The everyday weather is forecasted by combining atmospheric data (temperature, humidity, etc.) and our understanding of atmospheric dynamics (land-air-water exchanges etc.). Predictions hinge on the accuracy of the data collected and their spatial resolution. These data can have poor regional scale, so scientists have utilized the natural production of hard structures in the ocean (corals, sea shells, etc.), in a discipline called sclerochronology, to investigate regional climates in parts of the planet where data are limited or hard to obtain. Attend this How-to Talk to learn how the Ocean’s record-keeping of Earth’s climate helps us understand regional climates of the past and future.

Attend this How-to Talk to learn about all the things related to electron microscopy (EM). Dr. Raisch will cover how electron microscopy works, applications in scientific research, and what resources are available at VCU. This talk is perfect for those new to EM or new to EM at VCU.

When corals are under stress from elevated seawater temperatures it can cause oxidative stress which triggers the breakdown of the symbiotic relationship with their resident algae.The algae are expelled and the corals become ghostly white (i.e. coral bleaching). This can lead to coral death and disruptions to an important marine ecosystem. Can treating the oxidative stress save the coral? This talk will investigate whether coral bleaching can be treated with antioxidants delivered by engineered nanoparticles and how to go from theory to field applications.

We’ve all heard about the serious limitations of frequentist statistics: p-hacking, misinterpreted results, and unmet assumptions of normality, etc.. Bayesian statistics presents an alternative. In Bayesian statistics, using the powerful simplicity of Bayes’ theorem and modern computational muscle, the credibility of candidate parameter values is robustly reallocated to be most consistent with the data. In this seminar, after a brief introduction to the fundamentals of Bayesian statistics, we will apply our knowledge to that workhorse of experimental design: a comparison between two groups. We will execute both a standard t-test and a Bayesian estimation to determine the difference between groups, then compare the results.