Search Results (39)

Epstein–Barr virus (EBV) is a human pathogenic and oncogenic herpesvirus, with worldwide importance, at times associated with serious to life-threatening symptoms, especially in immunocompromised hosts. The available preventive options against EBV disease are limited to medically elaborate and cost-intensive measures of cell-based immunotherapy. The development of novel options of anti-EBV drug targeting is currently a matter of intense international efforts. A putative target of the antiviral therapy approach is the EBV-encoded protein kinase BGLF4, which fulfills a multifaceted role in productive viral replication. So far, viral BGLF4 interactor proteins and phosphorylated substrates have occasionally been reported, but in particular cellular interactors await further characterization concerning both, their relevance for BGLF4 functionality and their accessibility to antiviral drugs. In this study, we have analyzed host cell–BGLF4 interaction, BGLF4 kinase properties, and BGLF4-directed small molecules. The main results are as follows: (i) a mass spectrometry-based interactomic study was performed with EBV-producing Akata-BX1 cells, thereby identifying the human pyruvate dehydrogenase (PDH) as a relevant BGLF4 interactor; (ii) BGLF4–PDH interaction was confirmed by protein coimmunoprecipitation, subcellular cofractionation, and confocal imaging; (iii) the BGLF4-mediated phosphorylation of PDH was demonstrated by an in vitro kinase assay (IVKA); (iv) a reduction in PDH phosphorylation was shown for selected kinase inhibitors, which also exerted BGLF4-directed inhibitory potential in a quantitative qSox-IVKA, and (v) these hit compounds showed anti-EBV activity in lytically induced P3HR-1 cells using qPCR measurement, as well as PDH-inhibitory activity using standardized PDH assays. These data lead to an improved understanding of EBV–host interaction that may open novel anti-EBV preventive opportunities. Combined, the findings point to PDH as a new cellular interactor of the EBV kinase BGLF4. Also, notably, the data on pharmacological intervention with kinase activity or substrate phosphorylation may possibly provide as yet untapped options of antiviral drug targeting. Full article

Granitic soils in the Highlands support the cultivation of Arabica coffee in northern Thailand; however, their geochemical and radiological properties are inadequately defined. This study examined major oxides, trace elements, natural radionuclides, and extractable phosphorus in granitic-derived coffee soils from the Agricultural Innovation Research, Integration, Demonstration, and Training Center (AIRID) in Chiang Mai. Twenty soil samples were obtained from 10 locations at two depth intervals (0–30 cm and 30–60 cm). Major and trace elements were analyzed via X-ray fluorescence (XRF), natural radionuclides were analyzed through high-purity germanium (HPGe) gamma spectrometry, and extractable phosphorus was determined using the Bray II method. The soils demonstrate remarkably high ⁴⁰K activity concentrations (1.2–1.9 kBq kg⁻¹) and increased K₂O contents (4.9–7.8 wt%), about three to five times more than worldwide soil averages according to Reimann & de Caritat, indicating enrichment from potassium-rich granitic rocks. Major oxide compositions suggest extensive tropical weathering, characterized by elevated SiO₂ (>60 wt%) and Al₂O₃ (>14 wt%), alongside significant depletion of CaO and MgO (<1 wt%). In topsoil, Bray II–extractable phosphorus constitutes 10–25% of total phosphorus and has a robust positive connection with P₂O₅ (R² = 0.95, p < 0.001), signifying surface accumulation and restricted vertical mobility. Multivariate analysis indicates lithogenic grouping of trace elements with negligible vertical redistribution. These findings establish a geochemical and radiological baseline for highland coffee soils in northern Thailand, with implications for soil fertility assessment, soil–plant transfer research, and evaluations of natural radioactive exposure related to coffee production. Full article

