Localization of calcitonin receptor-like receptor and receptor activity modifying protein 1 in enteric neurons, dorsal root ganglia, and the spinal cord of the rat

Calcitonin receptor-like receptor (CLR) and receptor activity modifying protein 1 (RAMP1) comprise a receptor for calcitonin gene related peptide (CGRP) and intermedin. Although CGRP is widely expressed in the nervous system, less is known about the localization of CLR and RAMP1. To localize these proteins, we raised antibodies to CLR and RAMP1. Antibodies specifically interacted with CLR and RAMP1 in HEK cells coexpressing rat CLR and RAMP1, determined by Western blotting and immunofluorescence. Fluorescent CGRP specifically bound to the surface of these cells and CGRP, CLR, and RAMP1 internalized into the same endosomes. CLR was prominently localized in nerve fibers of the myenteric and submucosal plexuses, muscularis externa and lamina propria of the gastrointestinal tract, and in the dorsal horn of the spinal cord of rats. CLR was detected at low levels in the soma of enteric, dorsal root ganglia (DRG), and spinal neurons. RAMP1 was also localized to enteric and DRG neurons and the dorsal horn. CLR and RAMP1 were detected in perivascular nerves and arterial smooth muscle. Nerve fibers containing CGRP and intermedin were closely associated with CLR fibers in the gastrointestinal tract and dorsal horn, and CGRP and CLR colocalized in DRG neurons. Thus, CLR and RAMP1 may mediate the effects of CGRP and intermedin in the nervous system. However, mRNA encoding RAMP2 and RAMP3 was also detected in the gastrointestinal tract, DRG, and dorsal horn, suggesting that CLR may associate with other RAMPs in these tissues to form a receptor for additional peptides such as adrenomedullin.

Using reanalysis data to quantify extreme wind power generation statistics : a 33 year case study in Great Britain

With a rapidly increasing fraction of electricity generation being sourced from wind, extreme wind power generation events such as prolonged periods of low (or high) generation and ramps in generation, are a growing concern for the efficient and secure operation of national power systems. As extreme events occur infrequently, long and reliable meteorological records are required to accurately estimate their characteristics. Recent publications have begun to investigate the use of global meteorological “reanalysis” data sets for power system applications, many of which focus on long-term average statistics such as monthly-mean generation. Here we demonstrate that reanalysis data can also be used to estimate the frequency of relatively short-lived extreme events (including ramping on sub-daily time scales). Verification against 328 surface observation stations across the United Kingdom suggests that near-surface wind variability over spatiotemporal scales greater than around 300 km and 6 h can be faithfully reproduced using reanalysis, with no need for costly dynamical downscaling. A case study is presented in which a state-of-the-art, 33 year reanalysis data set (MERRA, from NASA-GMAO), is used to construct an hourly time series of nationally-aggregated wind power generation in Great Britain (GB), assuming a fixed, modern distribution of wind farms. The resultant generation estimates are highly correlated with recorded data from National Grid in the recent period, both for instantaneous hourly values and for variability over time intervals greater than around 6 h. This 33 year time series is then used to quantify the frequency with which different extreme GB-wide wind power generation events occur, as well as their seasonal and inter-annual variability. Several novel insights into the nature of extreme wind power generation events are described, including (i) that the number of prolonged low or high generation events is well approximated by a Poission-like random process, and (ii) whilst in general there is large seasonal variability, the magnitude of the most extreme ramps is similar in both summer and winter. An up-to-date version of the GB case study data as well as the underlying model are freely available for download from our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014/.