Infection status outcome, machine learning method and virus type interact to affect the optimised prediction of hepatitis virus immunoassay results from routine pathology laboratory assays in unbalanced data

BACKGROUND Advanced data mining techniques such as decision trees have been successfully used to predict a variety of outcomes in complex medical environments. Furthermore, previous research has shown that combining the results of a set of individually trained trees into an ensemble-based classifier can improve overall classification accuracy. This paper investigates the effect of data pre-processing, the use of ensembles constructed by bagging, and a simple majority vote to combine classification predictions from routine pathology laboratory data, particularly to overcome a large imbalance of negative Hepatitis B virus (HBV) and Hepatitis C virus (HCV) cases versus HBV or HCV immunoassay positive cases. These methods were illustrated using a never before analysed data set from ACT Pathology (Canberra, Australia) relating to HBV and HCV patients. RESULTS It was easier to predict immunoassay positive cases than negative cases of HBV or HCV. While applying an ensemble-based approach rather than a single classifier had a small positive effect on the accuracy rate, this also varied depending on the virus under analysis. Finally, scaling data before prediction also has a small positive effect on the accuracy rate for this dataset. A graphical analysis of the distribution of accuracy rates across ensembles supports these findings. CONCLUSIONS Laboratories looking to include machine learning as part of their decision support processes need to be aware that the infection outcome, the machine learning method used and the virus type interact to affect the enhanced laboratory diagnosis of hepatitis virus infection, as determined by primary immunoassay data in concert with multiple routine pathology laboratory variables. This awareness will lead to the informed use of existing machine learning methods, thus improving the quality of laboratory diagnosis via informatics analyses.The project was funded by The Medical Advances Without Animals Trust (MAWA)

Arc Phenomena in low-voltage current limiting circuit breakers

Circuit breakers are an important safety feature in most electrical circuits, and they act to prevent excessive currents caused by short circuits, for example. Low-voltage current limiting circuit breakers are activated by a trip solenoid when a critical current is exceeded. The solenoid moves two contacts apart to break the circuit. However, as soon as the contacts are separated an electric arc forms between them, ionising the air in the gap, increasing the electrical conductivity of air to that of the hot plasma that forms, and current continues to flow. The currents involved may be as large as 80,000 amperes. Critical to the success of the circuit breaker is that it is designed to cause the arc to move away from the contacts, into a widening wedge-shaped region. This lengthens the arc, and then moves it onto a series of separator plates called an arc divider or splitter. The arc divider raises the voltage required to sustain the arcs across it, above the voltage that is provided across the breaker, so that the circuit is broken and the arcing dies away. This entire process occurs in milliseconds, and is usually associated with a sound like an explosion and a bright ash from the arc. Parts of the contacts and the arc divider may melt and/or vapourise. The question to be addressed by the Study Group was to mathematically model the arc motion and extinction, with the overall aim of an improved understanding that would help the design of a better circuit breaker. Further discussion indicated that two key mechanisms are believed to contribute to the movement of the arc away from the contacts, one being self-magnetism (where the magnetic field associated with the arc and surrounding circuitry acts to push it towards the arc divider), and the other being air flow (where expansion of air combined with the design of the chamber enclosing the arc causes gas flow towards the arc divider). Further discussion also indicated that a key aspect of circuit breaker design was that it is desirable to have as fast a quenching of the arc as possible, that is, the faster the circuit breaker can act to stop current flow, the better. The relative importance of magnetic and air pressure effects on quenching speed is of central interest to circuit design

Building a community: the experience of RLadies

2018/19 eLife Ambassadors progra

#StayWoke: The Language and Literacies of the #BlackLivesMatter Movement

This paper examines the language, literacies, communicative, and rhetorical practices of the Black Lives Matter (BLM) movement. The work pays attention to the communication practices of the BLM and Hip Hop generation in its extension of Black and African American language traditions and prior liberation movements in their unapologetic performance of Black chants, Black grammar, phonology, vocabulary, Black fashion and music, to die-ins, hands-up, and the technologization of the movement through social media, Black Twitter, hashtags, and memes. The language and literacies of the Black Lives Matter movement represent diverse identities within Black community, vernacular associated with various economic and educational classes, diaspora, culturally rooted, Hip Hop generations, cis-gendered women, men, as well as LGBTQ and gender non-conforming. In this way, the language and literacies of BLM promote the value of ALL Black lives

Infection status outcome, machine learning method and virus type interact to affect the optimised prediction of hepatitis virus immunoassay results from routine pathology laboratory assays in unbalanced data

BACKGROUND: Advanced data mining techniques such as decision trees have been successfully used to predict a variety of outcomes in complex medical environments. Furthermore, previous research has shown that combining the results of a set of individually trained trees into an ensemble-based classifier can improve overall classification accuracy. This paper investigates the effect of data pre-processing, the use of ensembles constructed by bagging, and a simple majority vote to combine classification predictions from routine pathology laboratory data, particularly to overcome a large imbalance of negative Hepatitis B virus (HBV) and Hepatitis C virus (HCV) cases versus HBV or HCV immunoassay positive cases. These methods were illustrated using a never before analysed data set from ACT Pathology (Canberra, Australia) relating to HBV and HCV patients. RESULTS: It was easier to predict immunoassay positive cases than negative cases of HBV or HCV. While applying an ensemble-based approach rather than a single classifier had a small positive effect on the accuracy rate, this also varied depending on the virus under analysis. Finally, scaling data before prediction also has a small positive effect on the accuracy rate for this dataset. A graphical analysis of the distribution of accuracy rates across ensembles supports these findings. CONCLUSIONS: Laboratories looking to include machine learning as part of their decision support processes need to be aware that the infection outcome, the machine learning method used and the virus type interact to affect the enhanced laboratory diagnosis of hepatitis virus infection, as determined by primary immunoassay data in concert with multiple routine pathology laboratory variables. This awareness will lead to the informed use of existing machine learning methods, thus improving the quality of laboratory diagnosis via informatics analyses

