A point process framework for modeling electrical stimulation of the auditory nerve

Model-based studies of auditory nerve responses to electrical stimulation can provide insight into the functioning of cochlear implants. Ideally, these studies can identify limitations in sound processing strategies and lead to improved methods for providing sound information to cochlear implant users. To accomplish this, models must accurately describe auditory nerve spiking while avoiding excessive complexity that would preclude large-scale simulations of populations of auditory nerve fibers and obscure insight into the mechanisms that influence neural encoding of sound information. In this spirit, we develop a point process model of the auditory nerve that provides a compact and accurate description of neural responses to electric stimulation. Inspired by the framework of generalized linear models, the proposed model consists of a cascade of linear and nonlinear stages. We show how each of these stages can be associated with biophysical mechanisms and related to models of neuronal dynamics. Moreover, we derive a semi-analytical procedure that uniquely determines each parameter in the model on the basis of fundamental statistics from recordings of single fiber responses to electric stimulation, including threshold, relative spread, jitter, and chronaxie. The model also accounts for refractory and summation effects that influence the responses of auditory nerve fibers to high pulse rate stimulation. Throughout, we compare model predictions to published physiological data and explain differences in auditory nerve responses to high and low pulse rate stimulation. We close by performing an ideal observer analysis of simulated spike trains in response to sinusoidally amplitude modulated stimuli and find that carrier pulse rate does not affect modulation detection thresholds.Comment: 1 title page, 27 manuscript pages, 14 figures, 1 table, 1 appendi

Implementation Science to Accelerate Clean Cooking for Public Health

Clean cooking has emerged as a major concern for global health and development because of the enormous burden of disease caused by traditional cookstoves and fires. The World Health Organization has developed new indoor air quality guidelines that few homes will be able to achieve without replacing traditional methods with modern clean cooking technologies, including fuels and stoves. However, decades of experience with improved stove programs indicate that the challenge of modernizing cooking in impoverished communities includes a complex, multi-sectoral set of problems that require implementation research. The National Institutes of Health, in partnership with several government agencies and the Global Alliance for Clean Cookstoves, has launched the Clean Cooking Implementation Science Network that aims to address this issue. In this article, our focus is on building a knowledge base to accelerate scale-up and sustained use of the cleanest technologies in low- and middle-income countries. Implementation science provides a variety of analytical and planning tools to enhance effectiveness of clinical and public health interventions. These tools are being integrated with a growing body of knowledge and new research projects to yield new methods, consensus tools, and an evidence base to accelerate improvements in health promised by the renewed agenda of clean cooking.Fil: Rosenthal, Joshua. National Institutes Of Health. Fogarty International Center; Estados UnidosFil: Balakrishnan, Kalpana. Sri Ramachandra University; IndiaFil: Bruce, Nigel. University of Liverpool; Reino UnidoFil: Chambers, David. National Institutes of Health. National Cancer Institute; Estados UnidosFil: Graham, Jay. The George Washington University; Estados UnidosFil: Jack, Darby. Columbia University; Estados UnidosFil: Kline, Lydia. National Institutes Of Health. Fogarty International Center; Estados UnidosFil: Masera, Omar Raul. Universidad Nacional Autónoma de México; MéxicoFil: Mehta, Sumi. Global Alliance for Clean Cookstoves; Estados UnidosFil: Mercado, Ilse Ruiz. Universidad Nacional Autónoma de México; MéxicoFil: Neta, Gila. National Institutes of Health. National Cancer Institute; Estados UnidosFil: Pattanayak, Subhrendu. University of Duke; Estados UnidosFil: Puzzolo, Elisa. Global LPG Partnership; Estados UnidosFil: Petach, Helen. U.S. Agency for International Development; Estados UnidosFil: Punturieri, Antonello. National Heart, Lung, and Blood Institute; Estados UnidosFil: Rubinstein, Adolfo Luis. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sage, Michael. Centers for Disease Control and Prevention; Estados UnidosFil: Sturke, Rachel. National Institutes Of Health. Fogarty International Center; Estados UnidosFil: Shankar, Anita. University Johns Hopkins; Estados UnidosFil: Sherr, Kenny. University of Washington; Estados UnidosFil: Smith, Kirk. University of California at Berkeley; Estados UnidosFil: Yadama, Gautam. Washington University in St. Louis; Estados Unido

Common Genetic Variants and Modification of Penetrance of BRCA2-Associated Breast Cancer

Peer reviewe

Executive Control of Cognitive Processes in Task Switching

In 4 experiments, participants alternated between different tasks or performed the same task repeatedly. The tasks for 2 of the experiments required responding to geometric objects in terms of alternative classification rules, and the tasks for the other 2 experiments required solving arithmetic problems in terms of alternative numerical operations. Performance was measured as a function of whether the tasks were familiar or unfamiliar, the rules were simple or complex, and visual cues were present or absent about which tasks should be performed. Task alternation yielded switching-time costs that increased with rule complexity but decreased with task cuing. These factor effects were additive, supporting a model of executive control that has goal-shifting and rule-activation stages for task switching. It appears that rule activation takes more time for switching from familiar to unfamiliar tasks than for switching in the opposite direction

Rare-event probability estimation with conditional Monte Carlo

Estimation of rare-event probabilities in high-dimensional settings via importance sampling is a difficult problem due to the degeneracy of the likelihood ratio. In fact, it is generally recommended that Monte Carlo estimators involving likelihood ratios should not be used in such settings. In view of this, we develop efficient algorithms based on conditional Monte Carlo to estimate rare-event probabilities in situations where the degeneracy problem is expected to be severe. By utilizing an asymptotic description of how the rare event occurs, we derive algorithms that involve generating random variables only from the nominal distributions, thus avoiding any likelihood ratio. We consider two settings that occur frequently in applied probability: systems involving bottleneck elements and models involving heavy-tailed random variables. We first consider the problem of estimating ℙ(X+· · ·+X>γ), where X,...,X are independent but not identically distributed (ind) heavy-tailed random variables. Guided by insights obtained from this model, we then study a variety of more general settings. Specifically, we consider a complex bridge network and a generalization of the widely popular normal copula model used in managing portfolio credit risk, both of which involve hundreds of random variables. We show that the same conditioning idea, guided by an asymptotic description of the way in which the rare event happens, can be used to derive estimators that outperform existing ones