Cytotoxicity of advanced glycation endproducts is mediated by oxidative stress

Non-enzymatic glycation of proteins with reducing sugars and subsequent transition metal catalysed oxidations leads to the formation of protein bound "advanced glycation endproducts" (AGEs). They accumulate on long-lived proteins and are for example structural components of the beta-amyloid plaques in Alzheimer's disease. Since the oxidation of glycated proteins as well as the interaction of AGEs with cell surface receptors produces superoxide radicals, it was tested in BHK 21 hamster fibroblast cells and SH-SY5Y human neuroblastoma cells if AGEs can exert cytotoxic effects on cells. Cell viability was assessed with three independent tests: MTT-assay (activity of the mitochondrial respiratory chain), lactate dehydrogenase assay (release of cytoplasmatic enzymes, membrane integrity) and Neutral Red assay (active uptake of a hydrophilic dye). Two model AGEs, chicken egg albumin-AGE and BSA-AGE, both caused significant cell death in a dose-dependent manner. The cytotoxic effects of AGEs could be attenuated by alpha-ketoglutarate and pyruvate, by antioxidants such as thioctic acid and N-acetylcysteine, and by aminoguanidine, an inhibitor of nitric oxide synthase. This suggests that reactive oxygen species as well as reactive nitrogen species contribute to AGE mediated cytotoxicity. Since AGEs accumulate on beta-amyloid plaques in AD over time, they may additionally contribute to oxidative stress, cell damage, functional loss and even neuronal cell death in the Alzheimer's disease brain.     

Visual comparability of 3D regular sampling and reconstruction

The Body-Centered Cubic (BCC) and Face-Centered Cubic (FCC) lattices have been analytically shown to be more efficient sampling lattices than the traditional Cartesian Cubic (CC) lattice, but there has been no estimate of their visual comparability. Two perceptual studies (each with N = 12 participants) compared the visual quality of images rendered from BCC and FCC lattices to images rendered from the CC lattice. Images were generated from two signals: the commonly used Marschner-Lobb synthetic function and a computed tomography scan of a fish tail. Observers found that BCC and FCC could produce images of comparable visual quality to CC, using 30-35 percent fewer samples. For the images used in our studies, the L(2) error metric shows high correlation with the judgement of human observers. Using the L(2) metric as a proxy, the results of the experiments appear to extend across a wide range of images and parameter choices.     

Scan-based volume animation driven by locally adaptive articulated registrations

This paper describes a complete system to create anatomically accurate example-based volume deformation and animation of articulated body regions, starting from multiple in vivo volume scans of a specific individual. In order to solve the correspondence problem across volume scans, a template volume is registered to each sample. The wide range of pose variations is first approximated by volume blend deformation (VBD), providing proper initialization of the articulated subject in different poses. A novel registration method is presented to efficiently reduce the computation cost while avoiding strong local minima inherent in complex articulated body volume registration. The algorithm highly constrains the degrees of freedom and search space involved in the nonlinear optimization, using hierarchical volume structures and locally constrained deformation based on the biharmonic clamped spline. Our registration step establishes a correspondence across scans, allowing a data-driven deformation approach in the volume domain. The results provide an occlusion-free person-specific 3D human body model, asymptotically accurate inner tissue deformations, and realistic volume animation of articulated movements driven by standard joint control estimated from the actual skeleton. Our approach also addresses the practical issues arising in using scans from living subjects. The robustness of our algorithms is tested by their applications on the hand, probably the most complex articulated region in the body, and the knee, a frequent subject area for medical imaging due to injuries.     

ProbExplorer: Uncertainty‐guided Exploration and Editing of Probabilistic Medical Image Segmentation

In this paper, we develop an interactive analysis and visualization tool for probabilistic segmentation results in medical imaging. We provide a systematic approach to analyze, interact and highlight regions of segmentation uncertainty. We introduce a set of visual analysis widgets integrating different approaches to analyze multivariate probabilistic field data with direct volume rendering. We demonstrate the user's ability to identify suspicious regions (e.g. tumors) and correct the misclassification results using a novel uncertainty‐based segmentation editing technique. We evaluate our system and demonstrate its usefulness in the context of static and time‐varying medical imaging datasets.     

The Kemeny Constant for Finite Homogeneous Ergodic Markov Chains

A quantity known as the Kemeny constant, which is used to measure the expected number of links that a surfer on the World Wide Web, located on a random web page, needs to follow before reaching his/her desired location, coincides with the more well known notion of the expected time to mixing, i.e., to reaching stationarity of an ergodic Markov chain. In this paper we present a new formula for the Kemeny constant and we develop several perturbation results for the constant, including conditions under which it is a convex function. Finally, for chains whose transition matrix has a certain directed graph structure we show that the Kemeny constant is dependent only on the common length of the cycles and the total number of vertices and not on the specific transition probabilities of the chain.     

