Digital Commons Network^™

Term-Blast-Like Alignment Tool For Concept Recognition In Noisy Clinical Texts., Tudor Groza, Honghan Wu, Marcel E Dinger, Daniel Danis, Coleman Hilton, Anita Bagley, Jon R Davids, Ling Luo, Zhiyong Lu, Peter N Robinson

Faculty Research 2023

MOTIVATION: Methods for concept recognition (CR) in clinical texts have largely been tested on abstracts or articles from the medical literature. However, texts from electronic health records (EHRs) frequently contain spelling errors, abbreviations, and other nonstandard ways of representing clinical concepts.

RESULTS: Here, we present a method inspired by the BLAST algorithm for biosequence alignment that screens texts for potential matches on the basis of matching k-mer counts and scores candidates based on conformance to typical patterns of spelling errors derived from 2.9 million clinical notes. Our method, the Term-BLAST-like alignment tool (TBLAT) leverages a gold standard corpus for typographical …

Drone Swarms In Adversarial Environment, Bhavana Sai Yadav Akula

Theses

Drones are unmanned aerial vehicles (UAVs) operated remotely with the help of cameras, GPS, and on-device SD cards. These are used for many applications including civilian as well as military. On the other hand, drone swarms are a fleet of drones that work together to achieve a special goal through swarm intelligence approaches. These provide a lot of advantages such as better coverage, accuracy, increased safety, and improved flexibility when compared to a single drone. However, the deployment of such swarms in an adversarial environment poses significant challenges. This work provides an overview of the current state of research on …

Trademarks In An Algorithmic World, Christine Haight Farley

Washington Law Review

According to the sole normative foundation for trademark protection—“search costs” theory—trademarks transmit useful information to consumers, enabling an efficient marketplace. The marketplace, however, is in the midst of a fundamental change. Increasingly, retail is virtual, marketing is data-driven, and purchasing decisions are automated by AI. Predictive analytics are changing how consumers shop. Search costs theory no longer accurately describes the function of trademarks in this marketplace. Consumers now have numerous digital alternatives to trademarks that more efficiently provide them with increasingly accurate product information. Just as store shelves are disappearing from consumers’ retail experience, so are trademarks disappearing from their …

Predicting Multiple Sclerosis Severity With Multimodal Deep Neural Networks, Kai Zhang, John A Lincoln, Xiaoqian Jiang, Elmer V Bernstam, Shayan Shams

Journal Articles

Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) create opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning …

Predicting Multiple Sclerosis Severity With Multimodal Deep Neural Networks, Kai Zhang, John A Lincoln, Xiaoqian Jiang, Elmer V Bernstam, Shayan Shams

Journal Articles

Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) create opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning …

Hiking Trail Generation In Infinite Landscapes, Matthew Jensen

MS in Computer Science Project Reports

This project procedurally generates an infinite wilderness populated with deterministic hiking trails. Our approach recognizes that hiking trails depend on contextual information beyond the location of the path itself. To address this, we implemented a layered procedural system that orchestrates the generation process. This helps ensure the availability of contextual data at each stage. The first layer handles terrain generation, establishing the foundational landscape upon which trails will traverse. Subsequent layers handle point of interest identification and selection, trail network optimization through proximity graphs, and efficient pathfinding across the terrain. A notable feature of our approach is the deterministic nature …

Parameterizing The Spillage Left Behind: Datafication, Machine Learning Algorithms, And The Question Of Ecological Agency, Courtney Rosenthal

All HCAS Student Capstones, Theses, and Dissertations

With “datafication” practices becoming more common in digital ecologies, humans have become increasingly reliant on emerging technologies and other actors that can store, comprehend, and analyze information. This thesis offers a proposed model of mediative agency to address the importance of interrogating how non-human actors interpret and make meaning from data. Mediative agents contribute to the disbursement of rhetoric, as well as our understanding of information, by granting visibility and assigning value to data. These processes effectively play a role in shaping reality through agents’ parameterization of data broadly, allowing non-human actors to take on a complex agency that can …

An Open Guide To Data Structures And Algorithms, Paul W. Bible, Lucas Moser

Computer Science Faculty publications

This textbook serves as a gentle introduction for undergraduates to theoretical concepts in data structures and algorithms in computer science while providing coverage of practical implementation (coding) issues. The field of computer science (CS) supports a multitude of essential technologies in science, engineering, and communication as a social medium. The varied and interconnected nature of computer technology permeates countless career paths making CS a popular and growing major program. Mastery of the science behind computer science relies on an understanding of the theory of algorithms and data structures. These concepts underlie the fundamental tradeoffs that dictate performance in terms of …

