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Landslides are geological disasters that can cause great damage to natural and social environments. Landslide hazard assessments are crucial for disaster prevention and mitigation. Conventional regional landslide hazard assessment results are static and do not take into account the dynamic changes in landslides; thus, areas with landslides that have been treated and stabilized are often still identified as high-risk areas. Therefore, a new hazard assessment method is proposed in this paper that combines the deformation rate results obtained by interferometric synthetic aperture radar (InSAR) with the results of conventional hazard assessments to obtain the hazard assessment level while considering the deformation factor of the study area, with Zhouqu, Gansu Province, selected as the case study. First, to obtain the latest landslide inventory map of Zhouqu, the hazard assessment results of the study area were obtained based on a neural network and statistical analysis, and an innovative combination of the deformation rate results of the steepest slope direction from the ascending and descending data were obtained by InSAR technology. Finally, the hazard assessment level considering the deformation factor of Zhouqu was obtained. The method proposed in this paper allows for a near-term hazard assessment of the study area, which in turn enables dynamic regional landslide hazard assessments and improves the efficiency of authorities when conducting high-risk-area identification and management.

Multiple researchers have effectively utilized InSAR for early identification research in southwest China. However, the intricate geological structure in this region includes deeply buried and unpredictable anti-dip stratiform rock slopes, posing an additional challenge for InSAR remote sensing. This study establishes a coupling relationship between slope topple deformation patterns and surface macro-deformation characteristics through a centrifuge model test, analyzing slope bending and topple deformation stages. The InSAR interpretation of prototype slopes is employed to retrospectively infer the slope stage and visualize the slope deformation process. This research provides technical assistance for identifying and preventing slope failures. The result demonstrates that: (1) The failure process of anti-dip stratiform rocky slope involves four major stages. Failures often begin at the slope’s base and move to the middle and top regions, eventually resulting in entire slope failure when the stepped fracture surface forms. (2) Experimental image recognition analysis was conducted to monitor the rate changes in the deep and surface layers, establishing a corresponding coupling relationship between them. Observations reveal that significant deformation areas exhibit a bottom-to-top development pattern. (3) A comparison of the InSAR interpretation results for the Zhayong deformation with the test conclusions reveals that the landslide was in the progressive deformation stage. In summary, this study provides valuable technical support for utilizing InSAR technology to identify and prevent slope failures in complex geological conditions.

Interferometric synthetic aperture radar (InSAR) technology is known as one of the most effective methods for active landslide identification and deformation monitoring in large areas, and thus it is conducive to preventing and mitigating the losses caused by landslides. However, great uncertainty inevitably exists due to influences of complex terrains, dense vegetations, and atmospheric interferences in the southwestern mountainous area of China, and this is associated with false or erroneous judgment during the process of landslide identification. In this study, a landslide identification method is put forward by integrating InSAR technology and airborne light detection and ranging (LiDAR) technology. Via this method, surface deformation characteristics detected by InSAR technology and micro-geomorphic features reflected by LiDAR technology were used to identify and map landslides of large areas. Herein, the method was applied to process 224 Sentinel-1 images covering Danba County and its surrounding areas (540 km2) from October 2014 to September 2020. Firstly, 44 active landslides with total areas of 59 km2 were detected by stacking InSAR technology. Then, major regions up to 135 km2 were validated by data gained from the airborne LiDAR technology. Particularly, several large landslides with lengths and/or widths of more than 2 km were found. Further, the precipitation data were integrated with the above results to analyze the temporal deformation characteristics of three typical landslides from major regions via SBAS InSAR technology. The key findings were as follows: (1) The combination of InSAR and LiDAR technologies could improve the accuracy of landslide detection and identification; (2) there was a significant correlation between temporal deformation characteristics of some landslides and monthly rainfall, with an obvious hysteretic effect existing between the initiation timing of rainfall and that of deformation; (3) the results of this study will be important guidance for the prevention and control of geological hazards in Danba County and areas with similar complex geomorphological conditions by helping effectively identify and map landslides.

