Differences in grain growth of calcite: a field-based modeling approach

Normal grain growth of calcite was investigated by combining grain size analysis of calcite across the contact aureole of the Adamello pluton, and grain growth modeling based on a thermal model of the surroundings of the pluton. In an unbiased model system, i.e., location dependent variations in temperature-time path, 2/3 and 1/3 of grain growth occurs during pro- and retrograde metamorphism at all locations, respectively. In contrast to this idealized situation, in the field example three groups can be distinguished, which are characterized by variations in their grain size versus temperature relationships: Group I occurs at low temperatures and the grain size remains constant because nano-scale second phase particles of organic origin inhibit grain growth in the calcite aggregates under these conditions. In the presence of an aqueous fluid, these second phases decay at a temperature of about 350 °C enabling the onset of grain growth in calcite. In the following growth period, fluid-enhanced group II and slower group III growth occurs. For group II a continuous and intense grain size increase with T is typical while the grain growth decreases with T for group III. None of the observed trends correlate with experimentally based grain growth kinetics, probably due to differences between nature and experiment which have not yet been investigated (e.g., porosity, second phases). Therefore, grain growth modeling was used to iteratively improve the correlation between measured and modeled grain sizes by optimizing activation energy (Q), pre-exponential factor (k0) and grain size exponent (n). For n=2, Q of 350 kJ/mol, k0 of 1.7×1021 μmns−1 and Q of 35 kJ/mol, k0 of 2.5×10-5 μmns−1 were obtained for group II and III, respectively. With respect to future work, field-data based grain growth modeling might be a promising tool for investigating the influences of secondary effects like porosity and second phases on grain growth in nature, and to unravel differences between nature and experiment.

Multi-Scale Modeling for Image Analysis of Brain Tumor Studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Modelling the effects of land use and climate changes on hydrology in the Ursern Valley, Switzerland

While many studies have been conducted in mountainous catchments to examine the impact of climate change on hydrology, the interactions between climate changes and land use components have largely unknown impacts on hydrology in alpine regions. They need to be given special attention in order to devise possible strategies concerning general development in these regions. Thus, the main aim was to examine the impact of land use (i.e. bushland expansion) and climate changes (i.e. increase of temperature) on hydrology by model simulations. For this purpose, the physically based WaSiM-ETH model was applied to the catchment of Ursern Valley in the central Alps (191 km2) over the period of 1983−2005. Modelling results showed that the reduction of the mean monthly discharge during the summer period is due primarily to the retreat of snow discharge in time and secondarily to the reduction in the glacier surface area together with its retreat in time, rather than the increase in the evapotranspiration due to the expansion of the “green alder” on the expense of grassland. The significant decrease in summer discharge during July, August and September shows a change in the regime from b-glacio-nival to nivo-glacial. These changes are confirmed by the modeling results that attest to a temporal shift in snowmelt and glacier discharge towards earlier in the year: March, April and May for snowmelt and May and June for glacier discharge. It is expected that the yearly total discharge due to the land use changes will be reduced by 0.6% in the near future, whereas, it will be reduced by about 5% if climate change is also taken into account. Copyright © 2013 John Wiley & Sons, Ltd.

Individual and population level effects of partner notification for Chlamydia trachomatis

Partner notification (PN or contact tracing) is an important aspect of treating bacterial sexually transmitted infections (STIs), such as Chlamydia trachomatis. It facilitates the identification of new infected cases that can be treated through individual case management. PN also acts indirectly by limiting onward transmission in the general population. However, the impact of PN, both at the level of individuals and the population, remains unclear. Since it is difficult to study the effects of PN empirically, mathematical and computational models are useful tools for investigating its potential as a public health intervention. To this end, we developed an individual-based modeling framework called Rstisim. It allows the implementation of different models of STI transmission with various levels of complexity and the reconstruction of the complete dynamic sexual partnership network over any time period. A key feature of this framework is that we can trace an individual's partnership history in detail and investigate the outcome of different PN strategies for C. trachomatis. For individual case management, the results suggest that notifying three or more partners from the preceding 18 months yields substantial numbers of new cases. In contrast, the successful treatment of current partners is most important for preventing re-infection of index cases and reducing further transmission of C. trachomatis at the population level. The findings of this study demonstrate the difference between individual and population level outcomes of public health interventions for STIs.

