993 resultados para Neuronal signal modeling
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The neurotrophin receptor (p75NTR) is best known for mediating tropic support by participating in the formation of high-affinity nerve growth factor (NGF) receptor complexes with trkA, however, p75NTR more recently has been shown to act as a bona fide death-signaling receptor, which can signal independently of trkA. This article discusses the evidence for an active role of p75NTR in neuronal cell death and the mechanisms controlling this process, including roles for Bcl-2 family members, the c-jun stress kinase JNK, the transcription factor nuclear factor kappa B (NF kappa B), and caspases.
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Specific neuronal mRNAs are localized in dendrites, often concentrated in dendritic spines and spine synapses, where they are translated. The molecular mechanism of localization is mostly unknown. Here we have explored the roles of A2 response element (A2RE), a cis-acting signal for oligodendrocyte RNA trafficking, and its cognate trans-acting factor, heterogeneous nuclear ribonucleoprotein ( hnRNP) A2, in neurons. Fluorescently labeled chimeric RNAs containing A2RE were microinjected into hippocampal neurons, and RNA transport followed using confocal laser scanning microscopy. These RNA molecules, but not RNA lacking the A2RE sequence, were transported in granules to the distal neurites. hnRNP A2 protein was implicated as the cognate trans-acting factor: it was colocalized with RNA in cytoplasmic granules, and RNA trafficking in neurites was compromised by A2RE mutations that abrogate hnRNP A2 binding. Coinjection of antibodies to hnRNP A2 halved the number of trafficking cells, and treatment of neurons with antisense oligonucleotides also disrupted A2RE - RNA transport. Colchicine inhibited trafficking, whereas cells treated with cytochalasin were unaffected, implicating involvement of microtubules rather than microfilaments. A2RE-like sequences are found in a subset of dendritically localized mRNAs, which, together with these results, suggests that a molecular mechanism based on this cis-acting sequence may contribute to dendritic RNA localization.
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The growing heterogeneity of networks, devices and consumption conditions asks for flexible and adaptive video coding solutions. The compression power of the HEVC standard and the benefits of the distributed video coding paradigm allow designing novel scalable coding solutions with improved error robustness and low encoding complexity while still achieving competitive compression efficiency. In this context, this paper proposes a novel scalable video coding scheme using a HEVC Intra compliant base layer and a distributed coding approach in the enhancement layers (EL). This design inherits the HEVC compression efficiency while providing low encoding complexity at the enhancement layers. The temporal correlation is exploited at the decoder to create the EL side information (SI) residue, an estimation of the original residue. The EL encoder sends only the data that cannot be inferred at the decoder, thus exploiting the correlation between the original and SI residues; however, this correlation must be characterized with an accurate correlation model to obtain coding efficiency improvements. Therefore, this paper proposes a correlation modeling solution to be used at both encoder and decoder, without requiring a feedback channel. Experiments results confirm that the proposed scalable coding scheme has lower encoding complexity and provides BD-Rate savings up to 3.43% in comparison with the HEVC Intra scalable extension under development. © 2014 IEEE.
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Signal Processing, Vol. 86, nº 10
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Background: Brown adipose tissue (BAT) plays an important role in whole body metabolism and could potentially mediate weight gain and insulin sensitivity. Although some imaging techniques allow BAT detection, there are currently no viable methods for continuous acquisition of BAT energy expenditure. We present a non-invasive technique for long term monitoring of BAT metabolism using microwave radiometry. Methods: A multilayer 3D computational model was created in HFSS™ with 1.5 mm skin, 3-10 mm subcutaneous fat, 200 mm muscle and a BAT region (2-6 cm3) located between fat and muscle. Based on this model, a log-spiral antenna was designed and optimized to maximize reception of thermal emissions from the target (BAT). The power absorption patterns calculated in HFSS™ were combined with simulated thermal distributions computed in COMSOL® to predict radiometric signal measured from an ultra-low-noise microwave radiometer. The power received by the antenna was characterized as a function of different levels of BAT metabolism under cold and noradrenergic stimulation. Results: The optimized frequency band was 1.5-2.2 GHz, with averaged antenna efficiency of 19%. The simulated power received by the radiometric antenna increased 2-9 mdBm (noradrenergic stimulus) and 4-15 mdBm (cold stimulus) corresponding to increased 15-fold BAT metabolism. Conclusions: Results demonstrated the ability to detect thermal radiation from small volumes (2-6 cm3) of BAT located up to 12 mm deep and to monitor small changes (0.5°C) in BAT metabolism. As such, the developed miniature radiometric antenna sensor appears suitable for non-invasive long term monitoring of BAT metabolism.