Maxsim: Multi-Angle-Crossing Structured Illumination Microscopy With Height-Controlled Mirror For 3d Topological Mapping Of Live Cells, Pedro Felipe Gardeazabal Rodriguez, Yigal Lilach, Abhijit Ambegaonkar, Teresa Vitali, Haani Jafri, Hae Won Sohn, Matthew B. Dalva, Susan Pierce, Inhee Chung

Department of Neuroscience Faculty Papers

Mapping 3D plasma membrane topology in live cells can bring unprecedented insights into cell biology. Widefield-based super-resolution methods such as 3D-structured illumination microscopy (3D-SIM) can achieve twice the axial ( ~ 300 nm) and lateral ( ~ 100 nm) resolution of widefield microscopy in real time in live cells. However, twice-resolution enhancement cannot sufficiently visualize nanoscale fine structures of the plasma membrane. Axial interferometry methods including fluorescence light interference contrast microscopy and its derivatives (e.g., scanning angle interference microscopy) can determine nanoscale axial locations of proteins on and near the plasma membrane. Thus, by combining super-resolution lateral imaging of 2D-SIM …

Algorithms And Competition In The Digital Economy, Cary Coglianese, Alicia Lai

Articles

No abstract provided.

Water Quality Monitoring And Mapping Using Rapidly Deployable Sensor Nodes, Mohamed Abdelwahab

Theses and Dissertations

Efficient and continuous monitoring of water quality parameters plays a pivotal role in responding to pollution incidents and ensuring the safety of both human consumption and ecological resources. This research introduces an affordable and dependable in-situ water quality sensor package designed for seamless continuous monitoring, providing essential data to facilitate informed decision-making in water resource management. The sensor package enables comprehensive on-site assessment of key water characteristics, including pH, temperature, turbidity (measured in NTU), and total dissolved solids (TDS, measured in ppm). Spatial interpolation techniques, specifically Kriging, are employed to extrapolate variable values at unobserved locations based on nearby measurements. …

Simulation-Based Optimization Of A Dc Microgrid: With Machine-Learning-Based Models And Hybrid Meta-Heuristic Algorithms, Tyler Van Deese

Theses and Dissertations

The field of economic dispatch (ED) focuses on optimizing power flow in a power system to minimize costs. It has the potential to significantly enhance system effectiveness, and efficiency, and reduce operating costs. Various techniques have been employed to tackle this problem, each with its own strengths and weaknesses. One promising approach is simulation-based optimization (SBO), which allows for accurate modeling of system interactions and improved representation of expected results. However, SBO requires running numerous simulations to identify an optimal solution, and there is a possibility of not achieving the global optimum. This work aims to address these challenges using …

Self-Supervised Deep Clustering Of Single-Cell Rna-Seq Data To Hierarchically Detect Rare Cell Populations., Tianyuan Lei, Ruoyu Chen, Shaoqiang Zhang, Yong Chen

Faculty Scholarship for the College of Science & Mathematics

Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. …

Cracking The Shield: Cda Section 230, Algorithms, And Product Liability, Kevin Ofchus

University of Arkansas at Little Rock Law Review

No abstract provided.

To Democratize Algorithms, Ngozi Okidegbe

Faculty Scholarship

Jurisdictions increasingly employ algorithms in public sector decisionmaking. Facing public outcry about the use of such technologies, jurisdictions have begun to increase democratic participation in the processes by which algorithms are procured, constructed, implemented, used, and overseen. But what problem is the current approach to democratization meant to solve? Policymakers have tended to view the problem as the absence of public deliberation: agencies and courts often use algorithms without public knowledge or input. To redress this problem, jurisdictions have turned to deliberative approaches designed to foster transparency and public debate.

This Article contends that the current approach to democratization is …

How I Read An Article That Uses Machine Learning Methods, Aziz Nazha, Olivier Elemento, Shannon Mcweeney, Moses Miles, Torsten Haferlach

Kimmel Cancer Center Faculty Papers

No abstract provided.

Art Through A Digital Lens: A Study Of The Effects Of New Medias On The Museum, Its Works, And The Public., Shelley Kopp

Electronic Thesis and Dissertation Repository

Over the last two decades imagery viewed on the internet has grown immensely. Museums, though slow to embrace it, have begun to upload digital images of their traditional artwork to their websites and onto their social media channels. In large measure, the COVID pandemic accelerated this move to engage audiences they feared would dissipate as museum doors closed. Moving digital images online though means giving over control to the protocol and systems of the internet, to profit-seeking corporations, and the volatility of social media platforms. The museum’s long-established authority over artists, artworks, and exhibitions is usurped by power structures existing …

Phylogenetic Inference From Single-Cell Rna-Seq Data, Xuan Liu, Jason I Griffiths, Isaac Bishara, Jiayi Liu, Andrea H Bild, Jeffrey T Chang

