Spatiotemporal aspects of Hendra Virus infection in Pteropid Bats (Flying-Foxes) in Eastern Australia

Hendra virus (HeV) causes highly lethal disease in horses and humans in the eastern Australian states of Queensland (QLD) and New South Wales (NSW), with multiple equine cases now reported on an annual basis. Infection and excretion dynamics in pteropid bats (flying-foxes), the recognised natural reservoir, are incompletely understood. We sought to identify key spatial and temporal factors associated with excretion in flying-foxes over a 2300 km latitudinal gradient from northern QLD to southern NSW which encompassed all known equine case locations. The aim was to strengthen knowledge of Hendra virus ecology in flying-foxes to improve spillover risk prediction and exposure risk mitigation strategies, and thus better protect horses and humans. Monthly pooled urine samples were collected from under roosting flying-foxes over a three-year period and screened for HeV RNA by quantitative RT-PCR. A generalised linear model was employed to investigate spatiotemporal associations with HeV detection in 13,968 samples from 27 roosts. There was a non-linear relationship between mean HeV excretion prevalence and five latitudinal regions, with excretion moderate in northern and central QLD, highest in southern QLD/northern NSW, moderate in central NSW, and negligible in southern NSW. Highest HeV positivity occurred where black or spectacled flying-foxes were present; nil or very low positivity rates occurred in exclusive grey-headed flying-fox roosts. Similarly, little red flying-foxes are evidently not a significant source of virus, as their periodic extreme increase in numbers at some roosts was not associated with any concurrent increase in HeV detection. There was a consistent, strong winter seasonality to excretion in the southern QLD/northern NSW and central NSW regions. This new information allows risk management strategies to be refined and targeted, mindful of the potential for spatial risk profiles to shift over time with changes in flying-fox species distribution.

Not in all Walks of Life? : Social Differences in Suicide Mortality

In Finland, the suicide mortality trend has been decreasing during the last decade and a half, yet suicide was the fourth most common cause of death among both Finnish men and women aged 15 64 years in 2006. However, suicide does not occur equally among population sub-groups. Two notable social factors that position people at different risk of suicide are socioeconomic and employment status: those with low education, employed in manual occupations, having low income and those who are unemployed have been found to have an elevated suicide risk. The purpose of this study was to provide a systematic analysis of these social differences in suicide mortality in Finland. Besides studying socioeconomic trends and differences in suicide according to age and sex, different indicators for socioeconomic status were used simultaneously, taking account of their pathways and mutual associations while also paying attention to confounding and mediatory effects of living arrangements and employment status. Register data obtained from Statistics Finland were used in this study. In some analyses suicides were divided into two groups according to contributory causes of death: the first group consisted of suicide deaths that had alcohol intoxication as one of the contributory causes, and the other group is comprised of all other suicide deaths. Methods included Poisson and Cox regression models. Despite the decrease in suicide mortality trend, social differences still exist. Low occupation-based social class proved to be an important determinant of suicide risk among both men and women, but the strong independent effect of education on alcohol-associated suicide indicates that the roots of these differences are probably established in early adulthood when educational qualifications are obtained and health-behavioural patterns set. High relative suicide mortality among the unemployed during times of economic boom suggests that selective processes may be responsible for some of the employment status differences in suicide. However, long-term unemployment seems to have causal effects on suicide, which, especially among men, partly stem from low income. In conclusion, the results in this study suggest that education, occupation-based social class and employment status have causal effects on suicide risk, but to some extent selection into low education and unemployment are also involved in the explanations for excess suicide mortality among the socially deprived. It is also conceivable that alcohol use is to some extent behind social differences in suicide. In addition to those with low education, manual workers and the unemployed, young people, whose health-related behaviour is still to be adopted, would most probably benefit from suicide prevention programmes.

