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Abstract Jawless vertebrates, which diverged from jawed vertebrates approximately 550 million years ago, possess an adaptive immune system characterized by two distinct lineages of lymphocytes bearing unique antigen receptors. However, despite decades of research, the mechanism of antigen presentation in these organisms remains unclear. In this study, we report that chromosomes 22, 50, and 59 in the sea lamprey (Petromyzon marinus) exhibit synteny with the human major histocompatibility complex (MHC) region. The syntenic region spans fromDHX16toRING1(chromosome 6: 30,517,301–33,034,069) in the human genome (T2T-CHM13v2.0). Similar to the human MHC region,P. marinuschromosome 59 harbors homologs ofDDX39B,NOTCH,PBX,TNF,andBRDgenes, yet notably lacks the core MHC class I, II, and III (complement) genes. Genotyping of three independent lampreys showed no signatures of balancing selection, a characteristic feature of the human MHC. Additionally, these lamprey chromosomes exhibit synteny with chromosome 9 in amphioxus (Branchiostoma lanceolatum), which contains conserved homologs that form its “proto-MHC.” These findings suggest that chromosomes 22, 50, and 59 inP. marinusare a proto-MHC or its paralog. 

We show how to obtain improved active learning methods in the agnostic (adversarial noise) setting by combining marginal leverage score sampling with non- independent sampling strategies that promote spatial coverage. In particular, we propose an easily implemented method based on the pivotal sampling algorithm, which we test on problems motivated by learning-based methods for parametric PDEs and uncertainty quantification. In comparison to independent sampling, our method reduces the number of samples needed to reach a given target accuracy by up to 50%. We support our findings with two theoretical results. First, we show that any non-independent leverage score sampling method that obeys a weak one-sided l∞ independence condition (which includes pivotal sampling) can actively learn d dimensional linear functions with O(d log d) samples, matching independent sampling. This result extends recent work on matrix Chernoff bounds under l∞ independence, and may be of interest for analyzing other sampling strategies beyond pivotal sampling. Second, we show that, for the important case of polynomial regression, our pivotal method obtains an improved bound on O(d) samples.