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Reconstitution of SPO11-dependent double-strand break formation
Abstract Meiotic recombination starts with SPO11 generation of DNA double-strand breaks (DSBs) 1 . SPO11 is critical for meiosis in most species, but it generates dangerous DSBs with mutagenic 2 and gametocidal 3 potential. Cells must therefore utilize the beneficial functions of SPO11 while minimizing its risks 4 —how they do so remains poorly understood. Here we report reconstitution of DNA cleavage in vitro with purified recombinant mouse SPO11 bound to TOP6BL. SPO11–TOP6BL complexes are monomeric (1:1) in solution and bind tightly to DNA, but dimeric (2:2) assemblies cleave DNA to form covalent 5′ attachments that require SPO11 active-site residues, divalent metal ions and SPO11 dimerization. SPO11 can also reseal DNA that it has nicked. Structure modelling with AlphaFold 3 suggests that DNA is bent prior to cleavage 5 . In vitro cleavage displays a sequence bias that partially explains DSB site preferences in vivo. Cleavage is inefficient on complex DNA substrates, partly because SPO11 is readily trapped in DSB-incompetent (presumably monomeric) binding states that exchange slowly. However, cleavage is improved with substrates that favour dimer assembly or by artificially dimerizing SPO11. Our results inform a model in which intrinsically weak dimerization restrains SPO11 activity in vivo, making it exquisitely dependent on accessory proteins that focus and control DSB formation.
The past, present, and future evolution of Aurora Subglacial Basin’s subglacial drainage system
Detection of hydrophobicity grade of insulators based on AHC-YOLO algorithm
Abstract Thanks to the rapid development of image processing technology, the efficiency and accuracy of power inspection have been enhanced through deep learning techniques. However, during on-site inspections, the complexity of the background images of composite insulators often makes it difficult to directly extract key features for accurately assessing hydrophobicity levels. Moreover, considering the real-time requirements for insulator hydrophobicity detection in practical operations, the model must be lightweight to speed up the detection process. To address this issue, this paper proposes a YOLO algorithm for the precise detection of composite insulator hydrophobicity. The algorithm integrates a high-performance GPU network (HGNetv2), a mixed local channel attention mechanism (MLCA), lightweight convolution (CSPPC), and the Inner-WIoU loss function, significantly reducing the network’s burden and improving the accuracy of recognizing composite insulator sheds and classifying their hydrophobicity levels. By adopting a strategy of identifying insulator sheds and then classifying their hydrophobicity levels, precise detection of hydrophobicity is achieved. Experimental results show that the proposed AHC-YOLO algorithm has increased the detection accuracy of sheds by 5.77%, with GFLOPs reduced to 5.8. In the task of classifying hydrophobicity levels, the Top-1 accuracy has been improved by 4.994%, with GFLOPs reduced to 1.9. These achievements not only meet the needs for the detection and classification of composite insulator hydrophobicity but also further demonstrate the effectiveness and superiority of the algorithm through ablation and comparative experiments.
In vitro reconstitution of meiotic DNA double-strand-break formation
Narrowed pore conformations of aquaglyceroporins AQP3 and GlpF
Constructive-destructive neighbor search drives artificial bee colony algorithm for variable speed green hybrid flowshop scheduling problem
Robust enzyme discovery and engineering with deep learning using CataPro
Abstract Accurate prediction of enzyme kinetic parameters is crucial for enzyme exploration and modification. Existing models face the problem of either low accuracy or poor generalization ability due to overfitting. In this work, we first developed unbiased datasets to evaluate the actual performance of these methods and proposed a deep learning model, CataPro, based on pre-trained models and molecular fingerprints to predict turnover number (k c a t ), Michaelis constant (K m ), and catalytic efficiency (k c a t /K m ). Compared with previous baseline models, CataPro demonstrates clearly enhanced accuracy and generalization ability on the unbiased datasets. In a representational enzyme mining project, by combining CataPro with traditional methods, we identified an enzyme (SsCSO) with 19.53 times increased activity compared to the initial enzyme (CSO2) and then successfully engineered it to improve its activity by 3.34 times. This reveals the high potential of CataPro as an effective tool for future enzyme discovery and modification.
Internal friction evidence for the carbides-formation in the high Co–Ni martensite steel during heat treatment
Abstract The internal friction (IF) behaviors, combined with X-ray diffraction (XRD), dilatometry, and transmission electron microscopy (TEM) analysis of the cryogenic treated and tempered M54 steel were systematically investigated. In IF-temperature curves, the peak P1 was proved to be a Snoek-Ke-Koester (SKK) relaxation peak associated with interstitial carbon atoms in martensite matrix according to its activation energy. The peak P2 and P3 were attributed to reverse martensite transformation and martensite transformation, respectively, during the thermal cycle. Based on the analysis results of IF, XRD and TEM, M2C precipitation indeed occurred during tempering, leading to final ultra-high strength and hardness of the aged M54 steel.
