To determine the frequency, location, size, and risk factors for silent cerebral infarctions (SCIs) on brain CT, we identified 629 patients without a history of previous stroke who were enrolled in a multicenter clinical trial of therapy for acute ischemic stroke. On the baseline CT, 143 patients (22.7%) had SCIs; 34.3% of the lesions were in the right hemisphere, 38.5% in the left hemisphere, and 27.3% were bilateral. The lesion size was < 1 cm in 65.7%, and the most common site was the basal ganglia (48.3%). Patients with SCI were compared with controls without SCI to determine the odds ratios (ORs) for each risk factor. On univariate analysis, race (black versus white) had an OR of 1.80 (95% confidence interval [CI], 1.14 to 2.85), male sex an OR of 1.68 (95% CI, 1.12 to 2.51), and congestive heart failure an OR of 1.88 (95% CI, 1.07 to 3.31). Significant risk factors on multivariate analysis include age (OR 1.03 per year, p = 0.0070), male sex (OR 1.78, p = 0.0094), and race (OR 2.43, p = 0.0004). After including interaction terms with age and hypertension and age, sex, and race, hypertension was also a significant risk factor.

The prevalence of filariae in wild raccoons trapped in southeast Georgia was determined. Examination of blood samples revealed that 74 of 113 raccoons (66%) trapped in 6 southeastern Georgia counties were infected. Seventy-three of these raccoons (65%) were infected with Mansonella llewellyni and this parasite was observed in raccoons from every location examined. Dirofilaria tenuis was found in 22 raccoons (20%) and was observed in only 3 of the 6 counties surveyed. An adult specimen of Acanthocheilonema procyonis was found in the subcutaneous tissues of 1 of 5 necropsied raccoons. This is the first record of filariae in raccoons from Georgia. In addition, Dirofilaria-like larvae were found in Aedes taeniorhyncus mosquitoes collected in Liberty County.

17Increased technological methods have enabled the investigation of biology at nanoscale levels. 18 Nevertheless, such systems necessitate the use of computational methods to comprehend the complex 19 interactions occurring. Traditionally, dynamics of metabolic systems are described by ordinary differential 20 equations producing a deterministic result which neglects the intrinsic heterogeneity of biological systems. 21 More recently, stochastic modeling approaches have gained popularity with the capacity to provide more 22 realistic outcomes. Yet, solving stochastic algorithms tend to be computationally intensive processes. 23 Employing the queueing theory, an approach commonly used to evaluate telecommunication networks, 24 reduces the computational power required to generate simulated results, while simultaneously reducing 25 expansion of errors inherent to classical deterministic approaches. Herein, we present the application of 26 queueing theory to efficiently simulate stochastic metabolic networks. For the current model, we utilize 27 glycolysis to demonstrate the power of the proposed modeling methods, and we describe simulation and 28 pharmacological inhibition in glycolysis to further exemplify modeling capabilities. 30 Author Summary 31Computational biology is increasingly used to understand biological occurances and complex 32 dynamics. Biological modeling, in general, aims to represent a biological system with computational 33 approaches, as realistically and accurate as current methods allow. Metabolomics and metabolic systems 34 have emerged as an important aspect of cellular biology, allowing a more sentive view for understanding 35 the complex interactions occurring intracellularly as a result of normal or perturbed (or diseased) states. To 36 understand metabolic changes, many researchers have commonly used Ordianary Differential Equations to 37 produce in silico models of the in vitro system of interest. While these have been beneficial to date, 38 continuing to advance computational methods of analyzing such systems is of interest. Stochastic models 39 that include randomness have been known to produce more reaslistic results, yet the difficulty and intesive 40 time component urges additional methods and techniques to be developed. In the present research, we 3 41 propose using queueing networks as a technique to model complex metabolic systems, doing such with a 42 model of glycolysis, a core metabolic pathway. 43 44 58 are a representation of reality, aiming to accurately represent the system of study. Inclusion of all cellular 59 components indirectly or directly involved are considered far too complex to model. Consequently, 60 simplifications and assumptions must be made and often the perceived non-pivotal details, such as 61 stochasticity, omitted. Nevertheless, the accuracy and competence of the model is dependent on these 62 assumptions and simplifications. 63 Many approaches may be taken to model the dynamics of metabolic systems; importantly, the 64 categoriza...

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