Search Results (4,527)

Lignin constitutes a fundamental component of plant defense mechanisms against environmental stressors. 4-coumarate 3-hydroxylase (C3H) serves as a pivotal enzyme in lignin biosynthesis. However, its role in the halophyte Sesuvium portulacastrum remains uncharacterized. In this study, the SpC3H gene was cloned, and subsequent sequence alignment and phylogenetic analyses revealed the highest similarity (57.14%) with BvC3H from Beta vulgaris, exhibiting the closest evolutionary relationship with Beta vulgaris and Spinacia oleracea C3H protein. Quantitative real-time polymerase chain reaction demonstrated that SpC3H expression was markedly upregulated in both roots and leaves of S. portulacastrum under 800 mM NaCl treatment. Root expression peaked at 48 h (25.3-fold), whereas leaves displayed dual expression maxima at 12 h (7.9-fold) and 72 h (10.7-fold). Subcellular localization assays confirmed cytoplasmic distribution. Heterologous expression in Arabidopsis thaliana indicated that transgenic lines exhibited enhanced growth performance, higher fresh weight, and elevated lignin contents relative to wild-type plants under salt stress, accompanied by reduced reactive oxygen species (ROS) accumulation and lower relative electrical conductivity. Furthermore, activities of superoxide dismutase and peroxidase, together with expression of lignin biosynthesis-associated and antioxidant enzyme genes, were markedly elevated. Collectively, these findings establish that SpC3H confers salt tolerance by promoting lignin biosynthesis and activating antioxidant defenses to eliminate ROS, thereby providing a theoretical foundation for genetic improvement of plant salt tolerance. Full article

Long-term fertilization strategies are crucial for sustainable soil health and crop productivity. However, the synergistic effect of combining straw with lime in long-term fertilization remains underexplored, particularly regarding soil micronutrient availability and tomato yield. This study examined the 10-year effects of chicken manure (M) with straw (S) and/or lime (Ca) on soil properties, micronutrient availability, and tomato yield. The results demonstrated that all of the fertilization treatments significantly altered topsoil (0–20 cm) characteristics, reducing the pH but increasing the EC and nutrient content. The combined MSCa treatment was most effective, achieving the highest levels of total carbon (19 g/kg) and tomato yield (5.6 kg/m²), which was 12–87% higher than that achieved with the other treatments. Fertilization also significantly increased the diethylenetriamine pentaacetic acid (DTPA)-extractable Fe, Mn, Cu, and Zn concentrations in both bulk soil and aggregate fractions, with availability strongly correlated with the soil total carbon and pH. The straw and lime amendments significantly improved the fruit quality by increasing the vitamin C and soluble sugar content while reducing the nitrate content. Furthermore, these treatments altered the distribution of micronutrients within the tomato organs, increasing their proportion in roots and fruits specifically. This study concludes that the integrated application of chicken manure with straw and lime is a highly effective strategy for improving soil fertility, enhancing micronutrient bioavailability, and boosting both the yield and nutritional quality of tomatoes. Full article

Accurate satellite-based precipitation estimates are crucial for climate studies and water resource management, particularly in regions with sparse meteorological station coverage. This study evaluates the improvements of the Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run version 07 (V07) relative to the previous version (V06). The evaluation employed gridded data from the Brazilian Daily Weather Gridded Data (BR-DWGD) product and ground observations from 58 rain gauges distributed across the Parnaíba River Basin in Northeast Brazil. The analysis comprised three main stages: (i) an intercomparison between BR-DWGD gridded data and rain gauge records using correlation, bias, and Root Mean Square Error (RMSE) metrics; (ii) a comparative assessment of the IMERG Final V06 and V07 products, evaluated with statistical metrics (correlation, bias, and RMSE) and complemented by performance indicators including the Kling-Gupta Efficiency (KGE), Probability of Detection (POD), and False Alarm Ratio (FAR); and (iii) the application of cluster analysis to identify homogeneous regions and characterize seasonal rainfall variations across the basin. The results show that the IMERG Final V07 product provides notable improvements, with lower bias, reduced RMSE, and greater accuracy in representing the spatial distribution of precipitation, particularly in the central and southern regions of the basin, which feature complex topography. IMERG V07 also demonstrated higher consistency, with reduced random errors and improved seasonal performance, reflected in higher POD and lower FAR values during the rainy season. The cluster analysis identified four homogeneous regions, within which V07 more effectively captured seasonal rainfall patterns influenced by systems such as the Intertropical Convergence Zone (ITCZ) and Amazonian moisture advection. These findings highlight the potential of the IMERG Final V07 product to enhance precipitation estimation across diverse climatic and topographic settings, supporting applications in hydrological modeling and extreme-event monitoring. Full article

