Unions in Germany: Searching to regain the initiative

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Nanoporous Silica Films as Novel Biomaterial: Applications in the Middle Ear

We have introduced nanoporous silica as a novel biomaterial. Nanoporous silica is non-toxic and biocompatible. It provides a high surface area and pore volume, uniform and tunable pore sizes and the possibility for chemical modification. We have shown that nanoporous silica coatings on middle ear prostheses provide a suitable basis for installing various functionalizations which can improve the healing after the insertion of the implant.DF

Arctic sea ice area changes in CMIP3 and CMIP5 climate models’ ensembles

The shrinking Arctic sea ice cover observed during the last decades is probably the clearest manifestation of ongoing climate change. While climate models in general reproduce the sea ice retreat in the Arctic during the 20th century and simulate further sea ice area loss during the 21st century in response to anthropogenic forcing, the models suffer from large biases and the results exhibit considerable spread. Here, we compare results from the two last generations of climate models, CMIP3 and CMIP5, with respect to total and regional Arctic sea ice change. Different characteristics of sea ice area (SIA) in March and September have been analysed for the Entire Arctic, Central Arctic and Barents Sea. Further, the sensitivity of SIA to changes in Northern Hemisphere (NH) temperature is investigated and dynamical links between SIA and some atmospheric variability modes are assessed. CMIP3 (SRES A1B) and CMIP5 (RCP8.5) models not only simulate a coherent decline of the Arctic SIA but also depict consistent changes in the SIA seasonal cycle. The spatial patterns of SIC variability improve in CMIP5 ensemble, most noticeably in summer when compared to HadISST1 data. A better simulation of summer SIA in the Entire Arctic by CMIP5 models is accompanied by a slightly increased bias for winter season in comparison to CMIP3 ensemble. SIA in the Barents Sea is strongly overestimated by the majority of CMIP3 and CMIP5 models, and projected SIA changes are characterized by a high uncertainty. Both CMIP ensembles depict a significant link between the SIA and NH temperature changes indicating that a part of inter-ensemble SIA spread comes from different temperature sensitivity to anthropogenic forcing. The results suggest that, in general, a sensitivity of SIA to external forcing is enhanced in CMIP5 models. Arctic SIA interannual variability in the end of the 20th century is on average well simulated by both ensembles. To the end of the 21st century, September variability is strongly reduced in CMIP5 models under RCP8.5 scenario, whereas variability changes in CMIP3 and in both ensembles in March are relatively small. The majority of models in both CMIP ensembles demonstrate an ability to capture a negative correlation of interannual SIA variations in the Barents Sea with North Atlantic Oscillation and sea level pressure gradient in the western Barents Sea opening serving as an index of oceanic inflow to the Sea

The influence of basaltic islands on the oceanic REE distribution: A case study from the tropical South Pacific

The Rare Earth Elements (REEs) have been widely used to investigate marine biogeochemical processes as well as the sources and mixing of water masses. However, there are still important uncertainties about the global aqueous REE cycle with respect to the contributions of highly reactive basaltic minerals originating from volcanic islands and the role of Submarine Groundwater Discharge (SGD). Here we present dissolved REE concentrations obtained from waters at the island-ocean interface (including SGD, river, lagoon and coastal waters) from the island of Tahiti and from three detailed open ocean profiles on the Manihiki Plateau (including neodymium (Nd) isotope compositions), which are located in ocean currents downstream of Tahiti. Tahitian fresh waters have highly variable REE concentrations that likely result from variable water–rock interaction and removal by secondary minerals. In contrast to studies on other islands, the SGD samples do not exhibit elevated REE concentrations but have distinctive REE distributions and Y/Ho ratios. The basaltic Tahitian rocks impart a REE pattern to the waters characterized by a middle REE enrichment, with a peak at europium similar to groundwaters and coastal waters of other volcanic islands in the Pacific. However, the basaltic island REE characteristics (with the exception of elevated Y/Ho ratios) are lost during transport to the Manihiki Plateau within surface waters that also exhibit highly radiogenic Nd isotope signatures. Our new data demonstrate that REE concentrations are enriched in Tahitian coastal water, but without multidimensional sampling, basaltic island Nd flux estimates range over orders of magnitude from relatively small to globally significant. Antarctic Intermediate Water (AAIW) loses its characteristic Nd isotopic signature (-6 to-9) around the Manihiki Plateau as a consequence of mixing with South Equatorial Pacific Intermediate Water (SEqPIW), which shows more positive values (-1 to -2). However, an additional Nd input/exchange along the pathway of AAIW, eventually originating from the volcanic Society, Tuamotu and Tubuai Islands (including Tahiti), is indicated by an offset from the mixing array of AAIW and SEqPIW to more radiogenic Nd isotope compositions

