Recent evidence suggests insects might indirectly provide symbionts to their offspring by releasing them into the soil via feces. We investigated whether this is occurring in the Eastern leaf-footed bug, Leptoglosuss phyllopus. We hypothesized that if adults release viable symbiont cells into their environment, then nymphs will have a higher survival rate, develop faster, and be larger at adulthood when exposed to soil that has been in contact with adult insects for longer. To measure the rate and whether the bugs acquired Caballeronia from the soil, insect nymphs were exposed to soil that had been exposed to a lab colony of L. phyllopus for different lengths of time. After exposing the nymphs to the colony soil, their development time, survival, and mass in adulthood were measured. Contrary to our hypothesis, we observed that nymph survival decreased as the length of time soil was exposed to the lab colony increased. We speculate that diseases carried by adult insects in the colony might have overwhelmed any possible positive effect of the adults inoculating soil with the symbiont. By studying this symbiosis within insect host species, we can improve our knowledge of the complexities of microorganisms and how they shape the physiology of their host and its community.
Health professionals must diagnose correctly to avoid unnecessary costs in care and medicine. Different conditions, come with different treatments. Therefore, it is key to produce the right diagnoses promptly to help treat people appropriately and quickly. As lung cancer is the leading cancer type in the world, it is often diagnosed too late once symptoms appear which becomes difficult to treat at certain stages. In the race against time, recruiting the use of machine learning next to traditional methods of diagnosing will improve the confidence of professionals to predict specific conditions accurately and with speed. For our research, we will develop a convolutional neural network (CNN) model using the TinyVGG architecture to classify subtypes of lung cancer taken anonymously from patients. Pictures will be taken during the 10 days of incubating these cells. Over time, we will look at the results and determine if there are unique overgrowth patterns from different lung cancer cell lines. Alongside this, we will measure the accuracy of the CNN model and its ability to detect features that are not obvious to the human eye. This will aid in the understanding of this model by optimizing and adjusting its parameters to help classify these tumor growths correctly. A simultaneous goal is to determine if these unique overgrowth patterns are correlated with specific activation of certain oncogenes.
