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Abstract Models of near‐field tsunamis and an extreme hurricane provide further evidence for a great precolonial earthquake along the Puerto Rico Trench. The models are benchmarked to brain‐coral boulders and cobbles on Anegada, 125 km south of the trench. The models are screened by their success in flooding the mapped sites of these erratics, which were emplaced some six centuries ago. Among 25 tsunami scenarios, 19 have megathrust sources and the rest posit normal faulting on the outer rise. The modeled storm, the most extreme of 15 hurricanes of category 5, produces tsunami‐like bores from surf beat. In the tsunami scenarios, simulated flow depth is 1 m or more at all the clast sites, and 2 m or more at nearly all, given either a megathrust rupture 255 km long with 7.5 m of dip slip and M8.45, or an outer‐rise rupture 130 km long with 11.4 m of dip slip and M8.17. By contrast, many coral clasts lie beyond the reach of simulated flooding from the extreme hurricane. The tsunami screening may underestimate earthquake size by neglecting trees and shrubs that likely impeded both the simulated flows and the observed clasts; and it may overestimate earthquake size by leaving coastal sand barriers intact. The screening results broadly agree with those from previously published tsunami simulations. In either successful scenario, the average recurrence interval spans thousands of years, and flooding on the nearest Caribbean shores begins within a half‐hour. 

Allosteric regulation is common in protein–protein interactions and is thus promising in drug design. Previous experimental and simulation work supported the presence of allosteric regulation in the SARS-CoV-2 spike protein. Here the route of allosteric regulation in SARS-CoV-2 spike protein is examined by all-atom explicit solvent molecular dynamics simulations, contrastive machine learning, and the Ohm approach. It was found that peptide binding to the polybasic cleavage sites, especially the one at the first subunit of the trimeric spike protein, activates the fluctuation of the spike protein's backbone, which eventually propagates to the receptor-binding domain on the third subunit that binds to ACE2. Remarkably, the allosteric regulation routes starting from the polybasic cleavage sites share a high fraction (39–67%) of the critical amino acids with the routes starting from the nitrogen-terminal domains, suggesting the presence of an allosteric regulation network in the spike protein. Our study paves the way for the rational design of allosteric antibody inhibitors. 

Tsunamigenic megathrust earthquakes along the Cascadia subduction zone present a major hazard concern. We can better prepare to model the earthquake source in a rapid manner by imbuing fault geometry constraints based on prior knowledge and by evaluating the capabilities of using existing GNSS sensors. Near-field GNSS waveforms have shown promise in providing rapid coarse finite-fault model approximations of the earthquake rupture that can improve tsunami modeling and response time. In this study, we explore the performance of GNSS derived finite-fault inversions and tsunami forecasting predictions in Cascadia that highlights the impact and potential of geodetic techniques and data in operational earthquake and tsunami monitoring. We utilized 1300 Cascadia earthquake simulations (FakeQuakes) that provide realistic (M7.5-9.3) rupture scenarios to assess how feasibly finite-fault models can be obtained in a rapid earthquake early warning and tsunami response context. A series of fault models with rectangular dislocation patches spanning the Cascadia megathrust area is added to the GFAST inversion algorithm to calculate slip for each earthquake scenario. Another method used to constrain the finite-fault geometry is from the GNSS-derived CMT fault plane solution. For the Cascadia region, we show that fault discretization using two rectangular segments approximating the megathrust portion of the subduction zone leads to improvements in modeling magnitude, fault slip, tsunami amplitude, and inundation. In relation to tsunami forecasting capabilities, we compare coastal amplitude predictions spanning from Vancouver Island (Canada) to Northern California (USA). Generally, the coastal amplitudes derived using fault parameters from the CMT solutions show an overestimation bias compared to amplitudes derived from the fixed slab model. We also see improved prediction values of the run-up height and maximum amplitude at 10 tide gauge stations using the fixed slab model as well. 

A comprehensive understanding of the interfacial behaviors of biomolecules holds great significance in the development of biomaterials and biosensing technologies. In this work, we used discontinuous molecular dynamics (DMD) simulations and graphic contrastive learning analysis to study the adsorption of ubiquitin protein on a graphene surface. Our high-throughput DMD simulations can explore the whole protein adsorption process including the protein structural evolution with sufficient accuracy. Contrastive learning was employed to train a protein contact map feature extractor aiming at generating contact map feature vectors. Subsequently, these features were grouped using the k-means clustering algorithm to identify the protein structural transition stages throughout the adsorption process. The machine learning analysis can illustrate the dynamics of protein structural changes, including the pathway and the rate-limiting step. Our study indicated that the protein–graphene surface hydrophobic interactions and the π–π stacking were crucial to the seven-stage adsorption process. Upon adsorption, the secondary structure and tertiary structure of ubiquitin disintegrated. The unfolding stages obtained by contrastive learning-based algorithm were not only consistent with the detailed analyses of protein structures but also provided more hidden information about the transition states and pathway of protein adsorption process and structural dynamics. Our combination of efficient DMD simulations and machine learning analysis could be a valuable approach to studying the interfacial behaviors of biomolecules. 

