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.
Microbial drivers of DMSO reduction and DMS-dependent methanogenesis in saltmarsh sediments
Saltmarshes are highly productive environments, exhibiting high abundances of organosulfur compounds. Dimethylsulfoniopropionate (DMSP) is produced in large quantities by algae, plants, and bacteria and is a potential precursor for dimethylsulfoxide (DMSO) and dimethylsulfide (DMS). DMSO serves as electron acceptor for anaerobic respiration leading to DMS formation, which is either emitted or can be degraded by methylotrophic prokaryotes. Major products of these reactions are trace gases with positive (CO2, CH4) or negative (DMS) radiative forcing with contrasting effects on the global climate. Here, we investigated organic sulfur cycling in saltmarsh sediments and followed DMSO reduction in anoxic batch experiments. Compared to previous measurements from marine waters, DMSO concentrations in the saltmarsh sediments were up to ~300 fold higher. In batch experiments, DMSO was reduced to DMS and subsequently consumed with concomitant CH4 production. Changes in prokaryotic communities and DMSO reductase gene counts indicated a dominance of organisms containing the Dms-type DMSO reductases (e.g., Desulfobulbales, Enterobacterales). In contrast, when sulfate reduction was inhibited by molybdate, Tor-type DMSO reductases (e.g., Rhodobacterales) increased. Vibrionales increased in relative abundance in both treatments, and metagenome assembled genomes (MAGs) affiliated to Vibrio had all genes encoding the subunits of DMSO reductases. Molar conversion ratios of <1.3 CH4 per added DMSO were accompanied by a predominance of the methylotrophic methanogens Methanosarcinales. Enrichment of mtsDH genes, encoding for DMS methyl transferases in metagenomes of batch incubations indicate their role in DMS-dependent methanogenesis. MAGs affiliated to Methanolobus carried the complete set of genes encoding for the enzymes in methylotrophic methanogenesis.
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