The role and effects of the IL-33/ST2 pathway in disease is still unknown. The work described here centers upon two important members of the IL-33/ST2 axis: pro-IL-33 and the sST2 receptor. The first portion of the work focuses on the ligand pro-IL-33, an important signaling and transcriptional regulator for the IL-33/ST2 system whose structure and biophysical characteristics have yet to be elucidated due to lack of a high expression system. This need was addressed through the iterative testing and eventual purification of an Eschericia coli based expression system utilizing a His-SUMO construct coupled with autoinduction. The second portion of the work centers upon the role of glycosylations on the sST2 receptor. To address the role of glycosylations in the IL-33/ST2 axis, site specific occupancy was analyzed on the sST2 receptor revealing occupancy of 7 of 8 possible sites present. Subsequent enzymatic based characterization of these sites revealed a mixed population of tri- and tetra- antennary complex glycans present. The effects of these glycans were probed through the implementation of a novel add-on to the computational processing program SMOG2 to enable interpretation and simulation using GROMACS. Analysis of the trajectories revealed that N-linked glycosylations reduce dynamics present in sST2 in a global and local manner; and stabilize the structure of an unstructured loop present from Tyr34 to Val65 of the sST2 receptor when bound to IL-33. As such, this work lays the foundation for further exploration into the function and internal regulation abilities of the IL-33/ST2 axis

Biochemical investigations into DNA-binding and DNA-cutting proteins often benefit from the specific attachment of a radioactive label to one of the two DNA termini. In many cases, it is essential to perform two versions of the same experiment: one with the 5' DNA end labeled and one with the 3' DNA end labeled. While homogeneous 5'-radiolabeling can be accomplished using a single kinase-catalyzed phosphorylation step, existing procedures for 3'-radiolabeling often result in probe heterogeneity, prohibiting precise DNA fragment identification in downstream experiments. We present here a new protocol to efficiently attach a ³²P-phosphate to the 3' end of a DNA oligonucleotide of arbitrary sequence, relying on inexpensive DNA oligonucleotide modifications (2'-O-methylribonucleotide and ribonucleotide sugar substitutions), two enzymes (T4 polynucleotide kinase and T4 RNA ligase 2), and the differential susceptibility of DNA and RNA to hydroxide treatment. Radioactive probe molecules produced by this protocol are homogeneous and oxidant-compatible, and they can be used for precise cleavage-site mapping in the context of both DNase enzyme characterization and DNA footprinting assays. Graphic abstract.

Exemplar models (e.g., Nosofsky 1986) provide a highly influential account of the psychology of human category learning. However, the explanatory power of exemplar models may falter when applied to behavior outside of standard laboratory paradigms (Murphy, 2016) or even within the realm of traditional category learning experiments (Conaway & Kurtz, 2016; Kurtz & Wetzel, 2021). The present research poses new challenges that test the exemplar view within its wheelhouse of artificial classification learning tasks. Learners acquired categories based on two concentric circles (inner and outer) in feature space. Similarity-matched generalization tests reveal underlying global versus item-based category representation. Implications for exemplar and abstractive formal models of category learning are discussed.

Analyses of the 2016 election focused a great deal on the “White Evangelical vote,” signaling the important intersection of race and religion. But not all Evangelicals are White nor does Christianity encompass all American religion. The Asian American community brings these two social dimensions into broader perspective. We argue that Asian American religious communities encounter two major political parties with different cultural schemas: the White Christian ideal of American society on the right and Multicultural Liberalism, a racially and religiously pluralistic vision of America, on the left. Using data from the Collaborative Multiracial Post-Election Survey (CMPS) 2017 we illustrate the 2016 presidential candidate choice and party affiliations of Asian American voters and nonvoters disaggregated by religious identity. We suggest that the Republican Party’s candidate and thinly veiled White Christian nationalism likely alienated a potential non‒White Christian base. Similarly, most Asian American Buddhists, Hindus, Muslims, and Sikhs voted for Clinton despite great proportions of all groups identifying as political independents. Political practitioners will find important considerations when evaluating coalition building given the patterns we show here. We offer suggestions on how to frame the considerations of religious Asian Americans under our current racialized and religionized political culture.

Learning is important for honey bee fitness and the pollination services that they provide. Neonicotinoid pesticides impair learning, fitness, colony health, and pollination, but most studies on how they affect bee learning have focused on olfactory learning. We tested the effects of field realistic doses of 0.8 ng/bee and 1.34 ng/bee of the neonicotinoid pesticide, thiamethoxam (TMX), on bee visual learning. We adapted a T-maze bioassay and classically conditioned bees to associate sugar reward with a simulated flower color (blue or yellow light) in a choice assay. At 1.34 ng/bee, TMX significantly reduced correct choices in the final learning trial as compared to the control treatment. There was no TMX effect in our 1-h memory test. We found stronger effects on decision time and abnormal behaviors. TMX decreased bee decision times, a potential byproduct of induced hyperactivity since bees walked to make choices. Behaviors (falling, trembling, and rapid abnormal movements) were significantly increased by both TMX doses as compared to the control treatment. These results suggest that the effects of neonicotinoids on bee visual learning should be further studied and incorporated into Risk Assessment protocols.

We develop a method to reconstruct, from measured displacements of an underlying elastic substrate, the spatially dependent forces that cells or tissues impart on it. Given newly available high-resolution images of substrate displacements, it is desirable to be able to reconstruct small-scale, compactly supported focal adhesions that are often localized and exist only within the footprint of a cell. In addition to the standard quadratic data mismatch terms that define least-squares fitting, we motivate a regularization term in the objective function that penalizes vectorial invariants of the reconstructed surface stress while preserving boundaries. We solve this inverse problem by providing a numerical method for setting up a discretized inverse problem that is solvable by standard convex optimization techniques. By minimizing the objective function subject to a number of important physically motivated constraints, we are able to efficiently reconstruct stress fields with localized structure from simulated and experimental substrate displacements. Our method incorporates the exact solution for a stress tensor accurate to first-order finite differences and motivates the use of distance-based cutoffs for data inclusion and problem sparsification.

Shepard’s Universal Law of Generalization offered a com-pelling case for the first physics-like law in cognitive sciencethat should hold for all intelligent agents in the universe. Shep-ard’s account is based on a rational Bayesian model of general-ization, providing an answer to the question of why such a lawshould emerge. Extending this account to explain how humansuse multiple examples to make better generalizations requiresan additional assumption, called the size principle: hypothesesthat pick out fewer objects should make a larger contributionto generalization. The degree to which this principle warrantssimilarly law-like status is far from conclusive. Typically, eval-uating this principle has not been straightforward, requiringadditional assumptions. We present a new method for evaluat-ing the size principle that is more direct, and apply this methodto a diverse array of datasets. Our results provide support forthe broad applicability of the size principle.