Evolution of the cluster abundance in non-Gaussian models

We carry out N-body simulations of several non-Gaussian structure formation models, including Peebles' isocurvature cold dark matter model, cosmic string models, and a model with primordial voids. We compare the evolution of the cluster mass function in these simulations with that predicted by a modified version of the Press-Schechter formalism. We find that the Press-Schechter formula can accurately fit the cluster evolution over a wide range of redshifts for all of the models considered, with typical errors in the mass function of less than 25%, considerably smaller than the amount by which predictions for different models may differ. This work demonstrates that the Press-Schechter formalism can be used to place strong model independent constraints on non-Gaussianity in the universe.Comment: 11 pages, 12 postscipt figure

An automatic adaptive method to combine summary statistics in approximate Bayesian computation

To infer the parameters of mechanistic models with intractable likelihoods, techniques such as approximate Bayesian computation (ABC) are increasingly being adopted. One of the main disadvantages of ABC in practical situations, however, is that parameter inference must generally rely on summary statistics of the data. This is particularly the case for problems involving high-dimensional data, such as biological imaging experiments. However, some summary statistics contain more information about parameters of interest than others, and it is not always clear how to weight their contributions within the ABC framework. We address this problem by developing an automatic, adaptive algorithm that chooses weights for each summary statistic. Our algorithm aims to maximize the distance between the prior and the approximate posterior by automatically adapting the weights within the ABC distance function. Computationally, we use a nearest neighbour estimator of the distance between distributions. We justify the algorithm theoretically based on properties of the nearest neighbour distance estimator. To demonstrate the effectiveness of our algorithm, we apply it to a variety of test problems, including several stochastic models of biochemical reaction networks, and a spatial model of diffusion, and compare our results with existing algorithms

The impact of temporal sampling resolution on parameter inference for biological transport models

Imaging data has become widely available to study biological systems at various scales, for example the motile behaviour of bacteria or the transport of mRNA, and it has the potential to transform our understanding of key transport mechanisms. Often these imaging studies require us to compare biological species or mutants, and to do this we need to quantitatively characterise their behaviour. Mathematical models offer a quantitative description of a system that enables us to perform this comparison, but to relate these mechanistic mathematical models to imaging data, we need to estimate the parameters of the models. In this work, we study the impact of collecting data at different temporal resolutions on parameter inference for biological transport models by performing exact inference for simple velocity jump process models in a Bayesian framework. This issue is prominent in a host of studies because the majority of imaging technologies place constraints on the frequency with which images can be collected, and the discrete nature of observations can introduce errors into parameter estimates. In this work, we avoid such errors by formulating the velocity jump process model within a hidden states framework. This allows us to obtain estimates of the reorientation rate and noise amplitude for noisy observations of a simple velocity jump process. We demonstrate the sensitivity of these estimates to temporal variations in the sampling resolution and extent of measurement noise. We use our methodology to provide experimental guidelines for researchers aiming to characterise motile behaviour that can be described by a velocity jump process. In particular, we consider how experimental constraints resulting in a trade-off between temporal sampling resolution and observation noise may affect parameter estimates.Comment: Published in PLOS Computational Biolog

An automatic adaptive method to combine summary statistics in approximate Bayesian computation

To infer the parameters of mechanistic models with intractable likelihoods, techniques such as approximate Bayesian computation (ABC) are increasingly being adopted. One of the main disadvantages of ABC in practical situations, however, is that parameter inference must generally rely on summary statistics of the data. This is particularly the case for problems involving high-dimensional data, such as biological imaging experiments. However, some summary statistics contain more information about parameters of interest than others, and it is not always clear how to weight their contributions within the ABC framework. We address this problem by developing an automatic, adaptive algorithm that chooses weights for each summary statistic. Our algorithm aims to maximize the distance between the prior and the approximate posterior by automatically adapting the weights within the ABC distance function. Computationally, we use a nearest neighbour estimator of the distance between distributions. We justify the algorithm theoretically based on properties of the nearest neighbour distance estimator. To demonstrate the effectiveness of our algorithm, we apply it to a variety of test problems, including several stochastic models of biochemical reaction networks, and a spatial model of diffusion, and compare our results with existing algorithms

Survival of Dopaminergic Amacrine Cells after Near-Infrared Light Treatment in MPTP-Treated Mice

We examined whether near-infrared light (NIr) treatment (photobiomodulation) saves dopaminergic amacrine cells of the retina in an acute and a chronic 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse model of Parkinson disease. For the acute model, BALB/c mice had MPTP (100 mg/kg) or saline injections over 30 hours, followed by a six-day-survival period. For the chronic model, mice had MPTP (200 mg/kg) or saline injections over five weeks, followed by a three-week-survival period. NIr treatment was applied either at the same time (simultaneous series) or well after (posttreatment series) the MPTP insult. There were four groups within each series: Saline, Saline-NIr, MPTP, and MPTP-NIr. Retinae were processed for tyrosine hydroxylase (TH) immunochemistry, and cell number was analysed. In the MPTP groups, there was a significant reduction in TH+ cell number compared to the saline controls; this reduction was greater in the acute (~50%) compared to the chronic (~30%) cases. In the MPTP-NIr groups, there were significantly more TH+ cells than in the MPTP groups of both series (~30%). In summary, we showed that NIr treatment was able to both protect (simultaneous series) and rescue (posttreatment series) TH+ cells of the retina from parkinsonian insult

Corticosterone Acts in the Nucleus Accumbens to Enhance Dopamine Signaling and Potentiate Reinstatement of Cocaine Seeking

Stressful life events are important contributors to relapse in recovering cocaine addicts, but the mechanisms by which they influence motivational systems are poorly understood. Studies suggest that stress may “set the stage” for relapse by increasing the sensitivity of brain reward circuits to drug-associated stimuli. We examined the effects of stress and corticosterone on behavioral and neurochemical responses of rats to a cocaine prime after cocaine self-administration and extinction. Exposure of rats to acute electric footshock stress did not by itself reinstate drug-seeking behavior but potentiated reinstatement in response to a subthreshold dose of cocaine. This effect of stress was not observed in adrenalectomized animals, and was reproduced in nonstressed animals by administration of corticosterone at a dose that reproduced stress-induced plasma levels. Pretreatment with the glucocorticoid receptor antagonist RU38486 did not block the corticosterone effect. Corticosterone potentiated cocaine-induced increases in extracellular dopamine in the nucleus accumbens (NAc), and pharmacological blockade of NAc dopamine receptors blocked corticosterone-induced potentiation of reinstatement. Intra-accumbens administration of corticosterone reproduced the behavioral effects of stress and systemic corticosterone. Corticosterone treatment acutely decreased NAc dopamine clearance measured by fast-scan cyclic voltammetry, suggesting that inhibition of uptake2-mediated dopamine clearance may underlie corticosterone effects. Consistent with this hypothesis, intra-accumbens administration of the uptake2 inhibitor normetanephrine potentiated cocaine-induced reinstatement. Expression of organic cation transporter 3, a corticosterone-sensitive uptake2 transporter, was detected on NAc neurons. These findings reveal a novel mechanism by which stress hormones can rapidly regulate dopamine signaling and contribute to the impact of stress on drug intake