Search Results (8,108)

Adherence to dietary recommendations and the implementation of appropriate dietary habits after myocardial infarction (MI) can significantly improve health and reduce mortality from cardiac causes. The aim of this study was to analyse the adherence of patients with a history of MI to a healthy diet, which is one of the primary methods of cardiovascular disease (CVD) prevention. Following a proper diet involves limiting the consumption of saturated fats, salt, alcohol, and simple sugars. It is recommended to follow the Mediterranean diet, which is based on whole grains, fruits, vegetables, and fish. This study involved 120 patients hospitalised in the Invasive Cardiology and Cardiac Intensive Care Unit at the Independent Public Voivodship Hospital in Szczecin from August to December 2019. A self-designed questionnaire and the Questionnaire of Eating Behaviour (QEB) were used. The majority of respondents were hospitalised for a first-time MI (88.33%), and 65% of them reported adherence to the recommendations. The vast majority (78.33%) considered their diet good, with 50.83% of the respondents eating four meals a day and never eating fast food (49.17%). The analysis showed that although the respondents’ diets did not contain many unhealthy foods, they did not consume enough vegetables, fruits, fish, nuts, or fibre, which have a protective effect, lowering the risk of cardiovascular diseases and death. Furthermore, patients with a better education had a higher level of knowledge. Respondents’ knowledge of proper post-MI nutrition was sufficient, and their index of unhealthy diets was low, but they still made dietary mistakes and did not consume enough health-protective foods. These results indicate a need for further education. Full article

(1) Background: Diet plays an important role in the development of inflammatory bowel disease (IBD). There are a number of methods available to assess the diets of patients with IBD, including nutrients, dietary patterns, and various appraisal tools of diet quality. However, research on diet quality and dietary patterns in IBD populations is limited, and comparative evaluations of dietary intake in patients with IBD have not been performed. (2) Objectives: The aim of this study was to assess nutrients, the dietary patterns, and diet quality of patients with IBD and to investigate the relationship between dietary patterns, diet quality, and the adequacy of nutrient intake. (3) Methods: Three-day food records of 268 patients with ulcerative colitis (UC) and 126 patients with Crohn’s disease (CD) were collected to estimate nutrients and food groups, while dietary quality was assessed using the Dietary Inflammation Index (DII) and Mediterranean Diet Score (MDS). Dietary patterns were derived using principal component analysis (PCA). Participants’ nutrient intake, diet quality, and dietary patterns were compared. We used binary logistic regression to assess the relationship between dietary patterns (independent variable) and nutritional adequacy (dependent variable). (4) Results: In our sample, patients had inadequate energy, protein, and dietary fiber intake compared with Reference Nutrient Intake (RNI). Regarding micronutrients, intakes of potassium, zinc, selenium, vitamin A, vitamin C, vitamin E, sodium, calcium, iron, niacin, thiamin, and riboflavin were inadequate. Regarding food groups, the highest intakes were fruits, legumes, dairy products, and nuts. PCA revealed four dietary patterns, namely DP1, DP2, DP3, and DP4. Among UC patients, 96, 55, 69, and 48 patients adhered to DP1, DP2, DP3, and DP4 dietary patterns, respectively. Among CD patients, 41, 31, 34, and 20 patients complied with the dietary patterns of DP1, DP2, DP3, and DP4, respectively. There was no significant difference in dietary patterns between UC and CD patients. Compared with DP4 (high intake of mixed legumes and low intake of tubers), DP1 (high intake of cereals, tubers, vegetables and eggs) was more likely to ensure adequate intake of energy (OR, 2.96; 95% CI, 1.55, 5.62), protein (OR, 2.05; 95% CI, 1.06, 3.96), carbohydrates (OR, 3.55; 95% CI, 1.51, 6.59), thiamine (OR, 2.59; 95% CI, 1.36,4.93), niacin (OR, 2.75; 95% CI, 1.39, 5.42), phosphorus (OR, 2.04; 95% CI, 1.08, 3.85), zinc (OR, 2.43; 95% CI, 1.28, 4.63), and manganese (OR, 3.10; 95% CI, 1.60, 5.90), and DP2 (high intake of fruits, poultry, aquatic products, and nuts) was more likely to meet niacin requirements than DP4 (OR, 2.65; 95% CI, 1.28, 5.48). (5) Conclusion: This study clarifies our understanding of dietary intake, diet quality, and dietary patterns in adult patients with IBD. Future attention is needed to improve diet quality, emphasizing the importance of assessing and understanding patient dietary habits and increasing understanding of the factors that influence dietary intake in IBD in order to achieve optimal outcomes for patients with IBD. Full article