In this study, the biological activities of extracts obtained from Ramaria obtusissima were optimized using response surface methodology (RSM) and artificial neural networks-genetic algorithm (ANN-GA) approaches, and the chemical and biological profiles of the obtained extracts were evaluated with a holistic approach. Antioxidant potential was determined using FRAP, DPPH, TAS, TOS, and OSI parameters. It was found that the extract optimized with ANN-GA had significantly higher FRAP (242 ± 3 mg Trolox equivalent/g), TAS (6.64 ± 0.04 mmol/L), and DPPH (154 ± 3 mg Trolox equivalent/g) values compared to the RSM extract, while its OSI value was lower. Anticholinesterase activities were evaluated using IC₅₀ values, and it was determined that the ANN-GA extract exhibited a stronger inhibitory effect on acetylcholinesterase (95 ± 2 µg/mL) and butyrylcholinesterase (125 ± 3 µg/mL) compared to the RSM extract. Antiproliferative effects were investigated in A549, MCF-7, and DU-145 cell lines, and a significant and dose-dependent suppression of cell proliferation was observed in all three cell lines, particularly at concentrations of 100 and 200 µg/mL. The chemical profile was determined using LC-MS/MS and GC-MS techniques. Higher levels of phenolic compounds such as gallic acid (6694.5 ± 4.9 mg/kg), caffeic acid (3374.8 ± 4.9 mg/kg), and quercetin (1563.1 ± 2.3 mg/kg) were found in the ANN-GA extract. GC-MS analyses showed that the ANN-GA extract has a richer lipophilic component profile in terms of biologically active fatty acids and ester derivatives. The findings reveal that AI-assisted optimization offers a powerful and effective approach to enhancing the biological efficacy of mushroom-derived natural products. Full article

The comment raises three principal themes: (i) safeguarding statistical independence through grouping at the level of specimen/collector/site prior to splitting, (ii) preventing selection bias via fully nested preprocessing and hyperparameter tuning, and (iii) assessing shortcut learning and reporting probability calibration for decision thresholding in ecological workflows [...] Full article

This study was motivated by the persistent difficulty of accurately identifying morphologically similar macrofungi species, which remains a significant challenge in fungal taxonomy and biodiversity monitoring. This study presents a deep learning framework for the automated classification of seven morphologically similar coprinoid macrofungi species. A curated dataset of 1692 high-resolution images was used to evaluate ten state-of-the-art convolutional neural networks (CNNs) and three novel fusion models. The Dual Path Network (DPN) achieved the highest performance as a single model with 89.35% accuracy, a 0.8764 Matthews Correlation Coefficient (MCC), and a 0.9886 Area Under the Curve (AUC). The feature-level fusion of Xception and DPN yielded competitive results, reaching 88.89% accuracy and 0.8803 MCC, demonstrating the synergistic potential of combining architectures. In contrast, lighter models like LCNet and MixNet showed lower performance, achieving only 72.05% accuracy. Explainable AI (XAI) techniques, including Grad-CAM and Integrated Gradients, confirmed that high-performing models focused accurately on discriminative morphological structures such as caps and gills. The results underscore the efficacy of deep learning, particularly deeper architectures and strategic fusion models, in overcoming the challenges of fine-grained visual classification in mycology. This work provides a robust, interpretable computational tool for automated fungal identification, with significant implications for biodiversity research and taxonomic studies. Full article

Accurate identification of wild edible macrofungi is essential for biodiversity conservation, food safety, and ecological sustainability, yet remains challenging due to the morphological similarity between edible and toxic species. In this study, a curated dataset of 24 wild edible macrofungi species was analyzed using six state-of-the-art convolutional neural networks (CNNs) and four ensemble configurations, benchmarked across eight evaluation metrics. Among individual models, EfficientNetB0 achieved the highest performance (95.55% accuracy), whereas MobileNetV3-L underperformed (90.55%). Pairwise ensembles yielded inconsistent improvements, highlighting the importance of architectural complementarity. Notably, the proposed Combination Model, integrating EfficientNetB0, ResNet50, and RegNetY through a hierarchical voting strategy, achieved the best results with 97.36% accuracy, 0.9996 AUC, and 0.9725 MCC, surpassing all other models. To enhance interpretability, explainable AI (XAI) methods Grad-CAM, Eigen-CAM, and LIME were employed, consistently revealing biologically meaningful regions and transforming the framework into a transparent decision-support tool. These findings establish a robust and scalable paradigm for fine-grained fungal classification, demonstrating that carefully engineered ensemble learning combined with XAI not only advances mycological research but also paves the way for broader applications in plant recognition, spore analysis, and large-scale vegetation monitoring from satellite imagery. Full article

