The association between attributions of responsibility for motor vehicle accidents and patient satisfaction: a study within a no-fault injury compensation system

This study set out to test the relationship between attributions of responsibility for motor vehicle accidents and satisfaction with personal injury compensation systems.

The predictive validity of bipolar at-risk (prodromal) criteria in help-seeking adolescents and young adults: a prospective study.

There are no established tools to identify individuals at risk for developing bipolar disorder. We developed a set of ultra-high-risk criteria for bipolar disorder [bipolar at-risk (BAR)]. The primary aim of the present study was to determine the predictive validity of the BAR criteria.

Investigating the predictive ability of gait speed and quadriceps strength for incident falls in community-dwelling older women at high risk of fracture

Gait speed is a recommended geriatric assessment of physical performance, but may not be regularly examined in clinical settings. We aimed to investigate whether quadriceps strength tests demonstrate similar predictive ability for incident falls as gait speed in older women. We investigated 135 female volunteers aged mean±SD 76.7±5.0 years (range 70-92) at high risk of fracture. Participants completed gait speed assessments using the GAITRite Electronic Walkway System, and quadriceps strength assessments using a hand-held dynamometer (HHD). Participants reported incident falls monthly for 3.7±1.2 years. N=99 (73%) participants fell 355 times during the follow-up period (mean fall rate 83 per 100 person years). We observed a reduced odds ratio for multiple falls (0.83, 95% CI 0.70-0.98) and a reduced hazard ratio for time to first fall (0.90, 95% CI 0.83-0.98), according to quadriceps strength. There was also a significantly shorter time to first fall for those with low quadriceps strength (<7.0 kg; lowest tertile) compared with those with normal quadriceps strength (estimated means [95% CI] 1.54 [1.02, 2.06] vs. 2.23 [1.82, 2.64] years; P=0.019), but not for those with low (<1.0 m/s) vs. normal gait speed (P=0.15). Quadriceps strength is a significant predictor of incident falls over three years amongst community-dwelling older women at high risk of fracture. Quadriceps strength tests may be an acceptable alternative to gait speed for geriatric assessments of falls risk.

Edge patterns in aquatic invertebrates explained by predictive models

Predictive frameworks for understanding and describing how animals respond to habitat fragmentation, particularly across edges, have been largely restricted to terrestrial systems. Abundances of zooplankton and meiofauna were measured across seagrasssand edges and the patterns compared with predictive models of edge effects. Artificial seagrass patches were placed on bare sand, and zooplankton and meiofauna were sampled with tube traps at five positions (from patch edges: 12, 60 and 130 cm into seagrass; and 12 and 60 cm onto sand). Position effects consisted of the following three general patterns: (1) increases in abundance around the seagrasssand edge (total abundance and cumaceans); (2) declining abundance from seagrass onto sand (calanoid copepods, harpacticoid copepods and amphipods); and (3) increasing abundance from seagrass onto sand (crustacean nauplii and bivalve larvae). The first two patterns are consistent with resource-distribution models, either as higher resources at the confluence of adjacent habitats or supplementation of resources from high-quality to low-quality habitat. The third pattern is consistent with reductions in zooplankton abundance as a consequence of predation or attenuation of currents by seagrass. The results show that predictive models of edge effects can apply to aquatic animals and that edges are important in structuring zooplankton and meiofauna assemblages in seagrass. © 2010 CSIRO.

