Legal issues related to Accountable-eHealth systems in Australia

Information privacy requirements of patients and information requirements of healthcare providers (HCP) are competing concerns. Reaching a balance between these requirements have proven difficult but is crucial for the success of eHealth systems. The traditional approaches to information management have been preventive measures which either allow or deny access to information. We believe that this approach is inappropriate for a domain such as healthcare. We contend that introducing information accountability (IA) to eHealth systems can reach the aforementioned balance without the need for rigid information control. IA is a fairly new concept to computer science, hence; there are no unambiguously accepted principles as yet. But the concept delivers promising advantages to information management in a robust manner. Accountable-eHealth (AeH) systems are eHealth systems which use IA principles as the measure for privacy and information management. AeH systems face three main impediments; technological, social and ethical and legal. In this paper, we present the AeH model and focus on the legal aspects of AeH systems in Australia. We investigate current legislation available in Australia regarding health information management and identify future legal requirements if AeH systems are to be implemented in Australia.

Reliability of partial ambiguity fixing with multiple GNSS constellations

Reliable ambiguity resolution (AR) is essential to Real-Time Kinematic (RTK) positioning and its applications, since incorrect ambiguity fixing can lead to largely biased positioning solutions. A partial ambiguity fixing technique is developed to improve the reliability of AR, involving partial ambiguity decorrelation (PAD) and partial ambiguity resolution (PAR). Decorrelation transformation could substantially amplify the biases in the phase measurements. The purpose of PAD is to find the optimum trade-off between decorrelation and worst-case bias amplification. The concept of PAR refers to the case where only a subset of the ambiguities can be fixed correctly to their integers in the integer least-squares (ILS) estimation system at high success rates. As a result, RTK solutions can be derived from these integer-fixed phase measurements. This is meaningful provided that the number of reliably resolved phase measurements is sufficiently large for least-square estimation of RTK solutions as well. Considering the GPS constellation alone, partially fixed measurements are often insufficient for positioning. The AR reliability is usually characterised by the AR success rate. In this contribution an AR validation decision matrix is firstly introduced to understand the impact of success rate. Moreover the AR risk probability is included into a more complete evaluation of the AR reliability. We use 16 ambiguity variance-covariance matrices with different levels of success rate to analyse the relation between success rate and AR risk probability. Next, the paper examines during the PAD process, how a bias in one measurement is propagated and amplified onto many others, leading to more than one wrong integer and to affect the success probability. Furthermore, the paper proposes a partial ambiguity fixing procedure with a predefined success rate criterion and ratio-test in the ambiguity validation process. In this paper, the Galileo constellation data is tested with simulated observations. Numerical results from our experiment clearly demonstrate that only when the computed success rate is very high, the AR validation can provide decisions about the correctness of AR which are close to real world, with both low AR risk and false alarm probabilities. The results also indicate that the PAR procedure can automatically chose adequate number of ambiguities to fix at given high-success rate from the multiple constellations instead of fixing all the ambiguities. This is a benefit that multiple GNSS constellations can offer.

A virtual environment to simulate the experience of psychosis

Psychosis is a mental disorder that affects 1-2% of the population at some point in their lives. One of the main causes of psychosis is the mental illness schizophrenia. Sufferers of this illness often have terrifying symptoms such as hallucinations, delusions, and thought disorder. This project aims to develop a virtual environment to simulate the experience of psychosis, focusing on re-creating auditory and visual hallucinations. A model of a psychiatric ward was created and the psychosis simulation software was written to re-create the auditory and visual hallucinations of one particular patient. The patient was very impressed with the simulation, and commented that it effectively re-created the same emotions that she experienced on a day-to-day basis during her psychotic episodes. It is hoped that this work will result in a useful educational tool about schizophrenia, leading to improved training of clinicians, and fostering improved understanding and empathy toward sufferers of schizophrenia in the community, ultimately improving the quality of life and chances of recovery of patients.

Automatic particle picking of biological molecules imaged by electron microscopy

One of the next great challenges of cell biology is the determination of the enormous number of protein structures encoded in genomes. In recent years, advances in electron cryo-microscopy and high-resolution single particle analysis have developed to the point where they now provide a methodology for high resolution structure determination. Using this approach, images of randomly oriented single particles are aligned computationally to reconstruct 3-D structures of proteins and even whole viruses. One of the limiting factors in obtaining high-resolution reconstructions is obtaining a large enough representative dataset ($>100,000$ particles). Traditionally particles have been manually picked which is an extremely labour intensive process. The problem is made especially difficult by the low signal-to-noise ratio of the images. This paper describes the development of automatic particle picking software, which has been tested with both negatively stained and cryo-electron micrographs. This algorithm has been shown to be capable of selecting most of the particles, with few false positives. Further work will involve extending the software to detect differently shaped and oriented particles.