Risk minimization in water quality control problems of a river system

Methodologies are presented for minimization of risk in a river water quality management problem. A risk minimization model is developed to minimize the risk of low water quality along a river in the face of conflict among various stake holders. The model consists of three parts: a water quality simulation model, a risk evaluation model with uncertainty analysis and an optimization model. Sensitivity analysis, First Order Reliability Analysis (FORA) and Monte-Carlo simulations are performed to evaluate the fuzzy risk of low water quality. Fuzzy multiobjective programming is used to formulate the multiobjective model. Probabilistic Global Search Laussane (PGSL), a global search algorithm developed recently, is used for solving the resulting non-linear optimization problem. The algorithm is based on the assumption that better sets of points are more likely to be found in the neighborhood of good sets of points, therefore intensifying the search in the regions that contain good solutions. Another model is developed for risk minimization, which deals with only the moments of the generated probability density functions of the water quality indicators. Suitable skewness values of water quality indicators, which lead to low fuzzy risk are identified. Results of the models are compared with the results of a deterministic fuzzy waste load allocation model (FWLAM), when methodologies are applied to the case study of Tunga-Bhadra river system in southern India, with a steady state BOD-DO model. The fractional removal levels resulting from the risk minimization model are slightly higher, but result in a significant reduction in risk of low water quality. (c) 2005 Elsevier Ltd. All rights reserved.

A new ground motion intensity measure, peak filtered acceleration (PFA), to estimate collapse vulnerability of buildings in earthquakes

In this thesis, we develop an efficient collapse prediction model, the PFA (Peak Filtered Acceleration) model, for buildings subjected to different types of ground motions.

For the structural system, the PFA model covers modern steel and reinforced concrete moment-resisting frame buildings (potentially reinforced concrete shear wall buildings). For ground motions, the PFA model covers ramp-pulse-like ground motions, long-period ground motions, and short-period ground motions.

To predict whether a building will collapse in response to a given ground motion, we first extract long-period components from the ground motion using a Butterworth low-pass filter with suggested order and cutoff frequency. The order depends on the type of ground motion, and the cutoff frequency depends on the building’s natural frequency and ductility. We then compare the filtered acceleration time history with the capacity of the building. The capacity of the building is a constant for 2-dimentional buildings and a limit domain for 3-dimentional buildings. If the filtered acceleration exceeds the building’s capacity, the building is predicted to collapse. Otherwise, it is expected to survive the ground motion.

The parameters used in PFA model, which include fundamental period, global ductility and lateral capacity, can be obtained either from numerical analysis or interpolation based on the reference building system proposed in this thesis.

The PFA collapse prediction model greatly reduces computational complexity while archiving good accuracy. It is verified by FEM simulations of 13 frame building models and 150 ground motion records.

Based on the developed collapse prediction model, we propose to use PFA (Peak Filtered Acceleration) as a new ground motion intensity measure for collapse prediction. We compare PFA with traditional intensity measures PGA, PGV, PGD, and Sa in collapse prediction and find that PFA has the best performance among all the intensity measures.

We also provide a close form in term of a vector intensity measure (PGV, PGD) of the PFA collapse prediction model for practical collapse risk assessment.

Surface roughness noise prediction for silent aircraft experimental design SAX-40

Surface roughness noise is a potentially important contributor to airframe noise. In this paper, noise assessment due to surface roughness is performed for a conceptual Silent Aircraft design SAX-40 by means of a prediction model developed in previous theoretical work and validated experimentally. Estimates of three idealized test cases show that surface roughness could produce a significant noise level above that due to the trailing edge at high frequencies. Roughness height and roughness density are the two most significant parameters influencing surface roughness noise, with roughness height having the dominant effect. The ratio of roughness height to boundary-layer thickness is the relevant non-dimensional parameter and this decreases in the streamwise direction. The candidate surface roughness is selected for SAX-40 to meet an aggressive noise target and keep surface roughness noise at a negligible level. Copyright © 2008 by Yu Liu and Ann P. Dowling.

Design, construction and evaluation of systems to predict risk in obstetrics.

