Light modulates the melanophore response to alpha-MSH in Xenopus laevis: An analysis of the signal transduction crosstalk mechanisms involved

Melanin granule (melanosome) dispersion within Xenopus laevis melanophores is evoked either by light or alpha-MSH. We have previously demonstrated that the initial biochemical steps of light and alpha-MSH signaling are distinct, since the increase in cAMP observed in response to alpha-MSH was not seen after light exposure. cAMP concentrations in response to alpha-MSH were significantly lower in cells pre-exposed to light as compared to the levels in dark-adapted melanophores. Here we demonstrate the presence of an adenylyl cyclase (AC) in the Xenopus melanophore, similar to the mammalian type IX which is inhibited by Ca(2+)-calmodulin-activated phosphatase. This finding supports the hypothesis that the cyclase could be negatively modulated by a light-promoted Ca(2+) increase. In fact, the activity of calcineurin PP2B phosphatase was increased by light, which could result in AC IX inhibition, thus decreasing the response to alpha-MSH. St-Ht31, a disrupting agent of protein kinase A (PKA)-anchoring kinase A protein (AKAP) complex totally blocked the melanosome dispersing response to alpha-MSH, but did not impair the photo-response in Xenopus melanophores. Sequence comparison of a melanophore AKAP partial clone with GenBank sequences showed that the anchoring protein was a gravin-like adaptor previously sequenced from Xenopus non-pigmentary tissues. Co-immunoprecipitation of Xenopus AKAP and the catalytic subunit of PKA demonstrated that PKA is associated with AKAP and it is released in the presence of alpha-MSH. We conclude that in X laevis melanophores, AKAP12 (gravin-like) contains a site for binding the inactive PKA thus compartmentalizing PKA signaling and also possesses binding sites for PKC. Light diminishes alpha-MSH-induced increase of cAMP by increasing calcineurin (PP2B) activity, which in turn inhibits adenylyl cyclase type IX, and/or by activating PKC, which phosphorylates the gravin-like molecule, thus destabilizing its binding to the cell membrane. (C) 2009 Elsevier Inc. All rights reserved.

The serine and threonine residues in the Ig-α cytoplasmic tail negatively regulate immunoreceptor tyrosine-based activation motif-mediated signal transduction

The B cell antigen receptor (BCR) is a multiprotein complex consisting of the membrane-bound Ig molecule and the Ig-α/Ig-β heterodimer. On BCR engagement, Ig-α and Ig-β become phosphorylated not only on tyrosine residues of the immunoreceptor tyrosine-based activation motif but also on serine and threonine residues. We have mutated all serine and threonine residues in the Ig-α tail to alanine and valine, respectively. The mutated Ig-α sequence was expressed either as a single-chain Fv/Ig-α molecule or in the context of the complete BCR. In both cases, the mutated Ig-α showed a stronger tyrosine phosphorylation than the wild-type Ig-α and initiated increased signaling on stimulation. These findings suggest that serine/threonine kinases can negatively regulate signal transduction from the BCR.

