Ionized gas velocity dispersion in nearby dwarf galaxies: looking at supersonic turbulent motions

We present the results of ionized gas turbulent motions study in several nearby dwarf galaxies using a scanning Fabry-Perot interferometer with the 6-m telescope of the SAO RAS. Combining the `intensity-velocity dispersion' diagrams (I-sigma) with two-dimensional maps of radial velocity dispersion we found a number of common patterns pointing to the relation between the value of chaotic ionized gas motions and processes of current star formation. In five out of the seven analysed galaxies we identified expanding shells of ionized gas with diameters of 80-350 pc and kinematic ages of 1-4 Myr. We also demonstrate that the I-sigma diagrams may be useful for the search of supernova remnants, other small expanding shells or unique stars in nearby galaxies. As an example, a candidate luminous blue variable (LBV) was found in UGC 8508. We propose some additions to the interpretation, previously used by Munoz-Tunon et al. to explain the I-sigma diagrams for giant star formation regions. In the case of dwarf galaxies, a major part of the regions with high velocity dispersion belongs to the diffuse low surface brightness emission, surrounding the star forming regions. We attribute this to the presence of perturbed low density gas with high values of turbulent velocities around the giant HII regions.Comment: 21 pages, 8 figures. Accepted by MNRAS. The high-resolution version locates at http://www.sao.ru/hq/moisav/MoisLoz_full.pd

TrkB modulates fear learning and amygdalar synaptic plasticity by specific docking sites

Understanding the modulation of the neural circuitry of fear is clearly one of the most important aims in neurobiology. Protein phosphorylation in response to external stimuli is considered a major mechanism underlying dynamic changes in neural circuitry. TrkB (Ntrk2) neurotrophin receptor tyrosine kinase potently modulates synaptic plasticity and activates signal transduction pathways mainly through two phosphorylation sites [Y515/Shc site; Y816/PLCgamma (phospholipase Cgamma) site]. To identify the molecular pathways required for fear learning and amygdalar synaptic plasticity downstream of TrkB, we used highly defined genetic mouse models carrying single point mutations at one of these two sites (Y515F or Y816F) to examine the physiological relevance of pathways activated through these sites for pavlovian fear conditioning (FC), as well as for synaptic plasticity as measured by field recordings obtained from neurons of different amygdala nuclei. We show that a Y816F point mutation impairs acquisition of FC, amygdalar synaptic plasticity, and CaMKII signaling at synapses. In contrast, a Y515F point mutation affects consolidation but not acquisition of FC to tone, and also alters AKT signaling. Thus, TrkB receptors modulate specific phases of fear learning and amygdalar synaptic plasticity through two main phosphorylation docking sites

A tale of two yield curves: Modeling the joint term structure of dollar and euro interest rates

Modeling the joint term structure of interest rates in the United States and the European Union, the two largest economies in the world, is extremely important in international finance. In this article, we provide both theoretical and empirical analysis of multi-factor joint affine term structure models (ATSM) for dollar and euro interest rates. In particular, we provide a systematic classification of multi-factor joint ATSM similar to that of Dai and Singleton (2000). A principal component analysis of daily dollar and euro interest rates reveals four factors in the data. We estimate four-factor joint ATSM using the approximate maximum likelihood method of (Aït-Sahalia, 2002) and (Aït-Sahalia, forthcoming) and compare the in-sample and out-of-sample performances of these models using some of the latest nonparametric methods. We find that a new four-factor model with two common and two local factors captures the joint term structure dynamics in the US and the EU reasonably well.Affine term structure models International term structure models Approximate maximum likelihood LIBOR Euribor Specification analysis of term structure of interest rates Out-of-sample model evaluation

Validating forecasts of the joint probability density of bond yields: Can affine models beat random walk?

Most existing empirical studies on affine term structure models (ATSMs) have mainly focused on in-sample goodness-of-fit of historical bond yields and ignored out-of-sample forecast of future bond yields. Using an omnibus nonparametric procedure for density forecast evaluation in a continuous-time framework, we provide probably the first comprehensive empirical analysis of the out-of-sample performance of ATSMs in forecasting the joint conditional probability density of bond yields. We find that although the random walk models tend to have better forecasts for the conditional mean dynamics of bond yields, some ATSMs provide better forecasts for the joint probability density of bond yields. However, all ATSMs considered are still overwhelmingly rejected by our tests and fail to provide satisfactory density forecasts. There exists room for further improving density forecasts for bond yields by extending ATSMs. (c) 2005 Elsevier B.V. All rights reserved