Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures

Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.

ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection

To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20 sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts (eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.), (iii) Source of abnormally high amplitude ( �100 �V). We then evaluated the outcome of this cleaning by means of the classification of patients using multilayer perceptron neural networks. Results are very satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning procedure.

Derivation of an observer model adapted to irregular signals based on convolution channels.

Anthropomorphic model observers are mathe- matical algorithms which are applied to images with the ultimate goal of predicting human signal detection and classification accuracy across varieties of backgrounds, image acquisitions and display conditions. A limitation of current channelized model observers is their inability to handle irregularly-shaped signals, which are common in clinical images, without a high number of directional channels. Here, we derive a new linear model observer based on convolution channels which we refer to as the "Filtered Channel observer" (FCO), as an extension of the channelized Hotelling observer (CHO) and the nonprewhitening with an eye filter (NPWE) observer. In analogy to the CHO, this linear model observer can take the form of a single template with an external noise term. To compare with human observers, we tested signals with irregular and asymmetrical shapes spanning the size of lesions down to those of microcalfications in 4-AFC breast tomosynthesis detection tasks, with three different contrasts for each case. Whereas humans uniformly outperformed conventional CHOs, the FCO observer outperformed humans for every signal with only one exception. Additive internal noise in the models allowed us to degrade model performance and match human performance. We could not match all the human performances with a model with a single internal noise component for all signal shape, size and contrast conditions. This suggests that either the internal noise might vary across signals or that the model cannot entirely capture the human detection strategy. However, the FCO model offers an efficient way to apprehend human observer performance for a non-symmetric signal.

Performance benchmarking and incentive regulation – considerations of directing signals for electricity distribution companies

After the restructuring process of the power supply industry, which for instance in Finland took place in the mid-1990s, free competition was introduced for the production and sale of electricity. Nevertheless, natural monopolies are found to be the most efficient form of production in the transmission and distribution of electricity, and therefore such companies remained franchised monopolies. To prevent the misuse of the monopoly position and to guarantee the rights of the customers, regulation of these monopoly companies is required. One of the main objectives of the restructuring process has been to increase the cost efficiency of the industry. Simultaneously, demands for the service quality are increasing. Therefore, many regulatory frameworks are being, or have been, reshaped so that companies are provided with stronger incentives for efficiency and quality improvements. Performance benchmarking has in many cases a central role in the practical implementation of such incentive schemes. Economic regulation with performance benchmarking attached to it provides companies with directing signals that tend to affect their investment and maintenance strategies. Since the asset lifetimes in the electricity distribution are typically many decades, investment decisions have far-reaching technical and economic effects. This doctoral thesis addresses the directing signals of incentive regulation and performance benchmarking in the field of electricity distribution. The theory of efficiency measurement and the most common regulation models are presented. The chief contributions of this work are (1) a new kind of analysis of the regulatory framework, so that the actual directing signals of the regulation and benchmarking for the electricity distribution companies are evaluated, (2) developing the methodology and a software tool for analysing the directing signals of the regulation and benchmarking in the electricity distribution sector, and (3) analysing the real-life regulatory frameworks by the developed methodology and further develop regulation model from the viewpoint of the directing signals. The results of this study have played a key role in the development of the Finnish regulatory model.

Hepatic circadian clock oscillators and nuclear receptors integrate microbiome-derived signals.

The liver is a key organ of metabolic homeostasis with functions that oscillate in response to food intake. Although liver and gut microbiome crosstalk has been reported, microbiome-mediated effects on peripheral circadian clocks and their output genes are less well known. Here, we report that germ-free (GF) mice display altered daily oscillation of clock gene expression with a concomitant change in the expression of clock output regulators. Mice exposed to microbes typically exhibit characterized activities of nuclear receptors, some of which (PPARα, LXRβ) regulate specific liver gene expression networks, but these activities are profoundly changed in GF mice. These alterations in microbiome-sensitive gene expression patterns are associated with daily alterations in lipid, glucose, and xenobiotic metabolism, protein turnover, and redox balance, as revealed by hepatic metabolome analyses. Moreover, at the systemic level, daily changes in the abundance of biomarkers such as HDL cholesterol, free fatty acids, FGF21, bilirubin, and lactate depend on the microbiome. Altogether, our results indicate that the microbiome is required for integration of liver clock oscillations that tune output activators and their effectors, thereby regulating metabolic gene expression for optimal liver function.

Hepatic circadian clock oscillators and nuclear receptors integrate microbiome-derived signals.

The liver is a key organ of metabolic homeostasis with functions that oscillate in response to food intake. Although liver and gut microbiome crosstalk has been reported, microbiome-mediated effects on peripheral circadian clocks and their output genes are less well known. Here, we report that germ-free (GF) mice display altered daily oscillation of clock gene expression with a concomitant change in the expression of clock output regulators. Mice exposed to microbes typically exhibit characterized activities of nuclear receptors, some of which (PPARα, LXRβ) regulate specific liver gene expression networks, but these activities are profoundly changed in GF mice. These alterations in microbiome-sensitive gene expression patterns are associated with daily alterations in lipid, glucose, and xenobiotic metabolism, protein turnover, and redox balance, as revealed by hepatic metabolome analyses. Moreover, at the systemic level, daily changes in the abundance of biomarkers such as HDL cholesterol, free fatty acids, FGF21, bilirubin, and lactate depend on the microbiome. Altogether, our results indicate that the microbiome is required for integration of liver clock oscillations that tune output activators and their effectors, thereby regulating metabolic gene expression for optimal liver function.

