Catalase is a critical enzyme in the human body because it is capable of converting potentially dangerous hydrogen peroxide into water and oxygen. This work asks whether ethanol affects catalase activity, as alcohol consumption has been often linked to hepatitis occurring in the liver, where catalase level is especially high, and ethanol is known to be capable of denaturing proteins. Testing different concentrations of ethanol found that higher concentrations reduced the activity of catalase. This work has important implications on the negative effects of ethanol on metabolism, in which catalase plays an important role, and protein function more broadly.
In this study, the authors investigate the effect of a herbal formulation on Cyclooxygenase-2 (COX-2) expression in cancer cells. High levels of COX-2 correlates with worsened cancer outcomes and the authors hypothesize that the formulation will inhibit COX-2 levels.
Water scarcity affects upwards of a billion people worldwide today. This project leverages the potential of capturing humidity to build a high-efficiency water condensation device that can generate water and be used for personal and commercial purposes. This compact environment-friendly device would have low power requirements, which would potentially allow it to utilize renewable energy sources and collect water at the most needed location.
In this study, the authors study features of exoplanet 189733 b. This exoplanet, or planets that orbit stars other than the Sun, is found in the HD star system. Using a DSLR camera, they constructed a high caliber exoplanet transit detection tracker to study the orbital periods, radial velocity, and photometry of 189733 b. They then compared results from their system to data collected by other high precision studies. What they found was that their system produced results supporting previously published studies. These results are exciting results from the solar system demonstrating the importance of validating radial velocity and photometry data using high-precision studies.
In this study, the authors investigate whether antibiotic-resistant bacteria can be found in everyday locations. To do this, they collected samples from multiple high-trafficked areas in Cambridge, MA and grew them in the presence and absence of antibiotics. Interestingly, they grew bacterial colonies from many locations' samples, but not all could grow in the presence of ampicillin. These findings are intriguing and relevant given the rising concern about antibiotic-resistant bacteria.
Previously established data indicate that cemeteries have contributed to groundwater and soil pollution, as embalming fluids can impact the microbiomes that exist in decomposing remains. In this study, Caputo et al hypothesized that microbial variation would be high between cemeteries from different eras due to dissimilarities between embalming techniques employed, and furthermore, that specific microbes would act as an indication for certain contaminants. Overall, they found that there is a variation in the microbiomes of the different eras’ cemeteries according to the concentrations of the phyla and their more specific taxa.
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder and is difficult to diagnose in young children. Here magnetoencephalography was used to compare the brain activity in patients with ASD to patients in a control group. The results show that patients with ASD have a high level of activity in different areas of the brain than those in the control group.
Recent research confirms that climate change, driven by CO2 emissions from burning fossil fuels, poses a significant threat to humanity. In response, authors explore methods to remove CO2 from the atmosphere, including breaking its molecular bonds through high-speed collisions.
Although there has been great progress in the field of Natural language processing (NLP) over the last few years, particularly with the development of attention-based models, less research has contributed towards modeling keystroke log data. State of the art methods handle textual data directly and while this has produced excellent results, the time complexity and resource usage are quite high for such methods. Additionally, these methods fail to incorporate the actual writing process when assessing text and instead solely focus on the content. Therefore, we proposed a framework for modeling textual data using keystroke-based features. Such methods pay attention to how a document or response was written, rather than the final text that was produced. These features are vastly different from the kind of features extracted from raw text but reveal information that is otherwise hidden. We hypothesized that pairing efficient machine learning techniques with keystroke log information should produce results comparable to transformer techniques, models which pay more or less attention to the different components of a text sequence in a far quicker time. Transformer-based methods dominate the field of NLP currently due to the strong understanding they display of natural language. We showed that models trained on keystroke log data are capable of effectively evaluating the quality of writing and do it in a significantly shorter amount of time compared to traditional methods. This is significant as it provides a necessary fast and cheap alternative to increasingly larger and slower LLMs.
A central challenge of cancer therapy is identifying treatments that will effectively target cancer cells while minimizing effects on healthy cells. To identify potential targets for treating a multiple myeloma, a frequently incurable cancer, Kochenderfer and Kochenderfer analyze RNA sequencing data from the Cancer Cell Line Encyclopedia to find genes with high expression in multiple myeloma cells and low expression in normal tissues
