Fractional order modeling of large three-dimensional RC networks

An incidence matrix analysis is used to model a three-dimensional network consisting of resistive and capacitive elements distributed across several interconnected layers. A systematic methodology for deriving a descriptor representation of the network with random allocation of the resistors and capacitors is proposed. Using a transformation of the descriptor representation into standard state-space form, amplitude and phase admittance responses of three-dimensional random RC networks are obtained. Such networks display an emergent behavior with a characteristic Jonscher-like response over a wide range of frequencies. A model approximation study of these networks is performed to infer the admittance response using integral and fractional order models. It was found that a fractional order model with only seven parameters can accurately describe the responses of networks composed of more than 70 nodes and 200 branches with 100 resistors and 100 capacitors. The proposed analysis can be used to model charge migration in amorphous materials, which may be associated to specific macroscopic or microscopic scale fractal geometrical structures in composites displaying a viscoelastic electromechanical response, as well as to model the collective responses of processes governed by random events described using statistical mechanics.

Model estimation of cerebral hemodynamics between blood flow and volume changes: A data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV.

Stochastic climate theory and modeling

Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid-scale parameterizations (SSPs) as well as for model error representation, uncertainty quantification, data assimilation, and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large-scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochastic components and non-Markovian (memory) terms. Stochastic approaches in numerical weather and climate prediction models also lead to the reduction of model biases. Hence, there is a clear need for systematic stochastic approaches in weather and climate modeling. In this review, we present evidence for stochastic effects in laboratory experiments. Then we provide an overview of stochastic climate theory from an applied mathematics perspective. We also survey the current use of stochastic methods in comprehensive weather and climate prediction models and show that stochastic parameterizations have the potential to remedy many of the current biases in these comprehensive models.

Assessing the sensitivities of a distributed snow model to forcing data resolution

Highly heterogeneous mountain snow distributions strongly affect soil moisture patterns; local ecology; and, ultimately, the timing, magnitude, and chemistry of stream runoff. Capturing these vital heterogeneities in a physically based distributed snow model requires appropriately scaled model structures. This work looks at how model scale—particularly the resolutions at which the forcing processes are represented—affects simulated snow distributions and melt. The research area is in the Reynolds Creek Experimental Watershed in southwestern Idaho. In this region, where there is a negative correlation between snow accumulation and melt rates, overall scale degradation pushed simulated melt to earlier in the season. The processes mainly responsible for snow distribution heterogeneity in this region—wind speed, wind-affected snow accumulations, thermal radiation, and solar radiation—were also independently rescaled to test process-specific spatiotemporal sensitivities. It was found that in order to accurately simulate snowmelt in this catchment, the snow cover needed to be resolved to 100 m. Wind and wind-affected precipitation—the primary influence on snow distribution—required similar resolution. Thermal radiation scaled with the vegetation structure (~100 m), while solar radiation was adequately modeled with 100–250-m resolution. Spatiotemporal sensitivities to model scale were found that allowed for further reductions in computational costs through the winter months with limited losses in accuracy. It was also shown that these modeling-based scale breaks could be associated with physiographic and vegetation structures to aid a priori modeling decisions.

A new data set of soil mineralogy for dust-cycle modeling

The mineralogy of airborne dust affects the impact of dust particles on direct and indirect radiative forcing, on atmospheric chemistry and on biogeochemical cycling. It is determined partly by the mineralogy of the dust-source regions and partly by size-dependent fractionation during erosion and transport. Here we present a data set that characterizes the clay and silt-sized fractions of global soil units in terms of the abundance of 12 minerals that are important for dust–climate interactions: quartz, feldspars, illite, smectite, kaolinite, chlorite, vermiculite, mica, calcite, gypsum, hematite and goethite. The basic mineralogical information is derived from the literature, and is then expanded following explicit rules, in order to characterize as many soil units as possible. We present three alternative realizations of the mineralogical maps, taking the uncertainties in the mineralogical data into account. We examine the implications of the new database for calculations of the single scattering albedo of airborne dust and thus for dust radiative forcing.

Probabilistic downscaling of remote sensing data with applications for multi-scale biogeochemical flux modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.

Effect of plant neutrophil elastase inhibitor on leucocyte migration, adhesion and cytokine release in inflammatory conditions

BACKGROUND AND PURPOSE The serine and cysteine peptidase inhibitor, BbCI, isolated from Bauhinia bauhinioides seeds, is similar to the classical plant Kunitz inhibitor, STI, but lacks disulphide bridges and methionine residues. BbCI blocks activity of the serine peptidases, elastase (K(iapp) 5.3 nM) and cathepsin G (K(iapp) 160.0 nM), and the cysteine peptidase cathepsin L (K(iapp) 0.2 nM). These three peptidases play important roles in the inflammatory process. EXPERIMENTAL APPROACH We measured the effects of BbCI on paw oedema and on leucocyte accumulation in pleurisy, both induced by carrageenan. Leucocyte-endothelial cell interactions in scrotal microvasculature in Wistar rats were investigated using intravital microscopy. Cytokine levels in pleural exudate and serum were measured by ELISA. KEY RESULTS Pretreatment of the animals with BbCI (2.5 mg.kg(-1)), 30 min before carrageenan-induced inflammation, effectively reduced paw oedema and bradykinin release, neutrophil migration into the pleural cavity. The number of rolling, adhered and migrated leucocytes at the spermatic fascia microcirculation following carrageenan injection into the scrotum were reduced by BbCI pretreatment. Furthermore, levels of the rat chemokine cytokine-induced neutrophil chemo-attractant-1 were significantly reduced in both pleural exudates and serum from animals pretreated with BbCI. Levels of interleukin-1 beta or tumour necrosis factor-alpha, however, did not change. CONCLUSIONS AND IMPLICATIONS Taken together, our data suggest that the anti-inflammatory properties of BbCI may be useful in investigations of other pathological processes in which human neutrophil elastase, cathepsin G and cathepsin L play important roles.

On the efficiency of data representation on the modeling and characterization of complex networks

Specific choices about how to represent complex networks can have a substantial impact on the execution time required for the respective construction and analysis of those structures. In this work we report a comparison of the effects of representing complex networks statically by adjacency matrices or dynamically by adjacency lists. Three theoretical models of complex networks are considered: two types of Erdos-Renyi as well as the Barabasi-Albert model. We investigated the effect of the different representations with respect to the construction and measurement of several topological properties (i.e. degree, clustering coefficient, shortest path length, and betweenness centrality). We found that different forms of representation generally have a substantial effect on the execution time, with the sparse representation frequently resulting in remarkably superior performance. (C) 2011 Elsevier B.V. All rights reserved.

Understanding the "black box" of a health-promotion program : keys to enable health among older persons aging in the context of migration

Although the need to make health services more accessible to persons who have migrated has been identified, knowledge about health-promotion programs (HPPs) from the perspective of older persons born abroad is lacking. This study explores the design experiences and content implemented in an adapted version of a group-based HPP developed in a researcher-community partnership. Fourteen persons aged 70-83 years or older who had migrated to Sweden from Finland or the Balkan Peninsula were included. A grounded theory approach guided the data collection and analysis. The findings showed how participants and personnel jointly helped raise awareness. The participants experienced three key processes that could open doors to awareness: enabling community, providing opportunities to understand and be understood, and confirming human values and abilities. Depending on how the HPP content and design are being shaped by the group, the key processes could both inhibit or encourage opening doors to awareness. Therefore, this study provides key insights into how to enable health by deepening the understanding of how the exchange of health-promoting messages is experienced to be facilitated or hindered. This study adds to the scientific knowledge base of how the design and content of HPP may support and recognize the capabilities of persons aging in the context of migration.

Challenges in Radiative Transfer Modeling - W3 IRS5

By modeling the spectral energy distribution (SED) of the W3 IRS5 high-mass star formation region and matching this model to observed data, we can constrain the physical parameters of the basic system geometry and cloud mass distribution. From these parameters, we hope to add to the understanding of high-mass star formation processes. In particular, we hope to determine if the geometries associated with lowmass star formation carry over into the high-mass regime.

