995 resultados para Energy decomposition
Resumo:
There is growing evidence that astrocytes are involved in the neuropathology of major depression. In particular, decreases in glial cell density observed in the cerebral cortex of individuals with major depressive disorder are accompanied by a reduction of several astrocytic markers suggesting that astrocyte dysfunction may contribute to the pathophysiology of major depression. In rodents, glial loss in the prefrontal cortex is sufficient to induce depressive-like behaviors and antidepressant treatment prevents the stress-induced reduction of astrocyte number in the hippocampus. Collectively, these data support the existence of a link between astrocyte loss or dysfunction, depressive-like behavior and antidepressant treatment. Astrocytes are increasingly recognized to play important roles in neuronal development, neurotransmission, synaptic plasticity and maintenance of brain homeostasis. It is also well established that astrocytes provide trophic, structural, and metabolic support to neurons. In this article, we review evidence that antidepressants regulate energy metabolism and neurotrophic factor expression with particular emphasis on studies in astrocytes. These observations support a role for astrocytes as new targets for antidepressants. The contribution of changes in astrocyte glucose metabolism and neurotrophic factor expression to the therapeutic effects of antidepressants remains to be established.
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Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.
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Glucose is an important signal that regulates glucose and energy homeostasis but its precise physiological role and signaling mechanism in the brain are still uncompletely understood. Over the recent years we have investigated the possibility that central glucose sensing may share functional similarities with glucose sensing by pancreatic beta-cells, in particular a requirement for the expression of the glucose transporter Glut2. Using mice with genetic inactivation of Glut2, but rescued pancreatic beta-cell function by transgenic expression of a glucose transporter, we have established that extrapancreatic glucose sensors are involved: i) in the control of glucagon secretion in response to hypoglycemia, ii) in the control of feeding and iii) of energy expenditure. We have more recently shown that central Glut2-dependent glucose sensors are involved in the regulation of NPY and POMC expression by arcuate nucleus neurons and that the sensitivity to leptin of these neurons is enhanced by Glut2-dependent glucose sensors. Using mice with genetic tagging of Glut2-expressing cells, we determined that the NPY and POMC neurons did not express Glut2 but were connected to Glut2 expressing neurons located most probably outside of the arcuate nucleus. We are now defining the electrophysiological behavior of these Glut2 expressing neurons. Our data provide an initial map of glucose sensing neurons expressing Glut2 and link these neurons with the control of specific physiological function.
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In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.
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Artifacts are present in most of the electroencephalography (EEG) recordings, making it difficult to interpret or analyze the data. In this paper a cleaning procedure based on a multivariate extension of empirical mode decomposition is used to improve the quality of the data. This is achieved by applying the cleaning method to raw EEG data. Then, a synchrony measure is applied on the raw and the clean data in order to compare the improvement of the classification rate. Two classifiers are used, linear discriminant analysis and neural networks. For both cases, the classification rate is improved about 20%.
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BACKGROUND: This study validates the use of phycoerythrin (PE) and allophycocyanin (APC) for fluorescence energy transfer (FRET) analyzed by flow cytometry. METHODS: FRET was detected when a pair of antibody conjugates directed against two noncompetitive epitopes on the same CD8alpha chain was used. FRET was also detected between antibody conjugate pairs specific for the two chains of the heterodimeric alpha (4)beta(1) integrin. Similarly, the association of T-cell receptor (TCR) with a soluble antigen ligand was detected by FRET when anti-TCR antibody and MHC class I/peptide complexes (<<tetramers>>) were used. RESULTS: FRET efficiency was always less than 10%, probably because of steric effects associated with the size and structure of PE and APC. Some suggestions are given to take into account this and other effects (e.g., donor and acceptor concentrations) for a better interpretation of FRET results obtained with this pair of fluorochromes. CONCLUSIONS: We conclude that FRET assays can be carried out easily with commercially available antibodies and flow cytometers to study arrays of multimolecular complexes.
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The flow of two immiscible fluids through a porous medium depends on the complex interplay between gravity, capillarity, and viscous forces. The interaction between these forces and the geometry of the medium gives rise to a variety of complex flow regimes that are difficult to describe using continuum models. Although a number of pore-scale models have been employed, a careful investigation of the macroscopic effects of pore-scale processes requires methods based on conservation principles in order to reduce the number of modeling assumptions. In this work we perform direct numerical simulations of drainage by solving Navier-Stokes equations in the pore space and employing the Volume Of Fluid (VOF) method to track the evolution of the fluid-fluid interface. After demonstrating that the method is able to deal with large viscosity contrasts and model the transition from stable flow to viscous fingering, we focus on the macroscopic capillary pressure and we compare different definitions of this quantity under quasi-static and dynamic conditions. We show that the difference between the intrinsic phase-average pressures, which is commonly used as definition of Darcy-scale capillary pressure, is subject to several limitations and it is not accurate in presence of viscous effects or trapping. In contrast, a definition based on the variation of the total surface energy provides an accurate estimate of the macroscopic capillary pressure. This definition, which links the capillary pressure to its physical origin, allows a better separation of viscous effects and does not depend on the presence of trapped fluid clusters.
