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Integration of urinary EN2 protein & cell-free RNA data in the development of a multivariable risk model for the detection of prostate cancer prior to biopsy
Get PDFThe objective is to develop a multivariable risk model for the non-invasive detection of prostate cancer prior to biopsy by integrating information from clinically available parameters, Engrailed-2 (EN2) whole-urine protein levels and data from urinary cell-free RNA. Post-digital-rectal examination urine samples collected as part of the Movember Global Action Plan 1 study which has been analysed for both cell-free-RNA and EN2 protein levels were chosen to be integrated with clinical parameters (n = 207). A previously described robust feature selection framework incorporating bootstrap resampling and permutation was applied to the data to generate an optimal feature set for use in Random Forest models for prediction. The fully integrated model was named ExoGrail, and the out-of-bag predictions were used to evaluate the diagnostic potential of the risk model. ExoGrail risk (range 0–1) was able to determine the outcome of an initial trans-rectal ultrasound guided (TRUS) biopsy more accurately than clinical standards of care, predicting the presence of any cancer with an area under the receiver operator curve (AUC) = 0.89 (95% confidence interval(CI): 0.85–0.94), and discriminating more aggressive Gleason ≥ 3 + 4 disease returning an AUC=0.84(95%CI:0.78–0.89). The likelihood of more aggressive disease being detected significantly increased as ExoGrail risk score increased (Odds Ratio (OR) = 2.21 per 0.1 ExoGrail increase, 95% CI: 1.91–2.59). Decision curve analysis of the net benefit of ExoGrail showed the potential to reduce the numbers of unnecessary biopsies by 35% when compared to current standards of care. Integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy
Centralized graph based TSCH scheduling for IoT network applications
No full textThe current specification of the IEEE 802.15.4 standard supports several application specific Quality of Service (QoS) requirements for Internet of Things (IoT) network applications. Specifically, the Time Slotted Channel Hopping (TSCH) MAC mode provides effective latency and throughput performance through the use of dedicated timeslots between two communicating devices. Despite the impact TSCH MAC can facilitate in low-power lossy networks (LLNs), the standard does not explore either the building or maintaining of a schedule. The challenge is to build an energy-efficient TSCH schedule that repeats periodically over several channels. To address this problem, we propose a centralized cluster-level TSCH scheduling mechanism from the energy-efficiency perspective. The proposed mechanism derives a collision graph for each of the clusters in the network topology to schedule non-overlapping timeslots. The Bron–Kerbosch algorithm is used as a sub-procedure for finding the complete sub-graphs of a graph. In addition, we analytically compute the transmission and energy overhead with the help of a Markov Model for TSCH
Teaching here and there, episode 3, with Dom Pates, Dr. Ivan Sikora and James Rutherford
No full textThis episode of our podcast series features a conversation with Dr. Ashley Garlick is a Senior Lecturer specialising in event management at the University of West London and Eoin O'Sullivan, Associate Lecturer, University of St. Andrews. In a wide ranging discussion, our guests discuss their experiences of hybrid teaching pre and post pandemic. It is a fascinating conversation that covers the challenges and realities for students, how hybrid teaching should be approached as well as the impact on the timetable, students' well-being and the importance of being on campus
Nigeria's Legislation Against Discrimination of Persons With Disabilities: An Assessment
No full textAfter several years of appeals for the protection of persons with disabilities from discrimination in Nigeria, the federal government of Nigeria in early 2019 passed into law the Discrimination against Persons with Disabilities (Prohibition) Act 2018. While this is considered a welcome development, it remains to be ascertained whether the government has the political will to implement the act. This chapter examines the provisions of the Anti-Disability Disability Discrimination Law in Nigeria. The aim here is to consider how the law can be employed to better the lives of persons with disabilities in areas including but not limited to access to justice, employment, healthcare, education, and transportation. The methodology adopted for the study is a doctrinal review of the law and literature on disability rights, the plight of persons with disabilities, and the effect of the recently passed Act of 2018. The chapter concludes with recommendations
“And I dedicate this win to…”: Performing grief in high performance sport
Get PDFArthur Bochner wrote that even though he’d written about grief, he never really understood it until the death of his father. I suspect many of us recognise a truth in what he said. Often unannounced, death creeps like an unwelcome stain into our lives. I wonder how many of us are ready for it and what narrative scripts are available to us at these times as we try to make sense and bring meaning to our loss?
In sport, the path that I trod for my first occupation, the pervasiveness of the performance narrative means that following the death of a parent an athlete is expected to continue with their sport, regardless of their pain and grief. “The show” or that competition you trained so hard for, so the story goes, “must go on”. Within high performance sport the grief and loss experienced by well-known athletes seems to also provide an additional newsworthy opportunity for reporters and journalists to spice up their copy with stories of how, “I did it for dad”.
But is winning the only way to represent or honour this relationship? And what of those athletes who never win? How do they honour their dead?
Over the past twenty years I have often drawn on my own experiences in professional sport to challenge the dominant performance narrative which frames winning as the only accepted and valued goal of the athlete. In this performance autoethnography I hope to extending previous work by turning the spotlight on some of the ways the performance narrative frames how athletes and media alike represent grief and loss and what the father/child relationships means.
My aim and purpose with this work is to contribute to the creation of counterstories and alternative narrative maps. Through creating such resources it may provide some athletes whose live and experiences are currently disenfranchised or silenced, and I include myself in this group, to negotiate this difficult terrain in ways that are more authentic. In Mark Freeman’s terms, I seek to ‘break away’ from this powerful monologue and sap its “coercive power” (p. 12)
Artificial intelligence in prognostics and health management of engineering systems.