Analysis of the signal transduction in the hawk moth Manduca sexta

Fliegende Insekten orientieren sich in ihrer Umwelt mit Hilfe ihres hoch entwickelten olfaktorischen Systems. Es ermöglicht ihnen das Auffinden geeigneter Futter- und Eiablageplätze und ist unverzichtbar bei der innerartlichen Kommunikation. Der Geruchssinn muss dabei gleichzeitig sehr schnell und sensitiv sein um selbst geringste Mengen, z.B. des arteigenen Sexualpheromons, wahrnehmen zu können. Spezifische olfaktorische Rezeptoren (ORs) zur Detektion dieser Duftstoffe werden zusammen mit einem hoch konservierten Co-Rezeptor (Orco) in olfaktorischen Rezeptorneuronen (ORNs) auf den Insektenantennen exprimiert. Sie gehören zu den 7 Transmembran Rezeptoren, zeigen jedoch eine invertierte Membrantopologie im Vergleich zu den ORs der Vertebraten. Darüber hinaus bildet der OR/Orco-Komplex einen spontanaktiven Kationenkanal, die Bindung an ein G Protein ist allerdings umstritten. Daher ist noch ungeklärt, ob die Duftstoffbindung zu einer ionotropen Aktivierung des OR/Orco Kanals führt oder ob metabotrope Mechanismen die Bildung von zyklischem Adenosinmonophosphat (cAMP) oder Inositol 1,4,5-trisphosphat (IP3) bewirken. Mit Hilfe von extrazellulären Ableitungen einzelner Trichoidsensillen (tip recordings) auf den Antennen männlicher Manduca sexta wurde die Rolle von Orco sowie die Beteiligung einer Phospholipase Cβ (PLCβ)-abhängigen Transduktionskaskade untersucht. Es konnte gezeigt werden, dass die durch VUAA1 induzierte Spontanaktivität der ORNs durch OLC15 inhibiert und Orco somit kompetitiv gehemmt wurde. Eine Inhibition von Orco sollte die Antwort auf kurze Pheromonpulse sofort reduzieren, sollte die Transduktion über die Aktivierung des OR/Orco Kanals erfolgen. Die Ergebnisse dieser Arbeit zeigten jedoch keine Beeinflussung der primären Pheromonantwort, vielmehr wurde die späte, langanhaltende Antwort reduziert. Die ebenfalls als Orco-Antagonisten charakterisierten Amiloride MIA und HMA beeinflussen offensichtlich weitere Ziele, da eine substanz- und zeitgeberzeitabhängige Reduzierung der primären Antwort auftrat. Zusätzlich wurde die primäre Pheromonantwort durch die Inhibierung der PLCβ und der Proteinkinase C (PKC), sowie durch die Verwendung zweier Diacylglycerol (DAG)- Derivate signifikant beeinflusst. Hierbei zeigte die Inhibierung von PLCβ und PKC zeitgeberzeitabhängige Unterschiede in der Stärke der Antwortreduktion. Auch die Applikation des DAG-Derivates DOG reduzierte die Pheromonantwort, während die Zugabe von OAG die ORN Aktivität steigern oder reduzieren konnte, abhängig von der verwendeten Derivatkonzentration und der Pheromonkonzentration. Die Ergebnisse dieser Arbeit deuten somit auf einen metabotropen, sehr wahrscheinlich PLCβ-abhängigen Mechanismus für die Pheromontransduktion bei Manduca sexta.

Damage assessment of structures with uncertainty by using mode-shape curvatures and fuzzy logic

Uncertainties associated with the structural model and measured vibration data may lead to unreliable damage detection. In this paper, we show that geometric and measurement uncertainty cause considerable problem in damage assessment which can be alleviated by using a fuzzy logic-based approach for damage detection. Curvature damage factor (CDF) of a tapered cantilever beam are used as damage indicators. Monte Carlo simulation (MCS) is used to study the changes in the damage indicator due to uncertainty in the geometric properties of the beam. Variation in these CDF measures due to randomness in structural parameter, further contaminated with measurement noise, are used for developing and testing a fuzzy logic system (FLS). Results show that the method correctly identifies both single and multiple damages in the structure. For example, the FLS detects damage with an average accuracy of about 95 percent in a beam having geometric uncertainty of 1 percent COV and measurement noise of 10 percent in single damage scenario. For multiple damage case, the FLS identifies damages in the beam with an average accuracy of about 94 percent in the presence of above mentioned uncertainties. The paper brings together the disparate areas of probabilistic analysis and fuzzy logic to address uncertainty in structural damage detection.

Damage assessment in multiple-girder composite bridge using vibration characteristics

This paper uses dynamic computer simulation techniques to apply a procedure using vibration-based methods for damage assessment in multiple-girder composite bridge. In addition to changes in natural frequencies, this multi-criteria procedure incorporates two methods, namely the modal flexibility and the modal strain energy method. Using the numerically simulated modal data obtained through finite element analysis software, algorithms based on modal flexibility and modal strain energy change before and after damage are obtained and used as the indices for the assessment of structural health state. The feasibility and capability of the approach is demonstrated through numerical studies of proposed structure with six damage scenarios. It is concluded that the modal strain energy method is competent for application on multiple-girder composite bridge, as evidenced through the example treated in this paper.

