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Author Correction: Multifunctional nanoagents for ultrasensitive imaging and photoactive killing of Gram-negative and Gram-positive bacteria
β-Hydroxybutyrate promotes cancer metastasis through β-hydroxybutyrylation-dependent stabilization of Snail
Small RNA in sperm–Paternal contributions to human embryo development
Abstract Sperm not only delivers the paternal genome to the oocyte but also regulatory small RNA (sRNA). However, the role of sRNA in fertilisation and human embryo development remains poorly understood. Here, couples undergoing IVF are recruited, and sperm sRNA analysed to investigate their role in IVF treatment. Differential expression of mitochondrial sRNA and Y-RNA are observed in relation to sperm concentration. For fertilisation rate, sRNA from a single locus are significantly changed. Expression of microRNA (miRNA) and ribosomal sRNA correlates positively and negatively, respectively, to high-quality embryos. Notably, the top miRNA have an area under ROC of >0.8. Predicted targets of these miRNA are relevant for development, suggesting a role for sperm-borne miRNA in embryo development. In conclusion, sperm-borne sRNA are biomarkers for sperm concentration and embryo quality in IVF. These findings may contribute to clinical strategies improving embryo quality, lowering costs and reducing the need for additional treatment cycles.
The tumor-sentinel lymph node immuno-migratome reveals CCR7⁺ dendritic cells drive response to sequenced immunoradiotherapy
Abstract Surgical ablation or broad radiation of tumor-draining lymph nodes can eliminate the primary tumor response to immunotherapy, highlighting the crucial role of these nodes in mediating the primary tumor response. Here, we show that immunoradiotherapy efficacy is dependent on treatment sequence and migration of modulated dendritic cells from tumor to sentinel lymph nodes. Using a tamoxifen-inducible reporter paired with CITE-sequencing in a murine model of oral cancer, we comprehensively characterize tumor immune cellular migration through lymphatic channels to sentinel lymph nodes at single-cell resolution, revealing a unique immunologic niche defined by distinct cellular phenotypic and transcriptional profiles. Through a structured approach of sequential immunomodulatory radiotherapy and checkpoint inhibition, we show that sequenced, lymphatic-sparing, tumor-directed radiotherapy followed by PD-1 inhibition achieves complete and durable tumor responses. Mechanistically, this treatment approach enhances migration of activated CCR7+ dendritic cell surveillance across the tumor-sentinel lymph node axis, revealing a shift from their canonical role in promoting tolerance to driving antitumor immunity. Overall, this work supports rationally sequencing immune-sensitizing, lymphatic-preserving, tumor-directed radiotherapy followed by immune checkpoint inhibition to optimize tumor response to immunoradiotherapy by driving activated dendritic cells to draining sentinel lymph nodes.
Sustainable biomass-based filter for high-efficiency PM0.3 filtration
Quantum equilibrium propagation for efficient training of quantum systems based on Onsager reciprocity
Abstract The widespread adoption of machine learning and artificial intelligence in all branches of science and technology creates a need for energy-efficient, alternative hardware. While such neuromorphic systems have been demonstrated in a wide range of platforms, it remains an open challenge to find efficient and general physics-based training approaches. Equilibrium propagation (EP), the most widely studied approach, has been introduced for classical energy-based models relaxing to an equilibrium. Here, we show a direct connection between EP and Onsager reciprocity and exploit this to derive a quantum version of EP. For an arbitrary quantum system, this can now be used to extract training gradients with respect to all tuneable parameters via a single linear response experiment. We illustrate this new concept in examples in which the input or the task is of quantum-mechanical nature, e.g., the recognition of many-body ground states, phase discovery, sensing, and phase boundary exploration. Quantum EP may be used to solve challenges such as quantum phase discovery for Hamiltonians which are classically hard to simulate or even partially unknown. Our scheme is relevant for a variety of quantum simulation platforms such as ion chains, superconducting circuits, Rydberg atom tweezer arrays and ultracold atoms in optical lattices.
Spectrally sharp magnetic excitations above the critical temperature in a frustrated Weyl semimetal
Publisher Correction: 3D-printed spines for programmable liquid topographies and micromanipulation
Investigating the molecular ‘scars’ of PTSD in the human brain
Leveraging multinational enterprises to reduce the escalating regional carbon inequality in China
Abstract Multinational enterprises (MNEs) affect regional inequality as they exert substantial yet uneven economic and environmental effects. This study evaluates the impacts of MNEs on China’s regional carbon emission inequality against value-added gains using an environmentally extended interprovincial input-output model that differentiates MNEs activities across China’s provincial-level administrative divisions. We find that MNEs predominantly concentrated in developed coastal regions from 1997–2012, while less developed inland regions accounted for less than 30% of MNEs-generated value-added but over 50% of MNEs-related carbon emissions. This imbalance exacerbated regional inequality during this period. A pivotal change occurred over 2012–2017 as MNEs increasingly relocated inland and expanded their presence in clean technology-intensive industries, significantly reducing regional inequality. We also highlight the potential of leveraging MNEs’ knowledge spillovers to mitigate emissions and inequality. These insights offer policy implications for strategically deploying MNEs to address inequality and climate change, aligning with Sustainable Development Goals.
Eighteen million years of diverse enamel proteomes from the East African Rift
Type 1 innate lymphoid cell–immature neutrophil axis suppresses acute tissue inflammation
Workouts can help gut microbes to quell cancer
Cellular crosstalk mediated by TGF-β drives epithelial-mesenchymal transition in patient-derived multi-compartment biliary organoids
De novo designed protein guiding targeted protein degradation
Optical nonlinearities in excess of 500 through sublattice reconstruction
Global dissemination of npmA mediated pan-aminoglycoside resistance via a mobile genetic element in Gram-positive bacteria
Twinning mediated intralayer frustration governs structural degradation in layered Li-rich oxide cathode
AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer’s Disease clinical trial
Abstract Alzheimer’s Disease (AD) drug discovery has been hampered by patient heterogeneity, and the lack of sensitive tools for precise stratification. Here, we demonstrate that our robust and interpretable AI-guided tool (predictive prognostic model, PPM) enhances precision in patient stratification, improving outcomes and decreasing sample size for a AD clinical trial. The AMARANTH trial of lanabecestat, a BACE1 inhibitor, was deemed futile, as treatment did not change cognitive outcomes, despite reducing β-amyloid. Employing the PPM, we re-stratify patients precisely using baseline data and demonstrate significant treatment effects; that is, 46% slowing of cognitive decline for slow progressive patients at earlier stages of neurodegeneration. In contrast, rapid progressive patients did not show significant change in cognitive outcomes. Our results provide evidence for AI-guided patient stratification that is more precise than standard patient selection approaches (e.g. β-amyloid positivity) and has strong potential to enhance efficiency and efficacy of future AD trials.