Landslides are common natural disasters that cause serious damage to ecosystems and human societies. To effectively prevent and mitigate these disasters, an accurate assessment of landslide hazards is necessary. However, most traditional landslide hazard assessment methods rely on static assessment factors while ignoring the dynamic changes in landslides, which may lead to false-positive errors in the assessment results. This paper presents a novel landslide hazard assessment method for the Zagunao River basin, China. In this study, an updated landslide inventory was obtained for the Zagunao River basin using data from interferometric synthetic aperture radar (InSAR) and optical images. Based on this inventory, a landslide susceptibility map was developed using a random forest algorithm. Finally, an evaluation matrix was created by combining the results of deformation rates from both ascending and descending data to establish a hazard level that considers surface deformation. The method presented in this study can reflect recent landslide hazards in the region and produce dynamic assessments of regional landslide hazards. It provides a basis for the government to identify and manage high-risk areas.

Zhouqu County in Gansu Province, Northwest China, is typically highly prone to landslides. On July 12, 2018, a landslide blocked the Bailong River near Zhouqu County, posing a serious threat to the life and property of local residents and the safety of infrastructure. Small baseline subset interferometry synthetic aperture radar technology (SBAS-InSAR) was adopted to identify the potential active landslides in the surrounding area of Zhouqu County, using ascending and descending orbit Sentinel-1 satellite images taken from October 2017 to December 2018. The surface deformation areas detected by SBAS-InSAR were verified by optical remote sensing image interpretation and field investigation, and a total of 23 active landslides were identified finally. The deformation characteristics of four typical landslides are analysed in detail using deformation velocity and rainfall data. It is found that the deformation velocity of landslides in this area is mainly affected by rainfall and there is a lag effect. The results can provide a reference for the prevention and control of landslide risk in Zhouqu County.

Obesity is the major driver of the global epidemic in type 2 diabetes (T2D). In individuals with obesity, impaired insulin action leads to increased lipolysis in adipocytes, resulting in elevated plasma free fatty acid (FFA) levels that promote peripheral insulin resistance, a hallmark of T2D. Here we show, by using a combined genetic/biochemical/pharmacologic approach, that increased adipocyte lipolysis can be prevented by selective activation of adipocyte G q signaling in vitro and in vivo (in mice). Activation of this pathway by a G q -coupled designer receptor or by an agonist acting on an endogenous adipocyte G q -coupled receptor (CysLT 2 receptor) greatly improved glucose and lipid homeostasis in obese mice or in mice with adipocyte insulin receptor deficiency. Our findings identify adipocyte G q signaling as an essential regulator of whole-body glucose and lipid homeostasis and should inform the development of novel classes of GPCR-based antidiabetic drugs. Obesity impairs regulation of adipocyte lipolysis, which contributes towards development of insulin resistance. Here the authors report that adipocyte Gq signaling functions as a regulator of lipolysis and systemic glucose and lipid homeostasis in mice, suggesting that agents able to stimulate this pathway may prove useful as antidiabetic drugs.

Geohazard recognition and inventory mapping are absolutely the keys to the establishment of reliable susceptibility and hazard maps. However, it has been challenging to implement geohazards recognition and inventory mapping in mountainous areas with complex topography and vegetation cover. Progress in the light detection and ranging (LiDAR) technology provides a new possibility for geohazard recognition in such areas. Specifically, this study aims to evaluate the performances of the LiDAR technology in recognizing geohazard in the mountainous areas of Southwest China through visually analyzing airborne LiDAR DEM derivatives. Quasi-3D relief image maps are generated based on the sky-view factor (SVF), which makes it feasible to interpret precisely the features of geohazard. A total of 146 geohazards are remotely mapped in the entire 135 km2 study area in Danba County, Southwest China, and classified as landslide, rock fall, debris flow based on morphologic characteristics interpreted from SVF visualization maps. Field validation indicate the success rate of LiDAR-derived DEM in recognition and mapping geohazard with higher precision and accuracy. These mapped geohazards lie along both sides of the river, and their spatial distributions are related highly to human engineering activities, such as road excavation and slope cutting. The minimum geohazard that can be recognized in the 0.5 m resolution DEM is about 900 m2. Meanwhile, the SVF visualization method is demonstrated to be a great alternative to the classical hillshaded DEM method when it comes to the determination of geomorphological properties of geohazard. Results of this study highlight the importance of LiDAR data for creating complete and accurate geohazard inventories, which can then be used for the production of reliable susceptibility and hazard maps and thus contribute to a better understanding of the movement processes and reducing related losses.

