Narratives can motivate environmental action : the Whiskey Creek ocean acidification story

Author Posting. © The Author(s), 2014. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Ambio 43 (2014): 592-599, doi:10.1007/s13280-013-0442-2.Even when environmental data quantify the risks and benefits of delayed responses to rapid anthropogenic change, institutions rarely respond promptly. We propose that narratives complementing environmental datasets can motivate responsive environmental policy. To explore this idea, we relate a case study in which a narrative of economic loss due to regionally rapid ocean acidification—an anthropogenic change—helped connect knowledge with action. We pose three hypotheses to explain why narratives might be particularly effective in linking science to environmental policy, drawing from the literature of economics, environmental policy, and cognitive psychology. It seems that yet-untold narratives may hold similar potential for strengthening the feedback between environmental data and policy and motivating regional responses to other environmental problems.2015-09-0

Ocean Acidification Policy: Applying the Lessons of Washington to California and Beyond

This Article aims to distill the lessons of Washington’s experience with ocean acidification (OA) policy and apply them to the political framework that exists in California. More generally, this Article evaluates the political landscape in which OA policy is taking shape along the west coast of the United States and highlights elements of a political and policy strategy that would build current momentum on OA in California and elsewhere into a larger, more sustained policy infrastructure capable of addressing coastal issues of environmental resilience and water quality in the context of global change. It concludes by identifying some ways in which OA policy might benefit from action on—and constituencies for—the multiple interacting drivers of environmental chang

Ten Ways States Can Combat Ocean Acidification (and Why They Should)

The ocean is becoming more acidic worldwide as a result of increasing atmospheric concentrations of carbon dioxide (“CO2”) and other pollutants. This fundamental change is likely to have substantial ecological and economic consequences globally. In this Article, we provide a toolbox for understanding and addressing the drivers of ocean acidification. We begin with an overview of the relevant science, highlighting known causes of chemical change in the coastal ocean. Because of the difficulties associated with controlling diffuse atmospheric pollutants such as CO2, we then focus on controlling smaller-scale agents of acidification, discussing ten legal and policy tools that state government agencies can use to mitigate the problem. This bottom-up approach does not solve the global CO2 problem, but instead offers a more immediate means of addressing the challenges of a rapidly changing ocean. States have ample legal authority to address many of the causes of ocean acidification; what remains is to implement that authority to safeguard our iconic coastal resources. Republished with permission from 37 Harv. Envtl. L. Rev. 57 (2013)

Using atmospheric trajectories to model the isotopic composition of rainfall in central Kenya

Publisher’s version made available under a Creative Commons license.The isotopic composition of rainfall (δ2H and δ18O) is an important tracer in studies of the ecohydrology, plant physiology, climate and biogeochemistry of past and present ecosystems. The overall continental and global patterns in precipitation isotopic composition are fairly well described by condensation temperature and Rayleigh fractionation during rainout. However, these processes do not fully explain the isotopic variability in the tropics, where intra-storm and meso-scale dynamics may dominate. Here we explore the use of atmospheric back-trajectory modeling and associated meteorological variables to explain the large variability observed in the isotopic composition of individual rain events at the study site in central Kenya. Individual rain event samples collected at the study site (n = 41) range from −51‰ to 31‰ for δ2H and the corresponding monthly values (rain volume-weighted) range from −15‰ to 15‰. Using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, we map back-trajectories for all individual rain hours occurring at a research station in central Kenya from March 2010 through February 2012 (n = 544). A multiple linear regression analysis demonstrates that a large amount of variation in the isotopic composition of rainfall can be explained by two variables readily obtained from the HYSPLIT model: (1) solar radiation along the trajectory for 48 hours prior to the event, and (2) distance covered over land. We compare the measurements and regression model results to the isotopic composition expected from simple Rayleigh distillation along each trajectory. The empirical relationship described here has applications across temporal scales. For example, it could be used to help predict short-term changes in the isotopic composition of plant-available water in the absence of event-scale sampling. One can also reconstruct monthly, seasonal and annual weighted mean precipitation isotope signatures for a single location based only on hourly rainfall data and HYSPLIT model results. At the study site in East Africa, the annual weighted mean δ2H from measured and modeled values are −7.6‰ and −7.4‰, respectively, compared to −18‰ predicted for the study site by the Online Isotopes in Precipitation Calculator

Reactive oxygen species induce virus-independent MAVS-oligomerization in systemic lupus erythematosus