Damage assessment in structures using vibration characteristics

Changes in load characteristics, deterioration with age, environmental influences and random actions may cause local or global damage in structures, especially in bridges, which are designed for long life spans. Continuous health monitoring of structures will enable the early identification of distress and allow appropriate retrofitting in order to avoid failure or collapse of the structures. In recent times, structural health monitoring (SHM) has attracted much attention in both research and development. Local and global methods of damage assessment using the monitored information are an integral part of SHM techniques. In the local case, the assessment of the state of a structure is done either by direct visual inspection or using experimental techniques such as acoustic emission, ultrasonic, magnetic particle inspection, radiography and eddy current. A characteristic of all these techniques is that their application requires a prior localization of the damaged zones. The limitations of the local methodologies can be overcome by using vibration-based methods, which give a global damage assessment. The vibration-based damage detection methods use measured changes in dynamic characteristics to evaluate changes in physical properties that may indicate structural damage or degradation. The basic idea is that modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Changes in the physical properties will therefore cause changes in the modal properties. Any reduction in structural stiffness and increase in damping in the structure may indicate structural damage. This research uses the variations in vibration parameters to develop a multi-criteria method for damage assessment. It incorporates the changes in natural frequencies, modal flexibility and modal strain energy to locate damage in the main load bearing elements in bridge structures such as beams, slabs and trusses and simple bridges involving these elements. Dynamic computer simulation techniques are used to develop and apply the multi-criteria procedure under different damage scenarios. The effectiveness of the procedure is demonstrated through numerical examples. Results show that the proposed method incorporating modal flexibility and modal strain energy changes is competent in damage assessment in the structures treated herein.

Analysis of acoustic emission data for accurate damage assessment for structural health monitoring applications

Structural health monitoring (SHM) refers to the procedure used to assess the condition of structures so that their performance can be monitored and any damage can be detected early. Early detection of damage and appropriate retrofitting will aid in preventing failure of the structure and save money spent on maintenance or replacement and ensure the structure operates safely and efficiently during its whole intended life. Though visual inspection and other techniques such as vibration based ones are available for SHM of structures such as bridges, the use of acoustic emission (AE) technique is an attractive option and is increasing in use. AE waves are high frequency stress waves generated by rapid release of energy from localised sources within a material, such as crack initiation and growth. AE technique involves recording these waves by means of sensors attached on the surface and then analysing the signals to extract information about the nature of the source. High sensitivity to crack growth, ability to locate source, passive nature (no need to supply energy from outside, but energy from damage source itself is utilised) and possibility to perform real time monitoring (detecting crack as it occurs or grows) are some of the attractive features of AE technique. In spite of these advantages, challenges still exist in using AE technique for monitoring applications, especially in the area of analysis of recorded AE data, as large volumes of data are usually generated during monitoring. The need for effective data analysis can be linked with three main aims of monitoring: (a) accurately locating the source of damage; (b) identifying and discriminating signals from different sources of acoustic emission and (c) quantifying the level of damage of AE source for severity assessment. In AE technique, the location of the emission source is usually calculated using the times of arrival and velocities of the AE signals recorded by a number of sensors. But complications arise as AE waves can travel in a structure in a number of different modes that have different velocities and frequencies. Hence, to accurately locate a source it is necessary to identify the modes recorded by the sensors. This study has proposed and tested the use of time-frequency analysis tools such as short time Fourier transform to identify the modes and the use of the velocities of these modes to achieve very accurate results. Further, this study has explored the possibility of reducing the number of sensors needed for data capture by using the velocities of modes captured by a single sensor for source localization. A major problem in practical use of AE technique is the presence of sources of AE other than crack related, such as rubbing and impacts between different components of a structure. These spurious AE signals often mask the signals from the crack activity; hence discrimination of signals to identify the sources is very important. This work developed a model that uses different signal processing tools such as cross-correlation, magnitude squared coherence and energy distribution in different frequency bands as well as modal analysis (comparing amplitudes of identified modes) for accurately differentiating signals from different simulated AE sources. Quantification tools to assess the severity of the damage sources are highly desirable in practical applications. Though different damage quantification methods have been proposed in AE technique, not all have achieved universal approval or have been approved as suitable for all situations. The b-value analysis, which involves the study of distribution of amplitudes of AE signals, and its modified form (known as improved b-value analysis), was investigated for suitability for damage quantification purposes in ductile materials such as steel. This was found to give encouraging results for analysis of data from laboratory, thereby extending the possibility of its use for real life structures. By addressing these primary issues, it is believed that this thesis has helped improve the effectiveness of AE technique for structural health monitoring of civil infrastructures such as bridges.

