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Meiotic recombination between homologous chromosomes is vital for maximizing genetic variation among offspring. However, sex-determining regions are often rearranged and blocked from recombination. It remains unclear whether rearrangements or other mechanisms might be responsible for recombination suppression. Here, we uncover that the deficiency of the DNA cytosine methyltransferase DNMT1 in the green algaChlamydomonas reinhardtiicauses anomalous meiotic recombination at the mating-type locus (MT), generating haploid progeny containing bothplusandminusmating-type markers due to crossovers withinMT. The deficiency of a histone methyltransferase for H3K9 methylation does not lead to anomalous recombination. These findings suggest that DNA methylation, rather than rearrangements or histone methylation, suppresses meiotic recombination, revealing an unappreciated biological function for DNA methylation in eukaryotes. 

In this study, we propose to investigate triplet loss for the purpose of an alternative feature representation for ASR. We consider a general non-semantic speech representation, which is trained with a self-supervised criteria based on triplet loss called TRILL, for acoustic modeling to represent the acoustic characteristics of each audio. This strategy is then applied to the CHiME-4 corpus and CRSS-UTDallas Fearless Steps Corpus, with emphasis on the 100-hour challenge corpus which consists of 5 selected NASA Apollo-11 channels. An analysis of the extracted embeddings provides the foundation needed to characterize training utterances into distinct groups based on acoustic distinguishing properties. Moreover, we also demonstrate that triplet-loss based embedding performs better than i-Vector in acoustic modeling, confirming that the triplet loss is more effective than a speaker feature. With additional techniques such as pronunciation and silence probability modeling, plus multi-style training, we achieve a +5.42% and +3.18% relative WER improvement for the development and evaluation sets of the Fearless Steps Corpus. To explore generalization, we further test the same technique on the 1 channel track of CHiME-4 and observe a +11.90% relative WER improvement for real test data.