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Developing 3D Lung Rendering Algorithms for Computational Modeling of Gene Therapy Drug-Delivery Routes for ARDS and Ventilator-Induced Lung Injury.
(The Ohio State University, 2024-05) Nkwocha, Chikamnele; Ghadiali, Samir
Acute Respiratory Distress Syndrome (ARDS) is a severe condition defined by oxygen depletion and pulmonary edema. Mechanical ventilation, a common care method, can further exacerbate the damage created by ARDS by increasing inflammation and mechanical stress within the lungs. Thankfully, gene therapy technology has shown promise in mitigating inflammatory responses caused by ventilator-induced lung injury. Gene therapy for ARDS relies on successfully delivering aerosolized microRNA nanoparticles (which include miR-146a) to the injury site. The use of 3D computational models of damaged and healthy CT lung scans can help realize the effective drug-delivery route of miR-146a while minimizing pulmonary fluid occlusion. This project explores the use of three distinct algorithms for developing computationally compatible 3D lung models: active contours segmentation, trained neural network segmentation, and 3D spline lofting.
An Analysis of LEGO as an Investment Asset
(The Ohio State University, 2024-05) Al-E-Jalil, Ameen; Box-Steffensmeier, Janet; Logan, Trevon
In a world with increased information for investors, investors constantly seek new opportunities to maximize their return on investment. LEGO presents itself as a new, nontraditional investment asset type for investors. This paper collects information on approximately 3,300 LEGO sets released from 2000-2023 from brickset.com. From this data, the author performed a regression model and return on investment calculation to determine what aspects contribute to a LEGO set’s ROI and if LEGO can outperform the stock market. This paper finds that while, on average, a LEGO set will not outperform the stock market, an investor with a keen eye and understanding of LEGO may find opportunities to outperform the stock market. For this reason and because LEGO is exposed to risk factors different from traditional investment assets, it is a viable and noteworthy investment option for investors willing to invest in nontraditional investment assets.
Human Activity Recognition via Garment-embedded EMG Sensors
(The Ohio State University, 2024-05) Goetz, Danny; Srinivasan, Manoj
Human activity recognition (HAR) is a fast-growing field due to the increased desire to include this technology in many commercial electronic devices and its applicability to healthcare. The most common approach to HAR is to train a machine learning model with sensor data from human activity. Currently, accelerometers, inertial sensors, and gyroscopic sensors are the most common devices used to collect data for HAR. Another type of sensor that can provide data on human activity is an electromyography (EMG) sensor. EMG measures the electric signals due to person’s muscles activation. This thesis explores the possibility of using EMG sensors for HAR. To investigate this possibility, garments with embedded EMG sensors from ExoForce. The garments, when worn by participants, are designed to measure the activation of triceps, biceps, and calf muscles. The study consisted of participants who were asked to do a series of defined exercises (pushups, jumping jacks, curls, etc.) while wearing the EMG garments. A subset of the data from the participants was used to train a multinomial logistic regression machine learning model for classification. The rest of the data from the participants was used to test the accuracy of the model. The accuracy of the model was 45.31% on the test data (much higher than random guessing, which would be 12.5%), 98.44% on the training data, and 71.88% on the whole data set. The results from this study show that EMG sensors can be used as the basis for a device that accurately identifies various human activities, while future work could focus on improving accuracy and exploring specific applications. This finding could lead the way to improved commercial or medical devices aimed at HAR.
