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Background TCP proteins are plant specific transcription factors that play important roles in plant growth and development. Despite the known significance of these transcription factors in general plant development, their specific role in fruit growth remains largely uncharted. Therefore, this study explores the potential role of TCP transcription factors in the growth and development of sweet cherry fruits. Results Thirteen members of the PavTCP family were identified within the sweet cherry plant, with two, PavTCP1 and PavTCP4 , found to contain potential target sites for Pav-miR159, Pav-miR139a, and Pav-miR139b-3p. Analyses of cis-acting elements and Arabidopsis homology prediction analyses that the PavTCP family comprises many light-responsive elements. Homologs of PavTCP1 and PavTCP3 in Arabidopsis TCP proteins were found to be crucial to light responses. Shading experiments showed distinct correlation patterns between PavTCP1 , 2 , and 3 and total anthocyanins, soluble sugars, and soluble solids in sweet cherry fruits. These observations suggest that these genes may contribute significantly to sweet cherry light responses. In particular, PavTCP1 could play a key role, potentially mediated through Pav-miR159, Pav-miR139a, and Pav-miR139b-3p. Conclusion This study is the first to unveil the potential function of TCP transcription factors in the light responses of sweet cherry fruits, paving the way for future investigations into the role of this transcription factor family in plant fruit development.

The black soil region of northeast China is a critical global grain production base. The dynamic variations in soil organic carbon (SOC) are directly linked to the regional food security. To accurately monitor SOC content and evaluate the potential of integrating Landsat-9 and GF-1 satellite data for SOC inversion, we developed a machine learning framework that combines data from both satellite sources to model SOC. Using the typical black soil region of northeast China in the Tongken River Basin as the study area, we compared the MLR, PLSR, RF, and XGBoost algorithms. And XGBoost demonstrated the highest performance (R2 = 0.9130; RMSE = 0.3834%). Based on the optimal model, SOC in the study area was projected from 2020 to 2024. The multi-year average SOC exhibited an initial increase followed by a subsequent decline, with an overall increase of 22.78%. Spearman correlation analysis identified parent material as the dominant factor controlling SOC variation at the watershed scale (correlation coefficient = 0.38) while also modulating the influence of land use types on SOC dynamics. The “space–ground” multi-source collaborative inversion framework developed in this study offers a high-precision technical approach for the monitoring of SOC in black soil regions.

Northeast China is an important ecological barrier and commodity grain base in China. The coupling coordination relationship between geology–geomorphology and ecology has become a critical background condition for ecosystem protection and sustainable development. Taking Northeast China as a case (accounting for about 13% of China’s land area), 9 divisions are divided according to the characteristics of regional ecology and geology–geomorphology, and 17 indicators are selected to build an evaluation index system. Methods of analytic hierarchy process, entropy weight and game theory are used to determine the index weights. Based on the coupling coordination degree (CCD) model, the spatial coupling coordination characteristics of geology–geomorphology and ecology are studied. The variation characteristics of the Normalized Difference Vegetation Index (NDVI) are evaluated by Sen+Mann–Kendall (Sen+MK) method. Our results are as follows. (1) The coupling between geology–geomorphology and ecology is strong, but the spatial differentiation of CCD is obvious. Nine divisions are evaluated as two high–level, three medium–level and three low–level coordination types and one mild imbalance type. (2) The plain divisions Ⅰ and Ⅳ where the typical black soil belt is located are high coordination types. Restricted by geology–geomorphological conditions or ecological conditions, mountain divisions Ⅲ and Ⅶ and plain division Ⅴ are moderate coordination types, mountain divisions Ⅱ and Ⅷ and plateau division Ⅸ are low coordination types, and mountain division Ⅵ is mild imbalance type. (3) The variation trend of NDVI shows a significant increase in divisions Ⅲ, Ⅴ, Ⅰ, Ⅱ and Ⅶ. it shows a significant decrease in part of divisions Ⅳ, Ⅵ, Ⅷ and Ⅸ, and ecological management and construction should be strengthened in these divisions. The research shows that the CCD model method is feasible for evaluating the relationship between geology–geomorphology and ecology and can provide eco–geological background information for Northeast China.

Soil Organic Carbon (SOC) is crucial for soil health, agricultural productivity, and climate regulation. This study examines the temporal and spatial changes in SOC over a decade (2013–2023) in the Tongken River Basin, a key area within the black soil region of Northeast China. Using machine learning techniques and advanced spatial mapping techniques, the study identified temperature, precipitation, and soil managements as key drivers of SOC dynamics. The results revealed a significant increase in SOC content from 2.99% to 3.25%, and SOC density rose from 7.08 kg/m² to 7.72 kg/m², with precipitation exerting the strongest positive influence. These findings highlight the potential of climate-smart land-use strategies to enhance SOC storage and mitigate soil degradation. This research provides valuable insights for sustainable soil management and climate adaptation efforts in vulnerable agricultural regions.

Background Cupressus chengiana is mainly distributed in the Hengduan Mountains area in China. It is one of the Class II endangered plants, ex situ conservation is often used to the affected C. chengiana population due to the construction of the power station. However, population fragmentation and inbreeding in the ex situ conservation have led to decline in genetic diversity. It is therefore important to clarify the differences in genetic diversity between native populations and ex situ population. Results In this study, we used Genotyping-by-Sequencing to assess the genetic diversity of 30 C. chengiana trees from four populations in the Dadu River Basin, southwest China, including one ex situ conserved population (DK) and three native populations (BW, SA, RJ). The results showed that compared with the native populations, the DK population showed higher genetic diversity. Among the three native populations, SA population may experience inbreeding and has low genetic diversity. The population structure analysis further revealed that the DK population had higher gene flow and lower differentiation than other three populations. For ex situ populations, the primary determinant of genetic diversity is the genetic variation present in the seedlings sourced from natural populations. Conclusion These findings support the feasibility of ex situ conservation for C. chengiana conservation. This study provides a scientific foundation for the preservation, management, and restoration of C. chengiana , and would offer valuable insights for the conservation of other endangered plants.

