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Abstract Faults play a crucial role in shaping the formation and damage patterns of landslides in the mountainous region, particularly in Qinling-Daba (Qinba) area in China. On 6 October 2022, following a 4-day rainfall event totaling 221.5 mm, a landslide occurred in Hanwang Town, Shaanxi Province. The left boundary of the landslide coincided with a fault, which influence the formation and movement development of the landslide. To further understand and quantified the formation process and damage mechanism of the landslide, a comprehensive study was conducted, incorporating field investigations, local rainfall data, and various methods including unmanned aerial vehicles (UAVs), numerical simulations, and laboratory test. The results indicate that fault dictate the formation of the Lijiaping landslide by influencing the mechanical strength of the rock mass and the catchment topography in the landslide area. Due to fault, the rock mass in the landslide area is high fragmentation, with a softening coefficient of about 0.52. Weathering resulted in numerous residual and slope sediments in the landslide area, providing ample material for the landslide. Meanwhile, the fault activity led to a wedge-shaped topography in the landslide area, with an average Terrain Wetness Index (TWI) of 3.43, significantly higher than the Hanwang Township average of 1.47. This creates a hydrogeological structure favorable for landslides. Numerical simulations revealed that the maximum velocity of the landslide reached 5.05 m/s and the maximum displacement was 53.18 m, both occurring in the central part of the landslide. These findings offer crucial scientific insights for understanding and preventing similar geological hazards.

Abstract Gastric cancer (GC) contains subpopulations of cancer stem cells (CSCs), which are described as the main contributors in tumor initiation and metastasis. It is necessary to clarify the molecular mechanism underlying CSCs phenotype and develop novel biomarkers and therapeutic targets for gastric cancer. Here, we show that POLQ positively regulates stem cell-like characteristics of gastric cancer cells, knockdown of POLQ suppressed the stemness of GC cells in vitro and in vivo. Further mechanistic studies revealed that POLQ knockdown could downregulate the expression of dihydroorotate dehydrogenase (DHODH). DHODH overexpression rescued the reduced stemness resulted by POLQ knockdown. Furthermore, we found that POLQ expression correlated with resistance to ferroptosis, and POLQ inhibition renders gastric cancer cells more vulnerable to ferroptosis. Further investigation revealed that POLQ regulated DHODH expression via the transcription factors E2F4, thereby regulating ferroptosis resistance and stemness of gastric cancer cells. Given the importance of POLQ in stemness and ferroptosis resistance of GC, we further evaluated the therapeutic potential of POLQ inhibitor novobiocin, the results show that novobiocin attenuates the stemness of GC cells and increased ferroptosis sensitivity. Moreover, the combination of POLQ inhibitor and ferroptosis inducer synergistically suppressed MGC-803 xenograft tumor growth and diminished metastasis. Our results identify a POLQ-mediated stemness and ferroptosis defense mechanism and provide a new therapeutic strategy for gastric cancer.

The geological hazards of landslides in Hanwang Town, Ziyang County, Ankang City, Shaanxi Province, have emerged. Yet, the current understanding of the spatial distribution characteristics and influencing factors of landslides in this area remains unclear. Combining the results of remote sensing interpretation and field investigation, seven influencing factors, namely, elevation, slope direction, slope gradient, distance from rivers, distance from faults, engineering geologic lithology, and distance from roads, are selected for the study. The distribution characteristics of landslides in each influencing factor and the influence of the resolution of the Digital Elevation Model (DEM) on the results are statistically and analytically analyzed. Furthermore, two high-risk landslides within the study area were subjected to comprehensive analysis, integrating the findings from drilling and other field investigations in order to examine their deformation mechanisms. Based on this analysis, the following conclusions were derived: (1) 34 landslides in the study area, mainly small earth landslides, with a distribution density of 0.42/km2, threatening 414 people and property of about 55.87 million Yuan. (2)The landslides in the study area easily occur in the