Urban-scale aerosol pollution monitoring is of critical importance for both climate regulation and public health. To overcome the limitations of conventional kilometer-scale satellite aerosol optical depth (AOD) products in resolving urban pollution heterogeneity, this study develops a physical-guided transfer deep neural network (PT-DNN) model based on high-resolution Landsat 8 data. The PT-DNN introduces a novel physics-guided training framework, in which radiative transfer simulations are integrated to physically constrain the AOD retrieval. Pre-training was conducted using multi-scenario radiative transfer simulations, with subsequent fine-tuning via ground-based AERONET measurements. The model architecture integrates convolutional neural network (CNN) with residual connection. Validation results over impervious surfaces indicate that the PT-DNN model outperforms conventional data-driven models, with the coefficient of determination (R²) increasing from 0.81 to 0.86 and root mean square error (RMSE) decreasing from 0.122 to 0.104. Moreover, the AOD distributions retrieved at a high spatial resolution of 30 m effectively reveal fine-scale pollution gradients within urban environments, especially in densely built-up and industrial areas. Full article

The genus Eryngium (Apiaceae Lindley) includes over 250 species distributed worldwide. In Michoacán, Mexico, 22 species have been recorded, among them E. beecheyanum (EB), E. heterophyllum (EH), and E. mexiae (EM), which are commonly used in traditional medicine. However, our understanding of their biology and chemical composition remains limited. This study evaluated the phytochemical profile, as well as the antioxidant and hypoglycemic activities of leaves and roots from these three wild species. Flavonoids, phenolic compounds, and sterols were analyzed using high-performance thin-layer chromatography (HPTLC). Antioxidant activity was assessed in vitro using ABTS·+ and DPPH· assays, while antihyperglycemic activity was determined by α-glucosidase inhibition. Six metabolites were detected across all species, with organ-dependent variation. In the leaves, EB showed a high rutin content (241.3 µg/mL), EM contained catechin (137.3 µg/mL), and EH exhibited β sitosterol (315.9 µg/mL). Both leaves and roots of all species showed notable antioxidant activity. EB leaves exhibited inhibition rates of 69.5% and 85.5% in ABTS•+ and DPPH• assays, respectively (IC₅₀ = 22 and 23.47 µg/mL). EH roots showed higher activity, reaching 89.4% and 78.2% inhibition (IC₅₀ = 21.8 and 20.72 µg/mL). Conversely, EM organs exhibited relatively lower radical scavenging capacities; however, EM leaves showed the highest α-glucosidase inhibition (49.1%). Overall, these results suggest that roots generally possess stronger antioxidant potential than leaves, whereas EM leaves stand out for their enzymatic inhibitory activity. These findings highlight the diverse phytochemical and bioactive profiles of E. beecheyanum, E. heterophyllum, and E. mexiae. Full article

