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Morchellaspecies have considerable significance in terrestrial ecosystems, exhibiting a range of ecological lifestyles along the saprotrophism-to-symbiosis continuum. However, the mitochondrial genomes of these ascomycetous fungi have not been thoroughly studied, thereby impeding a comprehensive understanding of their genetic makeup and ecological role. In this study, we analysed the mitogenomes of 30Morchellaceaespecies, including yellow, black, blushing and false morels. These mitogenomes are either circular or linear DNA molecules with lengths ranging from 217 to 565 kbp and GC content ranging from 38% to 48%. Fifteen core protein-coding genes, 28–37tRNAgenes and 3–8rRNAgenes were identified in theseMorchellaceaemitogenomes. The gene order demonstrated a high level of conservation, with thecox1gene consistently positioned adjacent to thernSgene andcobgene flanked byaptgenes. Some exceptions were observed, such as the rearrangement ofatp6andrps3inMorchella importunaand the reversed order ofatp6andatp8in certain morel mitogenomes. However, the arrangement of thetRNAgenes remains conserved. We additionally investigated the distribution and phylogeny of homing endonuclease genes (HEGs) of the LAGLIDADG (LAGs) and GIY-YIG (GIYs) families. A total of 925 LAG and GIY sequences were detected, with individual species containing 19–48HEGs. These HEGs were primarily located in thecox1,cob,cox2andnad5introns and their presence and distribution displayed significant diversity amongst morel species. These elements significantly contribute to shaping their mitogenome diversity. Overall, this study provides novel insights into the phylogeny and evolution of theMorchellaceae. 

The inevitable appearance of spurious correlations in training datasets hurts the generalization of NLP models on unseen data. Previous work has found that datasets with paired inputs are prone to correlations between a specific part of the input (e.g., the hypothesis in NLI) and the label; consequently, models trained only on those outperform chance. Are these correlations picked up by models trained on the full input data? To address this question, we propose a new evaluation method, Counterfactual Attentiveness Test (CAT). CAT uses counterfactuals by replacing part of the input with its counterpart from a different example (subject to some restrictions), expecting an attentive model to change its prediction. Using CAT, we systematically investigate established supervised and in-context learning models on ten datasets spanning four tasks: natural language inference, reading comprehension, paraphrase detection, and visual & language reasoning. CAT reveals that reliance on such correlations is mainly data-dependent. Surprisingly, we find that GPT3 becomes less attentive with an increased number of demonstrations, while its accuracy on the test data improves. Our results demonstrate that augmenting training or demonstration data with counterfactuals is effective in improving models’ attentiveness. We show that models’ attentiveness measured by CAT reveals different conclusions from solely measuring correlations in data. 

Abstract DNA helicase activity is essential for the vital DNA metabolic processes of recombination, replication, transcription, translation, and repair. Recently, an unexpected, rapid exponential ATP‐stimulated DNA unwinding rate was observed from anArchaeoglobus fulgidushelicase (AfXPB) as compared to the slower conventional helicases fromSulfolobus tokodaii, StXPB1 and StXPB2. This unusual rapid activity suggests a “molecular wrench” mechanism arising from the torque applied by AfXPB on the duplex structure in transitioning from open to closed conformations. However, much remains to be understood. Here, we investigate the concentration dependence of DNA helicase binding and ATP‐stimulated kinetics of StXPB2 and AfXPB, as well as their binding and activity in Bax1 complexes, via an electrochemical assay with redox‐active DNA monolayers. StXPB2 ATP‐stimulated activity is concentration‐independent from 8 to 200 nM. Unexpectedly, AfXPB activity is concentration‐dependent in this range, with exponential rate constants varying from seconds at concentrations greater than 20 nM to thousands of seconds at lower concentrations. At 20 nM, rapid exponential signal decay ensues, linearly reverses, and resumes with a slower exponential decay. This change in AfXPB activity as a function of its concentration is rationalized as the crossover between the fast molecular wrench and slower conventional helicase modes. AfXPB‐Bax1 inhibits rapid activity, whereas the StXPB2‐Bax1 complex induces rapid kinetics at higher concentrations. This activity is rationalized with the crystal structures of these complexes. These findings illuminate the different physical models governing molecular wrench activity for improved biological insight into a key factor in DNA repair. 

Retracted papers often circulate widely on social media, digital news, and other websites before their official retraction. The spread of potentially inaccurate or misleading results from retracted papers can harm the scientific community and the public. Here, we quantify the amount and type of attention 3,851 retracted papers received over time in different online platforms. Comparing with a set of nonretracted control papers from the same journals with similar publication year, number of coauthors, and author impact, we show that retracted papers receive more attention after publication not only on social media but also, on heavily curated platforms, such as news outlets and knowledge repositories, amplifying the negative impact on the public. At the same time, we find that posts on Twitter tend to express more criticism about retracted than about control papers, suggesting that criticism-expressing tweets could contain factual information about problematic papers. Most importantly, around the time they are retracted, papers generate discussions that are primarily about the retraction incident rather than about research findings, showing that by this point, papers have exhausted attention to their results and highlighting the limited effect of retractions. Our findings reveal the extent to which retracted papers are discussed on different online platforms and identify at scale audience criticism toward them. In this context, we show that retraction is not an effective tool to reduce online attention to problematic papers.