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TAS2Rs are a family of G protein-coupled receptors that function as bitter taste receptors in vertebrates. Mammalian TAS2Rs have historically garnered the most attention, leading to our understanding of their roles in taste perception relevant to human physiology and behaviors. However, the evolution and functional implications of TAS2Rs in other vertebrate lineages remain less explored. Here, we identify 9,291 TAS2Rs from 661 vertebrate genomes. Large-scale phylogenomic analyses reveal that frogs and salamanders contain unusually high TAS2R gene content, in stark contrast to other vertebrate lineages. In most species, TAS2R genes are found in clusters; compared to other vertebrates, amphibians have additional clusters and more genes per cluster. We find that vertebrate TAS2Rs have few one-to-one orthologs between closely related species, although total TAS2R count is stable in most lineages. Interestingly, TAS2R count is proportional to the receptors expressed solely in extra-oral tissues. In vitro receptor activity assays uncover that many amphibian TAS2Rs function as tissue-specific chemosensors to detect ecologically important xenobiotics. 

Step Chemical Reaction Networks (step CRNs) are an augmentation of the Chemical Reaction Network (CRN) model where additional species may be introduced to the system in a sequence of “steps.” We study step CRN systems using a weak subset of reaction rules, void rules, in which molecular species can only be deleted. We demonstrate that step CRNs with only void rules of size (2,0) can simulate threshold formulas (TFs) under linear resources. These limited systems can also simulate threshold circuits (TCs) by modifying the volume of the system to be exponential. We then prove a matching exponential lower bound on the required volume for simulating threshold circuits in a step CRN with (2,0)-size rules under a restricted gate-wise simulation, thus showing our construction is optimal for simulating circuits in this way. 

This work-in-progress paper represents our initial approach to developing a procedure for identifying indicators of “overpersistence.” This approach is one facet of a larger NSF CAREER project, “Empowering students to be adaptive decision-makers,” to model student pathways using a ground-up curriculum-specific approach with the ultimate goal of helping students choose more strategic paths to graduation. We define “overpersisters” as those students who enter college with a specific major in mind and never sway from that choice, nor graduate in a timely manner. While persistence in and commitment to a major choice are generally viewed positively, some students become fixated on a major that may not be the best fit for them. These overpersisters often spend years in a degree program and eventually leave the institution with no degree, but potentially with a substantial amount of debt. Identifying academic events that cause these students to eventually withdraw from school is the first step towards creating better strategies through which they can persist and succeed in their undergraduate studies. The concept of overpersistence is defined relative to a particular major, so a student who tries a different major before leaving the institution would not be considered an overpersister. We selected the discipline of Mechanical Engineering as a starting point because of its large enrollment and the first author’s familiarity with the discipline. Our goal is to begin developing a procedure that will identify indicators of overpersistence and provide a foundation that will help to answer the larger research question: In Mechanical Engineering, what academic events commonly lead to late dropout without changes in academic major?