The Arctic Sea ice in the CMIP3 climate model ensemble – variability and anthropogenic change

The strongest manifestation of global warming is observed in the Arctic. The warming in the Arctic during the recent decades is about twice as strong as in the global average and has been accompanied by a summer sea ice decline that is very likely unprecedented during the last millennium. Here, Arctic sea ice variability is analyzed in the ensemble of CMIP3 models. Complementary to several previous studies, we focus on regional aspects, in particular on the Barents Sea. We also investigate the changes in the seasonal cycle and interannual variability. In all regions, the models predict a reduction in sea ice area and sea ice volume during 1900–2100. Toward the end of the 21st century, the models simulate higher sea ice area variability in September than in March, whereas the variability in the preindustrial control runs is higher in March. Furthermore, the amplitude and phase of the sea ice seasonal cycle change in response to enhanced greenhouse warming. The amplitude of the sea ice area seasonal cycle increases due to the very strong sea ice area decline in September. The seasonal cycle amplitude of the sea ice volume decreases due to the stronger reduction of sea ice volume in March. Multi-model mean estimates for the late 20th century are comparable with observational data only for the entire Arctic and the Central Arctic. In the Barents Sea, differences between the multi-model mean and the observational data are more pronounced. Regional sea ice sensitivity to Northern Hemisphere average surface warming has been investigated

The effect of continuous passive motion and sling exercise training on clinical and functional outcomes following total knee arthroplasty: a randomized active-controlled clinical study

BACKGROUND: The parallel-group randomized active-controlled clinical study was conducted to compare the effectiveness of two in-hospital range of motion (ROM) exercise programs following total knee arthroplasty (TKA). Continuous passive motion (CPM) is frequently used to increase ROM and improve postoperative recovery despite little conclusive scientific evidence. In contrast, a new active sling-based ROM therapy requires the activation of the knee joint muscles and dynamic joint stabilization. It was hypothesized that higher demands on muscle strength and muscle coordination during sling exercise training (ST) might be advantageous for early recovery following TKA. METHODS: A total of 125 patients undergoing primary TKA were assessed for eligibility. Thirty-eight patients were randomly assigned to receive ST or CPM (control intervention) during hospital stay. Patients were assessed before TKA for baseline measurement (pretest), 1 day before discharge (posttest) and 3 months after TKA (follow-up). The passive knee flexion range of motion (pFL) was the primary outcome measure. Secondary outcome measures included active knee flexion range of motion, active and passive knee extension ROM, static postural control, physical activity, pain, length of hospital stay as well as clinical, functional and quality-of-life outcomes (SF-36, HSS and WOMAC scores). Data were analyzed according to the intention-to-treat principle. Differences between the groups were tested for significance by the unpaired Student’s t test or an analysis of covariance (ANCOVA) adjusted for baseline, weight, sex, age, pain and physical activity. RESULTS: A between-group difference could be determined at posttest. The pFL was significantly higher by 6.0° (95% CI 0.9 to 11.2°; P = 0.022) in the ST group. No difference between groups in pFL was documented at follow-up. Furthermore, no significant differences could be observed for any secondary outcome measure at posttest and follow-up. CONCLUSIONS: ST seems to have a clinically relevant beneficial short-term effect on pFL compared to CPM. The results support the implementation of ST in rehabilitation programs following TKA. LEVEL OF EVIDENCE: Therapy, level 2

Different cell populations are inducible by BMP-2 covalently covered Bioverit® II implants in rabbit subcutis and middle ear

