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Self-assembled materials capable of modulating their assembly properties in response to specific enzymes play a pivotal role in advancing ‘intelligent’ encapsulation platforms for biotechnological applications. Here, we introduce a previously unreported class of synthetic nanomaterials that programmatically interact with histone deacetylase (HDAC) as the triggering stimulus for disassembly. These nanomaterials consist of co-polypeptides comprising poly (acetyl L-lysine) and poly(ethylene glycol) blocks. Under neutral pH conditions, they self-assemble into particles. However, their stability is compromised upon exposure to HDACs, depending on enzyme concentration and exposure time. Our investigation, utilizing HDAC8 as the model enzyme, revealed that the primary mechanism behind disassembly involves a decrease in amphiphilicity within the block copolymer due to the deacetylation of lysine residues within the particles’ hydrophobic domains. To elucidate the response mechanism, we encapsulated a fluorescent dye within these nanoparticles. Upon incubation with HDAC, the nanoparticle structure collapsed, leading to controlled release of the dye over time. Notably, this release was not triggered by denatured HDAC8, other proteolytic enzymes like trypsin, or the co-presence of HDAC8 and its inhibitor. We further demonstrated the biocompatibility and cellular effects of these materials and conducted a comprehensive computational study to unveil the possible interaction mechanism between enzymes and particles. By drawing parallels to the mechanism of naturally occurring histone proteins, this research represents a pioneering step toward developing functional materials capable of harnessing the activity of epigenetic enzymes such as HDACs. 

Recent observational and theoretical studies have suggested that supermassive black holes (SMBHs) grow mostly through non-merger (‘secular’) processes. Since galaxy mergers lead to dynamical bulge growth, the only way to observationally isolate non-merger growth is to study galaxies with low bulge-to-total mass ratio (e.g. $B/T\lt 10~{{\ \rm per\ cent}}$). However, bulge growth can also occur due to secular processes, such as disc instabilities, making disc-dominated selections a somewhat incomplete way to select merger-free systems. Here we use the Horizon-AGN simulation to select simulated galaxies which have not undergone a merger since z = 2, regardless of bulge mass, and investigate their location on typical black hole-galaxy scaling relations in comparison to galaxies with merger dominated histories. While the existence of these correlations has long been interpreted as co-evolution of galaxies and their SMBHs driven by galaxy mergers, we show here that they persist even in the absence of mergers. We find that the correlations between SMBH mass and both total mass and stellar velocity dispersion are independent of B/T ratio for both merger-free and merger-dominated galaxies. In addition, the bulge mass and SMBH mass correlation is still apparent for merger-free galaxies, the intercept for which is dependent on B/T. Galaxy mergers reduce the scatter around the scaling relations, with merger-free systems showing broader scatter. We show that for merger-free galaxies, the co-evolution is dominated by radio-mode feedback, and suggest that the long periods of time between galaxy mergers make an important contribution to the co-evolution between galaxies and SMBHs in all galaxies.

 

Here, we use the Horizon–active galactic nucleus (AGN) simulation to test whether the spins of supermassive black hole (SMBH) in merger-free galaxies are higher. We select samples using an observationally motivated bulge-to-total mass ratio of <0.1, along with two simulation-motivated thresholds selecting galaxies which have not undergone a galaxy merger since z = 2, and those SMBHs with $\lt 10~{{\ \rm per\ cent}}$ of their mass due to SMBH mergers. We find higher spins (>5σ) in all three sample compared to the rest of the population. In addition, we find that SMBHs with their growth dominated by BH mergers following galaxy mergers are less likely to be aligned with their galaxy spin than those that have grown through accretion in the absence of galaxy mergers (3.4σ). We discuss the implications this has for the impact of active galactic nucleus (AGN) feedback, finding that merger-free SMBHs spend on average 91 per cent of their lifetimes since z = 2 in a radio mode of feedback (88 per cent for merger-dominated galaxies). Given that previous observational and theoretical works have concluded that merger-free processes dominate SMBH-galaxy co-evolution, our results suggest that this co-evolution could be regulated by radio mode AGN feedback.

