Plant A Little Garden In Your Own Back Yard

Photograph of Bert Lewis and J. Walter Leipold; Man smoking pipe and using hoe in garden in back yard; Woman leaning over fence with rake and woman in background watering plantshttps://scholarsjunction.msstate.edu/cht-sheet-music/7926/thumbnail.jp

In Dear Old Dixieland

https://digitalcommons.library.umaine.edu/mmb-vp/5080/thumbnail.jp

Ready ... Go: Amplitude of the fMRI Signal Encodes Expectation of Cue Arrival Time

What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals

Thinking outside the channel : modeling nitrogen cycling in networked river ecosystems

Author Posting. © Ecological Society of America, 2011. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Frontiers in Ecology and the Environment 9 (2011): 229–238, doi:10.1890/080211.Agricultural and urban development alters nitrogen and other biogeochemical cycles in rivers worldwide. Because such biogeochemical processes cannot be measured empirically across whole river networks, simulation models are critical tools for understanding river-network biogeochemistry. However, limitations inherent in current models restrict our ability to simulate biogeochemical dynamics among diverse river networks. We illustrate these limitations using a river-network model to scale up in situ measures of nitrogen cycling in eight catchments spanning various geophysical and land-use conditions. Our model results provide evidence that catchment characteristics typically excluded from models may control river-network biogeochemistry. Based on our findings, we identify important components of a revised strategy for simulating biogeochemical dynamics in river networks, including approaches to modeling terrestrial–aquatic linkages, hydrologic exchanges between the channel, floodplain/riparian complex, and subsurface waters, and interactions between coupled biogeochemical cycles.This research was supported by NSF (DEB-0111410). Additional support was provided by NSF for BJP and SMT (DEB-0614301), for WMW (OCE-9726921 and DEB-0614282), for WHM and JDP (DEB-0620919), for SKH (DEB-0423627), and by the Gordon and Betty Moore Foundation for AMH, GCP, ESB, and JAS, and by an EPA Star Fellowship for AMH

Adverse drug events caused by three high-risk drug–drug interactions in patients admitted to intensive care units:A multicentre retrospective observational study

Aims: Knowledge about adverse drug events caused by drug–drug interactions (DDI-ADEs) is limited. We aimed to provide detailed insights about DDI-ADEs related to three frequent, high-risk potential DDIs (pDDIs) in the critical care setting: pDDIs with international normalized ratio increase (INR+) potential, pDDIs with acute kidney injury (AKI) potential, and pDDIs with QTc prolongation potential. Methods: We extracted routinely collected retrospective data from electronic health records of intensive care units (ICUs) patients (≥18 years), admitted to ten hospitals in the Netherlands between January 2010 and September 2019. We used computerized triggers (e-triggers) to preselect patients with potential DDI-ADEs. Between September 2020 and October 2021, clinical experts conducted a retrospective manual patient chart review on a subset of preselected patients, and assessed causality, severity, preventability, and contribution to ICU length of stay of DDI-ADEs using internationally prevailing standards. Results: In total 85 422 patients with ≥1 pDDI were included. Of these patients, 32 820 (38.4%) have been exposed to one of the three pDDIs. In the exposed group, 1141 (3.5%) patients were preselected using e-triggers. Of 237 patients (21%) assessed, 155 (65.4%) experienced an actual DDI-ADE; 52.9% had severity level of serious or higher, 75.5% were preventable, and 19.3% contributed to a longer ICU length of stay. The positive predictive value was the highest for DDI-INR+ e-trigger (0.76), followed by DDI-AKI e-trigger (0.57). Conclusion: The highly preventable nature and severity of DDI-ADEs, calls for action to optimize ICU patient safety. Use of e-triggers proved to be a promising preselection strategy.</p

Adverse drug events caused by three high-risk drug–drug interactions in patients admitted to intensive care units:A multicentre retrospective observational study

Aims: Knowledge about adverse drug events caused by drug–drug interactions (DDI-ADEs) is limited. We aimed to provide detailed insights about DDI-ADEs related to three frequent, high-risk potential DDIs (pDDIs) in the critical care setting: pDDIs with international normalized ratio increase (INR+) potential, pDDIs with acute kidney injury (AKI) potential, and pDDIs with QTc prolongation potential. Methods: We extracted routinely collected retrospective data from electronic health records of intensive care units (ICUs) patients (≥18 years), admitted to ten hospitals in the Netherlands between January 2010 and September 2019. We used computerized triggers (e-triggers) to preselect patients with potential DDI-ADEs. Between September 2020 and October 2021, clinical experts conducted a retrospective manual patient chart review on a subset of preselected patients, and assessed causality, severity, preventability, and contribution to ICU length of stay of DDI-ADEs using internationally prevailing standards. Results: In total 85 422 patients with ≥1 pDDI were included. Of these patients, 32 820 (38.4%) have been exposed to one of the three pDDIs. In the exposed group, 1141 (3.5%) patients were preselected using e-triggers. Of 237 patients (21%) assessed, 155 (65.4%) experienced an actual DDI-ADE; 52.9% had severity level of serious or higher, 75.5% were preventable, and 19.3% contributed to a longer ICU length of stay. The positive predictive value was the highest for DDI-INR+ e-trigger (0.76), followed by DDI-AKI e-trigger (0.57). Conclusion: The highly preventable nature and severity of DDI-ADEs, calls for action to optimize ICU patient safety. Use of e-triggers proved to be a promising preselection strategy.</p