In-hospital mortality rates after a cemented femoral component for displaced neck of femur fractures

Aim This prospective cohort study investigated whether the use of preoperative anticoagulants is an independent risk factor for the outcomes of surgical treatment of patients with a neck of femur fracture. Methods Data was obtained from a prospectively collected database. All patients admitted for a neck of femur fracture between Nov 2010 and Oct 2011 were included. This resulted in three hundred twenty-eight patients with 330 neck of femur fractures. Four groups were defined; patients preoperatively (i) on aspirin (n = 105); (ii) on clopidogrel (n = 28); (iii) on warfarin (n = 30), and; (iv) without any anticoagulation history (n = 167, the control group). The non-warfarin group included the aspirin group, clopidogrel group and the control group. Primary outcome was the in-hospital mortality. Secondary outcomes were the postoperative complications, return to theatre and length of stay. Results Thirteen in-hospital deaths were identified, 4 deaths in the aspirin group, 1 death in the clopidogrel group, 2 deaths in the warfarin group and 6 deaths in the control group. No significant difference in the mortality rates was found between the different groups. Also in the secondary outcomes, no significant difference was found between the four groups. A trend to a higher wound complication rate for the warfarin group was detected. Conclusion The use of clopidrogel or aspirin pre operatively is not an influence on short term patient outcome for patients with a neck of femur fracture. Surgical procedures should not be delayed to reverse their influence.

Component modeling for SCADA network mapping

Supervisory Control and Data Acquisition systems (SCADA) are widely used to control critical infrastructure automatically. Capturing and analyzing packet-level traffic flowing through such a network is an essential requirement for problems such as legacy network mapping and fault detection. Within the framework of captured network traffic, we present a simple modeling technique, which supports the mapping of the SCADA network topology via traffic monitoring. By characterizing atomic network components in terms of their input-output topology and the relationship between their data traffic logs, we show that these modeling primitives have good compositional behaviour, which allows complex networks to be modeled. Finally, the predictions generated by our model are found to be in good agreement with experimentally obtained traffic.

Conceptual and statistical framework for a water quality component of the integrated report card for the Great Barrier Reef catchments

This report presents the final deliverable from the project titled Conceptual and statistical framework for a water quality component of an integrated report card’ funded by the Marine and Tropical Sciences Research Facility (MTSRF; Project 3.7.7). The key management driver of this, and a number of other MTSRF projects concerned with indicator development, is the requirement for state and federal government authorities and other stakeholders to provide robust assessments of the present ‘state’ or ‘health’ of regional ecosystems in the Great Barrier Reef (GBR) catchments and adjacent marine waters. An integrated report card format, that encompasses both biophysical and socioeconomic factors, is an appropriate framework through which to deliver these assessments and meet a variety of reporting requirements. It is now well recognised that a ‘report card’ format for environmental reporting is very effective for community and stakeholder communication and engagement, and can be a key driver in galvanising community and political commitment and action. Although a report card it needs to be understandable by all levels of the community, it also needs to be underpinned by sound, quality-assured science. In this regard this project was to develop approaches to address the statistical issues that arise from amalgamation or integration of sets of discrete indicators into a final score or assessment of the state of the system. In brief, the two main issues are (1) selecting, measuring and interpreting specific indicators that vary both in space and time, and (2) integrating a range of indicators in such a way as to provide a succinct but robust overview of the state of the system. Although there is considerable research and knowledge of the use of indicators to inform the management of ecological, social and economic systems, methods on how to best to integrate multiple disparate indicators remain poorly developed. Therefore the objective of this project was to (i) focus on statistical approaches aimed at ensuring that estimates of individual indicators are as robust as possible, and (ii) present methods that can be used to report on the overall state of the system by integrating estimates of individual indicators. It was agreed at the outset, that this project was to focus on developing methods for a water quality report card. This was driven largely by the requirements of Reef Water Quality Protection Plan (RWQPP) and led to strong partner engagement with the Reef Water Quality Partnership.

Butterfly catastrophe for fronts in a three-component reaction–diffusion system

We study the dynamics of front solutions in a three-component reaction–diffusion system via a combination of geometric singular perturbation theory, Evans function analysis, and center manifold reduction. The reduced system exhibits a surprisingly complicated bifurcation structure including a butterfly catastrophe. Our results shed light on numerically observed accelerations and oscillations and pave the way for the analysis of front interactions in a parameter regime where the essential spectrum of a single front approaches the imaginary axis asymptotically.

