Optimal management of the critically Ill: Anaesthesia, monitoring, data capture, and point-of-care technological practices in ovine models of critical care

Animal models of critical illness are vital in biomedical research. They provide possibilities for the investigation of pathophysiological processes that may not otherwise be possible in humans. In order to be clinically applicable, the model should simulate the critical care situation realistically, including anaesthesia, monitoring, sampling, utilising appropriate personnel skill mix, and therapeutic interventions. There are limited data documenting the constitution of ideal technologically advanced large animal critical care practices and all the processes of the animal model. In this paper, we describe the procedure of animal preparation, anaesthesia induction and maintenance, physiologic monitoring, data capture, point-of-care technology, and animal aftercare that has been successfully used to study several novel ovine models of critical illness. The relevant investigations are on respiratory failure due to smoke inhalation, transfusion related acute lung injury, endotoxin-induced proteogenomic alterations, haemorrhagic shock, septic shock, brain death, cerebral microcirculation, and artificial heart studies. We have demonstrated the functionality of monitoring practices during anaesthesia required to provide a platform for undertaking systematic investigations in complex ovine models of critical illness.

Separable spatiotemporal priors for convex reconstruction of time-varying 3D point clouds

Reconstructing 3D motion data is highly under-constrained due to several common sources of data loss during measurement, such as projection, occlusion, or miscorrespondence. We present a statistical model of 3D motion data, based on the Kronecker structure of the spatiotemporal covariance of natural motion, as a prior on 3D motion. This prior is expressed as a matrix normal distribution, composed of separable and compact row and column covariances. We relate the marginals of the distribution to the shape, trajectory, and shape-trajectory models of prior art. When the marginal shape distribution is not available from training data, we show how placing a hierarchical prior over shapes results in a convex MAP solution in terms of the trace-norm. The matrix normal distribution, fit to a single sequence, outperforms state-of-the-art methods at reconstructing 3D motion data in the presence of significant data loss, while providing covariance estimates of the imputed points.

Milling innovations at Rocky Point

This paper reviews the innovations that have been introduced in the milling train at Rocky Point mill since 2001 and provides some operational, performance and maintenance comparisons of the technologies in use. The decision to install BHEM mills in the #2 and #3 mill positions to complement the six-roll mills in the #1 and #4 mill positions has proven a good one. Satisfactory performance is being obtained by these mills while maintenance costs are significantly less. Very good #1 mill extraction and final bagasse moisture content are being achieved. The innovation of using Hägglunds hydraulic drives at higher speed…

Turning gaming EEG peripherals into trainable brain computer interfaces

Companies such as NeuroSky and Emotiv Systems are selling non-medical EEG devices for human computer interaction. These devices are significantly more affordable than their medical counterparts, and are mainly used to measure levels of engagement, focus, relaxation and stress. This information is sought after for marketing research and games. However, these EEG devices have the potential to enable users to interact with their surrounding environment using thoughts only, without activating any muscles. In this paper, we present preliminary results that demonstrate that despite reduced voltage and time sensitivity compared to medical-grade EEG systems, the quality of the signals of the Emotiv EPOC neuroheadset is sufficiently good in allowing discrimina tion between imaging events. We collected streams of EEG raw data and trained different types of classifiers to discriminate between three states (rest and two imaging events). We achieved a generalisation error of less than 2% for two types of non-linear classifiers.

