Role of Positron Emission Tomography in Imaging of Non-neurologic Disorders of the Head, Neck, and Teeth in Veterinary Medicine.

Positron Emission Tomography (PET) is an imaging technique that provides functional information, in addition to structural information obtained with computed tomography (CT). The most common application is cancer staging, using 18F-Fluorodeoxyglucose (18F-FDG), a radioactive analog of glucose. Although limited data are available in the veterinary literature, human studies have demonstrated benefit with the addition of PET both for assessment of the primary tumor and for detection of metastatic disease. 18F-FDG PET appears to be more accurate at detecting the margin of oral neoplasia, in particular for tumors arising from highly vascularized tissue, such as the lingual and laryngeal areas. 18F-FDG PET has a high sensitivity for the detection of lymph node metastasis, however the specificity is variable between studies. Tracers beyond 18F-FDG can also be used for oncology imaging. 18F-Fluoride (18F-NaF) is an excellent osseous tracer, useful in assessing bone involvement of primary tumors or osseous metastasis. Other specific tracers can be used to assess cell proliferation or hypoxia for tumor characterization. 18F-FDG is also an excellent tracer for detection of inflammation. Human studies have demonstrated its value for the assessment of periodontitis and dental implant infection. 18F-NaF has been used to assess disorders of the temporomandibular joint in the human literature, demonstrating good correlation with arthralgia and therapeutic outcome. Both 18F-NaF and 18F-FDG had good concordance with localization of cervical pain in people. PET will likely have a growing role in veterinary medicine not only for oncologic imaging but also for assessment of inflammation and pain

Illuminating Lewis acidity strength

In this issue of Chem, Baumgartner, Caputo and co-workers describe a simple naked-eye litmus test for the determination of Lewis acid strength. This technique is based on fluorescence via highly tunable, luminescent phosphole oxide Lewis basic probes

Surrogate Modeling for Uncertainty Assessment with Application to Aviation Environmental System Models

Numerical simulation models to support decision-making and policy-making processes are often complex, involving many disciplines, many inputs, and long computation times. Inputs to such models are inherently uncertain, leading to uncertainty in model outputs. Characterizing, propagating, and analyzing this uncertainty is critical both to model development and to the effective application of model results in a decision-making setting; however, the many thousands of model evaluations required to sample the uncertainty space (e.g., via Monte Carlo sampling) present an intractable computational burden. This paper presents a novel surrogate modeling methodology designed specifically for propagating uncertainty from model inputs to model outputs and for performing a global sensitivity analysis, which characterizes the contributions of uncertainties in model inputs to output variance, while maintaining the quantitative rigor of the analysis by providing confidence intervals on surrogate predictions. The approach is developed for a general class of models and is demonstrated on an aircraft emissions prediction model that is being developed and applied to support aviation environmental policy-making. The results demonstrate how the confidence intervals on surrogate predictions can be used to balance the tradeoff between computation time and uncertainty in the estimation of the statistical outputs of interest.United States. Federal Aviation Administration (contract no. DTFAWA-05-D-00012, Task Order 0002

A MATHEMATICAL AND COMPUTATIONAL FRAMEWORK FOR MULTIFIDELITY DESIGN AND ANALYSIS WITH COMPUTER MODELS

A multifidelity approach to design and analysis for complex systems seeks to exploit optimally all available models and data. Existing multifidelity approaches generally attempt to calibrate low-fidelity models or replace low-fidelity analysis results using data from higher fidelity analyses. This paper proposes a fundamentally different approach that uses the tools of estimation theory to fuse together information from multifidelity analyses, resulting in a Bayesian-based approach to mitigating risk in complex system design and analysis. This approach is combined with maximum entropy characterizations of model discrepancy to represent epistemic uncertainties due to modeling limitations and model assumptions. Mathematical interrogation of the uncertainty in system output quantities of interest is achieved via a variance-based global sensitivity analysis, which identifies the primary contributors to output uncertainty and thus provides guidance for adaptation of model fidelity. The methodology is applied to multidisciplinary design optimization and demonstrated on a wing-sizing problem for a high altitude, long endurance vehicle.United States. Air Force Office of Scientific Research. Small Business Technology Transfer Program (Contract FA9550-09-C-0128

