Julia Gillard, Big Pharma, patent law and public health

It takes a lot of bravery for governments to stand up to big business. But the Gillard government has shown a lot of guts during its tenure. It stood up to Big Tobacco in the battle over plain packaging of tobacco products and has defended individuals and families affected by asbestos. It took on Big Oil in its Clean Energy Future reforms and stood up to the resource barons with the mining tax. The government is now considering Big Pharma - the pharmaceutical industry and their patents – and has launched several inquiries into patent law and pharmaceutical drugs...

No future? End the Future Fund's affair with Big Tobacco

Australia has shown outstanding leadership on tobacco control - but it could do more. The next step is surely for the Future Fund to quit its addiction to tobacco investments.

Quality of information for quality of life: Healthcare big data analytics

Big data analysis in healthcare sector is still in its early stages when comparing with that of other business sectors due to numerous reasons. Accommodating the volume, velocity and variety of healthcare data Identifying platforms that examine data from multiple sources, such as clinical records, genomic data, financial systems, and administrative systems Electronic Health Record (EHR) is a key information resource for big data analysis and is also composed of varied co-created values. Successful integration and crossing of different subfields of healthcare data such as biomedical informatics and health informatics could lead to huge improvement for the end users of the health care system, i.e. the patients.

Information Accountability of Healthcare Big Data

Huge amount of data are generated from a variety of information sources in healthcare while the data sources originate from a veracity of clinical information systems and corporate data warehouses. The data derived from the above data sources are used for analysis and trending purposes thus playing an influential role as a real time decision-making tool. The unstructured, narrative data provided by these data sources qualify as healthcare big-data and researchers argue that the application of big-data in healthcare might enable the accountability and efficiency.

Adopting healthcare big data in Sri Lankan healthcare sector

The concept of big data has already outperformed traditional data management efforts in almost all industries. Other instances it has succeeded in obtaining promising results that provide value from large-scale integration and analysis of heterogeneous data sources for example Genomic and proteomic information. Big data analytics have become increasingly important in describing the data sets and analytical techniques in software applications that are so large and complex due to its significant advantages including better business decisions, cost reduction and delivery of new product and services [1]. In a similar context, the health community has experienced not only more complex and large data content, but also information systems that contain a large number of data sources with interrelated and interconnected data attributes. That have resulted in challenging, and highly dynamic environments leading to creation of big data with its enumerate complexities, for instant sharing of information with the expected security requirements of stakeholders. When comparing big data analysis with other sectors, the health sector is still in its early stages. Key challenges include accommodating the volume, velocity and variety of healthcare data with the current deluge of exponential growth. Given the complexity of big data, it is understood that while data storage and accessibility are technically manageable, the implementation of Information Accountability measures to healthcare big data might be a practical solution in support of information security, privacy and traceability measures. Transparency is one important measure that can demonstrate integrity which is a vital factor in the healthcare service. Clarity about performance expectations is considered to be another Information Accountability measure which is necessary to avoid data ambiguity and controversy about interpretation and finally, liability [2]. According to current studies [3] Electronic Health Records (EHR) are key information resources for big data analysis and is also composed of varied co-created values [3]. Common healthcare information originates from and is used by different actors and groups that facilitate understanding of the relationship for other data sources. Consequently, healthcare services often serve as an integrated service bundle. Although a critical requirement in healthcare services and analytics, it is difficult to find a comprehensive set of guidelines to adopt EHR to fulfil the big data analysis requirements. Therefore as a remedy, this research work focus on a systematic approach containing comprehensive guidelines with the accurate data that must be provided to apply and evaluate big data analysis until the necessary decision making requirements are fulfilled to improve quality of healthcare services. Hence, we believe that this approach would subsequently improve quality of life.

Information accountability and health big data analytics: A consent-based model

With the ever increasing amount of eHealth data available from various eHealth systems and sources, Health Big Data Analytics promises enticing benefits such as enabling the discovery of new treatment options and improved decision making. However, concerns over the privacy of information have hindered the aggregation of this information. To address these concerns, we propose the use of Information Accountability protocols to provide patients with the ability to decide how and when their data can be shared and aggregated for use in big data research. In this paper, we discuss the issues surrounding Health Big Data Analytics and propose a consent-based model to address privacy concerns to aid in achieving the promised benefits of Big Data in eHealth.

