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Comparative Study of Real-Time Semantic Segmentation Networks in Aerial Images During Flooding Events
No full textThe prediction of Alzheimer’s disease through multi-trait genetic modeling
No full textTo better capture the polygenic architecture of Alzheimer’s disease (AD), we developed a joint genetic score, MetaGRS. We incorporated genetic variants for AD and 24 other traits from two independent cohorts, NACC (n = 3,174, training set) and UPitt (n = 2,053, validation set). One standard deviation increase in the MetaGRS is associated with about 57% increase in the AD risk [hazard ratio (HR) = 1.577, p = 7.17 E-56], showing little difference from the HR for AD GRS alone (HR = 1.579, p = 1.20E-56), suggesting similar utility of both models. We also conducted APOE-stratified analyses to assess the role of the e4 allele on risk prediction. Similar to that of the combined model, our stratified results did not show a considerable improvement of the MetaGRS. Our study showed that the prediction power of the MetaGRS significantly outperformed that of the reference model without any genetic information, but was effectively equivalent to the prediction power of the AD GRS.</jats:p
How Climate Literacy and Public Opinion Are the Driving Forces Behind Climate-Based Policy: A Student Perspective on COP27
No full textLike I\u27m a nobody: firearm-injured peoples\u27 perspectives on news media reporting about firearm violence
No full textPredicting Drug Loading in Extracellular Vesicles through Coarse-Grained Molecular Dynamics Simulation
No full textIn recent years, extracellular vesicles have emerged as a promising avenue for drug delivery. However, loading exogenous cargos into the vesicles without damaging their membrane poses a significant challenge. One commonly used method involves rapidly squeezing the vesicles through nanofluidic channels to create nanopores on the membrane, allowing for cargo loading. Unfortunately, the exact process and dynamics of nanopore formation and cargo loading through nanopores remain unknown due to the fast, transient nature of the process and the small size of the vesicles. To address this gap, we developed a comprehensive algorithm that simulates nanopore formation and predicts drug loading during extracellular vesicle squeezing. We leveraged the power of coarse-grain molecular dynamics simulations with fluid dynamics, coupling EV CG beads with implicit Fluctuating Lattice Boltzmann solvent. Our simulation analyzed the effects of various squeezing test parameters, such as EV size, flow velocity, channel width, and length, and EV properties on pore formation and drug loading efficiency. Our simulation results allowed us to generate a phase diagram to guide the design of nanochannel geometry and squeezing velocity, enabling us to generate nanopores on the membrane without damaging the EV. This approach can be used to optimize nanofluidic device configuration and flow setup to achieve optimal drug loading into EVs. Overall, this simulation method offers valuable insights into the drug delivery process and may lead to improved drug delivery platforms in the future
Deep Learning in Label-free Cell Sorting and Numerical Simulation with Reduced Training Data
No full textMany deep-learning-based problems depend on a significant amount and high quality of the dataset, while in the "small data" regime, less work and related applications have been established. The cost of microfluidic experiments and related fluid dynamics simulations limits the scale, predictability, and use in physics-conforming control systems for mobile and low-powered devices. On one hand, the feature extraction from data including images and sequential information in microfluidics is hard due to the potential noisy and limited scale. On the other hand, deep-learning-based simulations mostly rely on labeled training data. This dissertation presents innovative techniques based on data-driven approaches for better solutions in various experimental and computational applications. The first part of this dissertation presents an innovative technique for the detection of rare cells using a deep learning model on microscopic cell images. This proof-of-concept application demonstrates the potential for characterizing rare cells based on image data. Building on this success, we extend the application to an image-based cell sorting system that can identify distinct subpopulations of hematopoietic stem cells and multipotent progenitors. We also delve into the sensitivity of the deep learning-based classifier to the scale of the training dataset. In parallel, we tackle the problem of distortion correction in laser-based galvanometer fabrication systems. Leveraging a hybrid methodology that integrates computer vision algorithms and machine learning, we offer a cloud-based service that enables calibration and correction of lithography systems based on a set of calibration photos. Lastly, we establish a physics-informed model using a stacked U-Net architecture trained in a weakly-supervised manner on computationally-efficient, low-dimensional simulations. This model is created to generate numerical solutions for Navier-Stokes equations without the need for computationally expensive simulation results. An additional physics-informed model is developed to assist in the design of deterministic lateral displacement devices, indicating the potential for leveraging deep learning in microfluidic device design optimization. In essence, this dissertation bridges the gap between deep learning and microfluidics, bringing forth innovative solutions for cell characterization, system calibration, and computational problems within the \"small data\" regime
Healthcare and Political Obstacles in the Way of Quality Healthcare
No full textThis paper discusses several political and policy ramifications related to healthcare access for individuals in the US, specifically for undocumented and legally present immigrants in the United States. The political and policy ramifications of the research in this article center around the need for comprehensive healthcare reform to ensure that all individuals, regardless of immigration status, have access to healthcare and health services. The argument expands into the effect of healthcare worker shortages, insurance status, and immigration status. The research concludes that each negatively impact and pose an obstacle to individuals in the United States receiving quality healthcare. It is found that policy reform and new initiatives are needed to offset the effect of these obstacles in access to healthcare