Tree species and diversity effects on soil water seepage in a tropical plantation

Plant diversity has been shown to influence the water cycle of forest ecosystems by differences in water consumption and the associated effects on groundwater recharge. However, the effects of biodiversity on soil water fluxes remain poorly understood for native tree species plantations in the tropics. Therefore, we estimated soil water fluxes and assessed the effects of tree species and diversity on these fluxes in an experimental native tree species plantation in Sardinilla (Panama). The study was conducted during the wet season 2008 on plots of monocultures and mixtures of three or six tree species. Rainfall and soil water content were measured and evapotranspiration was estimated with the Penman-Monteith equation. Soil water fluxes were estimated using a simple soil water budget model considering water input, output, and soil water and groundwater storage changes and in addition, were simulated using the physically based one-dimensional water flow model Hydrus-1D. In general, the Hydrus simulation did not reflect the observed pressure heads, in that modeled pressure heads were higher compared to measured ones. On the other hand, the results of the water balance equation (WBE) reproduced observed water use patterns well. In monocultures, the downward fluxes through the 200 cm-depth plane were highest below Hura crepitans (6.13 mm day−1) and lowest below Luehea seemannii (5.18 mm day−1). The average seepage rate in monocultures (±SE) was 5.66 ± 0.18 mm day−1, and therefore, significantly higher than below six-species mixtures (5.49 ± 0.04 mm day−1) according to overyielding analyses. The three-species mixtures had an average seepage rate of 5.63 ± 0.12 mm day−1 and their values did not differ significantly from the average values of the corresponding species in monocultures. Seepage rates were driven by the transpiration of the varying biomass among the plots (r = 0.61, p = 0.017). Thus, a mixture of trees with different growth rates resulted in moderate seepage rates compared to monocultures of either fast growing or slow growing tree species. Our results demonstrate that tree-species specific biomass production and tree diversity are important controls of seepage rates in the Sardinilla plantation during the wet season.

Integrated Segmentation of Brain Tumor Images for Radiotherapy and Neurosurgery

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Distortion-sensitive insight into the pretransitional behavior of 4- n -octyloxy-4′-cyanobiphenyl (8OCB)

Results of studies of the static and dynamic dielectric properties in rod-like 4-n-octyloxy-4'-cyanobiphenyl (8OCB) with isotropic (I)–nematic (N)–smectic A (SmA)–crystal (Cr) mesomorphism, combined with measurements of the low-frequency nonlinear dielectric effect and heat capacity are presented. The analysis is supported by the derivative-based and distortion-sensitive transformation of experimental data. Evidence for the I–N and N–SmA pretransitional anomalies, indicating the influence of tricritical behavior, is shown. It has also been found that neither the N phase nor the SmA phase are uniform and hallmarks of fluid–fluid crossovers can be detected. The dynamics, tested via the evolution of the primary relaxation time, is clearly non-Arrhenius and described via τ(T) = τc(T−TC)−phgr. In the immediate vicinity of the I–N transition a novel anomaly has been found: Δτ ∝ 1/(T − T*), where T* is the temperature of the virtual continuous transition and Δτ is the excess over the 'background behavior'. Experimental results are confronted with the comprehensive Landau–de Gennes theory based modeling.

A glimpse into the future composition of marine phytoplankton communities

It is expected that climate change will have significant impacts on ecosystems. Most model projections agree that the ocean will experience stronger stratification and less nutrient supply from deep waters. These changes will likely affect marine phytoplankton communities and will thus impact on the higher trophic levels of the oceanic food web. The potential consequences of future climate change on marine microbial communities can be investigated and predicted only with the help of mathematical models. Here we present the application of a model that describes aggregate properties of marine phytoplankton communities and captures the effects of a changing environment on their composition and adaptive capacity. Specifically, the model describes the phytoplankton community in terms of total biomass, mean cell size, and functional diversity. The model is applied to two contrasting regions of the Atlantic Ocean (tropical and temperate) and is tested under two emission scenarios: SRES A2 or “business as usual” and SRES B1 or “local utopia.” We find that all three macroecological properties will decline during the next century in both regions, although this effect will be more pronounced in the temperate region. Being consistent with previous model predictions, our results show that a simple trait-based modeling framework represents a valuable tool for investigating how phytoplankton communities may reorganize under a changing climate.

Effect of surface restoring on subsurface variability in a climate model during 1949–2005

Initializing the ocean for decadal predictability studies is a challenge, as it requires reconstructing the little observed subsurface trajectory of ocean variability. In this study we explore to what extent surface nudging using well-observed sea surface temperature (SST) can reconstruct the deeper ocean variations for the 1949–2005 period. An ensemble made with a nudged version of the IPSLCM5A model and compared to ocean reanalyses and reconstructed datasets. The SST is restored to observations using a physically-based relaxation coefficient, in contrast to earlier studies, which use a much larger value. The assessment is restricted to the regions where the ocean reanalyses agree, i.e. in the upper 500 m of the ocean, although this can be latitude and basin dependent. Significant reconstruction of the subsurface is achieved in specific regions, namely region of subduction in the subtropical Atlantic, below the thermocline in the equatorial Pacific and, in some cases, in the North Atlantic deep convection regions. Beyond the mean correlations, ocean integrals are used to explore the time evolution of the correlation over 20-year windows. Classical fixed depth heat content diagnostics do not exhibit any significant reconstruction between the different existing bservation-based references and can therefore not be used to assess global average time-varying correlations in the nudged simulations. Using the physically based average temperature above an isotherm (14°C) alleviates this issue in the tropics and subtropics and shows significant reconstruction of these quantities in the nudged simulations for several decades. This skill is attributed to the wind stress reconstruction in the tropics, as already demonstrated in a perfect model study using the same model. Thus, we also show here the robustness of this result in an historical and observational context.

Biomechanical Role of Bone Anisotropy Estimated on Clinical CT Scans by Image Registration

Image-based modeling is a popular approach to perform patient-specific biomechanical simulations. Accurate modeling is critical for orthopedic application to evaluate implant design and surgical planning. It has been shown that bone strength can be estimated from the bone mineral density (BMD) and trabecular bone architecture. However, these findings cannot be directly and fully transferred to patient-specific modeling since only BMD can be derived from clinical CT. Therefore, the objective of this study was to propose a method to predict the trabecular bone structure using a µCT atlas and an image registration technique. The approach has been evaluated on femurs and patellae under physiological loading. The displacement and ultimate force for femurs loaded in stance position were predicted with an error of 2.5% and 3.7%, respectively, while predictions obtained with an isotropic material resulted in errors of 7.3% and 6.9%. Similar results were obtained for the patella, where the strain predicted using the registration approach resulted in an improved mean squared error compared to the isotropic model. We conclude that the registration of anisotropic information from of a single template bone enables more accurate patient-specific simulations from clinical image datasets than isotropic model.

Volcanic forcing for climate modeling: a new microphysics-based data set covering years 1600–present

As the understanding and representation of the impacts of volcanic eruptions on climate have improved in the last decades, uncertainties in the stratospheric aerosol forcing from large eruptions are now linked not only to visible optical depth estimates on a global scale but also to details on the size, latitude and altitude distributions of the stratospheric aerosols. Based on our understanding of these uncertainties, we propose a new model-based approach to generating a volcanic forcing for general circulation model (GCM) and chemistry–climate model (CCM) simulations. This new volcanic forcing, covering the 1600–present period, uses an aerosol microphysical model to provide a realistic, physically consistent treatment of the stratospheric sulfate aerosols. Twenty-six eruptions were modeled individually using the latest available ice cores aerosol mass estimates and historical data on the latitude and date of eruptions. The evolution of aerosol spatial and size distribution after the sulfur dioxide discharge are hence characterized for each volcanic eruption. Large variations are seen in hemispheric partitioning and size distributions in relation to location/date of eruptions and injected SO2 masses. Results for recent eruptions show reasonable agreement with observations. By providing these new estimates of spatial distributions of shortwave and long-wave radiative perturbations, this volcanic forcing may help to better constrain the climate model responses to volcanic eruptions in the 1600–present period. The final data set consists of 3-D values (with constant longitude) of spectrally resolved extinction coefficients, single scattering albedos and asymmetry factors calculated for different wavelength bands upon request. Surface area densities for heterogeneous chemistry are also provided.