A myosin from a higher plant has structural similarities to class V myosins

In plant cells, myosin is believed to be the molecular motor responsible for actin-based motility processes such as cytoplasmic streaming and directed vesicle transport. In an effort to characterize plant myosin, a cDNA encoding a myosin heavy chain was isolated from Arabidopsis thaliana. The predicted product of the MYA1 gene is 173 kDa and is structurally similar to the class V myosins. It is composed of the highly-conserved NH2-terminal "head" domain, a putative calmodulin-binding "neck" domain an alpha-helical coiled-coil domain, and a COOH-terminal domain. Northern blot analysis shows that the Arabidopsis MYA1 gene is expressed in all the major plant tissues (flower, leaf, root, and stem). We suggest that the MYA1 myosin may be involved in a general intracellular transport process in plant cells.

Analysis of acoustic emission data for structural health monitoring applications

Acoustic emission (AE) is the phenomenon where high frequency stress waves are generated by rapid release of energy within a material by sources such as crack initiation or growth. AE technique involves recording these stress waves by means of sensors placed on the surface and subsequent analysis of the recorded signals to gather information such as the nature and location of the source. It is one of the several diagnostic techniques currently used for structural health monitoring (SHM) of civil infrastructure such as bridges. Some of its advantages include ability to provide continuous in-situ monitoring and high sensitivity to crack activity. But several challenges still exist. Due to high sampling rate required for data capture, large amount of data is generated during AE testing. This is further complicated by the presence of a number of spurious sources that can produce AE signals which can then mask desired signals. Hence, an effective data analysis strategy is needed to achieve source discrimination. This also becomes important for long term monitoring applications in order to avoid massive date overload. Analysis of frequency contents of recorded AE signals together with the use of pattern recognition algorithms are some of the advanced and promising data analysis approaches for source discrimination. This paper explores the use of various signal processing tools for analysis of experimental data, with an overall aim of finding an improved method for source identification and discrimination, with particular focus on monitoring of steel bridges.

Seismic mitigation of building structural systems using passive dampers

This paper treats the seismic mitigation of medium rise frame-shear wall structures and building facade systems using passive damping devices. The frame shear wall structures have embedded viscoelastic and friction dampers in different configurations and placed in various locations in the structure. Influence of damper type, configuration and location are investigated. Results for tip deflections which provide an overall evaluation of the seismic response of the structure, are determined. Seismic mitigation of building facade systems in which visco-elastic dampers are fitted at the horizontal connections between the facades and the frame, instead of the traditional rigid connections, are also treated. Finite element techniques are used to model and analyse the two structural systems under different earthquake loadings, scaled to the same peak ground acceleration for meaningful comparison of responses. Results demonstrate the feasibility of these techniques for seismic mitigation.

Rumination, post-traumatic growth, and distress : structural equation modelling with cancer survivors

Objective Theoretical models of post-traumatic growth (PTG) have been derived in the general trauma literature to describe the post-trauma experience that facilitates the perception of positive life changes. To develop a statistical model identifying factors that are associated with PTG, structural equation modelling (SEM) was used in the current study to assess the relationships between perception of diagnosis severity, rumination, social support, distress, and PTG. Method A statistical model of PTG was tested in a sample of participants diagnosed with a variety of cancers (N=313). Results An initial principal components analysis of the measure used to assess rumination revealed three components: intrusive rumination, deliberate rumination of benefits, and life purpose rumination. SEM results indicated that the model fit the data well and that 30% of the variance in PTG was explained by the variables. Trauma severity was directly related to distress, but not to PTG. Deliberately ruminating on benefits and social support were directly related to PTG. Life purpose rumination and intrusive rumination were associated with distress. Conclusions The model showed that in addition to having unique correlating factors, distress was not related to PTG, thereby providing support for the notion that these are discrete constructs in the post-diagnosis experience. The statistical model provides support that post-diagnosis experience is simultaneously shaped by positive and negative life changes and that one or the other outcome may be prevalent or may occur concurrently. As such, an implication for practice is the need for supportive care that is holistic in nature.

