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ABSTRACT The mummichog,Fundulus heteroclitus, an abundant estuarine fish broadly distributed along the eastern coast of North America, has repeatedly evolved tolerance to otherwise lethal levels of aromatic hydrocarbon exposure. This tolerance is linked to reduced activation of the aryl hydrocarbon receptor (AHR) signaling pathway. In other animals, the AHR has been shown to influence the gastrointestinal-associated microbial community, particularly when activated by the model toxic pollutant 3,3′,4,4′,5-pentachlorobiphenyl (PCB-126) and other dioxin-like compounds. To understand host population and PCB-126 exposure effects on mummichog gut microbiota, we sampled two populations of wild fish, one from a PCB-contaminated environment (New Bedford Harbor, MA, USA) and the other from a much less polluted location (Scorton Creek, MA, USA), as well as laboratory-reared F2 generation fish originating from each of these populations. We examined the microbes associated with the gut of these fish using amplicon sequencing of bacterial and archaeal small subunit ribosomal RNA genes. Fish living in the PCB-polluted site had high microbial alpha and beta diversity compared to fish from the low PCB site. These differences between wild fish were not present in laboratory-reared F2 fish that originated from the same populations. Microbial compositional differences existed between wild and lab-reared fish, with the wild fish dominated by Vibrionaceae and the lab-reared fish by Enterococceae. These results suggest that mummichog habitat and/or environmental conditions have a stronger influence on the mummichog gut microbiome compared to population or hereditary-based influences. Mummichog are important eco-evolutionary model organisms; this work reveals their importance for exploring host-environmental-microbiome dynamics. IMPORTANCEThe mummichog fish, a common resident of North America's east coast estuaries, has evolved the ability to survive in waters contaminated with toxic chemicals that would typically be deadly. Our study investigates how living in and adapting to these toxic environments may affect their gut microbiomes. We compared mummichogs from a polluted area in Massachusetts with those from a non-polluted site and found significant differences in their gut microbes. Interestingly, when we raised the next generation of these fish in a lab, these differences disappeared, suggesting that the environment plays a more crucial role in shaping the gut microbiome than genetics. Understanding these changes helps shed light on how animals and their associated microbiomes adapt to pollution, which can inform conservation efforts and our broader understanding of environmental impacts on host-microbe dynamics. 

Polaritons in two-dimensional (2D) materials provide unique opportunities for controlling light at nanoscales. Tailoring these polaritons via gradient polaritonic surfaces with space-variant response can enable versatile light-matter interaction platforms with advanced functionalities. However, experimental progress has been hampered by the optical losses and poor light confinement of conventionally used artificial nanostructures. Here, we demonstrate natural gradient polaritonic surfaces based on superlattices of solitons—localized structural deformations—in a prototypical moiré system, twisted bilayer graphene on boron nitride. We demonstrate on-off switching and continuous modulation of local polariton-soliton interactions, which results from marked modifications of topological and conventional soliton states through variation of local strain direction. Furthermore, we reveal the capability of these structures to spatially modify the near-field profile, phase, and propagation direction of polaritons in record-small footprints, enabling generation and electrical switching of directional polaritons. Our findings open up new avenues toward nanoscale manipulation of light-matter interactions and spatial polariton engineering through gradient moiré superlattices. 

Understanding the petrological and geochemical processes shaping the Moho transition zone (MTZ) is crucial for advancing our knowledge of thermal and chemical exchanges between the oceanic crust and the residual upper mantle. In this study, we systematically investigate the MTZ outcropped within the Zedong ophiolite, located in the eastern part of the Yarlung-Tsangpo Suture Zone (YTSZ), with the aim of at reconstructing the magmatic processes responsible for generating the petrological Moho. The Zedong MTZ comprises a sequence of dunite, wehrlite, pyroxenite, and gabbro, with frequent occurrences of clinopyroxene-rich lithologies. Cyclicity within the MTZ sequences is characterized by the recurrence of olivine-rich intervals and the presence of zig-zag patterns in both major and trace elements of clinopyroxenes. Zircon Usingle bondPb dating on the Zedong gabbros supports the coeval formation of the Zedong ophiolite with other YTSZ ophiolites. Clinopyroxene in the Zedong MTZ follows a differentiation sequence characterized by an increase in contents of Al2O3 and TiO2, coupled with a decrease in Mg#. This differentiation sequence along with frequent occurrences of amphibole suggest the evolution of a primitive hydrous melt depleted in Al2O3, TiO2, and Na2O. The depleted Ndsingle bondHf isotopes and rare earth element patterns of the MTZ rocks indicate that their parental magmas originated from fluid-enhanced re-melting of a previously depleted mantle. Additionally, we proposed that the initiation of a new subduction zone results in the re-melting of the mantle peridotite, leading to the formation of primitive hydrous basaltic melts. The variable lithologies observed in the Zedong MTZ arise from fractional crystallization and repeated replenishment of hydrous melts. 

Modern machine learning frameworks support very large models by incorporating parallelism and optimization techniques. Yet, these very techniques add new layers of complexity in ensuring the correctness of the computation. An incorrect implementation of these techniques might lead to compile-time or runtime errors that can easily be observed and fixed, but it might also lead to silent errors that will result in incorrect computations in training or inference, which do not exhibit any obvious symptom until the model is used later. These subtle errors not only waste computation resources, but involve significant developer effort to detect and diagnose. In this work, we propose Aerify, a framework to automatically expose silent errors by verifying semantic equivalence of models with equality saturation. Aerify constructs equivalence graphs (e-graphs) from intermediate representations of tensor programs, and incrementally applies rewriting rules---derived from generic templates and refined via domain-specific analysis---to prove or disprove equivalence at scale. When discrepancies remain unproven, Aerify pinpoints the corresponding graph segments and maps them back to source code, simplifying debugging and reducing developer overhead. Our preliminary results show strong potentials of Aerify in detecting real-world silent errors. 

In synthetic biological systems, rare events can cause undesirable behavior leading to pathological effects. Due to their low observability, rare events are challenging to analyze using existing stochastic simulation methods. Chemical Reaction Networks (CRNs) are a general-purpose formal language for modeling chemical kinetics. This paper presents a fully automated approach to efficiently construct a large number of concurrent traces by expanding a sample of known traces. These traces constitute a partial state space containing only traces leading to a rare event of interest. This state space is then used to compute a lower bound for the rare event’s probability. We propose a novel approach for the analysis of highly concurrent CRNs, including a CRN reaction independence analysis and an algorithm that exploits CRN concurrency to rapidly enumerate parallel traces. We then present a novel algorithm to add cycles to a partial state space to further increase the rare event’s probability lower bound to its actual value. The resulting prototype tool, RAGTIMER, demonstrates improvement over stochastic simulation and probabilistic model checking.