Highly specialized insect-parasite interactions can vary greatly between populations, which can be attributed to differences in evolutionary history and contemporary ecological differences. Understanding how this variation arises and persists across insect-parasite populations requires knowledge of both genetic and ecological factors. Dietary variation across subpopulations in an insect's rage is among the most important ecological factors that can shape interactions with their parasites. However, the roles of dietary and genetic differences in shaping insect-parasite interactions are understood in only a handful of systems, mostly pest, managed or common model species. Given that genetic and dietary differences often covary, disentangling their effects requires experimentation to complement the observational data on which our understanding of many systems is based. Here, we experimentally tested the roles of diet and genetic background in shaping monarch butterfly resistance to its protozoan parasite Ophryocystis elektroscirrha using monarchs from mainland North America, Puerto Rico and hybrids of these two populations. By factorially manipulating host diet and genetic background, we found that parasite resistance conferred by a toxic host plant to mainland North American monarchs did not provide resistance to Puerto Rican monarchs. Instead, resistance in Puerto Rican monarchs was conferred by their genetic background, which was partially passed to hybrids of mainland North American-Puerto Rican monarchs. We then assessed cuticular melanin, a proxy for immune competency, as a potential explanation for the increased resistance observed in Puerto Rican monarchs. Although we found support for melanin-associated immune competency across populations, we did not find support for this hypothesis within populations. Overall, these findings add to a growing body of unique case studies that lend insight into the complex ecological and evolutionary dynamics of highly specialized host-parasite interactions.
How may a study of Chinese railway exports update our theoretical toolkit for understanding globalization? This article revisits globalization and argues that such exports exemplify a distinct mode of state-enabled globalization. Contrary to the corporate-led model dominated by multinational corporations based in the global North, China's strategy is driven by state institutions that create overseas market opportunities through diplomacy, industrial policy coordination and development finance. In turn, China's state-owned enterprises (SOEs) have become the mainstay of 'going global' for Chinese infrastructure projects and capital. We contribute to debates on state capitalism by showing how the Chinese state integrates strategic state objectives with quasi-autonomous, but still profit-oriented, SOEs through a system of market creation, standard export and resource orchestration. These go beyond the existing understanding of state capitalism, which has focused on the state's direct control of and material support to firms. Drawing on primarily Chinese-language sources, interviews, field visits and case-studies of two prominent China-exported railway projects in Kenya and Laos, this paper traces the evolution of Chinese railway exports as a microcosm of this model. The findings shed light on China's developmental state logic that extends globally, shaping the political economy of infrastructure and industrialization in the global South.
Background: The low-lying level structure of 13Be is still not fully understood; specifically, it has not been ruled out that the first 1/2- state could be located above the isomeric 0+ 2 state of 12Be and decay via that state. Purpose: Search for a possible decay path of 13Be excited states through the isomeric 0+ 2 state in 12Be. Method: The invariant mass technique was used to reconstruct (12Be + n) decay energies from neutron removal reactions of 14Be at 78 MeV/u on a 9Be target in coincidence with delayed gamma rays. Charged particles and neutrons were detected and identified with a telescope and a subset of the Modular Neutron Array (MoNA), respectively, around zero degrees. The CAESium-iodide scintillator ARray (CAESAR) was located around the telescope measuring the gamma rays. Results: No evidence for the population and decay of the 0+ 2 state in 12Be was observed. From the nonobservation of 511 keV gamma rays an upper limit of 6% of the p-wave contribution was extracted. Conclusions: The previously observed p-wave resonance at about 500 keV does not decay via the isomeric state and thus corresponds to the ground state of 13Be decaying to the 12Be ground state.
Aerial outdoor semantic navigation requires robots to explore large, unstructured environments to locate target objects. Recent advances in semantic navigation have demonstrated open-set object-goal navigation in indoor settings, but these methods remain limited by constrained spatial ranges and structured layouts, making them unsuitable for long-range outdoor search. While outdoor semantic navigation approaches exist, they either rely on reactive policies based on current observations, which tend to produce short-sighted behaviors, or precompute scene graphs offline for navigation, limiting adaptability to online deployment. We present RAVEN, a 3D memory-based, behavior tree framework for aerial semantic navigation in unstructured outdoor environments. It (1) uses a spatially consistent semantic voxel-ray map as persistent memory, enabling long-horizon planning and avoiding purely reactive behaviors, (2) combines short-range voxel search and long-range ray search to scale to large environments, (3) leverages a large vision-language model to suggest auxiliary cues, mitigating sparsity of outdoor targets. These components are coordinated by a behavior tree, which adaptively switches behaviors for robust operation. We evaluate RAVEN in 10 photorealistic outdoor simulation environments over 100 semantic tasks, encompassing single-object search, multi-class, multi-instance navigation and sequential task changes. Results show RAVEN outperforms baselines by 85.25% in simulation and demonstrate its real-world applicability through deployment on an aerial robot in outdoor field tests.
