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Prunus (P.) humilis is a small woody shrub that has been widely planted in northern China due to its high nutritional value and resistance to environmental abiotic stress. However, little information about the responses of photosynthetic performance and the anatomical structure of P. humilis to saline–alkaline stress (SAS) under field conditions is available. Here, we investigated the behavior of the photosynthetic apparatus of P. humilis by measuring the chlorophyll fluorescence parameters under moderate (MS) and severe (SS) saline–alkaline stress and analyzing their relationship to leaf anatomical traits. The results showed that SAS significantly decreased the net photosynthetic rate (An) but increased the substomatal CO2 concentration (Ci). The maximum photochemical quantum yield of PSII (Fv/Fm) and the efficient quantum yield of PSII [Y(II)] decreased under MS and SS conditions, and this decrease was greater in the distal (tip) than in the proximal (base) leaf. Compared to the leaf tip, the base of P. humilis leaves seemed to have a stronger ability to cope with MS, as was made evident by the increased quantum yield of regulated energy dissipation in PSII [Y(NPQ)] and decreased excitation pressure (1-qP). Under MS and SS conditions, the shapes of the chlorophyll a fluorescence transient (OJIP) changed markedly, accompanied by decreased PSII acceptor-side and donor-side activities. The palisade–spongy tissue ratio (PT/ST) increased significantly with increasing stress and showed a significant correlation with the chlorophyll fluorescence parameters in the leaf base. These results suggested that the activity of PSII electron transfer in the upper leaf position tended to be more sensitive to saline–alkaline stress, and a chlorophyll fluorescence analysis proved to be a good technique to monitor impacts of saline–alkaline stress on photosynthetic function, which may reflect the non-uniformity of leaf anatomy. In addition, among the anatomical structure parameters, the palisade–spongy tissue ratio (PT/ST) can be used as a sensitive indicator to reflect the non-uniform of photosynthetic function and leaf anatomy under stress. [ABSTRACT FROM AUTHOR]

Water resources are scarce, and secondary soil salinization is severe in the Hetao Irrigation District. Farmland shelterbelt systems (FSS) play a critical role in regulating soil water and salt dynamics within the irrigation district. However, the understanding of soil water and salt migration within FSS during the freeze–thaw period remains unclear due to the complex and multifaceted interactions between water and salt. This study focused on a typical FSS and conducted comprehensive monitoring of soil moisture, salinity, temperature, and meteorological parameters during the freeze–thaw period. The results revealed consistent trends in air temperature and soil temperature overall. Soil freezing durations exceeded thawing durations, and both decreased with an increasing soil depth. At the three critical freeze–thaw nodes, the soil moisture content at a 0–20 cm depth was significantly lower than at a 40–100 cm depth (p < 0.05). The soil water content increased with time and depth at varying distances from the shelterbelt, with an average increase of 7.63% after freezing and thawing. The surface water content at the forest edge (0.3H, 4H) was lower than inside the farmland (1H, 2H, 3H). Soil salt accumulation occurred during both freezing stable periods and melting–thawing periods in the 0–100 cm soil layer near the forest edge (0.3H, 4H), with the highest soil salinity reaching 0.62 g·kg−1. After the freeze–thaw period, the soil salt content in each layer increased by 11.41–47.26% compared to before the freeze–thaw period. Salt accumulation in farmland soil near the shelterbelt was stronger than in the far shelterbelt. The multivariate statistical model demonstrated goodness of fit for soil water and salt as 0.94 and 0.72, respectively, while the BP neural network model showed goodness of fit for soil water and salt as 0.82 and 0.85, respectively. Our results provide an efficient theoretical basis for FSS construction and agricultural water management practices. [ABSTRACT FROM AUTHOR]

