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In this study, the authors studied the ability for bacteria to develop antibiotic resistance over successive generations and modeled the trajectory to predict how antibiotic resistance is developed.

This manuscript describes a new method of on-the-go passwords using typing characteristics. The authors developed a keyboard and keystroke recording setup and tested it with 30 participants. The results indicated the five chosen parameters are distinct across participants yet consistent across time for each participant, making it a plausible candidate for a behavior-based password system.

Pediatric asthma remains a significant health issue for Dallas students. This study examined the relationship between microclimatic parameters, vegetation, and pediatric asthma vulnerability (PAV) in urban schools.

Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

The authors test various machine learning models to improve the accuracy and efficiency of pneumonia diagnosis from X-ray images.

The authors use machine learning and electroencephalogram data to propose a method for improving epilepsy diagnosis.

The authors surveyed individuals diagnosed with coronary artery disease about their mental health to study a potential connection between coronary artery disease and depression.

Skin cancer is a common and potentially deadly form of cancer. This study’s purpose was to develop an automated approach for early detection for skin cancer. We hypothesized that convolutional neural network-based models using transfer learning could accurately differentiate between benign and malignant moles using natural images of human skin.

With the advance of technology, artificial intelligence (AI) is now applied widely in society. In the study of AI, machine learning (ML) is a subfield in which a machine learns to be better at performing certain tasks through experience. This work focuses on the convolutional neural network (CNN), a framework of ML, applied to an image classification task. Specifically, we analyzed the performance of the CNN as the type of neural activation function changes.

Physical therapy, especially for patients with spinal cord injuries, can be a difficult and tedious experience. This can result in negative health outcomes, such as patients dropping out of physical therapy or developing additional health problems. In this study, the authors develop and test a potential solution to these challenges: a mixed reality game called Skyfarer that replaces a standard physical therapy regimen with an immersive experience that can be shared with their friends and family. The findings of this study suggest that mixed reality games such as Skyfarer could be effective alternatives to conventional physical therapy.