Search Results (189)

Background/Objectives: Cardiopulmonary bypass (CPB) facilitates complex cardiac surgery but can damage erythrocyte membranes, impairing microcirculation and oxygen transport. Standard rheological tests assess overall blood properties but fail to define specific cellular mechanisms. In this study, atomic force microscopy (AFM) was employed to characterize morphological, nanostructural, and mechanical changes in erythrocytes following CPB and CPB combined with hypothermic circulatory arrest (HCA). Methods: The study included 14 patients who underwent cardiac surgery with CPB. Patients were divided into two groups. Group 1 underwent heart valve surgery with normothermic CPB (n = 7), and Group 2 underwent aortic arch surgery with CPB combined with HCA and moderate hypothermia (28 °C) (n = 7). Arterial blood samples were collected before the induction of anesthesia and immediately after CPB. The morphology and surface roughness (Rtm) of the erythrocyte membrane were evaluated on air-dried blood smears. Young’s modulus (E) was estimated from force-distance curves on living cells; measurements were performed at 24 °C in PBS. Results: Following CPB, both groups exhibited a decrease in the proportion of discocytes and an increase in echinocytes. In the CPB+HCA group, discocytes were absent after surgery. The mean Rtm increased 1.4-fold in Group 1 and 1.6-fold in Group 2, indicating greater nanostructural membrane damage in the latter. In Group 1, Young’s modulus increased by an average of 1.6 times, indicating increased cell stiffness. In Group 2, the increase was smaller (mean: 1.1 times) and was not statistically significant in some patients. Conclusions: Normothermic CPB primarily affects the nanomechanical properties of erythrocytes, whereas CPB+HCA induces more severe morphological and membrane surface damage while relatively preserving cytoskeletal elasticity. AFM-derived parameters of membrane roughness and cell elasticity may serve as sensitive indicators of erythrocyte biophysical integrity. Full article

Google Earth Engine (GEE) has revolutionised remote sensing. The GEE cloud platform lets users quickly analyse large satellite imagery datasets with custom programmes, enhancing global-scale analysis. Crop condition monitoring using GEE would greatly help in decision-making and precision agriculture. Estimating canopy chlorophyll content (CCC) is an effective way to monitor crops using remote sensing because leaf chlorophyll is a key indicator. A hybrid model that combines radiative transfer models (RTMs), such as PROSAIL, with Gaussian Process Regression (GPR) can effectively estimate crop biophysical parameters using remote sensing images. GPR has proven to be one of the best methods for this purpose. This study aimed to develop a hybrid model to estimate CCC from S2 imagery and transfer it to the GEE platform for efficient data processing. In this work, the CCC (g/cm²) data from the S2 biophysical processor toolbox for the S2 imagery of the ICAR-Indian Agricultural Research Institute (IARI) on 23 February 2023 were used as observation data to train the hybrid algorithm. The hybrid model was successfully validated against the 155 input data with an R2 of 0.94, RMSE of 10.02, and NRMSE of 5.04%. The model was integrated into GEE to successfully generate a CCC-estimated map of IARI using S2 imagery from 23 February 2023. An R2 value of 0.96 was observed when GEE-estimated CCC values were compared against CCC values estimated locally. This establishes that the GEE-based CCC estimation with the PROSAIL + GPR hybrid model is an effective and accurate method for monitoring vegetation and crop conditions over large areas and extended periods. Full article