Get PDFPrognostics and health management (PHM) has become a crucial aspect of the management of engineering systems and structures, where sensor hardware and decision support tools are deployed to detect anomalies, diagnose faults and predict remaining useful lifetime (RUL). Methodologies for PHM are either model-driven, data-driven or a fusion of both approaches. Data-driven approaches make extensive use of large-scale datasets collected from physical assets to identify underlying failure mechanisms and root causes. In recent years, many data-driven PHM models have been developed to evaluate system’s health conditions using artificial intelligence (AI) and machine learning (ML) algorithms applied to condition monitoring data. The field of AI is fast gaining acceptance in various areas of applications such as robotics, autonomous vehicles and smart devices. With advancements in the use of AI technologies in Industry 4.0, where systems consist of multiple interconnected components in a cyber–physical space, there is increasing pressure on industries to move towards more predictive and proactive maintenance practices. In this paper, a thorough state-of-the-art review of the AI techniques adopted for PHM of engineering systems is conducted. Furthermore, given that the future of inspection and maintenance will be predominantly AI-driven, the paper discusses the soft issues relating to manpower, cyber-security, standards and regulations under such a regime. The review concludes that the current systems and methodologies for maintenance will inevitably become incompatible with future designs and systems; as such, continued research into AI-driven prognostics systems is expedient as it offers the best promise of bridging the potential gap
Spiking neural network-based multi-task autonomous learning for mobile robots
No full textSpiking Neural Networks (SNNs) are the new generation of artificial neural networks that closely mimic the time encoding and information processing aspects of the human brain. In this work, a multi-task autonomous learning paradigm is proposed for the mobile robot application, which employs a SNN to construct the controlling system of the mobile robot. The Reward-modulated Spiking-time-dependent Plasticity learning rule is developed for the SNN-based controller, which aims to achieve the capability of autonomous learning under multiple tasks. Reward signals are generated based on the instantaneous frequencies of pre- and post-synaptic spikes, which adapts to the sensory stimuli and environmental feedback. Meanwhile, inspired by lateral inhibition connections, a task switch mechanism is designed to enable the controller to switch the operations between multiple tasks. Two tasks of obstacle avoidance and target tracking are used for performance evaluation and results demonstrate that the mobile robot with the proposed paradigm is able to autonomously learn, switch and complete the tasks
A Novel Approach for Seizure Classification Using Patient Specific Triggers: Pilot Study
No full textWith advancements in personalised medicine, healthcare delivery systems have moved away from the one-size-fits-all approach towards tailored treatments that meet the needs of individuals and specific subgroups. As nearly one-third of those diagnosed with epilepsy are classed as refractory and are resistant to antiepileptic medication, there is a need for a personalised method of detecting epileptic seizures. Epidemiological studies show that up to 91% of those diagnosed identify one or more triggers as the causation of their seizure onset. These triggers are patient-specific and can affect those diagnosed in different ways dependent on each person’s idiosyncratic tolerance and threshold levels. Whilst these triggers are known to induce seizure onset, only a few studies have even considered their use as a preventive component. Therefore, this pilot study investigates the use of patient-specific triggers (PST) in diagnosed epileptics, and whether they can be used as an additional modality when detecting seizures. This study used a precision medicine approach with artificial intelligence (AI), to train and test several patient-specific algorithms that classified epileptic seizures based on the PST of each participant. Experimental results show accuracy, sensitivity, and specificity scores of 94.73%, 96.90% and 93.33% for participant 1 and 96.87%, 96.96% and 96.77% for participant 2, respectively
Fair Pricing Model for Smart Grids.
No full textThe demand for electricity is constantly growing, which leads to an increase in production, which in turn adversely affects the environment. Many countries integrate Renewable Energy Sources (RES) with the aim of decreasing the use of fossil fuels. Although there is more and more power coming from RES, some part of it is still lost due to the lack of motivation on the participants’ side to interact with each other. To attract more participants to the Smart Grid, thereby reducing the demand from the Utility Grid, it is necessary to ensure fair prices within the Smart Grid, that will be beneficial to all participants. Firstly, we consider the pricing issue considering the principle of how smart meters operate. Secondly, we propose a fair pricing model for the Smart Grid, as well as a method for determining an equilibrium price of buying and selling electricity. Finally, we evaluate the proposed model and provide the results, that prove its effectiveness
‘Frankly, as far as I can see, it has very little to do with teaching’. Exploring academics’ perceptions of the HEA Fellowships
Get PDFThe United Kingdom Professional Standards Framework (UKPSF) is a national framework that aims to enhance and raise the status of teaching and supporting learning in Higher Education (HE). This paper provides an overview of the adoption and an indication of the impact of Higher Education Academy (HEA) Fellowships through a document review and a qualitative study. The document review suggests that the adoption of HEA Fellowships has grown substantially, to half of the academics and related staff in the UK but shows no positive or negative relationship with the perceived quality of teaching in the National Student Survey (NSS) over the same period (2011-12 to 2017-18). The relationship between HEA Fellowships and the enhancement of teaching practice is the focus of the qualitative study. The analysis of in-depth interviews (n=11) conducted with senior academics who have obtained Senior Fellowship, at a post-1992 and a research-intensive university, reveals a complex relationship between the recognition schemes and the enhancement of practice. This needs to be understood against the managerial realities underpinning engagement, the limitations of the recognition schemes, and standards for the enhancement of teaching practices. The discussion explores the implications for academic developers, leaders, and policymakers involved in HEA Fellowships