Chondrocyte redifferentiation and construct mechanical property development in single-component photocrosslinkable hydrogels

Hydrogels are promising materials for cartilage repair, but the properties required for optimal functional outcomes are not yet known. In this study, we functionalized four materials that are commonly used in cartilage tissue engineering and evaluated them using in vitro cultures. Gelatin, hyaluronic acid, polyethylene glycol, and alginate were functionalized with methacrylic anhydride to make them photocrosslinkable. We found that the responses of encapsulated human chondrocytes were highly dependent on hydrogel type. Gelatin hydrogels supported cell proliferation and the deposition of a glycosaminoglycan rich matrix with significant mechanical functionality. However, cells had a dedifferentiated phenotype, with high expression of collagen type I. Chondrocytes showed the best redifferentiation in hyaluronic acid hydrogels, but the newly formed matrix was highly localized to the pericellular regions, and these gels degraded rapidly. Polyethylene glycol hydrogels, as a bioinert control, did not promote any strong responses. Alginate hydrogels did not support the deposition of new matrix, and the stiffness decreased during culture. The markedly different response of chondrocytes to these four photocrosslinkable hydrogels demonstrates the importance of material properties for chondrogenesis and extracellular matrix production, which are critical for effective cartilage repair.

Cultural beliefs about societal change: A three-mode principal component analysis in China, Australia, and Japan

People’s beliefs about where society has come from and where it is going have personal and political consequences. Here, we conduct a detailed investigation of these beliefs through re-analyzing Kashima et al.’s (Study 2, n = 320) data from China, Australia, and Japan. Kashima et al. identified a “folk theory of social change” (FTSC) belief that people in society become more competent over time, but less warm and moral. Using three-mode principal components analysis, an under-utilized analytical method in psychology, we identified two additional narratives: Utopianism/Dystopianism (people becoming generally better or worse over time) and Expansion/Contraction (an increase/decrease in both positive and negative aspects of character over time). Countries differed in endorsement of these three narratives of societal change. Chinese endorsed the FTSC and Utopian narratives more than other countries, Japanese held Dystopian and Contraction beliefs more than other countries, and Australians’ narratives of societal change fell between Chinese and Japanese. Those who believed in greater economic/technological development held stronger FTSC and Expansion/Contraction narratives, but not Utopianism/Dystopianism. By identifying multiple cultural narratives about societal change, this research provides insights into how people across cultures perceive their social world and their visions of the future.

Agent-based simulation as a component of mixed methods in entrepreneurship research

As a new research method supplementing the existing qualitative and quantitative approaches, agent-based modelling and simulation (ABMS) may fit well within the entrepreneurship field because the core concepts and basic premises of entrepreneurship coincide with the characteristics of ABMS (McKelvey, 2004; Yang & Chandra, 2013). Agent-based simulation is a simulation method based on agent-based models. The agentbased models are composed of heterogeneous agents and their behavioural rules. By repeatedly carrying out agent-based simulations on a computer, the simulations reproduce each agent’s behaviour, their interactive process, and the emerging macroscopic phenomenon according to the flow of time. Using agent-based simulations, researchers may investigate temporal or dynamic effects of each agent’s behaviours.

Mutations in the gene encoding IFT dynein complex component WDR34 cause jeune asphyxiating thoracic dystrophy

Bidirectional (anterograde and retrograde) motor-based intraflagellar transport (IFT) governs cargo transport and delivery processes that are essential for primary cilia growth and maintenance and for hedgehog signaling functions. The IFT dynein-2 motor complex that regulates ciliary retrograde protein transport contains a heavy chain dynein ATPase/motor subunit, DYNC2H1, along with other less well functionally defined subunits. Deficiency of IFT proteins, including DYNC2H1, underlies a spectrum of skeletal ciliopathies. Here, by using exome sequencing and a targeted next-generation sequencing panel, we identified a total of 11 mutations in WDR34 in 9 families with the clinical diagnosis of Jeune syndrome (asphyxiating thoracic dystrophy). WDR34 encodes a WD40 repeat-containing protein orthologous to Chlamydomonas FAP133, a dynein intermediate chain associated with the retrograde intraflagellar transport motor. Three-dimensional protein modeling suggests that the identified mutations all affect residues critical for WDR34 protein-protein interactions. We find that WDR34 concentrates around the centrioles and basal bodies in mammalian cells, also showing axonemal staining. WDR34 coimmunoprecipitates with the dynein-1 light chain DYNLL1 in vitro, and mining of proteomics data suggests that WDR34 could represent a previously unrecognized link between the cytoplasmic dynein-1 and IFT dynein-2 motors. Together, these data show that WDR34 is critical for ciliary functions essential to normal development and survival, most probably as a previously unrecognized component of the mammalian dynein-IFT machinery.

