20 resultados para 1-6


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To investigate mechanisms by which angiotensin converting enzyme (ACE)-inhibition increases insulin sensitivity, spontaneously hypertensive (SH) rats were treated with or without ramipril (1 mg/kg per day) for 12 weeks. Insulin binding and protein levels of insulin receptor substrate-1 (IRS-1), p85-subunit of phosphatidylinositol 3'-kinase (p85) and Src homology 2 domain-containing phosphatase-2 (SHP2) were then determined in hindlimb muscle and liver. Additionally, protein tyrosine phosphatase (PTPase) activities towards immobilized phosphorylated insulin receptor or phosphorylated IRS-1 of membrane (MF) and cytosolic fractions (CF) of these tissues were measured. Ramipril treatment increased IRS-1-protein content in muscle by 31+/-9% (P<0.05). No effects were observed on IRS-1 content in liver or on insulin binding or protein expression of p85 or SHP2 in both tissues. Ramipril treatment also increased dephosphorylation of insulin receptor by muscle CF (22.0+/-1.0%/60 min compared to 16.8+/-1.5%/60 min; P<0.05), and of IRS-1 by liver MF (37.2+/-1.7%/7.5 min compared to 33.8+/-1.7%/7.5 min; P<0.05) and CF (36.8+/-1.0%/7.5 min compared to 33.2+/-1.0%/7.5 min; P<0.05). We conclude that the observed effects of ACE-inhibition by ramipril on the protein expression of IRS-1 and on PTPase activity might contribute to its effect on insulin sensitivity.

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Lake water temperature (LWT) is an important driver of lake ecosystems and it has been identified as an indicator of climate change. Consequently, the Global Climate Observing System (GCOS) lists LWT as an essential climate variable. Although for some European lakes long in situ time series of LWT do exist, many lakes are not observed or only on a non-regular basis making these observations insufficient for climate monitoring. Satellite data can provide the information needed. However, only few satellite sensors offer the possibility to analyse time series which cover 25 years or more. The Advanced Very High Resolution Radiometer (AVHRR) is among these and has been flown as a heritage instrument for almost 35 years. It will be carried on for at least ten more years, offering a unique opportunity for satellite-based climate studies. Herein we present a satellite-based lake surface water temperature (LSWT) data set for European water bodies in or near the Alps based on the extensive AVHRR 1 km data record (1989–2013) of the Remote Sensing Research Group at the University of Bern. It has been compiled out of AVHRR/2 (NOAA-07, -09, -11, -14) and AVHRR/3 (NOAA-16, -17, -18, -19 and MetOp-A) data. The high accuracy needed for climate related studies requires careful pre-processing and consideration of the atmospheric state. The LSWT retrieval is based on a simulation-based scheme making use of the Radiative Transfer for TOVS (RTTOV) Version 10 together with ERA-interim reanalysis data from the European Centre for Medium-range Weather Forecasts. The resulting LSWTs were extensively compared with in situ measurements from lakes with various sizes between 14 and 580 km2 and the resulting biases and RMSEs were found to be within the range of −0.5 to 0.6 K and 1.0 to 1.6 K, respectively. The upper limits of the reported errors could be rather attributed to uncertainties in the data comparison between in situ and satellite observations than inaccuracies of the satellite retrieval. An inter-comparison with the standard Moderate-resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature product exhibits RMSEs and biases in the range of 0.6 to 0.9 and −0.5 to 0.2 K, respectively. The cross-platform consistency of the retrieval was found to be within ~ 0.3 K. For one lake, the satellite-derived trend was compared with the trend of in situ measurements and both were found to be similar. Thus, orbital drift is not causing artificial temperature trends in the data set. A comparison with LSWT derived through global sea surface temperature (SST) algorithms shows lower RMSEs and biases for the simulation-based approach. A running project will apply the developed method to retrieve LSWT for all of Europe to derive the climate signal of the last 30 years. The data are available at doi:10.1594/PANGAEA.831007.

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Three new coordination polymers [M(Pht)(1-MeIm)2]n (where M=Cu (1), Zn (2), Co (3); Pht2−=dianion of o-phthalic acid; 1-MeIm=1-methylimidazole) and two compounds [M(1-MeIm)6](HPht)2 · 2H2O (M=Co (4), Ni (5)) have been synthesized and characterized by X-ray crystallography. The structures of 1–3 (2 is isostructural to 3) consist of [M(1-MeIm)2] building units connected by 1,6-bridging phthalate ions to form infinite chains. In complex 1, each copper(II) center adopts a square coordination mode of N2O2 type by two O atoms from different phthalate ions and two N atoms of 1-MeIm, whereas in 3 two independent metal atoms are tetrahedrally (N2O2) coordinated to a pair of Pht ligands and a pair of 1-MeIm molecules. There are only van der Waals interactions between the chains in 1, while the three-dimensional network in 3 is assembled by C–H⋯O contacts. In contrast to polymers 1–3 the structures of 4 and 5 (complexes are also isostructural) are made up of the [M(1-MeIm)6]2+ cation, two hydrogen phthalate anions (HPht−) and two H2O solvate molecules. The coordination around each metal(II) atom is octahedral with six nitrogen atoms of 1-MeIm. Extended hydrogen bonding networks embracing the solvate water molecules and a phthalate residue as well as the weak C–H⋯O interactions stabilize the three-dimensional structures. Magnetic studies clearly show that the magnetic ions do not interact with each other. Furthermore, in compound 4 we have another example of a highly anisotropic Co2+ ion with a rhombic g-tensor and large zero-field-splitting. The complexes were also characterized by IR and 1H NMR spectroscopy, thermogravimetric analysis, and all data are discussed in the terms of known structures.

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Targeting hard-to-reach/marginalized populations is essential for preventing HIV-transmission. A unique opportunity to identify such populations in Switzerland is provided by a database of all genotypic-resistance-tests from Switzerland, including both sequences from the Swiss HIV Cohort Study (SHCS) and non-cohort sequences. A phylogenetic tree was built using 11,127 SHCS and 2,875 Swiss non-SHCS sequences. Demographics were imputed for non-SHCS patients using a phylogenetic proximity approach. Factors associated with non-cohort outbreaks were determined using logistic regression. Non-B subtype (univariable odds-ratio (OR): 1.9; 95% confidence interval (CI): 1.8-2.1), female gender (OR: 1.6; 95% CI: 1.4-1.7), black ethnicity (OR: 1.9; 95% CI: 1.7-2.1) and heterosexual transmission group (OR:1.8; 95% CI: 1.6-2.0), were all associated with underrepresentation in the SHCS. We found 344 purely non-SHCS transmission clusters, however, these outbreaks were small (median 2, maximum 7 patients) with a strong overlap with the SHCS'. 65% of non-SHCS sequences were part of clusters composed of >= 50% SHCS sequences. Our data suggests that marginalized-populations are underrepresented in the SHCS. However, the limited size of outbreaks among non-SHCS patients in-care implies that no major HIV outbreak in Switzerland was missed by the SHCS surveillance. This study demonstrates the potential of sequence data to assess and extend the scope of infectious-disease surveillance.