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We describe POInT_browse, a web portal that gives access to the orthology inferences made for polyploid genomes with POInT, the Polyploidy Orthology Inference Tool. Ancient, or paleo-, polyploidy events are widely distributed across the eukaryotic phylogeny, and the combination of duplicated and lost duplicated genes that these polyploidies produce can confound the identification of orthologous genes between genomes. POInT uses conserved synteny and phylogenetic models to infer orthologous genes between genomes with a shared polyploidy. It also gives confidence estimates for those orthology inferences. POInT_browsegives both graphical and query-based access to these inferences from 12 different polyploidy events, allowing users to visualize genomic regions produced by polyploidies and perform batch queries for each polyploidy event, downloading genes trees and coding sequences for orthologous genes meeting user-specified criteria. POInT_browseand the associated data are online athttps://wgd.statgen.ncsu.edu.

Ambitious climate packages promote the integration of variable renewable energy (VRE) and electrification of the economy. For the power sector, such a transformation means the emergence of so-called prosumers, i.e., agents that both consume and produce electricity. Due to their inflexible VRE output and flexible demand, prosumers will potentially add endogenous net sales with seasonal patterns to the power system. With its vast hydro reservoirs and ample transmission capacity, the Nordic region is seemingly well positioned to cope with such intermittent VRE output. However, the increased requirement for flexibility may be leveraged by incumbent producers to manipulate prices. Via a Nash-Cournot model with a representation of the Nordic region’s spatio-temporal features and reservoir volumes, we examine how hydro producers’ ability to manipulate electricity prices through temporal arbitrage is affected by (i) VRE-enabled prosumers and (ii) the latter plus a high CO$$_2$$${}_2$price. We find that hydro reservoirs could exploit prosumers’ patterns of net sales to conduct temporal arbitrage more effectively, viz., by targeting periods in which prosumers are net buyers (net sellers) to withhold (to “dump”) water. Meanwhile, a higher CO$$_2$$${}_2$price would further enhance hydro reservoirs’ market power because flexible price-taking thermal plants would be unable to ramp up productionmore »in order to counter such producers’ strategy to target VRE’s intermittency. Hence, in spite of a flexible demand side to complement additional intermittent VRE output, strategic hydro producers may still exacerbate price manipulation in a future power sector via more tailored exercise of market power.

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The task of learning a quantum circuit to prepare a given mixed state is a fundamental quantum subroutine. We present a variational quantum algorithm (VQA) to learn mixed states which is suitable for near-term hardware. Our algorithm represents a generalization of previous VQAs that aimed at learning preparation circuits for pure states. We consider two different ansätze for compiling the target state; the first is based on learning a purification of the state and the second on representing it as a convex combination of pure states. In both cases, the resources required to store and manipulate the compiled state grow with the rank of the approximation. Thus, by learning a lower rank approximation of the target state, our algorithm provides a means of compressing a state for more efficient processing. As a byproduct of our algorithm, one effectively learns the principal components of the target state, and hence our algorithm further provides a new method for principal component analysis. We investigate the efficacy of our algorithm through extensive numerical implementations, showing that typical random states and thermal states of many body systems may be learnt this way. Additionally, we demonstrate on quantum hardware how our algorithm can be usedmore »to study hardware noise-induced states.

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Prosumers adopt distributed energy resources (DER) to cover part of their own consumption and to sell surplus energy. Although individual prosumers are too dispersed to exert operational market power, they may collectively hold a strategic advantage over conventional generation in selecting DER capacity via aggregators. We devise a bilevel model to examine DER capacity sizing by a collective prosumer as a Stackelberg leader in an electricity industry where conventional generation may exert market power in operations. At the upper level, the prosumer chooses DER capacity in anticipation of lower-level operations by conventional generation and DER output. We demonstrate that exertion of market power in operations by conventional generation and the marginal cost of conventional generation affect DER investment by the prosumer in a nonmonotonic manner. Intuitively, in an industry where conventional generation exerts market power in operations similar to a monopoly (MO), the prosumer invests in more DER capacity than under perfectly competitive operations (PC) to take advantage of a high market-clearing price. However, if the marginal cost of conventional generation is high enough, then this intuitive result is reversed as the prosumer adopts more DER capacity under PC than under MO. This is because the high marginal costmore »of conventional generation prevents the market-clearing price from decreasing, thereby allowing for higher prosumer revenues. Moreover, competition relieves the chokehold on consumption under MO, which further incentivises the prosumer to expand DER capacity to capture market share. We prove the existence of a critical threshold for the marginal cost of conventional generation that leads to this counterintuitive result. Finally, we propose a countervailing regulatory mechanism that yields welfare-enhancing DER investment even in deregulated electricity industries.

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Scant research focuses on the resiliency of food supply chain networks to outbreaks, despite the estimated 600 million global foodborne illnesses annually. Outbreaks that cross country, state and provincial lines are virulent due to the number of people they can affect and difficulty controlling them. Research is needed on food supply chain networks, which are not well-characterized in relation to foodborne illnesses or generally. This paper introduces the United States Food, Energy, and State Transportation (US-FEAST) model and demonstrates its applicability via analysis of a hypothetical demand shock resulting from multistate food contamination. US-FEAST is an optimization-based model across all fifty states with yearly timesteps to 2030. It is a framework integrating food system data from multiple individual data sources. To calibrate, we develop a bilevel optimization routine to generate synthetic, state-level data and provide estimates of otherwise unavailable data at the intersections of the food and transportation systems. The results of US-FEAST elucidate potential heterogenous state-level variations in response, regional changes in food flows, vulnerabilities in the supply chain, and implications for food system resilience. While the generated data and scenarios are not empirical evidence, they provide insights to aid in planning by projecting outcomes and intervention effects. Ourmore »results estimate a 23% beef production decrease and 4% price decrease provide a road map toward data needs for quantifying food system resilience to foodborne illness. US-FEAST and its framework may have global utility for studying food safety in national and international food supply chain networks.« less

Abstract Throughout the past decade, many studies have reported adverse effects in biota following microplastic exposure. Yet, the field is still emerging as the current understanding of microplastic toxicity is limited. At the same time, recent legislative mandates have required environmental regulators to devise strategies to mitigate microplastic pollution and develop health-based thresholds for the protection of human and ecosystem health. The current publication rate also presents a unique challenge as scientists, environmental managers, and other communities may find it difficult to keep up with microplastic research as it rapidly evolves. At present, there is no tool that compiles and synthesizes the data from these studies to allow for visualization, interpretation, or analysis. Here, we present the Toxicity of Microplastics Explorer (ToMEx), an open access database and open source accompanying R Shiny web application that enables users to upload, search, visualize, and analyze microplastic toxicity data. Though ToMEx was originally created to facilitate the development of health-based thresholds to support California legislations, maintaining the database by the greater scientific community will be invaluable to furthering research and informing policies globally. The database and web applications may be accessed at https://microplastics.sccwrp.org/ . Graphical Abstract