Search Results (423)

Sea ice concentration (SIC) and thickness (SIT) are critical variables for polar research. In this study, the potential of Tianmu-1 GNSS-R observations for retrieving Arctic SIC and SIT is explored using machine learning algorithms. XGBoost demonstrated superior accuracy and efficiency in the comparison of the three methods. For SIC retrieval, 14 parameters from Tianmu-1 were employed directly, whereas SIT retrieval incorporated additional auxiliary parameters, including SIC, sea ice salinity (S), and temperature (T). Among the different GNSS systems, GLO achieved the lowest RMSE for SIC, at 7.750%, whereas GAL performed comparatively poorly, with an RMSE of 10.475%. In SIT retrieval, the GPS and BDS yielded the smallest RMSE values of 0.276 m and 0.278 m, respectively, while GLO resulted in a slightly higher RMSE of 0.309 m. Daily retrievals of both the SIC and SIT were conducted from 18 October 2023 to 12 April 2024, with consistently stable evaluation metrics throughout the freezing season. In high-concentration regions, the retrieved SIC and SIT closely matched the reference data, whereas larger errors occurred in marginal ice zones and coastal areas. This study reveals the potential of Tianmu-1 to complement existing satellite missions in Arctic sea ice monitoring during the freezing period. Full article

Colorectal cancer is one of the leading causes of cancer-associated mortality, and multifactorial resistance remains one of the main challenges in its treatment. Essential oils and their main compounds show interesting anticancer properties, but their mechanism of action is yet to be defined. This study aims to assess the cytotoxic effects of eugenol (EU) and cinnamaldehyde (CN) on colorectal cancer (CRC) cells, highlighting possible mechanisms of action. These compounds were tested on normal immortalized colonocytes (NCM-460) and two CRC cell lines: Caco-2, a human colon epithelial adenocarcinoma cell line, and SW-620, colon cancer cells derived from a lymph node metastatic site. The efficacy of EU and CN was evaluated through CellTiter-Glo^® and clonogenic assays and by determining proinflammatory cytokine secretion. Transcriptome analysis was used to identify possible pathways affected by EU and CN treatments. The results confirmed that EU and CN were selectively cytotoxic and pro-apoptotic against CRC cells, with different putative mechanisms. While EU drove cytotoxicity through robust transcriptional remodeling, CN yielded a stronger anti-inflammatory action. We confirmed that EU and CN are promising natural candidates in CRC prevention and treatment, even in association with chemotherapeutic drugs. Full article

This study aims to investigate the effects of dietary vitamin C supplementation on vitamin C synthesis, transport, and egg deposition in breeding geese. A total of 450 female and 90 male 221-day-old Yangzhou geese were randomly assigned to five treatment groups with six replicates each (15 females and 3 males per replicate). The control group received a basal diet, while the other four groups were fed diets supplemented with 100, 200, 300, and 400 mg/kg vitamin C over a 16-week feeding trial. The results showed that dietary vitamin C supplementation increased the vitamin C content in both serum and egg yolks and modulated the expression of key vitamin C-related genes. Specifically, the intestinal and ovarian sodium-dependent vitamin C transporters 1 and 2 (SVCT1/SVCT2) were upregulated, whereas hepatic and renal L-Gulonolactone oxidase (GLO) and SVCT1 were suppressed. These findings indicate that exogenous vitamin C enhances intestinal absorption, inhibits hepatic synthesis, and promotes yolk deposition, with 300 mg/kg emerging as an effective and practical supplementation level that provides a physiological basis for its application in poultry nutrition. Full article

