Andaliman (Zanthoxylum acanthopodium DC) is a native plant of North Sumatra province. Zanthoxylum acanthopodium is a member of Rutaceae family widely found in northern Sumatra, Indonesia. The aim of this study was to barcode Z. acanthopodium in North Sumatra province, Indonesia based on cpDNA maturase K (matK). Samples were collected in seven localities across six regions of North Sumatra province. Phylogenetic analysis was conducted using Maximum Likelihood method. The results of phylogenetic analysis indicate that Z. acanthopodium is a monophyletic group that is derived from a common ancestor. The results of the phylogenetic tree construction show that there is a grouping of accession between Z. acanthopodium species separate from other species in the Zanthoxylum genus as well as those of the Rutaceae family. The results showed that cpDNA matK marker can effectively be used as DNA barcoding to identify Z. acanthopodium.
Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results.
In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.
Sponge-derived fungi have recently attracted attention as an important source of interesting bioactive compounds. Aspergillus nomius NC06 was isolated from the marine sponge Neopetrosia chaliniformis. This fungus was cultured on rice medium and yielded four compounds including three new oxisterigmatocystins, namely, J, K, and L (1, 2, and 3), and one known compound, aspergillicin A (4). Structures of the compounds were elucidated by 1D and 2D NMR spectroscopy and by high-resolution mass spectrometry. The isolated compounds were tested for cytotoxic activity against HT 29 colon cancer cells, where compounds 1, 2, and 4 exhibited IC50 values of 6.28, 15.14, and 1.63 µM, respectively. Under the fluorescence microscope by using a double staining method, HT 29 cells were observed to be viable, apoptotic, and necrotic after treatment with the cytotoxic compounds 1, 2, and 4. The result shows that compounds 1 and 2 were able to induce apoptosis and cell death in HT 29 cells.
Methicillin-resistant Staphylococcus aureus (MRSA) is an emerging nosocomial pathogen among hospitalized patients, with high morbidity and mortality rates. The discovery of a novel antibacterial is urgently needed to address this resistance problem. The present study aims to explore the antibacterial potential of three depsidone compounds: 2-clorounguinol (1), unguinol (2), and nidulin (3), isolated from the marine sponge-derived fungus Aspergillus unguis IB1, both in vitro and in silico. The antibacterial activity of all compounds was evaluated by calculating the Minimum inhibitory concentration (MIC) and Minimum bactericidal concentration (MBC) against MRSA using agar diffusion and total plate count methods, respectively. Bacterial cell morphology changes were studied for the first time using scanning electron microscopy (SEM). Molecular docking, pharmacokinetics analysis, and molecular dynamics simulation were performed to determine possible protein-ligand interactions and the stability of the targeting penicillin-binding protein 2a (PBP2a) against 2-clorounguinol (1). The research findings indicated that compounds 1 to 3 exhibited MIC and MBC values of 2 µg/mL and 16 µg/mL against MRSA, respectively. MRSA cells displayed a distinct shape after the addition of the depsidone compound, as observed in SEM. According to the in silico study, 2-chlorounguinol exhibited the highest binding-free energy (BFE) with PBP2a (-6.7 kcal/mol). For comparison, (E)-3-(2-(4-cyanostyryl)-4-oxoquinazolin-3(4H)-yl) benzoic acid inhibits PBP2a with a BFE less than -6.6 kcal/mol. Based on the Lipinski's rule of 5, depsidone compounds constitute a class of compounds with good pharmacokinetic properties, being easily absorbed and permeable. These findings suggest that 2-chlorounguinol possesses potential antibacterial activity and could be developed as an antibiotic adjuvant to reduce antimicrobial resistance.