A statistical framework for natural feature representation

This paper presents a robust stochastic framework for the incorporation of visual observations into conventional estimation, data fusion, navigation and control algorithms. The representation combines Isomap, a non-linear dimensionality reduction algorithm, with expectation maximization, a statistical learning scheme. The joint probability distribution of this representation is computed offline based on existing training data. The training phase of the algorithm results in a nonlinear and non-Gaussian likelihood model of natural features conditioned on the underlying visual states. This generative model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The instantiated likelihoods are expressed as a Gaussian mixture model and are conveniently integrated within existing non-linear filtering algorithms. Example applications based on real visual data from heterogenous, unstructured environments demonstrate the versatility of the generative models.

A stochastic model for natural feature representation

This paper presents a robust stochastic model for the incorporation of natural features within data fusion algorithms. The representation combines Isomap, a non-linear manifold learning algorithm, with Expectation Maximization, a statistical learning scheme. The representation is computed offline and results in a non-linear, non-Gaussian likelihood model relating visual observations such as color and texture to the underlying visual states. The likelihood model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The likelihoods are expressed as a Gaussian Mixture Model so as to permit convenient integration within existing nonlinear filtering algorithms. The resulting compactness of the representation is especially suitable to decentralized sensor networks. Real visual data consisting of natural imagery acquired from an Unmanned Aerial Vehicle is used to demonstrate the versatility of the feature representation.

Multiple human single-stranded DNA binding proteins function in genome maintenance : structural, biochemical and functional analysis

DNA exists predominantly in a duplex form that is preserved via specific base pairing. This base pairing affords a considerable degree of protection against chemical or physical damage and preserves coding potential. However, there are many situations, e.g. during DNA damage and programmed cellular processes such as DNA replication and transcription, in which the DNA duplex is separated into two singlestranded DNA (ssDNA) strands. This ssDNA is vulnerable to attack by nucleases, binding by inappropriate proteins and chemical attack. It is very important to control the generation of ssDNA and protect it when it forms, and for this reason all cellular organisms and many viruses encode a ssDNA binding protein (SSB). All known SSBs use an oligosaccharide/oligonucleotide binding (OB)-fold domain for DNA binding. SSBs have multiple roles in binding and sequestering ssDNA, detecting DNA damage, stimulating strand-exchange proteins and helicases, and mediation of protein–protein interactions. Recently two additional human SSBs have been identified that are more closely related to bacterial and archaeal SSBs. Prior to this it was believed that replication protein A, RPA, was the only human equivalent of bacterial SSB. RPA is thought to be required for most aspects of DNA metabolism including DNA replication, recombination and repair. This review will discuss in further detail the biological pathways in which human SSBs function.

Use of acoustic emission technique for structural health monitoring of bridges

Bridges are valuable assets of every nation. They deteriorate with age and often are subjected to additional loads or different load patterns than originally designed for. These changes in loads can cause localized distress and may result in bridge failure if not corrected in time. Early detection of damage and appropriate retrofitting will aid in preventing bridge failures. Large amounts of money are spent in bridge maintenance all around the world. A need exists for a reliable technology capable of monitoring the structural health of bridges, thereby ensuring they operate safely and efficiently during the whole intended lives. Monitoring of bridges has been traditionally done by means of visual inspection. Visual inspection alone is not capable of locating and identifying all signs of damage, hence a variety of structural health monitoring (SHM) techniques is used regularly nowadays to monitor performance and to assess condition of bridges for early damage detection. Acoustic emission (AE) is one technique that is finding an increasing use in SHM applications of bridges all around the world. The chapter starts with a brief introduction to structural health monitoring and techniques commonly used for monitoring purposes. Acoustic emission technique, wave nature of AE phenomenon, previous applications and limitations and challenges in the use as a SHM technique are also discussed. Scope of the project and work carried out will be explained, followed by some recommendations of work planned in future.

Structural dynamics research and applications

Research in structural dynamics has received considerable attention due to problems associated with emerging slender structures, increased vulnerability of structures to random loads and aging infrastructure. This paper briefly describes some such research carried out on i) dynamics of composite floor structure, ii) dynamics of cable supported footbridge, iii) seismic mitigation of frame-shear wall structure using passive dampers and iv) development of a damage assessment model for use in structural health modelling.

