Undetected respiratory infections may adversely affect the intrapulmonary resistance after Stage 2 or Stage 3 Fontan palliation. A few studies describe a higher risk for viral pneumonia during respiratory virus season, but none of them have focused on the effect of symptomatic viral pneumonia on in-hospital clinical course after bidirectional Glenn shunt. We analysed 77 patients who underwent bidirectional Glenn shunt surgery. Six patients were detected with pneumonia and proof of viral ribonucleic acid in tracheal mucus in the very early postoperative time. We compared them retrospectively to the remaining 71 patients regarding preoperative inflammatory signs, mortality, paediatric ICU length of stay, and ventilation time. The infection rate was not seasonal dependent. Ventilation time was significantly elongated in the pneumonia group (558 h ± 634 vs. 8.7 h ± 1.9; p < 0.0001) and so was the paediatric ICU length of stay (29 days ± 26 vs. 3 days±1; p = 0.007). Significantly more patients in the pneumonia group required extracorporeal cardiac life support postoperatively. The mortality was significantly increased in patients with pneumonia. Even subclinical viral pneumonia may cause ventilation-to-perfusion mismatch by raising intrapulmonary resistance. Recorded parameters of postoperative paediatric ICU therapy showed a significant impact of a viral pneumonia on patients after bidirectional Glenn shunt. The respiratory syncytial virus vaccination does not protect these patients from infection with other respiratory viruses. The focus should be put on preoperative diagnosis of pulmonary infections in the vulnerable group of patients with univentricular hearts.

Sea-ice thickness in the Sea of Okhotsk is estimated for 2004–2008 from ICESat derived freeboard under the assumption of hydrostatic balance. Total ice thickness including snow depth (htot) averaged over 2004–2008 is 95 cm. The interannual variability of htot is large; from 77.5 cm (2008) to 110.4 cm (2005). The mode of htot varies from 50–60 cm (2007 and 2008) to 70–80 cm (2005). Ice thickness derived from ICESat data is validated from a comparison with that observed by Electromagnetic Induction Instrument (EM) aboard the icebreaker Soya near Hokkaido, Japan. Annual maps of htot reveal that the spatial distribution of htot is similar every year. Ice volume of 6.3 × 1011 m3 is estimated from the ICESat derived htot and AMSR-E derived ice concentration. A comparison with ice area demonstrates that the ice volume cannot always be represented by the area solely, despite the fact that the area has been used as a proxy of the volume in the Sea of Okhotsk. The ice volume roughly corresponds to that of annual ice production in the major coastal polynyas estimated based on heat budget calculations. This also supports the validity of the estimation of sea-ice thickness and volume using ICESat data.

There has been a marked decline in the summer extent of Arctic sea ice over the past few decades. Data from autonomous ice mass-balance buoys can enhance our understanding of this decline. These buoys monitor changes in snow deposition and ablation, ice growth, and ice surface and bottom melt. Results from the summer of 2008 showed considerable large-scale spatial variability in the amount of surface and bottom melt. Small amounts of melting were observed north of Greenland, while melting in the southern Beaufort Sea was quite large. Comparison of net solar heat input to the ice and heat required for surface ablation showed only modest correlation. However, there was a strong correlation between solar heat input to the ocean and bottom melting. As the ice concentration in the Beaufort Sea region decreased, there was an increase in solar heat to the ocean and an increase in bottom melting.

Preliminary results are presented from the first validation of geophysical data products (ice concentration, Snow thickness on Sea ice (hs) and ice temperature (TI) from the NASA EOS Aqua AMSR-E Sensor, in East Antarctica (in September–October 2003). The challenge of collecting Sufficient measurements with which to validate the coarse-resolution AMSR-E data products adequately was addressed by means of a hierarchical approach, using detailed in situ measurements, digital aerial photography and other Satellite data. Initial results from a circumnavigation of the experimental Site indicate that, at least under cold conditions with a dry Snow cover, there is a reasonably close agreement between Satellite- and aerial-photo-derived ice concentrations, i.e. 97.2±3.6% for NT2 and 96.5±2.5% for BBA algorithms vs 94.3% for the aerial photos. In general, the AMSR-E concentration represents a Slight overestimate of the actual concentration, with the largest discrepancies occurring in regions containing a relatively high proportion of thin ice. The AMSR-E concentrations from the NT2 and BBA algorithms are Similar on average, although differences of up to 5% occur in places, again related to thin-ice distribution. The AMSR-E ice temperature (TI) product agrees with coincident Surface measurements to approximately 0.5˚C in the limited dataset analyzed. Regarding Snow thickness, the AMSR hs retrieval is a Significant underestimate compared to in situ measurements weighted by the percentage of thin ice (and open water) present. For the case Study analyzed, the underestimate was 46% for the overall average, but 23% compared to Smooth-ice measurements. The Spatial distribution of the AMSR-E hs product follows an expected and consistent Spatial pattern, Suggesting that the observed difference may be an offset (at least under freezing conditions). Areas of discrepancy are identified, and the need for future work using the more extensive dataset is highlighted.

