988 resultados para Variedade Hermes


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One way developing embryos regulate the expression of their genes is by localizing mRNAs to specific subcellular regions. In the oocyte of the frog, Xenopus laevis, many RNAs are localized specifically to the animal or the vegetal halves of the oocyte. The localization of these RNAs contributes to the primary polarity of the oocyte, the asymmetry that is the basis for patterning and lineage specification in the embryo. I have screened a cDNA library for clones containing the Xlsirt repeat, an element known to target RNAs to the vegetal cortex of the oocyte. I have identified seventeen cDNA clones that contain this element. One of these cDNAs encodes the RNA binding protein Hermes. The Hermes mRNA is localized to the vegetal cortex of the oocyte. Additionally, Hermes protein is also vegetally localized in the oocyte and is found in subcellular structures known to contain localized mRNAs. This suggests that Hermes might interact with localized RNAs. While Hermes protein is present in oocytes, it disappears at germinal vesicle breakdown during maturation. We therefore believe that the time period during which Hermes functions is during oogenesis or maturation prior to the time of Hermes degradation. To determine Hermes function, an antisense depletion strategy was used that involved injecting morpholino oligos (HE-MO) into oocytes. Injection of these morpholinos causes the level of Hennes protein to drop prematurely during maturation. Embryos produced from these oocytes exhibit cleavage defects that are most prevalent in the vegetal blastomeres. The phenotype can be partially rescued by injection of a heterologous Hermes mRNA and is therefore specific to Hermes. The Hermes expression and depletion results are consistent with a model in which Hermes interacts with one or more vegetally localized mRNAs in the oocyte and during the early stages of maturation. The interaction is required for cleavage of the vegetal blastomeres. Therefore, it is likely that at least one mRNA that interacts with Hermes is a cell cycle regulator. ^

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The HERMES cold-water coral database is a combination of historical and published sclerectinia cold-water coral occurrences (mainly Lophelia pertusa) and new records of the HERMES project along the European margin. This database will be updated if new findings are reported. New or historical data can be sent to Ben De Mol (mailto:bendemol@ub.edu). Besides geocodes a second category indicates the coral species and if they are sampled alive or dead. If absolute dating is available of the corals this is provide together with the method. Only the framework building cold-water corals are selected: Lophelia pertusa, Madrepora oculata and common cold-water corals often associated with the framework builders like: Desmophyllum sp and Dendrophylia sp. in comments other observed corals are indicated. Another field indicates if the corals are part of a large build-up or solitary. A third category of parameters is referencing to the quality of the represented data. In this category are the following parameters indicated: source of reference, source type (such as Fishermen location, scientific paper, cruise reports). sample code and or name and sample type (e.g. rock dredge, grab, video line). These parameters must allow an assessment of the quality of the described parameters.

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The analysis of the interdependence between time series has become an important field of research, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, and the introduction of concepts such as Generalized (GS) and Phase synchronization (PS). This increase in the number of approaches to tackle the existence of the so-called functional (FC) and effective connectivity (EC) (Friston 1994) between two, (or among many) neural networks, along with their mathematical complexity, makes it desirable to arrange them into a unified toolbox, thereby allowing neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of them.

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The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ‘traditional’ set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified-easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.

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The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ?traditional? set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified, easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.

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Mode of access: Internet.

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"Quellenverzeichnis" : p. 231-239.

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Mode of access: Internet.

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Mode of access: Internet.

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Vols. 1-4 include "Anhang. Ausländische Literatur." Separately paged.