Fly ash from coal-fired power plants is a promising precursor for zeolite synthesis due to its aluminosilicate-rich composition. However, its direct utilization is often limited by impurities and a low silicon-to-aluminum ratio (SAR). This study demonstrates the conversion of Class C fly ash from the Soma thermal power plant (Turkey) into FAU- and CHA-type zeolites through optimized acid leaching and hydrothermal synthesis. Acid treatment increased the SAR from 1.33 to 2.85 and effectively reduced calcium-, sulfur-, and iron-bearing impurities. The SAR enhancement by acid leaching was found to be reproducible among Class C fly ashes, whereas Class F materials exhibited a limited response due to their acid-resistant framework. Subsequent optimization of alkaline fusion-assisted synthesis enabled selective crystallization of FAU and CHA, while GIS and MER appeared under prolonged crystallization or higher alkalinity. SEM revealed distinct morphologies, with MER forming rod-shaped clusters, and CHA exhibiting disc-like aggregates. Water sorption analysis showed superior uptake for metastable FAU (~23 wt%) and CHA (~18 wt%) compared to stable GIS and MER (~12–13 wt%). Overall, this study establishes a scalable and sustainable route for producing high-performance zeolites from industrial fly ash waste, offering significant potential for adsorption-based applications in dehumidification, heat pumps, and gas separation. Full article

The current study presents a multi-class, image-based classification of eight morphologically similar macroscopic Earthstar fungal species (Astraeus hygrometricus, Geastrum coronatum, G. elegans, G. fimbriatum, G. quadrifidum, G. rufescens, G. triplex, and Myriostoma coliforme) using deep learning and explainable artificial intelligence (XAI) techniques. For the first time in the literature, these species are evaluated together, providing a highly challenging dataset due to significant visual overlap. Eight different convolutional neural network (CNN) and transformer-based architectures were employed, including EfficientNetV2-M, DenseNet121, MaxViT-S, DeiT, RegNetY-8GF, MobileNetV3, EfficientNet-B3, and MnasNet. The accuracy scores of these models ranged from 86.16% to 96.23%, with EfficientNet-B3 achieving the best individual performance. To enhance interpretability, Grad-CAM and Score-CAM methods were utilised to visualise the rationale behind each classification decision. A key novelty of this study is the design of two hybrid ensemble models: EfficientNet-B3 + DeiT and DenseNet121 + MaxViT-S. These ensembles further improved classification stability, reaching 93.71% and 93.08% accuracy, respectively. Based on metric-based evaluation, the EfficientNet-B3 + DeiT model delivered the most balanced performance, with 93.83% precision, 93.72% recall, 93.73% F1-score, 99.10% specificity, a log loss of 0.2292, and an MCC of 0.9282. Moreover, this modeling approach holds potential for monitoring symbiotic fungal species in agricultural ecosystems and supporting sustainable production strategies. This research contributes to the literature by introducing a novel framework that simultaneously emphasises classification accuracy and model interpretability in fungal taxonomy. The proposed method successfully classified morphologically similar puffball species with high accuracy, while explainable AI techniques revealed biologically meaningful insights. All evaluation metrics were computed exclusively on a 10% independent test set that was entirely separate from the training and validation phases. Future work will focus on expanding the dataset with samples from diverse ecological regions and testing the method under field conditions. Full article

Antibiotic resistance poses a critical threat to global health, largely driven by bacterial efflux pumps that expel antibiotics and reduce their efficacy. This study investigated the potential of Phellinus hartigii ethyl acetate extract (Ph-Ace) to inhibit efflux pumps and restore antibiotic activity against multidrug-resistant Escherichia coli strains. In vitro assays demonstrated that Ph-Ace effectively inhibited efflux pumps, enhancing the efficacy of resistant antibiotics. GC/MS analysis identified key components such as nonadecane and octacosane. These findings suggest Ph-Ace as a promising natural efflux pump inhibitor. Further molecular and clinical studies are required to confirm its therapeutic potential. Full article

