Microglial diversity arises from the interplay between inherent genetic programs and external environmental signals. However, the mechanisms by which these processes develop and interact within the growing brain are not yet fully understood. Here, we show that radial glia-expressed integrin beta 8 (ITGB8) activates microglia-expressed TGFβ1 to drive microglial development. Domain-restricted deletion of Itgb8 in these progenitors results in regionally restricted and developmentally arrested microglia that persist into adulthood. In the absence of autocrine TGFβ1 signaling, microglia adopt a similar phenotype, leading to neuromotor symptoms almost identical to Itgb8 mutant mice. In contrast, microglia lacking the canonical TGFβ signal transducers Smad2 and Smad3 have a less polarized dysmature phenotype and correspondingly less severe neuromotor dysfunction. Our study describes the spatio-temporal regulation of TGFβ activation and signaling in the brain necessary to promote microglial development, and provides evidence for the adoption of microglial developmental signaling pathways in brain injury or disease.

Here, we present StimExo as a rational design strategy allowing various user-defined control signals to trigger calcium-dependent exocytosis and mediate on-demand protein secretion in cell-therapy settings. Using a modular framework incorporating inducible protein-protein interactions into an engineered bipartite activator of calcium release–activated calcium (CRAC) channels, Ca2+ influx mediated by the STIM/Orai1 machinery was flexibly adjusted to depend on different user-defined input signals. Application of StimExo to various endocrine cells enables instant secretion of therapeutic hormones upon administration of safe and patient-compliant trigger compounds. StimExo also mediated insulin exocytosis using a cell-based gene delivery strategy in vivo, accounting for real-time control of blood glucose homeostasis in male diabetic mice in response to the FDA-approved drug grazoprevir. This study achieves true “sense-and-respond” cell-based therapies and provides a platform for remote control of in vivo transgene activities using various trigger signals of interest.

What features of transcription can be learnt by fitting mathematical models of gene expression to mRNA count data? Given a suite of models, fitting to data selects an optimal one, thus identifying a probable transcriptional mechanism. Whilst attractive, the utility of this methodology remains unclear. Here, we sample steady-state, single-cell mRNA count distributions from parameters in the physiological range, and show they cannot be used to confidently estimate the number of inactive gene states, i.e. the number of rate-limiting steps in transcriptional initiation. Distributions from over 99% of the parameter space generated using models with 2, 3, or 4 inactive states can be well fit by one with a single inactive state. However, we show that for many minutes following induction, eukaryotic cells show an increase in the mean mRNA count that obeys a power law whose exponent equals the sum of the number of states visited from the initial inactive to the active state and the number of rate-limiting post-transcriptional processing steps. Our study shows that estimation of the exponent from eukaryotic data can be sufficient to determine a lower bound on the total number of regulatory steps in transcription initiation, splicing, and nuclear export.

Energy management technologies have significant potential to optimize electric vehicle performance and support global energy sustainability. However, despite extensive research, their real-world application remains limited due to reliance on simulations, which often fail to bridge the gap between theory and practice. This study introduces a real-world data-driven energy management framework based on offline reinforcement learning. By leveraging electric vehicle operation data, the proposed approach eliminates the need for manually designed rules or reliance on high-fidelity simulations. It integrates seamlessly into existing frameworks, enhancing performance after deployment. The method is tested on fuel cell electric vehicles, optimizing energy consumption and reducing system degradation. Real-world data from an electric vehicle monitoring system in China validate its effectiveness. The results demonstrate that the proposed method consistently achieves superior performance under diverse conditions. Notably, with increasing data availability, performance improves significantly, from 88% to 98.6% of the theoretical optimum after two updates. Training on over 60 million kilometers of data enables the learning agent to generalize across previously unseen and corner-case scenarios. These findings highlight the potential of data-driven methods to enhance energy efficiency and vehicle longevity through large-scale vehicle data utilization.

Obtaining a geocentric directional reference from a celestial compass requires compensation for the sun’s movement during the day (relative to the observer), which depends on the earth’s rotation, time of year and the observer’s latitude. We examine how insects could solve this problem, assuming they have clock neurons that represent time as a sinusoidal oscillation, and taking into account the neuroanatomy of their celestial compass pathway. We show how this circuit could exploit trigonometric identities to perform the required spatiotemporal calculations. Our basic model assumes a constant change in sun azimuth (the ‘hour angle’), which is recentred on solar noon for changing day lengths. In a more complete model, the time of year is represented by an oscillation with an annual period, and the latitude is estimated from the inclination of the geomagnetic field. Evaluating these models in simulated migration and foraging behaviours shows the hour angle may be sufficient.