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Dissertation presented to obtain the Ph.D degree in Biology
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MOTIVATION: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. RESULTS: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. AVAILABILITY: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.
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The current study investigates a new model of barrel cortex activation using stimulation of the infraorbital branch of the trigeminal nerve. A robust and reproducible activation of the rat barrel cortex was obtained following trigeminal nerve stimulation. Blood oxygen level-dependent (BOLD) effects were obtained in the primary somatosensory barrel cortex (S1BF), the secondary somatosensory cortex (S2) and the motor cortex. These cortical areas were reached from afferent pathways from the trigeminal ganglion, the trigeminal nuclei and thalamic nuclei from which neurons project their axons upon whisker stimulation. The maximum BOLD responses were obtained for a stimulus frequency of 1 Hz, a stimulus pulse width of 100 μs and for current intensities between 1.5 and 3 mA. The BOLD response was nonlinear as a function of frequency and current intensity. Additionally, modeling BOLD responses in the rat barrel cortex from separate cerebral blood flow (CBF) and cerebral metabolic rate of oxygen (CMRO(2)) measurements showed good agreement with the shape and amplitude of measured BOLD responses as a function of stimulus frequency and will potentially allow to identify the sources of BOLD nonlinearities. Activation of the rat barrel cortex using trigeminal nerve stimulation will contribute to the interpretation of the BOLD signals from functional magnetic resonance imaging studies.
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This research work deals with the problem of modeling and design of low level speed controller for the mobile robot PRIM. The main objective is to develop an effective educational tool. On one hand, the interests in using the open mobile platform PRIM consist in integrating several highly related subjects to the automatic control theory in an educational context, by embracing the subjects of communications, signal processing, sensor fusion and hardware design, amongst others. On the other hand, the idea is to implement useful navigation strategies such that the robot can be served as a mobile multimedia information point. It is in this context, when navigation strategies are oriented to goal achievement, that a local model predictive control is attained. Hence, such studies are presented as a very interesting control strategy in order to develop the future capabilities of the system
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In this study, hypothalamic activation was performed by dehydration-induced anorexia (DIA) and overnight food suppression (OFS) in female rats. The assessment of the hypothalamic response to these challenges by manganese-enhanced MRI showed increased neuronal activity in the paraventricular nuclei (PVN) and lateral hypothalamus (LH), both known to be areas involved in the regulation of food intake. The effects of DIA and OFS were compared by generating T-score maps. Increased neuronal activation was detected in the PVN and LH of DIA rats relative to OFS rats. In addition, the neurochemical profile of the PVN and LH were measured by (1) H MRS at 14.1T. Significant increases in metabolite levels were measured in DIA and OFS relative to control rats. Statistically significant increases in γ-aminobutyric acid were found in DIA (p=0.0007) and OFS (p<0.001) relative to control rats. Lactate increased significantly in DIA (p=0.03), but not in OFS, rats. This work shows that manganese-enhanced MRI coupled to (1) H MRS at high field is a promising noninvasive method for the investigation of the neural pathways and mechanisms involved in the control of food intake, in the autonomic and endocrine control of energy metabolism and in the regulation of body weight.
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A central feature of drugs of abuse is to induce gene expression in discrete brain structures that are critically involved in behavioral responses related to addictive processes. Although extracellular signal-regulated kinase (ERK) has been implicated in several neurobiological processes, including neuronal plasticity, its role in drug addiction remains poorly understood. This study was designed to analyze the activation of ERK by cocaine, its involvement in cocaine-induced early and long-term behavioral effects, as well as in gene expression. We show, by immunocytochemistry, that acute cocaine administration activates ERK throughout the striatum, rapidly but transiently. This activation was blocked when SCH 23390 [a specific dopamine (DA)-D1 antagonist] but not raclopride (a DA-D2 antagonist) was injected before cocaine. Glutamate receptors of NMDA subtypes also participated in ERK activation, as shown after injection of the NMDA receptor antagonist MK 801. The systemic injection of SL327, a selective inhibitor of the ERK kinase MEK, before cocaine, abolished the cocaine-induced ERK activation and decreased cocaine-induced hyperlocomotion, indicating a role of this pathway in events underlying early behavioral responses. Moreover, the rewarding effects of cocaine were abolished by SL327 in the place-conditioning paradigm. Because SL327 antagonized cocaine-induced c-fos expression and Elk-1 hyperphosphorylation, we suggest that the ERK intracellular signaling cascade is also involved in the prime burst of gene expression underlying long-term behavioral changes induced by cocaine. Altogether, these results reveal a new mechanism to explain behavioral responses of cocaine related to its addictive properties.
