This podcast considers the history of online courses in higher education and research into them, focusing on how well they serve a diverse student population. It considers how online learning developed, and how studies into the practices and effectiveness of online courses find inequality in academic outcomes and access. The podcast explores how research approaches bring to light these inequalities or fail to consider them. The future of online learning is also considered.

Health and Wealthness is a podcast where your hosts, Emily Weigel and Hanaa Khan discuss pressing and trending topics about health and wealth that everyone should know about. Our first four episodes focus on the opioid crisis. Both the science and healthcare sides. We then go on to talk about burnout and mental health in a conversational episode.

Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.

This research paper explores the effects of data variance on the quality of Artificial Intelligence image generation models and the impact on a viewer's perception of the generated images. The study examines how the quality and accuracy of the images produced by these models are influenced by factors such as size, labeling, and format of the training data. The findings suggest that reducing the training dataset size can lead to a decrease in image coherence, indicating that AI models get worse as the training dataset gets smaller. Moreover, the study makes surprising discoveries regarding AI image generation models that are trained on highly varied datasets. In addition, the study involves a survey in which people were asked to rate the subjective realism of the generated images on a scale ranging from 1 to 5 as well as sorting the images into their respective classes. The findings of this study emphasize the importance of considering dataset variance and size as a critical aspect of improving image generation models as well as the implications of using AI technology in the future.

Our current understanding of the mitochondrial genome was revolutionized in 2015 with the discovery of short open reading frames (sORFs) that produced protein products called mitochondrial-derived peptides (MDPs). Interestingly, unlike other proteins produced by the organelle, these MDPs are not directly involved in the electron transport chain but rather serve the role of metabolic regulators. In particular, one of these peptides called MOTS-c has been shown to regulate glucose and fat metabolism in an AMPK-dependent manner. With its capacity to enter the mitochondria and impact gene expression, MOTS-c has also displayed the ability to increase aerobic exercise performance by triggering elevated synthesis of the HO-1 antioxidant. Overall these findings position MOTS-c as a promising treatment for metabolic diseases as well as a potential dietary supplement to boost ATP availability.