The increased expression of genes induced by type I interferon (IFN) is characteristic of viral infections and systemic lupus erythematosus (SLE). We showed that mitochondrial antiviral signaling (MAVS) protein, which normally forms a complex with retinoic acid gene I (RIG-I)–like helicases during viral infection, was activated by oxidative stress independently of RIG-I helicases. We found that chemically generated oxidative stress stimulated the formation of MAVS oligomers, which led to mitochondrial hyperpolarization and decreased adenosine triphosphate production and spare respiratory capacity, responses that were not observed in similarly treated cells lacking MAVS. Peripheral blood lymphocytes of SLE patients also showed spontaneous MAVS oligomerization that correlated with the increased secretion of type I IFN and mitochondrial oxidative stress. Furthermore, inhibition of mitochondrial reactive oxygen species (ROS) by the mitochondria-targeted antioxidant MitoQ prevented MAVS oligomerization and type I IFN production. ROS-dependent MAVS oligomerization and type I IFN production were reduced in cells expressing the MAVS-C79F variant, which occurs in 30% of sub-Saharan Africans and is linked with reduced type I IFN secretion and milder disease in SLE patients. Patients expressing the MAVS-C79F variant also had reduced amounts of oligomerized MAVS in their plasma compared to healthy controls. Together, our findings suggest that oxidative stress–induced MAVS oligomerization in SLE patients may contribute to the type I IFN signature that is characteristic of this syndrome

Association of immune response with efficacy and safety outcomes in adults with phenylketonuria administered pegvaliase in phase 3 clinical trials

Background: This study assessed the immunogenicity of pegvaliase (recombinant Anabaena variabilis phenylalanine [Phe] ammonia lyase [PAL] conjugated with polyethylene glycol [PEG]) treatment in adults with phenylketonuria (PKU) and its impact on safety and efficacy. Methods: Immunogenicity was assessed during induction, upward titration, and maintenance dosing regimens in adults with PKU (n = 261). Total antidrug antibodies (ADA), neutralizing antibodies, immunoglobulin (Ig) M and IgG antibodies against PAL and PEG, IgG and IgM circulating immune complex (CIC) levels, complement components 3 and 4 (C3/C4), plasma Phe, and safety were assessed at baseline and throughout the study. Pegvaliase-specific IgE levels were measured in patients after hypersensitivity adverse events (HAE). Findings: All patients developed ADA against PAL, peaking by 6 months and then stabilizing. Most developed transient antibody responses against PEG, peaking by 3 months, then returning to baseline by 9 months. Binding of ADA to pegvaliase led to CIC formation and complement activation, which were highest during early treatment. Blood Phe decreased over time as CIC levels and complement activation declined and pegvaliase dosage increased. HAEs were most frequent during early treatment and declined over time. No patient with acute systemic hypersensitivity events tested positive for pegvaliase-specific IgE near the time of the event. Laboratory evidence was consistent with immune complex-mediated type III hypersensitivity. No evidence of pegvaliase-associated IC-mediated end organ damage was noted. Interpretation: Despite a universal ADA response post-pegvaliase administration, adult patients with PKU achieved substantial and sustained blood Phe reductions with a manageable safety profile. Fund: BioMarin Pharmaceutical Inc. Keywords: Enzyme replacement therapy, Antidrug antibody, Circulating immune complex, Hypersensitivity, Phenylalanin

Quark-gluon plasma phenomenology from anisotropic lattice QCD

The FASTSUM collaboration has been carrying out simulations of N_f=2+1 QCD at nonzero temperature in the fixed-scale approach using anisotropic lattices. Here we present the status of these studies, including recent results for electrical conductivity and charge diffusion, and heavy quarkonium (charm and beauty) physics.Comment: Talk given at Quark Confinement and the Hadron Spectrum (Confinement XI), 8-12 September, St. Petersburg, Russia. 8 pages, 7 figure

Preconditioned Neural Posterior Estimation for Likelihood-free Inference

Simulation based inference (SBI) methods enable the estimation of posterior distributions when the likelihood function is intractable, but where model simulation is feasible. Popular neural approaches to SBI are the neural posterior estimator (NPE) and its sequential version (SNPE). These methods can outperform statistical SBI approaches such as approximate Bayesian computation (ABC), particularly for relatively small numbers of model simulations. However, we show in this paper that the NPE methods are not guaranteed to be highly accurate, even on problems with low dimension. In such settings the posterior cannot be accurately trained over the prior predictive space, and even the sequential extension remains sub-optimal. To overcome this, we propose preconditioned NPE (PNPE) and its sequential version (PSNPE), which uses a short run of ABC to effectively eliminate regions of parameter space that produce large discrepancy between simulations and data and allow the posterior emulator to be more accurately trained. We present comprehensive empirical evidence that this melding of neural and statistical SBI methods improves performance over a range of examples, including a motivating example involving a complex agent-based model applied to real tumour growth data.Comment: 31 pages, 11 figure