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When particle dark matter is bound gravitationally around a massive black hole in sufficiently high densities, the dark matter will affect the rate of inspiral of a secondary compact object that forms a binary with the massive black hole. In this paper, we revisit previous estimates of the impact of dark-matter accretion by black-hole secondaries on the emitted gravitational waves. We identify a region of parameter space of binaries for which estimates of the accretion were too large (specifically, because the dark-matter distribution was assumed to be unchanging throughout the process, and the secondary black hole accreted more mass in dark matter than that enclosed within the orbit of the secondary). To restore consistency in these scenarios, we propose and implement a method to remove dark-matter particles from the distribution function when they are accreted by the secondary. This new feedback procedure then satisfies mass conservation, and when evolved with physically reasonable initial data, the mass accreted by the secondary no longer exceeds the mass enclosed within its orbital radius. Comparing the simulations with accretion feedback to those without this feedback, including feedback leads to a smaller gravitational-wave dephasing from binaries in which only the effects of dynamical friction are being modeled. Nevertheless, the dephasing can be hundreds to almost a thousand gravitational-wave cycles, an amount that should allow the effects of accretion to be inferred from gravitational-wave measurements of these systems. 

Sex chromosome dosage compensation is a model to understand the coordinated evolution of transcription; however, the advanced age of the sex chromosomes in model systems makes it difficult to study how the complex regulatory mechanisms underlying chromosome-wide dosage compensation can evolve. The sex chromosomes ofPoecilia pictahave undergone recent and rapid divergence, resulting in widespread gene loss on the male Y, coupled with complete X Chromosome dosage compensation, the first case reported in a fish. The recent de novo origin of dosage compensation presents a unique opportunity to understand the genetic and evolutionary basis of coordinated chromosomal gene regulation. By combining a new chromosome-level assembly ofP. pictawith whole-genome bisulfite sequencing and RNA-seq data, we determine that the YY1 transcription factor (YY1) DNA binding motif is associated with male-specific hypomethylated regions on the X, but not the autosomes. These YY1 motifs are the result of a recent and rapid repetitive element expansion on theP. pictaX Chromosome, which is absent in closely related species that lack dosage compensation. Taken together, our results present compelling support that a disruptive wave of repetitive element insertions carrying YY1 motifs resulted in the remodeling of the X Chromosome epigenomic landscape and the rapid de novo origin of a dosage compensation system.

 

The mainly deep-submarine Ontong Java Plateau (OJP) is the result of the largest outpouring of lava in the geologic record. Volcanic events of this magnitude can have dramatic environmental impacts due to volatile emissions. We report new S measurements in naturally glassy, olivine-hosted melt inclusions and pillow basalt glasses from the OJP. We combined these data with previous S measurements in OJP glasses to quantify S degassing in a suite of OJP glasses. Comparison with an S degassing model suggests OJP lavas that erupted at depths ~>1500 m did not degas S; OJP lavas that erupted at depths ~<1500 m degassed up to ~40% initial S, but these lavas likely made up a small fraction of OJP lavas. This result suggests that despite its large volume compared to continental large igneous provinces (LIPs), OJP lavas emitted less S, potentially contributing to its muted environmental impact. The OJP may provide a framework for the temporal evolution of S degassing at oceanic LIPs, with early eruptions at great water depths releasing limited to no S, and later eruptions at shallow water depths releasing larger, but still limited amounts of S. This framework may also have implications for continental LIP magmas, which may release significant amounts of CO2 but limited amounts of S during intrusive activity, with magmatic S emissions only becoming important during extrusive phases.

 

As renewed interest in human space-exploration intensifies, a coherent and modernized strategy for mission design and planning has become increasingly crucial. Biotechnology has emerged as a promising approach to increase resilience, flexibility, and efficiency of missions, by virtue of its ability to effectively utilize in situ resources and reclaim resources from waste streams. Here we outline four primary mission-classes on Moon and Mars that drive a staged and accretive biomanufacturing strategy. Each class requires a unique approach to integrate biomanufacturing into the existing mission-architecture and so faces unique challenges in technology development. These challenges stem directly from the resources available in a given mission-class—the degree to which feedstocks are derived from cargo and in situ resources—and the degree to which loop-closure is necessary. As mission duration and distance from Earth increase, the benefits of specialized, sustainable biomanufacturing processes also increase. Consequentially, we define specific design-scenarios and quantify the usefulness of in-space biomanufacturing, to guide techno-economics of space-missions. Especially materials emerged as a potentially pivotal target for biomanufacturing with large impact on up-mass cost. Subsequently, we outline the processes needed for development, testing, and deployment of requisite technologies. As space-related technology development often does, these advancements are likely to have profound implications for the creation of a resilient circular bioeconomy on Earth.

 

In this work, we explore multiplex graph (networks with different types of edges) generation with deep generative models. We discuss some of the challenges associated with multiplex graph generation that make it a more difficult problem than traditional graph generation. We propose TenGAN, the first neural network for multiplex graph generation, which greatly reduces the number of parameters required for multiplex graph generation. We also propose 3 different criteria for evaluating the quality of generated graphs: a graph-attribute-based, a classifier-based, and a tensor-based method. We evaluate its performance on 4 datasets and show that it generally performs better than other existing statistical multiplex graph generative models. We also adapt HGEN, an existing deep generative model for heterogeneous information networks, to work for multiplex graphs and show that our method generally performs better.