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Here the authors sought to understand the effects of different variables that may be tied to pollution and climate change on genetic variation of Pacific white-sided dolphins, a species that is currently threatened by water pollution. Based on environmental data collected alongside a genetic distance matrix, they found that ocean currents had the most significant impact on the genetic diversity of Pacific white-sided dolphins along the Japanese coast.

Many common respiratory illnesses like bronchitis, asthma, and chronic obstructive pulmonary disease (COPD) lead to bronchial inflammation and, subsequently, a blockage. However, there are many difficulties in measuring the severity of the blockage. A numeric metric to determine the degree of the blockage severity is necessary. To tackle this demand, we aimed to develop a novel human respiratory model and design a deep-learning program that can constantly monitor and report bronchial blockage by recording breath sounds in a non-intrusive way.

While some believe that ride-hailing services offer reduced CO₂ emissions compared to individual driving, studies have found that driving without passengers on ride-hailing trips or "deadheading" prevents this. Here, with a mathematical model, the authors investigated if the use of electric vehicles as ride-hailing vehicles could offer reduced CO₂ emissions. They found that the improved vehicle efficiency and cleaner generation could in fact lower emissions compared to the use of personal gas vehicles.

Breast cancer is the most common cancer in women, with approximately 300,000 diagnosed with breast cancer in 2023. It ranks second in cancer-related deaths for women, after lung cancer with nearly 50,000 deaths. Scientists have identified important genetic mutations in genes like BRCA1 and BRCA2 that lead to the development of breast cancer, but previous studies were limited as they focused on specific populations. To overcome limitations, diverse populations and powerful statistical methods like genome-wide association studies and whole-genome sequencing are needed. Explainable artificial intelligence (XAI) can be used in oncology and breast cancer research to overcome these limitations of specificity as it can analyze datasets of diagnosed patients by providing interpretable explanations for identified patterns and predictions. This project aims to achieve technological and medicinal goals by using advanced algorithms to identify breast cancer subtypes for faster diagnoses. Multiple methods were utilized to develop an efficient algorithm. We hypothesized that an XAI approach would be best as it can assign scores to genes, specifically with a 90% success rate. To test that, we ran multiple trials utilizing XAI methods through the identification of class-specific and patient-specific key genes. We found that the study demonstrated a pipeline that combines multiple XAI techniques to identify potential biomarker genes for breast cancer with a 95% success rate.

Here, the authors investigated the ability to use fruit peels to help soil retain moisture, a property that is essential to agriculture. Across a 96-hour observation period, orange, banana, and kiwi peel water emulsions were evaluated for their effects on soil moisture. They found that orange peels retained the most moisture, but banana and kiwi peels also offered improvements over their control sample.

One-third of the world's people do not have access to clean drinking water. Nadella and Nadella tackle this issue by testing a low-cost filtration system for removing heavy metal and bacteria from water.

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.

With increased screen time and exposure to blue light, an increasing number of people have sleep deprivation. Blue light suppresses the release of melatonin and hinders sleep at night. We hypothesized that people could get a greater amount of sleep by controlling the blue light exposure from screen time before bedtime. BBG’s effect on reducing time to fall asleep was significant within the teenage group, but not significant in the adult group. This indicated that BBG could improve the time taken to sleep for young teenagers post screen time in the evening.

The consumption of sugar substitute non-nutritive sweeteners (NNS) has dramatically increased in recent years. Despite being advertised as a healthy alternative, NNS have been linked to adverse effects on the body, such as neurodegenerative diseases (NDs). In NDs, neural stem cell function is impaired, which inhibits neuron regeneration. The purpose of this study was to determine if the NNS acesulfame potassium (Ace-K) and neotame affect planaria neuron regeneration rates. Since human neurons may regenerate, planaria, organisms with extensive regenerative capabilities due to stem cells called neoblasts, were used as the model organism. The heads of planaria exposed to either a control or non-toxic concentrations of NNS were amputated. The posterior regions of the planaria were observed every 24 hours to see the following regeneration stages: (1) wound healing, (2) blastema development, (3) growth, and (4) differentiation. The authors hypothesized that exposure to the NNS would slow planaria regeneration rates. The time it took for the planaria in the Ace-K group and the neotame group to reach the second, third, and fourth regeneration stage was significantly greater than that of the control. The results of this study indicated that exposure to the NNS significantly slowed regeneration rates in planaria. This suggests that the NNS may adversely impact neoblast proliferation rates in planaria, implying that it could impair neural stem cell proliferation in humans, which plays a role in NDs. This study may provide insight into the connection between NNS, human neuron regeneration, and NDs.

The application of machine learning techniques has facilitated the automatic annotation of behavior in video sequences, offering a promising approach for ethological studies by reducing the manual effort required for annotating each video frame. Nevertheless, before solely relying on machine-generated annotations, it is essential to evaluate the accuracy of these annotations to ensure their reliability and applicability. While it is conventionally accepted that there cannot be a perfect annotation, the degree of error associated with machine-generated annotations should be commensurate with the error between different human annotators. We hypothesized that machine learning supervised with adequate human annotations would be able to accurately predict body parts from video sequences. Here, we conducted a comparative analysis of the quality of annotations generated by humans and machines for the body parts of sheep during treadmill walking. For human annotation, two annotators manually labeled six body parts of sheep in 300 frames. To generate machine annotations, we employed the state-of-the-art pose-estimating library, DeepLabCut, which was trained using the frames annotated by human annotators. As expected, the human annotations demonstrated high consistency between annotators. Notably, the machine learning algorithm also generated accurate predictions, with errors comparable to those between humans. We also observed that abnormal annotations with a high error could be revised by introducing Kalman Filtering, which interpolates the trajectory of body parts over the time series, enhancing robustness. Our results suggest that conventional transfer learning methods can generate behavior annotations as accurate as those made by humans, presenting great potential for further research.