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Author Correction: Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types
Antimicrobial activity of cephamycins and β-lactam/β-lactamase inhibitors against ESBL-producing Escherichia coli and Klebsiella pneumoniae under standard and high bacterial inocula
RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning
Abstract RNAs are a vast reservoir of untapped drug targets. Structure-based virtual screening (VS) identifies candidate molecules by leveraging binding site information, traditionally using molecular docking simulations. However, docking struggles to scale with large compound libraries and RNA targets. Machine learning offers a solution but remains underdeveloped for RNA due to limited data and practical evaluations. We introduce a data-driven VS pipeline tailored for RNA, utilizing coarse-grained 3D modeling, synthetic data augmentation, and RNA-specific self-supervision. Our model achieves a 10,000x speedup over docking while ranking active compounds in the top 2.8% on structurally distinct test sets. It is robust to binding site variations and successfully screens unseen RNA riboswitches in a 20,000-compound in-vitro microarray, with a mean enrichment factor of 2.93 at 1%. This marks the first experimentally validated success of structure-based deep learning for RNA VS.
Molecular characterization and phylogenetic analysis of Cyprinid herpesvirus 3 genotypes in Iran from 2020 to 2023
Ultrafast thermo-optical control of spins in a 2D van der Waals semiconductor
Abstract Laser pulses provide one of the fastest means of manipulating electron spins in magnetic compounds and pave the way to ultrafast operation within magnetic recording, quantum computation and spintronics. However, effective management of the heat deposited during optical excitation is an open challenge. Layered two-dimensional (2D) van der Waals (vdW) materials possess unique thermal properties due to the highly anisotropic nature of their chemical bonding. Here we show how to control the rate of heat flow, and hence the magnetization dynamics, induced by an ultrafast laser pulse within the 2D ferromagnet Cr2Ge2Te6. Using time-resolved beam-scanning magneto-optical Kerr effect microscopy and microscopic spin modelling calculations, we show that by reducing the thickness of the magnetic layers, an enhancement of the heat dissipation rate into the adjacent substrate leads to a substantial reduction in the timescale for magnetization recovery from several nanoseconds down to a few hundred picoseconds. Finally, we demonstrate how the low thermal conductivity across vdW layers may be used to obtain magnetic domain memory behaviour, even after exposure to intense laser pulses. Our findings reveal the distinctive role of vdW magnets in the ultrafast control of heat conduction, spin dynamics and non-volatile memory.
Plasma levels of amino acids and osteoporosis: a cross-sectional study
Solid-like condensation of MORF8 inhibits RNA editing under heat stress in Arabidopsis
Minimum dietary diversity and its associated factors among children of 6–23 months in rural Ethiopia 2023
Testing the feasibility of multi-modular design in an HTR-PM nuclear plant
Experimental and numerical study on post-fire self-healing concrete for enhanced durability
A bipolar-redox tetraalkynylporphyrin macrocycle positive electrode with 12-electrons-transfer for high-energy aluminum-organic batteries
Data collaboration for causal inference from limited medical testing and medication data
Dependence of mitochondrial calcium signalling and dynamics on the disaggregase, CLPB
GOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative learning
Publisher Correction: Energy filtering enables macromolecular MicroED data at sub-atomic resolution
Advanced intrusion detection in internet of things using graph attention networks
Observation of multi-order polar radial vortices and their topological transition
Abstract Topological states have garnered enormous interest in both magnetic and ferroelectric materials for promising candidates of next-generation information carriers. Especially, multi-order topological structures with modulative topological charges are promising for multi-state storage. Here, by engineering boundary conditions, we directly observe the self-assembly two-order ferroelectric radial vortices in high-density BiFeO3 nanostructures. The as-observed two-order radial vortex features a doughnut-like out-of-plane polarization distribution and four-quadrant in-plane distribution, with the topological charge of Q = 0. Systematic dimensional control of the BiFeO3 nanostructures reveals size-dependent stabilization of distinct topological states, from elementary one-order to complex three-order radial vortices, which is further rationalized by phase-field simulations. The transition between different topological states with various topological charges is also realized under an external electric field. This study opens up an avenue for generating configurable polar topological states, offering potential advancements in designing high-performance multi-state memory devices.
Scalable intermediate-term earthquake forecasting with multimodal fusion neural networks
Abstract Seismology is witnessing rapid growth in both the volume and variety of earthquake observational data, but current tools for effectively integrating these heterogeneous data remain limited. Here, we propose SafeNet, a scalable deep learning framework designed to address these challenges through the use of multimodal fusion neural networks. SafeNet integrates 282-dimensional seismic indicators from earthquake catalogs, capturing long-, medium-, and short-term seismic patterns, and associates seismic activity with geological information using integrated maps. Its specialized fusion modules and adaptive attention mechanism enable dynamic spatiotemporal information exchange across regions. To validate SafeNet’s performance, we conducted a pseudo-prospective test using a 50-year earthquake catalog from China, demonstrating its superior forecasting performance over 13 state-of-the-art models. Additionally, the successful transfer of models trained on the China dataset to the Contiguous and Western United States further highlights SafeNet’s scalability.