Familial typical migraine: Linkage to chromosome 19p13 and evidence for genetic heterogeneity

Migraine is a frequent familial disorder that, in common with most multifactorial disorders, has an unknown etiology. The authors identified several families with multiple individuals affected by typical migraine using a single set of diagnostic criteria and studied these families for cosegregation between the disorder and markers on chromosome 19, the location of a mutation that causes a rare form of familial hemiplegic migraine (FHM). One large tested family showed both cosegregation and significant allele sharing for markers situated within or adjacent to the FHM locus. Multipoint GENEHUNTER results indicated significant excess allele sharing across a 12.6- cM region containing the FHM Ca2+ channel gene, CACNL1A4 (maximum nonparametric linkage Z score = 6.64, p = 0.0026), with a maximum parametric lod score of 1.92 obtained for a (CAG)(n) triplet repeat polymorphism situated in exon 47 of this gene. The CAG expansion did not, however, appear to be the cause of migraine in this pedigree. Other tested families showed neither cosegregation nor excess allele sharing to chromosome 19 markers. HOMOG analysis indicated heterogeneity, generating a maximum HLOD score of 3.6. It was concluded that Chr19 mutations either in the CACNL1A4 gene or a closely linked gene are implicated in some pedigrees with familial typical migraine, and that the disorder is genetically heterogeneous.

Spatio-temporal modelling of ultrafine particle number concentration

This thesis developed semi-parametric regression models for estimating the spatio-temporal distribution of outdoor airborne ultrafine particle number concentration (PNC). The models developed incorporate multivariate penalised splines and random walks and autoregressive errors in order to estimate non-linear functions of space, time and other covariates. The models were applied to data from the "Ultrafine Particles from Traffic Emissions and Child" project in Brisbane, Australia, and to longitudinal measurements of air quality in Helsinki, Finland. The spline and random walk aspects of the models reveal how the daily trend in PNC changes over the year in Helsinki and the similarities and differences in the daily and weekly trends across multiple primary schools in Brisbane. Midday peaks in PNC in Brisbane locations are attributed to new particle formation events at the Port of Brisbane and Brisbane Airport.

The UltraCommuter : a viable and desirable solar-powered commuter vehicle

The University of Queensland UltraCommuter project is the demonstration of an ultra-light weight, low drag, energy efficient and low polluting, electric commuter vehicle equipped with a 2.5m2 on-board solar array. A key goal of the project is to make the vehicle predominantly self-sufficient from solar power for normal driving purposes , so that it does not require charging or refuelling from off-board sources. This paper examines the technical feasibility of the solar-powered commuter vehicle concept, as it applies the UltraCommuter project. A parametric description of a solar-powered commuter vehicle is presented. Real solar insolation data is then used to predict the solar driving range for the UltraCommuter and this is compared to typical urban usage patterns for commuter vehicles in Queensland. A comparative analysis of annual greenhouse gas emissions from the vehicle is also presented. The results show that the UltraCommuter’s on-board solar array can provide substantial supplementation of the energy required for normal driving, powering 90% of annual travel needs for an average QLD passenger vehicle. The vehicle also has excellent potential to reduce annual greenhouse gas emissions from the private transport sector, achieving a 98% reduction in CO2 emissions when compared to the average QLD passenger vehicle. Lastly, the vehicle battery pack provides for tolerance to consecutive days of poor weather without resorting to grid charging, giving uninterrupted functionality to the user. These results hold great promise for the technical feasibility of the solar-powered commuter vehicle concept.

Real - time prediction of rainfall induced instability of residual soil slopes associated with deep cracks

