bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mitochondria Fusion and Fission
Authors: Benard Giovanni , Peng Guihong , Karbowski Mariusz 
Published: September 2009

Abstract

Mitochondrial structural dynamics is regulated by the fusion or fission of these organelles. Recently published evidence indicates the vital role of mitochondrial fusion and fission in cellular physiology, including progression of apoptosis. These reports indicate that in addition to intimate link between mitochondrial morphogenesis machineries and regulation of mitochondrial steps in apoptosis, certain proteins vital for the regulation of mitochondrial steps in apoptosis can also regulate mitochondrial fusion and fission in healthy cells. In this article, we focus on the regulation of mitochondrial network dynamics. The emerging evidence indicating that proteins implicated in mitochondrial network dynamics are vital for the mitochondrial steps in apoptosis is presented here, as well. Furthermore, the data demonstrating an unexpected role for the B‐cell lymphoma (Bcl)‐2 family members in the regulation of mitochondrial morphogenesis are also discussed.

Key concepts:

In healthy cells, mitochondrial cycle between several shapes, their morphology result from the equilibrium between mitochondrial fusion and fission.

The unique feature of mitochondrial fusion is the necessity of merging double membrane systems from the two fusing mitochondria. This process is mediated by the outer mitochondrial membrane‐associated mitofusin proteins (Mfn1 and Mfn2), and the inner mitochondrial membrane‐associated Opa1.

Regulation of mitochondrial fusion and fission has a significant impact on cell viability and early development.

The mitochondrial fragmentation occurs concomitantly with the outer mitochondrial membrane (OMM) permeabilization, a critical step in apoptosis.

The cooperation between proteins involved in mitochondrial fusion and fission and Bcl‐2 family proteins during apoptosis suggests that changes in mitochondrial network dynamics contribute to apoptotic signalling.

The mechanistic link between the core mitochondrial fusion and fission regulating proteins (e.g. Drp1, Mfn2 and Opa1) and proteins from Bcl‐2 family, suggest that Bcl‐2 family proteins also regulate mitochondrial dynamics in healthy cells.
Phylogenetic Relationships Deduced from Whole Genome Comparisons
Authors: Boussau Bastien 
Published: September 2009

Abstract

In the second half of the twentieth century, gene sequences have been used to study their evolution as well as the history of species. A decade ago, progress in sequencing techniques provided the opportunity to use whole genome sequences for reconstructing species phylogeny. Several phylogenomic methods have been used to exploit these large amounts of data, and those that are based on homologous or orthologous characters have been favoured. Among these, the supertree and supermatrix approaches have been very successful at resolving parts of the tree of life. However, these methods discard a great proportion of the evolutionary information present in genomes. New integrative models of genome evolution could make a better use of whole genome sequences and thus improve the resolution of the tree of life.

Key concepts

Genomes contain a very large amount of information relevant for reconstructing their history and the history of species.

Among phylogenomic methods, only those based on homologous/orthologous characters can reconstruct a species tree.

Rare genomic changes, supertree and supermatrice methods have been the most extensively used.

Genome‐sized datasets have permitted confirming key phylogenies, and have also provided new insights into important parts of the tree of life.

New methods that combine different types of genomic information could further improve the resolution of the tree of life.
Nonpathogenic Epsilonproteobacteria
Authors: Nakagawa Satoshi , Takaki Yoshihiro 
Published: September 2009

AbstractThe Epsilonproteobacteria is a class within the phylum Proteobacteria and has been primarily recognized as a group of pathogenic microorganisms. However, nonpathogenic Epsilonproteobacteria has been increasingly of great biological importance. They represent bacteria with the highest metabolic versatility. Nonpathogenic Epsilonproteobacteria occurs dominantly in various redoxclines such as deep‐sea vents, stratified ocean, terrestrial sulfidic caves, and oil fields as both epi‐ or endosymbionts and free‐living microorganisms. Recent genome analysis has revealed that some virulence determinants and the genomic plasticity of the pathogenic Epsilonproteobacteria appear to have roots in nonpathogenic, chemoautotrophic Epsilonproteobacteria. These work advantageously for pathogenic Epsilonproteobacteria to be efficiently and persistently infectious and for nonpathogenic Epsilonproteobacteria to thrive in extreme habitats. In the genomic era, previously unrecognized evolutionary links are emerging between important human/animal pathogens and their nonpathogenic, symbiotic, chemoautotrophic relatives.Key conceptsThe Epsilonproteobacteria is the class within the phylum Proteobacteria and represented by important human/animal pathogens.The perception of the class Epsilonproteobacteria has recently changed dramatically.The Epsilonproteobacteria comprise numerous nonpathogenic bacteria with diverse ecophysiology and phylogeny.Nonpathogenic Epsilonproteobacteria play key roles in biogeochemical cycles in various extreme environments on earth.Nonpathogenic Epsilonproteobacteria provide new insights into the origin of animal/human pathogens.
Fundamentals of Guided Elastic Waves in Solids
Authors: Cesnik Carlos E. S. , Raghavan Ajay 
Published: September 2009