IPA: Integrated predictive gene signature from gene expression based breast cancer patient samples

Background: Novel predictive markers are needed to accurately diagnose the breast cancer patients so they do not need to undergo any unnecessary aggressive therapies. Various gene expression studies based predictive gene signatureshave generated in the recent past to predict the binary estrogen-receptor subclass or to predict the therapy response subclass. However, the existing algorithms comes with many limitations, including low predictive performances over multiple cohorts of patients and non-significant or limited biological roles associated with thepredictive gene signatures. Therefore, the aim of this study is to develop novel predictive markers with improved performances.Methods: We propose a novel prediction algorithm called IPA to construct a predictive gene signature for performing multiple prediction tasks of predicting estrogen-receptor based binary subclass and predicting chemotherapy response (neoadjuvantly) based binary subclass. The constructed gene signature with considering multiple classification techniques was used to evaluate the algorithm performance on multiple cohorts of breast cancer patients.Results: The evaluation on multiple validation cohorts demonstrated that proposed algorithm achieved stable and high performance to perform prediction tasks, with consideration given to any classification techniques. We show that the predictive gene signature of our proposed algorithm reflects the mechanisms underlying the estrogen-receptors or response to therapy with significant greater biological interpretations, compared with the other existing algorithm.

Context sensitivity in action decreases along the autism spectrum: a predictive processing perspective

Recent predictive processing accounts of perception and action point towards a key challenge for the nervous system in dynamically optimizing the balance between incoming sensory information and existing expectations regarding the state of the environment. Here, we report differences in the influence of the preceding sensory context on motor function, varying with respect to both clinical and subclinical features of autism spectrum disorder (ASD). Reach-to-grasp movements were recorded subsequent to an inactive period in which illusory ownership of a prosthetic limb was induced. We analysed the sub-components of reach trajectories derived using a minimum-jerk fitting procedure. Non-clinical adults low in autistic features showed disrupted movement execution following the illusion compared to a control condition. By contrast, individuals higher in autistic features (both those with ASD and non-clinical individuals high in autistic traits) showed reduced sensitivity to the presence of the illusion in their reaching movements while still exhibiting the typical perceptual effects of the illusion. Clinical individuals were distinct from non-clinical individuals scoring high in autistic features, however, in the early stages of movement. These results suggest that the influence of high-level representations of the environment differs between individuals, contributing to clinical and subclinical differences in motor performance that manifest in a contextual manner. As high-level representations of context help to explain fluctuations in sensory input over relatively longer time scales, more circumscribed sensitivity to prior or contextual information in autistic sensory processing could contribute more generally to reduced social comprehension, sensory impairments and a stronger desire for predictability and routine.

Intervention-driven predictive framework for modeling healthcare data

Assessing prognostic risk is crucial to clinical care, and critically dependent on both diagnosis and medical interventions. Current methods use this augmented information to build a single prediction rule. But this may not be expressive enough to capture differential effects of interventions on prognosis. To this end, we propose a supervised, Bayesian nonparametric framework that simultaneously discovers the latent intervention groups and builds a separate prediction rule for each intervention group. The prediction rule is learnt using diagnosis data through a Bayesian logistic regression. For inference, we develop an efficient collapsed Gibbs sampler. We demonstrate that our method outperforms baselines in predicting 30-day hospital readmission using two patient cohorts - Acute Myocardial Infarction and Pneumonia. The significance of this model is that it can be applied widely across a broad range of medical prognosis tasks. © 2014 Springer International Publishing.

A first step towards a simple in-line shape compensation routine for the roll forming of high strength steel

The roll forming process is increasingly used in the automotive industry for the manufacture of structural and crash components from Ultra High Strength Steel (UHSS). Due to the high strength of UHSS (<1GPa) even small and commonly observed material property variations from coil to coil can result in significant changes in material yield and through that affect the final shape of the roll formed component. This requires the re-adjustment of tooling to compensate for shape defects and maintain part geometry resulting in costly downtimes of equipment. This paper presents a first step towards an in-line shape compensation method that based on the monitoring of roll load and torque allows for the estimation of shape defects and the subsequent re-adjustment of tooling for compensation. For this the effect of material property variation on common shape defects observed in the roll forming process as well as measurable process parameters such as roll load and torque needs to be understood. The effect of yield strength and material hardening on roll load and torque as well as longitudinal bow is investigated via experimental trials and numerical analysis. A regression analysis combined with Analysis of Variance (ANOVA) techniques is employed to establish the relationships between the process and material parameters and to determine their percentage influence on longitudinal bow, roll load and torque. The study will show that the level of longitudinal bow, one of the major shape defects observed in roll forming, can be estimated by variations in roll load and torque.

