A model for direct entry midwifery education and deployment in Ethiopia : transforming rural communities and health care to save lives

University of Technology, Sydney. Faculty of Health.Background: In Ethiopia, a landlocked country in the horn of Africa, only 10% of women give birth with a skilled attendant and the health workforce meets an estimated maternal and reproductive need of only 32%. Midwives save lives, however most midwives live in cities, while 83% of the Ethiopian population live in rural areas. There is therefore an urgent need to scale up the number of midwives and deploy them where they are needed. The aim of this study was to examine the outcomes of a new midwifery educational and rural deployment model which was implemented at the Hamlin College of Midwives in Ethiopia. Methods: A mixed methods design was used to investigate stakeholder experiences and associated health service and outcome data. A thematic analysis of qualitative semi structured interviews with students, new graduates and staff members of the College was undertaken. A descriptive analysis of selected health service data was also undertaken before and after the deployment of Hamlin midwives. Results: Three major themes emerged from the analysis. These are: the journey to midwifery; becoming a midwife; and innovation and transformation These themes revealed the challenges in accessing and pursuing education for rural girls, the transition academically, culturally and socially for midwifery students from rural areas, the passage of ‘novice to professional’ midwife as well as the emergence of professional midwives who are innovative and passionate advocates for women’s health within their own communities. Conclusion: Midwives who are recruited from rural areas, educated to fulfil the international competencies, thoughtfully deployed, supported and enabled with resources and referral networks can provide highly skilled, culturally sensitive woman centred care. Maternal health service usage and community engagement can be enhanced by the employment of local midwives who not only provide an important service but can be an agent of change through their action as a role model for girls, young women and their communities

The Assessment of Machine Learning Model Performance for Predicting Alluvial Deposits Distribution

This paper discusses the development and evaluation of distribution models for predicting alluvial mineral potential mapping. A number of existing models includes Weight of Evidence, Knowledge-driven Fuzzy, Data-driven Fuzzy, Neural-Network, Bayesian Classifier and Geostatistical Kriging. We offer classification models developed in our laboratory, where point pattern analysis was used to identify presence or absence of a known secondary alluvial (cassiterite) deposits in the Nigerian Younger Granite Region (NYGR) and the model performance assessed. We focused on the training and testing data split using longitudinal spatial data splitting (strips and halves) to ensure predictive attribute's independence. The spatial data split runs counter to the traditional random sample data selection as a procedure for checking overfitting of models mainly due to spatial data autocorrelation. Specifically, we used classification algorithms such as; Naive Bayes, Support Vector Machine, K-Nearest Neighbour, Decision Tree Bagging and Discriminant Analysis algorithms for training and testing. We analysed the model's performance results using model predictive accuracy and ROC curve values in two different approaches that improve spatial data independence among predictive attributes to give a meaningful model performance

Efficient fault-tolerant quantum computing

Fault tolerant quantum computing methods which work with efficient quantum error correcting codes are discussed. Several new techniques are introduced to restrict accumulation of errors before or during the recovery. Classes of eligible quantum codes are obtained, and good candidates exhibited. This permits a new analysis of the permissible error rates and minimum overheads for robust quantum computing. It is found that, under the standard noise model of ubiquitous stochastic, uncorrelated errors, a quantum computer need be only an order of magnitude larger than the logical machine contained within it in order to be reliable. For example, a scale-up by a factor of 22, with gate error rate of order $10^{-5}$, is sufficient to permit large quantum algorithms such as factorization of thousand-digit numbers.Comment: 21 pages plus 5 figures. Replaced with figures in new format to avoid problem

