Each year, millions of marine aquarium fish and invertebrates are harvested from coral reefs and enter the complex and largely unregulated marine aquarium trade (MAT). It is challenging to identify species at risk of overexploitation in this trade due to its data-limited and poorly monitored nature. We developed a new analytical approach based on a productivity-susceptibility analysis (PSA) to assess the vulnerability of wild-captured marine aquarium fish. The PSA was originally developed to assess food fisheries; however, species and operational characteristics between food fisheries and the MAT differ. Thus, we improved a prior PSA framework to assess the data-limited MAT through customization of productivity and susceptibility factors to align with the target fishery, improved data binning, calculation of susceptibility, and characterization of the vulnerability scores. Our vulnerability results align well with the most recent IUCN assessments, showing improved accuracy using this revised PSA compared to prior adaptions of the PSA to the MAT. Further, we show that this PSA approach can be used to assess species on either a global or country-specific scale. A Gaussian mixture model clustering algorithm was applied to the PSA results to objectively classify fish along a sustainability continuum. Among 32 species, a majority of species clustered as highly sustainable or sustainable indicating little management or over-harvest concern; however, the Bangaii cardinalfish Pterapogon kauderni and blue tang Paracanthurus hepatus indexed as unsustainable. This novel PSA method, and use of a clustering algorithm to classify results, provides a predictive tool for a wide range of fisheries. In addition to informing species management plans, the compilation of sustainability status data generated by our PSA can inform a consumer guide, allowing consumers and other stakeholders to make sustainable decisions when purchasing fish.

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Transcranial direct current stimulation (tDCS) continues to demonstrate success as a medical intervention for neurodegenerative diseases, psychological conditions, and traumatic brain injury recovery. One aspect of tDCS still not fully comprehended is the influence of the tDCS electric field on neural functionality. To address this issue, we present a mathematical, multiscale model that couples tDCS administration to neuron electrodynamics. We demonstrate the model's validity and medical applicability with computational simulations using an idealized two-dimensional domain and then an MRI-derived, three-dimensional human head geometry possessing inhomogeneous and anisotropic tissue conductivities. We exemplify the capabilities of these simulations with real-world tDCS electrode configurations and treatment parameters and compare the model's predictions to those attained from medical research studies. The model is implemented using efficient numerical strategies and solution techniques to allow the use of fine computational grids needed by the medical community.

BACKGROUNDSingle‐tool approaches often fail to provide effective long‐term suppression of pest populations, such that combining several tools into an integrated management strategy is critical. Yet studies that harness the power of population models to explore the relative efficacy of various management tools and their combinations remain rare. We constructed a Leslie matrix population model to evaluate the potential of crop resistance, acting alone or in combination with biological control, to reduce populations of the wheat stem sawfly, Cephus cinctus Norton, a major pest of wheat in North America.RESULTSOur model projections indicated that crop resistance reduced, but did not stop, C. cinctus population growth, suggesting that implementing multiple management tools will be necessary for longer term control of this pest. The levels of parasitism needed to curtail population growth were much lower in model projections for resistant solid‐stemmed compared with susceptible hollow‐stemmed cultivars (22% versus 86%). Furthermore, even when accounting for the reduced levels of parasitism observed in resistant cultivars, projected population growth rates for C. cinctus were always lower in resistant compared with susceptible wheat cultivars.CONCLUSIONDespite some empirical evidence for antagonistic interactions between resistance and biological control, our models suggest that combining these two approaches will always reduce population growth rates to lower levels than implementing either strategy alone. More work focused on integrating biological control into crop resistance breeding programs, and determining how these approaches affect performance of limiting life stages, will be important to optimize sustainable approaches to integrated pest management in this system and more broadly. Published 2020. This article is a U.S. Government work and is in the public domain in the USA.

Computational simulations of transcranial electrical stimulation (TES) are commonly utilized by the neurostimulation community, and while vastly different TES application areas can be investigated, the mathematical equations and physiological characteristics that govern this research are identical. The goal of this work was to develop a robust software framework for TES that efficiently supports the spectrum of computational simulations routinely utilized by the TES community and in addition easily extends to support alternative neurostimulation research objectives. Using well-established object-oriented software engineering techniques, we have designed a software framework based upon the physical and computational aspects of TES. The framework’s versatility is demonstrated with a set of diverse neurostimulation simulations that (i) reinforce the importance of using anisotropic tissue conductivities, (ii) demonstrate the enhanced precision of high-definition stimulation electrodes, and (iii) highlight the benefits of utilizing multigrid solution algorithms. Our approaches result in a framework that facilitates rapid prototyping of real-world, customized TES administrations and supports virtually any clinical, biomedical, or computational aspect of this treatment. Software reuse and maintainability are optimized, and in addition, the same code can be effortlessly augmented to provide support for alternative neurostimulation research endeavors.