In-line shape compensation for roll forming through process parameter monitoring

Buddhika’s Phd topic is In-line shape compensation for roll forming through process parameter monitoring. It mainly discussed about defects monitoring and compensation in high strength steel roll forming for automotive applications.

Predictive growth budgets in terns and gulls

Energy budgets for nestling growth are presented for sandwich tern Sterna sandvicensis, common tern S. hirundo, Arctic tern S. paradisaea, and herring gull Larus argentatus. Energy used in the production of body tissue averaged 27% (of which 7% for biosynthesis) while BMR accounted for 45%, the remainder being cost of activity and thermoregulation (28%). Where quantified, cost of temperature regulation accounted for only 10% of the total expenditure under field conditions. A regression made of metabolic energy (ME) intake over the entire nestling period against body mass of the fledgling based on eight studies of gulls and terns resulted in ME=35.14×M1.0105. -from Authors

Rebound or resignation: developing a predictive model of return to subjective wellbeing set-point

Though subjective wellbeing (SWB) is generally stable and consistent over time, it can fall below its set-point in response to adverse life events. However, deviations from set-point levels are usually only temporary, as homeostatic processes operate to return SWB to its normal state. Given that income and close interpersonal relationships have been proposed as powerful external resources that are coincident with higher SWB, access to these resources may be an important predictor of whether or not a person is likely to recover their SWB following a departure from their set-point. Under the guiding framework of SWB Homeostasis Theory, this study considers whether access to a higher income and a committed partner can predict whether people who score lower than normal for SWB at baseline will return to normal set-point levels of SWB (rebound) or remain below the normal range (resigned) at follow-up. Participants were 733 people (53.3 % female) from the Australian Unity Longitudinal Wellbeing Study who ranged in age from 20 to 92 years (M = 59.65 years; SD = 13.15). Logistic regression analyses revealed that participants’ demographic characteristics were poor predictors of whether they rebounded or resigned. Consistent with homeostasis theory, the extent of departure from the proposed normal SWB set-point at baseline was significantly associated with rebound or resignation at time 2. These findings have implications for the way that SWB measures can be used in professional practice to identify people who are particularly vulnerable to depression and to guide the provision of appropriate and effective therapeutic interventions.

Forests of the sea: predictive habitat modelling to assess the abundance of canopy forming kelp forests on temperate reefs

 Large brown seaweeds (kelps) form forests in temperate and boreal marine systems that serve as foundations to the structure and dynamics of communities. Mapping the distributions of these species is important to understanding the ecology of coastal environments, managing marine ecosystems (e.g., spatial planning), predicting consequences of climate change and the potential for carbon production. We demonstrate how combining seafloor mapping technologies (LiDAR and multibeam bathymetry) and models of wave energy to map the distribution and relative abundance of seaweed forests of Ecklonia radiata can provide complete coverage over hundreds of square kilometers. Using generalized linear mixed models (GLMMs), we associated observations of E. radiata abundance from video transects with environmental variables. These relationships were then used to predict the distribution of E. radiata across our 756.1km2 study area off the coast of Victoria, Australia. A reserved dataset was used to test the accuracy of these predictions. We found that the abundance distribution of E. radiata is strongly associated with depth, presence of rocky reef, curvature of the reef topography, and wave exposure. In addition, the GLMM methodology allowed us to adequately account for spatial autocorrelation in our sampling methods. The predictive distribution map created from the best GLMM predicted the abundance of E. radiata with an accuracy of 72%. The combination of LiDAR and multibeam bathymetry allowed us to model and predict E. radiata abundance distribution across its entire depth range for this study area. Using methods like those presented in this study, we can map the distribution of macroalgae species, which will give insight into ecological communities, biodiversity distribution, carbon uptake, and potential sequestration.