Monitoring indoor air quality is vital for health, as CO₂ is a major pollutant. An automated system that accurately forecasts CO₂ levels can optimize HVAC management, preventing sudden increases and reducing energy waste while maintaining occupant comfort. Traditionally, such systems require extensive datasets collected over months to train algorithms, making them computational expensive and inefficient. To address this limitation, an adaptive IoT-based platform has been developed, leveraging a limited set of recent data to forecast CO₂ trends. Tested in a real-world setting, the system analyzed parameters such as physical activity, temperature, humidity, and CO₂ to ensure accurate predictions. Data acquisition was performed using the Smartex WWS T-shirt for physical activity data and the UPSense UPAI3-CPVTHA environmental sensor for other measurements. The chosen sensor devices are wireless and minimally invasive, while data processing was carried out on a low-power embedded PC. The proposed forecasting model adopts an innovative approach. After a 5-day training period, a Generative Adversarial Network enhances the dataset by simulating a 10-day training period. The model utilizes a Generative Adversarial Network with a Long Short-Term Memory network as the generator to predict future CO₂ values based on historical data, while the discriminator, also a Long Short-Term Memory network, distinguishes between actual and generated CO₂ values. This approach, based on Conditional Generative Adversarial Networks, effectively captures data distributions, enabling more accurate multi-step probabilistic forecasts. In this way, the framework maintains a Root Mean Square Error of approximately 8 ppm, matching the performance of our previous approach, while reducing the need for real training data from 10 to just 5 days. Furthermore, it achieves accuracy comparable to other state-of-the-art methods that typically requires weeks or even months of training. This advancement significantly enhances computational efficiency and reduces data requirements for model training, improving the system’s practicality for real-world applications. Full article

Soil salinization has become a critical constraint on agricultural productivity and eco-logical sustainability in arid regions. The accurate mapping of its spatial distribution is essential for sustainable land management. Although many studies have used satellite remote sensing combined with machine learning or convolutional neural networks (CNN) for soil salinity monitoring, most CNN approaches rely on single-scale convolution kernels. This limits their ability to simultaneously capture fine local detail and broader spatial patterns. In this study, we developed a multi-scale deep learning framework to enhance salinity prediction accuracy. We target the root-zone soil salinity in the Wei-Ku Oasis. Sentinel-2 multispectral imagery and Sentinel-1 radar backscatter data, together with topographic, climatic, soil texture, and groundwater covariates, were integrated into a unified dataset. We implemented the workflow using the Google Earth Engine (GEE; earthengine-api 0.1.419) and Python (version 3.8.18) platforms, applying the Sequential Forward Selection (SFS) algorithm to identify the optimal feature subset for each model. A multi-branch convolutional neural network (MB-CNN) with parallel 1 × 1 and 3 × 3 convolutional branches was constructed and compared against random forest (RF), 1 × 1-CNN, and 3 × 3-CNN models. On the validation set, MB-CNN achieved the best performance (R² = 0.752, MAE = 0.789, RMSE = 1.051 dS∙m⁻¹, nRMSE = 0.104), showing stronger accuracy, lower error, and better stability than the other models. The soil salinity inversion map based on MB-CNN revealed distinct spatial patterns consistent with known hydrogeological and topographic controls. This study innovatively introduces a multi-scale convolutional kernel parallel architecture to construct the multi-branch CNN model. This approach captures environmental characteristics of soil salinity across multiple spatial scales, effectively enhancing the accuracy and stability of soil salinity inversion. It provides new insights for remote sensing modeling of soil properties. Full article