Snow depth on sea ice plays a critical role in the heat exchange between ocean and atmosphere because of its thermal insulation property. Furthermore, a heavy snow load on the relatively thin Southern Ocean sea-ice cover submerges the ice floes below sea level, causing snow-to-ice conversion. Snowfall is also an important freshwater source into the weakly stratified ocean. Snow-depth on sea-ice information can be used as an indirect measure of solid precipitation. Satellite passive microwave data are used to investigate the interannual and regional variability of the snow cover on sea ice. In this study we make use of 12 years (1992–2003) of Special Sensor Microwave/Imager (SSM/I) radiances to calculate average monthly snow depth on the Antarctic sea-ice cover. For the Antarctic sea-ice region as a whole, we find that September snow depth and sea-ice area are negatively correlated, which is not the case for individual regions. An analysis of the snow depth around Antarctica was undertaken. The results show an overall increase in snow depth for each of the five Antarctic sectors and the region as a whole, but only the Indian Ocean sector and the entire Southern Ocean show a statistically significant increase. There is a partial eastward propagation of maximum snow depths, which may be related to the Antarctic Circumpolar Wave. The overall trend and the variability of regional snow-depth distributions are also in agreement with cyclone density.

During spring and summer, the surface of the Arctic sea-ice cover undergoes rapid changes that greatly affect the surface albedo and significantly impact the further decay of the sea ice. These changes are primarily the development of a wet snow cover and the development of melt ponds. As melt ponds generally do not exceed a couple of meters in diameter, the spatial resolutions of sensors like the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer are too coarse for their identification. Landsat 7, on the other hand, has a spatial resolution of 30 m (15 m for the panchromatic band) and thus offers the best chance to map the distribution of melt ponds from space. The different wavelengths (bands) from blue to near-infrared offer the potential to distinguish among different surface conditions. Landsat 7 data for the Baffin Bay region for June 2000 have been analyzed. The analysis shows that different surface conditions, such as wet snow and melt-ponded areas, have different signatures in the individual Landsat bands. Consistent with in situ albedo measurements, melt ponds show up as blueish, whereas dry and wet ice have a white to gray appearance in the Landsat true-color image. These spectral differences enable areas with high fractions of melt ponds to be distinguished.

With the conclusion of the science phase of the Ice, Cloud and land Elevation Satellite (ICESat) mission in late 2009, and the planned launch of ICESat-2 in late 2015, NASA has recently established the IceBridge program to provide continuity between missions. A major goal of IceBridge is to obtain a sea-ice thickness time series via airborne surveys over the Arctic and Southern Oceans. Typically two laser altimeters, the Airborne Topographic Mapper (ATM) and the Land, Vegetation and Ice Sensor (LVIS), are utilized during IceBridge flights. Using laser altimetry simulations of conventional analogue systems such as ICESat, LVIS and ATM, with the multi-beam system proposed for ICESat-2, we investigate differences in measurements gathered at varying spatial resolutions and the impact on sea-ice freeboard. We assess the ability of each system to reproduce the elevation distributions of two sea-ice models and discuss potential biases in lead detection and sea-surface elevation, arising from variable footprint size and spacing. The conventional systems accurately reproduce mean freeboard over 25 km length scales, while ICESat-2 offers considerable improvements over its predecessor ICESat. In particular, its dense along-track sampling of the surface will allow flexibility in the algorithmic approaches taken to optimize the signal-to-noise ratio for accurate and precise freeboard retrieval.

