viraemia of carp virus (SVCV), cyprinid herpesvirus 1(CyHV-1), cyprinid herpesvirus 2(CyHV-2), type I grass carp reovirus (GCRV-I), type II GCRV (GCRV-II), type III GCRV (GCRV-III), and Aeromonas hydrophila.

DualGCN: Exploring Syntactic and Semantic Information for Aspect-Based Sentiment Analysis.

The task of aspect-based sentiment analysis aims to identify sentiment polarities of given aspects in a sentence. Recent advances have demonstrated the advantage of incorporating the syntactic dependency structure with graph convolutional networks (GCNs). However, their performance of these GCN-based methods largely depends on the dependency parsers, which would produce diverse parsing results for a sentence. In this article, we propose a dual GCN (DualGCN) that jointly considers the syntax structures and semantic correlations. Our DualGCN model mainly comprises four modules: 1) SynGCN: instead of explicitly encoding syntactic structure, the SynGCN module uses the dependency probability matrix as a graph structure to implicitly integrate the syntactic information; 2) SemGCN: we design the SemGCN module with multihead attention to enhance the performance of the syntactic structure with the semantic information; 3) Regularizers: we propose orthogonal and differential regularizers to precisely capture semantic correlations between words by constraining attention scores in the SemGCN module; and 4) Mutual BiAffine: we use the BiAffine module to bridge relevant information between the SynGCN and SemGCN modules. Extensive experiments are conducted compared with up-to-date pretrained language encoders on two groups of datasets, one including Restaurant14, Laptop14, and Twitter and the other including Restaurant15 and Restaurant16. The experimental results demonstrate that the parsing results of various dependency parsers affect their performance of the GCN-based models. Our DualGCN model achieves superior performance compared with the state-of-the-art approaches. The source code and preprocessed datasets are provided and publicly available on GitHub (see https://github.com/CCChenhao997/DualGCN-ABSA).

Challenges in representation learning: a report on three machine learning contests.

The ICML 2013 Workshop on Challenges in Representation Learning(1) focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions.