The ASAS criteria for axial spondyloarthritis: Strengths, weaknesses, and proposals for a way forward

Classification criteria should facilitate selection of similar patients for clinical and epidemiologic studies, therapeutic trials, and research on etiopathogenesis to enable comparison of results across studies from different centers. We critically appraise the validity and performance of the Assessment of SpondyloArthritis international Society (ASAS) classification criteria for axial spondyloarthritis (axSpA). It is still debatable whether all patients fulfilling these criteria should be considered as having true axSpA. Patients with radiographically evident disease by the ASAS criteria are not necessarily identical with ankylosing spondylitis (AS) as classified by the modified New York criteria. The complex multi-arm selection design of the ASAS criteria induces considerable heterogeneity among patients so classified, and applying them in settings with a low prevalence of axial spondyloarthritis (SpA) greatly increases the proportion of subjects falsely classified as suffering from axial SpA. One of the unmet needs in non-radiographic form of axial SpA is to have reliable markers that can identify individuals at risk for progression to AS and thereby facilitate early intervention trials designed to prevent such progression. We suggest needed improvements of the ASAS criteria for axSpA, as all criteria sets should be regarded as dynamic concepts open to modifications or updates as our knowledge advances.

Islamic fund and asset management: The opportunities and challenges of Islamic REITs

Purpose This paper aims to present a review on the issues and challenges for Islamic Funds and Asset Management, particularly the Islamic Real Estate Trusts (I-REITs) available in Malaysia. The key difference between the Islamic and the conventional investment vehicle is mainly the fund needs to adhere to the Shariah framework. Design/methodology/approach The paper reviews and synthesises the relevant literature on the framework of Islamic Asset and Fund Management, particularly the Islamic Real Estate Investment Trusts. The paper then provides insights for further research to address the issues and consider the Shariah framework applicable to other further research works. Findings The paper highlights the opportunities and challenges of Islamic REITs globally. There is a lack of the standardisation in the screening methodology used by the Malaysian I- REITs and Singapore I-REITs as the latter follows the Gulf Cooperation Council (GCC) guideline to capture the investors mainly from the Gulf countries. In term of tenants’ selection, there is similarity between I-REITs and the Socially Responsible Investment (SRI) or ethical investment. The gap between the investments can be bridged if the Islamic funds skewed the investment portfolio towards the social and ethical investment. Even though there is a limitation in the investment universe, I-REITs provide better diversification option and show better performance compared to the equity market during the economic crisis. The introduction of the Shariah-compliant REITs index for Asia Pacific allows the fund managers to benchmark the performance of either the funds or the sector with other investment vehicles. This will encourage more investors to consider I-REIT in the decision making of the asset allocation portfolio and broadening the horizon of the investment. Originality/value The contribution of the study is the examination and analysis of the Shariah framework currently adopted for Islamic REITs. This will assist in the identification of specific issues associated with Islamic REITs that will need to be addressed in the development and application of further research in the aspect of the management and operations to increase the efficiency level and better performance in order to capture more investors in this specific and promising market.

Dynamic, invivo, real-time detection of retinal oxidative status in a model of elevated intraocular pressure using a novel, reversibly responsive, profluorescent nitroxide probe

Changes to the redox status of biological systems have been implicated in the pathogenesis of a wide variety of disorders including cancer, Ischemia-reperfusion (I/R) injury and neurodegeneration. In times of metabolic stress e.g. ischaemia/reperfusion, reactive oxygen species (ROS) production overwhelms the intrinsic antioxidant capacity of the cell, damaging vital cellular components. The ability to quantify ROS changes in vivo, is therefore essential to understanding their biological role. Here we evaluate the suitability of a novel reversible profluorescent probe containing a redox-sensitive nitroxide moiety (methyl ester tetraethylrhodamine nitroxide, ME-TRN), as an in vivo, real-time reporter of retinal oxidative status. The reversible nature of the probe's response offers the unique advantage of being able to monitor redox changes in both oxidizing and reducing directions in real time. After intravitreal administration of the ME-TRN probe, we induced ROS production in rat retina using an established model of complete, acute retinal ischaemia followed by reperfusion. After restoration of blood flow, retinas were imaged using a Micron III rodent fundus fluorescence imaging system, to quantify the redox-response of the probe. Fluorescent intensity declined during the first 60 min of reperfusion. The ROS-induced change in probe fluorescence was ameliorated with the retinal antioxidant, lutein. Fluorescence intensity in non-Ischemia eyes did not change significantly. This new probe and imaging technology provide a reversible and real-time response to oxidative changes and may allow the in vivo testing of antioxidant therapies of potential benefit to a range of diseases linked to oxidative stress

