Growth Status Research Articles

Heat-shock transcription factor HsfA8a regulates heat stress response in Sorbus pohuashanensis.

The SpHsfA8a upregulated expression can induce the expression of multiple heat-tolerance genes, and increase the tolerance of Arabidopsis thaliana to high-temperature stress. Sorbus pohuashanensis is an ornamental tree used in courtyards. However, given its poor thermotolerance, the leaves experience sunburn owing to high temperatures in summer, severely affecting its ornamental value. Heat-shock transcription factors play a critical regulatory role in the plant response to heat stress. To explore the heat-tolerance-related genes of S. pohuashanensis to increase the tree's high-temperature tolerance, the SpHsfA8a gene was cloned from S. pohuashanensis, and its structure and expression patterns in different tissues and under abiotic stresswere analyzed, as well as its function in heat tolerance, was determined via overexpression in Arabidopsis thaliana. The results showed that SpHsfA8a encodes 416 amino acids with a predicted molecular weight of 47.18kDa and an isoelectric point of 4.63. SpHsfA8a is a hydrophilic protein without a signal peptide and multiple phosphorylation sites. It also contains a typical DNA-binding domain and is similar to MdHsfA8a in Malus domestica and PbHsfA8 in Pyrus bretschneideri. In S. pohuashanensis, SpHsfA8a is highly expressed in the roots and fruits and is strongly induced under high-temperature stress in leaves. The heterologous expression of SpHsfA8a in A. thaliana resulted in a considerably stronger growth status than that of the wild type after 6h of treatment at 45°C. Its proline content, catalase and peroxidase activities also significantly increased, indicating that the SpHsfA8a gene increased the tolerance of A. thaliana to high-temperature stress. SpHsfA8a could induce the expression of multiple heat-tolerance genes in A. thaliana, indicating that SpHsfA8a could strengthen the tolerance of A. thaliana to high-temperature stress through a complex regulatory network. The results of this study lay the foundation for further elucidation of the regulatory mechanism of SpHsfA8a in response of S. pohuashanensis to high-temperature stress.

Utilizing GaoFen-2 derived urban green space information to predict local surface temperature

Urban green spaces (UGS) significantly influence the distribution of surface heat and play a crucial role in regulating surface temperature. However, the quantitative relationship between UGS and surface temperature remains unclear, necessitating further research. This study aims to predict surface temperature based on green space information from GaoFen-2 satellite data. To achieve this, GaoFen-2 data were utilized to obtain spatial distribution and vegetation growth status in Urumqi, Xinjiang. Three machine learning models such as Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Regression Tree (GBRT) were constructed to predict surface temperature. Results indicated that UGS information extracted from GaoFen-2 data using the U-Net semantic segmentation model successfully predicted surface temperature. Among the three machine learning models, GBRT exhibited the highest predictive accuracy with an Radj2 of 0.81, RMSE of 0.44, and RPD of 2.29, followed by RF (Radj2 of 0.80, RMSE of 0.45, and RPD of 2.22), and SVM (Radj2of 0.79, RMSE of 0.47, and RPD of 2.15), In addition, a variable importance assessment reduced the original 44 variables to 28, maintaining predictive accuracy with the GBRT model achieving an Radj2 of 0.81, RMSE of 0.43, and RPD of 2.3. Our study demonstrates the effectiveness of using vegetation information derived from GaoFen-2 to predict surface temperature. This approach provides valuable recommendations for the layout of UGS in urban areas and serves as a comprehensive reference for urban planning and real estate development.

Research on Multi-Step Fruit Color Prediction Model of Tomato in Solar Greenhouse Based on Time Series Data

Color change is the most obvious characteristic of the tomato ripening stage and an important indicator of the tomato ripening condition, which directly affects the commodity value of tomato. To visualize the color change of tomato fruit during the mature stage, this paper proposes a gated recurrent unit network with an encoder–decoder structure. This structure dynamically simulates the growth and development of tomatoes using time-dependent lines, incorporating real-time information such as tomato color and shape. Firstly, the .json file was converted into a mask.png file, the tomato mask was extracted, and the tomato was separated from the complex background environment, thus successfully constructing the tomato growth and development dataset. The experimental results showed that for the gated recurrent unit network with the encoder–decoder structure proposed, when the hidden layer number was 1 and hidden layer number was 512, a high consistency and similarity between the model predicted image sequence and the actual growth and development image sequence was realized, and the structural similarity index measure was 0.746. It was proved that when the average temperature was 24.93 °C, the average soil temperature was 24.06 °C, and the average light intensity was 11.26 Klux, the environment was the most suitable for tomato growth. The environmental data-driven tomato growth model was constructed to explore the growth status of tomato under different environmental conditions, and thus, to understand the growth status of tomato in time. This study provides a theoretical foundation for determining the optimal greenhouse environmental conditions to achieve tomato maturity and it offers recommendations for investigating the growth cycle of tomatoes, as well as technical assistance for standardized cultivation in solar greenhouses.