Quantum Computing with Very Noisy Devices

In theory, quantum computers can efficiently simulate quantum physics, factor large numbers and estimate integrals, thus solving otherwise intractable computational problems. In practice, quantum computers must operate with noisy devices called ``gates'' that tend to destroy the fragile quantum states needed for computation. The goal of fault-tolerant quantum computing is to compute accurately even when gates have a high probability of error each time they are used. Here we give evidence that accurate quantum computing is possible with error probabilities above 3% per gate, which is significantly higher than what was previously thought possible. However, the resources required for computing at such high error probabilities are excessive. Fortunately, they decrease rapidly with decreasing error probabilities. If we had quantum resources comparable to the considerable resources available in today's digital computers, we could implement non-trivial quantum computations at error probabilities as high as 1% per gate.Comment: 47 page

Quantum Teleportation is a Universal Computational Primitive

We present a method to create a variety of interesting gates by teleporting quantum bits through special entangled states. This allows, for instance, the construction of a quantum computer based on just single qubit operations, Bell measurements, and GHZ states. We also present straightforward constructions of a wide variety of fault-tolerant quantum gates.Comment: 6 pages, REVTeX, 6 epsf figure

S100A4 Elevation Empowers Expression of Metastasis Effector Molecules in Human Breast Cancer.

Many human glandular cancers metastasize along nerve tracts, but the mechanisms involved are generally poorly understood. The calcium-binding protein S100A4 is expressed at elevated levels in human cancers, where it has been linked to increased invasion and metastasis. Here we report genetic studies in a Drosophila model to define S100A4 effector functions that mediate metastatic dissemination of mutant Ras-induced tumors in the developing nervous system. In flies overexpressing mutant RasVal12 and S100A4, there was a significant increase in activation of the stress kinase JNK and production of the matrix metalloproteinase MMP1. Genetic or chemical blockades of JNK and MMP1 suppressed metastatic dissemination associated with S100A4 elevation, defining required signaling pathway(s) for S100A4 in this setting. In clinical specimens of human breast cancer, elevated levels of the mammalian paralogs MMP2, MMP9, and MMP13 are associated with a 4- to 9-fold relative decrease in patient survival. In individual tumors, levels of MMP2 and MMP13 correlated more closely with levels of S100A4, whereas MMP9 levels correlated more closely with the S100 family member S100P. Overall, our results suggest the existence of evolutionarily conserved pathways used by S100A4 to promote metastatic dissemination, with potential prognostic and therapeutic implications for metastasis by cancers that preferentially exploit nerve tract migration routes. Cancer Res; 77(3); 1-10. ©2016 AACR

CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting

Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as "opioid epidemic," has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of prevention effort, in this work, we focus on forecasting opioid overdose using real-time crime dynamics. Previous work identified various types of links between opioid use and criminal activities, such as financial motives and common causes. Motivated by these observations, we propose a novel spatio-temporal predictive model for opioid overdose forecasting by leveraging the spatio-temporal patterns of crime incidents. Our proposed model incorporates multi-head attentional networks to learn different representation subspaces of features. Such deep learning architecture, called "community-attentive" networks, allows the prediction of a given location to be optimized by a mixture of groups (i.e., communities) of regions. In addition, our proposed model allows for interpreting what features, from what communities, have more contributions to predicting local incidents as well as how these communities are captured through forecasting. Our results on two real-world overdose datasets indicate that our model achieves superior forecasting performance and provides meaningful interpretations in terms of spatio-temporal relationships between the dynamics of crime and that of opioid overdose.Comment: Accepted as conference paper at ECML-PKDD 201

Training in paediatric virology in the UK: A brief overview (Review).

Paediatric virology is a bold, new scientific field, where paediatrics focuses on the newly acquired knowledge from clinical virology, enriched with current advances in epidemiology, molecular medicine, evidence-based medicine, clinical governance, quality improvement, pharmacology and immunology. Although there are several methods with which to obtain training in paediatric viral infections in the UK, paediatric virology does not currently exist as a specific subspecialty. The aim of the present article was to present the existing educational platforms and training options in paediatric virology in the UK available to trainees wanting to pursue a clinical and/or academic career in paediatric virology