Computational Modeling and Experimental Characterization of Martensitic Transformations in Nicoal for Self-Sensing Materials

Fundamental changes to aero-vehicle management require the utilization of automated health monitoring of vehicle structural components. A novel method is the use of self-sensing materials, which contain embedded sensory particles (SP). SPs are micron-sized pieces of shape-memory alloy that undergo transformation when the local strain reaches a prescribed threshold. The transformation is a result of a spontaneous rearrangement of the atoms in the crystal lattice under intensified stress near damaged locations, generating acoustic waves of a specific spectrum that can be detected by a suitably placed sensor. The sensitivity of the method depends on the strength of the emitted signal and its propagation through the material. To study the transition behavior of the sensory particle inside a metal matrix under load, a simulation approach based on a coupled atomistic-continuum model is used. The simulation results indicate a strong dependence of the particle's pseudoelastic response on its crystallographic orientation with respect to the loading direction and suggest possible ways of optimizing particle sensitivity. The technology of embedded sensory particles will serve as the key element in an autonomous structural health monitoring system that will constantly monitor for damage initiation in service, which will enable quick detection of unforeseen damage initiation in real-time and during onground inspections

Pathophysiology of acute experimental pancreatitis: Lessons from genetically engineered animal models and new molecular approaches

The incidence of acute pancreatitis is growing and worldwide population-based studies report a doubling or tripling since the 1970s. 25% of acute pancreatitis are severe and associated with histological changes of necrotizing pancreatitis. There is still no specific medical treatment for acute pancreatitis. The average mortality resides around 10%. In order to develop new specific medical treatment strategies for acute pancreatitis, a better understanding of the pathophysiology during the onset of acute pancreatitis is necessary. Since it is difficult to study the early acinar events in human pancreatitis, several animal models of acute pancreatitis have been developed. By this, it is hoped that clues into human pathophysiology become possible. In the last decade, while employing molecular biology techniques, a major progress has been made. The genome of the mouse was recently sequenced. Various strategies are possible to prove a causal effect of a single gene or protein, using either gain-of-function (i.e., overexpression of the protein of interest) or loss-of-function studies (i.e., genetic deletion of the gene of interest). The availability of transgenic mouse models and gene deletion studies has clearly increased our knowledge about the pathophysiology of acute pancreatitis and enables us to study and confirm in vitro findings in animal models. In addition, transgenic models with specific genetic deletion or overexpression of genes help in understanding the role of one specific protein in a cascade of inflammatory processes such as pancreatitis where different proteins interact and co-react. This review summarizes the recent progress in this field. Copyright (c) 2005 S. Karger AG, Basel

Variational Methods for Biomolecular Modeling

Structure, function and dynamics of many biomolecular systems can be characterized by the energetic variational principle and the corresponding systems of partial differential equations (PDEs). This principle allows us to focus on the identification of essential energetic components, the optimal parametrization of energies, and the efficient computational implementation of energy variation or minimization. Given the fact that complex biomolecular systems are structurally non-uniform and their interactions occur through contact interfaces, their free energies are associated with various interfaces as well, such as solute-solvent interface, molecular binding interface, lipid domain interface, and membrane surfaces. This fact motivates the inclusion of interface geometry, particular its curvatures, to the parametrization of free energies. Applications of such interface geometry based energetic variational principles are illustrated through three concrete topics: the multiscale modeling of biomolecular electrostatics and solvation that includes the curvature energy of the molecular surface, the formation of microdomains on lipid membrane due to the geometric and molecular mechanics at the lipid interface, and the mean curvature driven protein localization on membrane surfaces. By further implicitly representing the interface using a phase field function over the entire domain, one can simulate the dynamics of the interface and the corresponding energy variation by evolving the phase field function, achieving significant reduction of the number of degrees of freedom and computational complexity. Strategies for improving the efficiency of computational implementations and for extending applications to coarse-graining or multiscale molecular simulations are outlined.Comment: 36 page

Proceedings of the 2nd Workshop on Flavor Symmetries and Consequences in Accelerators and Cosmology (FLASY12)

These are the proceedings of the 2nd Workshop on Flavor Symmetries and Consequences in Accelerators and Cosmology, held 30 June 2012 - 4 July 2012, Dortmund, Germany.Comment: Order 400 pages, several figures including the group picture v2: corrected author list and contributio

Detection of variable VHE gamma-ray emission from the extra-galactic gamma-ray binary LMC P3

Context. Recently, the high-energy (HE, 0.1-100 GeV) $\gamma$-ray emission from the object LMC P3 in the Large Magellanic Cloud (LMC) has been discovered to be modulated with a 10.3-day period, making it the first extra-galactic $\gamma$-ray binary. Aims. This work aims at the detection of very-high-energy (VHE, >100 GeV) $\gamma$-ray emission and the search for modulation of the VHE signal with the orbital period of the binary system. Methods. LMC P3 has been observed with the High Energy Stereoscopic System (H.E.S.S.); the acceptance-corrected exposure time is 100 h. The data set has been folded with the known orbital period of the system in order to test for variability of the emission. Energy spectra are obtained for the orbit-averaged data set, and for the orbital phase bin around the VHE maximum. Results. VHE $\gamma$-ray emission is detected with a statistical significance of 6.4 $\sigma$. The data clearly show variability which is phase-locked to the orbital period of the system. Periodicity cannot be deduced from the H.E.S.S. data set alone. The orbit-averaged luminosity in the $1-10$ TeV energy range is $(1.4 \pm 0.2) \times 10^{35}$ erg/s. A luminosity of $(5 \pm 1) \times 10^{35}$ erg/s is reached during 20% of the orbit. HE and VHE $\gamma$-ray emissions are anti-correlated. LMC P3 is the most luminous $\gamma$-ray binary known so far.Comment: 5 pages, 3 figures, 1 table, accepted for publication in A&

Characterizing the gamma-ray long-term variability of PKS 2155-304 with H.E.S.S. and Fermi-LAT

Studying the temporal variability of BL Lac objects at the highest energies provides unique insights into the extreme physical processes occurring in relativistic jets and in the vicinity of super-massive black holes. To this end, the long-term variability of the BL Lac object PKS 2155-304 is analyzed in the high (HE, 100 MeV 200 GeV) gamma-ray domain. Over the course of ~9 yr of H.E.S.S observations the VHE light curve in the quiescent state is consistent with a log-normal behavior. The VHE variability in this state is well described by flicker noise (power-spectral-density index {\ss}_VHE = 1.10 +0.10 -0.13) on time scales larger than one day. An analysis of 5.5 yr of HE Fermi LAT data gives consistent results ({\ss}_HE = 1.20 +0.21 -0.23, on time scales larger than 10 days) compatible with the VHE findings. The HE and VHE power spectral densities show a scale invariance across the probed time ranges. A direct linear correlation between the VHE and HE fluxes could neither be excluded nor firmly established. These long-term-variability properties are discussed and compared to the red noise behavior ({\ss} ~ 2) seen on shorter time scales during VHE-flaring states. The difference in power spectral noise behavior at VHE energies during quiescent and flaring states provides evidence that these states are influenced by different physical processes, while the compatibility of the HE and VHE long-term results is suggestive of a common physical link as it might be introduced by an underlying jet-disk connection.Comment: 11 pages, 16 figure

A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature

The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein–protein interactions (PPIs) reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study shows that three kernels are clearly superior to the other methods