Nitrogen-fixing bacteria, such as the legume mutualist rhizobia, convert atmospheric nitrogen into a form that is usable by living organisms. Leguminous plants, like the model species Medicago truncatula, directly benefit from this process by forming a symbiotic relationship with rhizobia. Here, Rathod and Rowe investigate how M. truncatula responds to non-rhizobial bacterial partners.

As the world struggled to grapple with the emerging COVID-19 pandemic in 2020, many countries instated policies to help minimize the spread of the virus among residents. This inadvertently led to a decrease in travel, and in some cases, industrial output, two major sources of pollutants in today's world. Here, the authors investigate whether California's shelter-in-place policy was associated with a measurable decrease in water and air pollution in that state between June and July of 2020, compared to the preceeding five years. Their findings suggest that, by some metrics, air quality improved within certain areas while water quality was relatively unchanged. Overall, these findings suggest that changing human behavior can, indeed, help reduce the level of air pollutants that compromise air quality.

The disinterested willingness a person has for helping others is known as altruism. But is this willingness to help others dependent on external factors that make you more or less inclined to be generous? We hypothesized that generosity in adolescents would depend on external factors and that these factors would change the amount of help given. To evaluate altruism and generosity, we conducted non-anonymous and anonymous variations of the dictator game and ultimatum game experiments and explored the role of anonymity, fairness, and reciprocity in high school students.

Since school bathrooms are widely suspected to be unsanitary, we wanted to compare the total amount of bacteria with the amount of bacteria that had ampicillin or streptomycin resistance across different school bathrooms in the Boston area. We hypothesized that because people interact with the faucet, outdoor handle, and indoor handle of the bathroom, based on whether or not they have washed their hands, there would be differences in the quantity of the bacteria presented on these surfaces. Therefore, we predicted certain surfaces of the bathroom would be less sanitary than others.

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.

Household detergents have surfactants that can potentially harm the soil and broader ecosystems. In this study, the authors investigate whether eco-friendly and less-eco-friendly detergents affect soil pH, phosphorus, nitrogen, and potassium levels.

Minimal Change Disease (MCD) is a degenerative kidney disease. Researchers know very little about the cause of this disorder, however some research has suggested that T lymphocytes may be involved. In this study, the authors measure CD4 and CD8 T cell subpopulations in patients with MCD to investigate whether irregular T lymphocyte populations may be involved in MCD pathogenesis.

In this paper, we measured the privacy budgets and utilities of different differentially private mechanisms combined with different machine learning models that forecast traffic congestion at future timestamps. We expected the ANNs combined with the Staircase mechanism to perform the best with every value in the privacy budget range, especially with the medium high values of the privacy budget. In this study, we used the Autoregressive Integrated Moving Average (ARIMA) and neural network models to forecast and then added differentially private Laplacian, Gaussian, and Staircase noise to our datasets. We tested two real traffic congestion datasets, experimented with the different models, and examined their utility for different privacy budgets. We found that a favorable combination for this application was neural networks with the Staircase mechanism. Our findings identify the optimal models when dealing with tricky time series forecasting and can be used in non-traffic applications like disease tracking and population growth.

One disadvantage of antibiotic therapy is the potential for unpleasant gastrointestinal side effects. Here, the authors test whether some common antibiotics directly interfere with the digestion of protein, fat, or sugars. This study provides motivation to more carefully investigate the interactions between antibiotics and gut enzymes in order to inform treatment decisions and improve patient outcomes.

The most common atopic disease of the upper respiratory tract is allergic rhinitis. It is defined as a chronic inflammatory condition of nasal mucosa due to the effects of one or more allergens and is usually a long-term problem. The purpose of our study was to test the efficiency of apitherapy in allergic rhinitis healing by the application of honey bee pollen. Apitherapy is a branch of alternative medicine that uses honey bee products. Honey bee pollen can act as an allergen and cause new allergy attacks for those who suffer from allergic rhinitis. Conversely, we hoped to prove that smaller ingestion of honey bee pollen on a daily basis would desensitize participants to pollen and thus reduce the severity of allergic rhinitis.