In this study, extraction conditions were optimized to maximize the biological activities of extracts obtained from Paralepista flaccida, an edible mushroom species. Extraction processes were carried out using an ultrasonically assisted system, and two different optimization approaches were used as follows: Response Surface Methodology (RSM) and Artificial Neural Network–Genetic Algorithm (ANN-GA). The antioxidant potentials of the optimized extracts were evaluated using DPPH, FRAP, TAS, TOS, and OSI parameters; anticholinesterase activities were measured against AChE and BChE enzymes; and antiproliferative activities were investigated in A549, MCF-7, and DU-145 human cancer cell lines. In addition, phenolic contents were determined by LC-MS/MS analysis. The findings revealed that the extracts obtained by the RSM method exhibited a superior biological profile compared to ANN-GA extracts in terms of antioxidant, anticholinesterase, and antiproliferative activities. The high cytotoxicity observed, particularly in the MCF-7 line, supports the anticancer potential of this extract. These results demonstrate that optimization strategies are crucial for increasing not only extract yield but also biological functionality. Full article

Puffballs, a group of macrofungi belonging to the Basidiomycota, pose taxonomic challenges due to their convergent morphological features, including spherical basidiocarps and similar peridial structures, which often hinder accurate species-level identification. This study proposes a deep learning-based classification framework for eight ecologically and taxonomically important puffball species: Apioperdon pyriforme, Bovista plumbea, Bovistella utriformis, Lycoperdon echinatum, L. excipuliforme, L. molle, L. perlatum, and Mycenastrum corium. A balanced dataset of 1600 images (200 per species) was used, divided into 70% training, 15% validation, and 15% testing. To enhance generalizability, images were augmented to simulate natural variability in orientation, lighting, and background. In this study, five different deep learning models (ConvNeXt-Base, Swin Transformer, ViT, MaxViT, EfficientNet-B3) were comparatively evaluated on a balanced dataset of eight puffball species. Among these, the ConvNeXt-Base model achieved the highest performance, with 95.41% accuracy, and proved especially effective in distinguishing morphologically similar species such as Mycenastrum corium and Lycoperdon excipuliforme. The findings demonstrate that deep learning models can serve as powerful tools for the accurate classification of visually similar fungal species. This technological approach shows promise for developing automated mushroom identification systems that support citizen science, amateur naturalists, and conservation professionals. Full article

This study presents a novel approach for classifying Discomycetes species using deep learning and explainable artificial intelligence (XAI) techniques. The EfficientNet-B0 model achieved the highest performance, reaching 97% accuracy, a 97% F1-score, and a 99% AUC, making it the most effective model. MobileNetV3-L followed closely, with 96% accuracy, a 96% F1-score, and a 99% AUC, while ShuffleNet also showed strong results, reaching 95% accuracy and a 95% F1-score. In contrast, the EfficientNet-B4 model exhibited lower performance, achieving 89% accuracy, an 89% F1-score, and a 93% AUC. These results highlight the superior feature extraction and classification capabilities of EfficientNet-B0 and MobileNetV3-L for biological data. Explainable AI (XAI) techniques, including Grad-CAM and Score-CAM, enhanced the interpretability and transparency of model decisions. These methods offered insights into the internal decision-making processes of deep learning models, ensuring reliable classification results. This approach improves traditional taxonomy by advancing data processing and supporting accurate species differentiation. In the future, using larger datasets and more advanced AI models is recommended for biodiversity monitoring, ecosystem modeling, medical imaging, and bioinformatics. Beyond high classification performance, this study offers an ecologically meaningful approach by supporting biodiversity conservation and the accurate identification of fungal species. These findings contribute to developing more precise and reliable biological classification systems, setting new standards for AI-driven research in biological sciences. Full article

