Vascular proteomics

The characterization of patients with acute coronary syndromes (ACS) at the molecular and cellular levels provides a novel vision for understanding the pathological and clinical expression of the disease. Recent advances in proteomic technologies permit the evaluation of systematic changes in protein expression in many biological systems and have been extensively applied to cardiovascular diseases (CVD). The cardiovascular system is in permanent intimate contact with blood, making blood-based biomarker discovery a particularly worthwhile approach. Thus, proteomics can potentially yield novel biomarkers reflecting CVD, establish earlier detection strategies, and monitor response to therapy. Here we review the different proteomic strategies used in the study of atherosclerosis and the novel proteins differentially expressed and secreted by atherosclerotic lesions which constitute novel potential biomarkers (HSP-27, Cathepsin D). Special attention is paid to MS-Imaging of atheroma plaque and the generation, for the first time, of 2-D images of lipids, showing the distribution of these molecules in the different areas of the atherosclerotic lesions. In addition new potential biomarkers have been identified in plasma (amyloid A1α, transtherytin), circulating cells (protein profile in monocytes from ACS patients) and individual cells constituents of atheroma plaques (endothelial, VSMC, macrophages) which provide novel insights into vascular pathophysiology.     

The structure of subword graphs and suffix trees of Fibonacci words

We use automata-theoretic approach to analyze properties of Fibonacci words. The directed acyclic subword graph (dawg) is a useful deterministic automaton accepting all suffixes of the word. We show that dawg's of Fibonacci words have particularly simple structure. Our main result is a unifying framework for a large collection of relatively simple properties of Fibonacci words. The simple structure of dawgs of Fibonacci words gives in many cases simplified alternative proofs and new interpretation of several well-known properties of Fibonacci words. In particular, the structure of lengths of paths corresponds to a number-theoretic characterization of occurrences of any subword. Using the structural properties of dawg's it can be easily shown that for a string w$w$ we can check if w$w$ is a subword of a Fibonacci word in time O(|w|)$\mathrm{O}(|w|)$ and O(1)$\mathrm{O}(1)$ space. Compact dawg's of Fibonacci words show a very regular structure of their suffix trees and show how the suffix tree for the Fibonacci word grows (extending the leaves in a very simple way) into the suffix tree for the next Fibonacci word.     

Structural and spectroscopic characteristics of synthetic hydrohaematite

In order to investigate the identity of water-containing haematite, a method of preparation has been elaborated which leads to samples of hydrohaematite which are structurally pure and free of amorphous iron hydroxide. Differential thermal analysis by a lack of endothermic effect at 423 to 473 K on the DTA-curves and by a steady fall of the TG curves up to 1150 K revealed that water in the preparations must be tightly held in the lattice of haematite. Measurements of intensities of X-ray reflections of the (1 0 4) and (0 2 4) planes in relation to the (1 1 3) plane confirmed Fe³⁺-deficiency in the haematite cationic sublattice brought about by the presence of OH^– ions in the anionic sublattice. The infrared spectrum of hydrohaematite, in addition to six bands from Fe-O variations, exhibits three bands from hydroxyl groups. The effect of silicates on the pattern of the infrared spectra of natural and synthetic hydrohaematites and the discrepancies in the infrared spectra of haematites published to date are discussed.     

Evaluation of Crop Models for Simulating and Optimizing Deficit Irrigation Systems in Arid and Semi-arid Countries Under Climate Variability

The variability of fresh water availability in arid and semi-arid countries poses a serious challenge to farmers to cope with when depending on irrigation for crop growing. This has shifted the focus onto improving irrigation management and water productivity (WP) through controlled deficit irrigation (DI). DI can be conceived as a strategy to deal with these challenges but more knowledge on risks and chances of this strategy is urgently needed. The availability of simulation models that can reliably predict crop yield under the influence of soil, atmosphere, irrigation, and agricultural management practices is a prerequisite for deriving reliable and effective deficit irrigation strategies. In this context, this article discusses the performance of the crop models CropWat, PILOTE, Daisy, and APSIM when being part of a stochastic simulation-based approach to improve WP by focusing primarily on the impact of climate variability. The stochastic framework consists of: (i) a weather generator for simulating regional impacts of climate variability; (ii) a tailor-made evolutionary optimization algorithm for optimal irrigation scheduling with limited water supply; and (iii) the above mentioned models for simulating water transport and crop growth in a sound manner. The results present stochastic crop water production functions (SCWPFs) that can be used as basic tools for assessing the impact on the risk for the potential yield due to water stress and climate variability. Example simulations from India, Malawi, France and Oman are presented and the suitability of these crop models to be employed in a framework for optimizing WP is evaluated.     

