Settling velocities of coarse organic solids.

The settling velocity of a particle is an integral parameter in stormwater modeling and design. The settling velocity can be used to predict the fate and transport of stormwater particles and if the particles contribute to nutrient loading in a watershed. Prediction of settling velocity for inorganic particles is generally well-researched and well-understood. Organic particles tend to vary widely in their physical properties and there are currently no set standards or empirical equations for estimating the settling velocity of organic particles. This paper presents data from tree leaves and seeds settling velocity experiments to better understand how organic particles settle in the context of settling velocity equations such as the one developed by Ferguson and Church. Analysis of the collected data showed that the second of the two drag coefficients (C2) used in the Ferguson and Church Equation was sensitive to particle type and shape. By averaging C2 by particle type and species, there was a correlation between the observed settling velocity and the settling velocity predicted by the Ferguson and Church Equation (R2 = 0.83). With these results, stormwater modelers and designers are equipped with a better understanding of how to represent common organic particles in terms of settling velocity. Additional research on a wider variety of organic particle types and species would expand on the dataset presented here.

Three tips for creating an online course for nontraditional student populations.

In spring 2020, the University of Minnesota Erosion and Stormwater Management Certification Program temporarily ceased in-person workshops due to the spread of COVID-19. Twenty workshops were canceled, and the 1,233 attendees (all adult learners) were moved into asynchronous online course sections. These online workshops were the first remote courses that many of the attendees had ever attempted. Here, we provide tips for successfully creating online classes for nontraditional student populations.

Creating a model program for influenza surveillance in California: results from the 2005-2006 influenza season.

BACKGROUND: Influenza surveillance is valuable for monitoring trends in influenza-related morbidity and mortality. Using the 2005-2006 influenza season as an example, this paper describes a comprehensive influenza surveillance program used by the California Department of Public Health (CDPH). METHODS: Data collected from patients evaluated for acute respiratory illness in a given week were reported and summarized the following week, including (1) electronic hospital pneumonia and influenza admission and antiviral usage records from Kaiser Permanente, (2) sentinel provider influenza-like illness (ILI) reports, (3) severe pediatric influenza case reports (e.g., children either hospitalized in intensive care or expired), (4) school clinic ILI evaluations, and (5) positive influenza test results from a network of academic, hospital, commercial, and public health laboratories and the state CDPH Viral and Rickettsial Disease Laboratory. RESULTS: Influenza activity in California in the 2005-2006 season was moderate in severity; all clinical and laboratory markers rose and fell consistently. Extensive laboratory characterization identified the predominant circulating virus strain as A/California/7/2004(H3N2), which was a component of the 2005-2006 influenza vaccine; 96% of samples tested showed adamantane resistance. CONCLUSIONS: By using multiple, complementary surveillance methods coupled with a strong laboratory component, the CDPH has developed a simple, flexible, stable, and widely accepted influenza surveillance system that can monitor trends in statewide influenza activity, ascertain the correlation between circulating strains with vaccine strains, and assist with detection of new strain variants. The methods described can serve as a model for influenza surveillance in other states.

Brain event-related potentials: diagnosing early-stage Alzheimer's disease.

A pattern of components from brain event-related potentials (ERPs) (cognitive non-invasive electrical brain measures) performed well in separating early-stage Alzheimer's disease (AD) subjects from normal-aging control subjects and shows promise for developing a clinical diagnostic for probable AD. A Number-Letter task elicited brain activity related to cognitive processes. In response to the task stimuli, brain activity was recorded as ERPs, whose components were measured by principal components analysis (PCA). The ERP component scores to relevant and irrelevant stimuli were used in discriminant analyses to develop functions that successfully classified individuals as belonging to an early-stage Alzheimer's disease group or a like-aged Control group, with probabilities of an individual belonging to each group. Applying the discriminant function to the developmental half of the data showed 92% of the subjects were correctly classified into either the AD group or the Control group with a sensitivity of 1.00. The two crossvalidation results were good with sensitivities of 0.83 and classification accuracies of 0.75-0.79. P3 and CNV components, as well as other, earlier ERP components, e.g. C145 and the memory "Storage" component, were useful in the discriminant functions.