Search Results (220)

The Nitrogen Balance Index is a key indicator of crop nitrogen status, but conventional monitoring methods are invasive, costly, and unsuitable for large-scale application. This study targets early-season winter wheat in the Guanzhong Plain and proposes a framework that integrates Sentinel-2 imagery with Sen2Res super-resolution reconstruction, multi-feature optimization, and interpretable machine learning. Super-resolved imagery demonstrated improved spatial detail and enhanced correlations between reflectance, texture, and vegetation indices and the Nitrogen Balance Index compared to native imagery. A two-stage feature-selection strategy, combining correlation analysis and recursive feature elimination, identified a compact set of key variables. Among the tested algorithms, the random forest model achieved the highest accuracy, with R² = 0.77 and RMSE = 1.57, representing an improvement of about 20% over linear models. Shapley Additive Explanations revealed that red-edge and near-infrared features accounted for up to 75% of predictive contributions, highlighting their physiological relevance to nitrogen metabolism. Overall, this study contributes to the remote sensing of crop nitrogen status through three aspects: (1) integration of super-resolution with feature fusion to overcome coarse spatial resolution, (2) adoption of a two-stage feature optimization strategy to reduce redundancy, and (3) incorporation of interpretable modeling to improve transparency. The proposed framework supports regional-scale NBI monitoring and provides a scientific basis for precision fertilization. Full article

Background: Although nature exposure is recognized for its beneficial effects on psychological, cognitive, and physiological health, its impact on physical function has been underexplored. The main aim of this paper is to cover this gap. Methods: A systematic search of Cochrane, CINAHL Plus, and PubMed databases (2012–2023) was conducted using terms related to nature and physical function. Results: Eight intervention studies (total n = 209, age 25–91) met the inclusion criteria. NBIs, such as horticultural therapy and forest therapy, demonstrated generally positive effects across physical, psychosocial, and physiological outcomes, though effect size and quality varied. Study quality ranged from low to high. Conclusions: NBIs appear to promote multi-dimensional functioning in people living with physical chronic disease and offer promising complementary strategies to traditional rehabilitation. Full article

Background and Objectives: Early gastric cancer (EGC) has an excellent prognosis when detected, yet miss rates during endoscopy remain high. Narrow-band imaging (NBI) enhances mucosal and vascular visualization and is increasingly used, but its benefit over white-light imaging (WLI) in high-risk patients is uncertain. This study aimed to compare NBI with WLI for the detection of gastric neoplasia in patients undergoing gastroscopy. Materials and Methods: We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs), registered in PROSPERO (CRD42025649908) and reported according to PRISMA 2020 guidelines. PubMed, Scopus, and CENTRAL were searched up to October 2024. Eligible RCTs randomized adults undergoing gastroscopy for cancer surveillance or red-flag symptoms to NBI or WLI. Data extraction and risk of bias assessment were performed independently by two reviewers. Pooled relative risks (RRs) with 95% confidence intervals (CIs) were calculated using a random-effects model, and certainty of evidence was graded with GRADE. Results: From 21 records, 3 RCTs comprising 6003 patients were included. NBI did not significantly increase gastric neoplasm detection compared with WLI (2.79% vs. 2.74%; RR = 0.98; 95% CI: 0.66–1.45; I² = 22%). Focal gastric lesion detection rates (14.73% vs. 15.50%; RR = 1.05; 95% CI: 0.72–1.52; I² = 87%) and positive predictive value (29.56% vs. 20.56%; RR = 1.29; 95% CI: 0.84–1.99; I² = 61%) also showed no significant differences. Risk of bias was high for blinding, and overall evidence certainty was low. In practical terms, both NBI and WLI detected gastric cancers at similar rates, indicating that while NBI enhances visualization, it does not increase the likelihood of finding additional cancers in high-risk patients. Conclusions: NBI did not significantly improve gastric neoplasm detection compared with WLI in high-risk patients, though it remains valuable for mucosal and vascular assessment. Larger, multicenter RCTs across diverse populations are required to establish its role in surveillance strategies. Full article