In patients with atrophic age-related macular degeneration, subretinal photovoltaic implant (PRIMA) provided visual acuity up to 20/440, matching its 100 µm pixels size. Next-generation implants with smaller pixels should significantly improve the acuity. This study in rats evaluates removal of a subretinal implant, replacement with a newer device, and the resulting grating acuity in-vivo. Six weeks after the initial implantation with planar and 3-dimensional devices, the retina was re-detached, and the devices were successfully removed. Histology demonstrated a preserved inner nuclear layer. Re-implantation of new devices into the same location demonstrated retinal re-attachment to a new implant. New devices with 22 µm pixels increased the grating acuity from the 100 µm capability of PRIMA implants to 28 µm, reaching the limit of natural resolution in rats. Reimplanted devices exhibited the same stimulation threshold as for the first implantation of the same implants in a control group. This study demonstrates the feasibility of safely upgrading the subretinal photovoltaic implants to improve prosthetic visual acuity.

In this work, the effect of lattice structure on the corrosion behavior and passivation film properties of reinforced Al0.5Ti3Zr0.5NbxMo0.2 (x = 0.5,0.8,1) high-entropy alloys are investigated. A single-phase BCC Al0.5Ti3Zr0.5NbxMo0.2 (x = 0.5, 0.8, 1) high-entropy alloys, exhibiting good corrosion resistance, are synthesized using vacuum arc melting. Nb improves the corrosion resistance of high-entropy alloys in two main ways. On the one hand, the alloys show preferential corrosion at the {011} crystalline planes. Increasing Nb content reduced the {011} crystalline plane spacing, enhancing the corrosion resistance of Al0.5Ti3Zr0.5NbMo0.2. On the other hand, during the corrosion process, Nb, which has a large atomic radius and strong oxygenophilicity, interacts with each metal element, contributing to the uphill diffusion of Al/Ti and the downhill diffusion of O. The low-valent oxides form first continuously react with the inward-diffusing O to form high-valent oxides. This results in the formation of a layered passivation film with high breakdown potential and high stability. This work provides a basis for designing chemically robust alloys for extreme environments.

Surface acoustic wave devices are key components for processing radio frequency signals in wireless communication because these devices offer simultaneously high performance, compact size and low cost. The optimization of the device structure requires a quantitative understanding of energy conversion and loss mechanisms. Here we use stroboscopic full-field diffraction x-ray microscopy to reveal an unanticipated acoustic loss in a prototypical one-port resonator device. A non-uniform acoustic excitation in the active area was responsible for the substantial end and side leakages observed at the design frequency. Quantitative analysis of the strain amplitude using a wave decomposition method allowed the determination of several key device parameters. This high-resolution high-throughput spatiotemporal strain imaging technique is more generally applicable to the study of dynamic strain modulation in nanoscale acoustic, electronic, optical and quantum devices. The high sensitivity allows precise measurement of the strain modulation with picometer-scale amplitude.

Personalized synergistic antibacterial agents against diverse bacterial strains are receiving increasing attention in combating antimicrobial resistance. However, the current research has been struggling to strike a balance between strain specificity and broad-spectrum bactericidal activity. Here, we propose a bacterial cell wall-specific antibacterial strategy based on an in situ engineered nanocomposite consisting of carbon substrate and decorated TiOx dots, termed TiOx@C. The fiber-like carbon substrate of TiOx@C is able to penetrate the bacterial membrane of Pseudomonas aeruginosa (P. aeruginosa), but not that of Staphylococcus aureus (S. aureus) due to its thicker bacterial wall, thus achieving bacterial wall specificity. Furthermore, a series of experiments demonstrate the specific electro-mechanical co-sterilization effect of TiOx@C. On the one hand, TiOx@C can disrupt the electron transport chain and block the energy supply of S. aureus. On the other hand, TiOx@C capable of destroying the membrane structure of P. aeruginosa could cause severe mechanical damage to P. aeruginosa as well as inducing oxidative stress and protein leakage. In vivo experiments demonstrate the efficacy of TiOx@C in eliminating 97% of bacteria in wounds and promoting wound healing in wound-infected female mice. Overall, such a bacterial cell wall-specific nanomedicine presents a promising strategy for non-antibiotic treatments for bacterial diseases.

Age-related macular degeneration (AMD) is a prevalent neuroinflammation condition and the leading cause of irreversible blindness among the elderly population. Smoking significantly increases AMD risk, yet the mechanisms remain unclear. Here, we investigate the role of Sema4D-PlexinB1 axis in the progression of AMD, in which Sema4D-PlexinB1 is highly activated by smoking. Using patient-derived samples and mouse models, we discover that smoking increases the presence of Sema4D on the surface of CD8+ T cells that migrate into the choroidal neovascularization (CNV) lesion via CXCL12-CXCR4 axis and interact with its receptor PlexinB1 on choroidal pericytes. This leads to ROR2-mediated PlexinB1 phosphorylation and pericyte activation, thereby disrupting vascular homeostasis and promoting neovascularization. Inhibition of Sema4D reduces CNV and improves the benefit of anti-VEGF treatment. In conclusion, this study unveils the molecular mechanisms through which smoking exacerbates AMD pathology, and presents a potential therapeutic strategy by targeting Sema4D to augment current AMD treatments.