Climate records indicate that there has been a trend of decreasing annual snowfall totals throughout the United States during the peak winter season. However, New Jersey has seen a significant increase in snowfall over the past 126 years of recorded observations. The authors hypothesize that although annual snowfall has remained the same on average, the frequencies of major and minor snowfall events have noticeably increased. They found that there was no significant evidence for an increase in the frequency of minor events (1.1-inch to 4.0-inch events), but there was evidence for an increase in the frequency of major events (4.1+ inch events). The results imply that a warming climate might be opening up opportunities for more snowfall.
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.
A key barrier to adoption of solar energy technology is the low efficiency of solar cells converting solar energy into electricity. Sims and Sims tackle this problem by coding a Raspberry Pi as a multimeter to determine which wavelength of light generates the most voltage and current from a solar panel.
Greenspaces (urban and wetland areas that contain vegetation) are beneficial to reducing pollution, while airplanes are a highly-polluting method of transportation. The authors examine the intersection of these two environmental factors by processing satellite images to reveal what percentage of flight paths go over greenspaces at major US airports.
Coral bleaching is a fatal process that reduces coral diversity, leads to habitat loss for marine organisms, and is a symptom of climate change. This process occurs when corals expel their symbiotic dinoflagellates, algae that photosynthesize within coral tissue providing corals with glucose. Restoration efforts have attempted to repair damaged reefs; however, there are over 360,000 square miles of coral reefs worldwide, making it challenging to target conservation efforts. Thus, predicting the likelihood of bleaching in a certain region would make it easier to allocate resources for conservation efforts. We developed a machine learning model to predict global locations at risk for coral bleaching. Data obtained from the Biological and Chemical Oceanography Data Management Office consisted of various coral bleaching events and the parameters under which the bleaching occurred. Sea surface temperature, sea surface temperature anomalies, longitude, latitude, and coral depth below the surface were the features found to be most correlated to coral bleaching. Thirty-nine machine learning models were tested to determine which one most accurately used the parameters of interest to predict the percentage of corals that would be bleached. A random forest regressor model with an R-squared value of 0.25 and a root mean squared error value of 7.91 was determined to be the best model for predicting coral bleaching. In the end, the random model had a 96% accuracy in predicting the percentage of corals that would be bleached. This prediction system can make it easier for researchers and conservationists to identify coral bleaching hotspots and properly allocate resources to prevent or mitigate bleaching events.
A primary cause of diabetes is insulin resistance, which is caused by disruption of insulin signal transduction. The objective of this study was to maximize insulin sensitivity by creating a more effective, early intervention-based treatment to avert severe T2D. This treatment combined metformin, “the insulin sensitizer”, and medicinal plants, curcumin, fenugreek, and nettle.
Here, recognizing the difficulty associated with tracking the progression of dementia, the authors used machine learning models to predict between the presence of cognitive normalcy, mild cognitive impairment, and Alzheimer's Disease, based on blood DNA methylation levels, sex, and age. With four machine learning models and two dataset dimensionality reduction methods they achieved an accuracy of 53.33%.
In this study, the authors investigated the biological mechanism underlying the actions of a traditional medicinal plant, Astragalus membranaceus. Using C. elegans as an experimental model, they tested the effects of AM root on heat stress responses. Their results suggest that AM root extract may enhance the activity of endogenous pathways that mediate cellular responses to heat stress.
In this study, the authors characterized the trends of cigarette use amongst people who do and don't use electronic nicotine delivery systems (or ENDS). This was done to help determine if the use of ENDS is aiding in helping smokers quit, as the data on this has been controversial. They found that use of ENDS among people either with or without previous cigarette usage were more likely to continue using cigarettes in the future. This is important information contributing to our understanding of ways to effectively (and not effectively) reduce cigarette use.
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