AbstractGuided‐wave (GW) approaches have shown good promise as structural health monitoring (SHM) algorithms. However, to achieve their full potential, a sound understanding of the fundamentals of GWs in solids is very desirable and that is the objective of this article. After a brief introduction to the field of GW SHM, it starts with the simplest equation of elastic waves, that of one‐dimensional (1D) waves in a taut string. It covers basic concepts of 1D elastic waves to enable an understanding of fundamental concepts and nomenclature relating to wave theory. It then builds on these ideas to derive the 3D elasticity equations of free GWs (Lamb and horizontal shear waves) in isotropic plates. The notion of dispersiveness of GWs is then explained. This is followed by a brief derivation of the GW field excited by finite‐dimensional piezoelectric wafer transducers in isotropic plates based on the authors' earlier work. Subsequently, the equations of bulk waves in transversely isotropic solids are covered and these are used to build the solution for free GWs in multilayered fibrous composite laminates using the global matrix approach. The concept of GW steering in composites is discussed. Finally, more complex research issues in GW SHM are briefly introduced.
Civil Infrastructure Load Models for Structural Health Monitoring
Authors: Peil Udo 
Published: September 2009

AbstractLoad models play an important role in structural health monitoring (SHM). On the one hand, loads and load models are needed for system identification, which is the first step in obtaining a measure of structural health. Different possible loading procedures and their models are discussed in relation to the planned identification outcomes. On the other hand, the characteristics of realistic loads acting on a structure must be known to allow a precise lifetime prediction. In this article, only traffic and wind load models are discussed. Unexpected loads such as earthquakes, blasts, etc., cannot be predicted very accurately; thus, a lifetime assessment is not feasible. A similar situation exists with wind‐induced vibrations, e.g., due to vortex shedding, galloping, flutter, etc. These vibrations are usually always dangerous for a structure and may cause fatigue or other types of damage; thus, wind‐induced vibrations must be prevented rather than predicted.
Data Interrogation Approaches with Strain and Load Gauge Sensor Arrays
Authors: Todd Michael D. 
Published: September 2009

AbstractThis article considers how strain gauges and load gauges are being used as sensors (hardware) in conjunction with feature extraction algorithms (software) in structural health monitoring and damage prognosis applications. A concise review of mature strain gauge and load gauge technologies is presented, followed by a discussion on how the community has implemented data mining with these specific types of sensor arrays to make a system assessment of some kind. Examples from current SHM literature are provided for illustration where appropriate.
Nonlinear Acoustic Methods
Authors: Donskoy Dimitri M. 
Published: September 2009

AbstractThe nonlinear acoustic technique (NAT) developed for detecting and characterizing structural and material defects is an emerging field with unique advantages and challenges. NAT exploits nonlinear dynamic behavior produced by contact interfaces and micro‐ and mesoscopic inhomogeneities associated with structural defects and damages including cracks, disbonds, and delaminations. Nonlinearities across the defects lead to noticeable nonlinear transformations of the interrogating acoustic signals such as harmonic distortions, frequency mixing, modulations, etc. Digital signal processing is used to experimentally capture these transformations unlocking the potential of the NAT to monitor and characterize nonlinear defects and damages in a wide variety of materials and structures.
Guided‐Wave Array Methods
Authors: Wilcox Paul D. 
Published: September 2009

AbstractGuided waves are an attractive mechanism for detecting localized damage in structural health monitoring (SHM) applications because the damage can be remote from sensor locations. There are various techniques for transmitting and detecting guided waves using monolithic devices, but greater flexibility and superior performance can be achieved using arrays of transducer elements in conjunction with digital signal processing. In this article, the requirements for both deployable and permanently attached guided‐wave arrays are described and the differences between arrays used on one‐dimensional and two‐dimensional waveguides are highlighted. A distinction is made between compact arrays where the elements are in close proximity to one another, often in the same package, and sparse distributed arrays. The mathematical principles that are used to achieve mode separation and directionality with compact arrays are summarized and the practical consequences for array design are reviewed with reference to appropriate examples drawn from the literature. The final section of the article examines the somewhat different requirements of a permanently attached sparse array of sensors. Here the suppression of artifacts from structural features is the key challenge to be overcome for a successful SHM system, and it is shown how this dictates the maximum intersensor spacing that can be used.
Fiber‐Optic Sensor Principles
Authors: Peters Kara 
Published: September 2009

