Exploring Agricultural Production Systems and Their Fundamental Components with System Dynamics Modelling

Agricultural production in the United States is undergoing marked changes due to rapid shifts in consumer demands, input costs, and concerns for food safety and environmental impact. Agricultural production systems are comprised of multidimensional components and drivers that interact in complex ways to influence production sustainability. In a mixed-methods approach, we combine qualitative and quantitative data to develop and simulate a system dynamics model that explores the systemic interaction of these drivers on the economic, environmental and social sustainability of agricultural production. We then use this model to evaluate the role of each driver in determining the differences in sustainability between three distinct production systems: crops only, livestock only, and an integrated crops and livestock system. The result from these modelling efforts found that the greatest potential for sustainability existed with the crops only production system. While this study presents a stand-alone contribution to sector knowledge and practice, it encourages future research in this sector that employs similar systems-based methods to enable more sustainable practices and policies within agricultural production

The Effect of Cortex/Medulla Proportions on Molecular Diagnoses in Kidney Transplant Biopsies: Rejection and Injury Can Be Assessed in Medulla

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137720/1/ajt14233_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137720/2/ajt14233.pd

A missing dimension in measures of vaccination impacts

Immunological protection, acquired from either natural infection or vaccination, varies among hosts, reflecting underlying biological variation and affecting population-level protection. Owing to the nature of resistance mechanisms, distributions of susceptibility and protection entangle with pathogen dose in a way that can be decoupled by adequately representing the dose dimension. Any infectious processes must depend in some fashion on dose, and empirical evidence exists for an effect of exposure dose on the probability of transmission to mumps-vaccinated hosts [1], the case-fatality ratio of measles [2], and the probability of infection and, given infection, of symptoms in cholera [3]. Extreme distributions of vaccine protection have been termed leaky (partially protects all hosts) and all-or-nothing (totally protects a proportion of hosts) [4]. These distributions can be distinguished in vaccine field trials from the time dependence of infections [5]. Frailty mixing models have also been proposed to estimate the distribution of protection from time to event data [6], [7], although the results are not comparable across regions unless there is explicit control for baseline transmission [8]. Distributions of host susceptibility and acquired protection can be estimated from dose-response data generated under controlled experimental conditions [9]–[11] and natural settings [12], [13]. These distributions can guide research on mechanisms of protection, as well as enable model validity across the entire range of transmission intensities. We argue for a shift to a dose-dimension paradigm in infectious disease science and community health

Submarine groundwater discharge to a small estuary estimated from radon and salinity measurements and a box model

Author Posting. © 2005 Author(s). This work is licensed under a Creative Commons License. The definitive version was published Biogeosciences 2 (2005): 141-157, doi:10.5194/bg-2-141-2005.Submarine groundwater discharge was quantified by a variety of methods for a 4-day period during the early summer of 2004, in Salt Pond, adjacent to Nauset Marsh, on Cape Cod, USA. Discharge estimates based on radon and salinity took advantage of the presence of the narrow channel connecting Salt Pond to Nauset Marsh, which allowed constructing whole-pond mass balances as water flowed in and out due to tidal fluctuations. The data suggest that less than one quarter of the discharge in the vicinity of Salt Pond happened within the pond itself, while three quarters or more of the discharge occurred immediately seaward of the pond, either in the channel or in adjacent regions of Nauset Marsh. Much of this discharge, which maintains high radon activities and low salinity, is carried into the pond during each incoming tide. A box model was used as an aid to understand both the rates and the locations of discharge in the vicinity of Salt Pond. The model achieves a reasonable fit to both the salinity and radon data assuming submarine groundwater discharge is fresh and that most of it occurs either in the channel or in adjacent regions of Nauset Marsh. Salinity and radon data, together with seepage meter results, do not rule out discharge of saline groundwater, but suggest either that the saline discharge is at most comparable in volume to the fresh discharge or that it is depleted in radon. The estimated rate of fresh groundwater discharge in the vicinity of Salt Pond is 3000-7000 m3 d-1. This groundwater flux estimated from the radon and salinity data is comparable to a value of 3200-4500 m3 d-1 predicted by a recent hydrologic model (Masterson, 2004; Colman and Masterson, 2004), although the model predicts this rate of discharge to the pond whereas our data suggest most of the groundwater bypasses the pond prior to discharge. Additional work is needed to determine if the measured rate of discharge is representative of the long-term average, and to better constrain the rate of groundwater discharge seaward of Salt Pond.Financial support was provided by the US Geological Survey and by National Science Foundation grant #OCE-0346933 to MAC

FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model

Mathematical and computer models of epidemics have contributed to our understanding of the spread of infectious disease and the measures needed to contain or mitigate them. To help prepare for future influenza seasonal epidemics or pandemics, we developed a new stochastic model of the spread of influenza across a large population. Individuals in this model have realistic social contact networks, and transmission and infections are based on the current state of knowledge of the natural history of influenza. The model has been calibrated so that outcomes are consistent with the 1957/1958 Asian A(H2N2) and 2009 pandemic A(H1N1) influenza viruses. We present examples of how this model can be used to study the dynamics of influenza epidemics in the United States and simulate how to mitigate or delay them using pharmaceutical interventions and social distancing measures. Computer simulation models play an essential role in informing public policy and evaluating pandemic preparedness plans. We have made the source code of this model publicly available to encourage its use and further development

Reproducibility in modeling and simulation of the knee:Academic, industry, and regulatory perspectives

Stakeholders in the modeling and simulation (M&amp;S) community organized a workshop at the 2019 Annual Meeting of the Orthopaedic Research Society (ORS) entitled “Reproducibility in Modeling and Simulation of the Knee: Academic, Industry, and Regulatory Perspectives.” The goal was to discuss efforts among these stakeholders to address irreproducibility in M&amp;S focusing on the knee joint. An academic representative from a leading orthopedic hospital in the United States described a multi-institutional, open effort funded by the National Institutes of Health to assess model reproducibility in computational knee biomechanics. A regulatory representative from the United States Food and Drug Administration indicated the necessity of standards for reproducibility to increase utility of M&amp;S in the regulatory setting. An industry representative from a major orthopedic implant company emphasized improving reproducibility by addressing indeterminacy in personalized modeling through sensitivity analyses, thereby enhancing preclinical evaluation of joint replacement technology. Thought leaders in the M&amp;S community stressed the importance of data sharing to minimize duplication of efforts. A survey comprised 103 attendees revealed strong support for the workshop and for increasing emphasis on computational modeling at future ORS meetings. Nearly all survey respondents (97%) considered reproducibility to be an important issue. Almost half of respondents (45%) tried and failed to reproduce the work of others. Two-thirds of respondents (67%) declared that individual laboratories are most responsible for ensuring reproducible research whereas 44% thought that journals are most responsible. Thought leaders and survey respondents emphasized that computational models must be reproducible and credible to advance knee M&amp;S.</p

Impact of general practice endorsement on the social gradient in uptake in bowel cancer screening

This is a summary of independent research funded by the National Institute for Health. Research (NIHR)’s Programme Grants for Applied Research Programme (RP-PG-0609–10106