9 resultados para spatio-temporal data model

em National Center for Biotechnology Information - NCBI


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The HIV Reverse Transcriptase and Protease Sequence Database is an on-line relational database that catalogs evolutionary and drug-related sequence variation in the human immunodeficiency virus (HIV) reverse transcriptase (RT) and protease enzymes, the molecular targets of anti-HIV therapy (http://hivdb.stanford.edu). The database contains a compilation of nearly all published HIV RT and protease sequences, including submissions from International Collaboration databases and sequences published in journal articles. Sequences are linked to data about the source of the sequence sample and the antiretroviral drug treatment history of the individual from whom the isolate was obtained. During the past year 3500 sequences have been added and the data model has been expanded to include drug susceptibility data on sequenced isolates. Database content has also been integrated with didactic text and the output of two sequence analysis programs.

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A minimal hypothesis is proposed concerning the brain processes underlying effortful tasks. It distinguishes two main computational spaces: a unique global workspace composed of distributed and heavily interconnected neurons with long-range axons, and a set of specialized and modular perceptual, motor, memory, evaluative, and attentional processors. Workspace neurons are mobilized in effortful tasks for which the specialized processors do not suffice. They selectively mobilize or suppress, through descending connections, the contribution of specific processor neurons. In the course of task performance, workspace neurons become spontaneously coactivated, forming discrete though variable spatio-temporal patterns subject to modulation by vigilance signals and to selection by reward signals. A computer simulation of the Stroop task shows workspace activation to increase during acquisition of a novel task, effortful execution, and after errors. We outline predictions for spatio-temporal activation patterns during brain imaging, particularly about the contribution of dorsolateral prefrontal cortex and anterior cingulate to the workspace.

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Propagation of discharges in cortical and thalamic systems, which is used as a probe for examining network circuitry, is studied by constructing a one-dimensional model of integrate-and-fire neurons that are coupled by excitatory synapses with delay. Each neuron fires only one spike. The velocity and stability of propagating continuous pulses are calculated analytically. Above a certain critical value of the constant delay, these pulses lose stability. Instead, lurching pulses propagate with discontinuous and periodic spatio-temporal characteristics. The parameter regime for which lurching occurs is strongly affected by the footprint (connectivity) shape; bistability may occur with a square footprint shape but not with an exponential footprint shape. For strong synaptic coupling, the velocity of both continuous and lurching pulses increases logarithmically with the synaptic coupling strength gsyn for an exponential footprint shape, and it is bounded for a step footprint shape. We conclude that the differences in velocity and shape between the front of thalamic spindle waves in vitro and cortical paroxysmal discharges stem from their different effective delay; in thalamic networks, large effective delay between inhibitory neurons arises from their effective interaction via the excitatory cells which display postinhibitory rebound.

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In several biological systems, the electrical coupling of nonoscillating cells generates synchronized membrane potential oscillations. Because the isolated cell is nonoscillating and electrical coupling tends to equalize the membrane potentials of the coupled cells, the mechanism underlying these oscillations is unclear. Here we present a dynamic mechanism by which the electrical coupling of identical nonoscillating cells can generate synchronous membrane potential oscillations. We demonstrate this mechanism by constructing a biologically feasible model of electrically coupled cells, characterized by an excitable membrane and calcium dynamics. We show that strong electrical coupling in this network generates multiple oscillatory states with different spatio-temporal patterns and discuss their possible role in the cooperative computations performed by the system.

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In this study we investigate the mRNA expression of inhibitory factor κBα (IκBα) in cells of the rat brain induced by an intraperitoneal (i.p.) injection of lipopolysaccharide (LPS). IκB controls the activity of nuclear factor κB, which regulates the transcription of many immune signal molecules. The detection of IκB induction, therefore, would reveal the extent and the cellular location of brain-derived immune molecules in response to peripheral immune challenges. Low levels of IκBα mRNA were found in the large blood vessels and in circumventricular organs (CVOs) of saline-injected control animals. After an i.p. LPS injection (2.5 mg/kg), dramatic induction of IκBα mRNA occurred in four spatio-temporal patterns. Induced signals were first detected at 0.5 hr in the lumen of large blood vessels and in blood vessels of the choroid plexus and CVOs. Second, at 1–2 hr, labeling dramatically increased in the CVOs and choroid plexus and spread to small vascular and glial cells throughout the entire brain; these responses peaked at 2 hr and declined thereafter. Third, cells of the meninges became activated at 2 hr and persisted until 12 hr after the LPS injection. Finally, only at 12 hr, induced signals were present in ventricular ependyma. Thus, IκBα mRNA is induced in brain after peripheral LPS injection, beginning in cells lining the blood side of the blood–brain barrier and progressing to cells inside brain. The spatiotemporal patterns suggest that cells of the blood–brain barrier synthesize immune signal molecules to activate cells inside the central nervous system in response to peripheral LPS. The cerebrospinal fluid appears to be a conduit for these signal molecules.

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It is clear that the initial analysis of visual motion takes place in the striate cortex, where directionally selective cells are found that respond to local motion in one direction but not in the opposite direction. Widely accepted motion models postulate as inputs to directional units two or more cells whose spatio-temporal receptive fields (RFs) are approximately 90° out of phase (quadrature) in space and in time. Simple cells in macaque striate cortex differ in their spatial phases, but evidence is lacking for the varying time delays required for two inputs to be in temporal quadrature. We examined the space-time RF structure of cells in macaque striate cortex and found two subpopulations of (nondirectional) simple cells, some that show strongly biphasic temporal responses, and others that are weakly biphasic if at all. The temporal impulse responses of these two classes of cells are very close to 90° apart, with the strongly biphasic cells having a shorter latency than the weakly biphasic cells. A principal component analysis of the spatio-temporal RFs of directionally selective simple cells shows that their RFs could be produced by a linear combination of two components; these two components correspond closely in their respective latencies and biphasic characters to those of strongly biphasic and weakly biphasic nondirectional simple cells, respectively. This finding suggests that the motion system might acquire the requisite temporal quadrature by combining inputs from these two classes of nondirectional cells (or from their respective lateral geniculate inputs, which appear to be from magno and parvo lateral geniculate cells, respectively).

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The Biomolecular Interaction Network Database (BIND; http://binddb.org) is a database designed to store full descriptions of interactions, molecular complexes and pathways. Development of the BIND 2.0 data model has led to the incorporation of virtually all components of molecular mechanisms including interactions between any two molecules composed of proteins, nucleic acids and small molecules. Chemical reactions, photochemical activation and conformational changes can also be described. Everything from small molecule biochemistry to signal transduction is abstracted in such a way that graph theory methods may be applied for data mining. The database can be used to study networks of interactions, to map pathways across taxonomic branches and to generate information for kinetic simulations. BIND anticipates the coming large influx of interaction information from high-throughput proteomics efforts including detailed information about post-translational modifications from mass spectrometry. Version 2.0 of the BIND data model is discussed as well as implementation, content and the open nature of the BIND project. The BIND data specification is available as ASN.1 and XML DTD.

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We characterize a class of spatio-temporal illusions with two complementary properties. Firstly, if a vernier stimulus is flashed for a short time on a monitor and is followed immediately by a grating, the latter can express features of the vernier, such as its offset, its orientation, or its motion (feature inheritance). Yet the vernier stimulus itself remains perceptually invisible. Secondly, the vernier can be rendered visible by presenting gratings with a larger number of elements (shine-through). Under these conditions, subjects perceive two independent “objects” each carrying their own features. Transition between these two domains can be effected by subtle changes in the spatio-temporal layout of the grating. This should allow psychophysicists and electrophysiologists to investigate feature binding in a precise and quantitative manner.

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Global, near-surface temperature data sets and their derivations are discussed, and differences between the Jones and Intergovernmental Panel on Climate Change data sets are explained. Global-mean temperature changes are then interpreted in terms of anthropogenic forcing influences and natural variability. The inclusion of aerosol forcing improves the fit between modeled and observed changes but does not improve the agreement between the implied climate sensitivity value and the standard model-based range of 1.5–4.5°C equilibrium warming for a CO2 doubling. The implied sensitivity goes from below the model-based range of estimates to substantially above this range. The addition of a solar forcing effect further improves the fit and brings the best-fit sensitivity into the middle of the model-based range. Consistency is further improved when internally generated changes are considered. This consistency, however, hides many uncertainties that surround observed data/model comparisons. These uncertainties make it impossible currently to use observed global-scale temperature changes to narrow the uncertainty range in the climate sensitivity below that estimated directly from climate models.