1,050 years of hurricane strikes on Long Island in the Bahamas

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Wallace, E. J., Donnelly, J. P., van Hengstum, P. J., Winkler, T. S., McKeon, K., MacDonald, D., d'Entremont, N. E., Sullivan, R. M., Woodruff, J. D., Hawkes, A. D., & Maio, C. 1,050 years of hurricane strikes on long island in the Bahamas. Paleoceanography and Paleoclimatology, 36(3), (2021): e2020PA004156, https://doi.org/10.1029/2020PA004156.Sedimentary records of past hurricane activity indicate centennial-scale periods over the past millennium with elevated hurricane activity. The search for the underlying mechanism behind these active hurricane periods is confounded by regional variations in their timing. Here, we present a new high resolution paleohurricane record from The Bahamas with a synthesis of published North Atlantic records over the past millennium. We reconstruct hurricane strikes over the past 1,050 years in sediment cores from a blue hole on Long Island in The Bahamas. Coarse-grained deposits in these cores date to the close passage of seven hurricanes over the historical interval. We find that the intensity and angle of approach of these historical storms plays an important role in inducing storm surge near the site. Our new record indicates four active hurricane periods on Long Island that conflict with published records on neighboring islands (Andros and Abaco Island). We demonstrate these three islands do not sample the same storms despite their proximity, and we compile these reconstructions together to create the first regional compilation of annually resolved paleohurricane records in The Bahamas. Integrating our Bahamian compilation with compiled records from the U.S. coastline indicates basin-wide increased storminess during the Medieval Warm Period. Afterward, the hurricane patterns in our Bahamian compilation match those reconstructed along the U.S. East Coast but not in the northeastern Gulf of Mexico. This disconnect may result from shifts in local environmental conditions in the North Atlantic or shifts in hurricane populations from straight-moving to recurving storms over the past millennium.This work was funded by the National Science Foundation Graduate Research Fellowship (to E. J. W.), the Dalio Explore Foundation, and National Science Foundation grant OCE-1356708 (to J. P. D. and P. J. vH.)

Understanding the transfer of contemporary temperature signals into lake sediments via paired oxygen isotope ratios in carbonates and diatom silica: problems and potential

Although the oxygen isotope composition (δ18O) of calcite (δ18Ocalcite) and, to a lesser extent, diatom silica (δ18Odiatom) are widely used tracers of past hydroclimates (especially temperature and surface water hydrology), the degree to which these two hosts simultaneously acquire their isotope signals in modern lacustrine environments, or how these are altered during initial sedimentation, is poorly understood. Here, we present a unique dataset from a natural limnological laboratory to explore these issues. This study compares oxygen and hydrogen isotope data (δ18O, δ2H) of contemporary lake water samples at ~2-weekly intervals over a 2-year period (2010–12) with matching collections of diatoms (δ18Odiatom) and calcite (δ18Ocalcite) from sediment traps (at 10 m and 25 m) at Rostherne Mere (maximum depth 30 m), a well-monitored, eutrophic, seasonally stratified monomictic lake in the UK. The epilimnion shows a seasonal pattern of rising temperature and summer evaporative enrichment in 18O, and while there is a temperature imprint in both δ18Odiatom and δ18Ocalcite, there is significant inter-annual variability in both of these signals. The interpretation of δ18Odiatom and δ18Ocalcite values is complicated due to in-lake processes (e.g. non-equilibrium calcite precipitation, especially in spring, leading to significant 18Ocalcite depletion), and for δ18Odiatom, by post-mortem, depositional and possibly dissolution or diagenetic effects. For 2010 and 2011 respectively, there is a strong temperature dependence of δ18Ocalcite and δ18Odiatom in fresh trap material, with the fractionation slope for δ18Odiatom of ca. −0.2‰/°C, in agreement with several other studies. The δ18Odiatom data indicate the initiation of rapid post-mortem secondary alteration of fresh diatom silica (within ~6 months), with some trap material undergoing partial maturation in situ. Diatom δ18O of the trap material is also influenced by resuspension of diatom frustules from surface sediments (notably in summer 2011), with the net effect seen as an enrichment of deep-trap 18Odiatom by about +0.7‰ relative to shallow-trap values. Contact with anoxic water and anaerobic bacteria are potentially key to initiating this silica maturation process, as deep-trap samples that were removed prior to anoxia developing do not show enrichment. Dissolution (perhaps enhanced by anaerobic bacterial communities) may also be responsible for changes to δ18Odiatom that lead to increasing, but potentially predictable, error in inferred temperatures using this proxy. High resolution, multi-year monitoring can shed light on the complex dynamics affecting δ18Odiatom and δ18Ocalcite and supports the careful use of sedimentary δ18Odiatom and δ18Ocalcite as containing valuable hydroclimatic signals especially at a multi-annual resolution, although there remain substantial challenges to developing a reliable geothermometer on paired δ18Odiatom and δ18Ocalcite. In particular, δ18Odiatom needs cautious interpretation where silica post-mortem secondary alteration is incomplete and diatom preservation is not perfect, and we recommend dissolution be routinely assessed on diatom samples used for isotopic analyses

Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks

Neural networks are widely deployed models across many scientific disciplines and commercial endeavors ranging from edge computing and sensing to large-scale signal processing in data centers. The most efficient and well-entrenched method to train such networks is backpropagation, or reverse-mode automatic differentiation. To counter an exponentially increasing energy budget in the artificial intelligence sector, there has been recent interest in analog implementations of neural networks, specifically nanophotonic neural networks for which no analog backpropagation demonstration exists. We design mass-manufacturable silicon photonic neural networks that alternately cascade our custom designed "photonic mesh" accelerator with digitally implemented nonlinearities. These reconfigurable photonic meshes program computationally intensive arbitrary matrix multiplication by setting physical voltages that tune the interference of optically encoded input data propagating through integrated Mach-Zehnder interferometer networks. Here, using our packaged photonic chip, we demonstrate in situ backpropagation for the first time to solve classification tasks and evaluate a new protocol to keep the entire gradient measurement and update of physical device voltages in the analog domain, improving on past theoretical proposals. Our method is made possible by introducing three changes to typical photonic meshes: (1) measurements at optical "grating tap" monitors, (2) bidirectional optical signal propagation automated by fiber switch, and (3) universal generation and readout of optical amplitude and phase. After training, our classification achieves accuracies similar to digital equivalents even in presence of systematic error. Our findings suggest a new training paradigm for photonics-accelerated artificial intelligence based entirely on a physical analog of the popular backpropagation technique.Comment: 23 pages, 10 figure

COVID-19 Clinical Guidance For the Cardiovascular Care Team

COVID-19 is a quickly evolving public health emergency. The guidance provided in this document is based on the best available published information and expert evaluation. This document is intended to supplement, not supersede, relevant guidance from the Centers for Disease Control and Prevention, state and local health authorities, and your institution’s infectious disease containment, mitigation, and response plan

Complex host genetics influence the microbiome in inflammatory bowel disease

Background: Human genetics and host-associated microbial communities have been associated independently with a wide range of chronic diseases. One of the strongest associations in each case is inflammatory bowel disease (IBD), but disease risk cannot be explained fully by either factor individually. Recent findings point to interactions between host genetics and microbial exposures as important contributors to disease risk in IBD. These include evidence of the partial heritability of the gut microbiota and the conferral of gut mucosal inflammation by microbiome transplant even when the dysbiosis was initially genetically derived. Although there have been several tests for association of individual genetic loci with bacterial taxa, there has been no direct comparison of complex genome-microbiome associations in large cohorts of patients with an immunity-related disease. Methods: We obtained 16S ribosomal RNA (rRNA) gene sequences from intestinal biopsies as well as host genotype via Immunochip in three independent cohorts totaling 474 individuals. We tested for correlation between relative abundance of bacterial taxa and number of minor alleles at known IBD risk loci, including fine mapping of multiple risk alleles in the Nucleotide-binding oligomerization domain-containing protein 2 (NOD2) gene exon. We identified host polymorphisms whose associations with bacterial taxa were conserved across two or more cohorts, and we tested related genes for enrichment of host functional pathways. Results: We identified and confirmed in two cohorts a significant association between NOD2 risk allele count and increased relative abundance of Enterobacteriaceae, with directionality of the effect conserved in the third cohort. Forty-eight additional IBD-related SNPs have directionality of their associations with bacterial taxa significantly conserved across two or three cohorts, implicating genes enriched for regulation of innate immune response, the JAK-STAT cascade, and other immunity-related pathways. Conclusions: These results suggest complex interactions between genetically altered host functional pathways and the structure of the microbiome. Our findings demonstrate the ability to uncover novel associations from paired genome-microbiome data, and they suggest a complex link between host genetics and microbial dysbiosis in subjects with IBD across independent cohorts. Electronic supplementary material The online version of this article (doi:10.1186/s13073-014-0107-1) contains supplementary material, which is available to authorized users