Dynamic pattern of suicide in Australia, 1986-2005: A descriptive-analytic study

Objective This study explores the spatiotemporal variations of suicide across Australia from 1986 to 2005, discusses the reasons for dynamic changes, and considers future suicide research and prevention strategies. Design Suicide (1986–2005) and population data were obtained from the Australian Bureau of Statistics. A series of analyses were conducted to examine the suicide pattern by sex, method and age group over time and geography. Results Differences in suicide rates across sex, age groups and suicide methods were found across geographical areas. Male suicides were mainly completed by hanging, firearms, gases and self-poisoning. Female suicides were primarily completed by hanging and self-poisoning. Suicide rates were higher in rural areas than in urban areas (capital cities and regional centres). Suicide rates by firearms were higher in rural areas than in urban areas, while the pattern for self-poisoning showed the reverse trend. Suicide rates had relatively stable trend for the total population and those aged between 15 and 54, while suicide decreased among 55 years and over during the study period. There was a decrease in suicides by firearms during the study period especially after 1996 when a new firearm control law was implemented, while suicide by hanging continued to increase. Areas with a high proportion of indigenous population (eg, northwest of Queensland and top north of the Northern Territory) had shown a substantial increase in suicide incidence after 1995. Conclusions Suicide rates varied over time and space and across sexes, age groups and suicide methods. This study provides detailed patterns of suicide to inform suicide control and prevention strategies for specific subgroups and areas of high and increased risk.

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.

Complex crystallization dynamics in amorphous germanium observed with dynamic transmission electron microscopy

Crystallization of amorphous germanium (a-Ge) by laser or electron beam heating is a remarkably complex process that involves several distinct modes of crystal growth and the development of intricate microstructural patterns on the nanosecond to ten microsecond time scales. Here we use dynamic transmission electron microscopy (DTEM) to study the fast, complex crystallization dynamics with 10 nm spatial and 15 ns temporal resolution. We have obtained time-resolved real-space images of nanosecond laser-induced crystallization in a-Ge with unprecedentedly high spatial resolution. Direct visualization of the crystallization front allows for time-resolved snapshots of the initiation and roughening of the dendrites on submicrosecond time scales. This growth is followed by a rapid transition to a ledgelike growth mechanism that produces a layered microstructure on a time scale of several microseconds. This study provides insights into the mechanisms governing this complex crystallization process and is a dramatic demonstration of the power of DTEM for studying time-dependent material processes far from equilibrium.

In situ investigation of explosive crystallization in a-Ge: Experimental determination of the interface response function using dynamic transmission electron microscopy

The crystallization of amorphous semiconductors is a strongly exothermic process. Once initiated the release of latent heat can be sufficient to drive a self-sustaining crystallization front through the material in a manner that has been described as explosive. Here, we perform a quantitative in situ study of explosive crystallization in amorphous germanium using dynamic transmission electron microscopy. Direct observations of the speed of the explosive crystallization front as it evolves along a laser-imprinted temperature gradient are used to experimentally determine the complete interface response function (i.e., the temperature-dependent front propagation speed) for this process, which reaches a peak of 16 m/s. Fitting to the Frenkel-Wilson kinetic law demonstrates that the diffusivity of the material locally/immediately in advance of the explosive crystallization front is inconsistent with those of a liquid phase. This result suggests a modification to the liquid-mediated mechanism commonly used to describe this process that replaces the phase change at the leading amorphous-liquid interface with a change in bonding character (from covalent to metallic) occurring in the hot amorphous material.

Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.

A critical evaluation of literature on robot path planning in dynamic environment

Robot Path Planning (RPP) in dynamic environments is a search problem based on the examination of collision-free paths in the presence of dynamic and static obstacles. Many techniques have been developed to solve this problem. Trapping in a local minima and maintaining a Real-Time performance are known as the two most important challenges that these techniques face to solve such problem. This study presents a comprehensive survey of the various techniques that have been proposed in this domain. As part of this survey, we include a classification of the approaches and identify their methods.