Research on Rapeseed Above-Ground Biomass Estimation Based on Spectral and LiDAR Data

The study of estimating rapeseed above-ground biomass (AGB) is of significant importance, as it can reflect the growth status of crops, enhance the commercial value of crops, promote the development of modern agriculture, and predict yield. Previous studies have mostly estimated crop AGB by extracting spectral indices from spectral images. This study aims to construct a model for estimating rapeseed AGB by combining spectral and LiDAR data. This study incorporates LiDAR data into the spectral data to construct a regression model. Models are separately constructed for the overall rapeseed varieties, nitrogen application, and planting density to find the optimal method for estimating rapeseed AGB. The results show that the R² for all samples in the study reached above 0.56, with the highest overall R² being 0.69. The highest R² for QY01 and ZY03 varieties was 0.56 and 0.78, respectively. Under high- and low-nitrogen conditions, the highest R² was 0.64 and 0.67, respectively. At a planting density of 36,000 plants per mu, the highest R² was 0.81. This study has improved the accuracy of estimating rapeseed AGB.

Flexible Multimodal Sensors Enhanced by Electrospun Lead-Free Perovskite and PVDF-HFP Composite Form-Stable Mesh Membranes for In Situ Plant Monitoring.

The pH and humidity of the crop environment are essential indicators for monitoring crop growth status. This study reports a lead-free perovskite/polyvinylidene fluoride-hexafluoropropylene composite (LPPC) to enhance the stability and reliability of in situ plant pH and humidity monitoring. The mesh composite membrane of LPPC illustrates a hydrophobic contact angle of 101.982°, a tensile strain of 800%, and an opposing surface potential of less than -184.9 mV, which ensures fast response, high sensitivity, and stability of the sensor during long-term plant monitoring. The LPPC-coated pH electrode possesses a sensitivity of -63.90 mV/pH, which provides a fast response within 5 s and is inert to environmental temperature interference. The LPPC-coated humidity sensor obtains a sensitivity of -145.7 Ω/% RH, responds in 28 s, and works well under varying light conditions. The flexible multimodal sensor coated with an LPPC membrane completed real-time in situ monitoring of soilless strawberries for 17 consecutive days. Satisfactory consistency and accuracy performance are observed. The study provides a simple solution for developing reliable, flexible wearable multiparameter sensors for in situ monitoring of multiple parameters of crop environments.

Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level.

MutL Significantly Regulates the Formation of Biofilms in Bacillus amyloliquefaciens YT1

The purpose of this study is to discover and excavate more key factors and signaling pathways that regulate the formation intensity of biofilms and to fully reveal the possible models affecting biofilm formation. By using gene homologous recombination and bioinformatics technology, a MutL protein-directed deletion mutant strain was successfully constructed. The growth status of the mutant strain was observed, and it was confirmed that, except for the change in cell morphology, there were no significant differences in growth and reproduction between the mutant strain and the wild-type strain. By using the induced biofilm formation technique, the significant decrease in biofilm formation in the MutL mutant strain was successfully verified. The plate confrontation test confirmed that the inhibitory ability of the mutant strain against rice blast fungus was not significantly different from that of the WT strain. The colonization ability of the mutant strain on rice stems was tested, and it was confirmed that the colonization ability of the mutant strain was significantly lower than that of the WT strain. In terms of the prevention and control effect of rice blast disease, the mutant strain showed a significant decrease. By using transcriptomic big data, the gene and pathway expression differences between the mutant strain and the WT strain during biofilm formation were analyzed. The analysis revealed no significant correlation with the previously reported spo0A and tapA-sipW-tasA pathways. The key factor capB of the polyglutamic acid signaling pathway, which affects the formation of the biological model, was found to have a significant decrease in expression. A mechanical hypothesis was proposed: MutL may participate in regulating the formation intensity of Bacillus biofilms by regulating the formation of glutamic acid to polyglutamic acid.

