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.

Wound-healing involves a sequence of events, such as inflammation, proliferation, and migration of different cell types like fibroblasts. Zinc Finger CCCH-type with G-Patch Domain Containing Protein (ZGPAT), encodes a protein that has its main role as a transcription repressor by binding to a specific DNA sequence. The aim of the study was to find out whether inhibiting ZGPAT will expedite the wound healing process by accelerating cell migration. This treatment strategy can provide a key to the development of wound healing strategies in medicine and cellular biology.

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 developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.

In this study, the authors ask whether a Tau immunotherapy treatment, Hsp70 protein treatment, or dual treatment approach of both the Tau imunotherapy treatment and Hsp70 protein treatment leads to a greater reduction in Tau protein concentration in Alzheimer's disease. Overall, they conclude that the effectiveness of the treatment ultimately relies on the stage of Alzheimer’s.

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.

The authors test the levels of reducing sugars in avocados, soybeans, and cinnamon as part of a diet for individuals managing Type II diabetes.

This article investigates the study methodologies, learning strategies, and motives of spelling bee participants. The authors identify several important educational implications of this work.

Enzyme chemotaxis is a thermodynamic phenomenon in which enzymes move along a substrate concentration gradient towards regions with higher substrate concentrations and can be used to steer nanovehicles towards targets along natural substrate concentrations. In patients with Alzheimer’s disease, a gradient of tau protein forms in the bloodstream. Tau protein is a substrate of the enzyme CDK5, which catalyzes the phosphorylation of tau protein and can travel using chemotaxis along tau protein gradients to increasing concentrations of tau and amyloid-beta proteins. The authors hypothesized that CDK5 would be able to overcome these barriers of Brownian motion and developed a quantitative model using Michaelis-Menten kinetics to define the necessary parameters to confirm and characterize CDK5’s chemotactic behavior to establish its utility in drug delivery and other applications.

The authors were interested in whether they could observe the effects of climate change by analyzing historical temperature data of two U.S. cities. They predicted that they should observe a warming trend in both cities. Their results showed that despite yearly variations, warming trends can be observed both in Rochester, NY and Seattle, WA which fit the predictions of climate change forecasts.