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

National Centers for Coastal Ocean Science (NCCOS) research highlights in the Chesapeake Bay

The Chesapeake Bay is the largest estuary in the United States. It is a unique and valuable national treasure because of its ecological, recreational, economic and cultural benefits. The problems facing the Bay are well known and extensively documented, and are largely related to human uses of the watershed and resources within the Bay. Over the past several decades as the origins of the Chesapeake’s problems became clear, citizens groups and Federal, State, and local governments have entered into agreements and worked together to restore the Bay’s productivity and ecological health. In May 2010, President Barack Obama signed Executive Order number 13508 that tasked a team of Federal agencies to develop a way forward in the protection and restoration of the Chesapeake watershed. Success of both State and Federal efforts will depend on having relevant, sound information regarding the ecology and function of the system as the basis of management and decision making. In response to the executive order, the National Oceanic and Atmospheric Administration’s National Centers for Coastal Ocean Science (NCCOS) has compiled an overview of its research in Chesapeake Bay watershed. NCCOS has a long history of Chesapeake Bay research, investigating the causes and consequences of changes throughout the watershed’s ecosystems. This document presents a cross section of research results that have advanced the understanding of the structure and function of the Chesapeake and enabled the accurate and timely prediction of events with the potential to impact both human communities and ecosystems. There are three main focus areas: changes in land use patterns in the watershed and the related impacts on contaminant and pathogen distribution and concentrations; nutrient inputs and algal bloom events; and habitat use and life history patterns of species in the watershed. Land use changes in the Chesapeake Bay watershed have dramatically changed how the system functions. A comparison of several subsystems within the Bay drainages has shown that water quality is directly related to land use and how the land use affects ecosystem health of the rivers and streams that enter the Chesapeake Bay. Across the Chesapeake as a whole, the rivers that drain developed areas, such as the Potomac and James rivers, tend to have much more highly contaminated sediments than does the mainstem of the Bay itself. In addition to what might be considered traditional contaminants, such as hydrocarbons, new contaminants are appearing in measurable amounts. At fourteen sites studied in the Bay, thirteen different pharmaceuticals were detected. The impact of pharmaceuticals on organisms and the people who eat them is still unknown. The effects of water borne infections on people and marine life are known, however, and the exposure to certain bacteria is a significant health risk. A model is now available that predicts the likelihood of occurrence of a strain of bacteria known as Vibrio vulnificus throughout Bay waters.

A habitat-use model to determine essential fish habitat for juvenile brown shrimp (Farfantepenaeus aztecus) in Galveston Bay, Texas

A density prediction model for juvenile brown shrimp (Farfantepenaeus aztecus) was developed by using three bottom types, five salinity zones, and four seasons to quantify patterns of habitat use in Galveston Bay, Texas. Sixteen years of quantitative density data were used. Bottom types were vegetated marsh edge, submerged aquatic vegetation, and shallow nonvegetated bottom. Multiple regression was used to develop density estimates, and the resultant formula was then coupled with a geographical information system (GIS) to provide a spatial mosaic (map) of predicted habitat use. Results indicated that juvenile brown shrimp (<100 mm) selected vegetated habitats in salinities of 15−25 ppt and that seagrasses were selected over marsh edge where they co-occurred. Our results provide a spatially resolved estimate of high-density areas that will help designate essential fish habitat (EFH) in Galveston Bay. In addition, using this modeling technique, we were able to provide an estimate of the overall population of juvenile brown shrimp (<100 mm) in shallow water habitats within the bay of approximately 1.3 billion. Furthermore, the geographic range of the model was assessed by plotting observed (actual) versus expected (model) brown shrimp densities in three other Texas bays. Similar habitat-use patterns were observed in all three bays—each having a coefficient of determination >0.50. These results indicate that this model may have a broader geographic application and is a plausible approach in refining current EFH designations for all Gulf of Mexico estuaries with similar geomorphological and hydrological characteristics.

