Habiba, U. | bioRxiv Science

bioRxiv2024

Exploring Effector Protein Dynamics and Natural Fungicidal Potential in Rice Blast Pathogen Magnaporthe oryzae

Rice blast, caused by Magnaporthe oryzae, is a severe agricultural disease leading to significant global economic losses. Genetic and genomic investigations have identified crucial genes and pathways involved in its pathogenesis, particularly highlighting effector proteins like AvrPik variants and MAX proteins. These proteins interact with specific Pik alleles on rice chromosome 11, influencing host immune responses. This study focused on 35 plant-derived metabolites known for their antifungal properties, evaluating their potential as fungicidal agents against M. oryzae. Molecular docking analyses identified Hecogenin and Cucurbitacin E as highly effective binders to MAX40 and APIKL2A proteins, respectively, which are pivotal for fungal virulence and immune evasion. Molecular dynamics simulations further validated strong and stable interactions, affirming the therapeutic potential of these compounds. Additional assessments including Lipinskis rule of five criteria and toxicity predictions indicated their suitability for agricultural use. These findings underscore the promise of Hecogenin and Cucurbitacin E as lead candidates in developing novel fungicidal strategies against rice blast, offering prospects for enhanced crop protection and agricultural sustainability.

bioinformatics↗

bioRxiv2023

ODL-BCI: Optimal deep learning model for brain-computer interface to classify students concentration via hyper-parameter tuning

Brain-computer interface (BCI) research has gained increasing attention in educational contexts, offering the potential to monitor and enhance students cognitive states. Real-time classification of students confusion levels using electroencephalogram (EEG) data presents a significant challenge in this domain. Since real-time EEG data is dynamic and highly dimensional, current approaches have some limitations for predicting mental states based on this data. This paper introduces an optimal deep learning (DL) model for the BCI, ODL-BCI, optimized through hyperparameter tuning techniques to address the limitations of classifying students confusion in real time. Leveraging the "confused student EEG brainwave" dataset, we employ Bayesian optimization to fine-tune hyperparameters of the proposed DL model. The model architecture comprises input and output layers, with several hidden layers whose nodes, activation functions, and learning rates are determined utilizing selected hyperparameters. We evaluate and compare the proposed model with some state-of-the-art methods and standard machine learning (ML) classifiers, including Decision Tree, AdaBoost, Bagging, MLP, Nave Bayes, Random Forest, SVM, and XG Boost, on the EEG confusion dataset. Our experimental results demonstrate the superiority of the optimized DL model, ODL-BCI. It boosts the accuracy between 4% and 9% over the current approaches, outperforming all other classifiers in the process. The ODL-BCI implementation source codes can be accessed by anyone at https://github.com/MdOchiuddinMiah/ODL-BCI.

neuroscience↗