A principled experimental design approach to Big Data analysis

Big Datasets are endemic, but they are often notoriously difficult to analyse because of their size, heterogeneity, history and quality. The purpose of this paper is to open a discourse on the use of modern experimental design methods to analyse Big Data in order to answer particular questions of interest. By appealing to a range of examples, it is suggested that this perspective on Big Data modelling and analysis has wide generality and advantageous inferential and computational properties. In particular, the principled experimental design approach is shown to provide a flexible framework for analysis that, for certain classes of objectives and utility functions, delivers near equivalent answers compared with analyses of the full dataset under a controlled error rate. It can also provide a formalised method for iterative parameter estimation, model checking, identification of data gaps and evaluation of data quality. Finally, it has the potential to add value to other Big Data sampling algorithms, in particular divide-and-conquer strategies, by determining efficient sub-samples.

FlexAnalytics: A flexible data analytics framework for big data applications with I/O performance improvement

Increasingly larger scale applications are generating an unprecedented amount of data. However, the increasing gap between computation and I/O capacity on High End Computing machines makes a severe bottleneck for data analysis. Instead of moving data from its source to the output storage, in-situ analytics processes output data while simulations are running. However, in-situ data analysis incurs much more computing resource contentions with simulations. Such contentions severely damage the performance of simulation on HPE. Since different data processing strategies have different impact on performance and cost, there is a consequent need for flexibility in the location of data analytics. In this paper, we explore and analyze several potential data-analytics placement strategies along the I/O path. To find out the best strategy to reduce data movement in given situation, we propose a flexible data analytics (FlexAnalytics) framework in this paper. Based on this framework, a FlexAnalytics prototype system is developed for analytics placement. FlexAnalytics system enhances the scalability and flexibility of current I/O stack on HEC platforms and is useful for data pre-processing, runtime data analysis and visualization, as well as for large-scale data transfer. Two use cases – scientific data compression and remote visualization – have been applied in the study to verify the performance of FlexAnalytics. Experimental results demonstrate that FlexAnalytics framework increases data transition bandwidth and improves the application end-to-end transfer performance.

A CCG virtual system for big data application communication costs analysis

Network topology and routing are two important factors in determining the communication costs of big data applications at large scale. As for a given Cluster, Cloud, or Grid system, the network topology is fixed and static or dynamic routing protocols are preinstalled to direct the network traffic. Users cannot change them once the system is deployed. Hence, it is hard for application developers to identify the optimal network topology and routing algorithm for their applications with distinct communication patterns. In this study, we design a CCG virtual system (CCGVS), which first uses container-based virtualization to allow users to create a farm of lightweight virtual machines on a single host. Then, it uses software-defined networking (SDN) technique to control the network traffic among these virtual machines. Users can change the network topology and control the network traffic programmingly, thereby enabling application developers to evaluate their applications on the same system with different network topologies and routing algorithms. The preliminary experimental results through both synthetic big data programs and NPB benchmarks have shown that CCGVS can represent application performance variations caused by network topology and routing algorithm.

The case for “n«all”: Why the Big Data revolution will probably happen differently in the mining sector

Big Data and predictive analytics have received significant attention from the media and academic literature throughout the past few years, and it is likely that these emerging technologies will materially impact the mining sector. This short communication argues, however, that these technological forces will probably unfold differently in the mining industry than they have in many other sectors because of significant differences in the marginal cost of data capture and storage. To this end, we offer a brief overview of what Big Data and predictive analytics are, and explain how they are bringing about changes in a broad range of sectors. We discuss the “N=all” approach to data collection being promoted by many consultants and technology vendors in the marketplace but, by considering the economic and technical realities of data acquisition and storage, we then explain why a “n « all” data collection strategy probably makes more sense for the mining sector. Finally, towards shaping the industry’s policies with regards to technology-related investments in this area, we conclude by putting forward a conceptual model for leveraging Big Data tools and analytical techniques that is a more appropriate fit for the mining sector.

Big data

Making Sense of Mass Education provides an engaging and accessible analysis of traditional issues associated with mass education. The book challenges preconceptions about social class, gender and ethnicity discrimination; highlights the interplay between technology, media, popular culture and schooling; and inspects the relevance of ethics and philosophy in the modern classroom. This new edition has been comprehensively updated to provide current information regarding literature, statistics and legal policies, and significantly expands on the previous edition's structure of derailing traditional myths about education as a point of discussion. It also features two new chapters on Big Data and Globalisation and what they mean for the Australian classroom. Written for students, practising teachers and academics alike, Making Sense of Mass Education summarises the current educational landscape in Australia and looks at fundamental issues in society as they relate to education.

Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database. Conclusion The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.

The big pond debate.

Over recent decades, Australian piggeries have commonly employed anaerobic ponds to treat effluent to a standard suitable for recycling for shed flushing purposes and for irrigation onto nearby agricultural land. Anaerobic ponds are generally sized according to the Rational Design Standard (RDS) developed by Barth (1985), resulting in large ponds, which can be expensive to construct, occupy large land areas, and are difficult and expensive to desludge, potentially disrupting the whole piggery operation. Limited anecdotal and scientific evidence suggests that anaerobic ponds that are undersized according to the RDS, operate satisfactorily, without excessive odour emission, impaired biological function or high rates of solids accumulation. Based on these observations, this paper questions the validity of rigidly applying the principles of the RDS and presents a number of alternate design approaches resulting in smaller, more highly loaded ponds that are easier and cheaper to construct and manage. Based on limited data of pond odour emission, it is suggested that higher pond loading rates may reduce overall odour emission by decreasing the pond volume and surface area. Other management options that could be implemented to reduce pond volumes include permeable pond covers, various solids separation methods, and bio-digesters with impermeable covers, used in conjunction with biofilters and/or systems designed for biogas recovery. To ensure that new effluent management options are accepted by regulatory authorities, it is important for researchers to address both industry and regulator concerns and uncertainties regarding new technology, and to demonstrate, beyond reasonable doubt, that new technologies do not increase the risk of adverse impacts on the environment or community amenity. Further development of raw research outcomes to produce relatively simple, practical guidelines and implementation tools also increases the potential for acceptance and implementation of new technology by regulators and industry.

The performance of irrigated mixtures of tall fescue, ryegrass and white clover in subtropical Australia. 1. The effects of sowing mixture combinations, nitrogen and oversowing on establishment, productivity, botanical composition and persistence.

In the subtropics of Australia, the ryegrass component of irrigated perennial ryegrass (Lolium perenne) - white clover (Trifolium repens) pastures declines by approximately 40% in the summer following establishment, being replaced by summer-active C4 grasses. Tall fescue (Festuca arundinacea) is more persistent than perennial ryegrass and might resist this invasion, although tall fescue does not compete vigorously as a seedling. This series of experiments investigated the influence of ryegrass and tall fescue genotype, sowing time and sowing mixture as a means of improving tall fescue establishment and the productivity and persistence of tall fescue, ryegrass and white clover-based mixtures in a subtropical environment. Tall fescue frequency at the end of the establishment year decreased as the number of companion species sown in the mixture increased. Neither sowing mixture combinations nor sowing rates influenced overall pasture yield (of around 14 t/ha) in the establishment year but had a significant effect on botanical composition and component yields. Perennial ryegrass was less competitive than short-rotation ryegrass, increasing first-year yields of tall fescue by 40% in one experiment and by 10% in another but total yield was unaffected. The higher establishment-year yield (3.5 t/ha) allowed Dovey tall fescue to compete more successfully with the remaining pasture components than Vulcan (1.4 t/ha). Sowing 2 ryegrass cultivars in the mixture reduced tall fescue yields by 30% compared with a single ryegrass (1.6 t/ha), although tall fescue alone achieved higher yields (7.1 t/ha). Component sowing rate had little influence on composition or yield. Oversowing the ryegrass component into a 6-week-old sward of tall fescue and white clover improved tall fescue, white clover and overall yields in the establishment year by 83, 17 and 11%, respectively, but reduced ryegrass yields by 40%. The inclusion of red (T. pratense) and Persian (T. resupinatum) clovers and chicory (Cichorium intybus) increased first-year yields by 25% but suppressed perennial grass and clover components. Yields were generally maintained at around 12 t/ha/yr in the second and third years, with tall fescue becoming dominant in all 3 experiments. The lower tall fescue seeding rate used in the first experiment resulted in tall fescue dominance in the second year following establishment, whereas in Experiments 2 and 3 dominance occurred by the end of the first year. Invasion by the C4 grasses was relatively minor (<10%) even in the third year. As ryegrass plants died, tall fescue and, to a lesser extent, white clover increased as a proportion of the total sward. Treatment effects continued into the second, but rarely the third, year and mostly affected the yield of one of the components rather than total cumulative yield. Once tall fescue became dominant, it was difficult to re-introduce other pasture components, even following removal of foliage and moderate renovation. Severe renovation (reducing the tall fescue population by at least 30%) seems a possible option for redressing this situation.