Wildfire occurrence in arid and semiarid landscapes is increasingly driven by shifts in climatic and biophysical conditions, yet its dynamics remain poorly understood in the mountainous environments of western Saudi Arabia. This study modeled wildfire probabilities across the Aseer, Al Baha, Makkah Al-Mukarramah, and Jazan regions via multisource Earth observation datasets from 2012–2025. Active fire detections from VIIRS were integrated with ERA5-Land reanalysis variables, vegetation indices, and Copernicus DEM GLO30 topography. A random forest classifier was trained and validated via stratified sampling and cross-validation to predict monthly burn probabilities. Calibration, reliability assessment, and independent temporal validation confirmed strong model performance (AUC-ROC = 0.96; Brier = 0.03). Climatic dryness (dew-point deficit), vegetation structure (LAI_lv), and surface soil moisture emerged as dominant predictors, underscoring the coupling between energy balance and fuel desiccation. Temporal trend analyses (Kendall’s τ and Sen’s slope) revealed the gradual intensification of fire probability during the dry-to-transition seasons (February–April and September–November), with Aseer showing the most persistent risk. These findings establish a scalable framework for wildfire early warning and landscape management in arid ecosystems under accelerating climatic stress. Full article

Foundation models excel on general benchmarks but often underperform in clinical settings due to domain shift between internet-scale pretraining data and medical data. Multimodal deep learning, which jointly leverages medical images and clinical text, is promising for diagnosis, yet it remains unclear whether domain adaptation is better achieved by fine-tuning large vision–language models or by training lighter, task-specific architectures. We address this question by introducing PairDx, a balanced dataset of 22,665 image–caption pairs spanning six medical document classes, curated to reduce class imbalance and support fair, reproducible comparisons. Using PairDx, we develop and evaluate two approaches: (i) PairDxCLIP, a fine-tuned CLIP (ViT-B/32), and (ii) PairDxFusion, a custom hybrid model that combines ResNet-18 visual features and GloVe text embeddings with attention-based fusion. Both adapted models substantially outperform a zero-shot CLIP baseline (61.18% accuracy) and a specialized model, BiomedCLIP, which serves as an additional baseline and achieves 66.3% accuracy. Our fine-tuned CLIP (PairDxCLIP) attains 93% accuracy and our custom fusion model (PairDxFusion) reaches 94% accuracy on a held-out test set. Notably, PairDxFusion achieves this high accuracy with 17 min, 55 s of training time, nearly four times faster than PairDxCLIP (65 min, 52 s), highlighting a practical efficiency–performance trade-off for clinical deployment. The testing time also outperforms the specialized model—BiomedCLIP (0.387 s/image). Our results demonstrate that carefully constructed domain-specific datasets and lightweight multimodal fusion can close the domain gap while reducing computational cost in healthcare decision support. Full article

Moringa oleifera (MO) is a versatile non-traditional feed supplement rich in bioactive compounds. The objective of this study was to examine the effects of MO leaf (MOL) polysaccharide (MOLP) intake as a natural product on broiler chicken production and antioxidant indices. A polysaccharide with a molecular weight of 182.989 kDa was isolated from MOL in a previous study. Broiler chickens were allocated at random into four groups receiving varying doses of MOLP (0, 0.1, 0.2 and 0.4 g/kg feed) for three weeks. Feed intake (FI), average daily feed ingestion (ADFI), feed conversion ratio (FCR), and body weight gain (BWG) were monitored. Serological markers, including total protein (TP), albumin (ALB), globulin (GLO), albumin-to-globulin ratio (ALB/GLO), creatinine (CREA), as well as the activities of total superoxide dismutase (T-SOD), glutathione peroxidase (GSH-Px), total antioxidant capacity (T-AOC) and the concentrations of malondialdehyde (MDA) were assessed. Results from days 21 to 28 demonstrated that the high dose of MOLP significantly enhanced BWG, ADFI, liver and bursa indices compared to the control group. Additionally, TP and GLO, T-SOD, GSH-Px, T-AOC and MDA levels were elevated (p < 0.05). In conclusion, MOLP supplementation, particularly at 0.4 g/kg feed, positively impacted broiler chicken growth performance and antioxidant indices, suggesting its potential as a valuable feed additive. Full article

