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Abstract Although DNA methylation primarily represses TEs, it also represses select genes that are methylated in plant body tissues but demethylated by DNA glycosylases (DNGs) in endosperm or pollen. Either one of two DNGs, MATERNAL DEREPRESSION OF R1 (MDR1) or DNG102, is essential for pollen viability in maize. Using single-pollen mRNA sequencing on pollen-segregating mutations in both genes, we identify 58 candidate DNG target genes that account for 11.1% of the wild-type transcriptome but are silent or barely detectable in other tissues. They are unusual in their tendency to lack introns but even more so in their TE-like methylation (teM) in coding DNA. The majority have predicted functions in cell wall modification, and they likely support the rapid tip growth characteristic of pollen tubes. These results suggest a critical role for DNA methylation and demethylation in regulating maize genes with the potential for extremely high expression in pollen but constitutive silencing elsewhere. 

The 2nd Annual WPI-UMASS-UPENN EDM Data Min- ing Challenge required contestants to predict efficient test taking based on log data. In this paper, we describe our theory-driven and psychometric modeling approach. For feature engineering, we employed the Log-Normal Response Time Model for estimating latent person speed, and the Generalized Partial Credit Model for estimating latent person ability. Additionally, we adopted an n-gram feature approach for event sequences. For training a multi-label classifier, we distinguished inefficent test takers who were going too fast and those who were going too slow, instead of using the provided binary target label. Our best-performing ensemble classify er comprised three sets of low-dimensional classi ers, dominated by test-taker speed. While our classi- er reached moderate performance, relative to competition leaderboard, our approach makes two important contributions. First, we show how explainable classi ers could provide meaningful predictions if results can be contextualized to test administrators who wish to intervene or take action. Second, our re-engineering of test scores enabled us to incorporate person ability into the estimation. However, ability was hardly predictive of efficient behavior, leading to the conclusion that the target label's validity needs to be questioned. The paper concludes with tools that are helpful for substantively meaningful log data mining. 

The BICEP/Keck (BK) series of cosmic microwave background (CMB) polarization experiments has, over the past decade and a half, produced a series of field-leading constraints on cosmic inflation via measurements of the “B-mode” polarization of the CMB. Primordial B modes are directly tied to the amplitude of primordial gravitational waves (PGW), their strength parameterized by the tensor-to-scalar ratio, r, and thus the energy scale of inflation. Having set the most sensitive constraints to-date on r, σ(r) = 0.009 (r0.05 < 0.036, 95% C.L.) using data through the 2018 observing season (“BK18”), the BICEP/Keck program has continued to improve its dataset in the years since. We give a brief overview of the BK program and the “BK18” result before discussing the program’s ongoing efforts, including the deployment and performance of the Keck Array’s successor instrument, BICEP Array, improvements to data processing and internal consistency testing, new techniques such as delensing, and how those will ultimately serve to allow BK reach σ(r) ≲ 0.003 using data through the 2027 observing season.