The spatiotemporal dynamics of soil nutrients in the crop row zone are critical determinants of crop yield, necessitating precision fertilization for optimal plant growth. However, previous studies have predominantly focused on plant-available nutrient status at the scale of entire cropping systems, yet a granular understanding of their distribution patterns across precise temporal and spatial dimensions remains limited. Therefore, this study investigated maize–legume intercropping systems to quantify the dynamics of soil alkaline-hydrolyzable nitrogen (AN), available phosphorus (AP), and available potassium (AK) across distinct growth stages, soil depths, and row positions. The experiment comprised five treatments: maize–soybean intercropping, maize–peanut intercropping, and monocultures of maize, soybean, and peanut. Throughout the two-year study, maize–soybean intercropping significantly enhanced the plant height of both maize and soybean relative to their respective monocultures (p < 0.05). In contrast, within the maize–peanut system, intercropping significantly promoted peanut plant height but suppressed stem diameter in both species (p < 0.05); these effects were consistent across both study years. Both systems exhibited a “benefit-sacrifice” pattern, where dry matter was preferentially allocated to maize, thereby increasing total system productivity despite suppressing legume growth. Furthermore, during the mid-to-late growth stages, intercropped maize showed an enhanced capacity for nitrogen uptake from deeper soil layers. In contrast, the alkaline-hydrolyzable nitrogen content in intercropped soybean and peanut remained lower than in their respective monocultures throughout the growth period, with reductions ranging from 8.49% to 34.79%. Intercropping significantly increased the soil available phosphorus content in the root zones of maize, soybean, and peanut compared to their respective monocultures. The available phosphorus content in the 0–20 cm soil layer was consistently higher than in monoculture systems, with a maximum increase of 41.70%. Moreover, intercropping effectively mitigated soil potassium depletion, resulting in a smaller decline in available potassium. This effect was most pronounced in the maize–peanut intercropping pattern within the 20–40 cm soil layer. The distribution of soil available nutrients (N, P, K) was also influenced by drip tape placement. The levels of these nutrients for soybean and peanut were higher at 50 cm from the drip tape than at 30 cm, while for maize, levels were higher at 80 cm than at 40 cm. Intercropping increased the thousand-kernel weight of maize and soybean but decreased that of peanut. Overall, the strategic row configuration optimized the yield performance of both intercropping systems, resulting in land equivalent ratios greater than 1, which indicates distinct yield advantages for both intercropping patterns. Full article

Rhododendron hainanense Merr. is a tropical flowering shrub valued for its strong orna-mental and medicinal properties; however, its horticultural application is limited by its susceptibility to waterlogging disasters. The AP2/ERF transcription factor family plays crucial roles in plant growth, development, and responses to biotic and abiotic stresses; however, its regulatory mechanism in response to waterlogging stress remains unclear. This study conducted a genome-wide analysis of the AP2/ERF transcription factor family in R. hainanense, identifying 142 RhAP2/ERFs genes distributed across 13 chromosomes and classified into five subfamilies. Conserved motif analysis confirmed the characteristic AP2 domain structure. Gene duplication events revealed 16 segmental duplication pairs, indicating a potential role in adaptive evolution. Cis-element and protein interaction analyses suggested involvement in abiotic stress responses. Transcriptome and qRT-PCR results under waterlogging stress showed significant up-regulation of RhERF9 and RhERF95, with RhERF9 expression increasing 130-fold after 3 days, implying a positive regulatory role for the RhERF9 protein in early waterlogging response. Tissue-specific expression highlighted RhERF9’s strong induction in roots, associated with aerenchyma formation and hypoxia adaptation. The identified candidate AP2/ERF genes in R. hainanense play important roles in abiotic stress resistance and lay a foundation for future applications in breeding and horticulture. Full article

It has been argued that there are analogies between the nominal domain and the verbal domain in natural languages. Most approaches dealing with these analogies in Slavic languages investigate them from the semantic and aspectual points of view. In contrast to them, this article focuses on morphosyntactic parallels. It investigates all five aspectual markers of verbal predicates: prefixes, the secondary imperfective, the semelfactive morpheme, the iterative -a and the habitual suffix. The analysis follows the Distributed Morphology framework. This article addresses the question of which morphosyntactic correspondences these aspectual markers have in the nominal domain. It is argued that the iterative secondary imperfective is a parallel of the nominal number projection and that the habitual morpheme in North Slavic languages is the counterpart of the nominal determiner. Verbal prefixes are analogous to nominal classifiers, and in addition, lexical prefixes parallel the nominal complement, and superlexical prefixes correspond to adjectival modifiers of the nominal domain. The internal iterative -a, as a spell-out of the verbal categorizing head, is analogous to the categorizing head of nouns. Thus, it is argued that Slavic also has event-internal and event-external pluractional markers. The semelfactive morpheme parallels the singulative (diminutive) marker of the nominal domain, and we argue that these markers adjoin to the root before the categorizing head. This argues against the standard claim that semelfactives are derived from iteratives (multiplicatives). Full article