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Recently, there has been an increased interest on the neural mechanisms underlying perceptual decision making. However, the effect of neuronal adaptation in this context has not yet been studied. We begin our study by investigating how adaptation can bias perceptual decisions. We considered behavioral data from an experiment on high-level adaptation-related aftereffects in a perceptual decision task with ambiguous stimuli on humans. To understand the driving force behind the perceptual decision process, a biologically inspired cortical network model was used. Two theoretical scenarios arose for explaining the perceptual switch from the category of the adaptor stimulus to the opposite, nonadapted one. One is noise-driven transition due to the probabilistic spike times of neurons and the other is adaptation-driven transition due to afterhyperpolarization currents. With increasing levels of neural adaptation, the system shifts from a noise-driven to an adaptation-driven modus. The behavioral results show that the underlying model is not just a bistable model, as usual in the decision-making modeling literature, but that neuronal adaptation is high and therefore the working point of the model is in the oscillatory regime. Using the same model parameters, we studied the effect of neural adaptation in a perceptual decision-making task where the same ambiguous stimulus was presented with and without a preceding adaptor stimulus. We find that for different levels of sensory evidence favoring one of the two interpretations of the ambiguous stimulus, higher levels of neural adaptation lead to quicker decisions contributing to a speed–accuracy trade off.
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Nuclear receptors are a major component of signal transduction in animals. They mediate the regulatory activities of many hormones, nutrients and metabolites on the homeostasis and physiology of cells and tissues. It is of high interest to model the corresponding regulatory networks. While molecular and cell biology studies of individual promoters have provided important mechanistic insight, a more complex picture is emerging from genome-wide studies. The regulatory circuitry of nuclear receptor regulated gene expression networks, and their response to cellular signaling, appear highly dynamic, and involve long as well as short range chromatin interactions. We review how progress in understanding the kinetics and regulation of cofactor recruitment, and the development of new genomic methods, provide opportunities but also a major challenge for modeling nuclear receptor mediated regulatory networks.
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Since its introduction 16 years ago, the astrocyte-neuron lactate shuttle (ANLS) model has profoundly modified our understanding of neuroenergetics by bringing a cellular and molecular resolution. Praised or disputed, the concept has never ceased to attract attention, leading to critical advances and unexpected insights. Here, we summarize recent experimental evidence further supporting the main tenets of the model. Thus, evidence for distinct metabolic phenotypes between neurons (mainly oxidative) and astrocytes (mainly glycolytic) have been provided by genomics and classical metabolic approaches. Moreover, it has become clear that astrocytes act as a syncytium to distribute energy substrates such as lactate to active neurones. Glycogen, the main energy reserve located in astrocytes, is used as a lactate source to sustain glutamatergic neurotransmission and synaptic plasticity. Lactate is also emerging as a neuroprotective agent as well as a key signal to regulate blood flow. Characterization of monocarboxylate transporter regulation indicates a possible involvement in synaptic plasticity and memory. Finally, several modeling studies captured the implications of such findings for many brain functions. The ANLS model now represents a useful, experimentally based framework to better understand the coupling between neuronal activity and energetics as it relates to neuronal plasticity, neurodegeneration, and functional brain imaging.
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Synaptic plasticity involves a complex molecular machinery with various protein interactions but it is not yet clear how its components give rise to the different aspects of synaptic plasticity. Here we ask whether it is possible to mathematically model synaptic plasticity by making use of known substances only. We present a model of a multistable biochemical reaction system and use it to simulate the plasticity of synaptic transmission in long-term potentiation (LTP) or long-term depression (LTD) after repeated excitation of the synapse. According to our model, we can distinguish between two phases: first, a "viscosity" phase after the first excitation, the effects of which like the activation of NMDA receptors and CaMKII fade out in the absence of further excitations. Second, a "plasticity" phase actuated by an identical subsequent excitation that follows after a short time interval and causes the temporarily altered concentrations of AMPA subunits in the postsynaptic membrane to be stabilized. We show that positive feedback is the crucial element in the core chemical reaction, i.e. the activation of the short-tail AMPA subunit by NEM-sensitive factor, which allows generating multiple stable equilibria. Three stable equilibria are related to LTP, LTD and a third unfixed state called ACTIVE. Our mathematical approach shows that modeling synaptic multistability is possible by making use of known substances like NMDA and AMPA receptors, NEM-sensitive factor, glutamate, CaMKII and brain-derived neurotrophic factor. Furthermore, we could show that the heteromeric combination of short- and long-tail AMPA receptor subunits fulfills the function of a memory tag.