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BACKGROUND: Single-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. RESULTS: Our 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. CONCLUSION: Despite 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.
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Himenópteros , Animales , América del Norte , Control de Plagas , Control Biológico de Vectores , Dinámica PoblacionalRESUMEN
Transcranial Electrical Stimulation (TES) continues to demonstrate success as a medical intervention for individuals with neurodegenerative diseases. Despite promising results from these neuromodulation modalities, the cellular level mechanisms by which this neurotherapy operates are not fully comprehended. In particular, the effects of TES on ion channel gating and ion transport are not known. Using the Poisson-Nernst-Planck model of electrodiffusion, coupled with a Hodgkin-Huxley based model of cellular ion transport, we present a model of TES that, for the first time, integrates electric potential energy, individualized ion species, voltage-gated ion channels, and transmembrane ionic flux during TES administration. Computational simulations are executed on a biologically-inspired domain with medically-based TES treatment parameters and quantify neuron-level electrical processes resulting from this form of neurostimulation. Results confirm prior findings that show that TES polarizes the cell membrane, however, these are extended as simulations in this paper show that polarization occurs in a location specific manner, where the type and degree of polarization depends on the position on the membrane within a node of Ranvier. In addition, results demonstrate that TES causes ion channel gating variables to change in a location specific fashion and, as a result, transmembrane current from distinct ion species depends on both time and membrane location. Another simulation finding is that intracellular calcium concentrations increase significantly due to a TES-induced calcium influx. As cytosolic calcium is critical in intracellular signaling pathways that govern proper neurotransmitter secretion as well as support cell viability, this alteration in calcium homeostasis suggests a possible mechanism by which TES operates at the neuronal level to achieve neurotherapeutic success.
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Post-traumatic stress disorder (PTSD) is a neurological condition which results from a traumatic experience caused by physiological shock or physical harm. Clinical results show success in combating the symptoms of PTSD with a neurostimulation treatment called transcranial Direct Current Stimulation (tDCS). Though effective, the underlying mechanisms of the treatment and its success are not fully comprehended. In order to elucidate reasons for its efficacy, a mathematical model of tDCS has been implemented to quantify the electrical energy delivered by this treatment. Computational simulation results of various PTSD-focused electrode montages on a three-dimensional, MRI-derived cranial cavity with biologically-based tissue conductivities parallel results from published literature and clinical experiments. Specifically, regions of the brain thought to be targeted by tDCS treatments are confirmed with in silico experiments. Finally, an extension of this model to a unique multiscale mathematical model of tDCS is presented, which adds the ability to quantify neural tissue response via tDCS-induced transmembrane voltage polarization, the first of its kind for tDCS simulations for PTSD.
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Trastornos por Estrés Postraumático , Estimulación Transcraneal de Corriente Directa , Encéfalo , Simulación por Computador , Humanos , Modelos Teóricos , Trastornos por Estrés Postraumático/terapiaRESUMEN
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.