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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. 

Abstract Mpv17 (mitochondrial inner membrane protein MPV17) deficiency causes severe mitochondrial DNA depletion syndrome in mammals and loss of pigmentation of iridophores and a significant decrease of melanophores in zebrafish. The reasons for this are still unclear. In this study, we established an mpv17 homozygous mutant line in Nile tilapia. The developing mutants are transparent due to the loss of iridophores and aggregation of pigment granules in the melanophores and disappearance of the vertical pigment bars on the side of the fish. Transcriptome analysis using the skin of fish at 30 dpf (days post fertilization) revealed that the genes related to purine (especially pnp4a) and melanin synthesis were significantly downregulated. However, administration of guanine diets failed to rescue the phenotype of the mutants. In addition, no obvious apoptosis signals were observed in the iris of the mutants by TUNEL staining. Significant downregulation of genes related to iridophore differentiation was detected by qPCR. Insufficient ATP, as revealed by ATP assay, α-MSH treatment, and adcy5 mutational analysis, might account for the defects of melanophores in mpv17 mutants. Several tissues displayed less mtDNA and decreased ATP levels. Taken together, these results indicated that mutation of mpv17 led to mitochondrial dTMP deficiency, followed by impaired mtDNA content and mitochondrial function, which in turn, led to loss of iridophores and a transparent body color in tilapia. 

The opacity of deep neural networks remains a challenge in deploying solutions where explanation is as important as precision. We present ConceptX, a human-in-the-loop framework for interpreting and annotating latent representational space in pre-trained Language Models (pLMs). We use an unsupervised method to discover concepts learned in these models and enable a graphical interface for humans to generate explanations for the concepts. To facilitate the process, we provide auto-annotations of the concepts (based on traditional linguistic ontologies). Such annotations enable development of a linguistic resource that directly represents latent concepts learned within deep NLP models. These include not just traditional linguistic concepts, but also task-specific or sensitive concepts (words grouped based on gender or religious connotation) that helps the annotators to mark bias in the model. The framework consists of two parts (i) concept discovery and (ii) annotation platform. 

We introduce our Maximum-Entropy Rewarded Reinforcement Learning (MERRL) framework that selects training data for more accurate Natural Language Processing (NLP). Because conventional data selection methods select training samples based on the test domain knowledge and not on real life data, they frequently fail in unknown domains like patent and Twitter. Our approach selects training samples that maximize information uncertainty measured by entropy, including observation entropy like empirical Shannon entropy, Min-entropy, R\'enyi entropy, and prediction entropy using mutual information, to cover more possible queries that may appear in unknown worlds. Our MERRL using regularized A2C and SAC achieves up to -99.7 perplexity decrease (-43.4\% relatively) in language modeling, +25.0 accuracy increase (+40.0\% relatively) in sentiment analysis, and +5.0 F1 score increase (+30.8\% relatively) in named entity recognition over various domains, demonstrating strong generalization power on unknown test sets. 

"Multilingual neural machine translation (MNMT) jointly trains a shared model for translation with multiple language pairs. However, traditional subword-based MNMT approaches suffer from out-of-vocabulary (OOV) issues and representation bottleneck, which often degrades translation performance on certain language pairs. While byte tokenization is used to tackle the OOV problems in neural machine translation (NMT), until now its capability has not been validated in MNMT. Additionally, existing work has not studied how byte encoding can benefit endangered language translation to our knowledge. We propose a byte-based multilingual neural machine translation system (BMNMT) to alleviate the representation bottleneck and improve translation performance in endangered languages. Furthermore, we design a random byte mapping method with an ensemble prediction to enhance our model robustness. Experimental results show that our BMNMT consistently and significantly outperforms subword/word-based baselines on twelve language pairs up to +18.5 BLEU points, an 840{\%} relative improvement.",