Classical conditioning is a method of learning in which a neutral stimulus is repeatedly matched with a naturally occurring stimulus to produce a learnt response. Whereas operant conditioning involves utilizing consequences to change behavior. Operant conditioning, developed by B.F. Skinner, uses positive or negative consequences to increase or decrease behavior frequency. It's widely used in animal training, but only operant conditioning is used in numerical recognition tasks. Classical conditioning is often used with negative stimuli to create an aversive response, but it's easier to implement and has not been fully explored as an alternative. Our research investigates the use of both operant and classical conditioning in teaching numeral recognition to the common freshwater angelfish, Pterophylum sclarae. Two fish are housed and trained individually. Tanks were set up with one end displaying two windows for dot arrays; this end of the tank has a divider down the middle so the fish can only swim up to one set of dots at a time. They are presented with a single dot on one side, three on the other. The classically trained fish was offered a food reward in front of the window displaying three dots. The operantly trained fish was trained using free-shaping techniques, receiving a small reward for each movement towards the correct array, getting a “jackpot” reward when arriving at the correct window. Fish were assigned a score on a 100 point scale, where the number of seconds it took the fish to get to the correct side is subtracted from 100 to determine the score. Preliminary results show that the classically trained fish has higher accuracy and speed in determining the correct array. This demonstrates that the simpler method of classical conditioning may be a feasible training method for cognitive tasks in angelfish.
 

Road salts are commonly applied as deicer during the winter months in Washington and can enter freshwater systems through run-off. The salinized waters can harm aquatic ecosystems. Zooplankton, specifically Daphnia, play a crucial role in providing food to other trophic levels in many ecosystems and could threaten the stability of these systems if they are unable to tolerate salinized waters. We hypothesized that Daphnia exposed to low levels of salt through several generations would better adapt to salinized water than an untreated population over time. Two populations of Daphnia were cultivated in the lab, one control with standard media and another with low levels of additional salt. We then placed these two groups in varying salt concentrations for one week. Under each condition, the survival rate of Daphnia was recorded. Heart rate was also observed as an indicator of physiological stress. We expect the pre-treated Daphnia to adapt to the road salt while the non-treated will have higher mortality rates. Understanding the impact of road salts on Daphnia can help us predict the possible effects on the overall health of aquatic ecosystems.

With the sharp uptick of diagnosed depression cases since the start of COVID-19, it is likely that someone close to us suffers or has suffered from depression. The standby period when starting a new anti-depressant medication exceeds practical justification when patients must often wait two or more weeks before the efficacy of new medication can be assessed. This waiting game often leads to a loss of hope when other options have been unsuccessful. Predicting the effectiveness of a new medication based on a patient’s individualized makeup is vital to the medication onboarding process. Tests based on metabolizing properties and Pharmacogenomic capabilities provide a pathway into predictive care. The ability to assay target genes while identifying subpopulations can simultaneously reduce the likelihood of potential adverse drug reactions, as well as entice pharmaceutical companies to mass-produce medication directed to those with specific mutations to prevent adverse reactions. This research consists of a literature review detailing forms of potential adverse drug reaction prediction. Methods include the use of information from various scientific articles and the recognition of connections and disconnects between sources. It’s expected that metabolizing genes will be the primary component in efficacy prediction due to drug-gene interactions. The implications of reducing the waiting period for those suffering from depression is crucial to patient well-being. As rates of depression diagnoses increase, the implementation of personalized treatment will aid in decreasing the timespan of treatment. Particular drug manufacturers will lose money if their drug is proven ineffective ahead of time for specific individuals, though patients and physicians alike will benefit from quicker treatment options. Current tests are not mainstream due to ongoing research and strict guidelines that must be followed before clinical implementation. As innovation continues, preventative and personalized medicine will be a prominent supplementation to the treatment of individuals with depression.