This article analyses a GloCal Service-Learning experience conducted by a student from the University of Padova, during a 5-month mobility period in Brazil. The experience involved conducting educational meetings for in-service teachers and parents of children in conditions of high social vulnerability in the city of Juazeiro, Bahia. The meetings aimed to raise awareness about the importance of fostering healthy and psychologically stimulating environments during early infancy development. As part of a case study, the research focuses on teachers’ and parents’ evaluations of the meetings and the learning outcomes of the student involved. Qualitative data were analyzed using descriptive coding. Data analysis revealed, on the one hand, that both teachers and parents evaluated the meetings very positively and, on the other hand, the need to give the community a greater voice. With respect to student learning, the research highlighted that contextual immersion, language, and contextualized education—dimensions of the GloCal framework—emerged as interconnected and indispensable to translating care into practice. Despite its limitations, this experience offers valuable insights into how International Service-Learning can evolve into a truly intercultural and ethical practice, bringing care and GloCal responsibility to the heart of education. Full article

This study presents the design and evaluation of a multi-model artificial intelligence (AI) framework for proactive quality risk management in projects. A dataset comprising 2000 risk records was developed, containing four columns: Risk Description (input), Risk Category, Trigger, and Impact (outputs). Each output variable was modeled using three independent classifiers, forming a multi-step decision-making pipeline where one input is processed by multiple specialized models. Two feature extraction techniques, Term Frequency–Inverse Document Frequency (TF-IDF) and GloVe100 Word Embeddings, were compared in combination with several machine learning algorithms, including Logistic Regression, Support Vector Machines (SVMs), Random Forest, Multinomial Naive Bayes, and XGBoost. Results showed that model performance varied with task complexity and the number of output classes. Trigger prediction (28 classes), Logistic Regression, and SVM achieved the best performance, with a macro-average F1-score of 0.75, while XGBoost with TF-IDF features produced the highest accuracy for Risk Category classification (five classes). In Impact prediction (15 classes), SVM with Word Embeddings demonstrated superior results. The implementation, conducted in Python (v3.9.12, Anaconda), utilized Scikit-learn, XGBoost, SHAP, and Gensim libraries. SHAP visualizations and confusion matrices enhanced model interpretability. The proposed framework contributes to scalable, text-based, predictive, quality risk management, supporting real-time project decision-making. Full article

Sentiment analysis is essential for understanding consumer opinions, yet selecting the optimal models and embedding methods remains challenging, especially when handling ambiguous expressions, slang, or mismatched sentiment–rating pairs. This study provides a comprehensive comparative evaluation of sentiment classification models across three paradigms: traditional machine learning, pre-transformer deep learning, and transformer-based models. Using the Amazon Magazine Subscriptions 2023 dataset, we evaluate a range of embedding techniques, including static embeddings (GloVe, FastText) and contextual transformer embeddings (BERT, DistilBERT, etc.). To capture predictive confidence and model uncertainty, we include categorical cross-entropy as a key evaluation metric alongside accuracy, precision, recall, and F1-score. In addition to detailed quantitative comparisons, we conduct a systematic qualitative analysis of misclassified samples to reveal model-specific patterns of uncertainty. Our findings show that FastText consistently outperforms GloVe in both traditional and LSTM-based models, particularly in recall, due to its subword-level semantic richness. Transformer-based models demonstrate superior contextual understanding and achieve the highest accuracy (92%) and lowest cross-entropy loss (0.25) with DistilBERT, indicating well-calibrated predictions. To validate the generalisability of our results, we replicated our experiments on the Amazon Gift Card Reviews dataset, where similar trends were observed. We also adopt a resource-aware approach by reducing the dataset size from 25 K to 20 K to reflect real-world hardware constraints. This study contributes to both sentiment analysis and sustainable AI by offering a scalable, entropy-aware evaluation framework that supports informed, context-sensitive model selection for practical applications. Full article