Influence of road traffic environment and mobile phone distraction on the speed selection behaviour of young drivers

Travel speed is one of the most critical parameters for road safety; the evidence suggests that increased vehicle speed is associated with higher crash risk and injury severity. Both naturalistic and simulator studies have reported that drivers distracted by a mobile phone select a lower driving speed. Speed decrements have been argued to be a risk compensatory behaviour of distracted drivers. Nonetheless, the extent and circumstances of the speed change among distracted drivers are still not known very well. As such, the primary objective of this study was to investigate patterns of speed variation in relation to contextual factors and distraction. Using the CARRS-Q high-fidelity Advanced Driving Simulator, the speed selection behaviour of 32 drivers aged 18-26 years was examined in two phone conditions: baseline (no phone conversation) and handheld phone operation. The simulator driving route contained five different types of road traffic complexities, including one road section with a horizontal S curve, one horizontal S curve with adjacent traffic, one straight segment of suburban road without traffic, one straight segment of suburban road with traffic interactions, and one road segment in a city environment. Speed deviations from the posted speed limit were analysed using Ward’s Hierarchical Clustering method to identify the effects of road traffic environment and cognitive distraction. The speed deviations along curved road sections formed two different clusters for the two phone conditions, implying that distracted drivers adopt a different strategy for selecting driving speed in a complex driving situation. In particular, distracted drivers selected a lower speed while driving along a horizontal curve. The speed deviation along the city road segment and other straight road segments grouped into a different cluster, and the deviations were not significantly different across phone conditions, suggesting a negligible effect of distraction on speed selection along these road sections. Future research should focus on developing a risk compensation model to explain the relationship between road traffic complexity and distraction.

Model selection with misspecified spatial covariance structure

Spatial data analysis has become more and more important in the studies of ecology and economics during the last decade. One focus of spatial data analysis is how to select predictors, variance functions and correlation functions. However, in general, the true covariance function is unknown and the working covariance structure is often misspecified. In this paper, our target is to find a good strategy to identify the best model from the candidate set using model selection criteria. This paper is to evaluate the ability of some information criteria (corrected Akaike information criterion, Bayesian information criterion (BIC) and residual information criterion (RIC)) for choosing the optimal model when the working correlation function, the working variance function and the working mean function are correct or misspecified. Simulations are carried out for small to moderate sample sizes. Four candidate covariance functions (exponential, Gaussian, Matern and rational quadratic) are used in simulation studies. With the summary in simulation results, we find that the misspecified working correlation structure can still capture some spatial correlation information in model fitting. When the sample size is large enough, BIC and RIC perform well even if the the working covariance is misspecified. Moreover, the performance of these information criteria is related to the average level of model fitting which can be indicated by the average adjusted R square ( [GRAPHICS] ), and overall RIC performs well.

Intra-cluster correlation structure in longitudinal data analysis: Selection criteria and misspecification tests

Selection criteria and misspecification tests for the intra-cluster correlation structure (ICS) in longitudinal data analysis are considered. In particular, the asymptotical distribution of the correlation information criterion (CIC) is derived and a new method for selecting a working ICS is proposed by standardizing the selection criterion as the p-value. The CIC test is found to be powerful in detecting misspecification of the working ICS structures, while with respect to the working ICS selection, the standardized CIC test is also shown to have satisfactory performance. Some simulation studies and applications to two real longitudinal datasets are made to illustrate how these criteria and tests might be useful.

Working covariance model selection for generalized estimating equations

We investigate methods for data-based selection of working covariance models in the analysis of correlated data with generalized estimating equations. We study two selection criteria: Gaussian pseudolikelihood and a geodesic distance based on discrepancy between model-sensitive and model-robust regression parameter covariance estimators. The Gaussian pseudolikelihood is found in simulation to be reasonably sensitive for several response distributions and noncanonical mean-variance relations for longitudinal data. Application is also made to a clinical dataset. Assessment of adequacy of both correlation and variance models for longitudinal data should be routine in applications, and we describe open-source software supporting this practice.

Modeling strategies in longitudinal data analysis: Covariate, variance function and correlation structure selection

A modeling paradigm is proposed for covariate, variance and working correlation structure selection for longitudinal data analysis. Appropriate selection of covariates is pertinent to correct variance modeling and selecting the appropriate covariates and variance function is vital to correlation structure selection. This leads to a stepwise model selection procedure that deploys a combination of different model selection criteria. Although these criteria find a common theoretical root based on approximating the Kullback-Leibler distance, they are designed to address different aspects of model selection and have different merits and limitations. For example, the extended quasi-likelihood information criterion (EQIC) with a covariance penalty performs well for covariate selection even when the working variance function is misspecified, but EQIC contains little information on correlation structures. The proposed model selection strategies are outlined and a Monte Carlo assessment of their finite sample properties is reported. Two longitudinal studies are used for illustration.