Monocular 3D object detection plays a pivotal role in autonomous driving, presenting a formidable challenge by requiring the precise localization of 3D objects within a single image, devoid of depth information. Most existing methods in this domain fall short of harnessing the limited information available in monocular 3D detection tasks. They typically provide only a single detection outcome, omitting essential uncertainty analysis and result post-processing during model inference, thus limiting overall model performance. In this paper, we propose a comprehensive framework that maximizes information extraction from monocular images while encompassing diverse depth estimation and incorporating uncertainty analysis. Specifically, we mine additional information intrinsic to the monocular 3D detection task to augment supervision, thereby addressing the information scarcity challenge. Moreover, our framework handles depth estimation by recovering multiple sets of depth values from calculated visual heights. The final depth estimate and 3D confidence are determined through an uncertainty fusion process, effectively reducing inference errors. Furthermore, to address task weight allocation in multi-task training, we present a versatile training strategy tailored to monocular 3D detection. This approach leverages measurement indicators to monitor task progress, adaptively adjusting loss weights for different tasks. Experimental results on the KITTI and Waymo dataset confirm the effectiveness of our approach. The proposed method consistently provides enhanced performance across various difficulty levels compared to the original framework while maintaining real-time efficiency.

The Xihu Depression is the largest hydrocarbon-bearing depression of the East China Sea Shelf Basin (also referred to as the ECSSB). However, the depositional systems and reservoir distribution of the Oligocene Huagang Formation in the Xihu Depression are still controversial. Under the guidance of sedimentology and stratigraphy, this study documented a marine-terrestrial transitional environment in the restricted bay setting of the Oligocene Huagang Formation through core description, well logging, and seismic data analysis. This study also revealed that the Oligocene Huagang Formation is dominated by tidal delta, estuary, and gravity flow deposits in the central anticline zone of the Xihu Depression. The new understanding of the sedimentary systems and the discovery of the transgressive gap in the eastern Diaoyu Islands uplift explain the origin of fine-grained sediments and the EW-trending sand bodies in the central depression and the sand bodies parallel to shoreline in the west slope belt, which cannot be explained by previous study results, such as southern transgression or fluvial deltas and even lacustrine deposition. Moreover, the tidal channels, tidal sand flats, and gravity flow sand bodies formed by the transgressive tides are high-quality reservoirs. The study will provide a basis for well placement and serve as guidance for the selection of favorable hydrocarbon exploration areas in the Xihu Depression.

At present, the architecture modeling method of fluvial reservoirs are still developing. Traditional methods usually use grids to characterize architecture interbeds within the reservoir. Due to the thin thickness of this type of the interlayers, the number of the model grids must be greatly expanded. The number of grids in the tens of millions often makes an expensive computation; however, upscaling the model will generate a misleading model. The above confusion is the major reason that restricts the large-scale industrialization of fluvial reservoir architecture models in oilfield development and production. Therefore, this paper explores an intelligent architecture modeling method for multilevel fluvial reservoirs based on architecture interface and element. Based on the superpositional relationship of different architectural elements within the fluvial reservoir, this method uses a combination of multilevel interface constraints and non-uniform grid techniques to build a high-resolution 3D geological model for reservoir architecture. Through the grid upscaling technology of heterogeneous architecture elements, different upscaling densities are given to the lateral-accretion bedding and lateral-accretion bodies to simplify the model gridding. This new method greatly reduces the number of model grids while ensuring the accuracy of lateral-accretion bedding models, laying a foundation for large-scale numerical simulation of the subsequent industrialization of the architecture model. This method has been validated in A layer of X oilfield with meandering fluvial channel sands as reservoirs and B layer of Y oilfield with braided river sands as reservoirs. The simulation results show that it has a higher accuracy of production history matching and remaining oil distribution forecast of the targeted sand body. The numerical simulation results show that in the actual development process of oilfield, the injected water will not displace oil in a uniform diffusive manner as traditionally assumed, but in a more complex pattern with oil in upper part of sand body being left behind as residual oil due to the influences of different levels of architecture interfaces. This investigation is important to guiding reservoir evaluation, remaining oil analysis, profile control and potential tapping and well pattern adjustment.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications. [ABSTRACT FROM AUTHOR]