Kalirin12 interacts with dynamin

<p>Abstract</p> <p>Background</p> <p>Guanine nucleotide exchange factors (GEFs) and their target Rho GTPases regulate cytoskeletal changes and membrane trafficking. Dynamin, a large force-generating GTPase, plays an essential role in membrane tubulation and fission in cells. Kalirin12, a neuronal RhoGEF, is found in growth cones early in development and in dendritic spines later in development.</p> <p>Results</p> <p>The IgFn domain of Kalirin12, not present in other Kalirin isoforms, binds dynamin1 and dynamin2. An inactivating mutation in the GTPase domain of dynamin diminishes this interaction and the isolated GTPase domain of dynamin retains the ability to bind Kalirin12. Co-immunoprecipitation demonstrates an interaction of Kalirin12 and dynamin2 in embryonic brain. Purified recombinant Kalirin-IgFn domain inhibits the ability of purified rat brain dynamin to oligomerize in response to the presence of liposomes containing phosphatidylinositol-4,5-bisphosphate. Consistent with this, expression of exogenous Kalirin12 or its IgFn domain in PC12 cells disrupts clathrin-mediated transferrin endocytosis. Similarly, expression of exogenous Kalirin12 disrupts transferrin endocytosis in cortical neurons. Expression of Kalirin7, a shorter isoform which lacks the IgFn domain, was previously shown to inhibit clathrin-mediated endocytosis; the GTPase domain of dynamin does not interact with Kalirin7.</p> <p>Conclusion</p> <p>Kalirin12 may play a role in coordinating Rho GTPase-mediated changes in the actin cytoskeleton with dynamin-mediated changes in membrane trafficking.</p

Second-Order Belief Hidden Markov Models

Hidden Markov Models (HMMs) are learning methods for pattern recognition. The probabilistic HMMs have been one of the most used techniques based on the Bayesian model. First-order probabilistic HMMs were adapted to the theory of belief functions such that Bayesian probabilities were replaced with mass functions. In this paper, we present a second-order Hidden Markov Model using belief functions. Previous works in belief HMMs have been focused on the first-order HMMs. We extend them to the second-order model

HMM based scenario generation for an investment optimisation problem

This is the post-print version of the article. The official published version can be accessed from the link below - Copyright @ 2012 Springer-Verlag.The Geometric Brownian motion (GBM) is a standard method for modelling financial time series. An important criticism of this method is that the parameters of the GBM are assumed to be constants; due to this fact, important features of the time series, like extreme behaviour or volatility clustering cannot be captured. We propose an approach by which the parameters of the GBM are able to switch between regimes, more precisely they are governed by a hidden Markov chain. Thus, we model the financial time series via a hidden Markov model (HMM) with a GBM in each state. Using this approach, we generate scenarios for a financial portfolio optimisation problem in which the portfolio CVaR is minimised. Numerical results are presented.This study was funded by NET ACE at OptiRisk Systems

Inducing Probabilistic Grammars by Bayesian Model Merging

We describe a framework for inducing probabilistic grammars from corpora of positive samples. First, samples are {\em incorporated} by adding ad-hoc rules to a working grammar; subsequently, elements of the model (such as states or nonterminals) are {\em merged} to achieve generalization and a more compact representation. The choice of what to merge and when to stop is governed by the Bayesian posterior probability of the grammar given the data, which formalizes a trade-off between a close fit to the data and a default preference for simpler models (`Occam's Razor'). The general scheme is illustrated using three types of probabilistic grammars: Hidden Markov models, class-based $n$-grams, and stochastic context-free grammars.Comment: To appear in Grammatical Inference and Applications, Second International Colloquium on Grammatical Inference; Springer Verlag, 1994. 13 page

Reductions of Hidden Information Sources

In all but special circumstances, measurements of time-dependent processes reflect internal structures and correlations only indirectly. Building predictive models of such hidden information sources requires discovering, in some way, the internal states and mechanisms. Unfortunately, there are often many possible models that are observationally equivalent. Here we show that the situation is not as arbitrary as one would think. We show that generators of hidden stochastic processes can be reduced to a minimal form and compare this reduced representation to that provided by computational mechanics--the epsilon-machine. On the way to developing deeper, measure-theoretic foundations for the latter, we introduce a new two-step reduction process. The first step (internal-event reduction) produces the smallest observationally equivalent sigma-algebra and the second (internal-state reduction) removes sigma-algebra components that are redundant for optimal prediction. For several classes of stochastic dynamical systems these reductions produce representations that are equivalent to epsilon-machines.Comment: 12 pages, 4 figures; 30 citations; Updates at http://www.santafe.edu/~cm

Synaptic terminal density early in the course of schizophrenia: an in vivo UCB-J positron emission tomographic imaging study of synaptic vesicle glycoprotein 2A (SV2a).