Evaluation of strain energy based damage indices for flexural crack detection of RC beams

Vibration Based Damage Identification Techniques which use modal data or their functions, have received significant research interest in recent years due to their ability to detect damage in structures and hence contribute towards the safety of the structures. In this context, Strain Energy Based Damage Indices (SEDIs), based on modal strain energy, have been successful in localising damage in structuers made of homogeneous materials such as steel. However, their application to reinforced concrete (RC) structures needs further investigation due to the significant difference in the prominent damage type, the flexural crack. The work reported in this paper is an integral part of a comprehensive research program to develop and apply effective strain energy based damage indices to assess damage in reinforced concrete flexural members. This research program established (i) a suitable flexural crack simulation technique, (ii) four improved SEDI's and (iii) programmable sequentional steps to minimise effects of noise. This paper evaluates and ranks the four newly developed SEDIs and existing seven SEDIs for their ability to detect and localise flexural cracks in RC beams. Based on the results of the evaluations, it recommends the SEDIs for use with single and multiple vibration modes.

Damage assessment in reinforced concrete flexural members using modal strain energy based method

Damage assessment (damage detection, localization and quantification) in structures and appropriate retrofitting will enable the safe and efficient function of the structures. In this context, many Vibration Based Damage Identification Techniques (VBDIT) have emerged with potential for accurate damage assessment. VBDITs have achieved significant research interest in recent years, mainly due to their non-destructive nature and ability to assess inaccessible and invisible damage locations. Damage Index (DI) methods are also vibration based, but they are not based on the structural model. DI methods are fast and inexpensive compared to the model-based methods and have the ability to automate the damage detection process. DI method analyses the change in vibration response of the structure between two states so that the damage can be identified. Extensive research has been carried out to apply the DI method to assess damage in steel structures. Comparatively, there has been very little research interest in the use of DI methods to assess damage in Reinforced Concrete (RC) structures due to the complexity of simulating the predominant damage type, the flexural crack. Flexural cracks in RC beams distribute non- linearly and propagate along all directions. Secondary cracks extend more rapidly along the longitudinal and transverse directions of a RC structure than propagation of existing cracks in the depth direction due to stress distribution caused by the tensile reinforcement. Simplified damage simulation techniques (such as reductions in the modulus or section depth or use of rotational spring elements) that have been extensively used with research on steel structures, cannot be applied to simulate flexural cracks in RC elements. This highlights a big gap in knowledge and as a consequence VBDITs have not been successfully applied to damage assessment in RC structures. This research will address the above gap in knowledge and will develop and apply a modal strain energy based DI method to assess damage in RC flexural members. Firstly, this research evaluated different damage simulation techniques and recommended an appropriate technique to simulate the post cracking behaviour of RC structures. The ABAQUS finite element package was used throughout the study with properly validated material models. The damaged plasticity model was recommended as the method which can correctly simulate the post cracking behaviour of RC structures and was used in the rest of this study. Four different forms of Modal Strain Energy based Damage Indices (MSEDIs) were proposed to improve the damage assessment capability by minimising the numbers and intensities of false alarms. The developed MSEDIs were then used to automate the damage detection process by incorporating programmable algorithms. The developed algorithms have the ability to identify common issues associated with the vibration properties such as mode shifting and phase change. To minimise the effect of noise on the DI calculation process, this research proposed a sequential order of curve fitting technique. Finally, a statistical based damage assessment scheme was proposed to enhance the reliability of the damage assessment results. The proposed techniques were applied to locate damage in RC beams and slabs on girder bridge model to demonstrate their accuracy and efficiency. The outcomes of this research will make a significant contribution to the technical knowledge of VBDIT and will enhance the accuracy of damage assessment in RC structures. The application of the research findings to RC flexural members will enable their safe and efficient performance.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

