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Daily briefing: Dogs have a serious environmental impact — but owners can mitigate it
Physicists narrow down neutrino’s mysterious mass
Can peer-reviewed podcasts fast-track science?
Perisomatic ultrastructure efficiently classifies cells in mouse cortex
Abstract Mammalian neocortex contains a highly diverse set of cell types. These cell types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches1–4. Each modality offers new perspectives on the variation of biological processes underlying cell-type specialization. Cellular-scale electron microscopy provides dense ultrastructural examination and an unbiased perspective on the subcellular organization of brain cells, including their synaptic connectivity and nanometre-scale morphology. In data that contain tens of thousands of neurons, most of which have incomplete reconstructions, identifying cell types becomes a clear challenge for analysis5. Here, to address this challenge, we present a systematic survey of the somatic region of all cells in a cubic millimetre of cortex using quantitative features obtained from electron microscopy. This analysis demonstrates that the perisomatic region is sufficient to identify cell types, including types defined primarily on the basis of their connectivity patterns. We then describe how this classification facilitates cell-type-specific connectivity characterization and locating cells with rare connectivity patterns in the dataset.
Modulating product selectivity in lignin electroreduction with a robust metallic glass catalyst
Temperature-triggered inflatable hydrogel muscles with snap-through instability for untethered robots
An unconventional autophagic pathway that inhibits ATP secretion during apoptotic cell death
Antibacterial macrocyclic peptides reveal a distinct mode of BamA inhibition
Abstract Outer membrane proteins (OMPs) produced by Gram-negative bacteria contain a cylindrical amphipathic β-sheet (“β-barrel”) that functions as a membrane spanning domain. The assembly (folding and membrane insertion) of OMPs is mediated by the heterooligomeric β-barrel assembly machine (BAM). The central BAM subunit (BamA) is an attractive antibacterial target because its structure and cell surface localization are conserved, it catalyzes an essential reaction, and potent bactericidal compounds that inhibit its activity have been described. Here we utilize mRNA display to discover cyclic peptides that bind to Escherichia coli BamA with high affinity. We describe three peptides that arrest the growth of BAM deficient E. coli strains, inhibit OMP assembly in live cells and in vitro, and bind to unique sites within the BamA β-barrel lumen. Remarkably, we find that if the peptides are added to cultures after a slowly assembling OMP mutant binds to BamA, they accelerate its biogenesis. The data strongly suggest that the peptides trap BamA in conformations that block the initiation of OMP assembly but favor a later assembly step. Molecular dynamics simulations provide further evidence that the peptides bind stably to BamA and function by a previously undescribed mechanism.
Frequent land-ocean transboundary migration of tropical heatwaves under climate change
Publisher Correction: Mineralogical controls on PFAS and anthropogenic anions in subsurface soils and aquifers
Teaching is associated with the transmission of opaque culture and leadership across 23 egalitarian hunter-gatherer societies
Data-driven design of electrolyte additives supporting high-performance 5 V LiNi0.5Mn1.5O4 positive electrodes
Abstract LiNi 0.5 Mn 1.5 O 4 (LNMO) is a high-capacity spinel-structured material with an average lithiation/de-lithiation potential at ca. 4.6–4.7 V vs Li + /Li, far exceeding the stability limits of electrolytes. An efficient way to enable LNMO in lithium-ion batteries is to reformulate an electrolyte composition that stabilizes both graphitic (Gr) negative electrode with solid-electrolyte-interphase and LNMO with cathode-electrolyte-interphase. In this study, we select and test a diverse collection of 28 single and dual additives for the Gr||LNMO battery system. Subsequently, we train machine learning models on this dataset and employ the trained models to suggest 6 binary compositions out of 125, based on predicted final area-specific-impedance, impedance rise, and final specific-capacity. Such machine learning-generated new additives outperform the initial dataset. This finding not only underscores the efficacy of machine learning in identifying materials in a highly complicated application space but also showcases an accelerated material discovery workflow that directly integrates data-driven methods with battery testing experiments.
Author Correction: Effect of gut microbiome modulation on muscle function and cognition: the PROMOTe randomised controlled trial
Mechanistic understanding of UvrA damage detection and lesion hand-off to UvrB in Nucleotide Excision Repair
Enhancing catalytic durability in alkaline oxygen evolution reaction through squaric acid anion intercalation
Functional connectomics spanning multiple areas of mouse visual cortex
Abstract Understanding the brain requires understanding neurons’ functional responses to the circuit architecture shaping them. Here we introduce the MICrONS functional connectomics dataset with dense calcium imaging of around 75,000 neurons in primary visual cortex (VISp) and higher visual areas (VISrl, VISal and VISlm) in an awake mouse that is viewing natural and synthetic stimuli. These data are co-registered with an electron microscopy reconstruction containing more than 200,000 cells and 0.5 billion synapses. Proofreading of a subset of neurons yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Released as an open-access resource, this dataset includes the tools for data retrieval and analysis 1,2 . Accompanying studies describe its use for comprehensive characterization of cell types 3–6 , a synaptic level connectivity diagram of a cortical column 4 , and uncovering cell-type-specific inhibitory connectivity that can be linked to gene expression data 4,7 . Functionally, we identify new computational principles of how information is integrated across visual space 8 , characterize novel types of neuronal invariances 9 and bring structure and function together to uncover a general principle for connectivity between excitatory neurons within and across areas 10,11 .
Author Correction: Heritable polygenic editing: the next frontier in genomic medicine?
Pre-trained molecular representations enable antimicrobial discovery
Abstract The rise in antimicrobial resistance poses a worldwide threat, reducing the efficacy of common antibiotics. Determining the antimicrobial activity of new chemical compounds through experimental methods remains time-consuming and costly. While compound-centric deep learning models promise to accelerate this search and prioritization process, current strategies require large amounts of custom training data. Here, we introduce a lightweight computational strategy for antimicrobial discovery that builds on MolE (Molecular representation through redundancy reduced Embedding), a self-supervised deep learning framework that leverages unlabeled chemical structures to learn task-independent molecular representations. By combining MolE representation learning with available, experimentally validated compound-bacteria activity data, we design a general predictive model that enables assessing compounds with respect to their antimicrobial potential. Our model correctly identifies recent growth-inhibitory compounds that are structurally distinct from current antibiotics. Using this approach, we discover de novo, and experimentally confirm, three human-targeted drugs as growth inhibitors of Staphylococcus aureus . This framework offers a viable, cost-effective strategy to accelerate antibiotic discovery.