The adrenal secretory serine protease AsP is a short secretory isoform of the transmembrane airway trypsin-like protease

To further elucidate the role of proteases capable of cleaving N-terminal proopiomelanocortin (N-POMC)-derived peptides, we have cloned two cDNAs encoding isoforms of the airway trypsin-like protease (AT) from mouse (MAT) and rat ( RAT), respectively. The open reading frames comprise 417 amino acids (aa) and 279 aa. The mouse AT gene was located at chromosome 5E1 and contains 10 exons. The longer isoform, which we designated MAT1 and RAT1, has a simple type II transmembrane protein structure, consisting of a short cytoplasmic domain, a transmembrane domain, a SEA (63-kDa sea urchin sperm protein, enteropeptidase, agrin) module, and a serine protease domain. The human homolog of MAT1 and RAT1 is the human AT ( HAT). The shorter isoform, designated MAT2 and RAT2, which contains an alternative N terminus, was formerly described in the rat as adrenal secretory serine protease (AsP) and has been shown to be involved in the processing of N-POMC-derived peptides. In contrast to the long isoform, neither MAT2 and RAT2 ( AsP) contain a transmembrane domain nor a SEA domain but an N-terminal signal peptide to direct the enzyme to the secretory pathway. The C terminus, covering the catalytic triad, is identical in both isoforms. Immunohistochemically, MAT/RAT was predominantly expressed in tissues of the upper gastrointestinal and the respiratory tract - but also in the adrenal gland. Moreover, isoform-specific RT-PCR and quantitative PCR analysis revealed a complex expression pattern of the two isoforms with differences between mice and rats. These findings indicate a multifunctional role of these proteases beyond adrenal proliferation.

Faunal evidence of a Holocene pluvial phase in Southern Arabia with remarks on the morphological variability of Helenina anderseni

Although foraminifera have been found living in inland saline lakes isolated from the sea, this phenomenon has rarely been recognized in the fossil record. This study documents the occurrence of benthic foraminifera in Holocene lake sediments located nearly 500 km inland from the Red Sea, in the Al-Mundafan region of southern Saudi Arabia. The lake formed during a regional pluvial period, 10,500–6000 yr BP. The presence of foraminifera and brackish charophytes in the studied section represent an interval when the lake was slightly brackish due to high evaporation. The studied sediments yielded a bispecific benthic foraminiferal fauna comprised of Helenina anderseni and Trichohyalus aguayoi, as well as the brackish charophyte genus Lamprothamnium. The benthic foraminifera are species characteristic of mangrove swamps, salt marshes, and lagoons, which are environments currently widespread along the Red Sea coasts. Because the Al Mundafan area was never connected to the sea during the Quaternary, wading birds must have been the vector that transported the foraminifera to the paleolake.

Extended optimal estimation techniques for sea surface temperature from the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI)

Sea surface temperature (SST) can be estimated from day and night observations of the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) by optimal estimation (OE). We show that exploiting the 8.7 μm channel, in addition to the “traditional” wavelengths of 10.8 and 12.0 μm, improves OE SST retrieval statistics in validation. However, the main benefit is an improvement in the sensitivity of the SST estimate to variability in true SST. In a fair, single-pixel comparison, the 3-channel OE gives better results than the SST estimation technique presently operational within the Ocean and Sea Ice Satellite Application Facility. This operational technique is to use SST retrieval coefficients, followed by a bias-correction step informed by radiative transfer simulation. However, the operational technique has an additional “atmospheric correction smoothing”, which improves its noise performance, and hitherto had no analogue within the OE framework. Here, we propose an analogue to atmospheric correction smoothing, based on the expectation that atmospheric total column water vapour has a longer spatial correlation length scale than SST features. The approach extends the observations input to the OE to include the averaged brightness temperatures (BTs) of nearby clear-sky pixels, in addition to the BTs of the pixel for which SST is being retrieved. The retrieved quantities are then the single-pixel SST and the clear-sky total column water vapour averaged over the vicinity of the pixel. This reduces the noise in the retrieved SST significantly. The robust standard deviation of the new OE SST compared to matched drifting buoys becomes 0.39 K for all data. The smoothed OE gives SST sensitivity of 98% on average. This means that diurnal temperature variability and ocean frontal gradients are more faithfully estimated, and that the influence of the prior SST used is minimal (2%). This benefit is not available using traditional atmospheric correction smoothing.

Estimation of sea surface temperature from the Spinning Enhanced Visible and Infra Red Imager, improved using numerical weather prediction

Most of the operational Sea Surface Temperature (SST) products derived from satellite infrared radiometry use multi-spectral algorithms. They show, in general, reasonable performances with root mean square (RMS) residuals around 0.5 K when validated against buoy measurements, but have limitations, particularly a component of the retrieval error that relates to such algorithms' limited ability to cope with the full variability of atmospheric absorption and emission. We propose to use forecast atmospheric profiles and a radiative transfer model to simulate the algorithmic errors of multi-spectral algorithms. In the practical case of SST derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), we demonstrate that simulated algorithmic errors do explain a significant component of the actual errors observed for the non linear (NL) split window algorithm in operational use at the Centre de Météorologie Spatiale (CMS). The simulated errors, used as correction terms, reduce significantly the regional biases of the NL algorithm as well as the standard deviation of the differences with drifting buoy measurements. The availability of atmospheric profiles associated with observed satellite-buoy differences allows us to analyze the origins of the main algorithmic errors observed in the SEVIRI field of view: a negative bias in the inter-tropical zone, and a mid-latitude positive bias. We demonstrate how these errors are explained by the sensitivity of observed brightness temperatures to the vertical distribution of water vapour, propagated through the SST retrieval algorithm.

Probabilistic physically-based cloud screening of satellite infra-red imagery for operational sea surface temperature retrieval

We propose and demonstrate a fully probabilistic (Bayesian) approach to the detection of cloudy pixels in thermal infrared (TIR) imagery observed from satellite over oceans. Using this approach, we show how to exploit the prior information and the fast forward modelling capability that are typically available in the operational context to obtain improved cloud detection. The probability of clear sky for each pixel is estimated by applying Bayes' theorem, and we describe how to apply Bayes' theorem to this problem in general terms. Joint probability density functions (PDFs) of the observations in the TIR channels are needed; the PDFs for clear conditions are calculable from forward modelling and those for cloudy conditions have been obtained empirically. Using analysis fields from numerical weather prediction as prior information, we apply the approach to imagery representative of imagers on polar-orbiting platforms. In comparison with the established cloud-screening scheme, the new technique decreases both the rate of failure to detect cloud contamination and the false-alarm rate by one quarter. The rate of occurrence of cloud-screening-related errors of >1 K in area-averaged SSTs is reduced by 83%. Copyright © 2005 Royal Meteorological Society.