Computational Hemodynamics-Based Growth Prediction for Small Abdominal Aortic Aneurysms: Laminar Simulations Versus Large Eddy Simulations.

Prior studies have shown that computational fluid dynamics (CFD) simulations help assess patient-specific hemodynamics in abdominal aortic aneurysms (AAAs); patient-specific hemodynamic stressors are frequently used to predict an AAA's growth. Previous studies have utilized both laminar and turbulent simulation models to simulate hemodynamics. However, the impact of different CFD simulation models on the predictive modeling of AAA growth remains unknown and is thus the knowledge gap that motivates this study. Specifically, CFD simulations were performed for 70 AAA models derived from 70 patients' computed tomography angiography (CTA) data with known growth status (i.e., fast-growing [> 5mm/yr] or slowly growing [< 5mm/yr]). We used laminar and large eddy simulation (LES) models to obtain hemodynamic parameters to predict AAAs' growth status. Predicting the growth status of AAAs was based on morphological, hemodynamic, and patient health parameters in conjunction with three classical machine learning (ML) classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and generalized linear model (GLM). Our preliminary results estimated aneurysmal flow stability and wall shear stress (WSS) were comparable in both laminar and LES flow simulations. Moreover, computed WSS and velocity-related hemodynamic variables obtained from the laminar and LES simulations showed comparable abilities in differentiating the growth status of AAAs. More importantly, the predictive modeling performance of the three ML classifiers mentioned above was similar, with less than a 2% difference observed (p-value>0.05). In closing, our findings suggest that two different flow simulations investigated did not significantly affect outcomes of computational hemodynamics and predictive modeling of AAAs' growth status, given the data investigated.

Edukasi dan Screening Kesehatan sebagai Upaya Pencegahan Anemia pada Remaja Perempuan di MTs Nurul Falah Areman, Depok

Adolescent girls are a vulnerable group who can experience anemia due to low consumption and absorption of iron, blood loss during menstruation, and increased iron requirements. Anemia can cause impaired growth and health status as well as decreasing academic performance and cognitive development. Education regarding nutrition and screening for adolescent girls is an appropriate, effective, and sustainable method for preventing anemia due to iron deficiency. Therefore, in this community service activity, a combination of education and screening is carried out to prevent the development of anemia in adolescent girls at MTs Nurul Falah Areman. The community service was carried out using the lecture method and filling out a questionnaire in the form of a pretest and post-test to assess the increase in knowledge consisting of 20 multiple choice questions. The result obtained from 53 students showed that 52,8% had good knowledge regarding anemia and 47,2% had poor knowledge. Based on an examination of Hb levels, 25% of adolescent girls fall into the category of moderate and mild anemia. Education regarding anemia should be carried out routinely at MTs Nurul Falah Areman. Furthermore, MTs Nurul Falah Areman can be a target for blood supplement tablets from the nearest Community Health Center, so that in the future no more young women will be found suffering from anemia.

Transcription factor AbrB regulates ROS generation and clearance in Bacillus licheniformis

Oxidative damage caused by the accumulation of reactive oxygen species (ROS) is one of the main obstacles to the improvement of microbial cell growth and fermentation characteristics under adverse environments. And the antioxidant capacity of cells will increase with the cell growth. Here, we found that a transition state transcription factor AbrB related to changes in cell growth status could regulate the accumulation of ROS and antioxidant capacity in Bacillus licheniformis. The results showed that the accumulation of intracellular ROS was reduced by 23.91 % and the cell survival rates were increased by 1.77-fold under 0.5 mM H2O2 when AbrB was knocked out. We further mapped regulatory target genes of AbrB related to ROS generation or clearance based on our previously analyzed transcriptome sequencing. It proved that AbrB could promote ROS generation via upregulating the synthesis of oxidase and siderophores, and negatively regulating the synthesis of iron chelators (pulcherriminic acid, and H2S). Additionally, AbrB could inhibit ROS clearance by negatively regulating the synthesis of antioxidase (superoxide dismutase, catalase, peroxidase, thioredoxin, thioredoxin reductase) and cysteine. Those results illustrated that the inactivation of AbrB during the stationary phase, along with its control over ROS generation and clearance, might represent a vital self-protection mechanism during cell evolution. Overall, the systematic investigation of the multi-pathway regulation network of ROS generation and clearance highlights the important function of AbrB in maintaining intracellular redox balance.