Remote sensing technology is widely used to obtain information on floating green tides, and thresholding methods based on indices such as the normalized difference vegetation index (NDVI) and the floating algae index (FAI) play an important role in such studies. However, as the methods are influenced by many factors, the threshold values vary greatly; in particular, the error of data extraction clearly increases in situations of high-turbidity water (HTW) (NDVI > 0). In this study, high spatial resolution, multispectral images from the Sentinel-2 MSI mission were used as the data source. It was found that the International Commission on Illumination (CIE) hue angle calculated using remotely sensed equivalent multispectral reflectance data and the RGB method is extremely effective in distinguishing floating green tides from areas of HTW. Statistical analysis of Sentinel-2 MSI images showed that the threshold value of the hue angle that can effectively eliminate the effect of HTW is 218.94°. A test demonstration of the method for identifying the floating green tide in HTW in a Sentinel-2 MSI image was carried out using the identified threshold values of NDVI > 0 and CIE hue angle < 218.94°. The demonstration showed that the method effectively eliminates misidentification caused by HTW pixels (NDVI > 0), resulting in better consistency of the identification of the floating green tide and its distribution in the true color image. The method enables rapid and accurate extraction of information on floating green tide in HTW, and offers a new solution for the monitoring and tracking of green tides in coastal areas. Full article

To investigate the relationship between the lighting direction-induced morphophysiological traits and post-harvest storage of lettuce, the effects of different lighting directions (top, T; top + side, TS; top + bottom, TB; side + bottom, SB; and top + side + bottom, TSB; the light from different directions for a sum of light intensity of 600 μmol·m⁻²·s⁻¹ photosynthetic photon flux density (PPFD)) on the growth morphology, root development, leaf thickness, stomatal density, chlorophyll concentration, photosynthesis, and chlorophyll fluorescence, as well as the content of nutrition such as carbohydrates and soluble proteins in lettuce were analyzed. Subsequently, the changes in water loss rate, membrane permeability (measured as relative conductivity and malondialdehyde (MDA) content), brittleness (assessed by both brittleness index and β-galactosidase (β-GAL) activity), and yellowing degree (evaluated based on chlorophyll content, and activities of chlorophyllase (CLH) and pheophytinase (PPH)) were investigated during the storage after harvest. The findings indicate that the TS treatment can effectively reduce shoot height, increase crown width, enhance leaves’ length, width, number, and thickness, and improve chlorophyll fluorescence characteristics, photosynthetic capacity, and nutrient content in lettuce before harvest. Specifically, lettuce’s leaf thickness and stomatal density showed a significant increase. Reasonable regulation of water loss in post-harvested lettuce is essential for delaying chlorophyll degradation. It was utilized to mitigate the increase in conductivity and hinder the accumulation of MDA in lettuce. The softening speed of leafy vegetables was delayed by effectively regulating the activity of the β-GAL. Chlorophyll degradation was alleviated by affecting CLH and PPH activities. This provides a theoretical basis for investigating the relationship between creating a favorable light environment and enhancing the post-harvest preservation of leafy vegetables, thus prolonging their post-harvest storage period through optimization of their morphophysiological phenotypes. Full article

Agriculture plays a crucial role as a carbon sink in the atmosphere, contributing to a climate-neutral economy, which requires a comprehensive understanding of Earth’s complex biogeochemical processes. This study aims to quantify, for the first time, Gross Primary Productivity (GPP) and ecosystem water use efficiency (eWUE) in almond orchards during their vegetative phase. The study was conducted over six growing seasons (2017–2022) across two drip-irrigated commercial almond groves located in Albacete, SE Spain. Eddy covariance flux tower systems were used to measure Net Ecosystem Exchange (NEE) and evapotranspiration (ET), which were then used to calculate GPP and eWUE. A novel approach was developed to estimate eWUE by integrating the Normalized Difference Vegetation Index (NDVI), reference ET, and air temperature. The results show similar almond orchard carbon-fixing capacity rates to those of other natural and agro-ecosystems. Seasonal and interannual variability in GPP and eWUE were observed. The NDVI-ET combination proved to be effective for GPP estimations (regression coefficient of 0.78). Maximum carbon-fixing values were observed at ET values of around 4–5 mm/d. In addition, a novel method was developed to estimate eWUE from NDVI, reference ET and air temperature (RMSE of 0.38 g·C/kg·H₂O). This study highlights the carbon capture potential of almond orchards during their vegetative phase and introduces a novel approach for eWUE monitoring, with the intention of underscoring their significance in a climate change context and to encourage further research. Full article

