Nature Communications

Journal: Nature Communications

Abbreviation

Nat Commun

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Nature

ISSN

2041-1723

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Publications 1 - 10 of 1696

Ecological countermeasures to prevent pathogen spillover and subsequent pandemics
Tanshi I.; Plowright, Raina K.; Ahmed, Aliyu N.; et al. (2024)
Nature Communications
Substantial global attention is focused on how to reduce the risk of future pandemics. Reducing this risk requires investment in prevention, preparedness, and response. Although preparedness and response have received significant focus, prevention, especially the prevention of zoonotic spillover, remains largely absent from global conversations. This oversight is due in part to the lack of a clear definition of prevention and lack of guidance on how to achieve it. To address this gap, we elucidate the mechanisms linking environmental change and zoonotic spillover using spillover of viruses from bats as a case study. We identify ecological interventions that can disrupt these spillover mechanisms and propose policy frameworks for their implementation. Recognizing that pandemics originate in ecological systems, we advocate for integrating ecological approaches alongside biomedical approaches in a comprehensive and balanced pandemic prevention strategy.
Coupling artificial molecular spin states by photon-assisted tunnelling
Schreiber, L.R.; Braakman, Floris R.; Meunier, T.; et al. (2011)
Nature Communications
Artificial molecules containing just one or two electrons provide a powerful platform for studies of orbital and spin quantum dynamics in nanoscale devices. A well-known example of these dynamics is tunnelling of electrons between two coupled quantum dots triggered by microwave irradiation. So far, these tunnelling processes have been treated as electric-dipole-allowed spin-conserving events. Here we report that microwaves can also excite tunnelling transitions between states with different spin. We show that the dominant mechanism responsible for violation of spin conservation is the spin–orbit interaction. These transitions make it possible to perform detailed microwave spectroscopy of the molecular spin states of an artificial hydrogen molecule and open up the possibility of realizing full quantum control of a two-spin system through microwave excitation.
Distinct RPA domains promote recruitment and the helicase-nuclease activities of Dna2
Acharya, Ananya; Kasaciunaite, Kristina; Göse, Martin; et al. (2021)
Nature Communications
The Dna2 helicase-nuclease functions in concert with the replication protein A (RPA) in DNA double-strand break repair. Using ensemble and single-molecule biochemistry, coupled with structure modeling, we demonstrate that the stimulation of S. cerevisiae Dna2 by RPA is not a simple consequence of Dna2 recruitment to single-stranded DNA. The large RPA subunit Rfa1 alone can promote the Dna2 nuclease activity, and we identified mutations in a helix embedded in the N-terminal domain of Rfa1 that specifically disrupt this capacity. The same RPA mutant is instead fully functional to recruit Dna2 and promote its helicase activity. Furthermore, we found residues located on the outside of the central DNA-binding OB-fold domain Rfa1-A, which are required to promote the Dna2 motor activity. Our experiments thus unexpectedly demonstrate that different domains of Rfa1 regulate Dna2 recruitment, and its nuclease and helicase activities. Consequently, the identified separation-of-function RPA variants are compromised to stimulate Dna2 in the processing of DNA breaks. The results explain phenotypes of replication-proficient but radiation-sensitive RPA mutants and illustrate the unprecedented functional interplay of RPA and Dna2.
Constraints on soluble aerosol iron flux to the Southern Ocean at the Last Glacial Maximum
Conway, Timothy M.; Wolff, Eric W.; Röthlisberger, Regine; et al. (2015)
Nature Communications
Relief of iron (Fe) limitation in the Southern Ocean during ice ages, with potentially increased carbon storage in the ocean, has been invoked as one driver of glacial–interglacial atmospheric CO2 cycles. Ice and marine sediment records demonstrate that atmospheric dust supply to the oceans increased by up to an order of magnitude during glacial intervals. However, poor constraints on soluble atmospheric Fe fluxes to the oceans limit assessment of the role of Fe in glacial–interglacial change. Here, using novel techniques, we present estimates of water- and seawater-soluble Fe solubility in Last Glacial Maximum (LGM) atmospheric dust from the European Project for Ice Coring in Antarctica (EPICA) Dome C and Berkner Island ice cores. Fe solubility was very variable (1–42%) during the interval, and frequently higher than typically assumed by models. Soluble aerosol Fe fluxes to Dome C at the LGM (0.01–0.84 mg m−2 per year) suggest that soluble Fe deposition to the Southern Ocean would have been ≥10 × modern deposition, rivalling upwelling supply.
Mutational interactions define novel cancer subgroups
Kuipers, Jack; Thurnherr, Thomas; Moffa, Giusi; et al. (2018)
Nature Communications
Large-scale genomic data highlight the complexity and diversity of the molecular changes that drive cancer progression. Statistical analysis of cancer data from different tissues can guide drug repositioning as well as the design of targeted treatments. Here, we develop an improved Bayesian network model for tumour mutational profiles and apply it to 8198 patient samples across 22 cancer types from TCGA. For each cancer type, we identify the interactions between mutated genes, capturing signatures beyond mere mutational frequencies. When comparing mutation networks, we find genes which interact both within and across cancer types. To detach cancer classification from the tissue type we perform de novo clustering of the pancancer mutational profiles based on the Bayesian network models. We find 22 novel clusters which significantly improve survival prediction beyond clinical information. The models highlight key gene interactions for each cluster potentially allowing genomic stratification for clinical trials and identifying drug targets.
