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Data science is increasingly relevant to daily life and has garnered significant attention in education. While data science education has been traditionally focused on technical training, justice considerations are increasingly brought up given growing concerns over fairness and justice in data science. This paper introduces a framework for justice-oriented data science education that comprises five areas grounded in a broad range of literature. To explore and refine the framework in authentic contexts, we applied it to discourse data from one participatory design workshop with teachers. Analysis demonstrated the presence of this framework’s areas and their rich connections in teachers’ thinking. The framework offers educators a tool to integrate data science, justice issues, and disciplinary content in K-12 classrooms. 

Abstract Pathogenesis of COVID-19 by SARS-CoV-2 resulted in a global pandemic and public health emergency in 2020. Viral infection can induce oxidative stress through reactive oxygen species (ROS). Inflammation and environmental stress are major sources of oxidative stress after infection. Micronutrients such as iron, copper, zinc, and manganese play various roles in human tissues and their imbalance in blood can impact immune responses against pathogens including SARS CoV-2. We hypothesized that alteration of free metal ions during infection and metal-catalyzed oxidation plays a critical role towards pathogenesis after infection. We analyzed convalescent and hospitalized COVID-19 patient plasma using orthogonal analytical techniques to determine redox active metal concentrations, overall protein oxidation, oxidative modifications, and protein levels via proteomics to understand the consequences of metal-induced oxidative stress in COVID-19 plasma proteins. Metal analysis using ICP-MS showed significantly greater concentrations of copper in COVID-19 plasma compared to healthy controls. We demonstrate significantly greater total protein carbonylation, other oxidative modifications, and deamidation of plasma proteins in COVID-19 plasma compared to healthy controls. Proteomics analysis showed that levels of redox active proteins including hemoglobulin were elevated in COVID-19 plasma. Molecular modeling concurred with potential interactions between iron binding proteins and SARS CoV-2 surface proteins. Overall, increased levels of redox active metals and protein oxidation indicate that oxidative stress-induced protein oxidation in COVID-19 may be a consequence of the interactions of SARS-CoV-2 proteins with host cell metal binding proteins resulting in altered cellular homeostasis. 

Point processes provide a powerful framework for modeling the distribution and interactions of events in time or space. Their flexibility has given rise to a variety of sophisticated models in statistics and machine learning, yet model diagnostic and criticism techniques re- main underdeveloped. In this work, we pro- pose a general Stein operator for point pro- cesses based on the Papangelou conditional intensity function. We then establish a kernel goodness-of-fit test by defining a Stein dis- crepancy measure for general point processes. Notably, our test also applies to non-Poisson point processes whose intensity functions con- tain intractable normalization constants due to the presence of complex interactions among points. We apply our proposed test to sev- eral point process models, and show that it outperforms a two-sample test based on the maximum mean discrepancy. 

This work generalizes graph neural networks (GNNs) beyond those based on the Weisfeiler- Lehman (WL) algorithm, graph Laplacians, and diffusions. Our approach, denoted Relational Pooling (RP), draws from the theory of finite partial exchangeability to provide a framework with maximal representation power for graphs. RP can work with existing graph representation models and, somewhat counterintuitively, can make them even more powerful than the orig- inal WL isomorphism test. Additionally, RP allows architectures like Recurrent Neural Net- works and Convolutional Neural Networks to be used in a theoretically sound approach for graph classification. We demonstrate improved perfor- mance of RP-based graph representations over state-of-the-art methods on a number of tasks. 

We consider a simple and overarching representation for permutation-invariant functions of sequences (or multiset functions). Our approach, which we call Janossy pooling, expresses a permutation-invariant function as the average of a permutation-sensitive function applied to all reorderings of the input sequence. This allows us to leverage the rich and mature literature on permutation-sensitive functions to construct novel and flexible permutation-invariant functions. If car- ried out naively, Janossy pooling can be computationally prohibitive. To allow computational tractability, we consider three kinds of approximations: canonical orderings of sequences, functions with k-order interactions, and stochastic opti- mization algorithms with random permutations. Our framework unifies a variety of existing work in the literature, and suggests possible modeling and algorithmic extensions. We explore a few in our experiments, which demonstrate improved performance over current state-of-the-art methods.