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Abstract Understanding the occurrence of the intrusion of open ocean water onto continental shelves has scientific significance and societal relevance as the intrusion can significantly disrupt the marine ecosystem and fisheries. High-resolution numerical modeling is used to investigate the spatiotemporal occurrence and mechanisms of highly anomalous bottom intrusions on the southern New England shelf. Based on multi-year numerical simulations, this study reveals a hotspot of cross-isobath bottom-intensified intrusions at a topographic trough. Examination of multiple events portrays a robust mechanism of locally enhanced bottom intrusions. Persistent upwelling-favorable winds set up an enhanced pressure gradient field at the topographic trough and drive the intrusion a large-distance onshore. Numerical experiments with and without the topographic trough show that the localized pressure gradient results from a combination of the shelf orientation and local bathymetry. Although highly anomalous waters on the shelf relate to wind forcing, correlations between the wind stress anomaly and bottom salinity anomaly at the location of the enhanced intrusion is modest, implying the need to incorporate other environmental factors to develop more deterministic prediction models for subsurface conditions on the shelf. The results have important implications for marine environment and fisheries management. 

Abstract The monthly mean sea level along the U.S. Mid‐Atlantic Coast varies seasonally, reaching a minimum in January and a maximum in September during the 1960–2020 period. However, this seasonal cycle has changed significantly on multi‐decadal timescales. In the last two decades, the annual minimum has shifted from January to February. The amplitude of seasonal changes increased by 65% from 14.16 cm in 1980–1999 to 23.16 cm in 2000–2020. Even more concerning, the maximum sea level in September rose by 82%, from 6.81 to 12.38 cm, potentially exacerbating coastal flooding over the past 20 years. A two‐layer ocean model effectively replicates both the phase and magnitude of the observed changes and attributes these shifts to changes in wind stress near the coast, with relatively minor influence from deep ocean forcing. Both alongshore and cross‐shore wind stress changes are found to contribute to changes in the sea level's seasonal cycle. 

Abstract MotivationMany tasks in sequence analysis ask to identify biologically related sequences in a large set. The edit distance, being a sensible model for both evolution and sequencing error, is widely used in these tasks as a measure. The resulting computational problem—to recognize all pairs of sequences within a small edit distance—turns out to be exceedingly difficult, since the edit distance is known to be notoriously expensive to compute and that all-versus-all comparison is simply not acceptable with millions or billions of sequences. Among many attempts, we recently proposed the locality-sensitive bucketing (LSB) functions to meet this challenge. Formally, a (d1,d2)-LSB function sends sequences into multiple buckets with the guarantee that pairs of sequences of edit distance at most d1 can be found within a same bucket while those of edit distance at least d2 do not share any. LSB functions generalize the locality-sensitive hashing (LSH) functions and admit favorable properties, with a notable highlight being that optimal LSB functions for certain (d1,d2) exist. LSB functions hold the potential of solving above problems optimally, but the existence of LSB functions for more general (d1,d2) remains unclear, let alone constructing them for practical use. ResultsIn this work, we aim to utilize machine learning techniques to train LSB functions. With the development of a novel loss function and insights in the neural network structures that can potentially extend beyond this specific task, we obtained LSB functions that exhibit nearly perfect accuracy for certain (d1,d2), matching our theoretical results, and high accuracy for many others. Comparing to the state-of-the-art LSH method Order Min Hash, the trained LSB functions achieve a 2- to 5-fold improvement on the sensitivity of recognizing similar sequences. An experiment on analyzing erroneous cell barcode data is also included to demonstrate the application of the trained LSB functions. Availability and implementationThe code for the training process and the structure of trained models are freely available at https://github.com/Shao-Group/lsb-learn. 

Abstract Rationalizing synthetic pathways is crucial for material design and property optimization, especially for polymorphic and metastable phases. Over‐stoichiometric rocksalt (ORX) compounds, characterized by their face‐sharing configurations, are a promising group of materials with unique properties; however, their development is significantly hindered by challenges in synthesizability. Here, taking the recently identified Li superionic conductor, over‐stoichiometric rocksalt Li–In–Sn–O (o‐LISO) material as a prototypical ORX compound, the mechanisms of phase formation are systematically investigated. It is revealed that the spinel‐like phase with unconventional stoichiometry forms as coherent precipitate from the high‐temperature‐stabilized cation‐disordered rocksalt phase upon fast cooling. This process prevents direct phase decomposition and kinetically locks the system in a metastable state with the desired face‐sharing Li configurations. This insight enables us to enhance the ionic conductivity of o‐LISO to be >1 mS cm⁻¹at room temperature through low‐temperature post‐annealing. This work offers insights into the synthesis of ORX materials and highlights important opportunities in this new class of materials. 

Abstract A long‐standing hypothesis is that the steady along‐shelf circulation in the Northwest Atlantic (NWA) coastal ocean is driven by buoyancy input from continental freshwater runoff. However, the forcing from the freshwater runoff has not been adequately evaluated and compared with other potential driving mechanisms. This study investigates the roles of both wind stress and freshwater runoff in driving the mean along‐shelf flow in the NWA coastal ocean and examines other potential drivers using a newly developed high‐resolution regional model with realistic forcing conditions. The results reveal that wind stress has a larger impact than freshwater runoff on the overall mean circulation and along‐shelf sea‐level gradient on the NWA shelf. While the continental freshwater input consistently contributes to the equatorward along‐shelf flow and higher sea level along the coast, wind stress is more effective for the setup of the broad‐scale circulation pattern by driving the along‐shelf flow on the Labrador Shelf and opposing the flow in the Mid‐Atlantic Bight and on the Scotian Shelf. In addition to the local wind and continental runoff, the sub‐Arctic inflow from higher latitude is an essential part of the NWA shelf circulation system. This remote driver directly contributes to the along‐shelf flow and insulates the shelf flow from the Gulf Stream on the southern shelves.