Altered Hippocampal-Prefrontal Neural Dynamics in Mouse Models of Down Syndrome

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Dopamine Gates Visual Signals in Monkey Prefrontal Cortex Neurons

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KCNJ8/ABCC9-containing K-ATP channel modulates brain vascular smooth muscle development and neurovascular coupling

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Microbial production of d-amino acids

The production of d-aminoacylase by Alcaligenes denitrificans and Alcaligenes faecalis has been studied. The enzyme was inducibly produced and N-acetyl-d-leucine and N-acetyl-d-valine were the most effective inducers. d-methionine, d-valine, d-phenylalamine and d-leucine were produced by the enzymic hydrolysis of the appropriate N-acetyl-d-amino-acids with whole cell biomass. The hydrolysis of N-acetyl-d-methionine by A. denitrificans and N-acetyl-d-valine by A. faecalis was preferential. Maximum yields of d-methionine and d-valine were 94.3 and 84.7% at a specific product formation rate of 20.10 and 19.19 μmol min⁻¹ mg⁻¹ of wet cells at 20 mM substrate concentration and 5 mg ml⁻¹ of cell density.     

Mechanisms of madness: evolutionary psychiatry without evolutionary psychology

Delusions are currently characterised as false beliefs produced by incorrect inference about external reality (DSM IV). This inferential conception has proved hard to link to explanations pitched at the level of neurobiology and neuroanatomy. This paper provides that link via a neurocomputational theory, based on evolutionary considerations, of the role of the prefrontal cortex in regulating offline cognition. When pathologically neuromodulated the prefrontal cortex produces hypersalient experiences which monopolise offline cognition. The result is characteristic psychotic experiences and patterns of thought. This bottom-up account uses neural network theory to integrate recent theories of the role of dopamine in delusion with the insights of inferential accounts. It also provides a general model for evolutionary psychiatry which avoids theoretical problems imported from evolutionary psychology.     

Bayesian Nonparametric Modeling for Comparison of Single-Neuron Firing Intensities

Summary .  We propose a fully inferential model-based approach to the problem of comparing the firing patterns of a neuron recorded under two distinct experimental conditions. The methodology is based on nonhomogeneous Poisson process models for the firing times of each condition with flexible nonparametric mixture prior models for the corresponding intensity functions. We demonstrate posterior inferences from a global analysis, which may be used to compare the two conditions over the entire experimental time window, as well as from a pointwise analysis at selected time points to detect local deviations of firing patterns from one condition to another. We apply our method on two neurons recorded from the primary motor cortex area of a monkey's brain while performing a sequence of reaching tasks.