BACKGROUND: The synaptic hypothesis is an influential theory of the pathoaetiology of schizophrenia. Supporting this, there is lower uptake of the synaptic terminal density marker UCB-J in patients with chronic schizophrenia compared to controls. However, it is unclear whether these differences are present early in the illness. To address this, we investigated [11C]UCB-J volume of distribution (VT) in antipsychotic-naïve/free patients with schizophrenia (SCZ) recruited from first-episode services compared to healthy volunteers (HV). METHODS: Forty-two volunteers (SCZ n = 21, HV n = 21) underwent [11C]UCB-J positron emission tomography to index [11C]UCB-J VT and distribution volume ratio (DVR) in the anterior cingulate, frontal, and dorsolateral prefrontal cortices, temporal, parietal and occipital lobes, hippocampus, thalamus and amygdala. Symptom severity was assessed in the SCZ group using the Positive and Negative Syndrome Scale (PANSS). RESULTS: We found no significant effects of group on [11C]UCB-J VT or DVR in most regions of interest (effect sizes from d=0.0 to 0.7, p>0.05), other than lower DVR in the temporal lobe (d=0.7, uncorrected p<0.05) and lower VT/fp in the anterior cingulate cortex in patients (d=0.7, uncorrected p<0.05). PANSS total score was negatively associated with [11C]UCB-J VT in the hippocampus in the SCZ group (r =-0.48, p=0.03). CONCLUSIONS: These findings indicate that large differences in synaptic terminal density are not present early in schizophrenia, although there may be more subtle effects. When taken with prior evidence of lower [11C]UCB-J VT in patients with chronic illness, this may indicate synaptic density changes during the course of schizophrenia

Inverse Modeling for MEG/EEG data

We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients.Comment: 15 pages, 1 figur

A Method of Hidden Markov Model Optimization for Use with Geophysical Data Sets

Abstract. Geophysics research has been faced with a growing need for automated techniques with which to process large quantities of data. A successful tool must meet a number of requirements: it should be consistent, require minimal parameter tuning, and produce scienti¯cally meaningful results in reasonable time. We introduce a hidden Markov model (HMM)-based method for analysis of geophysical data sets that attempts to address these issues. Our method improves on standard HMM methods and is based on the systematic analysis of structural local maxima of the HMM objective function. Preliminary results of the method as applied to geodetic and seismic records are presented.

Widespread cell stress and mitochondrial dysfunction in early Alzheimer’s Disease

Cell stress and impaired oxidative phosphorylation are central to mechanisms of synaptic loss and neurodegeneration in the cellular pathology of Alzheimer’s disease (AD). We quantified the in vivo density of the endoplasmic reticulum stress marker, the sigma 1 receptor (S1R) using [11C]SA4503 PET, as well as that of mitochondrial complex I (MC1) with [18F]BCPP-EF and the pre-synaptic vesicular protein SV2A with [11C]UCB-J in 12 patients with early AD and in 16 cognitively normal controls. We integrated these molecular measures with assessments of regional brain volumes and brain perfusion (CBF) measured with MRI arterial spin labelling. 8 AD patients were followed longitudinally to estimate rates of change with disease progression over 12-18 months. The AD patients showed widespread increases in S1R (≤ 27%) and regional decreases in MC1 (≥ -28%), SV2A (≥ -25%), brain volume (≥ -23%), and CBF (≥ -26%). [18F]BCPP-EF PET MC1 density (≥ -12%) and brain volumes (≥ -5%) were further reduced at follow up in brain regions consistent with the differences between AD patients and controls at baseline. Exploratory analyses showing associations of MC1, SV2A and S1R density with cognitive changes at baseline and longitudinally with AD, but not in controls, suggested a loss of metabolic functional reserve with disease. Our study thus provides novel in vivo evidence for widespread cellular stress and bioenergetic abnormalities in early AD and that they may be clinically meaningful