To optimize the function of implants the formation of surrounding connective tissue should be adapted in dependence to the mechanical conditions. Therefore, with nanostructured silica coated Bioverit® II implants were only partly reacted with recombinant BMP-2. The histology was compared 28, 84 and 301 days after implantation in the rabbit middle ear and subcutis, respectively. The whole tissue blocks were embedded in Epon, sequentially grinded, stained with Toluidine Blue O and Eosin G. The granulation tissue covering the implants varies related to cell types, cell amounts, extracellular matrix and vessels. Whereas the high cell density and the angiogenesis predominated in the subcutis, the formation of new bone could only be recognized in the scar around the implants in the middle ear.SFB/599/project D1

In vivo testing of a bioabsorbable magnesium alloy serving as total ossicular replacement prostheses

Magnesium alloys have been investigated in different fields of medicine and represent a promising biomaterial for implants due to characteristics like bioabsorbability and osteoinduction. The objective of this study was to evaluate the usability of magnesium as implant material in middle ear surgery. Magnesium implants were placed into the right middle ear of eighteen New Zealand White rabbits. Nine animals were euthanized after four weeks and nine animals after three month. The petrous bones were removed and embedded in epoxy resin. The specimens were then polished, stained and evaluated with the aid of a light microscope. The histological examination revealed a good biocompatibility. After four weeks, a beginning corrosion of the implant's surface and low amount of trabecular bone formation in the area of the stapes base plate was observed. A considerable degradation of implants and obvious bone formation was found three month after implantation. The magnesium alloy used in the present study partly corroded too fast, so that a complete bone reconstruction could not be established in time. The increased osteoinduction on the stapes base plate resulted in a tight bone-implant bonding. Thus, a promising application of magnesium could be a coating of biomaterials in order to improve the bony integration of implants. © The Author(s) 2012 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav

Arctic sea ice area in CMIP3 and CMIP5 climate model ensembles – variability and change

The shrinking Arctic sea ice cover observed during the last decades is probably the clearest manifestation of ongoing climate change. While climate models in general reproduce the sea ice retreat in the Arctic during the 20th century and simulate further sea ice area loss during the 21st century in response to anthropogenic forcing, the models suffer from large biases and the model results exhibit considerable spread. The last generation of climate models from World Climate Research Programme Coupled Model Intercomparison Project Phase 5 (CMIP5), when compared to the previous CMIP3 model ensemble and considering the whole Arctic, were found to be more consistent with the observed changes in sea ice extent during the recent decades. Some CMIP5 models project strongly accelerated (non-linear) sea ice loss during the first half of the 21st century. Here, complementary to previous studies, we compare results from CMIP3 and CMIP5 with respect to regional Arctic sea ice change. We focus on September and March sea ice. Sea ice area (SIA) variability, sea ice concentration (SIC) variability, and characteristics of the SIA seasonal cycle and interannual variability have been analysed for the whole Arctic, termed Entire Arctic, Central Arctic and Barents Sea. Further, the sensitivity of SIA changes to changes in Northern Hemisphere (NH) averaged temperature is investigated and several important dynamical links between SIA and natural climate variability involving the Atlantic Meridional Overturning Circulation (AMOC), North Atlantic Oscillation (NAO) and sea level pressure gradient (SLPG) in the western Barents Sea opening serving as an index of oceanic inflow to the Barents Sea are studied. The CMIP3 and CMIP5 models not only simulate a coherent decline of the Arctic SIA but also depict consistent changes in the SIA seasonal cycle and in the aforementioned dynamical links. The spatial patterns of SIC variability improve in the CMIP5 ensemble, particularly in summer. Both CMIP ensembles depict a significant link between the SIA and NH temperature changes. Our analysis suggests that, on average, the sensitivity of SIA to external forcing is enhanced in the CMIP5 models. The Arctic SIA variability response to anthropogenic forcing is different in CMIP3 and CMIP5. While the CMIP3 models simulate increased variability in March and September, the CMIP5 ensemble shows the opposite tendency. A noticeable improvement in the simulation of summer SIA by the CMIP5 models is often accompanied by worse results for winter SIA characteristics. The relation between SIA and mean AMOC changes is opposite in September and March, with March SIA changes being positively correlated with AMOC slowing. Finally, both CMIP ensembles demonstrate an ability to capture, at least qualitatively, important dynamical links of SIA to decadal variability of the AMOC, NAO and SLPG. SIA in the Barents Sea is strongly overestimated by the majority of the CMIP3 and CMIP5 models, and projected SIA changes are characterized by a large spread giving rise to high uncertainty