Diagnosing gastrointestinal disorders (GIDs) remains a significant challenge, particularly when relying on wireless capsule endoscopy (WCE), which lacks advanced imaging enhancements like Narrow Band Imaging (NBI). To address this, we propose a novel framework, the Spectrum-Aided Vision Enhancer (SAVE), especially designed to transform standard white light (WLI) endoscopic images into spectrally enriched representations that emulate both hyperspectral imaging (HSI) and NBI formats. By leveraging color calibration through the Macbeth Color Checker, gamma correction, CIE 1931 XYZ transformation, and principal component analysis (PCA), SAVE reconstructs detailed spectral information from conventional RGB inputs. Performance was evaluated using the Kvasir-v2 dataset, which includes 6490 annotated images spanning eight GI-related categories. Deep learning models like Inception-Net V3, MobileNetV2, MobileNetV3, and AlexNet were trained on both original WLI- and SAVE-enhanced images. Among these, MobileNetV2 achieved an F1-score of 96% for polyp classification using SAVE, and AlexNet saw a notable increase in average accuracy to 84% when applied to enhanced images. Image quality assessment showed high structural similarity (SSIM scores of 93.99% for Olympus endoscopy and 90.68% for WCE), confirming the fidelity of the spectral transformations. Overall, the SAVE framework offers a practical, software-based enhancement strategy that significantly improves diagnostic accuracy in GI imaging, with strong implications for low-cost, non-invasive diagnostics using capsule endoscopy systems. Full article

The burden of depression is a public health concern, and traditional treatment approaches to mental health alone may be insufficient. The effects of contact with nature on wellbeing have been shown to reduce stress and improve mood, emotional wellbeing and mental health difficulties. Thus, self-guided nature-based interventions (NBIs) present a promising approach to improving mental health and wellbeing. However, there is limited evidence on the development of such interventions. This two-armed pilot randomised controlled trial aimed at determining the feasibility, acceptability and preliminary efficacy of a novel, 4-week, self-help NBI (Roots and Shoots©). Forty-seven participants were randomised (1:1) to either receive the Roots and Shoots intervention or a waitlist control. Participants in both conditions completed measures including wellbeing (primary outcome) and depressive symptoms, rumination, sleep and nature relatedness (secondary outcomes) at three timepoints: baseline (T0), 2 weeks (T1) and 4 weeks (T2). Those who completed the intervention period reported high acceptability and satisfaction with the intervention. The findings from this pilot study indicate potential for improvements in wellbeing following the intervention, which appears reasonably feasible and acceptable. Future research is warranted to further investigate the efficacy of this novel NBI in a larger, powered clinical trial. Full article

Ulcerative colitis, polyps, esophagitis, and other gastrointestinal (GI) diseases significantly impact health, making early detection crucial for reducing mortality rates and improving patient outcomes. Traditional white light imaging (WLI) is commonly used during endoscopy to identify abnormalities in the gastrointestinal tract. However, insufficient contrast often limits its effectiveness, making it challenging to distinguish between healthy and unhealthy tissues, particularly when identifying subtle mucosal and vascular abnormalities. These limitations have prompted the need for more advanced imaging techniques that enhance pathological visualization and facilitate early diagnosis. Therefore, this study investigates the integration of the Spectrum-Aided Vision Enhancer (SAVE) mechanism to improve WLI images and increase disease detection accuracy. This approach transforms standard WLI images into hyperspectral imaging (HSI) representations, creating narrow-band imaging (NBI-like) visuals with enhanced contrast and tissue differentiation, thereby improving the visualization of vascular and mucosal structures critical for diagnosing GI disorders. This transformation allows for a clearer representation of blood vessels and membrane formations, which is essential for determining the presence of GI diseases. The dataset for this study comprises WLI images alongside SAVE-enhanced images, including four categories: ulcerative colitis, polyps, esophagitis, and healthy GI tissue. These images are organized into training, validation, and test sets to develop a deep learning-based classification model. Utilizing principal component analysis (PCA) and multiple regression analysis for spectral standardization ensures that the improved images retain spectral characteristics that are vital for clinical applications. By merging deep learning techniques with advanced imaging enhancements, this study aims to create an artificial intelligence (AI)–driven diagnostic system capable of early and accurate detection of GI diseases. InceptionV3 attained an overall accuracy of 94% in both scenarios; SAVE produced a modest enhancement in the ulcerative colitis F1-score from 92% to 93%, while the F1-scores for other classes exceeded 96%. SAVE resulted in a 10% increase in YOLOv8x accuracy, reaching 89%, with ulcerative colitis F1 improving to 82% and polyp F1 rising to 76%. VGG16 enhanced accuracy from 85% to 91%, and the F1-score for polyps improved from 68% to 81%. These findings confirm that SAVE enhancement consistently improves disease classification across diverse architectures, offers a practical, hardware-independent approach to hyperspectral-quality images, and enhances the accuracy of gastrointestinal screening. Furthermore, this research seeks to provide a practical and effective solution for clinical applications, improving diagnostic accuracy and facilitating superior patient care. Full article

