Search

High-resolution landslide images are required for detailed geomorphological analysis in complex topographic environment with steep and vertical landslide distribution. This study proposed a vertical route planning method for unmanned aerial vehicles (UAVs), which could achieve rapid image collection based on strictly calculated route parameters. The effectiveness of this method was verified using a DJI Mavic 2 Pro, obtaining high-resolution landslide images within the Dongchuan debris flow gully, in the Xiaojiang River Basin, Dongchuan District, Yunnan, China. A three-dimensional (3D) model was constructed by the structure-from-motion and multi-view stereo (SfM-MVS). Micro-geomorphic features were analyzed through visual interpretation, geographic information system (GIS), spatial analysis, and mathematical statistics methods. The results demonstrated that the proposed method could obtain comprehensive vertical information on landslides while improving measurement accuracy. The 3D model was constructed using the vertically oriented flight route to achieve centimeter-level accuracy (horizontal accuracy better than 6 cm, elevation accuracy better than 3 cm, and relative accuracy better than 3.5 cm). The UAV technology could further help understand the micro internal spatial and structural characteristics of landslides, facilitating intuitive acquisition of surface details. The slope of landslide clusters ranged from 36° to 72°, with the majority of the slope facing east and southeast. Upper elevation levels were relatively consistent while middle to lower elevation levels gradually decreased from left to right with significant variations in lower elevation levels. During the rainy season, surface runoff was abundant, and steep topography exacerbated changes in surface features. This route method is suitable for unmanned aerial vehicle (UAV) landslide surveys in complex mountainous environments. The geomorphological analysis methods used will provide references for identifying and describing topographic features. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) and light detection and ranging (LiDAR) can be used to analyze the geomorphic features in complex plateau mountains. Accordingly, a UAV–LiDAR system was adopted in this study to acquire images and lidar point-cloud dataset in the annular structure of Lufeng, Yunnan. A three-dimensional (3D) model was constructed based on structure from motion and multi-view stereo (SfM–MVS) in combination with a high-resolution digital elevation model (DEM). Geomorphic identification, measurement, and analysis were conducted using integrated visual interpretation, DEM visualization, and geographic information system (GIS) topographic feature extraction. The results indicated that the 3D geomorphological visualization and mapping were based on DEM, which was employed to identify the dividing lines and ridges that were delineated of the pit rim structure. The high-resolution DEM retained more geomorphic detail information, and the topography and the variation between ridges were analyzed in depth. The catchment and ponding areas were analyzed using accurate morphological parameters through a multi-angle 3D visualization. The slope, aspect, and topographic wetness index (TWI) parameters were analyzed through mathematical statistics to qualitatively and accurately analyze the differences between different ridges. This study highlighted the significance of the UAV–LiDAR high-resolution topographic measurements and the SfM–MVS 3D scene modelling in accurately identifying geomorphological features and conducting refined analysis. An effective framework was established to acquire high-precision topographic datasets and to analyze geomorphological features in complex mountain areas, which was beneficial in deepening the research on numerical simulation analysis of geomorphological features and reveal the process evolution mechanism. [ABSTRACT FROM AUTHOR]

Previous genotyping‐based assays have identified non‐coding variants of several interleukins (ILs) being associated with genetic susceptibility to leprosy. However, understanding of the involvement of coding variants within all IL family genes in leprosy was still limited. To obtain the full mutation spectrum of all ILs in leprosy, we performed a targeted deep sequencing of coding regions of 58 ILs genes in 798 leprosy patients (age 56.2 ± 14.4; female 31.5%) and 990 healthy controls (age 38.1 ± 14.0; female 44.3%) from Yunnan, Southwest China. mRNA expression alterations of ILs in leprosy skin lesions or in response to M. leprae treatment were estimated by using publicly available expression datasets. Two coding variants in IL27 (rs17855750, p.S59A, p = 4.02 × 10−8, odds ratio [OR] = 1.748) and IL1RN (rs45507693, p.A106T, p = 1.45 × 10−5, OR = 3.629) were significantly associated with leprosy risk. mRNA levels of IL27 and IL1RN were upregulated in whole blood cells after M. leprae stimulation. These data showed that IL27 and IL1RN are leprosy risk genes. Further functional study is required for characterizing the exact role of ILs in leprosy. [ABSTRACT FROM AUTHOR]