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Abstract Fungal infection of grasses, including rice (Oryza sativa), sorghum (Sorghum bicolor), and barley (Hordeum vulgare), induces the formation and accumulation of flavonoid phytoalexins. In maize (Zea mays), however, investigators have emphasized benzoxazinoid and terpenoid phytoalexins, and comparatively little is known about flavonoid induction in response to pathogens. Here, we examined fungus-elicited flavonoid metabolism in maize and identified key biosynthetic enzymes involved in the formation of O-methylflavonoids. The predominant end products were identified as two tautomers of a 2-hydroxynaringenin-derived compound termed xilonenin, which significantly inhibited the growth of two maize pathogens, Fusarium graminearum and Fusarium verticillioides. Among the biosynthetic enzymes identified were two O-methyltransferases (OMTs), flavonoid OMT 2 (FOMT2), and FOMT4, which demonstrated distinct regiospecificity on a broad spectrum of flavonoid classes. In addition, a cytochrome P450 monooxygenase (CYP) in the CYP93G subfamily was found to serve as a flavanone 2-hydroxylase providing the substrate for FOMT2-catalyzed formation of xilonenin. In summary, maize produces a diverse blend of O-methylflavonoids with antifungal activity upon attack by a broad range of fungi. 

Neutrinoless double beta decay is one of the most sensitive probes for new physics beyond the Standard Model of particle physics. One of the isotopes under investigation is ${}^{136}\mathrm{Xe}$, which would double beta decay into ${}^{136}\mathrm{Ba}$. Detecting the single ${}^{136}\mathrm{Ba}$daughter provides a sort of ultimate tool in the discrimination against backgrounds. Previous work demonstrated the ability to perform single atom imaging of Ba atoms in a single-vacancy site of a solid xenon matrix. In this paper, the effort to identify signal from individual barium atoms is extended to Ba atoms in a hexa-vacancy site in the matrix and is achieved despite increased photobleaching in this site. Abrupt fluorescence turn-off of a single Ba atom is also observed. Significant recovery of fluorescence signal lost through photobleaching is demonstrated upon annealing of Ba deposits in the Xe ice. Following annealing, it is observed that Ba atoms in the hexa-vacancy site exhibit antibleaching while Ba atoms in the tetra-vacancy site exhibit bleaching. This may be evidence for a matrix site transfer upon laser excitation. Our findings offer a path of continued research toward tagging of Ba daughters in all significant sites in solid xenon. Published by the American Physical Society2024 

Abstract A new search for two-neutrino double-beta (2νββ) decay of¹³⁶Xe to theexcited state of¹³⁶Ba is performed with the full EXO-200 dataset. A deep learning-based convolutional neural network is used to discriminate signal from background events. Signal detection efficiency is increased relative to previous searches by EXO-200 by more than a factor of two. With the addition of the Phase II dataset taken with an upgraded detector, the median 90% confidence level half-life sensitivity of 2νββdecay to thestate of¹³⁶Ba isyr using a total¹³⁶Xe exposure of 234.1 kg yr. No statistically significant evidence for 2νββdecay to thestate is observed, leading to a lower limit ofyr at 90% confidence level, improved by 70% relative to the current world's best constraint. 

Abstract Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based on a Wasserstein Generative Adversarial Network — a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. Our network is trained on real calibration data using raw scintillation waveforms as input. We find that it is able to produce high-quality simulated waveforms an order of magnitude faster than the traditional simulation approach and, importantly, generalize from the training sample and discern salient high-level features of the data. In particular, the network correctly deduces position dependency of scintillation light response in the detector and correctly recognizes dead photodetector channels. The network output is then integrated into the EXO-200 analysis framework to show that the standard EXO-200 reconstruction routine processes the simulated waveforms to produce energy distributions comparable to that of real waveforms. Finally, the remaining discrepancies and potential ways to improve the approach further are highlighted.