Enhancing the yield and qualitative traits of horticultural crops without further hampering the environment constitutes an urgent challenge that could be addressed by implementing innovative agronomic tools, such as plant biostimulants. This study investigated the effects of three commercial biostimulants—BIO1 (fulvic/humic acids), BIO2 (leonardite-humic acids), and BIO3 (plant-based extracts)—on leaf ecophysiology, yield, and fruit quality in two raspberry cultivars, ‘Autumn Bliss’ (AB) and ‘Zeva’ (Z), grown in an open-field context, to assess their effectiveness in raspberry cultivation. Experimental activities involved two Research Years (RYs), namely, year 2023 (RY 1) and 2024 (RY 2). Leaf parameters such as chlorophyll, flavonols, anthocyanins, and the Nitrogen Balance Index (NBI) were predominantly influenced by the interaction between Treatment, Year and Cultivar factors, indicating context-dependent responses rather than direct biostimulant effects. BIO2 showed a tendency to increase yield (g plant⁻¹) and berry number plant⁻¹, particularly in RY 2 (417.50 g plant⁻¹, +33.93% vs. control). Fruit quality responses were cultivar and time-specific: BIO3 improved soluble solid content in AB (12.8 °Brix, RY 2, Intermediate Harvest) and Z (11.43 °Brix, +13.91% vs. BIO2). BIO2 reduced titratable acidity in AB (3.12 g L⁻¹) and increased pH in Z (3.02, RY 2) but also decreased °Brix in Z. These findings highlight the potential of biostimulants to modulate raspberry physiology and productivity but underscore the critical role of cultivar, environmental conditions, and specific biostimulant composition in determining the outcomes, which were found to critically depend on tailored application strategies. Full article

Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision Enhancer (SAVE), an innovative, software-driven framework that transforms standard WLI into high-fidelity hyperspectral imaging (HSI) and simulated narrow-band imaging (NBI) without any hardware modification. SAVE leverages advanced spectral reconstruction techniques, including Macbeth Color Checker-based calibration, principal component analysis (PCA), and multivariate polynomial regression, achieving a root mean square error (RMSE) of 0.056 and structural similarity index (SSIM) exceeding 90%. Trained and validated on the Kvasir v2 dataset (n = 6490) using deep learning models like ResNet-50, ResNet-101, EfficientNet-B2, both EfficientNet-B5 and EfficientNetV2-B0 were used to assess diagnostic performance across six key GI conditions. Results demonstrated that SAVE enhanced imagery and consistently outperformed raw WLI across precision, recall, and F1-score metrics, with EfficientNet-B2 and EfficientNetV2-B0 achieving the highest classification accuracy. Notably, this performance gain was achieved without the need for specialized imaging hardware. These findings highlight SAVE as a transformative solution for augmenting GI diagnostics, with the potential to significantly improve early detection, streamline clinical workflows, and broaden access to advanced imaging especially in resource constrained settings. Full article