Surface excavation with model predictive control

This paper describes the application of variable-horizon model predictive control to trajectory generation in surface excavation. A nonlinear dynamic model of a surface mining machine digging in oil sand is developed as a test platform. This model is then stabilised with an inner-loop controller before being linearised to generate a prediction model. The linear model is used to design a predictive controller for trajectory generation. A variable horizon formulation is augmented with extra terms in the cost function to allow more control over digging, whilst still preserving the guarantee of finite-time completion. Simulations show the generation of realistic trajectories, motivating new applications of variable horizon MPC for autonomy that go beyond the realm of vehicle path planning. ©2010 IEEE.

Bayesian Structured Prediction Using Gaussian Processes

We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M3N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct.

滑坡泥石流信息遥感获取与空间预测

On the issue of geological hazard evaluation（GHE）, taking remote sensing and GIS systems as experimental environment, assisting with some programming development, this thesis combines multi-knowledges of geo-hazard mechanism, statistic learning, remote sensing (RS), high-spectral recognition, spatial analysis, digital photogrammetry as well as mineralogy, and selects geo-hazard samples from Hong Kong and Three Parallel River region as experimental data, to study two kinds of core questions of GHE, geo-hazard information acquiring and evaluation model. In the aspect of landslide information acquiring by RS, three detailed topics are presented, image enhance for visual interpretation, automatic recognition of landslide as well as quantitative mineral mapping. As to the evaluation model, the latest and powerful data mining method, support vector machine (SVM), is introduced to GHE field, and a serious of comparing experiments are carried out to verify its feasibility and efficiency. Furthermore, this paper proposes a method to forecast the distribution of landslides if rainfall in future is known baseing on historical rainfall and corresponding landslide susceptibility map. The details are as following: (a) Remote sensing image enhancing methods for geo-hazard visual interpretation. The effect of visual interpretation is determined by RS data and image enhancing method, for which the most effective and regular technique is image merge between high-spatial image and multi-spectral image, but there are few researches concerning the merging methods of geo-hazard recognition. By the comparing experimental of six mainstream merging methods and combination of different remote sensing data source, this thesis presents merits of each method ,and qualitatively analyzes the effect of spatial resolution, spectral resolution and time phase on merging image. (b) Automatic recognition of shallow landslide by RS image. The inventory of landslide is the base of landslide forecast and landslide study. If persistent collecting of landslide events, updating the geo-hazard inventory in time, and promoting prediction model incessantly, the accuracy of forecast would be boosted step by step. RS technique is a feasible method to obtain landslide information, which is determined by the feature of geo-hazard distribution. An automatic hierarchical approach is proposed to identify shallow landslides in vegetable region by the combination of multi-spectral RS imagery and DEM derivatives, and the experiment is also drilled to inspect its efficiency. (c) Hazard-causing factors obtaining. Accurate environmental factors are the key to analyze and predict the risk of regional geological hazard. As to predict huge debris flow, the main challenge is still to determine the startup material and its volume in debris flow source region. Exerting the merits of various RS technique, this thesis presents the methods to obtain two important hazard-causing factors, DEM and alteration mineral, and through spatial analysis, finds the relationship between hydrothermal clay alteration minerals and geo-hazards in the arid-hot valleys of Three Parallel Rivers region. (d) Applying support vector machine (SVM) to landslide susceptibility mapping. Introduce the latest and powerful statistical learning theory, SVM, to RGHE. SVM that proved an efficient statistic learning method can deal with two-class and one-class samples, with feature avoiding produce ‘pseudo’ samples. 55 years historical samples in a natural terrain of Hong Kong are used to assess this method, whose susceptibility maps obtained by one-class SVM and two-class SVM are compared to that obtained by logistic regression method. It can conclude that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, only requires failed cases, has an advantage over the other two methods as only "failed" case information is usually available in landslide susceptibility mapping. (e) Predicting the distribution of rainfall-induced landslides by time-series analysis. Rainfall is the most dominating factor to bring in landslides. More than 90% losing and casualty by landslides is introduced by rainfall, so predicting landslide sites under certain rainfall is an important geological evaluating issue. With full considering the contribution of stable factors (landslide susceptibility map) and dynamic factors (rainfall), the time-series linear regression analysis between rainfall and landslide risk mapis presented, and experiments based on true samples prove that this method is perfect in natural region of Hong Kong. The following 4 practicable or original findings are obtained: 1) The RS ways to enhance geo-hazards image, automatic recognize shallow landslides, obtain DEM and mineral are studied, and the detailed operating steps are given through examples. The conclusion is practical strongly. 2) The explorative researching about relationship between geo-hazards and alteration mineral in arid-hot valley of Jinshajiang river is presented. Based on standard USGS mineral spectrum, the distribution of hydrothermal alteration mineral is mapped by SAM method. Through statistic analysis between debris flows and hazard-causing factors, the strong correlation between debris flows and clay minerals is found and validated. 3) Applying SVM theory (especially one-class SVM theory) to the landslide susceptibility mapping and system evaluation for its performance is also carried out, which proves that advantages of SVM in this field. 4) Establishing time-serial prediction method for rainfall induced landslide distribution. In a natural study area, the distribution of landslides induced by a storm is predicted successfully under a real maximum 24h rainfall based on the regression between 4 historical storms and corresponding landslides.