Distributional sensitivity analysis

Among the uses for global sensitivity analysis is factor prioritization. A key assumption for this is that a given factor can, through further research, be fixed to some point on its domain. For factors containing epistemic uncertainty, this is an optimistic assumption, which can lead to inappropriate resource allocation. Thus, this research develops an original method, referred to as distributional sensitivity analysis, that considers which factors would on average cause the greatest reduction in output variance, given that the portion of a particular factor's variance that can be reduced is a random variable. A key aspect of the method is that the analysis is performed directly on the samples that were generated during a global sensitivity analysis using acceptance/rejection sampling. In general, if for each factor, N model runs are required for a global sensitivity analysis, then those same N model runs are sufficient for a distributional sensitivity analysis

A decomposition-based approach to uncertainty analysis of feed-forward multicomponent systems

To support effective decision making, engineers should comprehend and manage various uncertainties throughout the design process. Unfortunately, in today's modern systems, uncertainty analysis can become cumbersome and computationally intractable for one individual or group to manage. This is particularly true for systems comprised of a large number of components. In many cases, these components may be developed by different groups and even run on different computational platforms. This paper proposes an approach for decomposing the uncertainty analysis task among the various components comprising a feed-forward system and synthesizing the local uncertainty analyses into a system uncertainty analysis. Our proposed decomposition-based multicomponent uncertainty analysis approach is shown to be provably convergent in distribution under certain conditions. The proposed method is illustrated on quantification of uncertainty for a multidisciplinary gas turbine system and is compared to a traditional system-level Monte Carlo uncertainty analysis approach.SUTD-MIT International Design CentreUnited States. Defense Advanced Research Projects Agency. META Program (United States. Air Force Research Laboratory Contract FA8650-10-C-7083)Vanderbilt University (Contract VU-DSR#21807-S7)United States. Federal Aviation Administration. Office of Environment and Energy (FAA Award 09-C-NE-MIT, Amendments 028, 033, and 038

Modeling of Transitional Channel Flow Using Balanced Proper Orthogonal Decomposition

We study reduced-order models of three-dimensional perturbations in linearized channel flow using balanced proper orthogonal decomposition (BPOD). The models are obtained from three-dimensional simulations in physical space as opposed to the traditional single-wavenumber approach, and are therefore better able to capture the effects of localized disturbances or localized actuators. In order to assess the performance of the models, we consider the impulse response and frequency response, and variation of the Reynolds number as a model parameter. We show that the BPOD procedure yields models that capture the transient growth well at a low order, whereas standard POD does not capture the growth unless a considerably larger number of modes is included, and even then can be inaccurate. In the case of a localized actuator, we show that POD modes which are not energetically significant can be very important for capturing the energy growth. In addition, a comparison of the subspaces resulting from the two methods suggests that the use of a non-orthogonal projection with adjoint modes is most likely the main reason for the superior performance of BPOD. We also demonstrate that for single-wavenumber perturbations, low-order BPOD models reproduce the dominant eigenvalues of the full system better than POD models of the same order. These features indicate that the simple, yet accurate BPOD models are a good candidate for developing model-based controllers for channel flow.Comment: 35 pages, 20 figure

Evaluation and pharmacovigilance of projects promoting cultivation and local use of Artemisia annua for malaria

<p>Abstract</p> <p>Background</p> <p>Several non-governmental organisations (NGOs) are promoting the use of <it>Artemisia annua </it>teas as a home-based treatment for malaria in situations where conventional treatments are not available. There has been controversy about the effectiveness and safety of this approach, but no pharmacovigilance studies or evaluations have been published to date.</p> <p>Method</p> <p>A questionnaire about the cultivation of <it>A. annua</it>, treatment of patients, and side-effects observed, was sent to partners of the NGO Anamed in Kenya and Uganda. Some of the respondents were then selected purposively for more in-depth semi-structured interviews.</p> <p>Results</p> <p>Eighteen partners in Kenya and 21 in Uganda responded. 49% reported difficulties in growing the plant, mainly due to drought. Overall about 3,000 cases of presumed malaria had been treated with <it>A. annua </it>teas in the previous year, of which about 250 were in children and 54 were in women in the first trimester of pregnancy. The commonest problem observed in children was poor compliance due to the bitter taste, which was improved by the addition of sugar or honey. Two miscarriages were reported in pregnant patients. Only four respondents reported side-effects in other patients, the commonest of which was vomiting. 51% of respondents had started using <it>A. annua </it>tea to treat illnesses other than malaria.</p> <p>Conclusions</p> <p>Local cultivation and preparation of <it>A. annua </it>are feasible where growing conditions are appropriate. Few adverse events were reported even in children and pregnant women. Where ACT is in short supply, it would make sense to save it for young children, while using <it>A. annua </it>infusions to treat older patients who are at lower risk. An ongoing pharmacovigilance system is needed to facilitate reporting of any adverse events.</p