Demand for mental health support has exerted unprecedented pressure on statutory services. Innovative solutions such as Green or Nature-Based Social Prescribing (NBSP) programmes may help address unmet need, improve access to personalised treatment, and support the sustainable delivery of primary services within a prevention model of population health. We piloted an innovative health economics-informed Social Return on Investment (SROI) analysis and forecast of a ‘Making Well’ therapeutic craft and horticulture programme for mental health between October 2021 and March 2022. Quantitative and qualitative outcome data were collected from participants with mild-to-moderate mental health conditions at baseline and nine-weeks follow-up using a range of validated measures, including the Short Warwick–Edinburgh Mental Well-being Scale, ICEpop CAPability measure for Adults (ICECAP-A), General Self-Efficacy Scale (GSES), and a bespoke Client Service Receipt Inventory (CSRI). The acceptability and feasibility of these measures were explored. Results indicate that the Making Well programme generated well-being-related social value in the range of British Pound Sterling (GBP) GBP 3.30 to GBP 4.70 for every GBP 1 invested. Our initial pilot forecast suggests that the programme has the potential to generate GBP 5.40 to GBP 7.70 for every GBP 1 invested as the programme is developed and delivered over a 12-month period. Despite the small sample size and lack of a control group, our results contribute to the evidence-base for the effectiveness and social return on investment of NBSP as a therapeutic intervention for improving health and well-being and provides an example of the use of health economic well-being outcome measures such as ICECAP-A and CSRIs in social value analysis. Combining SROI evaluation and forecast methodologies with validated quantitative outcome measures used in the field of health economics can provide valuable social cost–benefit evidence to decision-makers. Full article

Objective: The classification of skin cancer is very helpful in its early diagnosis and treatment, considering the complexity involved in differentiating AK from BCC and SK. These conditions are generally not easily detectable due to their comparable clinical presentations. Method: This paper presents a new approach to hyperspectral imaging for enhancing the visualization of skin lesions called the Spectrum-Aided Vision Enhancer (SAVE), which has the ability to convert any RGB image into a narrow-band image (NBI) by combining hyperspectral imaging (HSI) to increase the contrast of the area of the cancerous lesions when compared with the normal tissue, thereby increasing the accuracy of classification. The current study investigates the use of ten different machine learning algorithms for the purpose of classification of AK, BCC, and SK, including convolutional neural network (CNN), random forest (RF), you only look once (YOLO) version 8, support vector machine (SVM), ResNet50, MobileNetV2, Logistic Regression, SVM with stochastic gradient descent (SGD) Classifier, SVM with logarithmic (LOG) Classifier and SVM- Polynomial Classifier, in assessing the capability of the system to differentiate AK from BCC and SK with heightened accuracy. Results: The results demonstrated that SAVE enhanced classification performance and increased its accuracy, sensitivity, and specificity compared to a traditional RGB imaging approach. Conclusions: This advanced method offers dermatologists a tool for early and accurate diagnosis, reducing the likelihood of misclassification and improving patient outcomes. Full article

The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R² of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications. Full article

The production of high-quality horticultural seedlings is essential for successful field transplantation. Nevertheless, increasing soil salinization poses a significant challenge, particularly in salt-affected regions. Watermelon seedlings were cultivated in pots with a substrate (mixture of ground blonde peat (60%), black peat (30%), and perlite (10%) with pH 5.5–6.0) within a bamboo nethouse and subjected to varying salinity levels, i.e., 2–8 dS m⁻¹ (T1, T2, T3, and T4). At the end of the experimental period (4 weeks), the growth parameters, spectral vegetation indices, and chemical parameters of the sap and leachate were evaluated. The results demonstrated that increased salinity levels reduced the biomass of watermelon seedlings. In addition, elevated salinity levels were associated with increased values of B (48%) and NBI (46%) and decreased values of G (9%) and NGI (7%) at the end of the experimental period. The effects of the salinity levels were also evident in the sap chemical parameters, with marked increases in Cl⁻, Ca²⁺, and Na⁺ concentrations (9.6, 3.1, and 4.9 times, respectively) and decreases in the N-NO₃⁻, P, and K⁺ concentrations (51, 8, and 25%, respectively). The leachate analysis reported clear increases in the values of EC and concentrations of Cl⁻, Ca²⁺, and Na⁺ at the end of the experimental period. To validate the relevance of these findings, further research under field conditions and across a range of climatic environments is warranted. Full article

