OsAPX4 gene response to several environmental stresses in rice (Oryza sativa L.)

Expression of the gene, OsAPX4, coding for ascorbate peroxidase in leaves and roots of rice were induced by abiotic stresses, such as NaCl, NaHCO3 and Na2CO3, polyethylene glycol (PEG) 6000, H2O2, CuCl2. Yeast (Saccharomyces cerevisiae) over-expressing ascorbate peroxidase exhibited greater tolerance to NaCl and NaHCO3 and transgenic Arabidopsis over-expressing OsAPX4 had a greater salt tolerance than wild-type plants in 1/2 Murashige and Skoog (MS) medium with 150, 200 mM NaCl and 5, 7.5 mM NaHCO3. These results suggest that OsAPX4 plays an important role in multiple environmental stresses.Keywords: Arabidopsis, Oryza sativa, thaliana, carbonic anhydrase ascorbate peroxides, gene expression stressAfrican Journal of Biotechnology Vol. 9(36), pp. 5908-5913, 6 September, 201

Learning Discriminative Representations for Skeleton Based Action Recognition

Human action recognition aims at classifying the category of human action from a segment of a video. Recently, people have dived into designing GCN-based models to extract features from skeletons for performing this task, because skeleton representations are much more efficient and robust than other modalities such as RGB frames. However, when employing the skeleton data, some important clues like related items are also discarded. It results in some ambiguous actions that are hard to be distinguished and tend to be misclassified. To alleviate this problem, we propose an auxiliary feature refinement head (FR Head), which consists of spatial-temporal decoupling and contrastive feature refinement, to obtain discriminative representations of skeletons. Ambiguous samples are dynamically discovered and calibrated in the feature space. Furthermore, FR Head could be imposed on different stages of GCNs to build a multi-level refinement for stronger supervision. Extensive experiments are conducted on NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets. Our proposed models obtain competitive results from state-of-the-art methods and can help to discriminate those ambiguous samples. Codes are available at https://github.com/zhysora/FR-Head.Comment: Accepted by CVPR2023. 10 pages, 5 figures, 5 table

Counting dense objects in remote sensing images

Estimating accurate number of interested objects from a given image is a challenging yet important task. Significant efforts have been made to address this problem and achieve great progress, yet counting number of ground objects from remote sensing images is barely studied. In this paper, we are interested in counting dense objects from remote sensing images. Compared with object counting in natural scene, this task is challenging in following factors: large scale variation, complex cluttered background and orientation arbitrariness. More importantly, the scarcity of data severely limits the development of research in this field. To address these issues, we first construct a large-scale object counting dataset based on remote sensing images, which contains four kinds of objects: buildings, crowded ships in harbor, large-vehicles and small-vehicles in parking lot. We then benchmark the dataset by designing a novel neural network which can generate density map of an input image. The proposed network consists of three parts namely convolution block attention module (CBAM), scale pyramid module (SPM) and deformable convolution module (DCM). Experiments on the proposed dataset and comparisons with state of the art methods demonstrate the challenging of the proposed dataset, and superiority and effectiveness of our method