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Detecting slow slip events (SSEs) at offshore subduction zones is important to understand the slip behavior on offshore subduction megathrusts, where tsunamis can be generated. The most widely used method to detect SSEs is to measure the vertical seafloor deformation caused by SSEs using seafloor pressure data. However, due to the small signal‐to‐noise ratio and instrumental drift, such detection is very difficult. In this study, we trained a machine learning model using synthetic data to detect SSEs and applied it to real pressure data in New Zealand between 2014 and 2015. Our method detected five events, two of which are confirmed by the onshore GPS records. Besides, our model performs better than the traditional matched filter method. We conclude that machine learning could be used to detect SSEs in real seafloor pressure data. The method can be applied to other regions, especially where near trench GPS is not available. Plain Language Summary: We applied machine learning to detect a special tectonic signal recorded by pressure sensors sitting on the seafloor. This signal represents the release of tectonic stress between earthquakes, and thus, their existence indicates a lower likelihood of future large earthquakes and tsunamis. It is difficult to detect this signal because of the high noise. Here we show that machine learning successfully detected two such signals and three possible cases in data collected near New Zealand between 2014 and 2015. The method has the potential to transform our way of detecting such signals in seafloor pressure data offshore New Zealand and elsewhere, especially where the signal source is far away from the shoreline. Key Points: Using a machine learning method, we detected five events in seafloor pressure data offshore New Zealand, two of which are likely SSEsPerformance of the machine learning method is better than the traditional matched filter methodThis method can be especially useful where the trench is far from GPS onshore [ABSTRACT FROM AUTHOR]

Background: Human genetics research lacks diversity; over 80% of genome-wide association studies have been conducted on individuals of European ancestry. In addition to limiting insights regarding disease mechanisms, disproportionate representation can create disparities preventing equitable implementation of personalized medicine., Objective: This systematic review provides an overview of research involving Parkinson's disease (PD) genetics in underrepresented populations (URP) and sets a baseline to measure the future impact of current efforts in those populations., Methods: We searched PubMed and EMBASE until October 2021 using search strings for "PD," "genetics," the main "URP," and and the countries in Latin America, Caribbean, Africa, Asia, and Oceania (excluding Australia and New Zealand). Inclusion criteria were original studies, written in English, reporting genetic results on PD from non-European populations. Two levels of independent reviewers identified and extracted information., Results: We observed imbalances in PD genetic studies among URPs. Asian participants from Greater China were described in the majority of the articles published (57%), but other populations were less well studied; for example, Blacks were represented in just 4.0% of the publications. Also, although idiopathic PD was more studied than monogenic forms of the disease, most studies analyzed a limited number of genetic variants. We identified just nine studies using a genome-wide approach published up to 2021, including URPs., Conclusion: This review provides insight into the significant lack of population diversity in PD research highlighting the immediate need for better representation. The Global Parkinson's Genetics Program (GP2) and similar initiatives aim to impact research in URPs, and the early metrics presented here can be used to measure progress in the field of PD genetics in the future. © 2022 International Parkinson and Movement Disorder Society., (© 2022 International Parkinson and Movement Disorder Society.)