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Satellite cells are companions to voluntary muscle fibres, and are named for their intimate positional or ;satellite' relationship, as if revolving around fibres, like a satellite moon around the earth. Studies on the nature of at least some satellite cells, including their capabilities for self-renewal and for giving rise to multiple lineages in a stem cell-like function, are exploring the molecular basis of phenotypes described by markers of specialized function and gene expression in normal development, neuromuscular disease and aging. In adult skeletal muscle, the self-renewing capacity of satellite cells contributes to muscle growth, adaptation and regeneration. Muscle remodeling, such as demonstrated by changes in myofibre cross-sectional area and length, nerve and tendon junctions, and fibre-type distribution, occur in the absence of injury and provide broad functional and structural diversity among skeletal muscles. Those contributions to plasticity involve the satellite cell in at least five distinct roles, here described using metaphors for behaviour or the investigator's perspective. Satellite cells are the 'currency' of muscle; have a 'conveyance' role in adaptation by domains of cytoplasm along a myofibre; serve researchers, through a marker role, as 'clues' to various activities of muscle; are 'connectors' that physically, and through signalling and cell-fibre communications, bridge myofibres to the intra- and extra-muscular environment; and are equipped as metabolic and genetic filters or 'colanders' that can rectify or modulate particular signals. While all these roles are still under exploration, each contributes to the plasticity of skeletal muscle and thence to the overall biology and function of an organism. The use of metaphor for describing these roles helps to clarify and scrutinize the definitions that form the basis of our understanding of satellite cell biology: the metaphors provide the construct for various approaches to detect or test the nature of satellite cell functions in skeletal muscle plasticity.

The PROBA-V CubeSat Companion (PVCC) is an ESA in-orbit demonstration initiated by VITO Remote Sensing that aims not only to compare the performance of a payload designed for a small satellite on a CubeSat platform, but also to achieve high-quality Earth observation data suitable for monitoring global land surfaces and analysing the impact of climate change.

Download Companion Satellite

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Would it be possible for those having these problems to connect everything via the iphone/andriod and screen share it onto your TV via ATV? This is how I originally ran the program (as I didnt know any other way). Only down side is that it drains your phone battery quickly and you cant run the companion app at the same time.

I finally fixed it!! I replaced my Kickr snap with a Kickr Core, which has built in cadence.

So now everything hooks up through Apple TV, Power (trainer), Cadence, and HR. Now there are 0 issues. No need for companion in the mix!

Banxing or BX-1 (Chinese: ; lit. 'Companion Satellite'),[1] is a small Chinese technology development satellite which was deployed from the Shenzhou 7 spacecraft at 11:27 GMT on 27 September 2008.[1] Prior to deployment, the satellite was mounted on top of the Shenzhou 7 orbital module.

A few hours after Banxing was launched it and the Shenzhou 7 orbital module passed unusually close to the International Space Station. This provoked some speculation that the experiment was intended to test military anti-satellite interception technology.[3]

I use satellite data because a lot of my work happens at relatively large spatial and temporal scales, targets regions where ground-based data are simply unavailable or extremely difficult to gather and relies on being able to access data that have been collected in a systematic and scalable manner.

Yes, satellite-based techniques can address spatial and temporal domains inaccessible to traditional, on-the-ground, approaches, but I am the first to acknowledge that satellite remote sensing cannot match the accuracy, precision and thematic richness of in-situ measurement and monitoring.

Realising all this potential for satellite data to support ecological research and conservation is contingent on accessing relevant ground-based information though. Without contextualised knowledge gathered on the ground things like reliable data interpretation, adequate land cover classification and the development of new and useful satellite-based monitoring techniques would be impossible. There should be no competition between satellite remote sensing and fieldwork, there should only be collaborations.

However, there remain a series of challenges that must be overcome for satellite data to reach its full potential in terms of making a difference in ecology and conservation. For example, the cost of data acquisition and associated logistical requirements for processing and analysing large datasets can sometimes be prohibitive. In addition to this the integration of in-situ data, expert knowledge provided by local biologists and the technical expertise of remote sensing analysts is often limited. Opportunities to overcome these challenges have never been greater though. Clear desires have been expressed by the conservation and remote sensing communities to develop stronger, more efficient ties.

