In this study, the authors investigate the effects of acidity on the survival of commercial probiotic Lovebug bacterial strains.

Supernovas are powerful explosions that result from gravitational collapse of a massive star. Using photometric analysis Arora et al. set out to investigate whether 2020pni (located in galaxy UGC 9684) was a supernova. They were ultimately able to identify 2020pni as a Type II-L supernova and determine it's distance from earth.

In this study, the authors investigate whether Eisenia Fetida nerve signal speed correlates with Withania somnifera ingestion, a possible way to protect against demyelination.

E-cigarettes are often considered a healthier alternative to traditional cigarettes. This team of high school authors investigated the impact of common e-cigarette compounds on C. elegans, and found a number of harmful effects ultimately resulting in injury and neuronal damage.

Chronic alcohol consumption can cause cardiac myopathy, which afflicts about 500,000 Americans annually. Gunturi et al. wanted to understand the effects of alcohol on heart rate and confirm the role of nitric oxide (NO) signaling in heart rate regulation. Using the model organism Daphnia magna, a water crustacean with a large, transparent heart, they found that the heart rate of Daphnia magna was reduced after treatment with alcohol. This depression could be reversed after treatment with inhibitors of NO synthesis and signaling. Their work has important implications for how we understand alcohol-induced effects on heart rate and potential treatments to reverse heart rate depression as a result of alcohol consumption.

Van der Woude syndrome is a common birth defect caused by mutations in the gene Irf6. In this project, students used microarray expression analysis from wild-type and Irf6-deficient mice in order to identify gene networks or pathways differentially regulated due to the Irf6 mutation. They found NF-κB pathway to be activated in deficient mice.

In this study the authors looked at developing a more efficient particle collision classification method with the goal of being able to more efficiently analyze particle trajectories from large-scale particle collisions without loss of accuracy.

Metal-organic frameworks (MOFs) are promising new nanomaterials for use in the fight against climate change that can efficiently capture and convert CO₂ to other useful carbon products. This research used computational models to determine the reaction conditions under which MOFs can more efficiently capture and convert CO₂. In a cost-efficient manner, this analysis tested the hypothesis that pressure and temperature affect the efficacy of carbon capture and conversion, and contribute to understanding the optimal conditions for MOF performance to improve the use of MOFs for controlling greenhouse CO₂ emissions.

Based on the success of deep learning, recent works have attempted to develop a waste classification model using deep neural networks. This work presents federated learning (FL) for a solution, as it allows participants to aid in training the model using their own data. Results showed that with less clients, having a higher participation ratio resulted in less accuracy degradation by the data heterogeneity.

Here, in an effort to develop a model to predict future groundwater levels, the authors tested a tree-based automated artificial intelligence (AI) model against other methods. Through their analysis they found that groundwater levels in Texas aquifers are down significantly, and found that tree-based AI models most accurately predicted future levels.