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Paleomagnetic, rock magnetic, or geomagnetic data found in the MagIC data repository from a paper titled: Paleomagnetism of Miocene East African Rift sediments and the calibration of the geomagnetic reversal time scale 

Adaptive immunity is driven by specific binding of hypervariable receptors to diverse molecular targets. The sequence diversity of receptors and targets are both individually known but because multiple receptors can recognize the same target, a measure of the effective “functional” diversity of the human immune system has remained elusive. Here, we show that sequence near-coincidences within T cell receptors that bind specific epitopes provide a new window into this problem and allow the quantification of how binding probability covaries with sequence. We find that near-coincidence statistics within epitope-specific repertoires imply a measure of binding degeneracy to amino acid changes in receptor sequence that is consistent across disparate experiments. Paired data on both chains of the heterodimeric receptor are particularly revealing since simultaneous near-coincidences are rare and we show how they can be exploited to estimate the number of epitope responses that created the memory compartment. In addition, we find that paired-chain coincidences are strongly suppressed across donors with different human leukocyte antigens, evidence for a central role of antigen-driven selection in making paired chain receptors public. These results demonstrate the power of coincidence analysis to reveal the sequence determinants of epitope binding in receptor repertoires. 

This paper is the third in a series of three manuscripts under review and in press that have to do with mentoring in the midst of teachHOUSTON, an innovative urban Science-Technology-Engineering-Mathematics (STEM) teacher education program, which was developed to better meet the needs of underserved secondary students who mostly are of color. teachHOUSTON has been funded by several National Science Foundation (NSF) scholarship-awarding grants that additionally support program development. Each grant award has brought different iterations of mentoring to the surface, all of which are complementary. Our first paper (under review) demonstrates the multi-layered mentoring that takes place within the teachHOUSTON program. Our second manuscript, an in press chapter, focuses on generative mentoring; that is, the way that mentoring shoots out in all directions, with some of it being by teachHOUSTON design and other parts unspooling organically. In this paper, we dig more deeply into the relationships that developed and the interests shared between and among faculty, teachHOUSTONstudents (some being preservice teachers), teachers who are graduates of the program (teachHOUSTONalumni) and secondary school students who are the ultimate beneficiaries of the urban teacher education effort. This third mentoring paper is the “pandemic chapter” of the four-year study. Meetings, interviews and focus groups conducted on Zoom allowed us to map intricate connections between and among individuals participating in two grant programs: UH-LIFE and LEAD HOUSTON. In this work, we conceptualize chain mentoring as the formal and informal ways mentoring played out during the global pandemic in Houston, the fourth largest urban center in the U.S., which is the site where this narrative inquiry research took place. 

Abstract The ability to deploy a planar surface to a desired convex profile with a simple actuation can enhance foldable or morphing airfoils, deployable antennae and reflectors, and other applications where a specific profile geometry is desired from a planar sheet. A model using a system of rigid links joined by torsional springs of tailorable stiffness is employed to create an approximate curved surface when two opposing tip loads are applied. A system of equations describing the shape of the surface during deployment is developed. The physical implementation of the model uses compliant torsion bars as the torsion springs. A multidimensional optimization algorithm is presented to place joints to minimize the error from the rigid-link approximation and account for additional manufacturing and stress considerations in the torsion bars. A proof is presented to show that equal torsion spring spacing along the horizontal axis of deployed parabolic profiles will result in minimizing the area between the model’s rigid-link approximation and smooth curve. The model is demonstrated through the physical construction of a deployable airfoil surface and a metallic deployable parabolic reflector. 

Abstract The ability to deploy a planar surface to a desired convex profile can enhance foldable or morphing airfoils, deployable antennae and reflectors, and other applications where a specific profile geometry is desired from a planar sheet. A model using a system of rigid links joined by torsional springs of tailorable stiffness is employed to create an approximate curved surface when two opposing tip loads are applied. The physical implementation of the model uses compliant torsion bars as the torsion springs. A multidimensional optimization algorithm is presented to minimize the error from the rigid-link approximation and account for additional manufacturing and stress considerations in the torsion bars. A proof is presented to show that equal torsion spring spacing along the horizontal axis of deployed parabolic profiles will result in minimizing the area between the model’s rigid-link approximation and smooth curve. The model is demonstrated through the physical construction of a deployable airfoil surface and a metallic deployable parabolic reflector. 

Summary Despite the extreme diversity of T‐cell repertoires, many identical T‐cell receptor (TCR) sequences are found in a large number of individual mice and humans. These widely shared sequences, often referred to as “public,” have been suggested to be over‐represented due to their potential immune functionality or their ease of generation by V(D)J recombination. Here, we show that even for large cohorts, the observed degree of sharing of TCR sequences between individuals is well predicted by a model accounting for the known quantitative statistical biases in the generation process, together with a simple model of thymic selection. Whether a sequence is shared by many individuals is predicted to depend on the number of queried individuals and the sampling depth, as well as on the sequence itself, in agreement with the data. We introduce thedegree of publicnessconditional on the queried cohort size and the size of the sampled repertoires. Based on these observations, we propose a public/private sequence classifier, “PUBLIC” (Public Universal Binary Likelihood Inference Classifier), based on the generation probability, which performs very well even for small cohort sizes.