Wastewater Surveillance for SARS-CoV-2 on College Campuses: Initial Efforts, Lessons Learned, and Research Needs

Wastewater surveillance for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging approach to help identify the risk of a coronavirus disease (COVID-19) outbreak. This tool can contribute to public health surveillance at both community (wastewater treatment system) and institutional (e.g., colleges, prisons, and nursing homes) scales. This paper explores the successes, challenges, and lessons learned from initial wastewater surveillance efforts at colleges and university systems to inform future research, development and implementation. We present the experiences of 25 college and university systems in the United States that monitored campus wastewater for SARS-CoV-2 during the fall 2020 academic period. We describe the broad range of approaches, findings, resources, and impacts from these initial efforts. These institutions range in size, social and political geographies, and include both public and private institutions. Our analysis suggests that wastewater monitoring at colleges requires consideration of local information needs, sewage infrastructure, resources for sampling and analysis, college and community dynamics, approaches to interpretation and communication of results, and follow-up actions. Most colleges reported that a learning process of experimentation, evaluation, and adaptation was key to progress. This process requires ongoing collaboration among diverse stakeholders including decision-makers, researchers, faculty, facilities staff, students, and community members

A restatement of the natural science evidence base on the effects of endocrine disrupting chemicals on wildlife

Endocrine disrupting chemicals (EDCs) are substances that alter the function of the endocrine system and consequently cause adverse effects to humans or wildlife. The release of particular EDCs into the environment has been shown to negatively affect certain wildlife populations and has led to restrictions on the use of some EDCs. Current chemical regulations aim to balance the industrial, agricultural and/or pharmaceutical benefits of using these substances with their demonstrated or potential harm to human health or the environment. A summary is provided of the natural science evidence base informing the regulation of chemicals released into the environment that may have endocrine disrupting effects on wildlife. This summary is in a format (a ‘restatement’) intended to be policy-neutral and accessible to informed, but not expert, policy-makers and stakeholders

Farnesylated Nuclear Proteins Kugelkern and Lamin Dm0 Affect Nuclear Morphology by Directly Interacting with the Nuclear Membrane

Nuclear shape changes are observed during a variety of developmental processes, pathological conditions and ageing. Here, the molecular mechanism is analyzed how the farnesylated nuclear proteins interact with the nuclear envelope and deform the phospholipid bilayer

Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change

While the physical dimensions of climate change are now routinely assessed through multimodel intercomparisons, projected impacts on the global ocean ecosystem generally rely on individual models with a specific set of assumptions. To address these single-model limitations, we present standardized ensemble projections from six global marine ecosystem models forced with two Earth system models and four emission scenarios with and without fishing. We derive average biomass trends and associated uncertainties across the marine food web. Without fishing, mean global animal biomass decreased by 5% (±4% SD) under low emissions and 17% (±11% SD) under high emissions by 2100, with an average 5% decline for every 1 °C of warming. Projected biomass declines were primarily driven by increasing temperature and decreasing primary production, and were more pronounced at higher trophic levels, a process known as trophic amplification. Fishing did not substantially alter the effects of climate change. Considerable regional variation featured strong biomass increases at high latitudes and decreases at middle to low latitudes, with good model agreement on the direction of change but variable magnitude. Uncertainties due to variations in marine ecosystem and Earth system models were similar. Ensemble projections performed well compared with empirical data, emphasizing the benefits of multimodel inference to project future outcomes. Our results indicate that global ocean animal biomass consistently declines with climate change, and that these impacts are amplified at higher trophic levels. Next steps for model development include dynamic scenarios of fishing, cumulative human impacts, and the effects of management measures on future ocean biomass trends