Browse Articles

Thymoquinone is a compound of great therapeutic potential and scientific interest. However, its clinical administration and synthetic modifications are greatly limited by its instability in the presence of light. This study employed quantitative 1H nuclear magnetic resonance (NMR) spectroscopy to identify the effect of solvation on the degradation of thymoquinone under ultraviolet light (UV). It found that the rate of degradation is highly solvent dependent occurs maximally in chloroform.

The authors use pictures of candy wrappers and neural networks to improve nutritional accuracy of diet-tracking apps.

Several studies have applied different machine learning (ML) techniques to the area of forecasting solar photovoltaic power production. Most of these studies use weather data as inputs to predict power production; however, there are numerous practical issues with the procurement of this data. This study proposes models that do not use weather data as inputs, but rather use past power production data as a more practical substitute to weather-based models. Our proposed models demonstrate a better, cheaper, and more reliable alternatives to current weather models.

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 article, the authors explored the locomotory movement of Euglena sp. and Tetrahymena pyriformis in response to light. Such research bears relevance to the migration and distribution patterns of both T. pyriformis and Euglena as they differ in their method of finding sustenance in their native environments. With little previous research done on the exploration of a potential response to photostimulation enacted by T. pyriformis, the authors found that T. pyriformis do not bias in distribution towards areas of light - unlike Euglena, which displayed an increased prevalence in areas of light.

Chlorella sp. are unicellular green algae that use photosynthesis to reduce carbon dioxide into glucose. In this study, authors sought to determine the temperature that Chlorella sp. is maximally efficient at photosynthesis, and therefore removing the most carbon dioxide from the system. This activity could be harnessed to naturally remove carbon dioxide from the environment, fighting the effects of climate change.

Berberine is a natural product isoquinoline alkaloid derived from plants of the genus Berberis. When exposed to photoirradiation, it produces singlet oxygen through photosensitization of triplet oxygen. Through qNMR analysis of ¹H NMR spectra gathered through kinetic experiments, we were able to track the generation of a product between singlet oxygen and alpha terpinene, allowing us to quantitatively measure the photosensitizing properties of our scaffolds.

In this study, the authors investigate whether phototaxis and odortaxis in Drosophila melanogaster occurs through linear summation of cues including light and attractive odorants.

Here, seeking to better understand how facial features provide important visual cues to help convey emotions, the authors evaluated the accuracy and reaction time of participants in regards to experimental photographs where a person's eyebrows were obscured and ones where they were not. Their findings revealed that removing eyebrows resulted in a significant decrease in a participant's ability to recognize anger, with adolescents most likely to misidentify emotions.

Plant diseases can cause up to 50% crop yield loss for the popular tomato plant. A mobile device-based method to identify diseases from photos of symptomatic leaves via computer vision can be more effective due to its convenience and accessibility. To enable a practical mobile solution, a “shallow” convolutional neural networks (CNNs) with few layers, and thus low computational requirement but with high accuracy similar to the deep CNNs is needed. In this work, we explored if such a model was possible.