Improved damage detectability in a wind turbine blade by optimal selection of vibration signal correlation coefficients

Piotr Omenzetter and Simon Hoell’s work within the Lloyd’s Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by Lloyd’s Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research.

Improved damage detectability in a wind turbine blade by optimal selection of vibration signal correlation coefficients

Piotr Omenzetter and Simon Hoell’s work within the Lloyd’s Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by Lloyd’s Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research.

Infrastructure Sustainability : Damage Assessment in Multiple-Girder Composite Bridges Using Vibration Characteristics

Assessing the structural health state of urban infrastructure is crucial in terms of infrastructure sustainability. This chapter uses dynamic computer simulation techniques to apply a procedure using vibration-based methods for damage assessment in multiple-girder composite bridges. In addition to changes in natural frequencies, this multi-criteria procedure incorporates two methods, namely, the modal flexibility and the modal strain energy method. Using the numerically simulated modal data obtained through finite element analysis software, algorithms based on modal flexibility and modal strain energy change, before and after damage, are obtained and used as the indices for the assessment of structural health state. The feasibility and capability of the approach is demonstrated through numerical studies of a proposed structure with six damage scenarios. It is concluded that the modal strain energy method is capable of application to multiple-girder composite bridges, as evidenced through the example treated in this chapter.

Structural damage identification with noise polluted Frequency Response Functions (FRFs)

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage with noise polluted data which is unavoidable effect in real world. The measurement data are contaminated by noise because of test environment as well as electronic devices and this noise tend to give error results with structural damage identification methods. Therefore it is important to investigate a method which can perform better with noise polluted data. This paper introduces a new damage index using principal component analysis (PCA) for damage detection of building structures being able to accept noise polluted frequency response functions (FRFs) as input. The FRF data are obtained from the function datagen of MATLAB program which is available on the web site of the IASC-ASCE (International Association for Structural Control– American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group. The proposed method involves a five-stage process: calculation of FRFs, calculation of damage index values using proposed algorithm, development of the artificial neural networks and introducing damage indices as input parameters and damage detection of the structure. This paper briefly describes the methodology and the results obtained in detecting damage in all six cases of the benchmark study with different noise levels. The proposed method is applied to a benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health Monitoring, which was developed in order to facilitate the comparison of various damage identification methods. The illustrated results show that the PCA-based algorithm is effective for structural health monitoring with noise polluted FRFs which is of common occurrence when dealing with industrial structures.

An improved method to detect damage using modal strain energy based damage index

This paper presents two novel concepts to enhance the accuracy of damage detection using the Modal Strain Energy based Damage Index (MSEDI) with the presence of noise in the mode shape data. Firstly, the paper presents a sequential curve fitting technique that reduces the effect of noise on the calculation process of the MSEDI, more effectively than the two commonly used curve fitting techniques; namely, polynomial and Fourier’s series. Secondly, a probability based Generalized Damage Localization Index (GDLI) is proposed as a viable improvement to the damage detection process. The study uses a validated ABAQUS finite-element model of a reinforced concrete beam to obtain mode shape data in the undamaged and damaged states. Noise is simulated by adding three levels of random noise (1%, 3%, and 5%) to the mode shape data. Results show that damage detection is enhanced with increased number of modes and samples used with the GDLI.