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This article offers a comprehensive AI-centric review of deep learning in exploring landslides with remote-sensing techniques, breaking new ground beyond traditional methodologies. We categorize deep learning tasks into five key frameworks—classification, detection, segmentation, sequence, and the hybrid framework—and analyze their specific applications in landslide-related tasks. Following the presented frameworks, we review state-or-art studies and provide clear insights into the powerful capability of deep learning models for landslide detection, mapping, susceptibility mapping, and displacement prediction. We then discuss current challenges and future research directions, emphasizing areas like model generalizability and advanced network architectures. Aimed at serving both newcomers and experts on remote sensing and engineering geology, this review highlights the potential of deep learning in advancing landslide risk management and preservation.

Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, urban planning, etc. However, the tremendous details contained in the VFR image, especially the considerable variations in scale and appearance of objects, severely limit the potential of the existing deep learning approaches. Addressing such issues represents a promising research field in the remote sensing community, which paves the way for scene-level landscape pattern analysis and decision making. In this paper, we propose a Bilateral Awareness Network which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in VFR images. Specifically, the dependency path is conducted based on the ResT, a novel Transformer backbone with memory-efficient multi-head self-attention, while the texture path is built on the stacked convolution operation. In addition, using the linear attention mechanism, a feature aggregation module is designed to effectively fuse the dependency features and texture features. Extensive experiments conducted on the three large-scale urban scene image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid dataset, demonstrate the effectiveness of our BANet. Specifically, a 64.6% mIoU is achieved on the UAVid dataset.

Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) was proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user’s data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. Furthermore, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.

High‐resolution interseismic strain mapping is important for studying faulting behavior and for assessing seismic hazards. Interferometric Synthetic Aperture Radar has been widely applied to measure interseismic deformation along active strike‐slip faults. However, phase unwrapping errors and over‐smoothing effects limit its ability to map the extremely‐high strain due to shallow creep. Here, without the involvement of ground‐based measurement, we perform phase‐gradient stacking on wrapped Sentinel‐1 interferograms to directly map the shear strain rates along the North Anatolian Fault (NAF) with unprecedented resolution. The derived high‐resolution strain‐rate map reveals five strain‐concentrated segments on the NAF, implying shallow creeps. We find that their spatial distribution coincides with the lower coseismic slip of previous earthquakes that occurred since 1939. The proposed method can be applied to other less‐studied strike‐slip faults to distinguish segments with shallow creep and strong coupling, and thus to better quantify the shallow strain budget and its associated seismic hazards. Plain Language Summary Surface deformation measurements along active faults are important for understanding the elastic energy, namely the strain that is accumulated and released during earthquake cycles. Interferometric Synthetic Aperture Radar (InSAR), is an imaging geodetic method that allows mapping millimeter‐scale deformation from phase differences of microwave echoes, has been applied for studying interseismic deformation across active strike‐slip faults worldwide. However, obtaining strain from InSAR‐derived velocity fields is challenged by the high computational burdens and the low resolution. Here, we propose a new phase‐gradient stacking method to obtain high‐resolution shear strain rates along the entire North Anatolian Fault (NAF) in Turkey. Our results show that the presented method can clearly reveal the spatial extents of the creeping segment, indicating the overall non‐uniform strain rate distribution and the close relation with the previous earthquake ruptures along the NAF. We propose to apply the presented phase‐gradient stacking approach to other active strike‐slip faults to better understand their strain budgets and associated seismic hazards. Key Points We map the shear strain rates along the North Anatolian Fault (NAF) with an unprecedented resolution by stacking phase gradients of Sentinel‐1 interferograms Distribution of shear strain rates indicates a tight relation between creeping segments and surface ruptures of NAF earthquakes since 1939 We identify two creeping segments previously undetected along the NAF from the high‐resolution shear strain map

Conjugative plasmids can mediate the spread and maintenance of diverse traits and functions in microbial communities. This role depends on the plasmid’s ability to persist in a population. However, for a community consisting of multiple populations transferring multiple plasmids, the conditions underlying plasmid persistence are poorly understood. Here, we describe a plasmid-centric framework that makes it computationally feasible to analyze gene flow in complex communities. Using this framework, we derive the ‘persistence potential’: a general, heuristic metric that predicts the persistence and abundance of any plasmids. We validate the metric with engineered microbial consortia transferring mobilizable plasmids and with quantitative data available in the literature. We believe that our framework and the resulting metric will facilitate a quantitative understanding of natural microbial communities and the engineering of microbial consortia. Conjugative plasmids mediate the spread and maintenance of diverse traits in microbial communities, but the conditions underlying plasmid persistence are poorly understood. Here, Wang and You present a modeling framework for analysis of gene flow and prediction of plasmid persistence and abundance in complex communities.

