Deep Learning of Resting-state Electroencephalogram Signals for 3-class Classification of Alzheimer’s Disease, Mild Cognitive Impairment and Healthy Ageing

Objective. This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI) subjects and healthy ageing (HA) subjects using resting-state scalp electroencephalogram (EEG) signals. Approach. The raw EEG data were pre-processed to remove unwanted artefacts and sources of noise. The data were then processed with the continuous wavelet transform, using the Morse mother wavelet, to create time-frequency graphs with a wavelet coefficient scale range of 0-600. The graphs were combined into tiled topographical maps governed by the 10-20 system orientation for scalp electrodes. The application of this processing pipeline was used on a data set of resting-state EEG samples from age-matched groups of 52 AD subjects (82.3 ± 4.7 years of age), 37 MCI subjects (78.4 ± 5.1 years of age) and 52 HA subjects (79.6 ± 6.0 years of age). This resulted in the formation of a data set of 16197 topographical images. This image data set was then split into training, validation and test images and used as input to an AlexNet DL model. This model was comprised of five hidden convolutional layers and optimised for various parameters such as learning rate, learning rate schedule, optimiser, and batch size. Main results. The performance was assessed by a tenfold cross-validation strategy, which produced an average accuracy result of 98.9 ± 0.4% for the three-class classification of AD vs MCI vs HA. The results showed minimal overfitting and bias between classes, further indicating the strength of the model produced. Significance. These results provide significant improvement for this classification task compared to previous studies in this field and suggest that DL could contribute to the diagnosis of AD from EEG recordings

Junkie love : romance and addiction on the big screen

This article investigates the filmic construction of two disparate but intertwining cultural practices: those engaging in the life-affirming rituals of romantic love and those performing the potentially self-destructive rituals of hard drug consumption. Discussing a number of key feature films from the (mini) genre “junkie love”, it aims to show what happens when elements of mainstream romantic drama merge with the horror conventions of the heroin addiction film. Drawing amongst others on Murray Smith’s theory of “levels of [spectator] engagement” and Greg Smith’s concept of the “emotion system”, the article concludes that junkie love films, using tropes of the romantic tragedy in the tradition of Romeo and Juliet, present a more complex and nuanced approach to drug addicts than the predominantly condemnatory media coverage—one that arguably invites the spectator’s understanding and compassion

Towards effective small scale microbial fuel cells for energy generation from urine

© 2016 The Authors. Published by Elsevier Ltd. To resolve an increasing global demand in energy, a source of sustainable and environmentally friendly energy is needed. Microbial fuel cells (MFC) hold great potential as a sustainable and green bioenergy conversion technology that uses waste as the feedstock. This work pursues the development of an effective small-scale MFC for energy generation from urine. An innovative air-cathode miniature MFC was developed, and the effect of electrode length was investigated. Two different biomass derived catalysts were also studied. Doubling the electrode length resulted in the power density increasing by one order of magnitude (from 0.053 to 0.580 W m-3). When three devices were electrically connected in parallel, the power output was over 10 times higher compared to individual units. The use of biomass-derived oxygen reduction reaction catalysts at the cathode increased the power density generated by the MFC up to 1.95 W m-3, thus demonstrating the value of sustainable catalysts for cathodic reactions in MFCs

Machine Learning Models for Weight-Bearing Activity Type Recognition Based on Accelerometry in Postmenopausal Women

Hip-worn triaxial accelerometers are widely used to assess physical activity in terms of energy expenditure. Methods for classification in terms of different types of activity of relevance to the skeleton in populations at risk of osteoporosis are not currently available. This publication aims to assess the accuracy of four machine learning models on binary (standing and walking) and tertiary (standing, walking, and jogging) classification tasks in postmenopausal women. Eighty women performed a shuttle test on an indoor track, of which thirty performed the same test on an indoor treadmill. The raw accelerometer data were pre-processed, converted into eighteen different features and then combined into nine unique feature sets. The four machine learning models were evaluated using three different validation methods. Using the leave-one-out validation method, the highest average accuracy for the binary classification model, 99.61%, was produced by a k-NN Manhattan classifier using a basic statistical feature set. For the tertiary classification model, the highest average accuracy, 94.04%, was produced by a k-NN Manhattan classifier using a feature set that included all 18 features. The methods and classifiers within this study can be applied to accelerometer data to more accurately characterize weight-bearing activity which are important to skeletal health

Common Data Elements for National Institute of Mental Health–Funded Translational Early Psychosis Research

The National Institutes of Health has established the PhenX Toolkit as a web-based resource containing consensus measures freely available to the research community. The National Institute of Mental Health (NIMH) has introduced the Mental Health Research Core Collection as part of the PhenX Toolkit and recently convened the PhenX Early Psychosis Working Group to generate the PhenX Early Psychosis Specialty Collection. The Working Group consisted of two complementary panels for clinical and translational research. We review the process, deliberations, and products of the translational research panel. The Early Psychosis Specialty Collection rationale for measure selection as well as additional information and protocols for obtaining each measure are available on the PhenX website (https://www.phenxtoolkit.org). The NIMH strongly encourages investigators to use instruments from the PhenX Mental Health Research Collections in NIMH-funded studies and discourages use of alternative measures to collect similar data without justification. We also discuss some of the potential advances that can be achieved by collecting common data elements across large-scale longitudinal studies of early psychosis

The Paris Agreement: Development, the north-south divide and human rights

In December 2015, nations of the world joined together in Paris to negotiate a new legal instrument to address climate change. The debates which took place in the lead up to the adoption of the Paris Agreement reflected broader, ongoing tensions between developed and developing states within the international climate regime. They also demonstrated the divergence of opinion between states as to the relationship between climate change and human rights. While the human impacts of climate change are now well-understood, there is still debate as to what a human rights-based approach to climate change should look like. This chapter argues that these geopolitical dynamics and differing priorities will continue to shape the implementation of the Paris Agreement, as well as the specific debates over intellectual property, finance, technology transfer and innovation. The chapter therefore provides an important contextual backdrop for further analysis of these issues