An immunodiagnostic assay for quantitation of specific ige to the major pollen allergen component, pas n 1, of the subtropical bahia grass

Background Pollens of the Panicoideae subfamily of grasses including Bahia (Paspalum notatum) are important allergen sources in subtropical regions of the world. An assay for specific IgE to the major molecular allergenic component, Pas n 1, of Bahia grass pollen (BaGP) would have immunodiagnostic utility for patients with pollen allergy in these regions. Methods Biotinylated Pas n 1 purified from BaGP was coated onto streptavidin ImmunoCAPs. Subjects were assessed by clinical history of allergic rhinitis and skin prick test (SPT) to aeroallergens. Serum total, BaGP-specific and Pas n 1-specific IgE were measured. Results: Pas n 1 IgE concentrations were highly correlated with BaGP SPT (r = 0.795, p < 0.0001) and BaGP IgE (r = 0.915, p < 0.0001). At 0.23 kU/l Pas n 1 IgE, the diagnostic sensitivity (92.4%) and specificity (93.1%) for the detection of BaGP allergy was high (area under receiver operator curve 0.960, p < 0.0001). The median concentrations of Pas n 1 IgE in non-Atopic subjects (0.01 kU/l, n = 67) and those with other allergies (0.02 kU/l, n = 59) showed no inter-group difference, whilst grass pollen-Allergic patients with allergic rhinitis showed elevated Pas n 1 IgE (6.71 kU/l, n = 182, p < 0.0001). The inter-Assay coefficient of variation for the BaGP-Allergic serum pool was 6.92%. Conclusions Pas n 1 IgE appears to account for most of the BaGP-specific IgE. This molecular component immunoassay for Pas n 1 IgE has potential utility to improve the sensitivity and accuracy of diagnosis of BaGP allergy for patients in subtropical regions.

Introduction: Epitope mimicry as a component cause of autoimmune disease

The causes of autoimmune diseases have yet to be fully elucidated. Autoantibodies, autoreactive T cell responses, the presence of a predisposing major histocompatibility complex (MHC) haplotype and responsiveness to corticosteroids are features, and some are possibly contributory causes of autoimmune disease. The most challenging question is how autoimmune diseases are triggered. Molecular mimicry of host cell determinants by epitopes of infectious agents with ensuing cross-reactivity is one of the most popular yet still controversial theories for the initiation of autoimmune diseases [1]. Throughout the 1990s, hundreds of research articles focusing to various extents on epitope mimicry, as it is more accurately described in an immunological context, were published annually. Many of these articles presented data that were consistent with the hypothesis of mimicry but that did not actually prove the theory. Other equally convincing reports indicated that epitope mimicry was not the cause of the autoimmune disease despite sequence similarity between molecules of infectious agents and the host. Some 20 years ago, Rothman [2] proposed a model for disease causation and I have used this as a framework to examine the role of epitope mimicry in the development of autoimmune disease. The thesis of Rothman’s model is that an effect, in this instance autoimmune disease, arises as a result of a cause. In most cases, multiple-component causes contribute synergistically to yield the effect, and each of these components alone is insufficient as a cause. Logically, some component causes, such as the presence of a particular autoimmune response, are also necessary causes.

Robust principal component analysis in water quality index development

Some statistical procedures already available in literature are employed in developing the water quality index, WQI. The nature of complexity and interdependency that occur in physical and chemical processes of water could be easier explained if statistical approaches were applied to water quality indexing. The most popular statistical method used in developing WQI is the principal component analysis (PCA). In literature, the WQI development based on the classical PCA mostly used water quality data that have been transformed and normalized. Outliers may be considered in or eliminated from the analysis. However, the classical mean and sample covariance matrix used in classical PCA methodology is not reliable if the outliers exist in the data. Since the presence of outliers may affect the computation of the principal component, robust principal component analysis, RPCA should be used. Focusing in Langat River, the RPCA-WQI was introduced for the first time in this study to re-calculate the DOE-WQI. Results show that the RPCA-WQI is capable to capture similar distribution in the existing DOE-WQI.

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.