The early warning based on real-time prediction of rain-induced instability of natural residual slopes helps to minimise human casualties due to such slope failures. Slope instability prediction is complicated, as it is influenced by many factors, including soil properties, soil behaviour, slope geometry, and the location and size of deep cracks in the slope. These deep cracks can facilitate rainwater infiltration into the deep soil layers and reduce the unsaturated shear strength of residual soil. Subsequently, it can form a slip surface, triggering a landslide even in partially saturated soil slopes. Although past research has shown the effects of surface-cracks on soil stability, research examining the influence of deep-cracks on soil stability is very limited. This study aimed to develop methodologies for predicting the real-time rain-induced instability of natural residual soil slopes with deep cracks. The results can be used to warn against potential rain-induced slope failures. The literature review conducted on rain induced slope instability of unsaturated residual soil associated with soil crack, reveals that only limited studies have been done in the following areas related to this topic: - Methods for detecting deep cracks in residual soil slopes. - Practical application of unsaturated soil theory in slope stability analysis. - Mechanistic methods for real-time prediction of rain induced residual soil slope instability in critical slopes with deep cracks. Two natural residual soil slopes at Jombok Village, Ngantang City, Indonesia, which are located near a residential area, were investigated to obtain the parameters required for the stability analysis of the slope. A survey first identified all related field geometrical information including slope, roads, rivers, buildings, and boundaries of the slope. Second, the electrical resistivity tomography (ERT) method was used on the slope to identify the location and geometrical characteristics of deep cracks. The two ERT array models employed in this research are: Dipole-dipole and Azimuthal. Next, bore-hole tests were conducted at different locations in the slope to identify soil layers and to collect undisturbed soil samples for laboratory measurement of the soil parameters required for the stability analysis. At the same bore hole locations, Standard Penetration Test (SPT) was undertaken. Undisturbed soil samples taken from the bore-holes were tested in a laboratory to determine the variation of the following soil properties with the depth: - Classification and physical properties such as grain size distribution, atterberg limits, water content, dry density and specific gravity. - Saturated and unsaturated shear strength properties using direct shear apparatus. - Soil water characteristic curves (SWCC) using filter paper method. - Saturated hydraulic conductivity. The following three methods were used to detect and simulate the location and orientation of cracks in the investigated slope: (1) The electrical resistivity distribution of sub-soil obtained from ERT. (2) The profile of classification and physical properties of the soil, based on laboratory testing of soil samples collected from bore-holes and visual observations of the cracks on the slope surface. (3) The results of stress distribution obtained from 2D dynamic analysis of the slope using QUAKE/W software, together with the laboratory measured soil parameters and earthquake records of the area. It was assumed that the deep crack in the slope under investigation was generated by earthquakes. A good agreement was obtained when comparing the location and the orientation of the cracks detected by Method-1 and Method-2. However, the simulated cracks in Method-3 were not in good agreement with the output of Method-1 and Method-2. This may have been due to the material properties used and the assumptions made, for the analysis. From Method-1 and Method-2, it can be concluded that the ERT method can be used to detect the location and orientation of a crack in a soil slope, when the ERT is conducted in very dry or very wet soil conditions. In this study, the cracks detected by the ERT were used for stability analysis of the slope. The stability of the slope was determined using the factor of safety (FOS) of a critical slip surface obtained by SLOPE/W using the limit equilibrium method. Pore-water pressure values for the stability analysis were obtained by coupling the transient seepage analysis of the slope using finite element based software, called SEEP/W. A parametric study conducted on the stability of an investigated slope revealed that the existence of deep cracks and their location in the soil slope are critical for its stability. The following two steps are proposed to predict the rain-induced instability of a residual soil slope with cracks. (a) Step-1: The transient stability analysis of the slope is conducted from the date of the investigation (initial conditions are based on the investigation) to the preferred date (current date), using measured rainfall data. Then, the stability analyses are continued for the next 12 months using the predicted annual rainfall that will be based on the previous five years rainfall data for the area. (b) Step-2: The stability of the slope is calculated in real-time using real-time measured rainfall. In this calculation, rainfall is predicted for the next hour or 24 hours and the stability of the slope is calculated one hour or 24 hours in advance using real time rainfall data. If Step-1 analysis shows critical stability for the forthcoming year, it is recommended that Step-2 be used for more accurate warning against the future failure of the slope. In this research, the results of the application of the Step-1 on an investigated slope (Slope-1) showed that its stability was not approaching a critical value for year 2012 (until 31st December 2012) and therefore, the application of Step-2 was not necessary for the year 2012. A case study (Slope-2) was used to verify the applicability of the complete proposed predictive method. A landslide event at Slope-2 occurred on 31st October 2010. The transient seepage and stability analyses of the slope using data obtained from field tests such as Bore-hole, SPT, ERT and Laboratory tests, were conducted on 12th June 2010 following the Step-1 and found that the slope in critical condition on that current date. It was then showing that the application of the Step-2 could have predicted this failure by giving sufficient warning time.