Some Notes and Comments on the Efficient use of Information in Repeated Games with Poisson Signals

In the present paper we characterize the optimal use of Poisson signals to establish incentives in the "bad" and "good" news models of Abreu et al. [1]. In the former, for small time intervals the signals' quality is high and we observe a "selective" use of information; otherwise there is a "mass" use. In the latter, for small time intervals the signals' quality is low and we observe a "fine" use of information; otherwise there is a "non-selective" use. JEL: C73, D82, D86. KEYWORDS: Repeated Games, Frequent Monitoring, Public Monitoring, Infor- mation Characteristics.

Onset of Nucleate Boiling and Onset of Fully Developed Subcooled Boiling Using Pressure Transducers Signals Spectral Analysis

The experimental technique used for detection of subcooled boiling through analysis of the fluctuation contained in pressure transducer signals is presented. This work was partly conducted at the Institut für Kerntechnik und zertörungsfreie Prüfverfahren von Hannover (IKPH, Germany) in a thermal-hydraulic circuit with one electrically heated rod with annular geometry test section. Piezoresistive pressure sensors are used for onset of nucleate boiling (ONB) and onset of fully developed boiling (OFDB) detection using spectral analysis/ signal correlation techniques. Experimental results are interpreted by phenomenological analysis of these two points and compared with existing correlation. The results allow us to conclude that this technique is adequate for the detection and monitoring of the ONB and OFDB.

A comparative analysis of preprocessing techniques of cardiac event series for the study of heart rhythm variability using simulated signals

In the present study, using noise-free simulated signals, we performed a comparative examination of several preprocessing techniques that are used to transform the cardiac event series in a regularly sampled time series, appropriate for spectral analysis of heart rhythm variability (HRV). First, a group of noise-free simulated point event series, which represents a time series of heartbeats, was generated by an integral pulse frequency modulation model. In order to evaluate the performance of the preprocessing methods, the differences between the spectra of the preprocessed simulated signals and the true spectrum (spectrum of the model input modulating signals) were surveyed by visual analysis and by contrasting merit indices. It is desired that estimated spectra match the true spectrum as close as possible, showing a minimum of harmonic components and other artifacts. The merit indices proposed to quantify these mismatches were the leakage rate, defined as a measure of leakage components (located outside some narrow windows centered at frequencies of model input modulating signals) with respect to the whole spectral components, and the numbers of leakage components with amplitudes greater than 1%, 5% and 10% of the total spectral components. Our data, obtained from a noise-free simulation, indicate that the utilization of heart rate values instead of heart period values in the derivation of signals representative of heart rhythm results in more accurate spectra. Furthermore, our data support the efficiency of the widely used preprocessing technique based on the convolution of inverse interval function values with a rectangular window, and suggest the preprocessing technique based on a cubic polynomial interpolation of inverse interval function values and succeeding spectral analysis as another efficient and fast method for the analysis of HRV signals

Nonspecific blockade of vascular free radical signals by methylated arginine analogues

Methylated arginine analogues are often used as probes of the effect of nitric oxide; however, their specificity is unclear and seems to be frequently overestimated. This study analyzed the effects of NG-methyl-L-arginine (L-NMMA) on the endothelium-dependent release of vascular superoxide radicals triggered by increased flow. Plasma ascorbyl radical signals measured by direct electron paramagnetic resonance spectroscopy in 25 rabbits increased by 3.8 ± 0.7 nmol/l vs baseline (28.7 ± 1.4 nmol/l, P<0.001) in response to papaverine-induced flow increases of 121 ± 12%. In contrast, after similar papaverine-induced flow increases simultaneously with L-NMMA infusions, ascorbyl levels were not significantly changed compared to baseline. Similar results were obtained in isolated rabbit aortas perfused ex vivo with the spin trap a-phenyl-N-tert-butylnitrone (N = 22). However, in both preparations, this complete blockade was not reversed by co-infusion of excess L-arginine and was also obtained by N-methyl-D-arginine, thus indicating that it is not related to nitric oxide synthase. L-arginine alone was ineffective, as previously demonstrated for NG-methyl-L-arginine ester (L-NAME). In vitro, neither L-arginine nor its analogues scavenged superoxide radicals. This nonspecific activity of methylated arginine analogues underscores the need for careful controls in order to assess nitric oxide effects, particularly those related to interactions with active oxygen species.

Control of CD4 gene expression: connecting signals to outcomes in T cell development

The control of CD4 gene expression is essential for proper T lymphocyte development. Signals transmitted from the T-cell antigen receptor (TCR) during the thymic selection processes are believed to be linked to the regulation of CD4 gene expression during specific stages of T cell development. Thus, a study of the factors that control CD4 gene expression may lead to further insight into the molecular mechanisms that drive thymic selection. In this review, we discuss the work conducted to date to identify and characterize the cis-acting transcriptional control elements in the CD4 locus and the DNA-binding factors that mediate their function. From these studies, it is becoming clear that the molecular mechanisms controlling CD4 gene expression are very complex and differ at each stage of development. Thus, the control of CD4 expression is subject to many different influences as the thymocyte develops.