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Chuck Yeager Basketball Photo
No full textPhotograph of Chuck Yeager on his hometown of Hamlin\u27s basketball team. Taken between 1937 and 1941.https://mds.marshall.edu/yeager/1050/thumbnail.jp
Allyship for the Rural Health Care Workforce
Get PDFThe COVID-19 pandemic revealed a lot about the American impressive yet fragile and overtaxed health care system. Our support systems – both institutional and human- were taxed. Building our network through a variety of methods can help to strengthen our support system while also helping to dismantle the structural inequities that have negative consequences for our workforce and for patient care. Seeking allies in medicine has become an integral component of building one’s network and becoming an ally for those communities that are isolated or under resourced and for those who are underrepresented in medicine has become an important way to help promote structural change at the institutional level
Use of Mandibular Distraction Osteogenesis to Correct Micrognathia and Airway Obstruction in Newborn Female with Pierre Robin Sequence and Neonatal Abstinence Syndrome in Rural Appalachia
Get PDFWe present a case of Pierre Robin sequence and Neonatal Abstinence Syndrome (NAS) in a newborn female patient to highlight the surgical technique of mandibular distraction osteogenesis to correct airway obstruction due to micrognathia. The patient presented as a transport after delivery due to respiratory distress. She was noted to have a cleft palate and micrognathia. The absence of other dysmorphic features diagnosed her with non-syndromic Pierre Robin sequence. To solve her upper airway obstruction, mandibular distraction osteogenesis was performed. This procedure allowed the patient to be weaned from all respiratory support and nasogastric tube feeds by the end of her hospitalization. She was able to be discharged home weeks before her internal hardware was surgically removed. Mandibular distraction osteogenesis was previously unavailable in rural Appalachia, making this case novel to the area. The patient also developed NAS during her hospitalization, highlighting the ongoing substance abuse epidemic in Appalachia
Estimating animal pose using deep learning a trained deep learning model outperforms morphological analysis
Get PDFINTRODUCTION: Analyzing animal behavior helps researchers understand their decision-making process and helper tools are rapidly becoming an indispensable part of many interdisciplinary studies. However, researchers are often challenged to estimate animal pose because of the limitation of the tools and its vulnerability to a specific environment. Over the years, deep learning has been introduced as an alternative solution to overcome these challenges.
OBJECTIVES: This study investigates how deep learning models can be applied for the accurate prediction of animal behavior, comparing with traditional morphological analysis based on image pixels.
METHODS: Transparent Omnidirectional Locomotion Compensator (TOLC), a tracking device, is used to record videos with a wide range of animal behavior. Recorded videos contain two insects: a walking red imported fire ant (Solenopsis invicta) and a walking fruit fly (Drosophila melanogaster). Body parts such as the head, legs, and thorax, are estimated by using an open-source deep-learning toolbox. A deep learning model, ResNet-50, is trained to predict the body parts of the fire ant and the fruit fly respectively. 500 image frames for each insect were annotated by humans and then compared with the predictions of the deep learning model as well as the points generated from the morphological analysis.
RESULTS: The experimental results show that the average distance between the deep learning-predicted centroids and the human-annotated centroids is 2.54, while the average distance between the morphological analysis-generated centroids and the human-annotated centroids is 6.41 over the 500 frames of the fire ant. For the fruit fly, the average distance of the centroids between the deep learning- predicted and the human-annotated is 2.43, while the average distance of the centroids between the morphological analysis-generated and the human-annotated is 5.06 over the 477 image frames.
CONCLUSION: In this paper, we demonstrate that the deep learning model outperforms traditional morphological analysis in terms of estimating animal pose in a series of video frames