Background/Objectives: Intrauterine growth restriction (IUGR) is a condition in which a fetus does not reach its normal growth potential and is associated with increased neonatal morbidity. Surveillance relies on cardiotocography, a biophysical ultrasound, and a Doppler assessment, but placental pathology remains insufficiently integrated into clinical evaluations. This study aimed to compare placentas from IUGR and normal pregnancies. Methods: This cohort included 34 pregnancies (16 IUGR, 18 controls) managed at Hunedoara County Hospital (Romania). The ultrasound and Doppler parameters were documented. The placentas were collected after delivery, fixed in formalin, and processed using standard histopathological protocols. The villous morphology and maternal vascular malperfusion features were assessed on H&E sections, focusing on syncytial knots, villous caliber reduction, stromal fibrosis, fibrin deposition, and infarctions. Immunohistochemistry for CD34, cytokeratin 7 (CK7), CD68, vascular endothelial growth factor (VEGF), and Hypoxian inducible factor 1 (HIF-1α)was performed using a semi-quantitative 0–3 scoring system. A statistical analysis was performed using chi-squared testing for categorical variables and t-tests for continuous variables. Results: The ultrasound evaluation showed an estimated fetal weight below the 10th percentile and abnormal Doppler indices in the IUGR group. The histopathology demonstrated a strong association between IUGR and villous abnormalities, including an increased number of syncytial knots, stromal fibrosis, a reduced villous caliber, and placental infarctions. The immunohistochemistry showed a marked overexpression of VEGF and HIF-1α and increased CD68-positive Hofbauer cells in IUGR placentas (p < 0.0001), while CD34 and CK7 displayed preserved strong staining in both groups. Conclusions: Placentas from IUGR pregnancies exhibited advanced maternal vascular malperfusion with consistent hypoxic and inflammatory changes, correlating with Doppler alterations. These findings highlight the diagnostic relevance of placental pathology in pregnancies with IUGR. Full article

Leaf area index (LAI) is a pivotal biophysical parameter linking vegetation physiological processes and macro-ecological functions. Accurate large-scale LAI estimation is indispensable for agricultural management, climate change research, and ecosystem modeling. However, existing methods fail to efficiently extract integrated spatial-spectral-temporal features and lack targeted modeling of spatio-temporal dependencies, compromising the accuracy of LAI products. To address this gap, we propose STC-DeepLAINet, a Transformer-GCN hybrid deep learning architecture integrating spatio-temporal correlations via the following three synergistic modules: (1) a 3D convolutional neural networks (CNNs)-based spectral-spatial embedding module capturing intrinsic correlations between multi-spectral bands and local spatial features; (2) a spatio-temporal correlation-aware module that models temporal dynamics (by “time periods”) and spatial heterogeneity (by “spatial slices”) simultaneously; (3) a spatio-temporal pattern memory attention module that retrieves historically similar spatio-temporal patterns via an attention-based mechanism to improve inversion accuracy. Experimental results demonstrate that STC-DeepLAINet outperforms eight state-of-the-art methods (including traditional machine learning and deep learning networks) in a 500 m resolution LAI inversion task over China. Validated against ground-based measurements, it achieves a coefficient of determination (R²) of 0.827 and a root mean square error (RMSE) of 0.718, outperforming the GLASS LAI product. Furthermore, STC-DeepLAINet effectively captures LAI variability across typical vegetation types (e.g., forests and croplands). This work establishes an operational solution for generating large-scale high-precision LAI products, which can provide reliable data support for agricultural yield estimation and ecosystem carbon cycle simulation, while offering a new methodological reference for spatio-temporal correlation modeling in remote sensing inversion. Full article

Leaf Area Index (LAI) is a key biophysical descriptor of crop canopies and is essential for growth monitoring and yield estimation. We present a physics-driven machine-learning framework for operational LAI retrieval and end-to-end uncertainty quantification that couples the PROSAIL radiative transfer model with a genetic-algorithm-optimised multilayer perceptron (NN–GA). PROSAIL is sampled across plausible parameter priors and spectra are convolved with Sentinel-2B spectral response functions to build a 30,000-sample training library; a GA is used to globally optimise network weights and biases. Total retrieval uncertainty is decomposed into a simulation component (PROSAIL parameter variability) and a training component (variability across repeated NN–GA trainings) and combined via the law of propagation of uncertainty. The model was developed in Minqin (modelling/testing area; entirely maize) and transferred to Zhangye (transfer/validation area; predominantly maize, with one sunflower plot). Sentinel-2B validation results were RMSE/R² = 0.44/0.73 (Minqin) and 0.40/0.56 (Zhangye), indicating reasonable cross-site generalisation. The uncertainty split indicates physical-driven contributions of 11.42% and 11.48% and machine-learning contributions of 18.06% and 12.96%, respectively. The framework improves 10 m LAI retrieval accuracy and supplies a reproducible, per-pixel uncertainty budget to guide product use and refinement. Full article

