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2. Vascularized Biomaterials to Study Cancer Metastasis

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   Bittner, Katharine_R; Jiménez, Juan_M; Peyton, Shelly_R (January 2020, Advanced Healthcare Materials)

   Abstract Cancer metastasis, the spread of cancer cells to distant organs, is responsible for 90% of cancer‐related deaths. Cancer cells need to enter and exit circulation in order to form metastases, and the vasculature and endothelial cells are key regulators of this process. While vascularized 3D in vitro systems have been developed, few have been used to study cancer, and many lack key features of vessels that are necessary to study metastasis. This review focuses on current methods of vascularizing biomaterials for the study of cancer, and three main factors that regulate intravasation and extravasation: endothelial cell heterogeneity, hemodynamics, and the extracellular matrix of the perivascular niche. 

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3. How to Use Causal Inference to Study Use of Force

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4. An agent-based framework to study forced migration: A case study of Ukraine

   https://doi.org/10.1093/pnasnexus/pgae080  

   Mehrab, Zakaria; Stundal, Logan; Venkatramanan, Srinivasan; Swarup, Samarth; Lewis, Bryan; Mortveit, Henning S.; Barrett, Christopher L.; Pandey, Abhishek; Wells, Chad R.; Galvani, Alison P.; et al (March 2024, PNAS Nexus)

   Abstract The ongoing Russian aggression against Ukraine has forced over eight million people to migrate out of Ukraine. Understanding the dynamics of forced migration is essential for policy-making and for delivering humanitarian assistance. Existing work is hindered by a reliance on observational data which is only available well after the fact. In this work, we study the efficacy of a data-driven agent-based framework motivated by social and behavioral theory in predicting outflow of migrants as a result of conflict events during the initial phase of the Ukraine war. We discuss policy use cases for the proposed framework by demonstrating how it can leverage refugee demographic details to answer pressing policy questions. We also show how to incorporate conflict forecast scenarios to predict future conflict-induced migration flows. Detailed future migration estimates across various conflict scenarios can both help to reduce policymaker uncertainty and improve allocation and staging of limited humanitarian resources in crisis settings. 

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5. Research Methods to Study & Empower Crowd Workers

   Savage, S; Toxtli, C; Betanzos, E. (January 2021, Oxford)

   null (Ed.)

   The artificial intelligence (AI) industry has created new jobs that are essential to the real world deployment of intelligent systems. Part of the job focuses on labeling data for machine learning models or having workers complete tasks that AI alone cannot do. These workers are usually known as ‘crowd workers’—they are part of a large distributed crowd that is jointly (but separately) working on the tasks although they are often invisible to end-users, leading to workers often being paid below minimum wage and having limited career growth. In this chapter, we draw upon the field of human–computer interaction to provide research methods for studying and empowering crowd workers. We present our Computational Worker Leagues which enable workers to work towards their desired professional goals and also supply quantitative information about crowdsourcing markets. This chapter demonstrates the benefits of this approach and highlights important factors to consider when researching the experiences of crowd workers. 

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