 

We measure the mean free path ($\lambda _{\rm mfp,H\, \small {I}}$), photoionization rate ($\langle \Gamma _{\rm H\, \small {I}} \rangle$), and neutral fraction ($\langle f_{\rm H\, \small {I}} \rangle$) of hydrogen in 12 redshift bins at 4.85 < z < 6.05 from a large sample of moderate resolution XShooter and ESI QSO absorption spectra. The fluctuations in ionizing radiation field are modelled by post-processing simulations from the Sherwood suite using our new code ‘EXtended reionization based on the Code for Ionization and Temperature Evolution’ (ex-cite). ex-cite uses efficient Octree summation for computing intergalactic medium attenuation and can generate large number of high resolution $\Gamma _{\rm H\, \small {I}}$ fluctuation models. Our simulation with ex-cite shows remarkable agreement with simulations performed with the radiative transfer code Aton and can recover the simulated parameters within 1σ uncertainty. We measure the three parameters by forward-modelling the  Lyα forest and comparing the effective optical depth ($\tau _{\rm eff, H\, \small {I}}$) distribution in simulations and observations. The final uncertainties in our measured parameters account for the uncertainties due to thermal parameters, modelling parameters, observational systematics, and cosmic variance. Our best-fitting parameters show significant evolution with redshift such that $\lambda _{\rm mfp,H\, \small {I}}$ and $\langle f_{\rm H\, \small {I}} \rangle$ decreases and increases by a factor ∼6 and ∼104, respectively from z ∼ 5 to z ∼ 6. By comparing our $\lambda _{\rm mfp,H\, \small {I}}$, $\langle \Gamma _{\rm H\, \small {I}} \rangle$ and $\langle f_{\rm H\, \small {I}} \rangle$ evolution with that in state-of-the-art Aton radiative transfer simulations and the Thesan and CoDa-III simulations, we find that our best-fitting parameter evolution is consistent with a model in which reionization completes by z ∼ 5.2. Our best-fitting model that matches the $\tau _{\rm eff, H\, \small {I}}$ distribution also reproduces the dark gap length distribution and transmission spike height distribution suggesting robustness and accuracy of our measured parameters.

 

Dipolarizing flux bundles (DFBs) have been suggested to transport energy and momentum from regions of reconnection in the magnetotail to the high latitude ionosphere, where they can generate localized ionospheric currents that can produce large nighttime geomagnetic disturbances (GMDs). In this study we identified DFBs observed in the midnight sector from ∼7 to ∼10 R_Eby THEMIS A, D, and E during days in 2015–2017 whose northern hemisphere magnetic footpoints mapped to regions near Hudson Bay, Canada, and have compared them to isolated GMDs observed by ground magnetometers. We found 6 days during which one or more of these DFBs coincided to within ±3 min with ≥6 nT/s GMDs observed by latitudinally closely spaced ground‐based magnetometers located near those footpoints. Spherical elementary current systems (SECS) maps and all‐sky imager data provided further characterization of two events, showing short‐lived localized intense upward currents, auroral intensifications and/or streamers, and vortical perturbations of a westward electrojet. On all but one of these days the coincident DFB—GMD pairs occurred during intervals of high‐speed solar wind streams but low values of SYM/H. The observations reported here indicate that isolated DFBs generated under these conditions influence only limited spatial regions nearer Earth. In some events, in which the DFBs were observed closer to Earth and with lower Earthward velocities, the GMDs occurred slightly earlier than the DFBs, suggesting that braking had begun before the time of the DFB observation.

 

Responses of the terrestrial biosphere to rapidly changing environmental conditions are a major source of uncertainty in climate projections. In an effort to reduce this uncertainty, a wide range of global change experiments have been conducted that mimic future conditions in terrestrial ecosystems, manipulating CO₂, temperature, and nutrient and water availability. Syntheses of results across experiments provide a more general sense of ecosystem responses to global change, and help to discern the influence of background conditions such as climate and vegetation type in determining global change responses. Several independent syntheses of published data have yielded distinct databases for specific objectives. Such parallel, uncoordinated initiatives carry the risk of producing redundant data collection efforts and have led to contrasting outcomes without clarifying the underlying reason for divergence. These problems could be avoided by creating a publicly available, updatable, curated database. Here, we report on a global effort to collect and curate 57,089 treatment responses across 3644 manipulation experiments at 1145 sites, simulating elevated CO₂, warming, nutrient addition, and precipitation changes. In the resulting Manipulation Experiments Synthesis Initiative (MESI) database, effects of experimental global change drivers on carbon and nutrient cycles are included, as well as ancillary data such as background climate, vegetation type, treatment magnitude, duration, and, unique to our database, measured soil properties. Our analysis of the database indicates that most experiments are short term (one or few growing seasons), conducted in the USA, Europe, or China, and that the most abundantly reported variable is aboveground biomass. We provide the most comprehensive multifactor global change database to date, enabling the research community to tackle open research questions, vital to global policymaking. The MESI database, freely accessible atdoi.org/10.5281/zenodo.7153253, opens new avenues for model evaluation and synthesis‐based understanding of how global change affects terrestrial biomes. We welcome contributions to the database on GitHub.