Mathematical modelling of counter flow v-grove solar air collector

Double-pass counter flow v-grove collector is considered one of the most efficient solar air-collectors. In this design of the collector, the inlet air initially flows at the top part of the collector and changes direction once it reaches the end of the collector and flows below the collector to the outlet. A mathematical model is developed for this type of collector and simulation is carried out using MATLAB programme. The simulation results were verified with three distinguished research results and it was found that the simulation has the ability to predict the performance of the air collector accurately as proven by the comparison of experimental data with simulation. The difference between the predicted and experimental results is, at maximum, approximately 7% which is within the acceptable limit considering some uncertainties in the input parameter values to allow comparison. A parametric study was performed and it was found that solar radiation, inlet air temperature, flow rate and length have a significant effect on the efficiency of the air collector. Additionally, the results are compared with single flow V-groove collector.

Establishing definitions and modeling public transport travel time variability

Public transport travel time variability (PTTV) is essential for understanding deteriorations in the reliability of travel time, optimizing transit schedules and route choices. This paper establishes key definitions of PTTV in which firstly include all buses, and secondly include only a single service from a bus route. The paper then analyses the day-to-day distribution of public transport travel time by using Transit Signal Priority data. A comprehensive approach using both parametric bootstrapping Kolmogorov-Smirnov test and Bayesian Information Creation technique is developed, recommends Lognormal distribution as the best descriptor of bus travel time on urban corridors. The probability density function of Lognormal distribution is finally used for calculating probability indicators of PTTV. The findings of this study are useful for both traffic managers and statisticians for planning and researching the transit systems.

The impact of risk-reducing hysterectomy and bilateral salpingo-oophorectomy on survival in patients with a history of breast cancer – a population-based data linkage study

Prophylactic surgery including hysterectomy and bilateral salpingo-oophorectomy (BSO) is recommended in BRCA positive women, while in women from the general population, hysterectomy plus BSO may increase the risk of overall mortality. The effect of hysterectomy plus BSO on women previously diagnosed with breast cancer is unknown. We used data from a population-base data linkage study of all women diagnosed with primary breast cancer in Queensland, Australia between 1997 and 2008 (n=21,067). We fitted flexible parametric breast cancer specific and overall survival models with 95% confidence intervals (also known as Royston-Parmar models) to assess the impact of risk-reducing surgery (removal of uterus, one or both ovaries). We also stratified analyses by age 20-49 and 50-79 years, respectively. Overall, 1,426 women (7%) underwent risk-reducing surgery (13% of premenopausal women and 3% of postmenopausal women). No women who had risk-reducing surgery, compared to 171 who did not have risk-reducing surgery developed a gynaecological cancer. Overall, 3,165 (15%) women died, including 2,195 (10%) from breast cancer. Hysterectomy plus BSO was associated with significantly reduced risk of death overall (adjusted HR = 0.69, 95% CI 0.53-0.89; P =0.005). Risk reduction was greater among premenopausal women, whose risk of death halved (HR, 0.45; 95% CI, 0.25-0.79; P < 0.006). This was largely driven by reduction in breast cancer-specific mortality (HR, 0.43; 95% CI, 0.24-0.79; P < 0.006). This population-based study found that risk-reducing surgery halved the mortality risk for premenopausal breast cancer patients. Replication of our results in independent cohorts, and subsequently randomised trials are needed to confirm these findings.

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots

Hot spot identification (HSID) aims to identify potential sites—roadway segments, intersections, crosswalks, interchanges, ramps, etc.—with disproportionately high crash risk relative to similar sites. An inefficient HSID methodology might result in either identifying a safe site as high risk (false positive) or a high risk site as safe (false negative), and consequently lead to the misuse the available public funds, to poor investment decisions, and to inefficient risk management practice. Current HSID methods suffer from issues like underreporting of minor injury and property damage only (PDO) crashes, challenges of accounting for crash severity into the methodology, and selection of a proper safety performance function to model crash data that is often heavily skewed by a preponderance of zeros. Addressing these challenges, this paper proposes a combination of a PDO equivalency calculation and quantile regression technique to identify hot spots in a transportation network. In particular, issues related to underreporting and crash severity are tackled by incorporating equivalent PDO crashes, whilst the concerns related to the non-count nature of equivalent PDO crashes and the skewness of crash data are addressed by the non-parametric quantile regression technique. The proposed method identifies covariate effects on various quantiles of a population, rather than the population mean like most methods in practice, which more closely corresponds with how black spots are identified in practice. The proposed methodology is illustrated using rural road segment data from Korea and compared against the traditional EB method with negative binomial regression. Application of a quantile regression model on equivalent PDO crashes enables identification of a set of high-risk sites that reflect the true safety costs to the society, simultaneously reduces the influence of under-reported PDO and minor injury crashes, and overcomes the limitation of traditional NB model in dealing with preponderance of zeros problem or right skewed dataset.