RWater – A Cyber Enabled Tool For Hydrologic Modeling And Analysis

Existing distributed hydrologic models are complex and computationally demanding for using as a rapid-forecasting policy-decision tool, or even as a class-room educational tool. In addition, platform dependence, specific input/output data structures and non-dynamic data-interaction with pluggable software components inside the existing proprietary frameworks make these models restrictive only to the specialized user groups. RWater is a web-based hydrologic analysis and modeling framework that utilizes the commonly used R software within the HUBzero cyber infrastructure of Purdue University. RWater is designed as an integrated framework for distributed hydrologic simulation, along with subsequent parameter optimization and visualization schemes. RWater provides platform independent web-based interface, flexible data integration capacity, grid-based simulations, and user-extensibility. RWater uses RStudio to simulate hydrologic processes on raster based data obtained through conventional GIS pre-processing. The program integrates Shuffled Complex Evolution (SCE) algorithm for parameter optimization. Moreover, RWater enables users to produce different descriptive statistics and visualization of the outputs at different temporal resolutions. The applicability of RWater will be demonstrated by application on two watersheds in Indiana for multiple rainfall events.

Meteomap: Generation Of Meteorological Variables For Distributed Physically-Based Hydrological Modeling

Distributed energy and water balance models require time-series surfaces of the meteorological variables involved in hydrological processes. Most of the hydrological GIS-based models apply simple interpolation techniques to extrapolate the point scale values registered at weather stations at a watershed scale. In mountainous areas, where the monitoring network ineffectively covers the complex terrain heterogeneity, simple geostatistical methods for spatial interpolation are not always representative enough, and algorithms that explicitly or implicitly account for the features creating strong local gradients in the meteorological variables must be applied. Originally developed as a meteorological pre-processing tool for a complete hydrological model (WiMMed), MeteoMap has become an independent software. The individual interpolation algorithms used to approximate the spatial distribution of each meteorological variable were carefully selected taking into account both, the specific variable being mapped, and the common lack of input data from Mediterranean mountainous areas. They include corrections with height for both rainfall and temperature (Herrero et al., 2007), and topographic corrections for solar radiation (Aguilar et al., 2010). MeteoMap is a GIS-based freeware upon registration. Input data include weather station records and topographic data and the output consists of tables and maps of the meteorological variables at hourly, daily, predefined rainfall event duration or annual scales. It offers its own pre and post-processing tools, including video outlook, map printing and the possibility of exporting the maps to images or ASCII ArcGIS formats. This study presents the friendly user interface of the software and shows some case studies with applications to hydrological modeling.

Loss of Expression and Function of SOCS3 Is an Early Event in HNSCC: Altered Subcellular Localization as a Possible Mechanism Involved in Proliferation, Migration and Invasion

Background: Suppressor of cytokine signaling 3 (SOCS3) is an inducible endogenous negative regulator of signal transduction and activator of transcription 3 (STAT3). Epigenetic silencing of SOCS3 has been shown in head and neck squamous cell carcinoma (HNSCC), which is associated with increased activation of STAT3. There is scarce information on the functional role of the reduction of SOCS3 expression and no information on altered subcellular localization of SOCS3 in HNSCC.Methodology/Principal Findings: We assessed endogenous SOCS3 expression in different HNSCC cell lines by RT-qPCR and western blot. Immunofluorescence and western blot were used to study the subcellular localization of endogenous SOCS3 induced by IL-6. Overexpression of SOCS3 by CMV-driven plasmids and siRNA-mediated inhibition of endogenous SOCS3 were used to verify the role of SOCS3 on tumor cell proliferation, viability, invasion and migration in vitro. In vivo relevance of SOCS3 expression in HNSCC was studied by quantitative immunohistochemistry of commercially-available tissue microarrays. Endogenous expression of SOCS3 was heterogeneous in four HNSCC cell lines and surprisingly preserved in most of these cell lines. Subcellular localization of endogenous SOCS3 in the HNSCC cell lines was predominantly nuclear as opposed to cytoplasmic in non-neoplasic epithelial cells. Overexpression of SOCS3 produced a relative increase of the protein in the cytoplasmic compartment and significantly inhibited proliferation, migration and invasion, whereas inhibition of endogenous nuclear SOCS3 did not affect these events. Analysis of tissue microarrays indicated that loss of SOCS3 is an early event in HNSCC and was correlated with tumor size and histological grade of dysplasia, but a considerable proportion of cases presented detectable expression of SOCS3.Conclusion: Our data support a role for SOCS3 as a tumor suppressor gene in HNSCC with relevance on proliferation and invasion processes and suggests that abnormal subcellular localization impairs SOCS3 function in HNSCC cells.