Through my college journey, the terms “research” and “zero-emissions” have become central to my academic identity. Not just because they reflect both my lab experience and keen interest in sustainable tech, but because they evoke a personal question that many high school seniors and college underclassmen grapple with: what does it mean to be a scientist? I’m Trevon Collins, a mechanical engineering student at Tennessee Technological University. In this podcast episode, I’ll share the answers I’ve found through investigating age-old secrets of phase change and their implications for the green transition.
Kennedy Whiters lives by "Do not fear failure; rather, fear not trying." She participated in the REALIZE REU when she was a junior at Albany State University. Kennedy performed research on methanogens, a type of anaerobic bacteria that consumes carbon dioxide and turns it into natural gas, which we can use as a source of energy in power plants. In this episode, she describes her experience working in the lab of Prof Annie Rowe, where she found the lab group to be a good source of positive support, like her family.
Histopathology image analysis plays a pivotal role in disease diagnosis and treatment planning, relying heavily on accurate nuclei segmentation for extracting vital cellular information. In recent years, artificial intelligence (AI) and in particular deep learning models have been applied successfully in solving computational pathology image analysis tasks. The You Only Look Once (YOLO) object detection framework, which is based on a convolutional neural network (CNN) architecture has gained traction across various domains for its real-time processing capabilities. This systematic review aims to comprehensively explore and evaluate the advancements, challenges, and applications of YOLO-based methodologies in nuclei segmentation within the domain of histopathological images. The review encompasses a structured analysis of recent literature, focusing on the utilization of YOLO variants for nuclei segmentation. Key methodologies, training strategies, dataset specifics, and performance metrics are evaluated to elucidate the strengths and limitations of YOLO in this context. Additionally, the review highlights the unique characteristics of YOLO that enable efficient object detection and delineation of nuclei structures, offering a comparative analysis against traditional segmentation approaches. This systematic review underscores the promising outcomes achieved through YOLO-based architectures, emphasizing their potential for accurate and rapid nuclei segmentation. Furthermore, it identifies persistent challenges such as handling variances in nuclei appearances, optimizing model architectures for histopathological images, and improving generalization across diverse datasets. Insights derived from this review can provide a foundation for future research directions and enhancements in nuclei segmentation methodologies using YOLO within histopathology, fostering advancements in disease diagnosis and biomedical research.
By considering various depictions of the Adoration of the Magi between 1400 to 1485, I establish that the portrayal of the Magus within The Master of the Legend of Saint Lucy’s painting directly reflects the economic realities in Bruges throughout the same period. In turn, my close examination of marketplace standards alongside themes of pilgrimage, shifting racial ideals, and a growing interest in secular subjects, offers new insight into 15th century adaptations of the Adoration.
Data of monarchs subjected to righting response orientation trials under different artificial magnetic fields pre- and post-overwintering cold treatment.
Data collected for a project under the Dysphagia Rehabilitation Lab at the University of Cincinnati. The manuscript was submitted to the American Journal of Speech Language Pathology and is currently under review. Below is the abstract.
Purpose: Manometric measures of lingual function are widely used in clinical practice, premised on the assumption that lingual impairments affect oropharyngeal swallowing. This study assessed correlations between measures of lingual function and oropharyngeal swallowing impairments using the Modified Barium Swallow Impairment Profile (MBSImP).
Method: Participants undergoing routine Modified Barium Swallow Studies (MBSS) were recruited if able and willing to complete the lingual measurement protocol. Post-MBSS, participants completed the Eating Assessment Tool (EAT-10) and measures of lingual pressure during a saliva swallow, anterior maximum isometric press (aMIP), effortful swallow, anterior isometric endurance (ISO-M), and anterior isotonic endurance (ISO-T). Correlations between these measures and MBSImP Oral Total (OT) and Pharyngeal Total (PT) Overall Impression scores and Penetration-Aspiration Scale (PAS) scores were evaluated using Spearman’s correlation coefficients.
Results: The final sample included 41 patients (23 men, 18 women; M= 61.75 ± 14.72 years) with heterogenous diagnoses. Analyses showed a moderate, significant negative correlation between aMIP and Oral Total (rs = -0.44, p < .01), and ISO-M and OT (rs = -0.36, p = .02). Specifically, aMIP was strongly correlated with oral residue (Component 5; rs = -0.49, p < .01), and ISO-M with tongue control during bolus hold (Component 2; rs = -0.47, p < .01). No significant correlations were found with Pharyngeal Total, EAT-10 or PAS scores.
Conclusions: Some lingual measurements correlate with oral physiological impairments, suggesting potential utility as clinical indicators of oral swallowing physiology. Further research is needed to explore these relationships across different patient populations.
The dataset includes all the data used to generate figures for the article submitted to the journal of Neuron. This includes individual figure panels and the raw data used to generate each figure panel, as well as the statistical analyses for each experiment.