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Abstract Herbicide resistance represents one of the biggest threats to our natural environment and agricultural sector. Thus, new herbicides are urgently needed to tackle the rise in herbicide-resistant weeds. Here, we employed a novel strategy to repurpose a ‘failed’ antibiotic into a new and target-specific herbicidal compound. Specifically, we identified an inhibitor of bacterial dihydrodipicolinate reductase (DHDPR), an enzyme involved in lysine biosynthesis in plants and bacteria, that exhibited no antibacterial activity but severely attenuated germination of the plant Arabidopsis thaliana. We confirmed that the inhibitor targets plant DHDPR orthologues in vitro, and exhibits no toxic effects against human cell lines. A series of analogues were then synthesised with improved efficacy in germination assays and against soil-grown A. thaliana. We also showed that our lead compound is the first lysine biosynthesis inhibitor with activity against both monocotyledonous and dicotyledonous weed species, by demonstrating its effectiveness at reducing the germination and growth of Lolium rigidum (rigid ryegrass) and Raphanus raphanistrum (wild radish). These results provide proof-of-concept that DHDPR inhibition may represent a much-needed new herbicide mode of action. Furthermore, this study exemplifies the untapped potential of repurposing ‘failed’ antibiotic scaffolds to fast-track the development of herbicide candidates targeting the respective plant enzymes.

We present the Galaxy Assembly and Interaction Neural Networks ( Gainn ), a series of artificial neural networks for predicting the redshift, stellar mass, halo mass, and mass-weighted age of simulated galaxies based on James Webb Space Telescope (JWST) photometry. Our goal is to determine the best neural network for predicting these variables at 11 < z < 15. The parameters of the optimal neural network can then be used to estimate these variables for real, observed galaxies. The inputs of the neural networks are JWST filter magnitudes of a subset of five broadband filters (F150W, F200W, F277W, F356W, and F444W) and two medium-band filters (F162M and F182M). We compare the performance of the neural networks using different combinations of these filters, as well as different activation functions and numbers of layers. The best neural network predicted redshift with a normalized rms error of ${0.010}_{-0.001}^{+0.003}$ , stellar mass with rms = ${0.089}_{-0.022}^{+0.044}$ , halo mass with a mean-squared error of ${0.022}_{-0.008}^{+0.014}$ , and mass-weighted age with rms = ${12.466}_{-2.408}^{+5.065}$ . We also test the performance of Gainn on real data from MACS0647JD, an object observed by JWST. Predictions from Gainn for the first projection of the object (JD1) have normalized bias 〈Δ z 〉 < 0.00228, which is significantly smaller than found with template-fitting methods. We find that the optimal filter combination is F277W, F356W, F162M, and F200W when considering both theoretical accuracy and observational resources from JWST.

In this fourth paper from the AGORA Collaboration, we study the evolution down to redshift z = 2 and below of a set of cosmological zoom-in simulations of a Milky Way mass galaxy by eight of the leading hydrodynamic simulation codes. We also compare this CosmoRun suite of simulations with dark matter-only simulations by the same eight codes. We analyze general properties of the halo and galaxy at z = 4 and 3, and before the last major merger, focusing on the formation of well-defined rotationally supported disks, the mass–metallicity relation, the specific star formation rate, the gas metallicity gradients, and the nonaxisymmetric structures in the stellar disks. Codes generally converge well to the stellar-to-halo mass ratios predicted by semianalytic models at z ∼ 2. We see that almost all the hydro codes develop rotationally supported structures at low redshifts. Most agree within 0.5 dex with the observed mass–metallicity relation at high and intermediate redshifts, and reproduce the gas metallicity gradients obtained from analytical models and low-redshift observations. We confirm that the intercode differences in the halo assembly history reported in the first paper of the collaboration also exist in CosmoRun , making the code-to-code comparison more difficult. We show that such differences are mainly due to variations in code-dependent parameters that control the time stepping strategy of the gravity solver. We find that variations in the early stellar feedback can also result in differences in the timing of the low-redshift mergers. All the simulation data down to z = 2 and the auxiliary data will be made publicly available.

We analyze the circumgalactic medium (CGM) for eight commonly-used cosmological codes in the AGORA collaboration. The codes are calibrated to use identical initial conditions, cosmology, heating and cooling, and star formation thresholds, but each evolves with its own unique code architecture and stellar feedback implementation. Here, we analyze the results of these simulations in terms of the structure, composition, and phase dynamics of the CGM. We show properties such as metal distribution, ionization levels, and kinematics are effective tracers of the effects of the different code feedback and implementation methods, and as such they can be highly divergent between simulations. This is merely a fiducial set of models, against which we will in the future compare multiple feedback recipes for each code. Nevertheless, we find that the large parameter space these simulations establish can help disentangle the different variables that affect observable quantities in the CGM, e.g., showing that abundances for ions with higher ionization energy are more strongly determined by the simulation’s metallicity, while abundances for ions with lower ionization energy are more strongly determined by the gas density and temperature.

MACS0647–JD is a triply lensed z ∼ 11 galaxy originally discovered with the Hubble Space Telescope. The three lensed images are magnified by factors of ∼8, 5, and 2 to AB mag 25.1, 25.6, and 26.6 at 3.5 μ m. The brightest is over a magnitude brighter than other galaxies recently discovered at similar redshifts z > 10 with JWST. Here, we report new JWST imaging that clearly resolves MACS0647–JD as having two components that are either merging galaxies or stellar complexes within a single galaxy. The brighter larger component “A” is intrinsically very blue ( β ∼ −2.6 ± 0.1), likely due to very recent star formation and no dust, and is spatially extended with an effective radius ∼70 ± 24 pc. The smaller component “B” ( r ∼ 20 ${}_{-5}^{+8}\,$ pc) appears redder ( β ∼ −2 ± 0.2), likely because it is older (100–200 Myr) with mild dust extinction ( A _V ∼ 0.1 mag). With an estimated stellar mass ratio of roughly 2:1 and physical projected separation ∼400 pc, we may be witnessing a galaxy merger 430 million years after the Big Bang. We identify galaxies with similar colors in a high-redshift simulation, finding their star formation histories to be dissimilar, which is also suggested by the spectral energy distribution fitting, suggesting they formed further apart. We also identify a candidate companion galaxy “C” ∼3 kpc away, likely destined to merge with A and B. Upcoming JWST Near Infrared Spectrograph observations planned for 2023 January will deliver spectroscopic redshifts and more physical properties for these tiny magnified distant galaxies observed in the early universe.

Herbicides with novel modes of action are urgently needed to safeguard global agricultural industries against the damaging effects of herbicide-resistant weeds. We recently developed the first herbicidal inhibitors of lysine biosynthesis, which provided proof-of-concept for a promising novel herbicide target. In this study, we expanded upon our understanding of the mode of action of herbicidal lysine biosynthesis inhibitors. We previously postulated that these inhibitors may act as proherbicides. Here, we show this is not the case. We report an additional mode of action of these inhibitors, through their inhibition of a second lysine biosynthesis enzyme, and investigate the molecular determinants of inhibition. Furthermore, we extend our herbicidal activity analyses to include a weed species of global significance.