Nature最新文献

When humans assemble into face-to-face social networks, they create an extended social environment that permits exposure to the microbiome of others, thereby shaping the composition and diversity of the microbiome at individual and population levels^1,2,3,4,5,6. Here we use comprehensive social network mapping and detailed microbiome sequencing data in 1,787 adults within 18 isolated villages in Honduras⁷ to investigate the relationship between network structure and gut microbiome composition. Using both species-level and strain-level data, we show that microbial sharing occurs between many relationship types, notably including non-familial and non-household connections. Furthermore, strain-sharing extends to second-degree social connections, suggesting the relevance of a person’s broader network. We also observe that socially central people are more microbially similar to the overall village than socially peripheral people. Among 301 people whose microbiome was re-measured 2 years later, we observe greater convergence in strain-sharing in connected versus otherwise similar unconnected co-villagers. Clusters of species and strains occur within clusters of people in village social networks, meaning that social networks provide the social niches within which microbiome biology and phenotypic impact are manifested.

Human neural organoids, generated from pluripotent stem cells in vitro, are useful tools to study human brain development, evolution and disease. However, it is unclear which parts of the human brain are covered by existing protocols, and it has been difficult to quantitatively assess organoid variation and fidelity. Here we integrate 36 single-cell transcriptomic datasets spanning 26 protocols into one integrated human neural organoid cell atlas totalling more than 1.7 million cells^{1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26}. Mapping to developing human brain references^27,28,29,30 shows primary cell types and states that have been generated in vitro, and estimates transcriptomic similarity between primary and organoid counterparts across protocols. We provide a programmatic interface to browse the atlas and query new datasets, and showcase the power of the atlas to annotate organoid cell types and evaluate new organoid protocols. Finally, we show that the atlas can be used as a diverse control cohort to annotate and compare organoid models of neural disease, identifying genes and pathways that may underlie pathological mechanisms with the neural models. The human neural organoid cell atlas will be useful to assess organoid fidelity, characterize perturbed and diseased states and facilitate protocol development.

Green indium phosphide (InP)-based quantum dot light-emitting diodes (QD-LEDs) still suffer from low efficiency and short operational lifetime, posing a critical challenge to fully cadmium-free QD-LED displays and lighting^1,2,3. Unfortunately, the factors that underlie these limitations remain unclear and, therefore, no clear device-engineering guidelines are available. Here, by using electrically excited transient absorption spectroscopy, we find that the low efficiency of state-of-the-art green cadmium-free QD-LEDs (which ubiquitously adopt the InP–ZnSeS–ZnS core–shell–shell structure) originates from the ZnSeS interlayer because it imposes a high injection barrier that limits the electron concentration and trap saturation. We demonstrate, both experimentally and theoretically, that replacing the currently widely used ZnSeS interlayer with a thickened ZnSe interlayer enables improved electron injection and depressed leakage simultaneously, allowing to achieve a peak external quantum efficiency of 26.68% and T₉₅ lifetime (time for the luminance to drop to 95% of the initial value) of 1,241 h at an initial brightness of 1,000 cd m^–2 in green InP-based QD-LEDs emitting at 543 nm—exceeding the previous best values by a factor of 1.6 and 165, respectively^3,4.

arising from: J. Rockström et al. Nature https://doi.org/10.1038/s41586-023-06083-8 (2023).

The observed temperature record, which combines sea surface temperatures with near-surface air temperatures over land, is crucial for understanding climate variability and change^1,2,3,4. However, early records of global mean surface temperature are uncertain owing to changes in measurement technology and practice, partial documentation^5,6,7,8, and incomplete spatial coverage⁹. Here we show that existing estimates of ocean temperatures in the early twentieth century (1900–1930) are too cold, based on independent statistical reconstructions of the global mean surface temperature from either ocean or land data. The ocean-based reconstruction is on average about 0.26 °C colder than the land-based one, despite very high agreement in all other periods. The ocean cold anomaly is unforced, and internal variability in climate models cannot explain the observed land–ocean discrepancy. Several lines of evidence based on attribution, timescale analysis, coastal grid cells and palaeoclimate data support the argument of a substantial cold bias in the observed global sea-surface-temperature record in the early twentieth century. Although estimates of global warming since the mid-nineteenth century are not affected, correcting the ocean cold bias would result in a more modest early-twentieth-century warming trend¹⁰, a lower estimate of decadal-scale variability inferred from the instrumental record³, and better agreement between simulated and observed warming than existing datasets suggest².