Reponse of Giant Ragweed (Ambrosia trifida) Source Populations to Varying Soil Moisture Conditions
(The Ohio State University, 2024-05) Brown, Kristen; Hovick, Steve
Giant ragweed (Ambrosia trifida) is native to North America and an emerging invasive species in Europe and Asia. Giant ragweed is typically found in riparian areas, which are located near water sources and have relatively high soil moisture. Giant ragweed often escapes into agricultural fields where it is highly competitive with corn and soybean crops and the soil is much drier. Such incursions into crop fields have been occurring for much longer in the eastern part of the U.S. Corn Belt than in the western Corn Belt, which has led to evolved population differences in the east that are more pronounced than in the west. We hypothesized that giant ragweed populations would differ in drought tolerance due to variable abiotic conditions where they occur. Specifically, we predicted that populations of giant ragweed from crop fields would be more drought tolerant than non-crop populations and that populations from the drier western region would be more drought tolerant than populations from the east. We sourced seeds from agricultural and riparian populations in Ohio and Nebraska. In a greenhouse, we grew each population type and source under three conditions that spanned a soil moisture gradient, from saturated to dry soil moisture conditions. Performance was greatest in the driest conditions for all source populations, with higher germination percentages, higher total biomass, and earlier emergence than in either of the wetter treatments. However, Nebraska populations had a much higher germination percentage and emerged earlier than Ohio populations, which was exacerbated in drier conditions. Additionally, crop populations showed higher germination percentages than non-crop populations, especially in the driest conditions (habitat x treatment). These findings suggest that Nebraska and crop populations may be more drought tolerant than Ohio and non-crop populations, respectively. Overall, giant ragweed is more successful in areas with lower soil moisture content, such as agricultural fields, in comparison to areas with saturated soils. Furthermore, soil moisture similarly affects giant ragweed from varying population types and seed sources. The germination data suggests that there have been adaptive changes in crop populations of giant ragweed in response to soil moisture. This indicates that adaptive changes across the range of giant ragweed may be more extensive than previously thought.
The Medicalization of Everyday Experiences as Symptoms: A Digital Ethnography of ADHD-related Social Media Content
(2024-03) Holroyd, Deanna; Stevens, Maurice
Attention Deficit/Hyperactivity Disorder (ADHD) is increasingly prevalent in American society (Smith, 2012), evident not only in rising diagnoses, but also in the recent increase of ADHD-related social media content. Much of this content presents the personal experiences of living with the disorder as an adult. While previous research into ADHD-related social media posts has explored levels of misinformation within the content (Yeung et al. 2022), it is still unknown exactly how content creators are presenting their experiences of having ADHD and their symptoms. This paper aims to explore just that, by adopting a digital ethnographic approach to examine ADHD-related Facebook, Instagram and TikTok posts and comments to discover how ADHD is presented and understood on social media. Throughout my 18-month-long digital ethnographic inquiry across these three social media platforms, I analyzed the discursive, memetic, and visual aspects of this ADHD-related content, paying close attention to the symptoms that are listed in these posts, and the general sentiment attached to the experiences that content creators share. I find that many of the ‘symptoms’ presented in this content (such as zoning out, being forgetful, fidgeting, etc.) are actually best understood as quotidian lived experiences of our neoliberal socio-cultural context, that are being reframed by content creators as medicalized diagnostic criteria – despite not being recognized as official symptoms or diagnostic criteria by medical professionals. This, I claim, helps create an environment in which almost anyone can understand themselves to have ADHD and self-identify as having the disorder. I also discover that much of this social media content rejects the notion of ADHD as a debilitating disorder and reframes it as a neurological ‘superpower’ and modern-day ‘life-hack’. As such, my research shows that ADHD-related social media content both medicalizes and glorifies everyday struggles, thus reducing ADHD to an online trend and personality trait that contributes to the delegitimization of the disorder. This paper therefore argues that, contrary to previous research on online health discourse, ADHD social media content is not simply a source of advocacy, awareness, and activism; rather, this vernacular discourse of embodied ADHD knowledge on social media is 1) actively changing widespread conceptions of what ADHD is, 2) challenges medical definitions of what it means to have ADHD in the 21st century, and 3) impacts who is considered an authoritative source of knowledge on mental health and the body. With this in mind, this paper makes several significant contributions to the fields of medical humanities, science and technology studies, and qualitative social media studies. Firstly, it highlights how the socio-political context of neoliberalism impacts what behavior becomes medicalized and what can be considered normative and disordered behavior. Secondly, the paper illuminates how digital and media technologies shape understandings of mental health and symptoms of disorders.