The accurate estimation of grapevine biophysical parameters is important for decision support in precision viticulture. This study addresses the use of unmanned aerial vehicle (UAV) multispectral data and machine learning (ML) techniques to estimate leaf area index (LAI), pruning wood biomass, and yield, across mixed-variety vineyards in the Douro Region of Portugal. Data were collected at three phenological stages, from veraison to maturation and two modeling approaches were tested: one using only spectral features, and another combining spectral and geometric features derived from photogrammetric elevation data. Multiple linear regression (MLR) and five ML algorithms were applied, with feature selection performed using both forward and backward selection procedures. Logarithmic transformations were used to mitigate data skewness. Overall, ML algorithms provided better predictive performance than MLR, particularly when geometric features were included. At harvest-ready, Random Forest achieved the highest accuracy for LAI (R² = 0.83) and yield (R² = 0.75), while MLR produced the most accurate estimates for pruning wood biomass (R² = 0.83). Among geometric variables, canopy area was the most informative. For spectral data, the Modified Soil-Adjusted Vegetation Index (MSAVI) and the Soil-Adjusted Vegetation Index (SAVI) were the most relevant. The models performed well across grapevine varieties, indicating that UAV-based monitoring can serve as a practical, non-invasive, and scalable approach for vineyard management in heterogeneous vineyards. Full article

The Icelandic Government’s climate action plan proposes climate-neutral beef production, reduced methane emissions, and improved fertilizer management. However, a life cycle assessment (LCA) of cattle production is lacking to determine the current status of its environmental impacts. This study conducts a cradle-to-farm gate LCA of interconnected dairy and beef cattle systems. The functional unit (FU) is “1 kg of edible cattle meat” for the meat and “1 kg of fat and protein corrected milk” (FPCM) for milk produced in Iceland in 2019. The multifunctionality between meat and milk from the dairy system is handled using mass, economic, and biophysical allocations, respectively. The environmental impacts were estimated using the ReCiPe 2016 v1.08 mid-point (H) impact assessment method. Furthermore, this study conducts an uncertainty and global sensitivity analysis to understand the possible range of environmental impacts and identifies key influential parameters in the dairy and beef cattle system. Animal production is a hotspot for global warming, while the feed (hay and concentrate) is a hotspot for other environmental categories. The allocation method choice highly influences the environmental impacts. This study underscores the need to harmonize data collection and access to centralized, reliable data sources to reduce uncertainty and meet climate action plan goals on both the national and global scale. Full article

In Northeast Brazil, climatic factors and technology synergistically enhance melon productivity and fruit quality. However, the region requires further research on the efficient use of water resources, particularly in determining the crop coefficient (Kc), which comprises the evaporation coefficient (Ke) and the transpiration coefficient (Kcb). Air temperature affects crop growth and development, altering the spectral response and the Kcb. However, the direct influence of air temperature on Kcb and spectral response remains underemphasized. This study employed unmanned aerial vehicle (UAV) with RGB and Red-Green-NIR sensors imagery to extract biophysical parameters for improved water management in melon cultivation in semiarid northern Bahia. Field experiments were conducted during two distinct periods: warm (October–December 2019) and cool (June–August 2020). The ‘Gladial’ and ‘Cantaloupe’ cultivars exhibited higher Kcb values during the warm season (2.753–3.450 and 3.087–3.856, respectively) and lower during the cool season (0.815–0.993 and 1.118–1.317). NDVI-based estimates of Kcb showed strong correlations with field data (r > 0.80), confirming its predictive potential. The results demonstrate that UAV-derived NDVI enables reliable estimation of melon Kcb across seasons, supporting its application for evapotranspiration modeling and precision irrigation in the Brazilian semiarid context. Full article

Air pollution is known to affect skin health, but tools to objectively measure individual exposure based on skin responses are limited. This study introduces the Exposure Intensity Index (EII), a novel tool that correlates lifestyle-related pollution exposure with skin parameters. A panel of 250 women residing in Lombardy completed a detailed questionnaire on socio-demographic features and daily habits, from which an exposure score was derived. Non-invasive bioengineering techniques were used to assess skin parameters, focusing on inflammation-related signs. A positive correlation emerged between exposure scores and variations in specific skin parameters, suggesting a link between daily pollution exposure and skin alterations. The EII emerges as a preliminary exploratory approach to estimate environmental impact on the skin through its correlation with biophysical parameters. It may offer future value for subject selection in in vivo testing of antipollution cosmetic claims. Full article