This article chronicles the 50-year history of tsunami research and development at the NOAA Pacific Marine Environmental Laboratory (PMEL), beginning with the merger in 1973 of the Joint Tsunami Research Effort and PMEL. It traces the development of instrumentation and modeling that brought a better understanding of tsunamis and improved warning systems. The advantage of having observational engineering and flooding modeling under one roof are highlighted. Deep-ocean Assessment and Reporting of Tsunami (DART) research and development led to technology transfer to NOAA’s National Data Buoy Center (NDBC) that now operates and maintains 39 buoys and serves as real-time data distributor for other nations. This technology was also patented and licensed by PMEL to meet the needs of the international community. DART licensee Science Applications International Corporation (SAIC) has manufactured over 60 buoys for eight different countries. DART data are essential for accurate tsunami warnings, so the global society benefits by receiving lifesaving information before the arrival of a tsunami. PMEL’s tsunami flooding modeling research led to technology transfer to NOAA’s tsunami warning centers, the National Tsunami Hazard Mitigation Program, and international tsunami preparedness communities. Short-term flooding modeling research was initiated at PMEL to improve NOAA tsunami warning operations to better serve US coastal communities. The same validated modeling technology was then applied to produce hazard maps for coastal communities in the United States and internationally through the United Nations’ Intergovernmental Oceanographic Commission (IOC). Tsunami hazard maps are an essential first step in preparing a community for the next tsunami. Using these maps and other preparedness criteria, a community can become “Tsunami Ready” for the next event. Tsunami Ready has been adopted by the IOC as the global standard for preparedness of at-risk communities with total populations exceeding 890 million people. 

NOAA Pacific Marine Environmental Laboratory’s (PMEL’s) approach to tsunami research is unique among such laboratories in that tsunami observations and modeling are under one roof, offering the advantages of enhancing the speed and lowering the cost of developments. Here, we chronicle the history of the transfer of deep-ocean observational and flooding modeling technologies within and outside of NOAA and provide a case study for future transfers. PMEL and partners’ efforts in transferring tsunami technology have been very successful, resulting in improved protection of global communities with high tsunami risk while enhancing the new blue economy. The transfer of observational technology within NOAA required years of effort, while the transfer outside of NOAA only required a patent and license agreement. During the transfer process, three additional generations of observational technologies were created. The transfer of tsunami flooding modeling technology required a validation process for transfer into NOAA operations and an international training program to allow access to the technology by other countries. During this model development, a web-based product was created to simplify the use of and access to these models for both real-time and hazard assessment applications. We present lessons learned from these transfers, including the need for support as long as the technology is in use. The tsunami transfer process created a wealth of economic expansion while protecting coastal citizens from future tsunamis. 

The 11 October 1918 devastating tsunami in northwest Puerto Rico had been used as an example for earthquake-induced landslide tsunami hazard. Three pieces of evidence pointed to a landslide as the origin of the tsunami: the discovery of a large submarine landslide scar from bathymetry data collected by shipboard high-resolution multibeam sonar, reported breaks of submarine cable within the scar, and the fit of tsunami models to flooding observations. Newly processed seafloor imagery collected by remotely operated vehicle (ROV) show, however, pervasive Fe–Mn crust (patina) on the landslide walls and floor, indicating that the landslide scar is at least several hundred years old. C14 dates of sediment covering the landslide floor verify this interpretation. Although we have not searched the region systematically for an alternative tsunami source, we propose a possible source—a two-segment normal-fault rupture along the eastern wall of Mona rift. The proposed fault location matches the published normal faults with steep bathymetry and is close to the International Seismological Center–Global Earthquake Model catalog locations of the 1918 mainshock and aftershocks. The ROV observations further show fresh vertical slickensides and rock exposure along the proposed fault trace. Hydrodynamic models from an Mw 7.2 earthquake rupture along the eastern wall of the rift faithfully reproduce the reported tsunami amplitudes, polarities, and arrival times. Our analysis emphasizes the value of close-up observations and physical samples to augment remote sensing data in natural hazard studies