Green peppers (Capsicum annuum L.) are a fruit vegetable with great culinary versatility and present important nutritional properties for human health. Water deficit negatively affects the nutritional quality of green peppers’ fruits. This study aimed to investigate the influence of zinc oxide nanoparticles (ZnONPs), associated with plant growth-promoting bacteria (PGPB), on the post-harvest nutritional quality of green peppers subjected to water deficit. In an open-field experiment, two irrigation levels (50 and 100% of crop evapotranspiration (Etc)), four treatments composed of a combination of ZnONPs, zinc sulfate (ZnSO₄), and PGPB (T1 = ZnSO₄ via leaves, T2 = ZnONPs via leaves, T3 = ZnONPs via leaves + PGPB via soil, T4 = ZnSO₄ via soil + PGPB via soil), and a control treatment (Control) were tested. Water deficit or water deficit mitigation treatments did not interfere with the physical–chemical parameters (except vitamin C content) and physical color parameters (except the lightness) of green peppers. On average, the water deficit reduced the levels of Ca (−13.2%), Mg (−8.5%), P (−8.5%), K (−8.6%), Mn (−10.5%), Fe (−12.2%), B (−12.0%), and Zn (−11.5%) in the fruits. Under the water deficit condition, ZnONPs or ZnSO₄ via foliar, associated or not with PGPB, increased the levels of Ca (+57% in the T2 and +69.0% in the T2), P, Mg, and Fe in the fruits. At 50% Etc, the foliar application of ZnONPs in association with PGPB increases vitamin C and mineral nutrients’ contents and nutritional quality index (+12.0%) of green peppers. Applying Zn via foliar as ZnONPs or ZnSO₄ mitigated the negative effects of water deficit on the quality of pepper fruits that were enhanced by the Bacillus subtilis and B. amyloliquefaciens inoculation. The ZnONPs source was more efficient than the ZnSO₄ source. The water deficit alleviating effect of both zinc sources was enhanced by the PGPB. Full article

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R² of 0.72, RMSE 0.41) and FCOVER (R² 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management. Full article

Accurately mapping the surface water bodies through remote sensing technology is of great significance for water resources management, flood monitoring, and drought monitoring. At present, many scholars at home and abroad carry out research on deep learning image recognition algorithms based on convolutional neural networks, and a variety of variant-based convolutional neural networks are proposed to be applied to extract water bodies from remote sensing images. However, due to the low depth of convolutional layers employed and underutilization of water spectral feature information, most of the water body extraction methods based on convolutional neural networks (CNNs) for remote sensing images are limited in accuracy. In this study, we propose a novel surface water automatic extraction method based on the convolutional neural network (CRAUnet++) for Sentinel-2 images. The proposed method includes three parts: (1) substituting the feature extractor of the original Unet++ with ResNet34 to enhance the network’s complexity by increasing its depth; (2) Embedding the Spatial and Channel ‘Squeeze and Excitation’ (SCSE) module into the up-sampling stage of the network to suppress background features and amplify water body features; (3) adding the vegetation red edge-based water index (RWI) into the input data to maximize the utilization of water body spectral information of Sentinel-2 images without increasing the data processing time. To verify the performance and accuracy of the proposed algorithm, the ablation experiment under four different strategies and comparison experiment with different algorithms of RWI, FCN, SegNet, Unet, and DeepLab v3+ were conducted on Sentinel-2 images of the Poyang Lake. The experimental result shows that the precision, recall, F1, and IoU of CRAUnet++ are 95.99%, 96.41%, 96.19%, and 92.67%, respectively. CRAUnet++ has a good performance in extracting various types of water bodies and suppressing noises because it introduces SCSE attention mechanisms and combines surface water spectral features from RWI, exceeding that of the other five algorithms. The result demonstrates that CRAUnet++ has high validity and reliability in extracting surface water bodies based on Sentinel-2 images. Full article

Monitoring soil water content (SWC) is vital for various applications, particularly in agriculture. This study compares SWC estimated by means of SCATSAR-SWI remote sensing (RS) at different depths (T-values) with Cosmic Ray Neutron Sensing (CRNS) across four agricultural sites in northern Italy. Additionally, it examines the spatial mismatch and representativeness of SWC products’ footprints based on different factors within the following areas: the Normalized Difference Vegetation Index (NDVI), soil properties (sand, silt, clay, Soil Organic Carbon (SOC)), and irrigation information. The results reveal that RS-derived SWC, particularly at T = 2 depth, exhibits moderate positive linear correlation (mean Pearson correlation coefficient, R = 0.6) and a mean unbiased Root–Mean–Square Difference (ubRMSD) of 14.90%SR. However, lower agreement is observed during summer and autumn, attributed to factors such as high biomass growth. Sites with less variation in vegetation and soil properties within RS pixels rank better in comparing SWC products. Although a weak correlation (mean R = 0.35) exists between median NDVI differences of footprints and disparities in SWC product performance metrics, the influence of vegetation greenness on the results is clearly identified. Additionally, RS pixels with a lower percentage of sand and SOC and silt loam soil type correlate to decreased agreement between SWC products. Finally, localized irrigation practices also partially explain some differences in the SWC products. Overall, the results highlight how RS pixel variability of the different factors can explain differences between SWC products and how this information should be considered when selecting optimal ground-based measurement locations for remote sensing comparison. Full article

