Genetic incorporation of a cyclopropene amino acid, Nε-(1-methylcycloprop-2-enecarboxamido)-lysine (CpK), into sperm whale myoglobin site-specifically in E. coli as well as enhanced green fluorescent protein in mammalian cells was achieved through amber codon suppression employing an orthogonal aminoacyl-tRNA synthetase/tRNACUA pair. Because of its high ring strain, cyclopropene exhibited fast reaction kinetics (up to 58 ± 16 M−1 s−1) in the photoclick reaction and allowed rapid (~ 2 min) bioorthogonal labeling of proteins in mammalian cells.

Changing climatic scenarios affect plant growth and consequences are more malicious in drought conditions. This study was performed for better understanding of tolerance mechanisms under prevailing drought stress and succeeding recovery in Axonopus compressus by exogenously applied abscisic acid (ABA) and glycine betaine (GB). three A. compressus accessions (A-38, A-58 and A-59) were subjected to well-watered (100% field capacity) and drought (40% field capacity) conditions. Two weeks later, plants were recovered from drought by re-watering. Water (control), GB, ABA and their combination were foliar applied on plants under drought twice a week until recovery. Drought stress decreased photosynthetic pigments and increased reactive oxygen species, lipid peroxidation, osmolytes and antioxidants in all accessions of A. compressus. Nonetheless, exogenous ABA and GB alone or in combination improved drought tolerance in all accessions which was maintained even after recovery. Maximum decrease in hydrogen peroxide and malondialdehyde, and increase in soluble sugars, proteins, proline, phenolics and chlorophyll contents, and superoxide dismutase, catalase, peroxidase and ascorbate peroxidase activity was recorded when GB was applied alone under drought. Order of improvement in drought tolerance among accessions was A-58 > A-59 > A-38. In conclusion, improved drought tolerance mechanisms by ABA and GB in A. compressus were retained even after recovery. Plants experience recurrent drought conditions which is one of the major negative forces for growth and productivity of biological systems 1,2. Drought stress is one of the key ecological effectors which causes alterations at morpho-physiological, biochemical and molecular levels 3. Water deficit reduces cell division and elongation, disturbs tissue water status, causes stomatal closure, damages photosynthetic machinery, limits the photosynthesis, and hampers nutrient uptake and translocation ultimately affecting plant growth and productivity 3-5. In addition, drought stress enhances production of reactive oxygen species (ROS) viz. singlet oxygen (O *), hydroxyl radicals (OH • .), superoxide (O 2 −) and hydrogen peroxide (H 2 O 2) 6,7 , which causes exaggerated lipid peroxidation of cellular membranes 7,8 , denaturation of proteins, destruction of nucleic acid ultimately disrupting homeostasis 6,9,10. Plants have devised tolerance mechanisms which potentially promote their survival in drought stressed environments 11,12. Abiotic stress induced ROS production activates the signaling mechanisms in plants 8,13,14 , which induce hormonal modifications mainly increase in endogenous ABA, and activates the genes for production of osmolytes and antioxidant enzymes 3,15-17. Compatible solutes such as proline, glycine betaine, soluble proteins ad sugars, organic acids and phenolic compounds are accumulated in plants in response to drought which improve the water potential, detoxify ROS, and protect cellular membranes and macromolecules from lipid peroxidation 11,18,19. In addition, ac...

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The redundant information, noise data generated in the process of single-modal feature extraction, and traditional learning algorithms are difficult to obtain ideal recognition performance. A multi-modal fusion emotion recognition method for speech expressions based on deep learning is proposed. Firstly, the corresponding feature extraction methods are set up for different single modalities. Among them, the voice uses the convolutional neural network-long and short term memory (CNN-LSTM) network, and the facial expression in the video uses the Inception-Res Net-v2 network to extract the feature data. Then, long and short term memory (LSTM) is used to capture the correlation between different modalities and within the modalities. After the feature selection process of the chi-square test, the single modalities are spliced to obtain a unified fusion feature. Finally, the fusion data features output by LSTM are used as the input of the classifier LIBSVM to realize the final emotion recognition. The experimental results show that the recognition accuracy of the proposed method on the MOSI and MELD datasets are 87.56 and 90.06%, respectively, which are better than other comparison methods. It has laid a certain theoretical foundation for the application of multimodal fusion in emotion recognition.

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