Invasive fungal infections among organ transplant recipients: results of the Transplant-Associated Infection Surveillance Network (TRANSNET).

BACKGROUND: Invasive fungal infections (IFIs) are a major cause of morbidity and mortality among organ transplant recipients. Multicenter prospective surveillance data to determine disease burden and secular trends are lacking. METHODS: The Transplant-Associated Infection Surveillance Network (TRANSNET) is a consortium of 23 US transplant centers, including 15 that contributed to the organ transplant recipient dataset. We prospectively identified IFIs among organ transplant recipients from March, 2001 through March, 2006 at these sites. To explore trends, we calculated the 12-month cumulative incidence among 9 sequential cohorts. RESULTS: During the surveillance period, 1208 IFIs were identified among 1063 organ transplant recipients. The most common IFIs were invasive candidiasis (53%), invasive aspergillosis (19%), cryptococcosis (8%), non-Aspergillus molds (8%), endemic fungi (5%), and zygomycosis (2%). Median time to onset of candidiasis, aspergillosis, and cryptococcosis was 103, 184, and 575 days, respectively. Among a cohort of 16,808 patients who underwent transplantation between March 2001 and September 2005 and were followed through March 2006, a total of 729 IFIs were reported among 633 persons. One-year cumulative incidences of the first IFI were 11.6%, 8.6%, 4.7%, 4.0%, 3.4%, and 1.3% for small bowel, lung, liver, heart, pancreas, and kidney transplant recipients, respectively. One-year incidence was highest for invasive candidiasis (1.95%) and aspergillosis (0.65%). Trend analysis showed a slight increase in cumulative incidence from 2002 to 2005. CONCLUSIONS: We detected a slight increase in IFIs during the surveillance period. These data provide important insights into the timing and incidence of IFIs among organ transplant recipients, which can help to focus effective prevention and treatment strategies.

Evaluating functional network inference using simulations of complex biological systems.

MOTIVATION: Although many network inference algorithms have been presented in the bioinformatics literature, no suitable approach has been formulated for evaluating their effectiveness at recovering models of complex biological systems from limited data. To overcome this limitation, we propose an approach to evaluate network inference algorithms according to their ability to recover a complex functional network from biologically reasonable simulated data. RESULTS: We designed a simulator to generate data representing a complex biological system at multiple levels of organization: behaviour, neural anatomy, brain electrophysiology, and gene expression of songbirds. About 90% of the simulated variables are unregulated by other variables in the system and are included simply as distracters. We sampled the simulated data at intervals as one would sample from a biological system in practice, and then used the sampled data to evaluate the effectiveness of an algorithm we developed for functional network inference. We found that our algorithm is highly effective at recovering the functional network structure of the simulated system-including the irrelevance of unregulated variables-from sampled data alone. To assess the reproducibility of these results, we tested our inference algorithm on 50 separately simulated sets of data and it consistently recovered almost perfectly the complex functional network structure underlying the simulated data. To our knowledge, this is the first approach for evaluating the effectiveness of functional network inference algorithms at recovering models from limited data. Our simulation approach also enables researchers a priori to design experiments and data-collection protocols that are amenable to functional network inference.

An Empirically Based Stochastic Turbulence Simulator with Temporal Coherence for Wind Energy Applications

In this dissertation, we develop a novel methodology for characterizing and simulating nonstationary, full-field, stochastic turbulent wind fields.

In this new method, nonstationarity is characterized and modeled via temporal coherence, which is quantified in the discrete frequency domain by probability distributions of the differences in phase between adjacent Fourier components.

The empirical distributions of the phase differences can also be extracted from measured data, and the resulting temporal coherence parameters can quantify the occurrence of nonstationarity in empirical wind data.

This dissertation (1) implements temporal coherence in a desktop turbulence simulator, (2) calibrates empirical temporal coherence models for four wind datasets, and (3) quantifies the increase in lifetime wind turbine loads caused by temporal coherence.

The four wind datasets were intentionally chosen from locations around the world so that they had significantly different ambient atmospheric conditions.

The prevalence of temporal coherence and its relationship to other standard wind parameters was modeled through empirical joint distributions (EJDs), which involved fitting marginal distributions and calculating correlations.

EJDs have the added benefit of being able to generate samples of wind parameters that reflect the characteristics of a particular site.

Lastly, to characterize the effect of temporal coherence on design loads, we created four models in the open-source wind turbine simulator FAST based on the \windpact turbines, fit response surfaces to them, and used the response surfaces to calculate lifetime turbine responses to wind fields simulated with and without temporal coherence.

The training data for the response surfaces was generated from exhaustive FAST simulations that were run on the high-performance computing (HPC) facilities at the National Renewable Energy Laboratory.

This process was repeated for wind field parameters drawn from the empirical distributions and for wind samples drawn using the recommended procedure in the wind turbine design standard \iec.

The effect of temporal coherence was calculated as a percent increase in the lifetime load over the base value with no temporal coherence.