Selective binding of the scavenger receptor C-type lectin to Lewisx trisaccharide and related glycan ligands

The scavenger receptor C-type lectin (SRCL) is an endothelial receptor that is similar in organization to type A scavenger receptors for modified low density lipoproteins but contains a C-type carbohydrate-recognition domain (CRD). Fragments of the receptor consisting of the entire extracellular domain and the CRD have been expressed and characterized. The extracellular domain is a trimer held together by collagen-like and coiled-coil domains adjacent to the CRD. The amino acid sequence of the CRD is very similar to the CRD of the asialoglycoprotein receptor and other galactose-specific receptors, but SRCL binds selectively to asialo-orosomucoid rather than generally to asialoglycoproteins. Screening of a glycan array and further quantitative binding studies indicate that this selectivity results from high affinity binding to glycans bearing the Lewis(x) trisaccharide. Thus, SRCL shares with the dendritic cell receptor DC-SIGN the ability to bind the Lewis(x) epitope. However, it does so in a fundamentally different way, making a primary binding interaction with the galactose moiety of the glycan rather than the fucose residue. SRCL shares with the asialoglycoprotein receptor the ability to mediate endocytosis and degradation of glycoprotein ligands. These studies suggest that SRCL might be involved in selective clearance of specific desialylated glycoproteins from circulation and/or interaction of cells bearing Lewis(x)-type structures with the vascular endothelium

Connecting Cluster Substructure in Galaxy Cluster Cores at z=0.2 With Cluster Assembly Histories

We use semi-analytic models of structure formation to interpret gravitational lensing measurements of substructure in galaxy cluster cores (R<=250kpc/h) at z=0.2. The dynamic range of the lensing-based substructure fraction measurements is well matched to the theoretical predictions, both spanning f_sub~0.05-0.65. The structure formation model predicts that f_sub is correlated with cluster assembly history. We use simple fitting formulae to parameterize the predicted correlations: Delta_90 = tau_90 + alpha_90 * log(f_sub) and Delta_50 = tau_50 + alpha_50 * log(f_sub), where Delta_90 and Delta_50 are the predicted lookback times from z=0.2 to when each theoretical cluster had acquired 90% and 50% respectively of the mass it had at z=0.2. The best-fit parameter values are: alpha_90 = (-1.34+/-0.79)Gyr, tau_90 = (0.31+/-0.56)Gyr and alpha_50 = (-2.77+/-1.66)Gyr, tau_50 = (0.99+/-1.18)Gyr. Therefore (i) observed clusters with f_sub<~0.1 (e.g. A383, A1835) are interpreted, on average, to have formed at z>~0.8 and to have suffered <=10% mass growth since z~0.4, (ii) observed clusters with f_sub>~0.4 (e.g. A68, A773) are interpreted as, on average, forming since z~0.4 and suffering >10% mass growth in the ~500Myr preceding z=0.2, i.e. since z=0.25. In summary, observational measurements of f_sub can be combined with structure formation models to estimate the age and assembly history of observed clusters. The ability to ``age-date'' approximately clusters in this way has numerous applications to the large clusters samples that are becoming available.Comment: Accepted by ApJL, 4 pages, 2 figure

Adaptive foveated single-pixel imaging with dynamic super-sampling

As an alternative to conventional multi-pixel cameras, single-pixel cameras enable images to be recorded using a single detector that measures the correlations between the scene and a set of patterns. However, to fully sample a scene in this way requires at least the same number of correlation measurements as there are pixels in the reconstructed image. Therefore single-pixel imaging systems typically exhibit low frame-rates. To mitigate this, a range of compressive sensing techniques have been developed which rely on a priori knowledge of the scene to reconstruct images from an under-sampled set of measurements. In this work we take a different approach and adopt a strategy inspired by the foveated vision systems found in the animal kingdom - a framework that exploits the spatio-temporal redundancy present in many dynamic scenes. In our single-pixel imaging system a high-resolution foveal region follows motion within the scene, but unlike a simple zoom, every frame delivers new spatial information from across the entire field-of-view. Using this approach we demonstrate a four-fold reduction in the time taken to record the detail of rapidly evolving features, whilst simultaneously accumulating detail of more slowly evolving regions over several consecutive frames. This tiered super-sampling technique enables the reconstruction of video streams in which both the resolution and the effective exposure-time spatially vary and adapt dynamically in response to the evolution of the scene. The methods described here can complement existing compressive sensing approaches and may be applied to enhance a variety of computational imagers that rely on sequential correlation measurements.Comment: 13 pages, 5 figure

Quantifying land surface temperature variability for two Sahelian mesoscale regions during the wet season

Land-atmosphere feedbacks play an important role in the weather and climate of many semi-arid regions. These feedbacks are strongly controlled by how the surface responds to precipitation events, which regulate the return of heat and moisture to the atmosphere. Characteristics of the surface can result in both differing amplitudes and rates of warming following rain. We used spectral analysis to quantify these surface responses to rainfall events using land surface temperature (LST) derived from Earth Observations (EO). We analysed two mesoscale regions in the Sahel and identified distinct differences in the strength of the short-term (< 5–day) spectral variance, notably a shift towards lower frequency variability in forest pixels relative to non-forest areas, and an increase in amplitude with decreasing vegetation cover. Consistent with these spectral signatures, we found that areas of forest, and to a lesser extent grassland regions, warm up more slowly than sparsely vegetated or barren pixels. We applied the same spectral analysis method to simulated LST data from the the Joint UK Land Environment Simulator (JULES) land surface model. We found a reasonable level of agreement with the EO spectral analysis, for two contrasting land surface regions. However JULES shows a significant underestimate in the magnitude of the observed response to rain compared to EO. A sensitivity analysis of the JULES model highlights an unrealistically high level of soil water availability as a key deficiency, which dampens the models response to rainfall events