Is a multiple excitation of a single atom equivalent to a single excitation of an ensemble of atoms?

Recent technological advances have enabled to isolate, control and measure the properties of a single atom, leading to the possibility to perform statistics on the behavior of single quantum systems. These experiments have enabled to check a question which was out of reach previously: Is the statistics of a repeatedly excitation of an atom N times equivalent to a single excitation of an ensemble of N atoms? We present a new method to analyze quantum measurements which leads to the postulation that the answer is most probably no. We discuss the merits of the analysis and its conclusion.Comment: 3 pages, 3 figure

Single-molecule studies of conformational states and dynamics in the ABC importer OpuA

The current model of active transport via ABC importers is mostly based on structural, biochemical and genetic data. We here establish single-molecule Förster resonance energy transfer (smFRET) assays to monitor the conformational states and heterogeneity of the osmoregulatory type I ABC importer OpuA from Lactococcus~lactis. We present data probing both intradomain distances that elucidate conformational changes within the substrate-binding domain (SBD) OpuAC, and interdomain distances between SBDs or transmembrane domains. Using this methodology, we studied ligand-binding mechanisms, as well as ATP and glycine betaine dependences of conformational changes. Our work expands the scope of smFRET investigations towards a class of so far unstudied ABC importers, and paves the way for a full understanding of their transport cycle in the future

The histamine system and cognitive function: an in vivo H3 receptor PET imaging study in healthy volunteers and patients with schizophrenia

BACKGROUND: The histamine-3 receptor (H3R) is an auto- and heteroreceptor that inhibits the release of histamine and other neurotransmitters. Post-mortem evidence has found altered H3R expression in patients with psychotic disorders, which may underlie cognitive impairment associated with schizophrenia (CIAS). AIMS: We used positron emission tomography (PET) imaging to compare brain uptake of an H3R selective tracer between patients with schizophrenia and matched controls (healthy individuals). Regions of interest included the dorsolateral prefrontal cortex (DLPFC) and striatum. We explored correlations between tracer uptake and symptoms, including cognitive domains. METHODS: A total of 12 patients and 12 matched controls were recruited to the study and were assessed with psychiatric and cognitive rating scales. They received a PET scan using the H3R-specific radioligand [11C]MK-8278 to determine H3R availability. RESULTS: There was no statistically significant difference in tracer uptake between patients and controls in the DLPFC (t19 = 0.79, p = 0.44) or striatum (t21 = 1.18, p = 0.25). An exploratory analysis found evidence for lower volume of distribution in the left cuneus (pFWE-corrected = 0.01). DLPFC tracer uptake was strongly correlated with cognition in controls (trail making test (TMT) A: r = 0.77, p = 0.006; TMT B: rho = 0.74, p = 0.01), but not in patients (TMT A: r = -0.18, p = 0.62; TMT B: rho = -0.06, p = 0.81). CONCLUSIONS: These findings indicate H3R in the DLPFC might play a role in executive function and this is disrupted in schizophrenia in the absence of major alterations in H3R availability as assessed using a selective radiotracer for H3R. This provides further evidence for the role of H3R in CIAS

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

Characterization of 3 PET tracers for Quantification of Mitochondrial and Synaptic function in Healthy Human Brain: 18F-BCPP-EF, 11C-SA-4503, 11C-UCB-J

Mitochondrial complex 1 (MC1) is involved in maintaining brain bioenergetics, the sigma 1 receptor (σ1R) responds to neuronal stress and synaptic vesicle protein 2A (SV2A) reflects synaptic integrity. Expression of each of these proteins is altered in neurodegenerative diseases. Here we characterise the kinetic behaviour of three positron emission tomography (PET) radioligands 18F-BCPP-EF, 11C-SA-4503 and 11CUCB- J, for the measurement of MC1, σ1R and SV2A, respectively, and determine appropriate analysis workflows for their application in future studies of the in vivo molecular pathology of these diseases. Methods: Twelve human subjects underwent dynamic PET scans including associated arterial blood sampling with each radioligand. A range of kinetic models were investigated to identify an optimal kinetic analysis method for each radioligand and a suitable acquisition duration. Results: All three radioligands readily entered the brain and yielded heterogeneous uptake consistent with the known distribution of the targets. The optimal models determined for the regional estimates of volume of distribution (VT) were multilinear analysis 1 (MA1) and the 2-tissue compartment (2TC) model for 18F-BCPP-EF, MA1 for 11C-SA- 4503, and both MA1 and the 1-tissue compartment (1TC) model for 11C-UCB-J. Acquisition times of 70, 80 and 60 minutes for 18F-BCPP-EF, 11C-SA-4503, 11C-UCB-J, respectively, provided good estimates of regional VT values. An effect of age was observed on 18F-BCPP-EF and 11C-UCB-J signal in the caudate. Conclusion: These ligands can be assessed for their potential to stratify patients or monitor the progression of molecular neuropathology in neurodegenerative diseases

Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals

Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which can subsequently be used in a classifier. The architecture consists of a spiking convolutional/pooling layer followed by a fully connected spiking layer for feature discovery. The convolutional layer of leaky, integrate-and-fire (LIF) neurons represents primary acoustic features. The fully connected layer is equipped with a probabilistic spike-timing-dependent plasticity learning rule. This layer represents the discriminative features through probabilistic, LIF neurons. To assess the discriminative power of the learned features, they are used in a hidden Markov model (HMM) for spoken digit recognition. The experimental results show performance above 96% that compares favorably with popular statistical feature extraction methods. Our results provide a novel demonstration of unsupervised feature acquisition in an SNN

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

A transient component in the pulse profile of PSR J0738-4042

One of the tenets of the radio pulsar observational picture is that the integrated pulse profiles are constant with time. This assumption underpins much of the fantastic science made possible via pulsar timing. Over the past few years, however, this assumption has come under question with a number of pulsars showing pulse shape changes on a range of timescales. Here, we show the dramatic appearance of a bright component in the pulse profile of PSR J0738-4042 (B0736-40). The component arises on the leading edge of the profile. It was not present in 2004 but strongly present in 2006 and all observations thereafter. A subsequent search through the literature shows the additional component varies in flux density over timescales of decades. We show that the polarization properties of the transient component are consistent with the picture of competing orthogonal polarization modes. Faced with the general problem of identifying and characterising average profile changes, we outline and apply a statistical technique based on a Hidden Markov Model. The value of this technique is established through simulations, and is shown to work successfully in the case of low signal-to-noise profiles.Comment: Accepted for publication in MNRA

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

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