One of the best-known flavonoid chrysin was coupled at position 7 with several trisubstituted phosphine derivatives with a flexible spacer, and their in vitro anticancer activities were investigated on 60 human tumor cell lines (NCI60) and on several gynecological cancer cells. The trisubstituted phosphines contained different substituents on the aromatic ring(s), e.g., methyl and methoxy groups or fluoro atoms. The phosphorus atom was substituted not only with aromatic rings but with cyclohexyl substituents. The ionic phosphonium building block is important because it allows the therapeutic agents to transfer across the cell membrane. Therefore, the pharmacophores linked to it can exert their effects in the mitochondria. Instead of the ionic phosphonium element, a neutral moiety, namely the triphenylmethyl group, was also added to the side chain, being sterically similar but without a charge and phosphorus atom. Most of the hybrids exhibited low micromolar growth inhibition (GI₅₀) values against the majority of the tested cell lines. Notably, conjugate 3f stood out, demonstrating nanomolar antitumor activity against the K-562 leukemia cell line (GI₅₀ = 34 nM). One selected compound (3i) with promising cancer selectivity elicited cell cycle disturbances and inhibited the migration of breast cancer. The tumor-selectivity of 3a and 3f was assessed based on their effects on non-tumor Chinese hamster ovary (CHO) cells using the CellTiter-Glo Luminescent Cell Viability Assay. Given their estimated half-maximal inhibitory concentration (IC₅₀) values on non-tumor CHO cells (2.65 µM and 1.15 µM, respectively), these conjugates demonstrate promising selectivity toward several cancer cell lines. The excellent results obtained may serve as good starting points for further optimization and the design of even more effective flavonoid- and/or phosphonium-based drugs. Full article

Perioperative anesthesia might directly alter cancer cell biology. We investigated the effects of levobupivacaine treatment on lung adenocarcinoma cells. A549 cells were treated with levobupivacaine at concentrations of 0.1 mM and 0.5 mM for 2 h. Transfection with angiotensin-converting enzyme 2 (ACE2) small interfering RNA (siRNA) was performed 6 h before the levobupivacaine treatment. Cell proliferation was assessed using a cell counting kit 8 (CCK-8), and ATP synthesis was evaluated with the CellTiter-Glo^® 2.0 assay at 0 and 24 h after anesthesia exposure. RT-PCR was performed to examine various biomarkers. The levobupivacaine treatment suppressed ATP synthesis without affecting cell proliferation. This was associated with the upregulation of ACE2 and the downregulation of pro-cancer biomarkers, including HIF-1α, MMP-9, and β-catenin. The anticancer effect of levobupivacaine was negated when ACE2 siRNA was introduced, and it was further suppressed when combined with levobupivacaine. The RT-PCR results indicated that the expressions of B-cell/CLL lymphoma 2 (BCL2) and wingless/integrated 1 (WNT1) were reduced after levobupivacaine treatment, but these effects were reversed with ACE2 siRNA induction. The administration of levobupivacaine suppressed A549 cell metabolism and downregulated HIF-1α, MMP-9, WNT1, EGFR, and BCL2 in an ACE2-dependent manner. Full article

(1) Background: Digital Elevation Models (DEMs) encompass digital bare earth surface representations that are essential for spatial data analysis, such as hydrological and geological modelling, as well as for other applications, such as agriculture and environmental management. However, available bare-earth DEMs can have limited coverage or accessibility. Moreover, the majority of available global DEMs have lower spatial resolutions (∼30–90 m) and contain errors introduced by surface features such as buildings and vegetation. (2) Methods: This research presents an innovative method to convert global DEMs to bare-earth DEMs while enhancing their spatial resolution as measured by the improved vertical accuracy of each pixel, combined with reduced pixel size. We propose the Joint Spatial Propagation Super-Resolution network (JSPSR), which integrates Guided Image Filtering (GIF) and Spatial Propagation Network (SPN). By leveraging guidance features extracted from remote sensing images with or without auxiliary spatial data, our method can correct elevation errors and enhance the spatial resolution of DEMs. We developed a dataset for real-world bare-earth DEM Super-Resolution (SR) problems in low-relief areas utilising open-access data. Experiments were conducted on the dataset using JSPSR and other methods to predict 3 m and 8 m spatial resolution DEMs from 30 m spatial resolution Copernicus GLO-30 DEMs. (3) Results: JSPSR improved prediction accuracy by 71.74% on Root Mean Squared Error (RMSE) and reconstruction quality by 22.9% on Peak Signal-to-Noise Ratio (PSNR) compared to bicubic interpolated GLO-30 DEMs, and achieves 56.03% and 13.8% improvement on the same items against a baseline Single Image Super Resolution (SISR) method. Overall RMSE was 1.06 m at 8 m spatial resolution and 1.1 m at 3 m, compared to 3.8 m for GLO-30, 1.8 m for FABDEM and 1.3 m for FathomDEM, at either resolution. (4) Conclusions: JSPSR outperforms other methods in bare-earth DEM super-resolution tasks, with improved elevation accuracy compared to other state-of-the-art globally available datasets. Full article

