Assessment of the Calibration of Periodontal Diagnosis and Treatment Planning Among Dental Students at Three Dental Schools

Calibration in diagnosis and treatment planning is difficult to achieve due to variations that exist in clinical interpretation. To determine if dental faculty members are consistent in teaching how to diagnose and treat periodontal disease, variations among dental students can be evaluated. A previous study reported high variability in diagnoses and treatment plans of periodontal cases at Indiana University School of Dentistry. This study aimed to build on that one by extending the research to two additional schools: Marquette University School of Dentistry and West Virginia University School of Dentistry. Diagnosis and treatment planning by 40 third- and fourth-year dental students were assessed at each of the schools. Students were asked to select the diagnosis and treatment plans on a questionnaire pertaining to 11 cases. Their responses were compared using chi-square tests, and multirater kappa statistics were used to assess agreement between classes and between schools. Logistic regression models were used to evaluate the effects of school, class year, prior experience, and GPA/class rank on correct responses. One case had a statistically significant difference in responses between third- and fourth-year dental students. Kappas for school agreement and class agreement were low. The students from Indiana University had higher diagnosis and treatment agreements than the Marquette University students, and the Marquette students fared better than the West Virginia University students. This study can help restructure future periodontal courses for a better understanding of periodontal diagnosis and treatment planning

Myocarditis, disseminated infection, and early viral persistence following experimental coxsackievirus B infection of cynomolgus monkeys.

Coxsackievirus B (CVB) infection is a common cause of acute viral myocarditis. The clinical presentation of myocarditis caused by this enterovirus is highly variable, ranging from mildly symptoms to complete hemodynamic collapse. These variations in initial symptoms and in the immediate and long term outcomes of this disease have impeded development of effective treatment strategies. Nine cynomolgus monkeys were inoculated with myocarditic strains of CVB. Virological studies performed up to 28 days post-inoculation demonstrated the development of neutralizing antibody in all animals, and the presence of CVB in plasma. High dose intravenous inoculation (n = 2) resulted in severe disseminated disease, while low dose intravenous (n = 6) or oral infection (1 animal) resulted in clinically unapparent infection. Transient, minor, echocardiographic abnormalities were noted in several animals, but no animals displayed signs of significant acute cardiac failure. Although viremia rapidly resolved, signs of myocardial inflammation and injury were observed in all animals at the time of necropsy, and CVB was detected in postmortem myocardial specimens up to 28 days PI. This non-human primate system replicates many features of illness in acute coxsackievirus myocarditis and demonstrates that myocardial involvement may be common in enteroviral infection; it may provide a model system for testing of treatment strategies for enteroviral infections and acute coxsackievirus myocarditis

Parameter uncertainty of a dynamic multispecies size spectrum model

Dynamic size spectrum models have been recognized as an effective way of describing how size-based interactions can give rise to the size structure of aquatic communities. They are intermediate-complexity ecological models that are solutions to partial differential equations driven by the size-dependent processes of predation, growth, mortality, and reproduction in a community of interacting species and sizes. To be useful for quantitative fisheries management these models need to be developed further in a formal statistical framework. Previous work has used time-averaged data to “calibrate” the model using optimization methods with the disadvantage of losing detailed time-series information. Using a published multispecies size spectrum model parameterized for the North Sea comprising 12 interacting fish species and a background resource, we fit the model to time-series data using a Bayesian framework for the first time. We capture the 1967–2010 period using annual estimates of fishing mortality rates as input to the model and time series of fisheries landings data to fit the model to output. We estimate 38 key parameters representing the carrying capacity of each species and background resource, as well as initial inputs of the dynamical system and errors on the model output. We then forecast the model forward to evaluate how uncertainty propagates through to population- and community-level indicators under alternative management strategies

Workshop on Mars Sample Return Science

Martian magnetic history; quarantine issues; surface modifying processes; climate and atmosphere; sampling sites and strategies; and life sciences were among the topics discussed

Confined Quantum Time of Arrival for Vanishing Potential

We give full account of our recent report in [E.A. Galapon, R. Caballar, R. Bahague {\it Phys. Rev. Let.} {\bf 93} 180406 (2004)] where it is shown that formulating the free quantum time of arrival problem in a segment of the real line suggests rephrasing the quantum time of arrival problem to finding a complete set of states that evolve to unitarily arrive at a given point at a definite time. For a spatially confined particle, here it is shown explicitly that the problem admits a solution in the form of an eigenvalue problem of a class of compact and self-adjoint time of arrival operators derived by a quantization of the classical time of arrival. The eigenfunctions of these operators are numerically demonstrated to unitarilly arrive at the origin at their respective eigenvalues.Comment: accepted for publication in Phys. Rev.

Dynamical dark energy: Current constraints and forecasts

We consider how well the dark energy equation of state $w$ as a function of red shift $z$ will be measured using current and anticipated experiments. We use a procedure which takes fair account of the uncertainties in the functional dependence of $w$ on $z$, as well as the parameter degeneracies, and avoids the use of strong prior constraints. We apply the procedure to current data from WMAP, SDSS, and the supernova searches, and obtain results that are consistent with other analyses using different combinations of data sets. The effects of systematic experimental errors and variations in the analysis technique are discussed. Next, we use the same procedure to forecast the dark energy constraints achieveable by the end of the decade, assuming 8 years of WMAP data and realistic projections for ground-based measurements of supernovae and weak lensing. We find the $2 \sigma$ constraints on the current value of $w$ to be $\Delta w_0 (2 \sigma) = 0.20$, and on $dw/dz$ (between $z=0$ and $z=1$) to be $\Delta w_1 (2 \sigma)=0.37$. Finally, we compare these limits to other projections in the literature. Most show only a modest improvement; others show a more substantial improvement, but there are serious concerns about systematics. The remaining uncertainty still allows a significant span of competing dark energy models. Most likely, new kinds of measurements, or experiments more sophisticated than those currently planned, are needed to reveal the true nature of dark energy.Comment: 24 pages, 20 figures. Added SN systematic uncertainties, extended discussio

Inverse Problems in a Bayesian Setting

In a Bayesian setting, inverse problems and uncertainty quantification (UQ) --- the propagation of uncertainty through a computational (forward) model --- are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.Comment: arXiv admin note: substantial text overlap with arXiv:1312.504