Multimodal feature fusion machine learning for predicting chronic injury induced by engineered nanomaterials
Computationally designed multi-epitope vaccine construct targeting the SARS-CoV-2 spike protein elicits robust immune responses in silico
Sublethal systemic LPS in mice enables gut-luminal pathogens to bloom through oxygen species-mediated microbiota inhibition
Abstract Endotoxin-driven systemic immune activation is a common hallmark across various clinical conditions. During acute critical illness, elevated plasma lipopolysaccharide triggers non-specific systemic immune activation. In addition, a compositional shift in the gut microbiota, including an increase in gut-luminal opportunistic pathogens, is observed. Whether a causal link exists between acute endotoxemia and abundance of gut-luminal opportunistic pathogens is incompletely understood. Here, we model acute, pathophysiological lipopolysaccharide concentrations in mice and show that systemic exposure promotes a 100–10’000-fold expansion of Klebsiella pneumoniae, Escherichia coli, Enterococcus faecium and Salmonella Typhimurium in the gut within one day, without overt enteropathy. Mechanistically, this is driven by a Toll-like receptor 4-dependent increase in gut-luminal oxygen species levels, which transiently halts microbiota fermentation and fuels growth of gut-luminal facultative anaerobic pathogens through oxidative respiration. Thus, systemic immune activation transiently perturbs microbiota homeostasis and favours opportunistic pathogens, potentially increasing the risk of infection in critically ill patients.
Evolution of coopetition between real estate enterprises and urban investment construction companies under an agent construction system
Accessible homeostatic gastric organoids reveal secondary cell type-specific host-pathogen interactions in Helicobacter pylori infections
Neural correlates of suppressing and imagining future threat
Abstract Suppressing upsetting thoughts can cause psychological distress but might also enhance mental health when used flexibly to suppress the imagination of future threat during challenging times. To investigate the neural correlates of suppressing and imagining future threat, a cohort of 65 participants underwent a previously examined "Imagine/No-Imagine" paradigm while examining brain activation using magnetic resonance imaging. We observed activity of the inferior frontal gyrus, middle frontal gyrus (MFG), superior parietal lobule, and superior occipital sulcus during thought suppression, whereas imagining future threat elicited activation in the bilateral posterior cingulate cortex (PCC) and ventromedial prefrontal cortex (vmPFC). Subjective levels of anxiety, stress, and depression as covariates did not alter these results. To further examine the group and individual-level network dynamics, we conducted dynamic causal modeling (DCM) and group iterative multiple model estimations (GIMME). The DCM model showed that during suppression, the MFG positively influenced the vmPFC and right PCC. In contrast, the vmPFC and the left and right PCC showed positive connections to the MFG during imagining. This suggests that the neural correlates of self-regulation involve an information flow between the PCC and the PFC. In addition, GIMME identified group-level connections between the right and left PCC and between the left PCC and vmPFC, reflecting the information flow during suppression and imagination of future threat, respectively. Considerable interindividual heterogeneity in the connectivity patterns became apparent, pointing to the existence of different biotypes.
Computational memory capacity predicts aging and cognitive decline
Abstract Memory is a crucial cognitive function that deteriorates with age. However, this ability is normally assessed using cognitive tests instead of the architecture of brain networks. Here, we use reservoir computing, a recurrent neural network computing paradigm, to assess the linear memory capacities of neural-network reservoirs extracted from brain anatomical connectivity data in a lifespan cohort of 636 individuals. The computational memory capacity emerges as a robust marker of aging, being associated with resting-state functional activity, white matter integrity, locus coeruleus signal intensity, and cognitive performance. We replicate our findings in an independent cohort of 154 young and 72 old individuals. By linking the computational memory capacity of the brain network with cognition, brain function and integrity, our findings open new pathways to employ reservoir computing to investigate aging and age-related disorders.
Analysis of the mitochondrial genome of Neuroctenus hainanensis and the phylogenetic position of Aradoidea
Sparse haplotype-based fine-scale local ancestry inference at scale reveals recent selection on immune responses
Abstract Increasingly efficient methods for inferring the ancestral origin of genome regions are needed to gain insights into genetic function and history as biobanks grow in scale. Here we describe two near-linear time algorithms to learn ancestry harnessing the strengths of a Positional Burrows-Wheeler Transform. SparsePainter is a faster, sparse replacement of previous model-based ‘chromosome painting’ algorithms to identify recently shared haplotypes, whilst PBWTpaint uses further approximations to obtain lightning-fast estimation optimized for genome-wide relatedness estimation. The computational efficiency gains of these tools for fine-scale local ancestry inference offer the possibility to analyse large-scale genomic datasets using different approaches. Application to the UK Biobank shows that haplotypes better represent ancestries than principal components, whilst linkage-disequilibrium of ancestry identifies signals of recent changes to population-specific selection for many genomic regions associated with immune responses, suggesting avenues for understanding the pathogen-immune system interplay on a historical timescale.