Browse Articles
Discover research articles across all indexed journals
Diversity and correlation of endophytic microbiota and Shikonin accumulation in Arnebia Euchroma roots across geographical origins
Interplay between β-Chitin Nanocrystal Supramolecular Architecture and Water Structuring: Insights from Three-Dimensional Atomic force Microscopy Measurements and Molecular Dynamics Simulations
Ketamine and dodecyl maltoside synergy as a potential topical therapeutic approach for melanoma
Synthesis of Precisely Modified Ribonucleic Acid to Reveal the Site-Specific Effect of Modification on Message Ribonucleic Acid Translation
Molecular characterization of curry leaf genotypes using ISSR markers
Electronic States Modulation of BiVO <sub>4</sub> with Transition Metal-Substituted Polyoxometalates to Activate Lattice Oxygen Mechanism for Efficient Water Oxidation
Interpretable deep multimodal-based tomato disease diagnosis and severity estimation
Abstract Plant diseases pose a significant threat to global food security, particularly in regions that rely heavily on crops that are vulnerable to disease, such as tomatoes. This research addresses the inefficiencies of traditional farming solutions by presenting a novel multimodal deep learning algorithm. The algorithm leverages EfficientNetB0 for image-based disease classification and utilizes Recurrent Neural Networks (RNN) to predict disease severity based on environmental data. By integrating visual and climatological inputs, our model addresses the limitations of unimodal systems, enhancing classification accuracy and interpretability. The model achieved a disease classification accuracy of 96.40% and a severity prediction accuracy of 99.20%. Additionally, the use of LIME and SHAP explainable AI techniques improves the understanding of disease severity classification outcomes. The contributions of this study align with precision agriculture practices and advance the resilience of local food systems, particularly in economies heavily dependent on tomato production. The proposed approach has the potential to mitigate the impacts of plant diseases and enhance food security by utilizing innovative technological solutions.
Ultrafast Conversion of CO <sub>2</sub> into C <sub>3</sub> –C <sub>4</sub> Diols in a Synergistic Electrochemical and AI-Assisted Biosynthesis System
Allele-specific gene expression in F1 hybrid mice reveals structural variants affecting macrophage characteristics
Abstract Genetic diversity underlies the foundation for variations in gene expression, resulting in diverse phenotypic traits. In this study, we investigated the effect of genetic variation on macrophage function using two genetically distinct mouse strains, C57BL/6 (B6) and Japanese Fancy Mouse 1 (JF1), which exhibit substantial genetic polymorphisms. Gene expression analysis of macrophages derived from B6, JF1, and their F1 hybrids revealed strain-specific allelic expression of immune-related and glycolytic genes, indicating significant influence of cis -regulatory variants on macrophage function. These findings highlight the role of genetic variation in shaping immune responses and metabolic pathways and provide new insights into the genetic basis of phenotypic diversity in macrophages among subspecies of mice.