滑坡和泥石流致灾条件叠合技术及风险分析-以川藏公路然乌至鲁朗段为例

As one part of national road No. 318, Sichuan-Tibet (Chengdu-Lasha) Highway is one of traffic life lines connecting Tibet municipality to the inland, which is very important to the economic development of Tibet. In addition, it is still an important national defence routeway, with extremely important strategic position on maintaining the stability and solidarity of Tibet municipality and consolidating national defence. Particular geological condition, terrain and landform condition and hydrometeorological condition induce large-scale debris flows and landslides (including landslips) and the like geological hazards frequently occur along the highway. High frequency geological hazards not only result in high casualties and a great property loss, but also block traffic at every turn, obstructing the Sichuan-Tibet highway seriously. On the basis of considerable engineering geological investigation and analysis to the relative studying achievements of predecessors, it is found that one of the dominating reason incurring landslides or debris flows again and again in a place is that abundant loose materials are accumulated in valleys and slopes along the highway. Taking landslides' and debris flows along Ranwu-Lulang section of Sichuan-Tibet highway as studying objects, the sources and cause of formation of loose accumulation materials in the studying area are analyzed in detail, the major hazard-inducing conditions, hazard, dynamic risk, prediction of susceptibility degree of landslides and debris flows, and the relations between landslides and debris flows and various hazard-inducing conditions are systematically researched in this paper. All of these will provide scientific foundation for the future highway renovating and reducing and preventing geological hazards. For the purpose of quantitatively analyzing landslide and debris flow hazards, the conception of entropy and information entropy are extended, the conception of geological hazard entropy is brought forward, and relevant mathematics model is built. Additionally, a new approach for the dynamic risk analysis of landslide and debris flow is put forward based on the dynamic characteristics of the hazard of hazard-inducings and the vulnerability of hazard-bearings. The formation of landslide and debris flow is a non-linear process, which is synthetically affected by various factors, and whose formation mechanics is extremely complex. Aiming at this question, a muli-factors classifying and overlapping technique is brought forward on the basis of engineering geomechanics meta-synthesis (EGMS) thought and approach, and relevant mathematics model is also built to predict the susceptibility degree of landslide or debris flow. The example analysis result proves the validity of this thought and approach. To studying the problem that whether the formation and space distribution of landslides and debris flows are controlled by one or several hazard-inducing conditions, the theme graphics of landslides and debris flows hazard and various hazard-inducing conditions are overlapped to determine the relationship between hazard and hazard-inducing conditions. On this basis, the semi-quantitative engineering zonation of the studying area is carried out. In addition, the overlapping analysis method of the hazard-indue ing conditions of landslides and debris flows based on "digital graphics system" is advanced to orderly organize and effectively manage the spatial and attributive data of hazard and hazard-inducing conditions theme graphics, and to realize the effectively combination of graphics, images and figures.