Black soil has good properties and high fertility. Understanding the spatial distribution of black soil layer thickness is of great significance in promoting regional agricultural development, ecological environmental protection, and soil erosion control. However, traditional soil investigation methods often fail to provide detailed soil thickness information. This study focuses on a small watershed in Northeast China’s black soil region. By integrating topographical parameters and vegetation-climate indicators, random forest and kriging methods (classical bayesian, ordinary, and simple) were used to estimate the spatial distribution of thickness of black soil layer. An integrated evaluation framework was developed by combining RUSLE-derived erosion estimates with black soil layer thickness, systematically incorporating both external erosive forces and inherent soil erosion resistance attributes. The results show that the random forest model outperformed the kriging models, with smaller RMSE (34.05 cm) and larger R² (0.57), especially when handling nonlinear, high-dimensional data. The predicted thickness of the black soil layer ranged from 16.2 cm to 107 cm, with a mean of 48.31 cm, closely matching the measured value of 48 cm. Elevation (EL) was found to be the most significant factor affecting the thickness of black soil layer. Soil erosion risk assessment revealed that areas with no risk and low risk accounted for 21.91% and 62.21%, respectively, while medium and high-risk areas made up 15.87% and 0.01%. No-risk areas were soil accumulation zones, while low-risk areas were mainly sloped farmland, where measures like terracing, adjusting crop ridge directions, and planting pedunculated vegetation were recommended. Medium- and high-risk areas should be addressed by returning farmland to forests and implementing engineering practices. This study offers a reference for thickness of black soil layer estimation and provides valuable insights for soil erosion risk management.

In order to improve the low output accuracy caused by the elastic deformations of the branch chains, a finite element-based dynamic accuracy analysis method for parallel mechanisms is proposed in this paper. First, taking a 5-prismatic-spherical-spherical (PSS)/universal-prismatic-universal (UPU) parallel mechanism as an example, the error model is established by a closed vector chain method, while its influence on the dynamic accuracy of the parallel mechanism is analyzed through numerical simulation. According to the structural and error characteristics of the parallel mechanism, a vector calibration algorithm is proposed to reduce the position and pose errors along the whole motion trajectory. Then, considering the elastic deformation of the rod, the rigid-flexible coupling dynamic equations of each component are established by combining the finite element method with the Lagrange method. The elastodynamic model of the whole machine is obtained based on the constraint condition of each moving part, and the correctness of the model is verified by simulation. Moreover, the effect of component flexibility on the dimensionless root mean square error of the displacement, velocity and acceleration of the moving platform is investigated by using a Newmark method, and the mapping relationship of these dimensionless root mean square errors to dynamic accuracy is further studied. The research work provides a theoretical basis for the design of the parameter size of the prototype.

Porcine deltacoronavirus (PDCoV) is an emerging enteropathogenic coronavirus capable of infecting various animal species, including humans. In this study, we explored the roles of sirtuins (SIRTs), a conserved family of protein deacylases and mono-adenosine diphosphate-ribosyltransferases, in PDCoV replication. Surprisingly, we found that SIRT5—a unique member of SIRTs with distinct desuccinylation, demalonylation, and deglutarylation activities—is a proviral factor essential for PDCoV replication; its catalytic activities are crucial in this process. Mechanistically, SIRT5 interacts with and desuccinylates the PDCoV membrane (M) protein. This modification activates the ataxia-telangiectasia mutated (ATM) pathway, facilitates ubiquitination of peroxisomal biogenesis protein 5 (PEX5), and recruits sequestosome 1 (SQSTM1/p62) to initiate selective peroxisomal autophagy (pexophagy). The pexophagy process disrupts peroxisomal function, elevates reactive oxygen species (ROS) levels, and suppresses type I and III interferon production, thereby enhancing viral replication. We also identified lysine 207 (K207) as the primary succinylation site of the M protein. Mutations mimicking the desuccinylated or succinylated states of K207 substantially influence viral replication and the ability to induce pexophagy. These findings reveal a novel role for SIRT5 in regulating pexophagy during viral infection and suggest a therapeutic target for efforts to combat coronavirus infections.

Understanding urban spatial pattern of land use is of great significance to urban land management and resource allocation. Urban space has strong heterogeneity, and thus there were many researches focusing on the identification of urban land use. The emergence of multiple new types of geospatial data provide an opportunity to investigate the methods of mapping essential urban land use. The popularization of street view images represented by Baidu Maps is benificial to the rapid acquisition of high-precision street view data, which has attracted the attention of scholars in the field of urban research. In this study, OpenStreetMap (OSM) was used to delineate parcels which were recognized as basic mapping units. A semantic segmentation of street view images was combined to enrich the multi-dimensional description of urban parcels, together with point of interest (POI), Sentinel-2A, and Luojia-1 nighttime light data. Furthermore, random forest (RF) was applied to determine the urban land use categories. The results show that street view elements are related to urban land use in the perspective of spatial distribution. It is reasonable and feasible to describe urban parcels according to the characteristics of street view elements. Due to the participation of street view, the overall accuracy reaches 79.13%. The contribution of street view features to the optimal classification model reached 20.6%, which is more stable than POI features.