Leaf Area Index (LAI) is a critical biophysical parameter for characterizing vegetation canopy structure and function. However, fine-scale LAI estimation remains challenging due to limitations in spatial resolution and structural detail in traditional remote sensing data and the insufficiency of single-index models like the LiDAR Penetration Index (LPI) in capturing canopy complexity. This study proposes a multi-scale LAI estimation approach integrating high-density UAV-based LiDAR data with LPI and point cloud texture features. A total of 40 field-sampled plots were used to develop and validate the model. LPI was computed at three spatial scales (5 m, 10 m, and 15 m) and corrected using a scale-specific adjustment coefficient (μ). Texture features including roughness and curvature were extracted and combined with LPI in a multiple linear regression model. Results showed that μ = 15 provided the optimal LPI correction, with the 10 m scale yielding the best model performance (R² = 0.40, RMSE = 0.35). Incorporating texture features moderately improved estimation accuracy (R² = 0.49, RMSE = 0.32). The findings confirm that integrating structural metrics enhances LAI prediction and that spatial scale selection is crucial, with 10 m identified as optimal for this study area. This method offers a practical and scalable solution for improving LAI retrieval using UAV-based LiDAR in heterogeneous forest environments. Full article

Accurate crop yield prediction is essential for stabilizing food supply chains and reducing the uncertainties in financial risks related to agricultural production. Yet, it is even more essential to understand how crop yield models make predictions depending on their relationship to Earth Observation (EO) indicators. This study presents a state-of-the-art explainable artificial intelligence (XAI) method to estimate corn yield prediction over the Corn Belt in the continental United States (CONUS). We utilize the recently introduced Kolmogorov–Arnold Network (KAN) architecture, which offers an interpretable alternative to the traditional Multi-Layer Perceptron (MLP) approach by utilizing learnable spline-based activation functions instead of fixed ones. By including a KAN in our crop yield prediction framework, we are able to achieve high prediction accuracy and identify the temporal drivers behind crop yield variability. We create a multi-source dataset that includes biophysical parameters along the crop phenology, as well as meteorological, topographic, and soil parameters to perform end-of-season and in-season predictions of county-level corn yields between 2016–2023. The performance of the KAN model is compared with the commonly used traditional machine learning (ML) models and its architecture-wise equivalent MLP. The KAN-based crop yield model outperforms the other models, achieving an R² of 0.85, an RMSE of 0.84 t/ha, and an MAE of 0.62 t/ha (compared to MLP: R² = 0.81, RMSE = 0.95 t/ha, and MAE = 0.71 t/ha). In addition to end-of-season predictions, the KAN model also proves effective for in-season yield forecasting. Notably, even three months prior to harvest, the KAN model demonstrates strong performance in in-season yield forecasting, achieving an R² of 0.82, an MAE of 0.74 t/ha, and an RMSE of 0.98 t/ha. These results indicate that the model maintains a high level of explanatory power relative to its final performance. Overall, these findings highlight the potential of the KAN model as a reliable tool for early yield estimation, offering valuable insights for agricultural planning and decision-making. Full article

Forest ecosystems serve as pivotal components of the global carbon cycle, with canopy height representing a critical biophysical parameter for quantifying ecosystem functionality, thereby holding substantial implications for forest resource management and carbon sequestration assessments. The precise extraction of ground elevation and vegetation canopy height is essential for advancing topographic and ecological research. The Terrestrial Ecosystem Carbon Inventory Satellite (referred to as TECIS hereafter) offers unprecedented capabilities for the large-scale, high-precision extraction of ground elevation and vegetation canopy height. Using the Northeast China Tiger and Leopard National Park as our study area, we first processed TECIS data to derive topographic and canopy height profiles. Subsequently, the accuracy of TECIS-derived ground and canopy height estimates was validated using onboard light detection and ranging (LiDAR) measurements. Finally, we systematically evaluated the influence of multiple factors on estimation accuracy. Our analysis revealed that TECIS-derived ground and canopy height estimates exhibited mean errors of 0.7 m and −0.35 m, respectively, with corresponding root mean square error (RMSE) values of 3.83 m and 2.70 m. Furthermore, slope gradient, vegetation coverage, and forest composition emerged as the dominant factors influencing canopy height estimation accuracy. These findings provide a scientific basis for optimizing the screening and application of TECIS data in global forest carbon monitoring. Full article