Area factors and suicide: 5-year follow-up of the Northern Ireland population

Background: Suicide rates vary markedly between areas but it is unclear whether this is due to differences in population composition or to contextual factors operating at an area level.
Aims: To determine if area factors are independently related to suicide risk after adjustment for individual and family characteristics.
Method: A 5-year record linkage study was conducted of 1 116 748 non-institutionalised individuals aged 16-74 years, enumerated at the 2001 Northern Ireland census.
Results: The cohort experienced 566 suicides during follow-up. Suicide risks were lowest for women and for those who were married or cohabiting. Indicators of individual and household disadvantage and economic and health status at the time of the census were also strongly related to risk of suicide. The higher rates of suicide in the more deprived and socially fragmented areas disappeared after adjustment for individual and household factors. There was no significant relationship between population density and risk of suicide.
Conclusions: Differences in rates of suicide between areas are predominantly due to population characteristics rather than to area-level factors, which suggests that policies targeted at area-level factors are unlikely to significantly influence suicides rates.

Contribution of 30 Biomarkers to 10-year cardiovascular risk estimation in 2 population cohorts: The MONICA, risk, genetics, archiving and monograph (MORGAM) biomarker project

Background— Cardiovascular risk estimation by novel biomarkers needs assessment in disease-free population cohorts, followed up for incident cardiovascular events, assaying the serum and plasma archived at baseline. We report results from 2 cohorts in such a continuing study.
Methods and Results— Thirty novel biomarkers from different pathophysiological pathways were evaluated in 7915 men and women of the FINRISK97 population cohort with 538 incident cardiovascular events at 10 years (fatal or nonfatal coronary or stroke events), from which a biomarker score was developed and then validated in the 2551 men of the Belfast Prospective Epidemiological Study of Myocardial Infarction (PRIME) cohort (260 events). No single biomarker consistently improved risk estimation in FINRISK97 men and FINRISK97 women and the Belfast PRIME Men cohort after allowing for confounding factors; however, the strongest associations (with hazard ratio per SD in FINRISK97 men) were found for N-terminal pro-brain natriuretic peptide (1.23), C-reactive protein (1.23), B-type natriuretic peptide (1.19), and sensitive troponin I (1.18). A biomarker score was developed from the FINRISK97 cohort with the use of regression coefficients and lasso methods, with selection of troponin I, C-reactive protein, and N-terminal pro-brain natriuretic peptide. Adding this score to a conventional risk factor model in the Belfast PRIME Men cohort validated it by improved c-statistics (P=0.004) and integrated discrimination (P<0.0001) and led to significant reclassification of individuals into risk categories (P=0.0008).
Conclusions— The addition of a biomarker score including N-terminal pro-brain natriuretic peptide, C-reactive protein, and sensitive troponin I to a conventional risk model improved 10-year risk estimation for cardiovascular events in 2 middle-aged European populations. Further validation is needed in other populations and age groups.

Prediction-Based Power-Performance Adaptation of Multithreaded Scientific Codes

Computing has recently reached an inflection point with the introduction of multicore processors. On-chip thread-level parallelism is doubling approximately every other year. Concurrency lends itself naturally to allowing a program to trade performance for power savings by regulating the number of active cores; however, in several domains, users are unwilling to sacrifice performance to save power. We present a prediction model for identifying energy-efficient operating points of concurrency in well-tuned multithreaded scientific applications and a runtime system that uses live program analysis to optimize applications dynamically. We describe a dynamic phase-aware performance prediction model that combines multivariate regression techniques with runtime analysis of data collected from hardware event counters to locate optimal operating points of concurrency. Using our model, we develop a prediction-driven phase-aware runtime optimization scheme that throttles concurrency so that power consumption can be reduced and performance can be set at the knee of the scalability curve of each program phase. The use of prediction reduces the overhead of searching the optimization space while achieving near-optimal performance and power savings. A thorough evaluation of our approach shows a reduction in power consumption of 10.8 percent, simultaneous with an improvement in performance of 17.9 percent, resulting in energy savings of 26.7 percent.