Numerical modelling of load bearing light gauge steel frame wall systems exposed to realistic design fires

Fire safety has become an important part in structural design due to the ever increasing loss of properties and lives during fires. Conventionally the fire rating of load bearing wall systems made of Light gauge Steel Frames (LSF) is determined using fire tests based on the standard time-temperature curve in ISO834 [1]. However, modern commercial and residential buildings make use of thermoplastic materials, which mean considerably high fuel loads. Hence a detailed fire research study into the fire performance of LSF walls was undertaken using realistic design fire curves developed based on Eurocode parametric [2] and Barnett’s BFD [3] curves using both full scale fire tests and numerical studies. It included LSF walls without cavity insulation, and the recently developed externally insulated composite panel system. This paper presents the details of finite element models developed to simulate the full scale fire tests of LSF wall panels under realistic design fires. Finite element models of LSF walls exposed to realistic design fires were developed, and analysed under both transient and steady state fire conditions using the measured stud time-temperature curves. Transient state analyses were performed to simulate fire test conditions while steady state analyses were performed to obtain the load ratio versus time and failure temperature curves of LSF walls. Details of the developed finite element models and the results including the axial deformation and lateral deflection versus time curves, and the stud failure modes and times are presented in this paper. Comparison with fire test results demonstrate the ability of developed finite element models to predict the performance and fire resistance ratings of LSF walls under realistic design fires.

Visualization of predictive distributions for discrete spatial-temporal log Cox processes approximated with MCMC

An important aspect of decision support systems involves applying sophisticated and flexible statistical models to real datasets and communicating these results to decision makers in interpretable ways. An important class of problem is the modelling of incidence such as fire, disease etc. Models of incidence known as point processes or Cox processes are particularly challenging as they are ‘doubly stochastic’ i.e. obtaining the probability mass function of incidents requires two integrals to be evaluated. Existing approaches to the problem either use simple models that obtain predictions using plug-in point estimates and do not distinguish between Cox processes and density estimation but do use sophisticated 3D visualization for interpretation. Alternatively other work employs sophisticated non-parametric Bayesian Cox process models, but do not use visualization to render interpretable complex spatial temporal forecasts. The contribution here is to fill this gap by inferring predictive distributions of Gaussian-log Cox processes and rendering them using state of the art 3D visualization techniques. This requires performing inference on an approximation of the model on a discretized grid of large scale and adapting an existing spatial-diurnal kernel to the log Gaussian Cox process context.

Noise levels in a general intensive care unit : a descriptive study.

The aim of this small-scale study was to measure, analyse and compare levels of acoustic noise, in a nine-bedded general intensive care unit (ICU). Measurements were undertaken using the Norsonic 116 sound level meter recording noise levels in the internationally agreed ‘A’ weighted scale. Noise level data were obtained and recorded at 5 min over 3 consecutive days. Results of noise level analysis indicated that mean noise levels within this clinical area was 56·42 dB(A), with acute spikes reaching 80 dB(A). The quietest noise level attained was that of 50 dB(A) during sporadic intervals throughout the 24-h period. Parametric testing using analysis of variance found a positive relationship (p ≤ 0·001) between the nursing shifts and the day of the week. However, Scheffe multiple range testing showed significant differences between the morning shift, and the afternoon and night shifts combined (p ≤ 0·05). There was no statistical difference between the afternoon and night shifts (p ≥ 0·05). While the results of this study may seem self-evident in many respects, what it has highlighted is that the problem of excessive noise exposure within the ICU continues to go unabated. More concerning is that the prolonged effects of excessive noise exposure on patients and staff alike can have deleterious effect on the health and well-being of these individuals.

