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Abstract Subparsec binary supermassive black holes (BSBHs) should be common from galaxy mergers, yet direct evidence has been elusive. We present Hubble Space Telescope (HST)/WFC3IR F160W imaging for a sample of eight candidate subparsec BSBHs at redshiftsz ∼ 0.1–0.5, as well as cross-comparison with a sample of ordinary quasars with archival HST/WFC3 IR F160W images. These eight candidate subparsec BSBHs were identified from multiepoch spectroscopic surveys of quasars (including both typical quasars and those with single-peaked velocity-offset broad lines), whose broad Hβlines are significantly offset (by ≳ a few hundred kilometers per second) from the systemic redshifts. We directly test the prediction that the host galaxies of BSBHs would have a higher fraction of disturbed morphologies and younger stellar bulges from recent interactions than those of control quasars. After careful subtraction of the central quasar light, our candidate BSBH hosts show a statistically undifferentiated distribution of host asymmetry, indicative of a similar fraction of recent mergers. While a significantly larger sample is needed to place this result on a much firmer statistical ground, it opens questions as to the timescale differences between galaxy merger and BSBH formation, or the efficacy of the radial-velocity-shift-based selection of subparsec BSBH candidates. 

Abstract High spatial and spectral resolution observations are essential for identifying subarcsecond dual and lensed quasars and confirming their redshifts. We present Gemini/Gemini Multi-Object Spectrograph and Hubble Space Telescope/STIS optical spectra for 27 dual quasar candidates selected based on their variability-induced astrometric noise or double detections in Gaia (the Varstrometry for Off-nucleus and Dual sub-Kpc AGN (VODKA) project). From this follow-up, we spectroscopically identify 11 star superpositions and seven dual/lensed quasars. Among the remaining targets, two are likely dual/lensed quasars based on additional radio imaging, while the rest are quasars with unknown companions. Without prior photometric or spectroscopic selection, we find the star contamination rate to be 41%–67%, while the dual/lensed quasar fraction is ≳26% in the follow-up VODKA sample. However, when combined with existing unresolved spectra and spatially resolved two-band color cuts, the dual/lensed quasar fraction can be increased to ≳67%. Our study highlights the need for high-quality spectral data, including a signal-to-noise ratio of at least 20, spatial resolution that is at least twice finer than the source separation, and a spectral resolution ofR ≳ 1000, in order to separate close sources, exclude stellar superpositions, and reliably identify dual quasars. 

Abstract Dual active galactic nuclei (AGNs), a phase in some galaxy mergers during which both central supermassive black holes (SMBHs) are active, are expected to be a key observable stage leading up to SMBH mergers. Constraining the population of dual AGNs in both the nearby and high-zUniverse has proven to be elusive until very recently. We present a multiwavelength follow-up campaign to confirm the nature of a sample of 20 candidate dual AGNs at cosmic noon (z ∼ 2) from the VODKA sample. Through a combination of Hubble Space Telescope and Very Large Array imaging, we refute the possibility of gravitational lensing in all but one target. We find evidence of dual AGNs in three systems, while seven exhibit a single AGN in galaxy pairs, through either strong radio emission or ancillary emission-line data. The remaining systems are confirmed as either quasar−star superpositions (seven) or nonlensed pairs (two) that require further investigations to establish AGN activity. Among the systems with radio detections, we find a variety of radio spectral slopes and UV/optical colors suggesting that our sample contains a range of AGN properties, from obscured radio-quiet objects to those with powerful synchrotron-emitting jets. This study presents one of the largest dedicated multiwavelength follow-up campaigns to date searching for dual AGNs at high redshift. We confirm several of the highest-zsystems at small physical separations, thus representing some of the most evolved dual-AGN systems at the epoch of peak quasar activity known to date. 

Abstract Cooperative catalysis with an enzyme and a small‐molecule photocatalyst has recently emerged as a potentially general activation mode to advance novel biocatalytic reactions with synthetic utility. Herein, we report cooperative photobiocatalysis involving an engineered nonheme Fe enzyme and a tailored photoredox catalyst to achieve enantioconvergent decarboxylative azidation, thiocyanation, and isocyanation of redox‐active esters via a radical mechanism. We repurposed and further evolved metapyrocatechase (MPC), a nonheme Fe extradiol dioxygenase not previously studied in new‐to‐nature biocatalysis, for the enantioselective C─N₃, C─SCN, and C─NCO bond formation via an enzymatic Fe─X intermediate (X═N₃, NCS, and NCO). A range of primary, secondary, and tertiary alkyl radical precursors were effectively converted by our engineered MPC, allowing the syntheses of organic azides, thiocyanates, and isocyanates with good to excellent enantiocontrol. Further derivatization of these products furnished valuable compounds including enantioenriched amines, triazoles, ureas, and SCF₃‐containing products. DFT and MD simulations shed light on the mechanism as well as the binding poses of the alkyl radical intermediate in the enzyme active site and the π‐facial selectivity in the enantiodetermining radical rebound. Overall, cooperative photometallobiocatalysis with nonheme Fe enzymes provides a means to develop challenging asymmetric radical transformations eluding small‐molecule catalysis. 