Abstract

This article presents the fundamental principles common to all optical fiber sensors, as well as general issues concerning their application to structural health monitoring systems. More detailed descriptions of specific optical fiber sensors can be found in
Intensity‐, Interferometric‐, and Scattering‐based Optical‐fiber Sensors
and
Fiber Bragg Grating Sensors
. Lightwave propagation through an optical fiber is described with an emphasis on mode propagation constants and energy distributions. The four primary sensing mechanisms exploiting these parameters are presented: intensity, phase, spectrum, and polarization encoding. Common sensor uses for different optical fiber types are presented including single‐mode, multimode, high‐birefringence, and polymer optical fibers. Commonly applied laser sources are also described. Finally, the integration of optical fiber sensors into structural components is discussed including the strength and attenuation properties of the optical fibers themselves. Included are methods for embedment in composite laminates and concrete structures, the strain transfer between the host structure and the embedded sensor, and the integrity of composite structures with embedded optical fiber sensor networks.
Sensor Technologies for Direct Health Monitoring of Tires
Authors: Moffitt Ronald D. , Bland Scott M. , Sunny Mohammad R. , Kapania Rakesh K. 
Published: September 2009

AbstractStructural health monitoring of tires is increasingly becoming very important and a number of studies have been or are being conducted to develop appropriate means to accomplish this very difficult task. A key to achieving this goal would be the development of appropriate sensors for predicting, and thus ultimately preventing, onset of failure in tires. The failure modes in tires are many and it may require a large number of sensors to detect onset of these failure modes. This article summarizes some of the sensors that are currently available for sensing strain in tires. Specifically, we review noncontacting sensors such as the sidewall torsion sensor, the developments of the Apollo project, a laser‐sensing system, micropower impulse radar, ultrawideband (UWB) radar–based health monitoring, chromasonic sensors based on acoustic emission, acceleration sensing, force/friction sensing, and strain sensing.
Ship and Offshore Structures
Authors: Kim Myung Hyun , Kim Do Hyung 
Published: September 2009

Abstract
Structural health monitoring techniques for ship and offshore structures are summarized in this article. Owing to the increasing accidents associated with bulk carriers and large oil tankers, international regulating committees have recommended the use of hull stress monitoring systems for bulk carriers of 20 000 dwt and above to reduce the risks of structural failure since 1994. In this article, typical hull stress monitoring systems (HMS) and common sensors with monitored parameters are discussed. Long‐based strain gauge (LBSG), accelerometers, and other types of sensors, such as motion sensors and pressure sensors, that are employed in HMS are introduced. Particular requirements for HMS are summarized for each classification societies such as LR (Lloyed Register), ABS (American Bureau of Shipping), DNV (Det Norske Veritas), and IACS (International Association of Classification Societies). Moreover, emerging new technologies applicable for ship and offshore structures are also introduced. The basic principles and the application of fiber‐optic sensors (FOS), acoustic emission sensors (AE), and crack detection sensors (CD) are reviewed in the later part of this chapter. Finally, new requirements and trends in terms of structural health monitoring in marine industries are introduced. In particular, the importance of structural health monitoring technology that is applicable in cryogenic environment is presented.
Noncontact Rail Monitoring by Ultrasonic Guided Waves
Authors: Rizzo Piervincenzo , Coccia Stefano , Bartoli Ivan , di Scalea Francesco Lanza 
Published: September 2009

Abstract
Recent train accidents have reaffirmed the need for developing more effective rail defect detection systems than those used today. One of the recent developments in rail inspection is the use of ultrasonic guided waves and noncontact probing techniques to target transverse‐type defects. Besides the obvious advantages of noncontact probing, which include robustness and potential for large inspection speed, such a system can theoretically detect transverse defects under horizontal shelling or head checks. A rail inspection prototype based on these concepts and funded by the Federal Railroad Administration (FRA) is under development at the University of California at San Diego (UCSD). The first field test was carried out in March 2006. The second field test took place in April 2007. This article first presents the main aspects of ultrasonic guided‐wave inspection of rails, including wave propagation modeling and noncontact ultrasound probing. The discussion then focuses on the UCSD/FRA prototype and its defect detection performance as determined in both laboratory tests and field tests.
Statistical Time Series Methods forSHM
Authors: Fassois Spilios D. , Sakellariou John S. 
Published: September 2009

AbstractStatistical time series methods for structural health monitoring utilize random excitation and/or vibration response signals, statistical model building, and statistical decision making for inferring the health state of a structure. This includes fault detection, identification (localization), as well as magnitude estimation. The principles and operation of methods that utilize the time or frequency domains on a periodic inspection basis are explained, and the methods are classified into various categories under the general nonparametric and parametric classes. Representative methods from each category are outlined and their use is illustrated on a laboratory structure.
Nonlinear Features forSHMApplications
Authors: Nichols Jonathan M. , Todd Michael D. 
Published: September 2009

