Two new compounds, named aspergilins A (1) and B (2), were isolated from the mangrove endophytic fungus Aspergillus sp. A1E3 associated with the fruit of Rhizophora mucronata, together with averufanin (3). The planar structures and absolute configurations of these compounds were unambiguously established by extensive NMR investigations, quantum-chemical electronic circular dichroism (ECD) calculations, and single-crystal X-ray diffraction analysis. Most notably, the absolute configuration of aspergillin A (1) was determined by single-crystal X-ray diffraction analysis of its tri-pivaloyl derivative 4, conducted with Cu Kα radiation, whereas that of averufanin (3) was first clarified by quantum-chemical ECD calculations. Aspergilin A is the first orsellinic acid–ribose–​pyridazinone-Noxide hybrid containing a unique β-oxo-2,3-dihydropyridazine 1-oxide moiety, whereas aspergilin B and averufanin are the sterigmatocystin and anthraquinone derivative, respectively. From the perspective of biosynthesis, aspergilin A could be originated from the combined assembly of three building blocks, viz., orsellinic acid, β-D-ribofuranose, and L-glutamine. It is an unprecedented alkaloid-Noxide involving biosynthetic pathways of polyketide, pentose, and amino acid. Aspergilin B (2) exhibited potent antiproliferative activity against four cancer cell lines. In addition, Aspergilin B (2) dose-dependently induced G2/M phase arrest in HepG2 cells.

This study investigates anomaly detection of IoT device traffic using Convolutional Neural Networks (CNN) and Variational Autoencoders (VAE) to enhance the detection capability of security threats in IoT environments. A series of hardware configurations, software environments, and hyperparameters were utilized to optimize the training and testing processes of the models. The CNN model demonstrates robust classification performance, achieving an accuracy rate of 95.85% on the test dataset, effectively distinguishing between different types of IoT device traffic. Meanwhile, the VAE model exhibits proficient anomaly detection capabilities by effectively capturing abnormal patterns in the data using reconstruction loss and KL divergence. The combined use of CNN and VAE models offers a comprehensive solution to cybersecurity challenges in IoT environments. Future research directions include exploring diverse IoT traffic data, practical deployment for validation, and further optimization of model structures and parameters to improve performance and applicability.