Noise levels in a General Surgical Ward : a descriptive study

Aims and objectives.  This study was undertaken to measure and analyse levels of acoustic noise in a General Surgical Ward. Method.  Measurements were undertaken using the Norsonic 116 sound level meter (SLM) recording noise levels in the internationally agreed ‘A’ weighted scale. Noise level data and observational data as to the number of staff present were obtained and recorded at 5-min intervals over three consecutive days. Results.  Results of noise level analysis indicated that mean noise level within this clinical area was 42.28 dB with acute spikes reaching 70 dB(A). The lowest noise level attained was that of 36 dB(A) during the period midnight to 7 a.m. Non-parametric testing, using Spearman's Rho (two-tailed), found a positive relationship between the number of staff present and the level of noise recorded, indicating that the presence of hospital personnel strongly influences the level of noise within this area. Relevance to clinical practice.  Whilst the results of this may seem self-evident in many respects the problems of excessive noise production and the exposure to it for patients, hospital personnel and relatives alike continues unabated. What must be of concern is the psychophysiological effects excessive noise exposure has on individuals, for example, decreased wound healing, sleep deprivation and cardiovascular stimulation.

What knowledge do ICU nurses have with regard to the effects of noise exposure in the Intensive Care Unit?

This small-scale study was undertaken to assess what knowledge nursing staff from a General Intensive Care Unit held with regard to noise exposure. To assess knowledge a self-administered multiple-choice questionnaire was used. Rigorous peer-review insured content validity. This study produced poor results in terms of the knowledge nurses held with regard to noise related issues in particular the psychophysiological effects and current legislation concerning its safe exposure. Non-parametric testing, using Kruskal–Wallis found no significant difference between nursing grades, however, descriptive analysis demonstrated that the staff nurse grade (D and E) performed better overall. Whilst the results of this study may seem self-evident in some respects, it is the problems of exposure to excessive noise levels for both patients and hospital personnel, which are clearly not understood. The effects noise exposure has on individuals for example decreased wound healing; sleep deprivation and cardiovascular stimulation must be of concern especially in terms of patient care but more so for nursing staff especially the effects noise levels can have on cognitive task performance.

A spatial-indexing model for measuring neighbourhood-level land-use and transport integration

Significant attention has been given in urban policy literature to the integration of land-use and transport planning and policies—with a view to curbing sprawling urban form and diminishing externalities associated with car-dependent travel patterns. By taking land-use and transport interaction into account, this debate mainly focuses on how a successful integration can contribute to societal well-being, providing efficient and balanced economic growth while accomplishing the goal of developing sustainable urban environments and communities. The integration is also a focal theme of contemporary urban development models, such as smart growth, liveable neighbourhoods, and new urbanism. Even though available planning policy options for ameliorating urban form and transport-related externalities have matured—owing to growing research and practice worldwide—there remains a lack of suitable evaluation models to reflect on the current status of urban form and travel problems or on the success of implemented integration policies. In this study we explore the applicability of indicator-based spatial indexing to assess land-use and transport integration at the neighbourhood level. For this, a spatial index is developed by a number of indicators compiled from international studies and trialled in Gold Coast, Queensland, Australia. The results of this modelling study reveal that it is possible to propose an effective metric to determine the success level of city plans considering their sustainability performance via composite indicator methodology. The model proved useful in demarcating areas where planning intervention is applicable, and in identifying the most suitable locations for future urban development and plan amendments. Lastly, we integrate variance-based sensitivity analysis with the spatial indexing method, and discuss the applicability of the model in other urban contexts.

Fire performance of cold-formed steel wall panels and prediction of their fire resistance rating

Recent research at the Queensland University of Technology has investigated the structural and thermal behaviour of load bearing Light gauge Steel Frame (LSF) wall systems made of 1.15 mm G500 steel studs and varying plasterboard and insulation configurations (cavity and external insulation) using full scale fire tests. Suitable finite element models of LSF walls were then developed and validated by comparing with test results. In this study, the validated finite element models of LSF wall panels subject to standard fire conditions were used in a detailed parametric study to investigate the effects of important parameters such as steel grade and thickness, plasterboard screw spacing, plasterboard lateral restraint, insulation materials and load ratio on their performance under standard fire conditions. Suitable equations were proposed to predict the time–temperature profiles of LSF wall studs with eight different plasterboard-insulation configurations, and used in the finite element analyses. Finite element parametric studies produced extensive fire performance data for the LSF wall panels in the form of load ratio versus time and critical hot flange (failure) temperature curves for eight wall configurations. This data demonstrated the superior fire performance of externally insulated LSF wall panels made of different steel grades and thicknesses. It also led to the development of a set of equations to predict the important relationship between the load ratio and the critical hot flange temperature of LSF wall studs. Finally this paper proposes a simplified method to predict the fire resistance rating of LSF walls based on the two proposed set of equations for the load ratio–hot flange temperature and the time–temperature relationships.