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The authors looked at the ability to use audio clips to analyze the progression of Parkinson's disease.

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.

Ecological corridors are geographic features designated to allow the movement of wildlife populations between habitats that have been fragmented by human landscapes. Corridors can be a pivotal aspect in wildlife conservation because they preserve a suitable habitat for isolated populations to live and intermingle. Here, two students simulate the effect of introducing a safety corridor for cheetahs, based on real tracking data on cheetahs in Namibia.

The authors looked at the development of biodegradable bioplastic and its features compared to PET packaging films. They were able to develop a biodegradable plastic with sodium alginate that dissolved in water and degrade in microbial conditions while also being transparent and flexible similar to current plastic films.

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.

Rechargeable batteries are playing an increasingly prominent role in our lives due to the ongoing transition from fossil energy sources to green energy. The purpose of this study was to investigate variables that impact the effectiveness of rechargeable batteries. Alkaline (non-rechargeable) and rechargeable batteries share common features that are critical for the operation of a battery. The positive and negative electrodes, also known as the cathode and anode, are where the energy of the battery is stored. The electrolyte is what facilitates the transfer of cations and anions in a battery to generate electricity. Due to the importance of these components, we felt that a systematic investigation examining the surface area of the cathode and anode as well the impact of electrolytes with different properties on battery performance was justified. Utilizing a copper cathode and aluminum anode coupled with a water in salt electrolyte, a model rechargeable battery system was developed to test two hypotheses: a) increasing the contact area between the electrodes and electrolyte would improve battery capacity, and b) more soluble salt-based electrolytes would improve battery capacity. After soaking in an electrolyte solution, the battery was charged and the capacity, starting voltage, and ending voltage of each battery were measured. The results of this study supported our hypothesis that larger anode/cathodes surface areas and more ionic electrolytes such as sodium chloride, potassium chloride and potassium sulfate resulted in superior battery capacity. Incorporating these findings can help maximize the efficiency of commercial rechargeable batteries.

Cutibacterium acnes is a bacterium believed to play an important role in the pathogenesis of common skin diseases such as acne vulgaris. Currently, acne is known to be associated with strains from the type IA1 and IC clades of C. acnes, while those from the type IA2, IB, II, and III phylogroups are associated with skin health. This is the first study to explore the sequence space of individual gene products of different C. acnes phylogroups. Our analysis compared the sequence space topology of virulence factors to proteins with unknown functions and housekeeping proteins. We hypothesized that sequence space features of virulence factors are different from housekeeping protein features, which potentially provides an avenue to deduce unknown proteins’ functions. This proposition should be confirmed based on further experimental outcomes. A notable similarity in the sequence spaces’ topological features of previously known as housekeeping proteins encoded by recA and guaA genes to ‘putative virulence’ genes camp2 and tly was observed. Our research suggests further investigation of recA and guaA’s potential virulence properties to better understand acne pathogenesis and develop more targeted acne treatments.

The purpose of our study was to examine the correlation of glycosylated hemoglobin (HbA1c), blood pressure (BP) readings, and lipid levels with retinopathy. Our main hypothesis was that poor glycemic control, as evident by high HbA1c levels, high blood pressure, and abnormal lipid levels, causes an increased risk of retinopathy. We identified the top two features that were most important to the model as age and HbA1c. This indicates that older patients with poor glycemic control are more likely to show presence of retinopathy.

Every year, around 40% of undergraduate students in the United States discontinue their studies, resulting in a loss of valuable education for students and a loss of money for colleges. Even so, colleges across the nation struggle to discover the underlying causes of these high dropout rates. In this paper, the authors discuss the use of machine learning to find correlations between the built environment factors and the retention rates of colleges. They hypothesized that one way for colleges to improve their retention rates could be to improve the physical characteristics of their campus to be more pleasing. The authors used image classification techniques to look at images of colleges and correlate certain features like colors, cars, and people to higher or lower retention rates. With three possible options of high, medium, and low retention rates, the probability that their models reached the right conclusion if they simply chose randomly was 33%. After finding that this 33%, or 0.33 mark, always fell outside of the 99% confidence intervals built around their models’ accuracies, the authors concluded that their machine learning techniques can be used to find correlations between certain environmental factors and retention rates.

The purpose of the study was to determine whether graph-based machine learning techniques, which have increased prevalence in the last few years, can accurately classify data into one of many clusters, while requiring less labeled training data and parameter tuning as opposed to traditional machine learning algorithms. The results determined that the accuracy of graph-based and traditional classification algorithms depends directly upon the number of features of each dataset, the number of classes in each dataset, and the amount of labeled training data used.