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Browsing by Author "Hasan, Mohammad A."
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Item Community Studies of Antisemitism in Schools (CSAIS) Community Typology Explorer(2021) Price, Jeremy F.; Wilson, Jeffrey S.; Schall, Carly E.; Snorten, Clifton L.; Hasan, Mohammad A.; Luo, Xiao; Jahin, S. M. AbrarThis is a companion document to the CSAIS (Community Studies of Antisemitism In Schools) Community Typology Explorer which can be found at https://jeremyfprice.github.io/csais-dashboard/. Details about specific incidents, communities, and community types can be found at the CSAIS Community Typology Explorer. This project utilizes data from the ADL H.E.A.T. Map between 2016 and 2019 to identify incidents of antisemitism that specifically took place in schools. These incidents in schools are influenced by demographic, historical, social, and political factors. This project brings this data together to construct a community typology at the national level. This typology will provide insight into the ways that school-based incidents of hate are enacted and reported in context. Developing a community typology will allow providers to better target specific demographic, historical, and political attributes of the communities in which these incidents occur through curriculum and learning experiences.Item In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining(Wiley, 2021) Woloshuk, Andre; Khochare, Suraj; Almulhim, Aljohara F.; McNutt, Andrew T.; Dean, Dawson; Barwinska, Daria; Ferkowicz, Michael J.; Eadon, Michael T.; Kelly, Katherine J.; Dunn, Kenneth W.; Hasan, Mohammad A.; El-Achkar, Tarek M.; Winfree, Seth; Medicine, School of MedicineTo understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational structure is tightly linked to key physiological functions. Advances in imaging-based cell classification may be limited by the need to incorporate specific markers that can link classification to function. Multiplex imaging can mitigate these limitations, but requires cumulative incorporation of markers, which may lead to tissue exhaustion. Furthermore, the application of such strategies in large scale 3-dimensional (3D) imaging is challenging. Here, we propose that 3D nuclear signatures from a DNA stain, DAPI, which could be incorporated in most experimental imaging, can be used for classifying cells in intact human kidney tissue. We developed an unsupervised approach that uses 3D tissue cytometry to generate a large training dataset of nuclei images (NephNuc), where each nucleus is associated with a cell type label. We then devised various supervised machine learning approaches for kidney cell classification and demonstrated that a deep learning approach outperforms classical machine learning or shape-based classifiers. Specifically, a custom 3D convolutional neural network (NephNet3D) trained on nuclei image volumes achieved a balanced accuracy of 80.26%. Importantly, integrating NephNet3D classification with tissue cytometry allowed in situ visualization of cell type classifications in kidney tissue. In conclusion, we present a tissue cytometry and deep learning approach for in situ classification of cell types in human kidney tissue using only a DNA stain. This methodology is generalizable to other tissues and has potential advantages on tissue economy and non-exhaustive classification of different cell types.Item Temporal Event Modeling of Social Harm with High Dimensional and Latent Covariates(2022-08) Liu, Xueying; Mohler, George; Fang, Shiaofen; Wang, Honglang; Hasan, Mohammad A.The counting process is the fundamental of many real-world problems with event data. Poisson process, used as the background intensity of Hawkes process, is the most commonly used point process. The Hawkes process, a self-exciting point process fits to temporal event data, spatial-temporal event data, and event data with covariates. We study the Hawkes process that fits to heterogeneous drug overdose data via a novel semi-parametric approach. The counting process is also related to survival data based on the fact that they both study the occurrences of events over time. We fit a Cox model to temporal event data with a large corpus that is processed into high dimensional covariates. We study the significant features that influence the intensity of events.