Astrometry and geodesy with radio interferometry: experiments, models, results

Summarizes current status of radio interferometry at radio frequencies between Earth-based receivers, for astrometric and geodetic applications. Emphasizes theoretical models of VLBI observables that are required to extract results at the present accuracy levels of 1 cm and 1 nanoradian. Highlights the achievements of VLBI during the past two decades in reference frames, Earth orientation, atmospheric effects on microwave propagation, and relativity.Comment: 83 pages, 19 Postscript figures. To be published in Rev. Mod. Phys., Vol. 70, Oct. 199

A ground-based near-infrared emission spectrum of the exoplanet HD 189733b

Detection of molecules using infrared spectroscopy probes the conditions and compositions of exoplanet atmospheres. Water (H2O), methane (CH4), carbon dioxide (CO2), and carbon monoxide (CO) have been detected in two hot Jupiters. These previous results relied on space-based telescopes that do not provide spectroscopic capability in the 2.4 - 5.2 micron spectral region. Here we report ground-based observations of the dayside emission spectrum for HD 189733b between 2.0-2.4 micron and 3.1-4.1 micron, where we find a bright emission feature. Where overlap with space-based instruments exists, our results are in excellent agreement with previous measurements. A feature at ~3.25 micron is unexpected and difficult to explain with models that assume local thermodynamic equilibrium (LTE) conditions at the 1 bar to 1 x 10-6 bar pressures typically sampled by infrared measurements. The most likely explanation for this feature is that it arises from non-LTE emission from CH4, similar to what is seen in the atmospheres of planets in our own Solar System. These results suggest that non-LTE effects may need to be considered when interpreting measurements of strongly irradiated exoplanets.Comment: 12 pages, 2 figures, published in Natur

LSWAVE. A MATLAB software for the least-squares wavelet and cross-wavelet analyses

The least-squares wavelet analysis (LSWA) is a robust method of analyzing any type of time/data series without the need for editing and preprocessing of the original series. The LSWA can rigorously analyze any non-stationary and equally/unequally spaced series with an associated covariance matrix that may have trends and/or datum shifts. The least-squares cross-wavelet analysis complements the LSWA in the study of the coherency and phase differences of two series of any type. A MATLAB software package including a graphical user interface is developed for these methods to aid researchers in analyzing pairs of series. The package also includes the least-squares spectral analysis, the antileakage least-squares spectral analysis, and the least-squares cross-spectral analysis to further help researchers study the components of interest in a series. We demonstrate the steps that users need to take for a successful analysis using three examples: two synthetic time series, and a Global Positioning System time series

Least-squares wavelet analysis of unequally spaced and non-stationary time series and its applications

Least-squares spectral analysis, an alternative to the classical Fourier transform, is a method of analyzing unequally spaced and non-stationary time series in their first and second statistical moments. However, when a time series has components with low or high amplitude and frequency variability over time, it is not appropriate to use either the least-squares spectral analysis or Fourier transform. On the other hand, the classical short-time Fourier transform and the continuous wavelet transform do not consider the covariance matrix associated with a time series nor do they consider trends or datum shifts. Moreover, they are not defined for unequally spaced time series. A new method of analyzing time series, namely, the least-squares wavelet analysis is introduced, which is a natural extension of the least-squares spectral analysis. This method decomposes a time series to the time–frequency domain and obtains its spectrogram. In addition, the probability distribution function of the spectrogram is derived that identifies statistically significant peaks. The least-squares wavelet analysis can analyze any non-stationary and unequally spaced time series with components of low or high amplitude and frequency variability, including datum shifts, trends, and constituents of known forms, by taking into account the covariance matrix associated with the time series. The outstanding performance of the proposed method on synthetic time series and a very long baseline interferometry series is demonstrated, and the results are compared with the weighted wavelet Z-transform

Least-squares cross-wavelet analysis and its applications in geophysical time series

The least-squares wavelet analysis, an alternative to the classical wavelet analysis, was introduced in order to analyze unequally spaced and non-stationary time series exhibiting components with variable amplitude and frequency over time. There are a few methods such as cross-wavelet transform and wavelet coherence that can analyze two time series together. However, these methods cannot generally be used to analyze unequally spaced and non-stationary time series with associated covariance matrices that may have trends and/or datum shifts. A new method of analyzing two time series together, namely the least-squares cross-wavelet analysis, is developed and applied to study the disturbances in the gravitational gradients observed by GOCE satellite that arise from plasma flow in the ionosphere represented by Poynting flux. The proposed method also shows its outstanding performance on the Westford–Wettzell very long baseline interferometry baseline length and temperature series

TGFβ-TAZ/SRF signalling regulates vascular smooth muscle cell differentiation

Vascular smooth muscle cells (VSMCs) do not terminally differentiate; they modulate their phenotype between proliferative and differentiated states, which is a major factor contributing to vascular diseases. TGFβ signalling has been implicated in inducing VSMC differentiation, although the exact mechanism remains largely unknown. Our goal was to assess the network of transcription factors involved in the induction of VSMC differentiation, and to determine the role of TAZ in promoting the quiescent VSMC phenotype. TGFβ robustly induces VSMC marker genes in 10T1/2 mouse embryonic fibroblast cells and the potent transcriptional regulator TAZ has been shown to retain Smad complexes on DNA. Thus, the role of TAZ in regulation of VSMC differentiation was studied. Using primary aortic VSMCs coupled with siRNA-mediated gene silencing, our studies reveal that TAZ is required for TGFβ induction of smooth muscle genes and is also required for the differentiated VSMC phenotype; synergy between TAZ and SRF, and TAZ and Myocardin (MyoC856), in regulating smooth muscle gene activation was observed. These data provide evidence of components of a novel signalling pathway that links TGFβ signalling to induction of smooth muscle genes through a mechanism involving regulation of TAZ and SRF proteins. In addition, we report a physical interaction of TAZ and MyoC856. These observations elucidate a novel level of control of VSMC induction which may have implications for vascular diseases and congenital vascular malformations