Salt stress, particularly short-term salt stress, is among the most serious abiotic factors limiting plant survival and growth in China. It has been established that exogenous spermidine (Spd) stimulates plant tolerance to salt stress. The present study utilized two zoysiagrass cultivars commonly grown in China that exhibit either sensitive (cv. Z081) or tolerant (cv. Z057) adaptation capacity to salt stress. The two cultivars were subjected to 200 mM salt stress and treated with different exogenous Spd concentrations for 8 days. Polyamine [diamine putrescine (Put), tetraamine spermine (Spm), and Spd], H2O2 and malondialdehyde (MDA) contents and polyamine metabolic (ADC, ODC, SAMDC, PAO, and DAO) and antioxidant (superoxide dismutase, catalase, and peroxidase) enzyme activities were measured. The results showed that salt stress induced increases in Spd and Spm contents and ornithine decarboxylase (ODC), S-adenosylmethionine decarboxylase (SAMDC), and diamine oxidase (DAO) activities in both cultivars. Exogenous Spd application did not alter polyamine contents via regulation of polyamine-degrading enzymes, and an increase in polyamine biosynthetic enzyme levels was observed during the experiment. Increasing the concentration of exogenous Spd resulted in a tendency of the Spd and Spm contents and ODC, SAMDC, DAO, and antioxidant enzyme activities to first increase and then decrease in both cultivars. H2O2 and MDA levels significantly decreased in both cultivars treated with Spd. Additionally, in both cultivars, positive correlations between polyamine biosynthetic enzymes (ADC, SAMDC), DAO, and antioxidant enzymes (SOD, POD, CAT), but negative correlations with H2O2 and MDA levels, and the Spd + Spm content were observed with an increase in the concentration of exogenous Spd.

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Deep learning has become the dominant approach in coping with various tasks in Natural Language Processing (NLP). Although text inputs are typically represented as a sequence of tokens, there is a rich variety of NLP problems that can be best expressed with a graph structure. As a result, there is a surge of interests in developing new deep learning techniques on graphs for a large number of NLP tasks. In this survey, we present a comprehensive overview on Graph Neural Networks (GNNs) for Natural Language Processing. We propose a new taxonomy of GNNs for NLP, which systematically organizes existing research of GNNs for NLP along three axes: graph construction, graph representation learning, and graph based encoder-decoder models. We further introduce a large number of NLP applications that are exploiting the power of GNNs and summarize the corresponding benchmark datasets, evaluation metrics, and open-source codes. Finally, we discuss various outstanding challenges for making the full use of GNNs for NLP as well as future research directions. To the best of our knowledge, this is the first comprehensive overview of Graph Neural Networks for Natural Language Processing.

The celebrated Seq2Seq technique and its numerous variants achieve excellent performance on many tasks such as neural machine translation, semantic parsing, and math word problem solving. However, these models either only consider input objects as sequences while ignoring the important structural information for encoding, or they simply treat output objects as sequence outputs instead of structural objects for decoding. In this paper, we present a novel Graph-to-Tree Neural Networks, namely Graph2Tree consisting of a graph encoder and a hierarchical tree decoder, that encodes an augmented graph-structured input and decodes a tree-structured output. In particular, we investigated our model for solving two problems, neural semantic parsing and math word problem. Our extensive experiments demonstrate that our Graph2Tree model outperforms or matches the performance of other state-of-the-art models on these tasks.

Bladder cancer (BC) is a complex disease and could be classified into nonmuscle‐invasive BC (NMIBC) or muscle‐invasive BC (MIBC) subtypes according to the distinct genetic background and clinical prognosis. Until now, the golden standard and confirmed diagnosis of BC is cystoscopy and the major problems of BC are the high rate of recurrence and high costs in the clinic. Recent molecular and genetic studies have provided perspectives on the novel biomarkers and potential therapeutic targets of BC. In this article, we provided an overview of the traditional diagnostic approaches of BC, and introduced some new imaging, endoscopic, and immunological diagnostic technology in the accurate diagnosis of BC. Meanwhile, the minimally invasive precision treatment technique, immunotherapy, chemotherapy, gene therapy, and targeted therapy of BC were also included. Here, we will overview the diagnosis and therapy methods of BC used in clinical practice, focusing on their specificity, efficiency, and safety. On the basis of the discussion of the benefits of precision medicine in BC, we will also discuss the challenges and limitations facing the non‐invasive methods of diagnosis and precision therapy of BC. The molecularly targeted and immunotherapeutic approaches, and gene therapy methods to BC treatment improved the prognosis and overall survival of BC patients.