A NAC-EXPANSIN module enhances maize kernel size by controlling nucellus elimination

Maize early endosperm development is initiated in coordination with elimination of maternal nucellar tissues. However, the underlying mechanisms are largely unknown. Here, we characterize a major quantitative trait locus for maize kernel size and weight that encodes an EXPANSIN gene, ZmEXPB15. The encoded β-expansin protein is expressed specifically in nucellus, and positively controls kernel size and weight by promoting nucellus elimination. We further show that two nucellus-enriched transcription factors (TFs), ZmNAC11 and ZmNAC29, activate ZmEXPB15 expression. Accordingly, these two TFs also promote kernel size and weight through nucellus elimination regulation, and genetic analyses support their interaction with ZmEXPB15. Importantly, hybrids derived from a ZmEXPB15 overexpression line have increased kernel weight, demonstrates its potential value in breeding. Together, we reveal a pathway modulating the cellular processes of maternal nucellus elimination and early endosperm development, and an approach to improve kernel weight

Spatial Distribution Analysis of Landslide Deformations and Land-Use Changes in the Three Gorges Reservoir Area by Using Interferometric and Polarimetric SAR

Landslides are geological events that frequently cause major disasters. Research on landslides is essential, but current studies mostly use historical landslide data and do not reflect dynamic, real-time research results. In this study, landslide deformations and land-use changes were used to analyze the landslide distribution in Fengjie County and Wushan County in the Three Gorges Reservoir Area (TGRA) by using interferometric and polarimetric SAR. In this study, the mean annual rate of landslide deformations was obtained using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) for the ALOS-2 (2014–2019) data. Land-use changes were based on the 2007 and 2017 land-use results from dual-polarization ALOS-1 and ALOS-2 data, respectively. To address the problem of classification accuracy reduction caused by geometric distortion in mountainous areas, we first used texture maps and pseudocolor maps synthesized with dual-polarization intensity maps to perform classification with random forest (RF), and then we used coherence and slope maps to run the K-Means algorithm (KMA). We named this the secondary classification method. It is an improvement on the single classification method, exhibiting a 94% classification accuracy, especially in rugged areas. Combined with land-use changes, GIS spatial analysis was used to analyze the spatial distribution of landslides, and it was found that the landslide rate was significantly correlated with the type after change, with a correlation coefficient of 0.7. In addition, land-use types associated with human activities, such as cultivated vegetation, were more likely to cause landslide deformation, which can be used to guide local land-use planning

MUSEnet: High Temporal-Frequency Estimation of Landslide Deformation Field Through Joint InSAR and Hydrological Observations Using Deep Learning

The Three Gorges hydropower station in China creates a large reservoir by diverting water from the Yangtze River, increasing the risk of geological disasters, especially massive landslides along the reservoir shoreline. To mitigate these risks, improving geological monitoring and early warning systems is crucial. Interferometric Synthetic Aperture Radar (InSAR) is widely used to monitor reservoir bank landslides. However, its potential in early warning systems is limited due to temporal resolution constraints, preventing timely warnings. To address this, we propose integrating daily hydrological data (precipitation and water level observations) with historical InSAR deformation sequences using our deep learning-based multivariate united state estimation network, “MUSEnet.” This approach generates customized daily landslide deformation products for high-risk areas, greatly enhancing early warning capabilities by providing timely and accurate information on landslide occurrence and magnitude. We validated our method using 161 Sentinel-1 A images of the Xinpu landslide in the Three Gorges Reservoir area. Through statistical analysis, we identified different degrees of influence from rainfall and reservoir water level on the deformation of the Xinpu landslide at various locations. Additionally, we observed distinct lag times between deformation and corresponding rainfall and reservoir water level events. By utilizing deep learning, our method estimates nonlinear states by considering hysteresis and intelligently accounts for the impact of rainfall and reservoir water level, resulting in more accurate estimations compared to traditional models