In October 2003 a campaign on board the Australian icebreaker Aurora Australis had the objective to validate standard Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sea-ice products. Additionally, the satellite laser altimeter on the Ice, Cloud and land Elevation Satellite (ICESat) was in operation. To capture the large-scale information on the sea-ice conditions necessary for satellite validation, the measurement strategy was to obtain large-scale sea-ice statistics using extensive sea-ice measurements in a Lagrangian approach. A drifting buoy array, spanning initially 50 km × 100 km, was surveyed during the campaign. In situ measurements consisted of 12 transects, 50–500 m, with detailed snow and ice measurements as well as random snow depth sampling of floes within the buoy array using helicopters. In order to increase the amount of coincident in situ and satellite data an approach has been developed to extrapolate measurements in time and in space. Assuming no change in snow depth and freeboard occurred during the period of the campaign on the floes surveyed, we use buoy ice-drift information as well as daily estimates of thin-ice fraction and rough-ice vs smooth-ice fractions from AMSR-E and QuikSCAT, respectively, to estimate kilometer-scale snow depth and freeboard for other days. the results show that ICESat freeboard estimates have a mean difference of 1.8 cm when compared with the in situ data and a correlation coefficient of 0.6. Furthermore, incorporating ICESat roughness information into the AMSR-E snow depth algorithm significantly improves snow depth retrievals. Snow depth retrievals using a combination of AMSR-E and ICESat data agree with in situ data with a mean difference of 2.3 cm and a correlation coefficient of 0.84 with a negligible bias.

Melt-season duration, melt-onset and freeze-up dates are derived from Satellite passive microwave data and analyzed from 1979 to 2005 over Arctic Sea ice. Results indicate a Shift towards a longer melt Season, particularly north of Alaska and Siberia, corresponding to large retreats of Sea ice observed in these regions. Although there is large interannual and regional variability in the length of the melt Season, the Arctic is experiencing an overall lengthening of the melt Season at a rate of about 2 weeks decade–1. In fact, all regions in the Arctic (except for the central Arctic) have Statistically Significant (at the 99% level or higher) longer melt Seasons by >1 week decade–1. The central Arctic Shows a Statistically Significant trend (at the 98% level) of 5.4 days decade–1. In 2005 the Arctic experienced its longest melt Season, corresponding with the least amount of Sea ice Since 1979 and the warmest temperatures Since the 1880s. Overall, the length of the melt Season is inversely correlated with the lack of Sea ice Seen in September north of Alaska and Siberia, with a mean correlation of –0.8.

Sea ice is generally covered with snow, which can vary in thickness from a few centimeters to >1 m. Snow cover acts as a thermal insulator modulating the heat exchange between the ocean and the atmosphere, and it impacts sea-ice growth rates and overall thickness, a key indicator of climate change in polar regions. Snow depth is required to estimate sea-ice thickness using freeboard measurements made with satellite altimeters. The snow cover also acts as a mechanical load that depresses ice freeboard (snow and ice above sea level). Freeboard depression can result in flooding of the snow/ice interface and the formation of a thick slush layer, particularly in the Antarctic sea-ice cover. The Center for Remote Sensing of Ice Sheets (CReSIS) has developed an ultra-wideband, microwave radar capable of operation on long-endurance aircraft to characterize the thickness of snow over sea ice. The low-power, 100 mW signal is swept from 2 to 8 GHz allowing the air/snow and snow/ ice interfaces to be mapped with 5 cm range resolution in snow; this is an improvement over the original system that worked from 2 to 6.5 GHz. From 2009 to 2012, CReSIS successfully operated the radar on the NASA P-3B and DC-8 aircraft to collect data on snow-covered sea ice in the Arctic and Antarctic for NASA Operation IceBridge. The radar was found capable of snow depth retrievals ranging from 10 cm to >1 m. We also demonstrated that this radar can be used to map near-surface internal layers in polar firn with fine range resolution. Here we describe the instrument design, characteristics and performance of the radar.

A method to detect sub-pixel-scale polynyas with passive microwave data has been developed. Models for the brightness temperature transition from sea ice to shelf ice, with and without a coastal polynya, are generated by simulating the satellite overflight across the coast. The models are compared with image data along transects across the coast; correlation coefficients are used to determine the best-fit model and thereby detect the influence of open water. Polynya areas derived from a single SSM/I overpass compare well with coincident AVHRR data showing a mean difference of 156 km2. A longer time series based on daily averaged SSM/I data over a study area adjacent to research station Halley indicates that the opening and closing of polynyas are highly correlated with station wind data.