Improving metastable impact electron spectroscopy and ultraviolet photoelectron spectroscopy signals by means of a modified time-of-flight separation

The separation of ultraviolet photoelectron spectroscopy (UPS) and metastable impact electron spectroscopy (MIES) is usually performed by a time-of-flight (ToF) separation using pre-set ToF for both types of signal. In this work, we present a new, improved ex situ signal separation method for the separation of MIES and UPS for every single measurement. Signal separation issues due to changes of system parameters can be overcome by changing the ToF separation and therefore allowing for the application of a wider range of measuring conditions. The method also enables to identify and achieve separation of the two signals without any time consuming calibration and the use of any special material for the calibration. Furthermore, changes made to the discharge source are described that enable to operate an existing MIES/UPS source over a broader range of conditions. This allows for tuning of the yield of UV photons and metastable rare gas atoms leading to an improved signal to noise ratio. First results of this improved setup are well in agreement with spectra reported in literature and show increased resolution and higher signal intensities for both MIE and UP spectra compared to the previous, non-optimized setup.     

A symbolic summation approach to Feynman integral calculus

Given a Feynman parameter integral, depending on a single discrete variable N$N$ and a real parameter ε$\epsilon$, we discuss a new algorithmic framework to compute the first coefficients of its Laurent series expansion in ε$\epsilon$. In a first step, the integrals are expressed by hypergeometric multi-sums by means of symbolic transformations. Given this sum format, we develop new summation tools to extract the first coefficients of its series expansion whenever they are expressible in terms of indefinite nested product–sum expressions. In particular, we enhance the known multi-sum algorithms to derive recurrences for sums with complicated boundary conditions, and we present new algorithms to find formal Laurent series solutions of a given recurrence relation.     

Autonomous landing at unprepared sites by a full-scale helicopter

Helicopters are valuable since they can land at unprepared sites; however, current unmanned helicopters are unable to select or validate landing zones (LZs) and approach paths. For operation in unknown terrain it is necessary to assess the safety of a LZ. In this paper, we describe a lidar-based perception system that enables a full-scale autonomous helicopter to identify and land in previously unmapped terrain with no human input.We describe the problem, real-time algorithms, perception hardware, and results. Our approach has extended the state of the art in terrain assessment by incorporating not only plane fitting, but by also considering factors such as terrain/skid interaction, rotor and tail clearance, wind direction, clear approach/abort paths, and ground paths.In results from urban and natural environments we were able to successfully classify LZs from point cloud maps. We also present results from 8 successful landing experiments with varying ground clutter and approach directions. The helicopter selected its own landing site, approaches, and then proceeds to land. To our knowledge, these experiments were the first demonstration of a full-scale autonomous helicopter that selected its own landing zones and landed.     

Maximum margin Bayesian network classifiers

We present a maximum margin parameter learning algorithm for Bayesian network classifiers using a conjugate gradient (CG) method for optimization. In contrast to previous approaches, we maintain the normalization constraints on the parameters of the Bayesian network during optimization, i.e., the probabilistic interpretation of the model is not lost. This enables us to handle missing features in discriminatively optimized Bayesian networks. In experiments, we compare the classification performance of maximum margin parameter learning to conditional likelihood and maximum likelihood learning approaches. Discriminative parameter learning significantly outperforms generative maximum likelihood estimation for naive Bayes and tree augmented naive Bayes structures on all considered data sets. Furthermore, maximizing the margin dominates the conditional likelihood approach in terms of classification performance in most cases. We provide results for a recently proposed maximum margin optimization approach based on convex relaxation. While the classification results are highly similar, our CG-based optimization is computationally up to orders of magnitude faster. Margin-optimized Bayesian network classifiers achieve classification performance comparable to support vector machines (SVMs) using fewer parameters. Moreover, we show that unanticipated missing feature values during classification can be easily processed by discriminatively optimized Bayesian network classifiers, a case where discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.     

Progressive Virtual Beam Lights

A recent technique that forms virtual ray lights (VRLs) from path segments in media, reduces the artifacts common to VPL approaches in participating media, however, distracting singularities still remain. We present Virtual Beam Lights (VBLs), a progressive many‐lights algorithm for rendering complex indirect transport paths in, from, and to media. VBLs are efficient and can handle heterogeneous media, anisotropic scattering, and moderately glossy surfaces, while provably converging to ground truth. We inflate ray lights into beam lights with finite thicknesses to eliminate the remaining singularities. Furthermore, we devise several practical schemes for importance sampling the various transport contributions between camera rays, light rays, and surface points. VBLs produce artifact‐free images faster than VRLs, especially when glossy surfaces and/or anisotropic phase functions are present. Lastly, we employ a progressive thickness reduction scheme for VBLs in order to render results that converge to ground truth.     

Grid-Controlled Lightpaths for High Performance Grid Applications

Grid applications call for high performance networking support. One attractive solution is to deploy Grids over optical networks. However, resource management in optical domains is traditionally very rigid and cannot successfully meet the requirements of Grid applications, such as flexible provisioning and configuration. In this paper, we present a customizable resource management solution for optical networks where users can create lightpaths on demand and manage their own network resources. Thanks to a Grid-centric system architecture, lightpath resources can be shared among users and easily integrated with data and computation Grids.