Ectomycorrhizal fungi (ECMF) function as critical mediators connecting plant roots and associated microorganisms. These fungi establish intimate associations with the root systems of diverse higher plants, particularly Pinaceae species, constituting essential components of forest ecosystems. The current understanding of ECMF community structure in Pinus elliottii and its potential associations with soil characteristics remains inadequate. This investigation examined seasonal variations in rhizosphere soil physicochemical properties and fungal community dynamics between susceptible (YB) and healthy (YJ) P. elliottii using amplicon sequencing. The results demonstrated significant seasonal differences in fungal community composition between YB and YJ. Dominant ECMF genera exhibited distinct distribution patterns, with Rhizopogon predominating in YJ and Tricholoma in YB. Correlation analyses revealed strong associations between these ECMF taxa and key soil parameters (available potassium, total phosphorus, and available phosphorus), indicating substantial seasonal influences of phosphorus and potassium cycling on ECMF development. Ericoid mycorrhizal fungi displayed higher abundance in YJ samples during spring, suggesting their dual role in facilitating nutrient acquisition and enhancing host plant resilience against biotic and abiotic stresses. These findings provide novel insights into seasonal dynamics of fungal communities in P. elliottii ecosystems and offer practical implications for sustainable plantation management under global change scenarios. Full article

Root-knot nematodes (RKNs) of the genus Meloidogyne spp., are among the most economically important groups of plant-parasitic nematodes worldwide, causing significant economic losses through yield reduction across a wide range of crops. In Croatia, although the presence of Meloidogyne spp. has been documented for decades, data at the species level was limited. As accurate identification is crucial for implementation of effective management strategies, we attempted to fill this gap. This study presents the results of a national survey of RKNs affecting potato crops as well as an early warning programme targeting vegetable crops, conducted across Croatia between 2022 and 2024. Nematodes were identified using morphological analyses (female perineal patterns and second-stage juveniles) and molecular methods (PCR with group-specific and species-specific primers, as well as DNA sequencing). Meloidogyne spp. were detected in 61 out of 210 samples, corresponding to an infestation rate of 29%. Four species were identified: M. incognita, M. hapla, M. arenaria, and M. javanica. Notably, M. incognita and M. javanica are reported here for the first time in Croatia. These results provide updated insights into the distribution and identity of RKNs in Croatia, thereby establishing a foundation for the implementation of sustainable management strategies. Full article

The accurate registration of image pairs is an indispensable key step in the process of disaster assessment, environmental monitoring, and change detection. However, obtaining correct matches from input images is difficult, especially from images with significant resolution and regional variations. The current image-registration algorithms perform poorly in this application scenario. In this article, a spatial point feature-based registration method is proposed for remote sensing images with large regional variations. First, a new edge keypoint extraction method is designed that selects points with gradient magnitude maxima around the neighborhood of the edge line segments as keypoint features. Then, the feature descriptors for each keypoint are constructed based on the geometrical distribution (distance and orientation) of each keypoint. Considering the stability of the distribution of the edge contours, our constructed descriptor vectors can be well used for image pairs with large resolution and regional variations. In addition, all feature descriptors in this method are constructed and matched in the rotated image pyramid. Finally, the fast sampling consensus algorithm is applied to eliminate mismatches. In test images with various scales, rotation angles, and regional variations, the proposed method achieved pixel-level root mean square error, and the average registration precision is nearly 100%. Meanwhile, our proposed method’s rotation and scale invariance are verified by rotating and downsampling the image pairs extensively. In addition, compared with the comparison algorithms, the method proposed in this paper has better registration performance for images with resolution and regional variations. Full article

