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The invasive black tiger shrimp has caused serious ecological problems in the America. However, since it can be directly eaten or made into feed, it may be beneficial to other countries. In order to ensure ecological security, it is necessary to control the invasion of the black tiger shrimp through international cooperation. Common control modes of the black tiger shrimp include the introducing natural enemy mode, making feed mode and the "bringing to the table" mode. In order to derive the applicable scope of various control modes of the black tiger shrimp and provide suggestions for the security and sustainability of the ecological supply chain of the America and cooperative country, this article constructs three differential game models and compares and analyzes the equilibrium results obtained by the models. Finally, the study shows that the higher the price of feed and the price of black tiger shrimp, the greater the degree of control of the black tiger shrimp. If the price of the black tiger shrimp and the reputation of the America for controlling the black tiger shrimp are lower, the America can gain more benefits under the feed production mode. Otherwise, the America prefers to sell the black tiger shrimp directly, thus directly "bringing to the table". Compared with the feed production or "bringing to the table" mode, cooperative country prefer to control the black tiger shrimp flooding through the natural enemy introduction mode. [ABSTRACT FROM AUTHOR]

Birth-death models play a key role in phylodynamic analysis for their interpretation in terms of key epidemiological parameters. In particular, models with piecewise-constant rates varying at different epochs in time, to which we refer as episodic birth-death-sampling (EBDS) models, are valuable for their reflection of changing transmission dynamics over time. A challenge, however, that persists with current time-varying model inference procedures is their lack of computational efficiency. This limitation hinders the full utilization of these models in large-scale phylodynamic analyses, especially when dealing with high-dimensional parameter vectors that exhibit strong correlations. We present here a linear-time algorithm to compute the gradient of the birth-death model sampling density with respect to all time-varying parameters, and we implement this algorithm within a gradient-based Hamiltonian Monte Carlo (HMC) sampler to alleviate the computational burden of conducting inference under a wide variety of structures of, as well as priors for, EBDS processes. We assess this approach using three different real world data examples, including the HIV epidemic in Odesa, Ukraine, seasonal influenza A/H3N2 virus dynamics in New York state, America, and Ebola outbreak in West Africa. HMC sampling exhibits a substantial efficiency boost, delivering a 10- to 200-fold increase in minimum effective sample size per unit-time, in comparison to a Metropolis-Hastings-based approach. Additionally, we show the robustness of our implementation in both allowing for flexible prior choices and in modeling the transmission dynamics of various pathogens by accurately capturing the changing trend of viral effective reproductive number. Author summary: Epidemic control and forecasting relies on accurate quantification of transmission and recovery dynamics. This quantification is achievable through the analysis of phylogenetic relationships among pathogen strains obtained from infected individuals. As a key analytical tool for such inference, we concentrate on the study of the episodic birth-death-sampling (EBDS) models. These models define a probability distribution on time-calibrated phylogenies that enable estimation of time-varying rates of pathogens' transmission, recovery, and sampling. Advances in sequencing technology, however, have led to an increasing amount of genetic data collected from these pathogens. Consequently, the traditional computational methods for analyzing these large-scale data under EBDS models have become inadequate due to their high computational load. We aimed to break this computational bottleneck by developing a new approach, based on the Hamiltonian Monte Carlo sampling, that considerably accelerates inference of all rate parameters compared to the traditional random-walk Metropolis-Hasting method. Our method greatly improves the ability to explore the complex distributions that arise when we want to understand disease dynamics at a more granular temporal resolution. This advancement delivers value to public health as it helps rapid data-driven decision-making during outbreaks and enhances our understanding of the spread of infectious diseases. [ABSTRACT FROM AUTHOR]

Partisan animosity has been on the rise in America. Partisan animosity involves blame, wherein political partisans blame outparty members for their beliefs and actions. Here, we examine whether a historicist thinking intervention—drawn from research on blame mitigation—can reduce partisan animosity. The intervention consisted of three components: (1) a narrative about the idiosyncratic development of one political opponent paired with (2) a message about how unique life experiences shape everyone's political beliefs and (3) a suggestion that outparty members can be changed by future formative experiences. Experiments 1 and 2 showed that the intervention reduced cold feelings—measured via Feeling Thermometer—towards the outparty for both Democrats and Republicans. Experiments 3 and 4 focused on more specific emotional changes. Experiment 3 showed that, for Democrats, the intervention increased compassion. Experiment 4 showed that, for Republicans, the intervention reduced disgust, disapproval, anger, and contempt, but had no impact on compassion. For Democrats, but not for Republicans, reductions in animosity were mediated by reduced perceptions of control of self-formation, the mediator identified in prior work on historicist thinking and blame mitigation. [ABSTRACT FROM AUTHOR]