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Aneuploidy confers a unique transcriptional and phenotypic profile to Candida albicans
Design and evaluation of a solar powered smart irrigation system for sustainable urban agriculture
Abstract Urban areas face significant challenges, including a lack of green spaces, scarce water resources, environmental pollution, and elevated heat emissions, particularly in developing countries experiencing rapid population growth. Therefore, the study aims to advance sustainable urban agriculture by designing and evaluating a solar-powered smart rooftop irrigation system for peppermint cultivation. The system incorporates two drip irrigation setups—conventional and smart irrigation—powered by photovoltaic (PV) panels. The smart system integrates real-time monitoring of critical variables, including (1) soil moisture, (2) relative humidity, (3) PV panel temperature, and (4) PV panel current and voltage. Key performance metrics such as water and energy consumption, water use efficiency, energy productivity, and carbon dioxide emissions were evaluated for both systems. In addition, the economic analysis of the smart system was determined. Results revealed that the smart system reduced water and energy consumption by 28.1% compared to conventional irrigation. Additionally, the smart system achieved a notable reduction in carbon footprint, with CO2 emissions of 0.181 kg CO₂/m2/year compared to 0.252 kg CO₂/m2/year for the conventional system. The system’s economic analysis demonstrated a payback period of 5.6 years, highlighting its financial viability. This study underscores the transformative potential of solar-powered smart irrigation systems in enhancing food security, conserving water, reducing energy consumption, and mitigating carbon emissions in urban agriculture.
High selectivity framework polymer membranes chemically tuned towards fast anion conduction
Construction of ubiquitination-related risk model for predicting prognosis in lung adenocarcinoma
Abstract Lung adenocarcinoma is the most prevalent lung cancer type. Ubiquitination, a critical post-translational modification process that regulates protein degradation and signaling pathways, has been implicated in various cancers, including LUAD. We aimed to explore the associations between ubiquitination and lung adenocarcinoma. TCGA-LUAD cohort served as the training set. Unsupervised clustering, univariate Cox regression, Random Survival Forests, and least absolute shrinkage and selection operator (LASSO) Cox regression were applied to identify ubiquitination-related genes (URGs), then ubiquitination-related risk scores (URRS) were calculated using gene expression and the univariate Cox’s coefficient. Comparisons between the high and the low URRS group regarding chemotherapy drug response, immune infiltration level, tumor mutation burden (TMB), tumor neoantigen load (TNB), PD1/L1 expression, and enriched pathways were performed. URRS was calculated based on the expression of DTL, UBE2S, CISH, and STC1. Patients with higher URRS had a worse prognosis (Hazard Ratio [HR] = 0.54, 95% Confidence Interval [CI]: 0.39–0.73, p < 0.001), and the prognosis of the URRS was further confirmed in 6 external validation cohorts (Hazard Ratio [HR] = 0.58, 95% Confidence Interval [CI]: 0.36–0.93, pmax = 0.023). The high URRS group had higher PD1/L1 expression level (p < 0.05), TMB (p < 0.001), TNB (p < 0.001), and TME scores (p < 0.001). The IC50 values of various chemotherapy drugs were lower in the high URRS group. In addition, we found that upregulation of STC1, UBE2S, and DTL was associated with worse, while upregulation of CISH was associated with better prognosis. We also performed a reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for validation. In conclusion, the ubiquitination-based signature might serve as a biomarker to help evaluate the prognosis, biological features, and appropriate treatment for patients with lung adenocarcinoma.
Enhancing thermoelectric output in a molecular heat engine utilizing Yu-Shiba-Rusinov bound states
Deep spatio-temporal dependent convolutional LSTM network for traffic flow prediction
ESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties
Applying deep learning for style transfer in digital art: enhancing creative expression through neural networks
The solvent extraction and stripping process using Alamine 336 with a case study of uranium
VGG-MFO-orange for sweetness prediction of Linhai mandarin oranges
Design of displacement-based viscous damper damping structures
Development and validation of interpretable machine learning models to predict distant metastasis and prognosis of muscle-invasive bladder cancer patients
Early transcriptomic changes in cucumber and maize roots in response to FePO4 nanoparticles as a source of P and Fe
Real-time fear emotion recognition in mice based on multimodal data fusion
An explainable hybrid feature aggregation network with residual inception positional encoding attention and EfficientNet for cassava leaf disease classification
Abstract Cassava is a tuberous edible plant native to the American tropics and is essential for its versatile applications including cassava flour, bread, tapioca, and laundry starch. Cassava leaf diseases reduce crop yields, elevate production costs, and disrupt market stability. This places significant burdens on farmers and economies while highlighting the need for effective management strategies. Traditional methods of manual disease diagnosis are costly, labor-intensive, and time-consuming. This research aims to address the challenge of accurate disease classification by overcoming the limitations of existing methods, which encounter difficulties with the complexity and variability of leaf disease symptoms. To the best of our knowledge, this is the first study to propose a novel dual-track feature aggregation architecture that integrates the Residual Inception Positional Encoding Attention (RIPEA) Network with EfficientNet for the classification of cassava leaf diseases. The proposed model employs a dual-track feature aggregation architecture which integrates the RIPEA Network with EfficientNet. The RIPEA track extracts significant features by leveraging residual connections for preserving gradients and uses multi-scale feature fusion for combining fine-grained details with broader patterns. It also incorporates Coordinate and Mixed Attention mechanisms which focus on cross-channel and long-range dependencies. The extracted features from both tracks are aggregated for classification. Furthermore, it incorporates an image augmentation method and a cosine decay learning rate schedule to improve model training. This improves the ability of the model to accurately differentiate between Cassava Bacterial Blight (CBB), Brown Streak Disease (CBSD), Green Mottle (CGM), Mosaic Disease (CMD), and healthy leaves, addressing both local textures and global structures. Additionally, to enhance the interpretability of the model, we apply Grad-CAM to provide visual explanations for the model’s decision-making process, helping to understand which regions of the leaf images contribute to the classification results. The proposed network achieved a classification accuracy of 93.06%.
Developing a standardized framework for evaluating health apps using natural language processing
Multi-scale adversarial diffusion network for image super-resolution
Predator crow search optimization with explainable AI for cardiac vascular disease classification
Abstract The proposed framework optimizes Explainable AI parameters, combining Predator crow search optimization to refine the predictive model’s performance. To prevent overfitting and enhance feature selection, an information acquisition-based technique is introduced, improving the model’s robustness and reliability. An enhanced U-Net model employing context-based partitioning is proposed for precise and automatic left ventricular segmentation, facilitating quantitative assessment. The methodology was validated using two datasets: the publicly available ACDC challenge dataset and the imATFIB dataset from internal clinical research, demonstrating significant improvements. The comparative analysis confirms the superiority of the proposed framework over existing cardiovascular disease prediction methods, achieving remarkable results of 99.72% accuracy, 96.47% precision, 98.6% recall, and 94.6% F1 measure. Additionally, qualitative analysis was performed to evaluate the interpretability and clinical relevance of the model’s predictions, ensuring that the outputs align with expert medical insights. This comprehensive approach not only advances the accuracy of CVD predictions but also provides a robust tool for medical professionals, potentially improving patient outcomes through early and precise diagnosis.