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In dynamic models of energy allocation, assimilated energy is allocated to reproduction, somatic growth, maintenance or storage, and the allocation pattern can change with age. The expected evolutionary outcome is an optimal allocation pattern, but this depends on the environment experienced during the evolutionary process and on the fitness costs and benefits incurred by allocating resources in different ways. Here we review existing treatments which encompass some of the possibilities as regards constant or variable environments and their predictability or unpredictability, and the ways in which production rates and mortality rates depend on body size and composition and age and on the pattern of energy allocation. The optimal policy is to allocate resources where selection pressures are highest, and simultaneous allocation to several body subsystems and reproduction can be optimal if these pressures are equal. This may explain balanced growth commonly observed during ontogeny. Growth ceases at maturity in many models; factors favouring growth after maturity include non-linear trade-offs, variable season length, and production and mortality rates both increasing (or decreasing) functions of body size. We cannot yet say whether these are sufficient to account for the many known cases of growth after maturity and not all reasonable models have yet been explored. Factors favouring storage are also reviewed.
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We study energy relaxation in thermalized one-dimensional nonlinear arrays of the Fermi-Pasta-Ulam type. The ends of the thermalized systems are placed in contact with a zero-temperature reservoir via damping forces. Harmonic arrays relax by sequential phonon decay into the cold reservoir, the lower-frequency modes relaxing first. The relaxation pathway for purely anharmonic arrays involves the degradation of higher-energy nonlinear modes into lower-energy ones. The lowest-energy modes are absorbed by the cold reservoir, but a small amount of energy is persistently left behind in the array in the form of almost stationary low-frequency localized modes. Arrays with interactions that contain both a harmonic and an anharmonic contribution exhibit behavior that involves the interplay of phonon modes and breather modes. At long times relaxation is extremely slow due to the spontaneous appearance and persistence of energetic high-frequency stationary breathers. Breather behavior is further ascertained by explicitly injecting a localized excitation into the thermalized arrays and observing the relaxation behavior.
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The influence of external factors on food preferences and choices is poorly understood. Knowing which and how food-external cues impact the sensory processing and cognitive valuation of food would provide a strong benefit toward a more integrative understanding of food intake behavior and potential means of interfering with deviant eating patterns to avoid detrimental health consequences for individuals in the long run. We investigated whether written labels with positive and negative (as opposed to 'neutral') valence differentially modulate the spatio-temporal brain dynamics in response to the subsequent viewing of high- and low-energetic food images. Electrical neuroimaging analyses were applied to visual evoked potentials (VEPs) from 20 normal-weight participants. VEPs and source estimations in response to high- and low- energy foods were differentially affected by the valence of preceding word labels over the ~260-300 ms post-stimulus period. These effects were only observed when high-energy foods were preceded by labels with positive valence. Neural sources in occipital as well as posterior, frontal, insular and cingulate regions were down-regulated. These findings favor cognitive-affective influences especially on the visual responses to high-energetic food cues, potentially indicating decreases in cognitive control and goal-adaptive behavior. Inverse correlations between insular activity and effectiveness in food classification further indicate that this down-regulation directly impacts food-related behavior.
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Components of daily energy expenditure were measured serially by whole-body calorimetry in Gambian women before pregnancy and at 6, 12, 18, 24, 30, and 36 wk gestation. Weight gain was (mean +/- SD) 6.8 +/- 2.8 kg, fat deposition was 2.0 +/- 2.5 kg and lean tissue deposition was 5.0 +/- 2.5 kg. Basal metabolic rate (BMR) was depressed during the first 18 wk of gestation, causing total cumulative maintenance costs by week 36 to be 8.4 MJ. Individual responses to pregnancy correlated with changes in body mass (36 wk: delta BMR vs delta weight; r = 0.60, P < 0.01 delta BMR vs delta LBM; r = 0.62, P < 0.01). There was no significant increase in the cost of treadmill exercise (0% slope: F = 0.71, P = 0.64; 5% slope: F = 1.97, P = 0.10), 24-h energy expenditure (F = 0.72, P = 0.64), activity or diet-induced thermogenesis (F = 1.02, P = 0.43), during pregnancy in spite of body weight gain. Total metabolic costs over 36 wk were 144 MJ (fetus 43 MJ, fat deposition 92 MJ, cumulative maintenance costs 8.4 MJ). These were far lower than reported for well-nourished Western populations.