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Protein disulfide isomerases (PDIs) and PDI-like proteins catalyze the oxidation and reduction in protein disulfide bonds, inhibit aggregation of misfolded proteins, and participate in isomerization and abiotic stress responses. The wild type 'duli' pear (Pyrus betulaefolia) is an important rootstock commonly used for commercial pear tree grafting in northern China. In this study, we identified 24 PDI genes, named PbPDIs, from the genome of 'duli' pear. With 12 homologous gene pairs, these 24 PbPDIs distribute on 12 of its 17 chromosomes. Phylogenetic analysis placed the 24 PbPDIs into four clades and eleven groups. Collinearity analysis of the PDIs between P. betulaefolia, Arabidopsis thaliana, and Oryza sativa revealed that the PbPDIs of 'duli' pear show a strong collinear relationship with those from Arabidopsis, a dicot; but a weak collinear relationship with those from rice, a monocot. Quantitative RT-PCR analysis showed that most of the PbPDIs were upregulated by salt stress. Identification and expression analysis of 'duli' pear PbPDIs under salt stress conditions could provide useful information for further research in order to generate salt-resistant rootstock for pear grafting in the future. [ABSTRACT FROM AUTHOR]

To address the problem of poor semantic reasoning of models in multiple-choice Chinese machine reading comprehension (MRC), this paper proposes an MRC model incorporating multi-granularity semantic reasoning. In this work, we firstly encode articles, questions and candidates to extract global reasoning information; secondly, we use multiple convolution kernels of different sizes to convolve and maximize pooling of the BERT-encoded articles, questions and candidates to extract local semantic reasoning information of different granularities; we then fuse the global information with the local multi-granularity information and use it to make an answer selection. The proposed model can combine the learned multi-granularity semantic information for reasoning, solving the problem of poor semantic reasoning ability of the model, and thus can improve the reasoning ability of machine reading comprehension. The experiments show that the proposed model achieves better performance on the C 3 dataset than the benchmark model in semantic reasoning, which verifies the effectiveness of the proposed model in semantic reasoning. [ABSTRACT FROM AUTHOR]

Polycrystalline zinc selenide is widely used in advanced optical systems due to its superior optical properties. However, the soft and brittle properties bring a challenge for high-quality surface processing. In recent years, elliptical vibration cutting has been proven as a promising method for machining brittle materials. In the present research, a series of grooving and planning experiments were carried out to investigate the machinability of zinc selenide with elliptical vibration cutting. The removal mechanism was analyzed from fracture characteristics, chip morphology, and phase transformation. The results show that elliptical vibration cutting is effective in suppressing cleavage-induced craters. Reducing the nominal cutting speed is beneficial to inhibit the spring back-induced tearing of grains. A 94-time increase in the critical depth of cut was achieved by vibration trajectory optimization compared to ordinary cutting. Moreover, the influence mechanism of feed on the evolution of surface morphology was revealed. Finally, a zinc selenide microlens array was successfully fabricated. The performance was evaluated by geometric parameter measurements and a multiple imaging test. The findings provide a prospective method for ductile regime machining of zinc selenide.