The remote sensing community increasingly demands precise ecosystem monitoring, environmental change detection, and natural resource management, particularly in forestry. Key metrics such as biomass and total area index require accurate estimation, necessitating extensive experiments and reliable scattering models. Recent advances in radar interferometry introduce two essential parameters—interferogram phase and correlation coefficient—containing crucial target information. Understanding their relationship to forest biophysical parameters requires analyzing wave interactions with vegetation particles. This study presents a discrete interferometric model for a random medium layer, establishing the link between radar interferometry and forest biophysical properties. Correlation analysis plays a vital role in estimating one variable based on another, reducing uncertainty in random media. The research introduces a novel modeling approach that enhances theoretical foundations and supports empirical studies in the literature. Bridging theoretical analysis and practical observations, this work enhances the precision and applicability of radar interferometry for vegetation monitoring. The findings contribute to improving remote sensing methodologies and expanding their potential in ecological and environmental research. Ultimately, this study advances the use of interferometric models in extracting critical forest parameters with greater accuracy. Full article

The leaf area index (LAI) is a critical biophysical parameter that reflects crop growth conditions and the canopy photosynthetic potential, serving as a cornerstone in precision agriculture and dynamic crop monitoring. However, traditional LAI estimation methods relying on single-source remote sensing data and often suffer from insufficient accuracy in high-density vegetation scenarios, limiting their capacity to reflect crop growth variability comprehensively. To overcome these limitations, this study introduces an innovative multi-source feature fusion framework utilizing unmanned aerial vehicle (UAV) multispectral imagery for precise LAI estimation in winter wheat. RGB and multispectral datasets were collected across seven different growth stages (from regreening to grain filling) in 2024. Through the extraction of color attributes, spatial structural information, and eight representative vegetation indices (VIs), a robust multi-source dataset was developed to integrate diverse data types. A convolutional neural network (CNN)-based feature extraction backbone, paired with a multi-source feature fusion network (MSF-FusionNet), was designed to effectively combine spectral and spatial information from both RGB and multispectral imagery. The experimental results revealed that the proposed method achieved superior estimation performance compared to single-source models, with an R² of 0.8745 and RMSE of 0.5461, improving the R² by 36.67% and 5.54% over the RGB and VI models, respectively. Notably, the fusion method enhanced the accuracy during critical growth phases, such as the regreening and jointing stages. Compared to traditional machine learning techniques, the proposed framework exceeded the performance of the XGBoost model, with the R² rising by 4.51% and the RMSE dropping by 12.24%. Furthermore, our method facilitated the creation of LAI spatial distribution maps across key growth stages, accurately depicting the spatial heterogeneity and temporal dynamics in the field. These results highlight the efficacy and potential of integrating UAV multi-source data fusion with deep learning for precise LAI estimation in winter wheat, offering significant insights for crop growth evaluation and precision agricultural management. Full article

Traditional maize (Zea mays L.) breeding approaches use directly measured phenotypic performance to make decisions for the next generation of crosses. Indirect assessment of cultivar performance can be utilized using various methods such as genomic predictions and remote sensing. However, some secondary traits might expand the breeder’s ability to make informed decisions within a single season, facilitating an increase in breeding speed. We hypothesized that assessment of photosynthetic performance with chlorophyll a fluorescence (ChlF) might be efficient for in-season predictions of yield and grain moisture. The experiment was set with 16 maize hybrids over three consecutive years (2017–2019). ChlF was measured on dark-adapted leaves in the morning during anthesis. Partial least squares models were fitted and the efficiency of indirect selection was assessed. The results showed variability in the traits used in this study. Genetic correlations among all traits were mainly very weak and negative. Heritability estimates for all traits were moderately high to high. The model with 10 latent variables showed a higher predictive ability for grain yield (GY) than other models. The efficiency of the indirect selection for GY using biophysical parameters was lower than direct selection efficiency, while the indirect selection efficiency for grain moisture using biophysical parameters was relatively high. The results of this study highlight the significance and applicability of the ChlF transients in maize breeding programs. Full article