The redshifts of the primary lens and the source are zd,P = 0.3530 0.0005 and zs = 1.2680 0.0003, respectively (Limousin et al. 2009a, 2010). The redshift of galaxy S is zd,S = 0.3514 0.0004 (Muoz et al., in prep.). The uncertainties in the redshift measurements are statistical only; systematic errors due to, e.g., wavelength calibration and dependence on section of spectrum for redshift extraction, are ~0.0005 (Cabanac 2010, priv. comm.). We note that a difference of z = 0.001 between galaxy P and galaxy S corresponds to a line-of-sight velocity difference of ~200 kms-1 at zd,P, which is consistent with the expected radial velocities in a galaxy group. Based on the proximity of galaxy S to galaxy P and their measured zd,P and zd,S, we interpret galaxy S as being physically associated with the primary lens galaxy, and we refer to galaxy S as the satellite lens galaxy. For the analysis, we use zd = zd,P as the lens redshift for the system.

For the reconstruction of the lensed arcs, it is important to remove any light contribution to the arcs from the lens galaxies. This is done using the GALFIT software package (Peng et al. 2002). Following, e.g., Marshall et al. (2007) and Suyu et al. (2009), the point spread function (PSF) is estimated from a star in the field. The regions around the lensed arcs, as well as those of the remaining cosmic rays, are masked for extracting the lens galaxy light. The light distribution of the primary lens is bimodal with a concentrated circular component and a smoother, more elliptical component. The satellite lens and the two components of the primary lens are fitted simultaneously with Srsic profiles (Sersic 1968). To investigate the light distribution of the primary lens as a whole, we also fit a single Srsic profile to the primary lens. In doing so, we mask out the core of the primary lens that shows clear bimodality in the light distribution. We set the origin of the image coordinate system to be located at the centroid of the single-component primary lens galaxy. The best fit values for the Srsic profiles are tabulated in Table 1. The profile parameters for the satellite lens are nearly the same in both the single- and two-component modelling of the primary lens light.

Despite the dwarfish appearance of the satellite galaxy when juxtaposed with the primary lens, the satellite galaxy is a normal elliptical galaxy based on its effective radius in Table 1 and the lensing derived velocity dispersion in Sect. 6.2 (Tollerud et al. 2010; Graves et al. 2009; Geha et al. 2003).

In this section, we describe our lens modelling of the HST data for constraining the halo size of the satellite galaxy. We model the lens system using simply-parametrised mass profiles (Sect. 4.1), and sample the posterior probability distribution of the lens parameters using Markov chain Monte Carlo (MCMC) methods (Sect. 4.2). To constrain the lens parameters, we use either the image positions of the multiply imaged source (Sect. 4.3) or the extended surface brightness distribution of the lensed source galaxy (Sect. 4.4). This allows us to quantify the amount of additional information the extended images provide on the mass distribution of the lenses.

For a review on gravitational lensing, we refer the reader to Schneider et al. (2006). We describe the mass distribution for the lens system as two lens galaxies (the primary and the satellite) in the presence of a constant external shear.

Table 2 lists the marginalised lens parameters in the second column. The value of t, which nearly spans the entire range of the prior, shows that we cannot constrain the halo size of the satellite galaxy using image positions as constraints. The remedy is to use more information, such as the surface brightness of each pixel in the extended lensed images that is described next.

This is the first time the halo size of a satellite galaxy is measured without assuming a scaling relation and without fixing any of the satellite lens parameters based on the observed light distribution.

The measured satellite halo size is in agreement with its estimated tidal radius, indicating that galaxies in group environments experience tidal stripping of their halos. This confirms the tidal stripping of galaxy halos in dense environments found in numerical simulations and in previous lensing analysis of galaxy clusters. e24fc04721