 

Abstract Benchmark datasets and benchmark problems have been a key aspect for the success of modern machine learning applications in many scientific domains. Consequently, an active discussion about benchmarks for applications of machine learning has also started in the atmospheric sciences. Such benchmarks allow for the comparison of machine learning tools and approaches in a quantitative way and enable a separation of concerns for domain and machine learning scientists. However, a clear definition of benchmark datasets for weather and climate applications is missing with the result that many domain scientists are confused. In this paper, we equip the domain of atmospheric sciences with a recipe for how to build proper benchmark datasets, a (nonexclusive) list of domain-specific challenges for machine learning is presented, and it is elaborated where and what benchmark datasets will be needed to tackle these challenges. We hope that the creation of benchmark datasets will help the machine learning efforts in atmospheric sciences to be more coherent, and, at the same time, target the efforts of machine learning scientists and experts of high-performance computing to the most imminent challenges in atmospheric sciences. We focus on benchmarks for atmospheric sciences (weather, climate, and air-quality applications). However, many aspects of this paper will also hold for other aspects of the Earth system sciences or are at least transferable. Significance Statement Machine learning is the study of computer algorithms that learn automatically from data. Atmospheric sciences have started to explore sophisticated machine learning techniques and the community is making rapid progress on the uptake of new methods for a large number of application areas. This paper provides a clear definition of so-called benchmark datasets for weather and climate applications that help to share data and machine learning solutions between research groups to reduce time spent in data processing, to generate synergies between groups, and to make tool developments more targeted and comparable. Furthermore, a list of benchmark datasets that will be needed to tackle important challenges for the use of machine learning in atmospheric sciences is provided. 

The mean free path of ionizing photons,λ_mfp, is a critical parameter for modeling the intergalactic medium (IGM) both during and after reionization. We present direct measurements ofλ_mfpfrom QSO spectra over the redshift range 5 <z< 6, including the first measurements atz≃ 5.3 and 5.6. Our sample includes data from the XQR-30 VLT large program, as well as new Keck/ESI observations of QSOs nearz∼ 5.5, for which we also acquire new [Cii] 158μm redshifts with ALMA. By measuring the Lyman continuum transmission profile in stacked QSO spectra, we find${\lambda }_{\mathrm{mfp}}={9.33}_{-1.80}^{+2.06}$,${5.40}_{-1.40}^{+1.47}$,${3.31}_{-1.34}^{+2.74}$, and${0.81}_{-0.48}^{+0.73}$pMpc atz= 5.08, 5.31, 5.65, and 5.93, respectively. Our results demonstrate thatλ_mfpincreases steadily and rapidly with time over 5 <z< 6. Notably, we find thatλ_mfpdeviates significantly from predictions based on a fully ionized and relaxed IGM as late asz= 5.3. By comparing our results to model predictions and indirectλ_mfpconstraints based on IGM Lyαopacity, we find that the evolution ofλ_mfpis consistent with scenarios wherein the IGM is still undergoing reionization and/or retains large fluctuations in the ionizing UV background well below redshift 6.

 

Proximity zones of high-redshift quasars are unique probes of their central supermassive black holes as well as the intergalactic medium (IGM) in the last stages of reionization. We present 22 new measurements of proximity zones of quasars with redshifts between 5.8 and 6.6, using the enlarged XQR-30 sample of high-resolution, high-SNR quasar spectra. The quasars in our sample have ultraviolet magnitudes of M1450 ∼ −27 and black hole masses of 109–1010 M⊙. Our inferred proximity zone sizes are 2–7 physical Mpc, with a typical uncertainty of less than 0.5 physical Mpc, which, for the first time, also includes uncertainty in the quasar continuum. We find that the correlation between proximity zone sizes and the quasar redshift, luminosity, or black hole mass, indicates a large diversity of quasar lifetimes. Two of our proximity zone sizes are exceptionally small. The spectrum of one of these quasars, with z  = 6.02, displays, unusually for this redshift, damping wing absorption without any detectable metal lines, which could potentially originate from the IGM. The other quasar has a high-ionization absorber ∼0.5 pMpc from the edge of the proximity zone. This work increases the number of proximity zone measurements available in the last stages of cosmic reionization to 87. This data will lead to better constraints on quasar lifetimes and obscuration fractions at high redshift, that in turn will help probe the seed mass and formation redshift of supermassive black holes.