“What Happens When Pollinators Get Sick? Behavioral Impacts of Honeybee Viruses”

Like all organisms on the planet, honeybees (Apis mellifera) are susceptible to infection with a wide variety of viruses. These viruses may produce infections with no visible symptoms or may have devastating consequences on both the individual bee and the entire hive. Deformed Wing Virus, a member of the Iflavirus group of viruses, has an RNA genome and has had a particularly important impact on bee health. It can be spread between bees in a several ways – bees can infect each other during feeding or grooming activities, drones can pass the virus to the queen during mating and queens can lay infected eggs. The primary and most devastating way that these viruses are transmitted within and between hives involves a parasitic mite, an animal known ominously as Varroa destructor. The talk will discuss the effect that viruses have on the health and behavior of honeybees and will outline the collaborative research activities of Drs. Evans and Pizzorno over the last 7 years.

Measuring Correlation Between HIV Sequence Variation and Disease Stage of HIV-Infected Individuals

With a virus such as Human Immunodeficiency Virus (HIV) that has infected millions of people worldwide, and with many unaware that they are infected, it becomes vital to understand how the virus works and how it functions at the molecular level. Because there currently is no vaccine and no way to eradicate the virus from an infected person, any information about how the virus interacts with its host greatly increases the chances of understanding how HIV works and brings scientists one step closer to being able to combat such a destructive virus. Thousands of HIV viruses have been sequenced and are available in many online databases for public use. Attributes that are linked to each sequence include the viral load within the host and how sick the patient is currently. Being able to predict the stage of infection for someone is a valuable resource, as it could potentially aid in treatment options and proper medication use. Our approach of analyzing region-specific amino acid composition for select genes has been able to predict patient disease state up to an accuracy of 85.4%. Moreover, we output a set of classification rules based on the sequence that may prove useful for diagnosing the expected clinical outcome of the infected patient.