Low soil water potential limits or prevents germination and emergence of rainfed winter wheat (Triticum aestivum L.). This phenomenon is particularly pronounced in the winter wheat–summer fallow region of the US Inland Pacific Northwest, where wheat is routinely sown deep to reach moisture with 12–15 cm of soil covering the seed. Wide differences in seedling emergence among winter wheat cultivars have been reported, but few previous experiments have examined germination differences among cultivars as a function of water potential. The objective of our laboratory study was to quantify seed germination of five commonly sown winter wheat cultivars (Moro, Xerpha, Eltan, Buchanan and Finley) at seven water potentials, ranging from 0 to − 1.5 MPa. Germination was measured as a function of time for a period of 30 d. At higher water potentials (0 to − 0.5 MPa), all cultivars had germination of more than 90%. At the lowest water potentials ( − 1.0 to − 1.25 MPa), however, Moro consistently exceeded the other cultivars for speed and extent of germination, with total germination of 74% at − 1.0 MPa and 43% at − 1.25 MPa. Since its release in 1966, Moro has been sown by farmers when seed-zone water conditions are marginal. Scientists have long known that coleoptile length is an important factor controlling winter wheat seedling emergence from deep sowing depths. In addition to having a long coleoptile, our data suggest that Moro's known excellent emergence ability from deep sowing depths in dry soils can also be attributed to the ability to germinate at lower water potentials than other cultivars.

We present Zeeman-Doppler images of the active K2 star II Peg for the years 2004 and 2007. The surface magnetic field was reconstructed with our new ZDI code iMap which provides a full polarized radiative transfer driven inversion to simultaneously reconstruct the surface temperature and magnetic vector field distribution. II Peg shows a remarkable large scale magnetic field structure for both years. The magnetic field is predominantly located at high latitudes and is arranged in active longitudes. A dramatic evolution in the magnetic field structure is visible for the two years, where a dominant and largely unipolar field in 2004 has changed into two distinct and large scale bipolar structures in 2007.

Zeeman-Doppler Imaging (ZDI) is a powerful inversion method to reconstruct stellar magnetic surface fields. The reconstruction process is usually solved by translating the inverse problem into a regularized least-square or optimization problem. In this contribution we will emphasize that ZDI is an inherent non-linear problem and the corresponding regularized optimization is, like many non-linear problems, potentially prone to local minima. We show how this problem will be exacerbated by using an inadequate forward model. To facilitate a more consistent full radiative transfer driven approach to ZDI we describe a two-stage strategy that consist of a principal component analysis (PCA) based line profile reconstruction and a fast approximate polarized radiative transfer method to synthesize local Stokes profiles. Moreover, we introduce a novel statistical inversion method based on artificial neural networks (ANN) which provide a fast calculation of a first guess model and allows to incorporate better physical constraints into the inversion process.

Except for blocks with a cyclic or Klein four defect group, it is not known in general whether the Morita equivalence class of a block algebra over a field of prime characteristic determines that of the corresponding block algebra over a p-adic ring. We prove this to be the case when the defect group is quaternion of order 8 and the block algebra over an algebraically closed field k of characteristic 2 is Morita equivalent to kÃ4. The main ingredients are Erdmann's classification of tame blocks [6] and work of Cabanes and Picaronny [4, 5] on perfect isometries between tame blocks.

Many feasibility and design studies during the last years have shown that devices based on 3D chip stacking and integration can significantly outperform traditional planar (2D) devices. Furthermore, as packaging of the devices is a major cost factor, the possibility to integrate multiple functional entities in one package offers a huge potential for cost reduction. On research level the technical feasibility has been proven for a variety of processes. Today's focus lies on innovative manufacturing technologies and process integration schemes, which meet both, the economic and the technical demands.

Stacking of individual chips (both chip-to-wafer and wafer-to-wafer) has the inherent advantage that different functional subsystems like logic and memory can be processed on separate wafers thereby reducing the complexity and the number of process steps greatly. The individual chips can be processed on heterogeneous materials, in different fabs and by different producers.

Wafer-level integration has the advantage of higher throughput, enhanced cleanliness and the flexibility that standard fab equipment can be used for further processing. 3D integration applying chip-to-wafer bonding focuses on the yield (“good known die”) and enables to stack dies of different size e.g. several small dies on one big base die. This allows e.g. the integration of a logic device from a 300mm Si wafer with RF devices from a 150mm GaAs wafer.

In this paper a higher emphasis lies on the key enabling manufacturing technologies and supported processes.