BACKGROUND: Vacuum packaging has the ability to reduce oxidative deterioration and microbial‐induced spoilage of meat. However, in an oxygen‐free environment, it can lead to the development of an unappealing purplish‐red color and a decrease in the water‐holding capacity of meat, thereby impacting the overall meat quality. Portulaca oleracea L. (POL) is a homology of medicine and food known for its exceptional antioxidant and antimicrobial properties. RESULTS: The aim of our present study was to investigate the antioxidant and antimicrobial ability of n‐butanol phase extract of POL and the effect of POL extract incorporation on the quality (water‐holding capacity, shear force, color, and texture) and physicochemical (pH, total volatile base nitrogen, and total viable counts) attributes of vacuum‐packed seasoned steaks at 4 °C over a 15‐day period. Results showed that the POL extract had excellent antioxidant and antimicrobial capacity. Furthermore, the addition of POL extract significantly inhibited protein oxidation and microbial growth of steaks (P < 0.05), and improved the water‐holding capacity, color properties, and tenderness (P < 0.05). Moreover, there were no significant differences (P > 0.05) in the color, water‐holding capacity, or tenderness between the 0.5 and 1 g kg−1 POL extract treatment groups compared to the sodium nitrite control group. CONCLUSION: These results indicate that POL extract had the potential to replace sodium nitrite due to its ability to hinder protein oxidation and microbial growth of vacuum‐packed seasoned steaks, while enhancing the color, water‐holding capacity, and tenderness of seasoned steaks. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Background Pediatric native kidney diseases are common worldwide. The pathological diagnosis of kidney lesions is crucial for clinical treatment and prognosis. The aim of the current study was therefore to evaluate the value of electron microscopy (EM) to the final diagnosis of native kidney biopsies in children. Methods A retrospective evaluation of 855 pediatric kidney biopsies obtained from the Department of Pediatrics in Peking University First Hospital between November 2010 and December 2017 was performed to assess the contribution of EM to the final diagnosis. Results The role of EM in the final diagnosis was determined to be crucial in 300 cases (35.1%), important in 280 cases (32.7%), and auxiliary in 275 cases (32.2%). EM is considered most valuable in a large percentage of glomerular diseases, mainly including minimal change disease, early-stage membranous nephropathy, postinfectious glomerulonephritis, Alport syndrome, thin basement membrane nephropathy, and thrombotic microangiopathy. EM also provided helpful diagnostic information in cases of focal segmental glomerulosclerosis, lupus nephritis, IgA nephropathy, and IgA vasculitis (Henoch-Schonlein purpura nephritis). Additionally, EM was crucial in 90.0% of cases of subtle pathological changes observed with light microscopy (LM) and immunofluorescence (IF) and in 69.3% of the IF-negative specimens. Patients with nephrotic syndrome or hematuria also benefit from ultrastructural examination. Conclusions The present study demonstrated the crucial or important role of EM in the diagnosis of a majority of native kidney biopsies in children. The application of EM should be integrated together with LM and IF as a routine method of assessing pediatric kidney specimens., Author(s): Xu Zhang [sup.1] , Jin Xu [sup.1] , Huijie Xiao [sup.2] , Yong Yao [sup.2] , Hui Wang [sup.1] , Yali Ren [sup.1] , Mingming Liu [sup.3] [sup.4] , [...]

Monitoring the water pollution level in real time is the most critical issue for protecting the water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial Vehicles (UAV), four sensitive regions with the area of 1–2 km2 were first selected in this study based on the spatial distribution of total nitrogen (TN) concentration changes estimated by the Landsat remote sensing data. And then twelve machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks (ANN), Bayesian Ridge Regression (BRR), Lasso Regression (Lasso), Elastic Net (EN), Linear Regression (LR), Decision Tree Regression (DTR), K Neighbors Regression (KNR), Random Forest Regression (RFR), Extra Trees Regression (ETR), AdaBoost Regression (ABR) and Gradient Boosting Regression (GBR) were compared to construct a more accurate retrieval model by using the UAV hyper spectral remote sensing and ground monitoring data. And then the TN concentration was estimated after the process of dimensionality reduction and compressed sensing denoing. Finally, spatial heterogeneity of the TN concentration was analyzed in four sensitive areas of the Miyun reservoir. The results demonstrated that among the tested algorithms the Extra Trees Regression was best suitable for the construction of a TN concentration retrieval model on the basis of UAV hyper spectral data, and its absolute squared error was 0.000065. The spatial distribution of the TN showed that the concentration was highest within the water area of the Bulaotun village and the Houbajiazhuang village, while it was relatively low for the Chao river dam and Bai river dam. Additionally, no significant differences regarding the concentrations were shown in the single UAV flight area except the Houbajia village, which indicated that the water quality in Miyun reservoir was relatively stable and changed in a small interval. These conclusions can provide scientific references for water quality monitoring and management in the water reservoir.

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We examined the growth decline and health status oi farmland protective forest belt ( Populus alba var. pyramidalis and Populus simonii shelterbelts) in Ulanbuh Desert Oasis by using airborne hyperspectral and ground- based LiDAR to collect the hyperspectral images and point cloud data oi the whole forest belt respectively. Through correlation analysis and stepwise regression analysis, we constructed the evaluation model of the decline degree of farmland protection forest with the spectral differential value, vegetation index, and forest structure parameters as independent variables and the tree canopy dead branch index of the field survey as dependent variables. We further tested the accuracy of the model. The results showed that the evaluation accuracy of the decline degree of P. alba var. pyramidalis and P. simonii by LiDAR method was better than that by hyperspectral method, and that the evaluation accuracy of the combined LiDAR and hyperspectral method was the highest. Using the LiDAR method, hyper- spectral method, the combined method, the optimal model of P. alba var. pyramidalis was all light gradient boosting machine model, with the overall classification accuracy being 0.75, 0.68, 0.80, and Kappa coefficient being 0.58, 0.43, 0.66, respectively. The optimal model of P. simonii was random forest model, random forest model, and multilayer perceptron model, with the overall classification accuracy being 0.76, 0.62, 0.81, and Kappa coefficient being 0.60, 0.34, 0.71, respectively. This research method could accurately check and monitor the decline of plan-tations. [ABSTRACT FROM AUTHOR]