Vegetation quality is crucial for maintaining ecological health, and remote sensing techniques offer precise assessments of vegetation’s environmental quality. Although existing indicators and remote sensing approaches provide extensive spatial coverage, challenges remain in effectively integrating diverse indicators for a comprehensive evaluation. This study introduces a comprehensive ecological quality index (EQI) to assess vegetation quality on the Mongolian Plateau from 2001 to 2020 and to identify the determinants of EQI variations over space and time. We developed the EQI using remotely sensed normalized difference vegetation index (NDVI) data and the net primary productivity (NPP). Our analysis revealed distinct spatial patterns, with high ecological quality concentrated in northern Mongolia and eastern Inner Mongolia. Temporal fluctuations, indicative of ecological shifts, were primarily observed in eastern Mongolia and specific zones of Inner Mongolia. We employed a Geographically Optimal Zones-based Heterogeneity (GOZH) model to analyze the spatial scales and interactions influencing EQI patterns. This study found that precipitation, with an Omega value of 0.770, was the dominant factor affecting the EQI, particularly at spatial scales of 40–50 km. The GOZH model provided deeper insights into the spatial determinants of the EQI compared with previous models, highlighting the importance of climatic variables and their interactions in driving ecological quality. This research enhanced our understanding of vegetation quality dynamics and established a foundation for ecosystem conservation and informed management strategies, emphasizing the critical role of climate, especially precipitation, in shaping ecological landscapes. Full article

The development of methods to detect plant stress is not only a scientific challenge, but is also of great importance for agriculture and forestry. However, at present, stress diagnostics based on plant spectral characteristics has several limitations: (1) the high dependence of stress assessment on plant species identity; (2) the poor differentiation of different types of stress; and (3) the difficulty of detecting stress before visible symptoms appear. In this regard, the development of plant spectral metrics represents a significant area of research. Ficus elastica plants were exposed under the photosynthetic photon flux density (PPFD) from 0 to 1200 μmol photons m⁻²s⁻¹. Exposure of F. elastica leaves to excess light (EL) (≥400 μmol photons m⁻²s⁻¹) resulted in an increase in reflectance in the yellow-green region (522–594 nm) and a decrease in reflectance in the red region (666–682 nm) of the spectrum, accompanied by a shift of the red edge point toward the longer wavelength. These changes were revealed using the previously proposed light stress index (LSI = mean(R_666:682)/mean(R_522:594)). Based on the results obtained, two new vegetation indices (VIs) were proposed: LSI_Red = R₆₇₄/R₆₅₄ and LSI_Norm = (R₆₇₄ − R₆₅₄)/(R₆₇₄ + R₆₅₄), indicating light stress by changes in the red region of the spectrum. The results of the study showed that LSI, LSI_Red, and LSI_Norm have a high degree of coupling strength with maximal quantum yields of photosystem II values. The plant response to EL exposure, as assessed by the values of these three VIs, was well expressed regardless of the PPFD levels. The effect of EL at non-stressful PPFDs (50–200 μmol photons m⁻²s⁻¹) was found to disappear within one hour after cessation of exposure. In contrast, the effect of the stressful PPFD (800 μmol photons m⁻²s⁻¹) was found to persist for at least 80 h after cessation of exposure. The results of the study indicate the need to consider light history in spectral monitoring of vegetation. Full article

