In this study, the authors observe if the symptoms of Rett Syndrome, a neurodegenerative disease in humans, are reflected in Drosophila melanogaster. This was achieved by differentiating the behavior and physical aspects of wild-type flies from flies expressing the full-length MeCP2 gene and the mutated MeCP2 gene (R106W). After conducting these experiments, some of the Rett Syndrome symptoms were recapitulated in Drosophila, and a subset of those were partially ameliorated by the introduction of pifithrin-alpha.

This study hypothesized that a machine learning model could accurately predict the severity of California wildfires and determine the most influential meteorological factors. It utilized a custom dataset with information from the World Weather Online API and a Kaggle dataset of wildfires in California from 2013-2020. The developed algorithms classified fires into seven categories with promising accuracy (around 55 percent). They found that higher temperatures, lower humidity, lower dew point, higher wind gusts, and higher wind speeds are the most significant contributors to the spread of a wildfire. This tool could vastly improve the efficiency and preparedness of firefighters as they deal with wildfires.

Oxidative damage and neuro-inflammation were the key pathways implicated in the pathogenesis of Alzheimer’s disease. In this study, 30 natural extracts from plant roots and leaves with extensive anti-inflammatory and anti-oxidative properties were consumed by Drosophila melanogaster. Several assays were performed to evaluate the efficacy of these combinational extracts on delaying the progression of Alzheimer’s disease. The experimental group showed increased motor activity, improved associative memory, and decreased lifespan decline relative to the control group.

The authors trained a machine learning model to detect kidney stones based on characteristics of urine. This method would allow for detection of kidney stones prior to the onset of noticeable symptoms by the patient.

The authors created a board game to teach young children about healthy eating habits to see whether an interactive and family-oriented method would be effective at introducing and maintaining a love for fruits and veggies. Results showed that children developed a liking for fruits and vegetables, and none regressed. Half maintained their level of enjoyment for fruits and vegetables during the research period, while the other half had a positive increase. The results show that a simple interactive game can shape how young children relate to food and encourage them to maintain healthy habits.

Dhar and colleagues established a Type II diabetes mellitus (T2DM) model in fruit flies, using this model to induce insulin resistance and characterize the effects Resveratrol and Pterostilbene on a number of growth and activity metrics. Resveratrol and Pterostilbene treatment notably overturned the weight gain and glucose levels. The results of this study suggest that Drosophila can be utilized as a model organism to study T2DM and novel pharmacological treatments.

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.

In this article the authors address the complex and life quality-diminishing neurodegenerative disease known as Parkinson's. Although genetic and/or environmental factors contribute to the etiology of the disease, the diagnostic symptoms are the same. By genetically modifying fruit flies to exhibit symptoms of Parkinson's disease, they investigate whether drugs that inhibit mitochondrial calcium uptake or activate the lysosomal degradation of proteins could improve the symptoms of Parkinson's these flies exhibit. The authors report the most promising outcome to be that when both types of drugs were used together. Their data provides encouraging evidence to support further investigation of the utility of such drugs in the treatment of human Parkinson's patients.

In this study, the authors determine which house model is most resistant to high winds by building smaller prototypes that could be tested with a handheld source of wind.

The authors compare the predicted versus actual geyser eruption times for the Old Faithful and Beehive Geysers at Yellowstone National Park.