Background: Parkinson's disease (PD) gradually degrades the functionality of the brain. Because of its relevance to the abnormality of the brain, electroencephalogram (EEG) signal is used for the early detection of this disease. This paper introduces a novel computer-aided diagnosis method to detect PD, which is an efficient deep learning method based on a pooling-based deep recurrent neural network (PDRNN). Therefore, the purpose of this study is to detect Parkinson's disease based on deep recurrent neural network of EEG signal Methods: The EEG signals of 20 patients with Parkinson's disease and 20 healthy people in Henan ProvincialPeople's Hospital (People's Hospital of Zhengzhou University) were examined, and a PDRNN learning method was applied on the dataset for managing the demand of the traditional feature presentation step. Results:The suggested DPRNN network gives the precision, sensitivity and specificity of 88.31%, 84.84% and 91.81%, respectively. Nevertheless, 11.28% of the healthy cases are wrongly categorized in Parkinson class. Also, 11.49% percent of Parkinson cases are classified wrongly in the healthy class. Conclusions:The experimental model has high efficiency and can be used as a reliable tool for clinical PD detection. In future research, more cases should be used to test and develop the proposed model.

No abstract

to using a deep recurrent neural network with EEG signal to detect Parkinson's disease.

Background: Amber-like compounds form in tobacco (Nicotiana tabacum) during leaf curing and impact aromatic quality. In particular, cis-abienol, a polycyclic labdane-related diterpenoid, is of research interest as a precursor of these compounds. Glandular trichome cells specifically express copalyl diphosphate synthase (NtCPS2) at high levels in tobacco, which, together with NtABS, are major regulators of cis-abienol biosynthesis in tobacco. Results: To identify the genes involved in the biosynthesis of cis-abienol in tobacco, we constructed transgenic tobacco lines based on an NtCPS2 gene-knockout model, using CRISPR/Cas9 genome-editing technology to inhibit NtCPS2 function in vitro. In mutant plants, cis-abienol and labdene-diol contents decreased, whereas gibberellin and abscisic acid (ABA) contents increased compared with those in wild-type tobacco plants. RNA sequencing analysis revealed the presence of 9,514 differentially expressed genes (DEGs; 4,279 upregulated, 5,235 downregulated) when the leaves of wild-type and NtCPS2-knockout tobacco plants were screened. Among these DEGs, the genes encoding cis-abienol synthase, ent-kaurene oxidase, auxin/ABA-related proteins, and transcription factors were found to be involved in various biological and physiochemical processes, including diterpenoid biosynthesis, plant hormone signal transduction, plant-pathogen interactions, etc. Conclusions: Our findings provide clues to the molecular regulatory mechanism underlying NtCPS2 activity, allowing for a better understanding of the interactions among related genes in tobacco.