999 resultados para EEG. Realidade virtual. Emoção. Fisioterapia


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In this work, we have used molecular dynamics, density functional theory, virtual screening, ADMET predictions, and molecular interaction field studies to design and propose eight novel potential inhibitors of CDK2. The eight molecules proposed showed interesting structural characteristics that are required for inhibiting the CDK2 activity and show potential as drug candidates for the treatment of cancer. The parameters related to the Rule of Five were calculated, and only one of the molecules violated more than one parameter. One of the proposals and one of the drug-like compounds selected by virtual screening indicated to be promising candidates for CDK2-based cancer therapy.

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Dietary changes associated with drug therapy can reduce high serum cholesterol levels and dramatically decrease the risk of coronary artery disease, stroke, and overall mortality. Statins are hypolipemic drugs that are effective in the reduction of cholesterol serum levels, attenuating cholesterol synthesis in liver by competitive inhibition regarding the substrate or molecular target HMG-CoA reductase. We have herewith used computer-aided molecular design tools, i.e., flexible docking, virtual screening in large data bases, molecular interaction fields to propose novel potential HMG-CoA reductase inhibitors that are promising for the treatment of hypercholesterolemia.

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Monoamine oxidase is a flavoenzyme bound to the mitochondrial outer membranes of the cells, which is responsible for the oxidative deamination of neurotransmitter and dietary amines. It has two distinct isozymic forms, designated MAO-A and MAO-B, each displaying different substrate and inhibitor specificities. They are the well-known targets for antidepressant, Parkinson`s disease, and neuroprotective drugs. Elucidation of the x-ray crystallographic structure of MAO-B has opened the way for the molecular modeling studies. In this work we have used molecular modeling, density functional theory with correlation, virtual screening, flexible docking, molecular dynamics, ADMET predictions, and molecular interaction field studies in order to design new molecules with potential higher selectivity and enzymatic inhibitory activity over MAO-B.

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The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively).

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This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

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The concept of the virtual organization (VO) has engendered great interest in the literature, yet there is still little common understanding of the concept, as evidenced by the multitude of labels applied to VOs. In this article, we focus on a “Weberian-ideal-type” definition of the interorganizational VO, posited in our earlier work (Kasper-Fuehrer and Ashkanasy 2001). We argue, however, that this definition left unanswered critical questions relating to the nature and effects of interorganizational VOs. We answer these questions here by explicating the terms in the definition and deriving ten corollaries, or “natural consequences” of our definition. The corollaries posit that interorganizational VOs are temporary in nature, are network organizations, are independent, and are based on swift trust. We suggest further that interorganizational VOs enable small to medium enterprises to exploit market opportunities, and enable VO member organizations to create a value-adding partnership. We also identify information and communication technology (ICT) as the essential enabler of VOs. Finally, we argue that interorganizational VOs act as a single organizational unit and that they therefore constitute a uniquely distinguishable organizational form. We conclude with suggestions for further research, including trust, organizational behavior, transaction economics, virtual HRM, and business strategy.

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It is known that some Virtual Reality (VR) head-mounted displays (HMDs) can cause temporary deficits in binocular vision. On the other hand, the precise mechanism by which visual stress occurs is unclear. This paper is concerned with a potential source of visual stress that has not been previously considered with regard to VR systems: inappropriate vertical gaze angle. As vertical gaze angle is raised or lowered the 'effort' required of the binocular system also changes. The extent to which changes in vertical gaze angle alter the demands placed upon the vergence eye movement system was explored. The results suggested that visual stress may depend, in part, on vertical gaze angle. The proximity of the display screens within an HMD means that a VR headset should be in the correct vertical location for any individual user. This factor may explain some previous empirical results and has important implications for headset design. Fortuitously, a reasonably simple solution exists.

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Antigen recognition by cytotoxic CD8 T cells is dependent upon a number of critical steps in MHC class I antigen processing including proteosomal cleavage, TAP transport into the endoplasmic reticulum, and MHC class 1 binding. Based on extensive experimental data relating to each of these steps there is now the capacity to model individual antigen processing steps with a high degree of accuracy. This paper demonstrates the potential to bring together models of individual antigen processing steps, for example proteosome cleavage, TAP transport, and MHC binding, to build highly informative models of functional pathways. In particular, we demonstrate how an artificial neural network model of TAP transport was used to mine a HLA-binding database so as to identify H LA-binding peptides transported by TAP. This integrated model of antigen processing provided the unique insight that HLA class I alleles apparently constitute two separate classes: those that are TAP-efficient for peptide loading (HLA-B27, -A3, and -A24) and those that are TAP-inefficient (HLA-A2, -B7, and -B8). Hence, using this integrated model we were able to generate novel hypotheses regarding antigen processing, and these hypotheses are now capable of being tested experimentally. This model confirms the feasibility of constructing a virtual immune system, whereby each additional step in antigen processing is incorporated into a single modular model. Accurate models of antigen processing have implications for the study of basic immunology as well as for the design of peptide-based vaccines and other immunotherapies. (C) 2004 Elsevier Inc. All rights reserved.

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We conducted two psychophysical experiments to investigate the relationship between processing mechanisms for exocentric distance and direction. In the first experiment, the task was to discriminate exocentric distances. In the second one, the task was to discriminate exocentric directions. The individual effects of distance and direction on each task were dissociated by analyzing their corresponding psychophysical functions. Under stereoscopicviewing conditions, distancejudgments of excentric intervals were not affected by exocentric direction. However, directionjudgments were influenced by the distance between the pair of stimuli. Therefore, the mechanism processing exocentric direction is dependent on exocentric distance, but the mechanism processing exocentric distance does not require exocentric: direction measures. As a result, we suggest that exocentric distance and direction are hierarchically processed, with distance preceding direction. Alternatively, and more probably, a necessary condition for processing the exocentric direction between two stimuli may be to know the location of each of them.