Explainable AI-based analysis of human pancreas sections identifies traits of type 2 diabetes
Klein, Lukas; Ziegler, Sebastian; Gerst, Felicia; et al. (2026)
Nature Communications
Type 2 diabetes (T2D) is a chronic disease currently affecting around 500 million people worldwide with often severe health consequences. Yet, histopathological analyses are still inadequate to infer the glycaemic state of a person based on morphological alterations linked to impaired insulin secretion and β-cell failure in T2D. Giga-pixel microscopy can capture subtle morphological changes, but data complexity exceeds human analysis capabilities. In response, we generate a dataset of pancreas whole-slide images from living donors with multiple chromogenic and multiplex immunofluorescence stainings and train deep learning models to predict the T2D status. Using explainable AI, we make the learned relationships interpretable, quantify them as biomarkers, and assess their association with T2D. Remarkably, the highest prediction performance is achieved by simultaneously focusing on islet α- and δ-cells and neuronal axons, alongside subtle pancreatic alterations in T2D donors such as larger adipocyte clusters, altered islet-adipocyte proximity and smaller islets. This data-driven approach provides a foundation for future research into relevant diagnostic and therapeutic targets, refining several hypotheses regarding tissue alterations associated with T2D.
Monitoring the mass, eigenfrequency, and quality factor of mammalian cells
Herzog, Sophie; Fläschner, Gotthold Viktor; Incaviglia, Ilaria; et al. (2024)
Nature Communications
The regulation of mass is essential for the development and homeostasis of cells and multicellular organisms. However, cell mass is also tightly linked to cell mechanical properties, which depend on the time scales at which they are measured and change drastically at the cellular eigenfrequency. So far, it has not been possible to determine cell mass and eigenfrequency together. Here, we introduce microcantilevers oscillating in the Ångström range to monitor both fundamental physical properties of the cell. If the oscillation frequency is far below the cellular eigenfrequency, all cell compartments follow the cantilever motion, and the cell mass measurements are accurate. Yet, if the oscillating frequency approaches or lies above the cellular eigenfrequency, the mechanical response of the cell changes, and not all cellular components can follow the cantilever motions in phase. This energy loss caused by mechanical damping within the cell is described by the quality factor. We use these observations to examine living cells across externally applied mechanical frequency ranges and to measure their total mass, eigenfrequency, and quality factor. The three parameters open the door to better understand the mechanobiology of the cell and stimulate biotechnological and medical innovations.
Large optical nonlinearity enhancement under electronic strong coupling
Wang, Kuidong; Seidel, Marcus; Nagarajan, Kalaivanan; et al. (2021)
Nature Communications
Nonlinear optical responses provide a powerful way to understand the microscopic interactions between laser fields and matter. They are critical for plenty of applications, such as in lasers, integrated photonic circuits, biosensing and medical tools. However, most materials exhibit weak optical nonlinearities or long response times when they interact with intense optical fields. Here, we strongly couple the exciton of cyanine dye J-aggregates to an optical mode of a Fabry-Perot (FP) cavity, and achieve an enhancement of the complex nonlinear refractive index by two orders of magnitude compared with that of the uncoupled condition. Moreover, the coupled system shows an ultrafast response of ~120 fs that we extract from optical cross-correlation measurements. The ultrafast and large enhancement of the optical nonlinar coefficients in this work paves the way for exploring strong coupling effects on various third-order nonlinear optical phenomena and for technological applications.
An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG
Sharifshazileh, Mohammadali; Burelo, Karla; Sarnthein, Johannes; et al. (2021)
Nature Communications
The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies.
Dynamic anticrack propagation in snow
Gaume, Johan; Gast, Theodore F.; Teran, Joseph M.; et al. (2018)
Nature Communications
Continuum numerical modeling of dynamic crack propagation has been a great challenge over the past decade. This is particularly the case for anticracks in porous materials, as reported in sedimentary rocks, deep earthquakes, landslides, and snow avalanches, as material inter-penetration further complicates the problem. Here, on the basis of a new elastoplasticity model for porous cohesive materials and a large strain hybrid EulerianLagrangian numerical method, we accurately reproduced the onset and propagation dynamics of anticracks observed in snow fracture experiments. The key ingredient consists of a modified strain-softening plastic flow rule that captures the complexity of porous materials under mixed-mode loading accounting for the interplay between cohesion loss and volumetric collapse. Our unified model represents a significant step forward as it simulates solid-fluid phase transitions in geomaterials which is of paramount importance to mitigate and forecast gravitational hazards.

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