Population genomic structure of a widespread, urban-dwelling mammal: The eastern grey squirrel (Sciurus carolinensis).

Urbanization is a persistent and widespread driver of global environmental change, potentially shaping evolutionary processes due to genetic drift and reduced gene flow in cities induced by habitat fragmentation and small population sizes. We tested this prediction for the eastern grey squirrel (Sciurus carolinensis), a common and conspicuous forest-dwelling rodent, by obtaining 44K SNPs using reduced representation sequencing (ddRAD) for 403 individuals sampled across the species' native range in eastern North America. We observed moderate levels of genetic diversity, low levels of inbreeding, and only a modest signal of isolation-by-distance. Clustering and migration analyses show that estimated levels of migration and genetic connectivity were higher than expected across cities and forested areas, specifically within the eastern portion of the species' range dominated by urbanization, and genetic connectivity was less than expected within the western range where the landscape is fragmented by agriculture. Landscape genetic methods revealed greater gene flow among individual squirrels in forested regions, which likely provide abundant food and shelter for squirrels. Although gene flow appears to be higher in areas with more tree cover, only slight discontinuities in gene flow suggest eastern grey squirrels have maintained connected populations across urban areas in all but the most heavily fragmented agricultural landscapes. Our results suggest urbanization shapes biological evolution in wildlife species depending strongly on the composition and habitability of the landscape matrix surrounding urban areas.

Predicting in vivo MRI Gradient-Field Induced Voltage Levels on Implanted Deep Brain Stimulation Systems Using Neural Networks.

INTRODUCTION: MRI gradient-fields may induce extrinsic voltage between electrodes and conductive neurostimulator enclosure of implanted deep brain stimulation (DBS) systems, and may cause unintended stimulation and/or malfunction. Electromagnetic (EM) simulations using detailed anatomical human models, therapy implant trajectories, and gradient coil models can be used to calculate clinically relevant induced voltage levels. Incorporating additional anatomical human models into the EM simulation library can help to achieve more clinically relevant and accurate induced voltage levels, however, adding new anatomical human models and developing implant trajectories is time-consuming, expensive and not always feasible. METHODS: MRI gradient-field induced voltage levels are simulated in six adult human anatomical models, along clinically relevant DBS implant trajectories to generate the dataset. Predictive artificial neural network (ANN) regression models are trained on the simulated dataset. Leave-one-out cross validation is performed to assess the performance of ANN regressors and quantify model prediction errors. RESULTS: More than 180,000 unique gradient-induced voltage levels are simulated. ANN algorithm with two fully connected layers is selected due to its superior generalizability compared to support vector machine and tree-based algorithms in this particular application. The ANN regression model is capable of producing thousands of gradient-induced voltage predictions in less than a second with mean-squared-error less than 200 mV. CONCLUSION: We have integrated machine learning (ML) with computational modeling and simulations and developed an accurate predictive model to determine MRI gradient-field induced voltage levels on implanted DBS systems.

Safety evaluation of a leadless transcatheter pacemaker for magnetic resonance imaging use.

BACKGROUND: Increased magnetic resonance imaging (MRI) adoption and demand are driving the need for device patients to have safe access to MRI. OBJECTIVE: The aim of this study was to address the interactions of MRI with the Micra transcatheter pacemaker system. METHODS: A strategy was developed to evaluate potential MRI risks including device heating, unintended cardiac stimulation, force, torque, vibration, and device malfunction. Assessment of MRI-induced device heating was conducted using a phantom containing gelled saline, and Monte Carlo simulations incorporating these results were conducted to simulate numerous combinations of human body models, position locations in the MRI scanner bore, and a variety of coil designs. Lastly, a patient with a Micra pacemaker who underwent a clinically indicated MRI scan is presented. RESULTS: Compared to traditional MRI conditional pacemakers, the overall risk with Micra was greatly reduced because of the small size of the device and the absence of a lead. The modeling results predicted that the nonperfused temperature rise of the device would be less than 0.4°C at 1.5 T and 0.5°C at 3 T and that the risk of device heating with multiple device implants was not increased as compared with a single device. The clinical case study revealed no MRI-related complications. CONCLUSION: The MRI safety assessment tests conducted for the Micra pacemaker demonstrate that patients with a single device or multiple devices can safely undergo MRI scans in both 1.5- and 3-T MRI scanners. No MRI-related complications were observed in a patient implanted with a Micra pacemaker undergoing a clinically indicated scan.

Safe magnetic resonance imaging scanning of patients with cardiac rhythm devices: a role for computer modeling.

BACKGROUND: Although there are several hazards for patients with implanted pacemakers and defibrillators in the magnetic resonance imaging (MRI) environment, evaluation of lead electrode heating is the most complex because of the many influencing variables: patient size, anatomy, body composition, patient position in the bore, scan sequence (radiofrequency power level), lead routing, and lead design. Although clinical studies are an important step in demonstrating efficacy, demonstrating safety through clinical trials alone is not practical because of this complexity. OBJECTIVE: The purpose of this study was to develop a comprehensive modeling framework to predict the probability of pacing capture threshold (PCT) change due to lead electrode heating in the MRI environment and thus provide a robust safety evaluation. METHODS: The lead heating risk was assessed via PCT change because this parameter is the most clinically relevant measure of lead heating. The probability for PCT change was obtained by combining the prediction for power at the electrode-tissue interface obtained via simulations with a prediction for PCT change as a function of radiofrequency power obtained via an in vivo canine study. RESULTS: The human modeling framework predicted that the probability of a 0.5-V PCT change due to an MRI scan for the Medtronic CapSureFix MRI SureScan model 5086 MRI leads is <1/70,000 for chest scans and <1/10,000,000 for either head scans or lower torso scans. CONCLUSION: The framework efficiently models millions of combinations, delivering a robust evaluation of the lead electrode heating hazard. This modeling approach provides a comprehensive safety evaluation that is impossible to achieve using phantom testing, animal studies, or clinical trials alone.