Calcium signaling has been postulated to be critical for both heat and chilling tolerance in plants, but its molecular mechanisms are not fully understood. Here, we investigated the function of two closely related cyclic nucleotide-gated ion channel (CNGC) proteins, OsCNGC14 and OsCNGC16, in temperature-stress tolerance in rice (Oryza sativa) by examining their loss-of-function mutants generated by genome editing. Under both heat and chilling stress, both the cngc14 and cngc16 mutants displayed reduced survival rates, higher accumulation levels of hydrogen peroxide, and increased cell death. In the cngc16 mutant, the extent to which some genes were induced and repressed in response to heat stress was altered and some Heat Shock factor (HSF) and Heat Shock Protein (HSP) genes were slightly more induced compared to the wild type. Furthermore, the loss of either OsCNGC14 or OsCNGC16 reduced or abolished cytosolic calcium signals induced by either heat or chilling stress. Therefore, OsCNGC14 and OsCNGC16 are required for heat and chilling tolerance and are modulators of calcium signals in response to temperature stress. In addition, loss of their homologs AtCNGC2 and AtCNGC4 in Arabidopsis (Arabidopsis thaliana) also led to compromised tolerance of low temperature. Thus, this study indicates a critical role of CNGC genes in both chilling and heat tolerance in plants, suggesting a potential overlap in calcium signaling in response to high-and low-temperature stress.
BackgroundClinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number of professional and uncommon clinical terms in Chinese electronic medical records (EMRs), there are still many difficulties in clinical named entity recognition of Chinese EMRs. It is of great importance to eliminate semantic interference and improve the ability of autonomous learning of internal features of the model under the small training corpus.MethodsFrom the perspective of deep learning, we integrated the attention mechanism into neural network, and proposed an improved clinical named entity recognition method for Chinese electronic medical records called BiLSTM-Att-CRF, which could capture more useful information of the context and avoid the problem of missing information caused by long-distance factors. In addition, medical dictionaries and part-of-speech (POS) features were also introduced to improve the performance of the model.ResultsBased on China Conference on Knowledge Graph and Semantic Computing (CCKS) 2017 and 2018 Chinese EMRs corpus, our BiLSTM-Att-CRF model finally achieved better performance than other widely-used models without additional features(F1-measure of 85.4% in CCKS 2018, F1-measure of 90.29% in CCKS 2017), and achieved the best performance with POS and dictionary features (F1-measure of 86.11% in CCKS 2018, F1-measure of 90.48% in CCKS 2017). In particular, the BiLSTM-Att-CRF model had significant effect on the improvement of Recall.ConclusionsOur work preliminarily confirmed the validity of attention mechanism in discovering key information and mining text features, which might provide useful ideas for future research in clinical named entity recognition of Chinese electronic medical records. In the future, we will explore the deeper application of attention mechanism in neural network.
Core Ideas
Proper calibration and application is important when using herbicides on buffalograss.
Established ‘Bowie’ buffalograss was most tolerant to carfentrazone + quinclorac, halosulfuron, indaziflam, quinclorac, quinclorac + MCCP + dicamba, simazine, sulfosulfuron, or thiencarbazone + iodosulfuron + dicamba.
Herbicide injury on established buffalograss is relatively short‐lived with the exception of imazapic at the 2× rate.
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