Journal Articles

Tumors are comprised of subpopulations of cancer cells that harbor distinct genetic profiles and phenotypes that evolve over time and during treatment. By reconstructing the course of cancer evolution, we can understand the acquisition of the malignant properties that drive tumor progression. Unfortunately, recovering the evolutionary relationships of individual cancer cells linked to their phenotypes remains a difficult challenge. To address this need, we have developed PhylinSic, a method that reconstructs the phylogenetic relationships among cells linked to their gene expression profiles from single cell RNA-sequencing (scRNA-Seq) data. This method calls nucleotide bases using a probabilistic smoothing approach and then …

Selection Of Optimal Quantile Protein Biomarkers Based On Cell-Level Immunohistochemistry Data, Misung Yi, Tingting Zhan, Amy R. Peck, Jeffrey A. Hooke, Albert J. Kovatich, Craig D. Shriver, Hai Hu, Yunguang Sun, Hallgeir Rui, Inna Chervoneva

Department of Pharmacology, Physiology, and Cancer Biology Faculty Papers

BACKGROUND: Protein biomarkers of cancer progression and response to therapy are increasingly important for improving personalized medicine. Advanced quantitative pathology platforms enable measurement of protein expression in tissues at the single-cell level. However, this rich quantitative cell-by-cell biomarker information is most often not exploited. Instead, it is reduced to a single mean across the cells of interest or converted into a simple proportion of binary biomarker-positive or -negative cells.

RESULTS: We investigated the utility of retaining all quantitative information at the single-cell level by considering the values of the quantile function (inverse of the cumulative distribution function) estimated from a …

Segmentation Of Human Functional Tissue Units In Support Of A Human Reference Atlas, Yashvardhan Jain, Leah L Godwin, Yingnan Ju, Naveksha Sood, Ellen M Quardokus, Andreas Bueckle, Teri Longacre, Aaron Horning, Yiing Lin, Edward D Esplin, John W Hickey, Michael P Snyder, Nathan Heath Patterson, Jeffrey M Spraggins, Katy Börner

2020-Current year OA Pubs

The Human BioMolecular Atlas Program (HuBMAP) aims to compile a Human Reference Atlas (HRA) for the healthy adult body at the cellular level. Functional tissue units (FTUs), relevant for HRA construction, are of pathobiological significance. Manual segmentation of FTUs does not scale; highly accurate and performant, open-source machine-learning algorithms are needed. We designed and hosted a Kaggle competition that focused on development of such algorithms and 1200 teams from 60 countries participated. We present the competition outcomes and an expanded analysis of the winning algorithms on additional kidney and colon tissue data, and conduct a pilot study to understand spatial …

Chatgpt, Lamda, And The Hype Around Communicative Ai: The Automation Of Communication As A Field Of Research In Media And Communication Studies, Andreas Hepp, Wiebke Loosen, Stephan Dreyer, Juliane Jarke, Sigrid Kannengießer, Christian Katzenbach, Rainer Malaka, Michaela Pfadenhauer, Cornelius Puschmann, Wolfgang Schulz

Human-Machine Communication

The aim of this article is to more precisely define the field of research on the automation of communication, which is still only vaguely discernible. The central thesis argues that to be able to fully grasp the transformation of the media environment associated with the automation of communication, our view must be broadened from a preoccupation with direct interactions between humans and machines to societal communication. This more widely targeted question asks how the dynamics of societal communication change when communicative artificial intelligence—in short: communicative AI—is integrated into aspects of societal communication. To this end, we recommend an approach that …

Radiomics Characterization Of Tissues In An Animal Brain Tumor Model Imaged Using Dynamic Contrast Enhanced (Dce) Mri, Hassan Bagher-Ebadian, Stephen L. Brown, Mohammad M. Ghassemi, Tavarekere N. Nagaraja, Benjamin Movsas, James R. Ewing, Indrin J. Chetty

Radiation Oncology Articles

Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps …

#Dubailiving And Digital Placemaking On Tiktok: Migrant, Domestic, And Service Workers’ Affective Social Mediascapes, Zoe Hurley

All Works

While Dubai, the small emirate in the United Arab Emirates, tends to be associated with luxurious social media images of elite social actors, startling architecture, and consumer status symbols, this study addresses migrant, domestic, and service workers’ everyday digital placemaking. To explore these issues, a global semiotic framework reorientates traditional notions of the geopolitical context in terms of Dubai’s social mediascape. TikTok is taken as a case to explore a corpus of Dubai-related hashtags and content being shared by migrant, domestic, and service workers. The central argument of the article is that, while Dubai’s social media cultures reflect hegemonies of …

What Quantifies Good Primary Care In The United States? A Review Of Algorithms And Metrics Using Real-World Data, Yun Wang, Jianwei Zheng, Todd Schneberk, Yu Ke, Alexandre Chan, Tao Hu, Jerika Lam, Mary Gutierrez, Ivan Portillo, Dan Wu, Chi-Hung Chang, Yang Qu, Lawrence Brown, Michael B. Nichol

