A comparison of the antimicrobial efficacy of silver diamine fluoride and silver nitrate: an in vitro study

A COMPARISON OF THE ANTIMICROBIAL EFFICACY OF SILVER DIAMINE FLUORIDE AND SILVER NITRATE: AN IN VITRO STUDY By: Nicholas L Luke, D.D.S. A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Dentistry at Virginia Commonwealth University. Virginia Commonwealth University, May 2018 Thesis Advisor: William O. Dahlke Jr., D.M.D. Pediatric Dentistry, Department Chair Purpose: To determine the antimicrobial efficacy of SDF and SN/NaF. Methods: Three bacterial species were combined to create an in vitro biofilm. Treatment was completed with SN, SN/NaF, SDF, SDF½ or untreated (control). Results: The untreated group demonstrated significantly higher growth than all other treatment groups across the study. On the BHI-plates (1-day), there were significant differences between all treatments except SDF and SDF½. On the BHI-plates (3-days), SN/NaF was not significantly different from SDF or SDF½. On the L-MRS-plates (1-day), both SN treatment groups yielded significantly higher growth than the SDF groups. On the L-MRS-plates (3-days), SN yielded significantly higher growth than SN/NaF, SDF, and SDF½. Conclusion: SDF is more effective than SN/NaF, with the exception of BHI-plates (3-days) only and SN/NaF is more effective than SN on primarily S. mutans and L. acidophilus. There is evidence of a possible antimicrobial tolerance of oral bacteria to silver

Time-symmetric quantization in spacetimes with event horizons

The standard quantization formalism in spacetimes with event horizons implies a non-unitary evolution of quantum states, as initial pure states may evolve into thermal states. This phenomenon is behind the famous black hole information loss paradox which provoked long-standing debates on the compatibility of quantum mechanics and gravity. In this paper we demonstrate that within an alternative time-symmetric quantization formalism thermal radiation is absent and states evolve unitarily in spacetimes with event horizons. We also discuss the theoretical consistency of the proposed formalism. We explicitly demonstrate that the theory preserves the microcausality condition and suggest a "reinterpretation postulate" to resolve other apparent pathologies associated with negative energy states. Accordingly as there is a consistent alternative, we argue that choosing to use time-asymmetric quantization is a necessary condition for the black hole information loss paradox.Comment: 9 page

Man-systems requirements for the control of teleoperators in space

The microgravity of the space environment has profound effects on humans and, consequently, on the design requirements for subsystems and components with which humans interact. There are changes in the anthropometry, vision, the perception of orientation, posture, and the ways in which we exert energy. The design requirements for proper human engineering must reflect each of the changes that results, and this is especially true in the exercise of control over remote and teleoperated systems where the operator is removed from any direct sense of control. The National Aeronautics and Space Administration has recently completed the first NASA-wide human factors standard for microgravity. The Man-Systems Integration Standard, NASA-STD-3000, contains considerable information on the appropriate design criteria for microgravity, and there is information that is useful in the design for teleoperated systems. There is not, however, a dedicated collection of data which pertains directly to the special cases of remote and robotic operations. The design considerations for human-system interaction in the control of remote systems in space are discussed, with brief details on the information to be found in the NASA-STD-3000, and arguments for a dedicated section within the Standard which deals with robotic, teleoperated and remote systems and the design requirements for effective human control of these systems in the space environment, and from the space environment

Meyniel's conjecture holds for random graphs

In the game of cops and robber, the cops try to capture a robber moving on the vertices of the graph. The minimum number of cops required to win on a given graph $G$ is called the cop number of $G$. The biggest open conjecture in this area is the one of Meyniel, which asserts that for some absolute constant $C$, the cop number of every connected graph $G$ is at most $C \sqrt{|V(G)|}$. In this paper, we show that Meyniel's conjecture holds asymptotically almost surely for the binomial random graph. We do this by first showing that the conjecture holds for a general class of graphs with some specific expansion-type properties. This will also be used in a separate paper on random $d$-regular graphs, where we show that the conjecture holds asymptotically almost surely when $d = d(n) \ge 3$.Comment: revised versio

Prediction of infectious disease epidemics via weighted density ensembles

Accurate and reliable predictions of infectious disease dynamics can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task, using different model structures, covariates, and targets for prediction. Experience has shown that the performance of these models varies; some tend to do better or worse in different seasons or at different points within a season. Ensemble methods combine multiple models to obtain a single prediction that leverages the strengths of each model. We considered a range of ensemble methods that each form a predictive density for a target of interest as a weighted sum of the predictive densities from component models. In the simplest case, equal weight is assigned to each component model; in the most complex case, the weights vary with the region, prediction target, week of the season when the predictions are made, a measure of component model uncertainty, and recent observations of disease incidence. We applied these methods to predict measures of influenza season timing and severity in the United States, both at the national and regional levels, using three component models. We trained the models on retrospective predictions from 14 seasons (1997/1998 - 2010/2011) and evaluated each model's prospective, out-of-sample performance in the five subsequent influenza seasons. In this test phase, the ensemble methods showed overall performance that was similar to the best of the component models, but offered more consistent performance across seasons than the component models. Ensemble methods offer the potential to deliver more reliable predictions to public health decision makers.Comment: 20 pages, 6 figure

Family memory and the Crusades

No abstract available