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1. Conceptual Design and Prototyping for a Primate Health History Model

   Zhao, Martin Q; Maldonado, Elizabeth; Kensler, Terry B; Kohn, Luci AP; Guatelli-Steinberg, Debbie; Wang, Q. (January 2021, Transactions on computational science and computational intelligence)

   Arabnia, Hamid R.; Deligiannidis, Leonidas; Tinetti, Fernando G.; Tran, Quoc-Nam (Ed.)

   Primate models are important for understanding human conditions, especially in studies of ageing, pathology, adaptation, and evolution. However, how to integrate data from multiple disciplines and render them compatible with each other for datamining and in-depth study is always challenging. In a long-term project, we have started a collaborative research endeavor to examine the health history of a free-ranging rhesus macaque colony at Cayo Santiago, and build a knowledge model for anthropological and biomedical/translational studies of the effects of environment and genetics on bone development, aging, and pathologies. This paper discusses the conceptual design as well as the prototyping of this model and related graphical user interfaces, and how these will help future scientific queries and studies. 

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2. A Case for Cognitive Models in Visualization Research

   Padilla, Lace (October 2018, Seventh Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization)

   The visualization community has seen a rise in the adoption of user studies. Empirical user studies systematically test the assumptions that we make about how visualizations can help or hinder viewers’ performance of tasks. Although the increase in user studies is encouraging, it is vital that research on human reasoning with visualizations be grounded in an understanding of how the mind functions. Previously, there were no sufficient models that illustrate the process of decision-making with visualizations. However, Padilla et al. [41] recently proposed an integrative model for decision-making with visualizations, which expands on modern theories of visualization cognition and decision-making. In this paper, we provide insights into how cognitive models can accelerate innovation, improve validity, and facilitate replication efforts, which have yet to be thoroughly discussed in the visualization community. To do this, we offer a compact overview of the cognitive science of decision-making with visualizations for the visualization community, using the Padilla et al. [41] cognitive model as a guiding framework. By detailing examples of visualization research that illustrate each component of the model, this paper offers novel insights into how visualization researchers can utilize a cognitive framework to guide their user studies. We provide practical examples of each component of the model from empirical studies of visualizations, along with visualization implications of each cognitive process, which have not been directly addressed in prior work. Finally, this work offers a case study in utilizing an understanding of human cognition to generate a novel solution to a visualization reasoning bias in the context of hurricane forecast track visualizations. 

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3. FAIL Is Not a Four-Letter Word: A Theoretical Framework for Exploring Undergraduate Students’ Approaches to Academic Challenge and Responses to Failure in STEM Learning Environments

   https://doi.org/10.1187/cbe.18-06-0108  

   Henry, Meredith A.; Shorter, Shayla; Charkoudian, Louise; Heemstra, Jennifer M.; Corwin, Lisa A.; Gardner, Stephanie (March 2019, CBE—Life Sciences Education)

   Navigating scientific challenges, persevering through difficulties, and coping with failure are considered hallmarks of a successful scientist. However, relatively few studies investigate how undergraduate science, technology, engineering, and mathematics (STEM) students develop these skills and dispositions or how instructors can facilitate this development in undergraduate STEM learning contexts. This is a critical gap, because the unique cultures and practices found in STEM classrooms are likely to influence how students approach challenges and deal with failures, both during their STEM education and in the years that follow. To guide research aimed at understanding how STEM students develop a challenge-engaging disposition and the ability to adaptively cope with failure, we generate a model representing hypotheses of how students might approach challenges and respond to failures in undergraduate STEM learning contexts. We draw from theory and studies investigating mindset, goal orientations, attributions, fear of failure, and coping to inform our model. We offer this model as a tool for the community to test, revise, elaborate, or refute. Finally, we urge researchers and educators to consider the development, implementation, and rigorous testing of interventions aimed at helping students develop a persevering and challenge-engaging disposition within STEM contexts. 

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4. Visualizing and Annotating Protein Sequences using A Deep Neural Network

   https://doi.org/10.1109/IEEECONF51394.2020.9443364  

   Zhao, Zhengqiao; Rosen, Gail (November 2020, Visualizing and Annotating Protein Sequences using A Deep Neural Network)

   null (Ed.)

   It is critical for biological studies to annotate amino acid sequences and understand how proteins function. Protein function is important to medical research in the health industry (e.g., drug discovery). With the advancement of deep learning, accurate protein annotation models have been developed for alignment free protein annotation. In this paper, we develop a deep learning model with an attention mechanism that can predict Gene Ontology labels given a protein sequence input. We believe this model can produce accurate predictions as well as maintain good interpretability. We further show how the model can be interpreted by examining and visualizing the intermediate layer output in our deep neural network. 

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5. Evidence for exon shuffling is sensitive to model choice

   https://doi.org/10.1142/S0219720021400138  

   Cui, Xiaoyue; Stolzer, Maureen; Durand, Dannie (December 2021, Journal of Bioinformatics and Computational Biology)

   The exon shuffling theory posits that intronic recombination creates new domain combinations, facilitating the evolution of novel protein function. This theory predicts that introns will be preferentially situated near domain boundaries. Many studies have sought evidence for exon shuffling by testing the correspondence between introns and domain boundaries against chance intron positioning. Here, we present an empirical investigation of how the choice of null model influences significance. Although genome-wide studies have used a uniform null model, exclusively, more realistic null models have been proposed for single gene studies. We extended these models for genome-wide analyses and applied them to 21 metazoan and fungal genomes. Our results show that compared with the other two models, the uniform model does not recapitulate genuine exon lengths, dramatically underestimates the probability of chance agreement, and overestimates the significance of intron-domain correspondence by as much as 100 orders of magnitude. Model choice had much greater impact on the assessment of exon shuffling in fungal genomes than in metazoa, leading to different evolutionary conclusions in seven of the 16 fungal genomes tested. Genome-wide studies that use this overly permissive null model may exaggerate the importance of exon shuffling as a general mechanism of multidomain evolution. 

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