Pharmacy Faculty Articles and Research

Primary care physicians (PCPs) play an indispensable role in providing comprehensive care and referring patients for specialty care and other medical services. As the COVID-19 outbreak disrupts patient access to care, understanding the quality of primary care is critical at this unprecedented moment to support patients with complex medical needs in the primary care setting and inform policymakers to redesign our primary care system. The traditional way of collecting information from patient surveys is time-consuming and costly, and novel data collection and analysis methods are needed. In this review paper, we describe the existing algorithms and metrics that use the …

On The Dependent Recognition Of Some Long Zinc Finger Proteins., Zheng Zuo, Timothy Billings, Michael Walker, Petko M. Petkov, Polly M Fordyce, Gary D Stormo

Faculty Research 2023

The human genome contains about 800 C2H2 zinc finger proteins (ZFPs), and most of them are composed of long arrays of zinc fingers. Standard ZFP recognition model asserts longer finger arrays should recognize longer DNA-binding sites. However, recent experimental efforts to identify in vivo ZFP binding sites contradict this assumption, with many exhibiting short motifs. Here we use ZFY, CTCF, ZIM3, and ZNF343 as examples to address three closely related questions: What are the reasons that impede current motif discovery methods? What are the functions of those seemingly unused fingers and how can we improve the motif discovery algorithms based …

On The Dependent Recognition Of Some Long Zinc Finger Proteins, Zheng Zuo, Timothy Billings, Michael Walker, Petko M Petkov, Polly M Fordyce, Gary D Stormo

2020-Current year OA Pubs

The human genome contains about 800 C2H2 zinc finger proteins (ZFPs), and most of them are composed of long arrays of zinc fingers. Standard ZFP recognition model asserts longer finger arrays should recognize longer DNA-binding sites. However, recent experimental efforts to identify in vivo ZFP binding sites contradict this assumption, with many exhibiting short motifs. Here we use ZFY, CTCF, ZIM3, and ZNF343 as examples to address three closely related questions: What are the reasons that impede current motif discovery methods? What are the functions of those seemingly unused fingers and how can we improve the motif discovery algorithms based …

National Telecommunications And Information Administration: Comments From Researchers At Boston University And The University Of Chicago, Ran Canetti, Aloni Cohen, Chris Conley, Mark Crovella, Stacey Dogan, Marco Gaboardi, Woodrow Hartzog, Rory Van Loo, Christopher Robertson, Katharine B. Silbaugh

Faculty Scholarship

These comments were composed by an interdisciplinary group of legal, computer science, and data science faculty and researchers at Boston University and the University of Chicago. This group collaborates on research projects that grapple with the legal, policy, and ethical implications of the use of algorithms and digital innovation in general, and more specifically regarding the use of online platforms, machine learning algorithms for classification, prediction, and decision making, and generative AI. Specific areas of expertise include the functionality and impact of recommendation systems; the development of Privacy Enhancing Technologies (PETs) and their relationship to privacy and data security laws; …

Dynamic Contrast Enhanced (Dce) Mri Estimation Of Vascular Parameters Using Knowledge-Based Adaptive Models, Hassan Bagher-Ebadian, Stephen L. Brown, Mohammad M. Ghassemi, Tavarekere N. Nagaraja, Olivia G. Valadie, Prabhu C. Acharya, Glauber Cabral, George Divine, Robert A. Knight, Ian Y. Lee, Jun Xu, Benjamin Movsas, Indrin J. Chetty, James R. Ewing

Radiation Oncology Articles

We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, K(trans), plasma volume fraction, v(p), and extravascular, extracellular space, v(e), directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions …

Racializing Algorithms, Jessica M. Eaglin

Articles by Maurer Faculty

There is widespread recognition that algorithms in criminal law’s administration can impose negative racial and social effects. Scholars tend to offer two ways to address this concern through law—tinkering around the tools or abolishing the tools through law and policy. This Article contends that these paradigmatic interventions, though they may center racial disparities, legitimate the way race functions to structure society through the intersection of technology and law. In adopting a theoretical lens centered on racism and the law, it reveals deeply embedded social assumptions about race that propel algorithms as criminal legal reform in response to mass incarceration. It …

Comparison Between Gradients And Parcellations For Functional Connectivity Prediction Of Behavior, Ru Kong, Yan Rui Tan, Naren Wulan, Leon Qi Rong Ooi, Seyedeh-Rezvan Farahibozorg, Samuel Harrison, Janine D Bijsterbosch, Boris C Bernhardt, Simon Eickhoff, B T Thomas Yeo

2020-Current year OA Pubs

Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average "hard" parcellations (Schaefer et al., 2018), individual-specific "hard" parcellations (Kong et al., 2021a), and an individual-specific "soft" parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known …