366 research outputs found

    Validation of modelling the radiation exposure due to solar particle events at aircraft altitudes

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    Dose assessment procedures for cosmic radiation exposure of aircraft crew have been introduced in most European countries in accordance with the corresponding European directive and national regulations. However, the radiation exposure due to solar particle events is still a matter of scientific research. Here we describe the European research project CONRAD, WP6, Subgroup-B, about the current status of available solar storm measurements and existing models for dose estimation at flight altitudes during solar particle events leading to ground level enhancement (GLE). Three models for the numerical dose estimation during GLEs are discussed. Some of the models agree with limited experimental data reasonably well. Analysis of GLEs during geomagnetically disturbed conditions is still complex and time consuming. Currently available solar particle event models can disagree with each other by an order of magnitude. Further research and verification by on-board measurements is still neede

    Lack of functional GABA receptors alters Kiss1 , Gnrh1 and Gad1 mRNA expression in the medial basal hypothalamus at postnatal day 4

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    Background/Aims: Adult mice lacking functional GABAB receptors (GABAB1KO) show altered Gnrh1 and Gad1 expressions in the preoptic area-anterior hypothalamus (POA-AH) and females display disruption of cyclicity and fertility. Here we addressed whether sexual differentiation of the brain and the proper wiring of the GnRH and kisspeptin systems were already disturbed in postnatal day 4 (PND4) GABAB1KO mice. Methods: PND4 wild-type (WT) and GABAB1KO mice of both sexes were sacrificed; tissues were collected to determine mRNA expression (qPCR), amino acids (HPLC), and hormones (RIA and/or IHC). Results: GnRH neuron number (IHC) did not differ among groups in olfactory bulbs or OVLT-POA. Gnrh1 mRNA (qPCR) in POA-AH was similar among groups. Gnrh1 mRNA in medial basal hypothalamus (MBH) was similar in WTs but was increased in GABAB1KO females compared to GABAB1KO males. Hypothalamic GnRH (RIA) was sexually different in WTs (males < females), but this sex difference was lost in GABAB1KOs; the same pattern was observed when analyzing only the MBH, but not in the POA-AH. Arcuate nucleus Kiss1 mRNA (micropunch-qPCR) was higher in WT females than in WT males and GABAB1KO females. Gad1 mRNA in MBH was increased in GABAB1KO females compared to GABAB1KO males. Serum LH and gonadal estradiol content were also increased in GABAB1KOs. Conclusion: We demonstrate that GABABRs participate in the sexual differentiation of the ARC/MBH, because sex differences in several reproductive genes, such as Gad1, Kiss1 and Gnrh1, are critically disturbed in GABAB1KO mice at PND4, probably altering the organization and development of neural circuits governing the reproductive axis. (c) 2013 S. Karger AG, Basel

    Survey on solar X-ray flares and associated coherent radio emissions

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    The radio emission during 201 X-ray selected solar flares was surveyed from 100 MHz to 4 GHz with the Phoenix-2 spectrometer of ETH Zurich. The selection includes all RHESSI flares larger than C5.0 jointly observed from launch until June 30, 2003. Detailed association rates of radio emission during X-ray flares are reported. In the decimeter wavelength range, type III bursts and the genuinely decimetric emissions (pulsations, continua, and narrowband spikes) were found equally frequently. Both occur predominantly in the peak phase of hard X-ray (HXR) emission, but are less in tune with HXRs than the high-frequency continuum exceeding 4 GHz, attributed to gyrosynchrotron radiation. In 10% of the HXR flares, an intense radiation of the above genuine decimetric types followed in the decay phase or later. Classic meter-wave type III bursts are associated in 33% of all HXR flares, but only in 4% they are the exclusive radio emission. Noise storms were the only radio emission in 5% of the HXR flares, some of them with extended duration. Despite the spatial association (same active region), the noise storm variations are found to be only loosely correlated in time with the X-ray flux. In a surprising 17% of the HXR flares, no coherent radio emission was found in the extremely broad band surveyed. The association but loose correlation between HXR and coherent radio emission is interpreted by multiple reconnection sites connected by common field lines.Comment: Solar Physics, in pres

    Spectrum of Solar Type I Continuum Noise Storm in the 50 - 80 MHz band, and Plasma characteristics in the associated source region

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    Continuum observations of a solar noise storm in the frequency range of 50 - 80 MHz observed with the Gauribidanur radio spectrograph during 2000 September, 26 & 27, are presented here. The radio spectral index of the noise storm continuum in the band 50 - 80 MHz is found to be ~3.65 during the above period. The Noise Storm continuum radiation is explained as a consequence of the non-thermal, plasma emission mechanism. The beam-density of suprathermal electrons is estimated for the coronal plasma near the source region of storm radiation. Supplementary evidence for the density-estimate is provided by way of analysing the imaging data from the SXT on-board the Yohkoh spacecraft, and the LASCO, MDI, and EIT on board the SoHO spacecraft.Comment: 43 pages; 5 tables; 15 figures (9 color). ApJ (Part I : accepted

    Sialic Acid Glycobiology Unveils Trypanosoma cruzi Trypomastigote Membrane Physiology.

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    Trypanosoma cruzi, the flagellate protozoan agent of Chagas disease or American trypanosomiasis, is unable to synthesize sialic acids de novo. Mucins and trans-sialidase (TS) are substrate and enzyme, respectively, of the glycobiological system that scavenges sialic acid from the host in a crucial interplay for T. cruzi life cycle. The acquisition of the sialyl residue allows the parasite to avoid lysis by serum factors and to interact with the host cell. A major drawback to studying the sialylation kinetics and turnover of the trypomastigote glycoconjugates is the difficulty to identify and follow the recently acquired sialyl residues. To tackle this issue, we followed an unnatural sugar approach as bioorthogonal chemical reporters, where the use of azidosialyl residues allowed identifying the acquired sugar. Advanced microscopy techniques, together with biochemical methods, were used to study the trypomastigote membrane from its glycobiological perspective. Main sialyl acceptors were identified as mucins by biochemical procedures and protein markers. Together with determining their shedding and turnover rates, we also report that several membrane proteins, including TS and its substrates, both glycosylphosphatidylinositol-anchored proteins, are separately distributed on parasite surface and contained in different and highly stable membrane microdomains. Notably, labeling for α(1,3)Galactosyl residues only partially colocalize with sialylated mucins, indicating that two species of glycosylated mucins do exist, which are segregated at the parasite surface. Moreover, sialylated mucins were included in lipid-raft-domains, whereas TS molecules are not. The location of the surface-anchored TS resulted too far off as to be capable to sialylate mucins, a role played by the shed TS instead. Phosphatidylinositol-phospholipase-C activity is actually not present in trypomastigotes. Therefore, shedding of TS occurs via microvesicles instead of as a fully soluble form

    A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA

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    Coronal mass ejections (CMEs) are arguably the most violent eruptions in the solar system. CMEs can cause severe disturbances in interplanetary space and can even affect human activities in many aspects, causing damage to infrastructure and loss of revenue. Fast and accurate prediction of CME arrival time is vital to minimize the disruption that CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar-wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full halo CMEs and using algorithms of the Support Vector Machine. We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions made after applying CAT-PUMA to a test set unknown to the engine show a mean absolute prediction error of ∼5.9 hr within the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hr. Comparisons with other models reveal that CAT-PUMA has a more accurate prediction for 77% of the events investigated that can be carried out very quickly, i.e., within minutes of providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA
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