The Glucose-Glutamine Metabolic Interplay in MCF-7 Cells, a Hormone-Sensitive Breast Cancer Model.

Selective deprivation of glutamine has been shown to accelerate the generation of reactive oxygen species (ROS) and to impair the activity of a specific pentose phosphate pathway (PPP) located within the endoplasmic reticulum (ER). The consequent oxidative damage suggests that glucose flux through this reticular pathway might contribute to the redox stress of breast cancer cells. We thus evaluated whether this response is reproduced when the glutamine shortage is coupled with the glucose deprivation. Cancer growth, metabolic plasticity and redox status were evaluated under saturating conditions and after 48 h starvation (glucose 2.5 mM, glutamine 0.5 mM). The Seahorse technology was used to estimate adenosine triphosphate (ATP)-linked and ATP-independent oxygen consumption rate (OCR) as well as proton efflux rate (PER). 18F-fluoro-deoxy-glucose (FDG) uptake was evaluated through the LigandTracer device. Proliferation rate was estimated by the carboxyfluorescein-diacetate-succinimidyl ester (CFSE) staining, while cell viability by the propidium iodide exclusion assay. Starvation reduced the proliferation rate of MCF-7 cells without affecting their viability. It also decreased lactate release and PER. Overall OCR was left unchanged although ATP-synthase dependent fraction was increased under nutrient shortage. Glutaminolysis inhibition selectively impaired the ATP-independent and the oligomycin-sensitive OCR in control and starved cultures, respectively. The combined nutrient shortage decreased the cytosolic and mitochondrial markers of redox stress. It also left unchanged the expression of the reticular unfolded protein marker GRP78. By contrast, starvation decreased the expression of hexose-6P-dehydrogenase (H6PD) thus decreasing the glucose flux through the ER-PPP as documented by the profound impairment in the uptake rate of FDG. When combined with glucose deprivation, glutamine shortage does not elicit the expected enhancement of ROS generation in the studied breast cancer cell line. Combined with the decreased activity of ER-PPP, this observation suggests that glutamine interferes with the reticular glucose metabolism to regulate the cell redox balance.

Combined use of chemical dust suppressant and herbaceous plants for tailings dust control.

Tailings dust can negatively affect the surrounding environment and communities because the tailings are vulnerable to wind erosion. In this study, the effects of halides (sodium chloride [NaCl], calcium chloride [CaCl2], and magnesium chloride hexahydrate [MgCl2·6H2O]), and polymer materials (polyacrylamide [PAM], polyvinyl alcohol [PVA], and calcium lignosulfonate [LS]) were investigated for the stabilization of tailings for dust control. Erect milkvetch (Astragalus adsurgens), ryegrass (Lolium perenne L.), and Bermuda grass (Cynodon dactylon) were planted in the tailings and sprayed with chemical dust suppressants. The growth status of the plants and their effects on the mechanical properties of tailings were also studied. The results show that the weight loss of tailings was stabilized by halides and polymers, and decreased with increasing concentration and spraying amount of the solutions. The penetration resistance of tailings stabilized by halides and polymers increased with increasing concentration and spraying amount of the solutions. Among the halides and polymers tested, the use of CaCl2 and PAM resulted in the best control of tailings dust, respectively. CaCl2 solution reduces the adaptability of plants and therefore makes it difficult for grass seeds to germinate normally. PAM solutions are beneficial for the development of herbaceous plants. Among the three herbaceous species, ryegrass exhibited the best degree of development and was more suitable for growth in the tailings. The ryegrass plants planted in the tailings sprayed with PAM grew the best, and the root-soil complex that formed increased the shear strength of the tailings.

Multimodal rapid identification of growth stages and discrimination of growth status for Morchella