Mosquitoes of the Aedes and Culex genera are primary vectors of arboviruses such as the dengue, Zika, chikungunya (CHIKV), Oropouche, and West Nile viruses, causing millions of infections annually. Standard virus detection in mosquitoes requires capturing, transporting, and processing samples with a cold chain to preserve RNA, which is challenging in resource-limited areas. FTA cards preserve viral RNA at room temperature and have been used to collect mosquito saliva, a key sample for assessing transmission. However, most FTA-based traps require electricity or CO₂, limiting use in low-resource settings. This study adapted and evaluated the BR-ArboTrap, a low-cost trap derived from an oviposition trap, integrating a sugar-based attractant with FTA cards to collect mosquito saliva, without electricity or refrigeration. Aedes aegypti exposed to CHIKV were used in three experiments to evaluate: (i) RNA preservation under different conditions, (ii) the minimum number of positive mosquitoes for detection, and (iii) RNA amounts on FTA versus blood. RT-qPCR detected CHIKV RNA in 90% of FTA cards and 96% of exposed mosquitoes. RNA remained stable under varying conditions, with no significant difference compared to blood. BR-ArboTrap is an effective, affordable, and field-ready tool to enhance arbovirus surveillance in remote and low-resource areas. Full article

Oat (Avena sativa L.) is one of the most important self-fertilizing field plants belonging to the Poaceae family. It has no significant requirements regarding growing conditions but has a very good reaction to fertilization. The current research evaluated the significance of the effects of individual applications of mineral (NPK) and organo-mineral (OMF) fertilizers, as well as their individual combination with slaked lime (calcium hydroxide, Ca(OH)₂), on the yield, morphological traits [mean number of leaves per plant—MNLP, minimum leaf length (cm) per plant—MinLL, maximum leaf length (cm) per plant—MaxLL, number of ears per plant—NEP], and physiological parameters (nitrogen balance index—NBI, content of chlorophyll—Chl, flavonoids—Flv, anthocyanins—Ant) of organic and pelleted (graded) oat plants, comparing the treatments and in relation to the control. The experiment was performed in semi-controlled glasshouse conditions, in pots, from the fourth week of March to the fourth week of June 2024, using Vertisol soil. This soil is characterized as light clay with an acid reaction. Physiological parameters were measured using a Dualex leaf clip sensor. The results obtained showed that physiological parameters in both oat types significantly differed (p < 0.05) between the treatments applied and in relation to the control, whereas the morphological traits did not significantly differ (p > 0.05) between the treatments. Statistically significant differences (p < 0.05) in the yield of both oat types were most pronounced in the OMF + Slaked Lime treatment (organic: 4.49 g pot⁻¹; pelleted: 4.61 g pot⁻¹) in relation to the control (organic: 2.48 g pot⁻¹; pelleted: 2.63 g pot⁻¹). The pelleted oats showed slightly better results for the effects of different treatments across all tested parameters compared to organic oats. In conclusion, the best results were obtained with the use of OMF + Slaked Lime, which could be proposed as the optimal fertilization treatment for pelleted and organic oat cultivation based on this research. Full article

Nature-based interventions (NBIs) grounded in mindfulness have been shown to be beneficial for improving mental wellbeing in adults. With increasing mental health challenges among children and adolescents, accessible and cost-effective interventions are essential to enhance their well-being. Brief mindfulness-based NBIs may be helpful in this regard, but there is a dearth of evidence testing such NBIs in young adolescents. The aim of this study was to test the effect of a brief nature-based meditation on mental wellbeing in community groups of adolescents (n = 38; aged 12–17) and adults (n = 39; aged 18–26). We hypothesised that the meditation would reduce depressive rumination and stress in both age groups. In a repeated-measures design, participants completed self-report measures, indexing mental wellbeing (state rumination and stress) before and immediately after listening to a brief (13 min) nature-based meditation. Rumination and stress improved overall, and the pattern in the data suggested that effects were larger for adults when compared to adolescents. This study provides preliminary evidence for the use of a brief nature-based meditation in improving mental wellbeing in adolescents. Future research should make NBIs age appropriate and examine their effectiveness for clinical adolescent populations. Full article