eXplainable Artificial Intelligence (XAI) has garnered significant attention for enhancing transparency and trust in machine learning models. However, the scopes of most existing explanation techniques focus either on offering a holistic view of the explainee model (global explanation) or on individual instances (local explanation), while the middle ground, i.e., cohort-based explanation, is less explored. Cohort explanations offer insights into the explainee's behavior on a specific group or cohort of instances, enabling a deeper understanding of model decisions within a defined context. In this paper, we discuss the unique challenges and opportunities associated with measuring cohort explanations, define their desired properties, and create a generalized framework for generating cohort explanations based on supervised clustering. 

Abstract Wave‐particle duality, intertwining two inherently contradictory properties of quantum systems, remains one of the most conceptually profound aspects of quantum mechanics. By using the concept of energy capacity, the ability of a quantum system to store and extract energy, a device‐independent uncertainty relation is derived for wave‐particle duality. This relation is shown to be independent of both the representation space and the measurement basis of the quantum system. Furthermore, it is experimentally validated that this wave‐particle duality relation using a photon‐based platform. 

Abstract In this paper, we explore the benefits that digitalization and Big Data analytics can bring to animal health, emphasizing the need to advance toward precision veterinary epidemiology. This concept takes advantage of multilevel animal health–related data to better understand disease dynamics in a population and design more cost-effective interventions, particularly focusing on livestock health. However, to translate this concept into practice, critical advancements and changes are needed in how we collect, standardize, integrate, share, and use data. Fostering interdisciplinary teams that integrate epidemiologists, veterinarians, and other domain experts with computer scientists, engineers, and data scientists is essential to implement this approach and better address complex animal health issues. 

Abstract Periodic variability in active galactic nuclei (AGNs) is a promising method for studying subparsec supermassive black hole binaries (SMBHBs), which are a challenging detection target. While extensive searches have been made in the optical, X-ray, and gamma-ray bands, systematic infrared (IR) studies remain limited. Using data from the Wide-field Infrared Survey Explorer (WISE), which provides unique decade-long mid-IR light curves with a six-month cadence, we have conducted the first systematic search for SMBHB candidates based on IR periodicity. Analyzing a parent sample of 48,932 objects selected from about half a million AGNs, we have identified 28 candidate periodic AGNs with periods ranging from 1268 to 2437 days (in the observer frame), by fitting their WISE light curves with sinusoidal functions. However, our mock simulation of the parent sample indicates that stochastic variability can actually produce a similar number of periodic sources, underscoring the difficulty in robustly identifying real periodic signals with WISE light curves, given their current sampling. Notably, we find no overlap between our sample and optical periodic sources, which can be explained by a distinct preference for certain periods due to selection bias. By combining archived data from different surveys, we have identified a candidate exhibiting periodic behavior in both the optical and IR bands, a phenomenon that warrants further validation through observational tests. Our results highlight the potential of IR time-domain surveys, including future missions such as the Nancy Grace Roman Space Telescope, for identifying periodic AGNs, but complementary tests are still needed to determine their physical origins, such as SMBHBs. 

Abstract Designing CRISPR single guide RNA (sgRNA) libraries targeting entire kingdoms of life will significantly advance genetic research in diverse and underexplored taxa. Current sgRNA design tools are often species-specific and fail to scale to large, phylogenetically diverse datasets, limiting their applicability to comparative genomics, evolutionary studies, and biotechnology. Here, we present ALLEGRO, a combinatorial optimization algorithm able to design minimal, yet highly effective sgRNA libraries targeting thousands of species. Leveraging integer linear programming, ALLEGRO identified compact sgRNA sets simultaneously targeting several genes of interest for over 2,000 species across the fungal kingdom. We experimentally validated the sgRNAs designed by ALLEGRO inKluyveromyces marxianus, Komagataella phaffii, andYarrowia lipolytica. In addition, we adopted a generalized Cas9-Ribonucleoprotein delivery system coupled with protoplast transformation to extend ALLEGRO’s sgRNA libraries to other untested fungal genomes, such asRhodotorula araucariae. Our experimental results, along with cross-validation, show that ALLEGRO enables efficient CRISPR genome editing, supporting the development of universal sgRNA libraries applicable to entire taxonomic groups.