Matching high-redshift galaxy counts with ΛCDM
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Photometric detection at 7.7 μm of a galaxy beyond redshift 14 with JWST/MIRI
The James Webb Space Telescope (JWST) has spectroscopically confirmed numerous galaxies at z > 10. While weak rest-frame ultraviolet emission lines have only been seen in a handful of sources, the stronger rest-frame optical emission lines are highly diagnostic and accessible at mid-infrared wavelengths with the Mid-Infrared Instrument (MIRI) of JWST. We report the photometric detection of the distant spectroscopically confirmed galaxy JADES-GS-z14-0 at (z=14.3{2}_{-0.20}^{+0.08}) with MIRI at 7.7 μm. The most plausible solution for the stellar-population properties is that this galaxy contains half a billion solar masses in stars with a strong burst of star formation in the most recent few million years. For this model, at least one-third of the flux at 7.7 μm originates from the rest-frame optical emission lines Hβ and/or [O iii]λλ4959, 5007. The inferred properties of JADES-GS-z14-0 suggest rapid mass assembly and metal enrichment during the earliest phases of galaxy formation. This work demonstrates the unique power of mid-infrared observations in understanding galaxies at the redshift frontier.
Discovering fully semantic representations via centroid- and orientation-aware feature learning
Learning meaningful representations of images in scientific domains that are robust to variations in centroids and orientations remains an important challenge. Here we introduce centroid- and orientation-aware disentangling autoencoder (CODAE), an encoder–decoder-based neural network that learns meaningful content of objects in a latent space. Specifically, a combination of a translation- and rotation-equivariant encoder, Euler encoding and an image moment loss enables CODAE to extract features invariant to positions and orientations of objects of interest from randomly translated and rotated images. We evaluate this approach on several publicly available scientific datasets, including protein images from life sciences, four-dimensional scanning transmission electron microscopy data from material science and galaxy images from astronomy. The evaluation shows that CODAE learns centroids, orientations and their invariant features and outputs, as well as aligned reconstructions and the exact view reconstructions of the input images with high quality.
Microglial mechanisms drive amyloid-β clearance in immunized patients with Alzheimer’s disease
Alzheimer’s disease (AD) therapies utilizing amyloid-β (Aβ) immunization have shown potential in clinical trials. Yet, the mechanisms driving Aβ clearance in the immunized AD brain remain unclear. Here, we use spatial transcriptomics to explore the effects of both active and passive Aβ immunization in the AD brain. We compare actively immunized patients with AD with nonimmunized patients with AD and neurologically healthy controls, identifying distinct microglial states associated with Aβ clearance. Using high-resolution spatial transcriptomics alongside single-cell RNA sequencing, we delve deeper into the transcriptional pathways involved in Aβ removal after lecanemab treatment. We uncover spatially distinct microglial responses that vary by brain region. Our analysis reveals upregulation of the triggering receptor expressed on myeloid cells 2 (TREM2) and apolipoprotein E (APOE) in microglia across immunization approaches, which correlate positively with antibody responses and Aβ removal. Furthermore, we show that complement signaling in brain myeloid cells contributes to Aβ clearance after immunization. These findings provide new insights into the transcriptional mechanisms orchestrating Aβ removal and shed light on the role of microglia in immune-mediated Aβ clearance. Importantly, our work uncovers potential molecular targets that could enhance Aβ-targeted immunotherapies, offering new avenues for developing more effective therapeutic strategies to combat AD.
Cell2fate infers RNA velocity modules to improve cell fate prediction
RNA velocity exploits the temporal information contained in spliced and unspliced RNA counts to infer transcriptional dynamics. Existing velocity models often rely on coarse biophysical simplifications or numerical approximations to solve the underlying ordinary differential equations (ODEs), which can compromise accuracy in challenging settings, such as complex or weak transcription rate changes across cellular trajectories. Here we present cell2fate, a formulation of RNA velocity based on a linearization of the velocity ODE, which allows solving a biophysically more accurate model in a fully Bayesian fashion. As a result, cell2fate decomposes the RNA velocity solutions into modules, providing a biophysical connection between RNA velocity and statistical dimensionality reduction. We comprehensively benchmark cell2fate in real-world settings, demonstrating enhanced interpretability and power to reconstruct complex dynamics and weak dynamical signals in rare and mature cell types. Finally, we apply cell2fate to the developing human brain, where we spatially map RNA velocity modules onto the tissue architecture, connecting the spatial organization of tissues with temporal dynamics of transcription.
Bottom-up fabrication of 2D Rydberg exciton arrays in cuprous oxide
Solid-state platforms provide exceptional opportunities for advancing on-chip quantum technologies by enhancing interaction strengths through coupling, scalability, and robustness. Cuprous oxide (Cu2O) has recently emerged as a promising medium for scalable quantum technology due to its high-lying Rydberg excitonic states, akin to those in hydrogen atoms. To harness these nonlinearities for quantum applications, the confinement dimensions must match the Rydberg blockade size, which can reach several microns in Cu2O. Using a CMOS-compatible growth technique, this study demonstrates the bottom-up fabrication of site-selective arrays of Cu2O microparticles. We observed Rydberg excitons up to the principal quantum number n = 5 within these Cu2O arrays on a quartz substrate and analyzed the spatial variation of their spectrum across the array, showing robustness and reproducibility on a large chip. These results lay the groundwork for the deterministic growth of Cu2O around photonic structures, enabling substantial light-matter interaction on integrated photonic platforms and paving the way for scalable, on-chip quantum devices.
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