Spring production of Calanus finmarchicus at the Iceland-Scotland Ridge

Distribution and reproduction activity of the calanoid copepod Calanus finmarchicus were studied in the waters between Scotland and Iceland in April 1997 during the expected time of the animals' ascent to surface waters following diapause. Ascent was taking place on both sides of the Iceland-Scotland Ridge, apparently from two separate overwintering centers. The population on the Faroe Shelf (FS) most likely came from the overwintering population in the Faroe Shetland Channel (FSC). Per capita egg production was highest on the FS (> 30 eggs female -1d-1) and lowest in the Iceland Basin (10 eggs female -1d-1). The maximum clutch size recorded was on the FS (145 eggs). As the maximum clutch sizes that females produced were between 40% and 77% (area averages of the station maximum rates) of their size-specific reproduction potential, it is argued that egg production rates were generally food-limited. Chlorophyll a concentrations were, at all but one station, under 1 ugL-1. Chlorophyll-based ingestion could, theoretically, support the observed average egg production rates in the Iceland Basin and on the FS but only about 30% of the observed production at the stations in the East Icelandic Current (EIC). The carbon assimilated through ingestion of phytoplankton, Calanus own eggs andnauplii in the EIC was estimated to be too low to support the frequently observed production of clutches consisting of over 100 eggs. Cannibalism on eggs and nauplii was not likely to have constituted a significant component of dietary carbon intake. However, a combination of feeding and assimilation of reserved lipid remaining from overwintering could be sufficient to explain the observed per capita egg production rates. C. finmarchicus copepod stages 1-3 were only recorded in considerable numbers only on the FS. This suggests higher survival rates of eggs in the shelf waters

Supporting holistic care for patients with tuberculosis in a remote Indigenous community: a case report

Context: Tuberculosis (TB) is a serious infectious disease with high rates of morbidity and mortality if left untreated. In Australia, TB has been virtually eradicated in non-Indigenous Australian-born populations but in remote Aboriginal and/or Torres Strait Islander communities TB presents a rare but significant public health issue. Remote health services are most likely to encounter patients with suspected and confirmed TB diagnosis but may be unprepared for supporting someone with this disease and the complexities of balancing public health risk with patient autonomy. Issue: This case study will outline the process for diagnosis and treatment of a TB patient in a remote Cape York community. This case involved significant delay in diagnosis and required several strategies to achieve successful disease eradication. The process of treatment, however, had a significant effect on the patient’s physical health, and social and emotional wellbeing. Lessons learned: This case highlights the importance of early collaboration between medical, nursing, Indigenous health worker and allied health services and the importance of technology such as electronic information records to support opportunistic access to diagnostic services and treatment. The enactment of the TB protocol should include discussions about the consequences of any restrictions of movement, employment or social/community roles. Identifying alternative opportunities to engage in meaningful roles may reduce the impact the disease has on a patient’s quality of life

Clinical chemistry in higher dimensions: machine-learning and enhanced prediction from routine clinical chemistry data

Big Data is having an impact on many areas of research, not the least of which is biomedical science. In this review paper, big data and machine learning are defined in terms accessible to the clinical chemistry community. Seven myths associated with machine learning and big data are then presented, with the aim of managing expectation of machine learning amongst clinical chemists. The myths are illustrated with four examples investigating the relationship between biomarkers in liver function tests, enhanced laboratory prediction of hepatitis virus infection, the relationship between bilirubin and white cell count, and the relationship between red cell distribution width and laboratory prediction of anaemia.This work was supported by the Quality Use of Pathology Programme (QUPP), The Commonwealth Department of Health

Interventionism in the Family: Does Adoption Law in England and Wales Advocate the ‘Theft’ of Children?

This article questions the use of interventionism by the State in child protection. The law of adoption is used to contrast the public’s apparent demand for the reduction of state control with children’s need and right of protection from neglect and maltreatment. Particular reference is made to the recent controversial rickets cases, as well as the Pacchieri case of 2013. This article points to academic, legal and public perspectives in order to include all competing arguments involved. Finally, the article aims to demonstrate that it is sometimes necessary to undermine the sanctity of the private sphere in order to protect the society’s most vulnerable - children.

Maximum likelihood estimation of variance components

In this thesis, the Maximum Likelihood and Restricted Maximum Likelihood methods of estimating variance components are investigated for the one-way model. Expressions for the estimators and their variances are obtained, and algorithms for finding the estimates are tested by means of a Monte Carlo study. The quantitative effects of non-normality on the variability of estimates are discussed. Finally, diagnostic tests for identifying outliers and non-normality are proposed, and illustrated with data concerning soybean plant growth