Chromatin remodeling is essential for gene expression regulation in plant development and response to stresses. Brahma (BRM) is a conserved ATPase in the SWI/SNF chromatin remodeling complex and is involved in various biological processes in plant cells, but the regulation mechanism on BRM protein remains unclear. Here, we report that BRM interacts with AtMMS21, a SUMO ligase in Arabidopsis (). The interaction was confirmed in different approaches in vivo and in vitro. The mutants of and displayed a similar defect in root development. In the mutant, the protein level of BRM-GFP was significantly lower than that in wild type, but the RNA level of did not change. Biochemical evidence indicated that BRM was modified by SUMO3, and the reaction was enhanced by AtMMS21. Furthermore, overexpression of wild-type AtMMS21 but not the mutated AtMMS21 without SUMO ligase activity was able to recover the stability of BRM in Overexpression of in partially rescued the developmental defect of roots. Taken together, these results supported that AtMMS21 regulates the protein stability of BRM in root development.
With the continuous development of portable noninvasive human sensor technologies such as brain–computer interfaces (BCI), multimodal emotion recognition has attracted increasing attention in the area of affective computing. This paper primarily discusses the progress of research into multimodal emotion recognition based on BCI and reviews three types of multimodal affective BCI (aBCI): aBCI based on a combination of behavior and brain signals, aBCI based on various hybrid neurophysiology modalities and aBCI based on heterogeneous sensory stimuli. For each type of aBCI, we further review several representative multimodal aBCI systems, including their design principles, paradigms, algorithms, experimental results and corresponding advantages. Finally, we identify several important issues and research directions for multimodal emotion recognition based on BCI.
Brain science accelerates the study of intelligence and behavior, contributes fundamental insights into human cognition, and offers prospective treatments for brain disease. Faced with the challenges posed by imaging technologies and deep learning computational models, big data and high-performance computing (HPC) play essential roles in studying brain function, brain diseases, and large-scale brain models or connectomes. We review the driving forces behind big data and HPC methods applied to brain science, including deep learning, powerful data analysis capabilities, and computational performance solutions, each of which can be used to improve diagnostic accuracy and research output. This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible, by improving data standardization and sharing, and by providing new neuromorphic insights.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.