AbstractIn structural health monitoring, it is often the case that the dynamics of the structure being monitored are appropriately described by a nonlinear model. The statistical properties of the structural response data are therefore different from those contained in a linear structure's response. Taking these properties into account when computing damage‐sensitive features results in a more powerful approach to assessing damage presence, magnitude, location, and type over methods that rely on linear statistical properties. This article describes a variety of ways in which nonlinearity appears in structural dynamics and reviews a number of strategies for incorporating nonlinearity in the feature‐extraction process.
Dimensionality Reduction Using Linear and Nonlinear Transformation
Authors: Kerschen Gaëtan , Golinval Jean‐Claude 
Published: September 2009

AbstractWith a clear trend toward very large experimental data sets, dimensionality reduction has become an important step of structural health monitoring. The objective of this article is to support that the proper orthogonal decomposition and its nonlinear generalizations are a meaningful addition to the dynamicist's toolbox in this context. The techniques are demonstrated using two application examples, namely, reduced‐order modeling and damage detection under varying environmental conditions.
Damage Detection Using theHilbert–Huang Transform
Authors: Pines Darryll J. , Salvino Liming W. 
Published: September 2009

AbstractIn this article, a new structural health monitoring and damage identification method is presented. The method developed here exploits a new time–frequency signal‐processing analysis tool now commonly referred to as theempirical mode decompositionmethod coupled with the Hilbert–Huang transform. The empirical mode decomposition is a time‐series analysis method that extracts a custom set of basis functions to describe the vibratory response of a system. The ability of the empirical mode decomposition to extract embedded oscillations, to reveal hidden reflections in the data and to provide a high‐resolution energy–time–frequency spectrum is used to characterize guided Lamb wave interactions with various damaged regions. Case studies are used to illustrate the effectiveness of the empirical mode decomposition method and various damage detection metrics to infer damage in one‐ and two‐dimensional structures.
Artificial Neural Networks
Authors: Reed Steve 
Published: September 2009

AbstractArtificial neural networks (ANNs) are able to learn from experience, generalize from examples, and identify underlying information from within noisy data. These characteristics, and the explosion in ANN techniques over the past 15 or 20 years, have led to an ever‐increasing role for ANNs within structural health monitoring (SHM) systems. Within this article, the development of ANNs and the basic principles behind their functionality, training, and deployment are described. Emphasis is placed upon the multilayer perceptron (MLP), as the workhorse of ANN methods; however, other techniques, including radial basis functions (RBF) and self‐organizing feature maps (SOFMs), are also detailed. Examples of the use of ANNs within SHM systems are presented and reference has been made to a wide range of texts throughout this article.
Data Fusion of Multiple Signals from the Sensor Network
Authors: Su Zhongqing , Wang Xiaoming , Ye Lin 
Published: September 2009

Abstract

Data fusion
is a
multilevel
and
multifaceted
process of combining
multiple
features extracted from a
multitude
of spatially distributed
independent
sources, so as to provide capabilities in automatic detection, classification, and identification. The goal of this article is to introduce the currently prevailing data fusion approaches with the purpose of structural health monitoring (SHM). Emphases are particularly placed on the principle of data fusion, fusion architecture, and major fusion algorithms. Representative case studies for evaluating structural damage by using fusion algorithms including correlation measures (CMs), artificial neural network (ANN), and Bayesian inference (BI) are followed.
Uncertainty Analysis
Authors: Manson Graeme , Worden Keith , Pierce S. Gareth , Denoeux Thierry 
Published: September 2009

AbstractThis article considers the issue of uncertainty analysis in general and also its relevance to structural health monitoring. Brief descriptions of the most popular of the many frameworks for uncertainty representation are given. The three main uncertainty‐related problems of relevance to structural dynamics, namely, quantification, fusion, and propagation, are then discussed. In order to illustrate the preceding ideas in a realistic scenario, two case studies conducted on an aerospace structure, namely the wing of a Gnat trainer aircraft, are provided. The first case study considers the issue of attaining certification for the artificial neural network damage classifiers through the assessment of the network's robustness to uncertainty. This case study involves the propagation of intervals through the network structure and it was found that it is likely that the networks which would be considered as optimal, in the traditional sense, would not be the network that is most robust to uncertainty. The second case study considers evidence‐based classifiers as an alternative to probabilistic classifiers for the problem of damage location. The Dempster–Shafer theory is employed to construct neural network classifiers with the potential to admit ignorance, rather than misclassify. Issues of propagation and fusion in an evidence‐based framework are considered. It was also found that Dempster–Shafer networks give a slight improvement over their probabilistic counterparts.