Greenscaping, a key sustainable practice, helps cities combat rising temperatures and climate change. Urban parks, a pivotal greenscaping element, mitigate the urban heat island (UHI) effect. In this study, we utilized high-resolution remote sensing imagery (GF-2 and Landsat 8, 9) and in situ measurements to analyze the seasonal thermal regulation of different park types in Zhengzhou, China. We calculated vegetation characteristic indices (VCIs) and landscape patterns (LMs) and employed boosted regression tree models to explore their relative contributions to land surface temperature (LST) across different seasons. Our findings revealed that urban parks lowered temperatures by 0.65 °C, 1.41 °C, and 2.84 °C in spring, summer, and autumn, respectively, but raised them by 1.92 °C in winter. Amusement parks, comprehensive parks, large parks, and water-themed parks had significantly lower LSTs. The VCI significantly influenced LST in autumn, with trees having a stronger cooling effect than shrubs. LMs showed a more prominent effect than VCIs on LST during spring, summer, and winter. Parks with longer perimeters, larger and more dispersed green patches, higher plant species richness, higher vegetation heights, and larger canopies were associated with more efficient thermal reduction in an urban setting. The novelty of this study lies in its detailed analysis of the seasonal thermal regulation effects of different types of urban parks, providing new insights for more effective urban greenspace planning and management. Our findings assist urban managers in mitigating the urban surface heat effect through more effective urban greenspace planning, vegetation community design, and maintenance, thereby enhancing cities’ potential resilience to climate change. Full article

To address the high cost associated with acquiring hyperspectral data, spectral reconstruction (SR) has emerged as a prominent research area. However, contemporary SR techniques are more focused on image processing tasks in computer vision than on practical applications. Furthermore, the prevalent approach of employing single-dimensional features to guide reconstruction, aimed at reducing computational overhead, invariably compromises reconstruction accuracy, particularly in complex environments with intricate ground features and severe spectral mixing. Effectively utilizing both local and global information in spatial and spectral dimensions for spectral reconstruction remains a significant challenge. To tackle these challenges, this study proposes an integrated network of 3D CNN and U-shaped Transformer for heterogeneous spectral reconstruction, ICTH, which comprises a shallow feature extraction module (CSSM) and a deep feature extraction module (TDEM), implementing a coarse-to-fine spectral reconstruction scheme. To minimize information loss, we designed a novel spatial–spectral attention module (S2AM) as the foundation for constructing a U-transformer, enhancing the capture of long-range information across all dimensions. On three hyperspectral datasets, ICTH has exhibited remarkable strengths across quantitative, qualitative, and single-band detail assessments, while also revealing significant potential for subsequent applications, such as generalizability and vegetation index calculations) in two real-world datasets. Full article

The ecotone zone, located between the Cerrado and Amazon biomes, has been under intensive anthropogenic pressures due to the expansion of commodity agriculture and extensive cattle ranching. This has led to habitat loss, reducing biodiversity, depleting biomass, and increasing CO₂ emissions. In this study, we employed an artificial neural network, field data, and remote sensing techniques to develop a model for estimating biomass in the remaining native vegetation within an 18,864 km² ecotone region between the Amazon and Cerrado biomes in the state of Mato Grosso, Brazil. We utilized field data from a plant ecology laboratory and vegetation indices from Sentinel-2 satellite imagery and trained artificial neural networks to estimate aboveground biomass (AGB) in the study area. The optimal network was chosen based on graphical analysis, mean estimation errors, and correlation coefficients. We validated our chosen network using both a Student’s t-test and the aggregated difference. Our results using an artificial neural network, in combination with vegetation indices such as AFRI (Aerosol Free Vegetation Index), EVI (Enhanced Vegetation Index), and GNDVI (Green Normalized Difference Vegetation Index), which show an accurate estimation of aboveground forest biomass (Root Mean Square Error (RMSE) of 15.92%), can bolster efforts to assess biomass and carbon stocks. Our study results can support the definition of environmental conservation priorities and help set parameters for payment for ecosystem services in environmentally sensitive tropical regions. Full article

The School Meal Index-Lunch Evaluation (SMI-LE) has been developed to assess school lunch quality. The aim of this study was to use the SMI-LE index for a comparative analysis of the quality of school meals planned in primary schools in Warsaw and Zagreb. The SMI-LE index was used to assess 4-week school meals in both cities. The collected menus were analyzed both in terms of overall quality as an average of points over 4 weeks and in terms of individual categories. According to the SMI-LE index, 4-week school lunches were rated on average 64 and 62 points out of a total of 140 points, in Warsaw and Zagreb, respectively. The majority of school lunches in Zagreb were classified as medium quality, while in Warsaw, over 50% were classified as good quality. Aspects that could be improved include an increase in vegetable availability and variety, limiting the meat dishes, and providing alternatives for children on vegetarian diets, as well as the ability to choose portion sizes. Polish schools need to change their approach to school meals. Following the example of schools in Zagreb, these could be one-course hot meals but with a wider range of raw vegetables and fruits. The current findings also highlight the importance of future research to develop standards for school food policies and investigate whether such a school food program could improve the eating habits and nutritional status of primary school children in the long term. Using the SMI-LE index to design new school meals could be a good solution to improve the quality of school meals. Full article