The Cooperative Extension Service as a Boundary Organization for Diffusion of Climate Forecasts: A 5-Year Study

This article compares responses from two surveys in Florida to estimate how climate literacy has evolved as a result of the partnership of the Southeast Climate Consortium with the Cooperative Extension Services for diffusion of climate information. A 32-question survey was developed and posted to the Internet in 2004 and again in 2009. We found that climate knowledge evolved over the 5-year interval in two principal ways. Knowledge and willingness to use and provide information to end users increased on average, and agents had refined what types of climate information are actually useful and to what extent for their clients

Graduates

https://thekeep.eiu.edu/commencement_fall2015/1029/thumbnail.jp

Practical recipes for the model order reduction, dynamical simulation, and compressive sampling of large-scale open quantum systems

This article presents numerical recipes for simulating high-temperature and non-equilibrium quantum spin systems that are continuously measured and controlled. The notion of a spin system is broadly conceived, in order to encompass macroscopic test masses as the limiting case of large-j spins. The simulation technique has three stages: first the deliberate introduction of noise into the simulation, then the conversion of that noise into an equivalent continuous measurement and control process, and finally, projection of the trajectory onto a state-space manifold having reduced dimensionality and possessing a Kahler potential of multi-linear form. The resulting simulation formalism is used to construct a positive P-representation for the thermal density matrix. Single-spin detection by magnetic resonance force microscopy (MRFM) is simulated, and the data statistics are shown to be those of a random telegraph signal with additive white noise. Larger-scale spin-dust models are simulated, having no spatial symmetry and no spatial ordering; the high-fidelity projection of numerically computed quantum trajectories onto low-dimensionality Kahler state-space manifolds is demonstrated. The reconstruction of quantum trajectories from sparse random projections is demonstrated, the onset of Donoho-Stodden breakdown at the Candes-Tao sparsity limit is observed, a deterministic construction for sampling matrices is given, and methods for quantum state optimization by Dantzig selection are given.Comment: 104 pages, 13 figures, 2 table

Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely

Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely

The dynamic North Florida dairy farm model: A user-friendly computerized tool for increasing profits while minimizing N leaching under varying climatic conditions

This paper describes the computer implementation of the Dynamic North Florida Dairy farm model (DyNoFlo Dairy). The DyNoFlo Dairy is a decision support system that integrates nutrient budgeting, crop, and optimization models created to assess nitrogen (N) leaching from North Florida dairy farm systems and the economic impacts resulting from reducing it under different climatic conditions. The decision support system, based on Excel ® and Visual Basic ® software, responds to dairy-specific environmental (climate and soils) and managerial characteristics (livestock management, waste management, crop systems management) and can be used to study the economic and ecologic sustainability of these systems. The DyNoFlo Dairy model is a dynamic adaptation of the framework “balance” of nutrients in dairy farms, commonly used in Florida. The DyNoFlo Dairy model incorporates Markov-chain probabilistic simulation of cow-flows and crop simulation for historical climatic years El Niño southern oscillation (ENSO), automated optimization of managerial options, participatory modeling, and user friendliness. This paper discusses the model components and its computer implementation in a user-friendly application. The model was parameterized for conditions found in North Florida dairy farm systems. It is intended to be a tool for producers, regulatory agencies, and extension services, and because of that, participatory and interdisciplinary work was pursued during model creation, calibration, and validation. A case study for a synthesized North Florida dairy farm using the DyNoFlo Dairy model found substantial differences in the N leaching for different ENSO phases and other managerial factors; and the possibility of decreasing N leaching up to 25% while still maintaining profitability levels

From Climate Variability to Climate Change: Challenges and Opportunities to Extension

Interest of farmers in climate change has recently increased in response to intense media coverage of climate change, recent weather extremes in Florida such as 2 years of intense hurricane activity and a drought in 2007, and additional revenue possibilities in the carbon market. This article discusses the challenges involved and potential opportunities for the development and implementation of a climate change extension program at the University of Florida, complementing a recently established climate Extension program aimed at helping farmers cope with seasonal climate variability