Soil temperature, a key factor in subsurface geochemical processes, is influenced by environmental and geological dynamics. This study analyzed hourly soil temperature variations at depths of 10 to 100 cm near the Sakurajima volcano, alongside concurrent ambient temperature measurements. By applying temperature models and statistical methods, we characterized both seasonal and short-term thermal dynamics, including soil-atmosphere thermal coupling. Our findings revealed a depth-dependent thermal diffusivity, establishing distinct thermal regimes within the soil profile. The soil’s strong thermal buffering capacity, evidenced by increasing amplitude attenuation and temporal lag with depth, allowed us to identify optimal instrument placement depths (80–100 cm) for minimal diurnal temperature influence. We also quantified the relationship between ambient temperature fluctuations and soil thermal response at various depths, as well as the impact of these temperature variations on soil permeability. These results enhance our understanding of subsurface thermal behaviour in volcanic environments and offer practical guidance for environmental monitoring and geohazard studies. Full article

Background: Epstein-Barr virus (EBV) infects approximately 95% of the global population, causing numerous malignancy-related cases annually and some autoimmune diseases. EBV-encoded gp350, gH, gL, gp42 and gB glycoproteins are identified as antigen candidates for their key role in viral entry, and nanoparticle vaccines displaying them were developed for the advantage of inducing cross-reactive B cell responses. Methods: To develop liposomes displaying nanoparticle vaccine, we synthesized liposomes to present the well-identified EBV-encoded gp350D₁₂₃ glycoprotein on their surface to imitate the viral structure, through the conjugation between N-hydroxysuccinimide (NHS) groups on the liposomes and primary amine of antigens to form stable amide bond. Then we assessed the immunogenicity of the biomimetic Lipo-gp350D₁₂₃ nanoparticle vaccine in Balb/c mice immunized experiments. Results: The results showed that the sera samples from Lipo-gp350D₁₂₃ nanoparticle vaccine immunized mice collected at weeks 8, 10 and 12 had higher titers of gp350D₁₂₃ protein-specific antibodies, compared to monomer gp350D₁₂₃ protein control, and higher titers of neutralizing antibodies to block EBV-GFP infection in AKATA cells. Meanwhile, the Lipo-gp350D₁₂₃ nanoparticle vaccine also induced higher percentage of CD8+ IFN-γ+ T cells in the spleen, but without significance in CD4+ IFN-γ+ T cells, and these isolated splenocytes showed a higher level of secreted IFN-γ. Moreover, no significant histopathological changes were observed in all vaccinated mice. Conclusions: Altogether these data demonstrated that the liposome displaying promoted the immunogenicity of antigens, and the Lipo-gp350D₁₂₃ nanoparticle vaccine candidate had potential application in blocking EBV infection. The liposome nanoparticle was a useful vector for antigen displaying to elicit effective immunity. Full article

The Niger Basin is a typical marginal basin with complex internal structures and abundant oil and gas resources, exhibiting unique marine geological characteristics and processes. The distribution and deformation characteristics of Akata Formation mudstone in the basin significantly influence hydrocarbon accumulation. In this study, four analogue models were used to analyze the main factors affecting mudstone tectonics and establish an evolution model of mudstone structures. The results show that the tectonic features in the basin reflect the combined influence of gravity sliding and spreading. The main mechanism driving mudstone deformation is gravity spreading caused by differential loading. The basement morphology is the decisive factor in the development of zonation involving extension, translation, and contraction zones. The development of mudstone structures is also affected by the inclination of the basement slope and the thicknesses of both the mudstone layer and overlying layers. A relatively large basement slope inclination is conducive to the rapid flow of mudstone, leading to the rapid development of mudstone formations. A thin mudstone layer with weak plastic mobility is favorable for the full development of mudstone formations. A relatively thick overburden leads to enhanced gravity spreading, which in turn leads to the formation of larger and more numerous mudstone structures. The formation and evolution of mudstone structures in the Niger Basin involved through three stages: (1) during the Paleocene–Middle Oligocene, thick marine mudstone was deposited; (2) in the Middle Oligocene–Late Oligocene, the mudstone and overlying layers were strongly deformed, and numerous mudstone structures developed with tectonic zonation; and (3) since the Pliocene, the tectonic activity in the basin weakened. The simulation of the evolutionary process and evolutionary model established in this study improves the understanding of mudstone tectonics and provides a reference for analyzing the genetic mechanism and hydrocarbon exploration in the basin. Full article