We introduce a multimodal rapid identification and growth status discrimination method for morchella. Based on the unique biological characteristics and growth environmental requirements of morchella, the efficient and accurate identification of key growth stages of morchella is achieved through the integration of multimodal information acquisition technology. During the rapid identification process of the growth stage of Morchella, the Multi Stage Vision Enhanced Position Encoding Vision Transformer (MS-EP ViT) model is adopted. By introducing multi-stage input embedding, enhanced position encoding, and optimized Transformer Encoder layers, the performance of the model in identifying different growth stages of Morchella mushrooms is significantly improved. In the multimodal Morchella growth state discrimination method, text and image modalities are integrated, a Non downsampled Contourlet Transform Mask Region based Convolutional Neural Network (NSCT Mask R-CNN) model is designed, and a multimodal feature extraction strategy combining Non downsampled Contourlet Transform (NSCT) features with environmental features is explored. This strategy effectively achieves the goals of object detection and instance segmentation, and thus we have accurately evaluated the growth status of Morchella in the later stages of mulberry, young mushroom, and mature. The experimental results show that both models have achieved significant improvements in recognition accuracy and stability, and the rationality of hyperparameter settings has been verified through convergence and parameter sensitivity experiments. Overall, we provide a more accurate and efficient identification method for monitoring the growth of Morchella, which helps to better understand the growth of Morchella and provides scientific basis for optimizing its growth environment.

Removal of Nitrogen and Phosphorus in Low Polluted Wastewater by Aquatic Plants: Impact of Monochromatic Light Radiation

Plant absorption via aquatic plants is vital for the deep purification of treated wastewater. This study aimed to determine the removal efficiencies of nitrogen and phosphorus for different aquatic plants and the effect of monochromatic light as compared to white light. Five plants (i.e., Iris pseudacorus, Oenanthe javanica, Zantedeschia aethiopica, Ipomoea aquatica Forssk. and Sagittaria trifolia) were cultured in prepared wastewater and radiated by white, red, green and blue LED lamps with 8 h radiation per day, respectively. After 4 d of cultivation, the O. javanica and S. trifolia exhibited relatively better growth status and higher TP removal rates (90%). The blue light radiation played a key role in the TP uptake of the tested plants. The N removal rates of plants were relatively lower (10–40%), limited by the low COD content. The S. trifolia exhibited the highest efficiency, and red light promoted the removal of TN and NO3−-N, whereas NH4+-N removal was driven by blue light radiation. So, O. javanica and S. trifolia coupled with blue and red lamps as supplementary light were suggested for the deep purification of municipal treated wastewater. The effect of intensity and ratio of monochromatic lights could be a direction for further research.

Potassium Application Impacts Chickpea Yield and Nutritional Status

ABSTRACT As one of the three primary plant nutrients, plant growth and development depend on soil potassium (K) availability. However, plants often face K deficiency in the soil. Chickpea (Cicer arietinum L.) is an excellent source of vegetable protein. However, optimum chickpea growth is affected by K limitation. This study evaluated whether K application in an Oxisol influences chickpea growth, grain yield, and nutritional status. We conducted a greenhouse experiment in an Oxisol with four K application rates (0, 40, 80, and 150 kg ha−1) and four replicates. Our results showed that K application significantly increased chickpea grain yield (R2 = 0.968), shoot weight, root dry weight, and the number of pods. In addition, the K application rate in the soil increased with increasing K application rate. In addition, the concentrations of K and P in the grain are affected by K application. In addition, K improved the K concentration in the shoot (R2 = 0.756) and grain (R2 = 0.833). However, the application of high levels of K did not increase K accumulation in the seeds, indicating that the use of low doses of K (≤80 kg ha−1) is sufficient to support plant growth and nutrition and avoid K loss. Finally, the benefits of K in plants are more than an increase in the grain yield of crops. However, K plays a dominant role in the root system. For instance, this benefit enhances K acquisition from the soil for plants.

Screening of New Dendrobium officinale Strains Adapted to Karst Forest Environmental Stress Based on Electrophysiological Detection Method