Turning up the Heat on Admissions: A Study of the Impacts of Extreme Heat Events on Tasmanian Hospital Admissions 2003-2010

Introduction: Extreme heat events (both heat waves and extremely hot days) are increasing in frequency and duration globally and cause more deaths in Australia than any other extreme weather event. Numerous studies have demonstrated a link between extreme heat events and an increased risk of morbidity and death. In this study, the researchers sought to identify if extreme heat events in the Tasmanian population were associated with any changes in emergency department admissions to the Royal Hobart Hospital (RHH) for the period 2003-2010. Methods: Non-identifiable RHH emergency department data and climate data from the Australian Bureau of Meteorology were obtained for the period 2003-2010. Statistical analyses were conducted using the computer statistical computer software ‘R’ with a distributed lag non-linear model (DLNM) package used to fit a quassi-Poisson generalised linear regression model. Results: This study showed that RR of admission to RHH during 2003-2010 was significant over temperatures of 24 C with a lag effect lasting 12 days and main effect noted one day after the extreme heat event. Discussion: This study demonstrated that extreme heat events have a significant impact on public hospital admissions. Two limitations were identified: admissions data rather than presentations data were used and further analysis could be done to compare types of admissions and presentations between heat and non-heat events. Conclusion: With the impacts of climate change already being felt in Australia, public health organisations in Tasmania and the rest of Australia need to implement adaptation strategies to enhance resilience to protect the public from the adverse health effects of heat events and climate change.

The problem of simplification: Think-tanks, recipes, equity and ‘Turning around low-performing schools’

Non-government actors such as think-tanks are playing an important role in Australian policy work. As governments increasingly outsource policy work previously done by education departments and academics to these new policy actors, more think-tanks have emerged that represent a wide range of political views and ideological positions. This paper looks at the emergence of the Grattan Institute as one significant player in Australian education policy with a particular emphasis on Grattan’s report ‘Turning around low-performing schools’. Grattan exemplifies many of the facets of Barber’s ‘deliverology’, as they produce reports designed to be easily digested, simply actioned and provide reassurance that there is an answer, often through focusing on ‘what works’ recipes. ‘Turning around low-performing schools’ is a perfect example of this deliverology. However, a close analysis of the Report suggests that it contains four major problems which seriously impact its usefulness for schools and policymakers: it ignores data that may be more important in explaining the turn-around of schools, the Report is overly reliant on NAPLAN data, there are reasons to be suspicious about the evidence assembled, and finally the Report falls into a classic trap of logic—the post hoc fallacy.

The death of newspapers: Have we reached the tipping point?

News Corp’s only substantial competitor in the print journalism sector may be on the brink of giving up the ghost, as has long been speculated even by its natural supporters such as Beecher. If it does, hundreds more jobs will go, along with the many hundreds of experienced, skilled journalists and editors already shown the door by the company.

Multi-GNSS precise point positioning with raw single-frequency and dual-frequency measurement models

The emergence of multiple satellite navigation systems, including BDS, Galileo, modernized GPS, and GLONASS, brings great opportunities and challenges for precise point positioning (PPP). We study the contributions of various GNSS combinations to PPP performance based on undifferenced or raw observations, in which the signal delays and ionospheric delays must be considered. A priori ionospheric knowledge, such as regional or global corrections, strengthens the estimation of ionospheric delay parameters. The undifferenced models are generally more suitable for single-, dual-, or multi-frequency data processing for single or combined GNSS constellations. Another advantage over ionospheric-free PPP models is that undifferenced models avoid noise amplification by linear combinations. Extensive performance evaluations are conducted with multi-GNSS data sets collected from 105 MGEX stations in July 2014. Dual-frequency PPP results from each single constellation show that the convergence time of undifferenced PPP solution is usually shorter than that of ionospheric-free PPP solutions, while the positioning accuracy of undifferenced PPP shows more improvement for the GLONASS system. In addition, the GLONASS undifferenced PPP results demonstrate performance advantages in high latitude areas, while this impact is less obvious in the GPS/GLONASS combined configuration. The results have also indicated that the BDS GEO satellites have negative impacts on the undifferenced PPP performance given the current “poor” orbit and clock knowledge of GEO satellites. More generally, the multi-GNSS undifferenced PPP results have shown improvements in the convergence time by more than 60 % in both the single- and dual-frequency PPP results, while the positioning accuracy after convergence indicates no significant improvements for the dual-frequency PPP solutions, but an improvement of about 25 % on average for the single-frequency PPP solutions.