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Cosmogenic-nuclide concentrations in subglacial bedrock cores show that theWest Antarctic Ice Sheet (WAIS) at a site between Thwaites and Pope glaciers was at least 35m thinner than present in the past several thousand years and then subsequently thickened. This is important because of concern that present thinning and grounding line retreat at these and nearby glaciers in the Amundsen Sea Embayment may irreversibly lead to deglaciation of significant portions of the WAIS, with decimeter- to meter-scale sea level rise within decades to centuries. A past episode of ice sheet thinning that took place in a similar, although not identical, climate was not irreversible.We propose that the past thinning– thickening cycle was due to a glacioisostatic rebound feedback, similar to that invoked as a possible stabilizing mechanism for current grounding line retreat, in which isostatic uplift caused by Early Holocene thinning led to relative sea level fall favoring grounding line advance. 

Environmental DNA (eDNA) data make it possible to measure and monitor biodiversity at unprecedented resolution and scale. As use-cases multiply and scientific consensus grows regarding the value of eDNA analysis, public agencies have an opportunity to decide how and where eDNA data fit into their mandates. Within the United States, many federal and state agencies are individually using eDNA data in various applications and developing relevant scientific expertise. A national strategy for eDNA implementation would capitalize on recent scientific developments, providing a common set of next-generation tools for natural resource management and public health protection. Such a strategy would avoid patchwork and possibly inconsistent guidelines in different agencies, smoothing the way for efficient uptake of eDNA data in management. Because eDNA analysis is already in widespread use in both ocean and freshwater settings, we focus here on applications in these environments. However, we foresee the broad adoption of eDNA analysis to meet many resource management issues across the nation because the same tools have immediate terrestrial and aerial applications. 

Alzheimer's disease (AD) is a serious neurodegenerative condition that affects millions of people across the world. Recently machine learning models have been used to predict the progression of AD, although they frequently do not take advantage of the longitudinal and structural components associated with multi-modal medical data. To address this, we present a new algorithm that uses the multi-block alternating direction method of multipliers to optimize a novel objective that combines multi-modal longitudinal clinical data of various modalities to simultaneously predict the cognitive scores and diagnoses of the participants in the Alzheimer's Disease Neuroimaging Initiative cohort. Our new model is designed to leverage the structure associated with clinical data that is not incorporated into standard machine learning optimization algorithms. This new approach shows state-of-the-art predictive performance and validates a collection of brain and genetic biomarkers that have been recorded previously in AD literature.