Immunohistochemical analysis of structural changes in collagen for the assessment of osteoarthritis

Collagen fibrillation within articular cartilage (AC) plays a key role in joint osteoarthritis (OA) progression and, therefore, studying collagen synthesis changes could be an indicator for use in the assessment of OA. Various staining techniques have been developed and used to determine the collagen network transformation under microscopy. However, because collagen and proteoglycan coexist and have the same index of refraction, conventional methods for specific visualization of collagen tissue is difficult. This study aimed to develop an advanced staining technique to distinguish collagen from proteoglycan and to determine its evolution in relation to OA progression using optical and laser scanning confocal microscopy (LSCM). A number of AC samples were obtained from sheep joints, including both healthy and abnormal joints with OA grades 1 to 3. The samples were stained using two different trichrome methods and immunohistochemistry (IHC) to stain both colourimetrically and with fluorescence. Using optical microscopy and LSCM, the present authors demonstrated that the IHC technique stains collagens only, allowing the collagen network to be separated and directly investigated. Fluorescently-stained IHC samples were also subjected to LSCM to obtain three-dimensional images of the collagen fibres. Changes in the collagen fibres were then correlated with the grade of OA in tissue. This study is the first to successfully utilize the IHC staining technique in conjunction with laser scanning confocal microscopy. This is a valuable tool for assessing changes to articular cartilage in OA.

Structural reliability prediction of a steel bridge element using Dynamic Object Oriented Bayesian Network (DOOBN)

Different from conventional methods for structural reliability evaluation, such as, first/second-order reliability methods (FORM/SORM) or Monte Carlo simulation based on corresponding limit state functions, a novel approach based on dynamic objective oriented Bayesian network (DOOBN) for prediction of structural reliability of a steel bridge element has been proposed in this paper. The DOOBN approach can effectively model the deterioration processes of a steel bridge element and predict their structural reliability over time. This approach is also able to achieve Bayesian updating with observed information from measurements, monitoring and visual inspection. Moreover, the computational capacity embedded in the approach can be used to facilitate integrated management and maintenance optimization in a bridge system. A steel bridge girder is used to validate the proposed approach. The predicted results are compared with those evaluated by FORM method.

Structural inquiry, human agency and the contribution of Harre and Bhaskar : a case study of Wright’s “Classes”

This paper examines the contribution of aspects of critical and referential realism to the “logic” of structural explanation through an analysis of Erik Olin Wright’s Classes and the debate surrounding this work. Wright’s Classes has been selected as a case study because it offers an opportunity to examine issues pertaining to “objective” and “subjective” determinations of class and related questions of agency and structure at the level of actual methodological strategies. A close examination of the structure of Wright’s inquiry reveals a number of places where Harre’s and Bhaskar’s approaches may contribute to the prescription of methodological strategies which could overcome some of the antinomies on which the debate on Classes is based. As a case study, the paper underlines the important “underlabourer” role of critical and referential realism and their contribution to questions of agency and structure in the context of actual stages involved in structural explanation

Women and representation in local government : international case studies

Women and Representation in Local Government opens up an opportunity to critique and move beyond suppositions and labels in relation to women in local government. Presenting a wealth of new empirical material, this book brings together international experts to examine and compare the presence of women at this level and features case studies on the US, UK, France, Germany, Spain, Finland, Uganda, China, Australia and New Zealand. Divided into four main sections, each explores a key theme related to the subject of women and representation in local government and engages with contemporary gender theory and the broader literature on women and politics. The contributors explore local government as a gendered environment; critiquing strategies to address the limited number of elected female members in local government and examine the impact of significant recent changes on local government through a gender lens. Addressing key questions of how gender equality can be achieved in this sector, it will be of strong interest to students and academics working in the fields of gender studies, local government and international politics.

Analysis of micro-structural changes and measurement of their parameters of a food material

Food microstructure represents the way their elements arrangement and their interaction. Researchers in this field benefit from identifying new methods of examination of the microstructure and analysing the images. Experiments were undertaken to study micro-structural changes of food material during drying. Micro-structural images were obtained for potato samples of cubical shape at different moisture contents during drying using scanning electron microscopy. Physical parameters such as cell wall perimeter, and area were calculated using an image identification algorithm, based on edge detection and morphological operators. The algorithm was developed using Matlab.