Edge aware anisotropic diffusion for 3D scalar data

In this paper we present a novel anisotropic diffusion model targeted for 3D scalar field data. Our model preserves material boundaries as well as fine tubular structures while noise is smoothed out. One of the major novelties is the use of the directional second derivative to define material boundaries instead of the gradient magnitude for thresholding. This results in a diffusion model that has much lower sensitivity to the diffusion parameter and smoothes material boundaries consistently compared to gradient magnitude based techniques. We empirically analyze the stability and convergence of the proposed diffusion and demonstrate its de-noising capabilities for both analytic and real data. We also discuss applications in the context of volume rendering.     

Modelling water dynamics with DNDC and DAISY in a soil of the North China Plain: A comparative study

The performance of the DNDC and Daisy model to simulate the water dynamics in a floodplain soil of the North China Plain was tested and compared. While the DNDC model uses a simple cascade approach, the Daisy model applies the physically based Richard's equation for simulating water movement in soil. For model testing a three years record of the soil water content from the Dong Bei Wang experimental station near Beijing was used. There, the effect of nitrogen fertilization, irrigation and straw removal on soil water and nitrogen dynamics was investigated in a three factorial field experiment applying a split-split-plot design with 4 replications. The dataset of one treatment was used for model testing and calibration. Two other independent datasets from further treatments were employed for validating the models. For both models, the simulation results were not satisfying using default parameters. After parameter optimisation and the use of site-specific van Genuchten parameters, however, the Daisy model performed well. But, for the DNDC model, parameter optimisation failed to improve the simulation result. Owing to the fact that many biological processes such as plant growth, nitrification or denitrification depend strongly on the soil water content, our findings bring us to the conclusion that the site-specific suitability of the DNDC model for simulating the soil water dynamics should be tested before further simulation of other processes.     

Automated bidding in computational markets: an application in market-based allocation of computing services

Autonomous agents are widely applied to automate interactions in robotics, e.g. for selling and purchasing goods on eBay, and in financial markets, e.g. in the form of quote machines and algorithmic traders. Current research investigates efficient economic mechanisms that fully automate the provisioning and usage processes of Grid-based services. On the one hand, consumers want to allocate resources on demand for their various applications, e.g. data sharing, stream processing, email, business applications and simulations. On the other hand, providers of Grid services want to govern business policies to meet their utilization and profit goals. The above-mentioned processes are not manually manageable, however, because decisions need to be taken within milliseconds. Therefore, such processes have to be automated to minimize human interactions. Hence, market mechanisms and strategic behavior play important roles when it comes to achieving automated and efficient allocation of Grid services. The paper begins by presenting a framework for automated bidding, providing a methodology for the design and implementation of configurable bidding strategies. Second, it presents a novel bidding strategy based on a reinforcement learning technique. This strategy is designed to automate the bid generation processes of consumers and providers in various market mechanisms. Third, the behavior and convergence of the strategy is evaluated in a centralized Continuous Double Auction and a decentralized on-line machine scheduling mechanism against selected benchmark bidding strategies. Fourth, we define a bidding language for communicating consumer and provider preferences to the market as well as report back the match of the market-based allocation process.     

Constant-phase-mode operation of the light-addressable potentiometric sensor

The constant-phase-mode operation of the light-addressable potentiometric sensor (LAPS) is proposed and demonstrated. In this new operation mode, the temporal change and the spatial distribution of the analyte concentration are recorded in the form of the bias voltage applied to the LAPS sensor plate, which is servo-controlled to maintain the phase of the photocurrent at a constant value with respect to the light modulation. The constant-phase-mode LAPS is advantageous for its wider measurement range and reduction of artifacts.     

A high-density multi-point LAPS set-up using a VCSEL array and FPGA control

A new LAPS (light-addressable potentiometric sensor) set-up will be introduced, in which the light sources are miniaturised by the utilisation of a VCSEL (vertical-cavity surface-emitting laser) array to increase the measurement spot density. An FPGA (field-programmable gate array) is used to generate modulation signals for individual illumination of each measurement spot of the LAPS. The new set-up can operate a large number of measurement spots simultaneously by reading out the sum photocurrent and separate the signals of the individual measurement spots by an FFT analysis. The frequency, amplitude and offset of the modulation signal can be configured for each measurement spot by software. The new system can be combined with a positioning stage allowing the parallel read out of a single line of measurement spots and a scan perpendicular to that line in a similar manner, like for an optical scanner set-up. First measurements demonstrate the functionality of the new LAPS set-up as a chemical imaging system.