Exploration of the Neuronal Subtype Specificity of an Ethanol Responsive Gene: Glycogen Synthase Kinase 3 Beta (Gsk3b)

Exploration of the Neuronal Subtype Specificity of an Ethanol Responsive Gene: Glycogen Synthase Kinase 3 Beta (Gsk3b) Dalton Huey, Depts. of Bioinformatics, Biology & Chemistry, A. van der Vaart, G. M. Harris, and M. F. Miles, with Dr. Sarah Golding, Dept. of Biology Previous work done in our laboratory revealed that Glycogen Synthase Kinase 3 Beta (Gsk3b) functions as a hub gene in a network of genes regulated by acute ethanol in the medial prefrontal cortex (mPFC) across a mouse genetic panel. Adult mice treated with acute ethanol showed increased phosphorylation of GSK3B on the Ser9 residue in prefrontal cortex. Subsequent viral-mediated overexpression of Gsk3bin mouse mPFC caused an increase in ethanol consumption and pharmacological inhibition of GSK3B decreased ethanol consumption. However, it is unknown what neuron subtypes are driving this change in behavior. Here, we provide evidence that deletion of Gsk3bin Camk2a+ glutamatergic neurons of the mPFC results in a decrease in ethanol consumption in both continuous and intermittent access drinking paradigms. Furthermore, we have recently designed and validated a plasmid for Cre-dependent overexpression of Gsk3b, along with a Cre-dependent reporter as a control. These plasmids are planned for use in conjunction with different Cre drivers for viral-mediated expression in any cell type. Dissection of the neural circuitry of this ethanol responsive pathway can lead to a better assessment of Gsk3bas a potential target for the treatment of alcohol use disorders. Work supported by grants R01A027581 and P50AA022537 to MFM.https://scholarscompass.vcu.edu/uresposters/1313/thumbnail.jp

Taking error into account when fitting models using approximate Bayesian computation

Stochastic computer simulations are often the only practical way of answering questions relating to ecological management. However, due to their complexity, such models are difficult to calibrate and evaluate. Approximate Bayesian Computation (ABC) offers an increasingly popular approach to this problem, widely applied across a variety of fields. However, ensuring the accuracy of ABC's estimates has been difficult. Here, we obtain more accurate estimates by incorporating estimation of error into the ABC protocol. We show how this can be done where the data consist of repeated measures of the same quantity and errors may be assumed to be normally distributed and independent. We then derive the correct acceptance probabilities for a probabilistic ABC algorithm, and update the 'coverage test' with which accuracy is assessed. We apply this method - which we call 'error-calibrated ABC' - to a toy example and a realistic 14-parameter simulation model of earthworms that is used in environmental risk assessment. A comparison with exact methods and the diagnostic 'coverage test' show that our approach improves estimation of parameter values and their credible intervals for both models

Posterior Contraction Rates for the Bayesian Approach to Linear Ill-Posed Inverse Problems

We consider a Bayesian nonparametric approach to a family of linear inverse problems in a separable Hilbert space setting with Gaussian noise. We assume Gaussian priors, which are conjugate to the model, and present a method of identifying the posterior using its precision operator. Working with the unbounded precision operator enables us to use partial differential equations (PDE) methodology to obtain rates of contraction of the posterior distribution to a Dirac measure centered on the true solution. Our methods assume a relatively weak relation between the prior covariance, noise covariance and forward operator, allowing for a wide range of applications

Environmental Perturbations Induce Correlations in Midge Swarms

Although collectively behaving animal groups often show large-scale order (such as in bird flocks), they need not always (such as in insect swarms). It has been suggested that the signature of collective behavior in disordered groups is a residual long-range correlation. However, results in the literature have reported contradictory results as to the presence of long-range correlation in insect swarms, with swarms in the wild displaying correlation but those in a controlled laboratory environment not. We resolve these apparently incompatible results by showing the external perturbations generically induce the emergence of correlations. We apply a range of different external stimuli to laboratory swarms of the non-biting midge Chironomus riparius, and show that in all cases correlations appear when perturbations are introduced. We confirm the generic nature of these results by showing that they can be reproduced in a stochastic model of swarms. Given that swarms in the wild will always have to contend with environmental stimuli, our results thus harmonize previous findings

Проблеми управління експлуатаційним потенціалом газорозподільчих підприємств міста

У статті узагальнені проблеми управління експлуатаційним потенціалом газорозподільчих підприємств міста, визначено причини їх виникнення та запропоновано шляхи вирішення. Визначено коло завдань держави, які мають бути вирішені, щоб експлуатація газорозподільчих мереж у місті була більш ефективною та економічно доцільною

An equation of state for insect swarms

Collective behaviour in flocks, crowds, and swarms occurs throughout the biological world. Animal groups are generally assumed to be evolutionarily adapted to robustly achieve particular functions, so there is widespread interest in exploiting collective behaviour for bio-inspired engineering. However, this requires understanding the precise properties and function of groups, which remains a challenge. Here, we demonstrate that collective groups can be described in a thermodynamic framework. We define an appropriate set of state variables and extract an equation of state for laboratory midge swarms. We then drive swarms through “thermodynamic” cycles via external stimuli, and show that our equation of state holds throughout. Our findings demonstrate a new way of precisely quantifying the nature of collective groups and provide a cornerstone for potential future engineering design

Interaction Energy Decomposition in Protein–Protein Association: A Quantum Mechanical Study of Barnase–Barstar Complex

Protein–protein interactions are very important in the function of a cell. Computational studies of these interactions have been of interest, but often they have utilized classical modelling techniques. In recent years, quantum mechanical (QM) treatment of entire proteins has emerged as a powerful approach to study biomolecular systems. Herein, we apply a semi-empirical divide and conquer (DC) methodology coupled with a dielectric continuum model for the solvent, to explore the contribution of electrostatics, polarization and charge transfer to the interaction energy between barnase and barstar in their complex form. Molecular dynamic (MD) simulation was performed to account for the dynamic behavior of the complex. The results show that electrostatics, charge transfer and polarization favor the formation of the complex. Our study shows that electrostatics dominates the interaction between barnase and barstar (∼ 73%), while charge transfer and polarization are ∼ 21% and ∼ 6%, respectively. Close inspection of the polarization and charge-transfer effects on the charge distribution of the complex reveals the existence of two, well localized, regions in barstar. The first region includes the residues between P27 and Y47 and the second region is between N65 and D83. Since no such regions could be detected in barnase clearly suggests that barstar is well optimized for efficiently binding barnase. Furthermore, using our interaction energy decomposition scheme, we were able to identify all residues that have been experimentally determined to be important for the complex formation and to suggest other residues never have been investigated. This suggests that our approach will be useful as an aid in further understanding protein–protein contacts for the ultimate goal to produce successful inhibitors for protein complexes