At present, the main methods of breeding Dendrobium officinale (D. officinale) include introduction and domestication, selective breeding, hybrid breeding, and mutation breeding. In the process, traditional methods of field investigation of agronomic traits are often used to select resistant varieties. Although these breeding methods are effective, they have a certain degree of subjectivity and empiricism, and the breeding cycle is long. Electrophysiological instruments were used in this experiment to test the material transport and metabolic capacity of D. officinale in a karst forest epiphytic environment and to quickly evaluate the suitability of different strains of D. officinale in a drought environment. These instruments detected the data on electrophysiological information of leaves of different strains of D. officinale under long-term drought conditions, providing immediate access to the inherent electrophysiological information of the leaves of these strains. Based on the electrophysiological parameters of D. officinale leaves as defined by the inherent electrophysiological information of plants, the water metabolism, nutrient transport, and metabolic capacity in different leaves were evaluated. The key electrophysiological indexes were verified by combining the results of chlorophyll fluorescence and chlorophyll content. The results indicate the following: (1) Parameters defined based on electrophysiological information effectively characterized the differences in intracellular water utilization, the nutrient transport status, and the metabolic capacity of different D. officinale strains. (2) The intrinsic physiological resistance, intrinsic physiological reactance, active nutrient transport capacity, and passive nutrient transport capacity were closely related to the growth status and chlorophyll function of D. officinale leaves. These electrophysiological parameters could serve as critical indicators for evaluating the drought resistance of D. officinale. (3) Under severe drought stress, strain LH1 exhibited less leaf damage, adequate water/nutrient supply, vigorous life activities, and excellent drought resistance. We found that strain LH1 demonstrates better adaptation to the arid environment of karst forest lands. The electrophysiological detection method employed in this study offers a new technique for screening wild-cultivated D. officinale resistance strains. The results indicate that the real-time online leaf electrophysiological information measured by the method in this study can characterize the energy and material metabolism of crops, greatly improving the efficiency of crop-variety selection and reducing costs. These conclusions can be used to obtain real-time information on the transmission of water and nutrients within plant cells and can provide theoretical support for studying the adaptation mechanisms of crops to adverse environments.

Reconstruction of dense time series high spatial resolution NDVI data using a spatiotemporal optimal weighted combination estimation model based on Sentinel-2 and MODIS

A regular, dense time series of the normalized difference vegetation index (NDVI) is a crucial remote sensing parameter that provides insights into the growth status of plants across different temporal and spatial scales. However, the production of high-resolution, equal-interval, and dense-time-series NDVI products currently faces technical challenges, limiting their application in agriculture and forestry-related fields. This study proposes a new method for reconstructing a dense Sentinel-2 NDVI time series based on the spatiotemporal optimal weighted combination estimation model (SOWCEM). The SOWCEM is developed using state-of-the-art spatial and temporal reconstruction algorithms. This model considers the distribution characteristics of errors in spatiotemporal dimensions, allowing for adaptive determination of the optimal combination weight at the pixel level. Then, the reconstructed regular dense-time-series NDVI data covering the region for five days/time are evaluated through a comparative analysis. The experimental results demonstrate that the regional NDVI reconstruction image based on the SOWCEM algorithm has a determination coefficient (R2) of 0.9082, a root mean square error (RMSE) of 0.0403, and a comprehensive overall error (ERGAS) of 5.7726. Compared with single spatiotemporal dimension models, the SOWCEM algorithm shows an average improvement of 5.78% in R2, while the RMSE and ERGAS decrease by 6.5% and 6.86%, respectively. Moreover, the stability and robustness of the models are also enhanced. These results indicate that the SOWCEM algorithm can effectively achieve a high-quality fusion reconstruction of regional images, significantly enhancing the accuracy of high-spatial-resolution NDVI dense temporal spectral reconstruction. Furthermore, it exhibits clear superiority and good applicability. This algorithmic framework provides a feasible approach for the dense reconstruction of high-spatial-resolution regular time-series NDVI data, enabling more accurate and efficient high-resolution NDVI remote sensing monitoring services in related field applications. The core code of SOWCEM is available at https://github.com/GISerZkun/SOWCEM.

Spatial Scale Effect on Fractional Vegetation Coverage Changes and Driving Factors in the Henan Section of the Yellow River Basin

Vegetation plays a crucial role in terrestrial ecosystems, and the FVC (Fractional Vegetation Coverage) is a key indicator reflecting the growth status of vegetation. The accurate quantification of FVC dynamics and underlying driving factors has become a hot topic. However, the scale effect on FVC changes and driving factors has received less attention in previous studies. In this study, the changes and driving factors of FVC at multiple scales were analyzed to reveal the spatial and temporal change in vegetation in the Henan section of the Yellow River basin. Firstly, based on the pixel dichotomy model, the FVC at different times and spatial scales was calculated using Landsat-8 data. Then, the characteristics of spatial and temporal FVC changes were analyzed using simple linear regression and CV (Coefficient of Variation). Finally, a GD (Geographic Detector) was used to quantitatively analyze the driving factors of FVC at different scales. The results of this study revealed that (1) FVC showed an upward trend at all spatial scales, increasing by an average of 0.55% yr−1 from 2014 to 2022. The areas with an increasing trend in FVC were 10.83% more than those with a decreasing trend. (2) As the spatial scale decreased, the explanatory power of the topography factors (aspect, elevation, and slope) for changes in FVC was gradually strengthened, while the explanatory power of climate factors (evapotranspiration, temperature, and rainfall) and anthropogenic activities (night light) for changes in FVC decreased. (3) The q value of evapotranspiration was always the highest across different scales, peaking notably at a spatial scale of 1000 m ( q = 0.48).