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Some patients are no longer able to communicate effectively or even interact with the outside world in ways that most of us take for granted. In the most severe cases, tetraplegic or post-stroke patients are literally `locked in` their bodies, unable to exert any motor control after, for example, a spinal cord injury or a brainstem stroke, requiring alternative methods of communication and control. But we suggest that, in the near future, their brains may offer them a way out. Non-invasive electroencephalogram (EEG)-based brain-computer interfaces (BCD can be characterized by the technique used to measure brain activity and by the way that different brain signals are translated into commands that control an effector (e.g., controlling a computer cursor for word processing and accessing the internet). This review focuses on the basic concepts of EEG-based BC!, the main advances in communication, motor control restoration and the down-regulation of cortical activity, and the mirror neuron system (MNS) in the context of BCI. The latter appears to be relevant for clinical applications in the coming years, particularly for severely limited patients. Hypothetically, MNS could provide a robust way to map neural activity to behavior, representing the high-level information about goals and intentions of these patients. Non-invasive EEG-based BCIs allow brain-derived communication in patients with amyotrophic lateral sclerosis and motor control restoration in patients after spinal cord injury and stroke. Epilepsy and attention deficit and hyperactive disorder patients were able to down-regulate their cortical activity. Given the rapid progression of EEG-based BCI research over the last few years and the swift ascent of computer processing speeds and signal analysis techniques, we suggest that emerging ideas (e.g., MNS in the context of BC!) related to clinical neuro-rehabilitation of severely limited patients will generate viable clinical applications in the near future.

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Oral squamous cell carcinoma (OSCC) is associated with high morbidity and mortality which is due, at least in part, to late detection. Precancerous and cancerous oral lesions may mimic any number of benign oral lesions, and as such may be left without investigation and treatment until they are well advanced. Over the past several years there has been renewed interest in oral cytology as an adjuvant clinical tool in the investigation of oral mucosal lesions. The purpose of the present study was to compare the usefulness of ploidy analysis after Feulgen stained cytological thin-prep specimens with traditional incisional biopsy and routine histopathological examination for the assessment of the pre-malignant potential of oral mucosal lesions. An analysis of the cytological specimens was undertaken with virtual microscopy which allowed for rapid and thorough analysis of the complete cytological specimen. 100 healthy individuals between 30 and 70 years of age, who were non-smokers, non-drinkers and not taking any medication, had cytological specimens collected from both the buccal mucosa and lateral margin of tongue to establish normal cytology parameters within a control population. Patients with a presumptive clinical diagnosis of lichen planus, leukoplakia or OSCC had lesional cytological samples taken prior to their diagnostic biopsy. Standardised thin preparations were prepared and each specimen stained by both Feuglen and Papanicolau methods. High speed scanning of the complete slide at 40X magnification was undertaken using the Aperio Scanscope TM and the green channel of the resultant image was analysed after threshold segmentation to isolate only nuclei and the integrated optical density of each nucleus taken as a gross measure of the DNA content (ploidy). Preliminary results reveal that ploidy assessment of oral cytology holds great promise as an adjunctive prognostic factor in the analysis of the malignant potential of oral mucosal lesions.

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Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to disentangle the description of brain processes by exploiting the advantages of each technique. Most studies in this field focus on exploring the relationships between fMRI signals and the power spectrum at some specific frequency bands (alpha, beta, etc.). On the other hand, brain mapping of EEG signals (e.g., interictal spikes in epileptic patients) usually assumes an haemodynamic response function for a parametric analysis applying the GLM, as a rough approximation. The integration of the information provided by the high spatial resolution of MR images and the high temporal resolution of EEG may be improved by referencing them by transfer functions, which allows the identification of neural driven areas without strong assumptions about haemodynamic response shapes or brain haemodynamic`s homogeneity. The difference on sampling rate is the first obstacle for a full integration of EEG and fMRI information. Moreover, a parametric specification of a function representing the commonalities of both signals is not established. In this study, we introduce a new data-driven method for estimating the transfer function from EEG signal to fMRI signal at EEG sampling rate. This approach avoids EEG subsampling to fMRI time resolution and naturally provides a test for EEG predictive power over BOLD signal fluctuations, in a well-established statistical framework. We illustrate this concept in resting state (eyes closed) and visual simultaneous fMRI-EEG experiments. The results point out that it is possible to predict the BOLD fluctuations in occipital cortex by using EEG measurements. (C) 2010 Elsevier Inc. All rights reserved.

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Recent advances in computer technology have made it possible to create virtual plants by simulating the details of structural development of individual plants. Software has been developed that processes plant models expressed in a special purpose mini-language based on the Lindenmayer system formalism. These models can be extended from their architectural basis to capture plant physiology by integrating them with crop models, which estimate biomass production as a consequence of environmental inputs. Through this process, virtual plants will gain the ability to react to broad environmental conditions, while crop models will gain a visualisation component. This integration requires the resolution of the fundamentally different time scales underlying the approaches. Architectural models are usually based on physiological time; each time step encompasses the same amount of development in the plant, without regard to the passage of real time. In contrast, physiological models are based in real time; the amount of development in a time step is dependent on environmental conditions during the period. This paper provides a background on the plant modelling language, then describes how widely-used concepts of thermal time can be implemented to resolve these time scale differences. The process is illustrated using a case study. (C) 1997 Elsevier Science Ltd.