Many biological functions are leaky, and organisms that perform them contribute some of their products to a community "marketplace" in which nonperforming individuals may compete for them. Leaky functions are partitioned unequally in microbial communities, and the evolutionary forces determining which species perform them and which become beneficiaries are poorly understood. Here, we demonstrate that the market principle of comparative advantage determines the distribution of a leaky antibiotic resistance gene in an environment occupied by two "species"-strains of growing on mutually exclusive resources and thus occupying separate niches. Communities comprised of antibiotic-resistant cells were rapidly invaded by sensitive cells of both types. While the two phenotypes coexisted stably for 500 generations, in 15/18 replicates, antibiotic sensitivity became fixed in one species. Fixation always occurred in the same species despite both species being genetically identical except for their niche-defining mutation. In the absence of antibiotic, the fitness cost of resistance was identical in both species. However, the intrinsic resistance of the species that ultimately became the sole helper was significantly lower, and thus its reward for expressing the resistance gene was higher. Opportunity cost of resistance, not absolute cost or efficiency of antibiotic removal, determined which species became the helper, consistent with the economic theory of comparative advantage. We present a model that suggests that this market-like dynamic is a general property of Black Queen systems and, in communities dependent on multiple leaky functions, could lead to the spontaneous development of an equitable and efficient division of labor.
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http://dx.doi.org/10.1073/pnas.2109813118 | DOI Listing |
Cogn Neurodyn
December 2025
Shanghai University, Shanghai, China.
Neurodynamic observations indicate that the cerebral cortex evolved by self-organizing into functional networks, These networks, or distributed clusters of regions, display various degrees of attention maps based on input. Traditionally, the study of network self-organization relies predominantly on static data, overlooking temporal information in dynamic neuromorphic data. This paper proposes Temporal Self-Organizing (TSO) method for neuromorphic data processing using a spiking neural network.
View Article and Find Full Text PDFBiomaterials
January 2025
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States; Advanced Platform Technology Center, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, OH, United States. Electronic address:
Intracortical microelectrodes (IMEs) are essential for neural signal acquisition in neuroscience and brain-machine interface (BMI) systems, aiding patients with neurological disorders, paralysis, and amputations. However, IMEs often fail to maintain robust signal quality over time, partly due to neuroinflammation caused by vascular damage during insertion. Platelet-inspired nanoparticles (PIN), which possess injury-targeting functions, mimic the adhesion and aggregation of active platelets through conjugated collagen-binding peptides (CBP), von Willebrand Factor-binding peptides (VBP), and fibrinogen-mimetic peptides (FMP).
View Article and Find Full Text PDFThis work presents the generation of an Airy beam by a leaky-wave structure (LWS) designed from a substrate-integrated waveguide (SIW) with dimension-varying slots. The Airy beam is radiated by judiciously designing the length of the slots to modulate the phase distribution. Compared to Airy beams generated by phased array antennas or metasurfaces, no complex feeding network associated with phase shifters and no space-wave illumination is required, thus allowing one to reach a low-profile structure.
View Article and Find Full Text PDFNanophotonics
February 2024
Theoretical Quantum Physics Laboratory, Cluster for Pioneering Research, RIKEN, Wakoshi, Saitama 351-0198, Japan.
Neural Netw
February 2025
Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Center for Long-term Artificial Intelligence, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China. Electronic address:
Inspired by the brain's information processing using binary spikes, spiking neural networks (SNNs) offer significant reductions in energy consumption and are more adept at incorporating multi-scale biological characteristics. In SNNs, spiking neurons serve as the fundamental information processing units. However, in most models, these neurons are typically simplified, focusing primarily on the leaky integrate-and-fire (LIF) point neuron model while neglecting the structural properties of biological neurons.
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