Global navigation satellite system (GNSS) differential code bias (DCB) and topside ionosphere vertical electron content (VEC) can be estimated using onboard data from low-earth-orbit (LEO) satellites. These satellites provide the potential to make up for the lack of ground-based stations in the oceanic and polar regions and establish a high-precision global ionosphere model. In order to study the influences of different LEO-topside VEC processing methods on estimates, we creatively analyzed and compared the results and accuracy of the DCBs and LEO-topside VEC estimates using two topside VEC solutions—the SH-topside VEC (spherical harmonic-topside vertical electron content) and EP-topside VEC (epoch parameter-topside vertical electron content) methods. Some conclusions are drawn as follows. (1) Using GRACE-A data (400 km in 2016), the monthly stabilities (STDs) of GPS satellite DCBs and LEO receiver DCBs using the EP-topside VEC method are better than those using the SH-topside VEC method. For JASON-2 data (1350 km), the STD results of GPS DCBs using the SH-topside VEC method are slightly superior to those using the EP-topside VEC method, and LEO DCBs using the two methods have similar STD results. However, the root mean square (RMS) results for GPS DCBs using the SH-topside VEC model relative to the Center for Orbit Determination in Europe (CODE) products are slightly superior to those using the EP-topside VEC method. (2) The peak ranges of the actual GRACE-A-topside VEC results using the SH-topside VEC and EP-topside VEC methods are within 42 and 35 TECU, respectively, while the peak ranges of the JASON-2-topside VEC results are both within 6 TECU. Additionally, only the SH-topside VEC model results are displayed due to the EP-topside VEC method not modeling VEC. Due to the difference in orbital altitude, the results and distributions of the GRACE-topside VECs differ from those of the JASON-topside VECs, with the former being more consistent with the ground-based results, indicating that there may be different height structures in the LEO-topside VECs. In addition, we applied the IRI-GIM (International Reference Ionosphere model–Global Ionosphere Map) method to compare the LEO-based topside VEC results, which indicate that the accuracy of GRACE-A-topside VEC using the EP-topside VEC method is better than that using the SH-topside VEC method, whereas for JASON-2, the two methods have similar accuracy. Meanwhile, we note that the temporal and spatial resolutions of the SH-topside VEC method are higher than those of the EP-topside VEC method, and the former has a wide range of usability and predictive characteristics. The latter seems to correspond to the single-epoch VEC mean of the former to some extent. Full article

This paper presents an intelligent hybrid framework for short-term energy consumption forecasting and real-time energy management in photovoltaic (PV)-integrated smart homes with Vehicle-to-Home (V2H) systems, tailored to the Tunisian context. The forecasting module employs an Attention-based Long Short-Term Memory (LSTM) neural network, whose hyperparameters (learning rate, hidden units, temporal window size) are optimized using the Harris Hawks Optimization (HHO) algorithm. Simulation results show that the proposed LSTM-HHO model achieves a Root Mean Square Error (RMSE) of 269 Wh, a Mean Absolute Error (MAE) of 187 Wh, and a Mean Absolute Percentage Error (MAPE) of 9.43%, with R² = 0.97, substantially outperforming conventional LSTM (RMSE: 945 Wh, MAPE: 51.05%) and LSTM-PSO (RMSE: 586 Wh, MAPE: 28.72%). These accurate forecasts are exploited by the Energy Management System (EMS) to optimize energy flows through dynamic appliance scheduling, HVAC load shifting, and coordinated operation of home and EV batteries. Compared with baseline operation, PV self-consumption increased by 18.6%, grid reliance decreased by 25%, and household energy costs were reduced by 17.3%. Cost savings are achieved via predictive and adaptive control that prioritizes PV utilization, shifts flexible loads to surplus periods, and hierarchically manages distributed storage (home battery for short-term balancing, EV battery for extended deficits). Overall, the proposed LSTM-HHO-based EMS provides a practical and effective pathway toward smart, sustainable, and cost-efficient residential energy systems, contributing directly to Tunisia’s energy transition goals. Full article