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Using facial recognition as a use-case scenario, we attempt to identify sources of bias in a model developed using transfer learning. To achieve this task, we developed a model based on a pre-trained facial recognition model, and scrutinized the accuracy of the model’s image classification against factors such as age, gender, and race to observe whether or not the model performed better on some demographic groups than others. By identifying the bias and finding potential sources of bias, his work contributes a unique technical perspective from the view of a small scale developer to emerging discussions of accountability and transparency in AI.

The authors used RNA-seq datasets to assess the effect of CDK7 inhibition on transcriptional pathways in castration-resistant prostate cancer cells.

Antibiotic resistance is a growing global health threat, and one strategy to combat it is using bacteriophages to enhance the effectiveness of existing antibiotics. This study tested whether targeting the TolC protein in E. coli with the TLS bacteriophage would increase bacterial sensitivity to antibiotics.

The authors studied the impact of AI face-tracking technology on the immersiveness of videogames as an alternative to virtual reality gaming.

The author looked at how dance styles can vary by individuals, even between a student and their teacher.

The authors looked at four members of the tetracycline antibiotic family/class against four different species of bacteria.

Current osteosarcoma (OS) treatments rely on surgery and chemotherapy, but drug resistance remains a major challenge that lowers patient survival rates. Ferroptosis, a form of regulated cell death, has shown promise in cancer therapy but is not well understood in OS. This study explores the use of Ferroptosis in OS.

The authors looked at the ability of different deep learning models to predict the presence of lung cancer from chest CT scans. They found that a pre-trained CNN model performed better than an autoencoder model.

Depression affects millions globally, yet identifying symptoms remains challenging. This study explored detecting depression-related patterns in social media texts using natural language processing and machine learning algorithms, including decision trees and random forests. Our findings suggest that analyzing online text activity can serve as a viable method for screening mental disorders, potentially improving diagnosis accuracy by incorporating both physical and psychological indicators.

Authors examine the impact of environmental noise on cognitive processes in teenagers, focusing on five different noise conditions: two types of noise (aircraft and construction) at two different decibel levels (30 dBA and 60 dBA) and a quiet condition.