Nutritional improvement status of primary and secondary school students in the pilot nutrition improvement areas of Hainan, China from 2014 to 2021

ObjectiveBy meticulously tracking the evolving growth, development, and nutritional status of primary and secondary school students in Qiongzhong County from 2014 to 2021 post-implementation of the “Nutrition Improvement Program for Rural Compulsory Education Students”(NIPRCES, This project provides a supplementary food allowance of at least ￥4 per person per day for primary and secondary school students. The project area undergoes annual routine monitoring.), this study aims to offer a scientific basis for enhancing and promoting the project. Through thorough monitoring of students’ nutritional status changes influenced by this program, we strive to establish a comprehensive and evidence-based framework for its future advancement.MethodsFrom 2014 to 2021, this study employed a multi-stage sampling method utilizing cluster sampling to select six primary and six secondary schools in Qiongzhong County, Hainan Province. Data on the growth and development of respondents were collected. This cohort was a dynamic cohort with a total of 18,762 final data recovered. The prevalence of malnutrition was evaluated using the Cochran Armitage Trend Test (CATT) to assess year-to-year changes. Furthermore, height/weight and the prevalence of malnutrition between groups were compared using the t-test, χ2 test, and Bonferroni’s corrected analysis.ResultsThe average height of both boys and girls has increased. In 2021, boys and girls of all ages showed an average height increase of 2.31 cm and 1.98 cm, respectively, compared to 2014. Nevertheless, the growth and development levels, and rate of improvement of these students remain comparatively lower than their rural counterparts across China, who are of the same age. From 2014 to 2021, the prevalence of undernutrition (mainly wasting) showed a significant downward trend (P < 0.05) from 29.30% to 22.19%, and the prevalence of overnutrition showed an upward trend (both P < 0.05). The prevalence of undernutrition was higher among boys, students in grades 1–3 and those of Li nationality. Meanwhile, the prevalence of overnutrition was higher among boys, students in grades 1–3 and those of Han nationality.ConclusionsOver the 8-year period of NIPRCES, there has been progress in the growth and development of students, yet levels still lag behind the national average for rural students of the same age. While malnutrition prevalence have decreased, they remain high, with a concerning rise in overnutrition prevalence. Undernutrition and overweight/obesity are more prevalent among boys and younger students. Li students show higher prevalence of undernutrition, while overnutrition is a growing issue among Han students. Simultaneously, local education and health care departments must acknowledge the disparities in growth and nutritional status among primary and secondary school students residing in rural areas within the tropics and those in rural areas across the entire nation. Nutritional improvement measures should be tailored to local conditions.

Impact of alternate partial root-zone irrigation on the rhizosphere microbiota of alfalfa plants inoculated with rhizobia.

Water is an important constraint on alfalfa (Medicago sativa) production in arid and semiarid areas, and alternate irrigation in root areas has water-saving potential for alfalfa production. To investigate the impact of alternate partial root-zone irrigation (APRI) on the rhizosphere soil microorganisms of alfalfa, this study subjected alfalfa plants to different irrigation methods and irrigation levels. The growth status and rhizosphere soil microbial community diversity of alfalfa plants under alternate root-zone watering treatment were analyzed through laboratory experiments and high-throughput sequencing. The results showed that at soil moisture levels of 80% field moisture capacity (FMC) and 60% FMC, APRI had no significant impact on the biomass or nodule number of alfalfa. However, 40% FMC significantly reduced the individual plant dry weight, chlorophyll content, and nodule number of the alfalfa plants. APRI increased the relative abundance of Actinomycetes in the alfalfa rhizosphere soil. Moreover, at 60% FMC, the MBC and MBN of rhizosphere, relative abundance of Actinobacteria and unclassified K fungi and Chao 1 index of bacteria significantly increased under APRI treatment. While relative abundance of Ascomycetes and Proteobacteria in the alfalfa rhizosphere significantly reduced under 60% FMC + APRI treatment. In summary, under the same irrigation conditions, APRI did not significantly affect the growth of alfalfa in the short term. And 60%FMC + APRI treatment did significantly affect the groups, structure and diversity of the rhizosphere soil microbial communities.