Natural microbial ecosystems harbor substantial diversity of competing species. Explaining such diversity is challenging, because in classic theories it is extremely infeasible for a large community of competing species to stably coexist in homogeneous environments. One important aspect mostly overlooked in these theories, however, is that microbes commonly share genetic materials with their neighbors through horizontal gene transfer (HGT), which enables the dynamic change of species growth rates due to the fitness effects of the mobile genetic elements (MGEs). Here, we establish a framework of species competition by accounting for the dynamic gene flow among competing microbes. Combining theoretical derivation and numerical simulations, we show that in many conditions HGT can surprisingly overcome the biodiversity limit predicted by the classic model and allow the coexistence of many competitors, by enabling dynamic neutrality of competing species. In contrast with the static neutrality proposed by previous theories, the diversity maintained by HGT is highly stable against random perturbations of microbial fitness. Our work highlights the importance of considering gene flow when addressing fundamental ecological questions in the world of microbes and has broad implications for the design and engineering of complex microbial consortia. Combining theoretical derivation and numerical simulations, this study predicts an ecological mechanism for maintaining microbial coexistence via dynamic neutrality enabled by horizontal gene transfer. This mechanism allows the emergence of microbial diversity beyond the limit predicted by previous theories.

A 3-D conjugated bond-pair-based peridynamic model is developed to comprehensively investigate failure characteristics of rock-like materials with intermittent fissures in the compressive-shear loading tests. Rock-like specimens containing one single central fissure are first simulated. Numerical results indicate that the 3-D conjugated bond-pair-based peridynamic model can faithfully reproduce failure characteristics of rock-like materials under compressive-shear loads. Then, the failure characteristics of rock-like specimens containing two parallel central intermittent fissures are numerically investigated. Effects of fissure inclination angle, fissure ligament length and rock bridge angle on fracturing behaviors, such as crack coalescence patterns, are also studied as well as crack initiation stress and coalescence stress.

There is increasing evidence that bone morphogenetic proteins (BMP) are involved in the proliferation and drug tolerance of kidney cancer. However, the molecular mechanism of BMP8A in renal cell proliferation and drug tolerance is not clear. Here we showed that BMP8A was highly expressed in renal cell carcinoma, which suggests a poor prognosis of ccRCC. Promotion of cell proliferation and inhibition of apoptosis were detected by CCK‐8 assay, Trypan Blue staining, flow cytometry and bioluminescence. BMP8A promoted resistance of As2O3 by regulating Nrf2 and Wnt pathways in vitro and in vivo. Mechanistically, BMP8A enhanced phosphorylation of Nrf2, which, in turn, inhibited Keap1‐mediated Nrf2 ubiquitination and, ultimately, promoted nuclear translocation and transcriptional activity of Nrf2. Nrf2 regulates the transcription of TRIM24 detected by ChIP‐qPCR. BMP8A was highly expressed in ccRCC, which suggests a poor prognosis. BMP8A was expected to be an independent prognostic molecule for ccRCC. On the one hand, activated Nrf2 regulated reactive oxygen balance, and on the other hand, by regulating the transcription level of TRIM24, it was involved in the regulation of the Wnt pathway to promote the proliferation, invasion and metastasis of ccRCC and the resistance of As2O3. Taken together, our findings describe a regulatory axis where BMP8A promotes Nrf2 phosphorylation and activates TRIM24 to promote survival and drug resistance in ccRCC. BMP8A can promote Nrf2 phosphorylation and nuclear translocation to exert antioxidative stress and transcriptional activity. At the same time, Nrf2 acts as a transcription factor of TRIM24, promotes the expression of TRIM24, activates the Wnt pathway and increases chemotherapy tolerance.

Polysaccharides are biopolymers made up of a large number of monosaccharides joined together by glycosidic bonds. Polysaccharides are widely distributed in nature: Some, such as peptidoglycan and cellulose, are the components that make up the cell walls of bacteria and plants, and some, such as starch and glycogen, are used as carbohydrate storage in plants and animals. Fungi exist in a variety of natural environments and can exploit a wide range of carbon sources. They play a crucial role in the global carbon cycle because of their ability to break down plant biomass, which is composed primarily of cell wall polysaccharides, including cellulose, hemicellulose, and pectin. Fungi produce a variety of enzymes that in combination degrade cell wall polysaccharides into different monosaccharides. Starch, the main component of grain, is also a polysaccharide that can be broken down into monosaccharides by fungi. These monosaccharides can be used for energy or as precursors for the biosynthesis of biomolecules through a series of enzymatic reactions. Industrial fermentation by microbes has been widely used to produce traditional foods, beverages, and biofuels from starch and to a lesser extent plant biomass. This review focuses on the degradation and utilization of plant homopolysaccharides, cellulose and starch; summarizes the activities of the enzymes involved and the regulation of the induction of the enzymes in well-studied filamentous fungi.

The single-mode Dicke model is well known to undergo a quantum phase transition from the so-called normal phase to the superradiant phase (hereinafter called the 'superradiant quantum phase transition'). Normally, quantum phase transitions can be identified by the critical behavior of quantities such as entanglement, quantum fluctuations, and fidelity. In this paper, we study the role of the quantum Fisher information (QFI) of both the field mode and the atoms in the ground state of the Dicke Hamiltonian. For a finite but large number of atoms, our numerical results show that near the critical atom-field coupling, the QFI of the atomic and the field subsystems can surpass their classical limits, due to the appearance of nonclassical quadrature squeezing. As the coupling increases far beyond the critical point, each subsystem becomes a highly mixed state, which degrades the QFI and hence the ultimate phase sensitivity. In the thermodynamic limit, we present the analytical results of the QFI and their relationship with the reduced variances of the field mode and the atoms. For each subsystem, we find that there is a singularity in the derivative of the QFI at the critical point, a clear signature of the quantum criticality in the Dicke model.