Biofilms are involved in both acute and chronic respiratory infections. While cigarette smoke extract (CSE) has been shown to increase biofilm formation by certain respiratory pathogens, the impact of emerging heated tobacco products (HTPs) remains unclear. We compared the effects of CSE with two HTP aerosol extracts on biofilm biomass and metabolic activity of common respiratory pathogens. Reference strains of Staphylococcus aureus, Pseudomonas aeruginosa, Klebsiella pneumoniae, Streptococcus pneumoniae, and non-typeable Haemophilus influenzae (NTHi), known respiratory pathogens, were grown as 24 h biofilms in 96-well plates (48 h for S. aureus and P. aeruginosa). These were exposed to CSE and HTP extracts from iQOS™ (Terea™ Turquoise, ILUMA™ device) and glo™ (neo™ Azure, HyperPro™ device), prepared in liquid culture media. Biofilm density was quantified by the crystal violet assay. Metabolic activity (planktonic and biofilm) was assessed by MTT reduction to formazan. At 24 h, CSE markedly reduced H. influenzae biomass versus iQOS™, glo™, and control, while K. pneumoniae, S. aureus, and P. aeruginosa showed no significant biomass differences. At 48 h, CSE significantly increased biomass in P. aeruginosa and S. aureus versus other exposures. Biofilm MTT assay measured metabolic activity increased in CSE exposure for K. pneumoniae versus iQOS™ and control, and for S. aureus versus control. Overall, HTP extracts showed limited, inconsistent effects compared with CSE, indicating combustion-derived constituents more strongly promote biofilm maturation in this model. Full article

Background: Malnutrition is a serious health concern among older adults in residential aged care (RAC), and timely identification is critical for effective intervention. Recent advancements in transformer-based large language models (LLMs), such as RoBERTa, provide context-aware embeddings that improve predictive performance in clinical tasks. Fine-tuning these models on domain-specific corpora, like nursing progress notes, can further enhance their applicability in healthcare. Methodology: We developed a RAC domain-specific LLM by training RoBERTa on 500,000 nursing progress notes from RAC electronic health records (EHRs). The model’s embeddings were used for two downstream tasks: malnutrition note identification and malnutrition prediction. Long sequences were truncated and processed in segments of up to 1536 tokens to fit RoBERTa’s 512-token input limit. Performance was compared against Bag of Words, GloVe, baseline RoBERTa, BlueBERT, ClinicalBERT, BioClinicalBERT, and PubMed models. Results: Using 5-fold cross-validation, the RAC domain-specific LLM outperformed other models. For malnutrition note identification, it achieved an F1-score of 0.966, and for malnutrition prediction, it achieved an F1-score of 0.687. Conclusions: This approach demonstrates the feasibility of developing specialised LLMs for identifying and predicting malnutrition among older adults in RAC. Future work includes further optimisation of prediction performance and integration with clinical workflows to support early intervention. Full article

Melanoma is an aggressive malignancy with poor prognosis in advanced stages, and current therapeutic options provide only limited benefits, highlighting the need for novel treatments. Chetomin, a fungal metabolite isolated from Chaetomium cochliodes, has been reported to exhibit diverse biological activities, yet its effects on melanoma cells remain poorly understood. In this study, we evaluated the antitumor potential of chetomin using the human A375 melanoma cell line. Cell viability was assessed with MTT and CellTiter-Glo^® assays, which revealed a significant dose- and time-dependent reduction in proliferation following chetomin exposure. Apoptotic effects were confirmed through Annexin V staining, and immunocytochemical analysis demonstrated a concentration-dependent increase in cleaved PARP1, indicating activation of programmed cell death pathways. Collectively, these findings demonstrate that chetomin effectively inhibits melanoma cell growth and promotes apoptosis. The results suggest that chetomin represents a promising lead compound for melanoma therapy, warranting further investigation into its precise molecular mechanisms. Full article