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Abstract The shallow portion of a megathrust represents the zone of first contact between two colliding plates, and its rheological properties control the seismic and tsunami hazards generated by the fault. The high cost of underwater geodetic data collection results in sparse observations, leading to limited constraints on the interseismic behavior of megathrusts. The Rakhine‐Bangladesh megathrust offers a unique opportunity to probe the behavior of the shallow megathrust as it is the only ocean‐continent subduction zone where the near‐trench region is fully accessible on land. Here, we use observations from ALOS‐2 wide‐swath imagery spanning 2015 to 2022 to conduct an InSAR timeseries analysis of the overriding plate within Bangladesh and the Indo‐Myanmar Ranges. We identify a narrow pattern of alternating uplift and subsidence associated with mapped anticlines but show that it cannot be explained by slip on the megathrust or other fault structures. Instead, we argue that the deformation is likely caused by active aseismic folding within the wedge above a shallow decollement. We show that estimates of the decollement depth derived from a viscous folding model and the observed anticline spacing are in agreement with previous seismic observations of the decollement depth across the fold belt. We suggest that the role of ductile deformation in the overriding plate in subduction zones may be more important than previously recognized. 

Abstract BackgroundProtein–protein interaction (PPI) is vital for life processes, disease treatment, and drug discovery. The computational prediction of PPI is relatively inexpensive and efficient when compared to traditional wet-lab experiments. Given a new protein, one may wish to find whether the protein has any PPI relationship with other existing proteins. Current computational PPI prediction methods usually compare the new protein to existing proteins one by one in a pairwise manner. This is time consuming. ResultsIn this work, we propose a more efficient model, called deep hash learning protein-and-protein interaction (DHL-PPI), to predict all-against-all PPI relationships in a database of proteins. First, DHL-PPI encodes a protein sequence into a binary hash code based on deep features extracted from the protein sequences using deep learning techniques. This encoding scheme enables us to turn the PPI discrimination problem into a much simpler searching problem. The binary hash code for a protein sequence can be regarded as a number. Thus, in the pre-screening stage of DHL-PPI, the string matching problem of comparing a protein sequence against a database withMproteins can be transformed into a much more simpler problem: to find a number inside a sorted array of lengthM. This pre-screening process narrows down the search to a much smaller set of candidate proteins for further confirmation. As a final step, DHL-PPI uses the Hamming distance to verify the final PPI relationship. ConclusionsThe experimental results confirmed that DHL-PPI is feasible and effective. Using a dataset with strictly negative PPI examples of four species, DHL-PPI is shown to be superior or competitive when compared to the other state-of-the-art methods in terms of precision, recall or F1 score. Furthermore, in the prediction stage, the proposed DHL-PPI reduced the time complexity from$$O(M^2)$$ $O\left(M^2\right)$to$$O(M\log M)$$ $O(MlogM)$for performing an all-against-all PPI prediction for a database withMproteins. With the proposed approach, a protein database can be preprocessed and stored for later search using the proposed encoding scheme. This can provide a more efficient way to cope with the rapidly increasing volume of protein datasets.