Following a meal, glucagon-like peptide 1 (GLP1) and glucose-dependent insulinotropic polypeptide (GIP), the 2 major incretins promoting insulin release, are secreted from specialized enteroendocrine cells (L and K cells, respectively). Although GIP is the dominant incretin in humans, the detailed molecular mechanisms governing its release remain to be explored. GIP secretion is regulated by the activity of G protein–coupled receptors (GPCRs) expressed by K cells. GPCRs couple to 1 or more specific classes of heterotrimeric G proteins. In the present study, we focused on the potential metabolic roles of K cell G s . First, we generated a mouse model that allowed us to selectively stimulate K cell G s signaling. Second, we generated a mouse strain harboring an inactivating mutation of Gnas , the gene encoding the α-subunit of G s , selectively in K cells. Metabolic phenotyping studies showed that acute or chronic stimulation of K cell G s signaling greatly improved impaired glucose homeostasis in obese mice and in a mouse model of type 2 diabetes, due to enhanced GIP secretion. In contrast, K cell–specific Gnas -KO mice displayed markedly reduced plasma GIP levels. These data strongly suggest that strategies aimed at enhancing K cell G s signaling may prove useful for the treatment of diabetes and related metabolic diseases.

The Upper Jinsha River (UJSR) has great water resource potential, but large-scale active landslides hinder water resource development and utilization. It is necessary to understand the spatial distribution and deformation trend of active landslides in the UJSR. In areas of high elevations, steep terrain or otherwise inaccessible to humans, extensive landslide studies remain challenging using traditional geological surveys and monitoring equipment. Stacking interferometry synthetic aperture radar (stacking-InSAR) technology, optical satellite images and unmanned aerial vehicle (UAV) photography are applied to landslide identification. Small baseline subset interferometry synthetic aperture radar (SBAS-InSAR) was used to obtain time-series deformation curves of samples to reveal the deformation types of active landslides. A total of 246 active landslides were identified within the study area, of which 207 were concentrated in three zones (zones I, II and III). Among the 31 landslides chosen as research samples, six were linear-type landslides, three were upward concave-type landslides, 10 were downward concave-type landslides, and 12 were step-type landslides based on the curve morphology. The results can aid in monitoring and early-warning systems for active landslides within the UJSR and provide insights for future studies on active landslides within the basin.

Impaired functioning of pancreatic β cells is a key hallmark of type 2 diabetes. β cell function is modulated by the actions of different classes of heterotrimeric G proteins. The functional consequences of activating specific β cell G protein signaling pathways in vivo are not well understood at present, primarily due to the fact that β cell G protein-coupled receptors (GPCRs) are also expressed by many other tissues. To circumvent these difficulties, we developed a chemical-genetic approach that allows for the conditional and selective activation of specific β cell G proteins in intact animals. Specifically, we created two lines of transgenic mice each of which expressed a specific designer GPCR in β cells only. Importantly, the two designer receptors differed in their G protein-coupling properties (Gq/₁₁ versus Gs). They were unable to bind endogenous ligand(s), but could be efficiently activated by an otherwise pharmacologically inert compound (clozapine-N-oxide), leading to the conditional activation of either β cell Gq/₁₁ or Gs G proteins. Here we report the findings that conditional and selective activation of β cell Gq/₁₁ signaling in vivo leads to striking increases in both first- and second-phase insulin release, greatly improved glucose tolerance in obese, insulin-resistant mice, and elevated β cell mass, associated with pathway-specific alterations in islet gene expression levels. Selective stimulation of β cell Gs triggered qualitatively similar in vivo metabolic effects. Thus, this developed chemical-genetic strategy represents a powerful approach to study G protein regulation of β cell function in vivo.