The authors investigated prophages present in Streptococcus bacteria that may increase their survival in different environments.

In order for cells to successfully multiply, a number of proteins are needed to correctly coordinate the replication and division process. In this study, students use fluorescence microscopy and molecular methods to study CCDC11, a protein critical in the formation of cilia. Interestingly, they uncover a new role for CCDC11, critical in the cell division across multiple human cell lines.

Here, recognizing the recognizing the growing threat of non-biodegradable plastic waste, the authors investigated the ability to use a modified enzyme identified in bacteria to decompose polyethylene terephthalate (PET). They used simulations to screen and identify an optimized enzyme based on machine learning models. Ultimately, they identified a potential mutant PETases capable of decomposing PET with improved thermal stability.

In this study, the authors investigate a potential case of cross antibiotic-resistance. Using swabs from an individual who received long-term treatments of azithromycin, they addressed the question of whether any bacteria in this individual might develop resistance to not only azithromycin, but also other antibiotics with similar structures. This study cleverly addresses the important issue of antibiotic resistance from a new and thoughtful approach.

Using facial recognition as a use-case scenario, we attempt to identify sources of bias in a model developed using transfer learning. To achieve this task, we developed a model based on a pre-trained facial recognition model, and scrutinized the accuracy of the model’s image classification against factors such as age, gender, and race to observe whether or not the model performed better on some demographic groups than others. By identifying the bias and finding potential sources of bias, his work contributes a unique technical perspective from the view of a small scale developer to emerging discussions of accountability and transparency in AI.

Long hospital stays can be stressful for the patient for many reasons. We hypothesized that age would be the greatest predictor of hospital stay among patients who underwent orthopedic surgery. Through our models, we found that severity of illness was indeed the highest factor that contributed to determining patient length of stay. The other two factors that followed were the facility that the patient was staying in and the type of procedure that they underwent.

Here, the authors sought to develop a new metric to evaluate the efficacy of baseball pitchers using machine learning models. They found that the frequency of balls, was the most predictive feature for their walks/hits allowed per inning (WHIP) metric. While their machine learning models did not identify a defining trait, such as high velocity, spin rate, or types of pitches, they found that consistently pitching within the strike zone resulted in significantly lower WHIPs.

Here, seeking to better understand the genetic associations underlying non-small cell lung cancer, the authors screened hundreds of genes, identifying that KCNMB2 upregulation was significantly correlated with poor prognoses in lung cancer patients. Based on this, they used small interfering RNA to decrease the expression of KCNMB2 in A549 lung cancer cells, finding decreased cell proliferation and increased lung cancer cell death. They suggest this could lead to a new potential target for lung cancer therapies.

Here, seeking to develop more efficient solar cells, the authors investigated photo-electrochemical (PEC) solar cells, specifically molybdenum diselenide (MoSe₂) based on its high resistance to corrosion. They found that the percentage efficiency of these PEC solar cells was proportional to light intensity^–0.9 and that performance was positively influenced by increasing the electrolyte volume. They suggest that studies such as these can lead to new insight into reaction-based solar cells.

The authors looked at the relation between stress and motivation in high school students with 4+ AP classes that also played a varsity sport. No distinct correlation was observed, however, results indicated that there are other factors at play that influence both stress and motivation.