Domestic heat pumps in the UK: user behaviour, satisfaction and performance

Consumer adoption of microgeneration technologies is part of the UK strategy to reduce carbon emissions from buildings. Domestic heat pumps are viewed as a potentially important carbon saving technology, given the ongoing decarbonisation of the electricity supply system. To address the lack of independent evaluation of heat pump performance, the Energy Saving Trust undertook the UK’s first large-scale heat pump field trial, which monitored 83 systems in real installations. As part of the trial, the Open University studied the consumers’ experience of using a domestic heat pump. An in-depth user survey investigated the characteristics, behaviour, and satisfactions of private householders and social housing residents using ground source and air source heat pumps for space and/or water heating, and examined the influence of user-related factors on measured heat pump system efficiency. The surveys found that most users were satisfied with the reliability, heating, hot water, warmth and comfort provided by their system. Analysis of user characteristics showed that higher system efficiencies were associated with greater user understanding of their heat pump system, and more continuous heat pump operation, although larger samples are needed for robust statistical confirmation. The analysis also found that the more efficient systems in the sample were more frequently located in the private dwellings than at the social housing sites and this difference was significant. This is explained by the interaction between differences in the systems, dwellings and users at the private and social housing sites. The implications for heat pump research, practice and policy are discussed

MEART: The Semi-Living Artist

Here, we and others describe an unusual neurorobotic project, a merging of art and science called MEART, the semi-living artist. We built a pneumatically actuated robotic arm to create drawings, as controlled by a living network of neurons from rat cortex grown on a multi-electrode array (MEA). Such embodied cultured networks formed a real-time closed-loop system which could now behave and receive electrical stimulation as feedback on its behavior. We used MEART and simulated embodiments, or animats, to study the network mechanisms that produce adaptive, goal-directed behavior. This approach to neural interfacing will help instruct the design of other hybrid neural-robotic systems we call hybrots. The interfacing technologies and algorithms developed have potential applications in responsive deep brain stimulation systems and for motor prosthetics using sensory components. In a broader context, MEART educates the public about neuroscience, neural interfaces, and robotics. It has paved the way for critical discussions on the future of bio-art and of biotechnology

Hybrid Representation Learning for Cognitive Diagnosis in Late-Life Depression Over 5 Years with Structural MRI

Late-life depression (LLD) is a highly prevalent mood disorder occurring in older adults and is frequently accompanied by cognitive impairment (CI). Studies have shown that LLD may increase the risk of Alzheimer's disease (AD). However, the heterogeneity of presentation of geriatric depression suggests that multiple biological mechanisms may underlie it. Current biological research on LLD progression incorporates machine learning that combines neuroimaging data with clinical observations. There are few studies on incident cognitive diagnostic outcomes in LLD based on structural MRI (sMRI). In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data. Specifically, we first extract prediction-oriented MRI features via a deep neural network, and then integrate them with handcrafted MRI features via a Transformer encoder for cognitive diagnosis prediction. Two tasks are investigated in this work, including (1) identifying cognitively normal subjects with LLD and never-depressed older healthy subjects, and (2) identifying LLD subjects who developed CI (or even AD) and those who stayed cognitively normal over five years. To the best of our knowledge, this is among the first attempts to study the complex heterogeneous progression of LLD based on task-oriented and handcrafted MRI features. We validate the proposed HRL on 294 subjects with T1-weighted MRIs from two clinically harmonized studies. Experimental results suggest that the HRL outperforms several classical machine learning and state-of-the-art deep learning methods in LLD identification and prediction tasks

BTS clinical statement for the diagnosis and management of ocular tuberculosis

The BTS clinical statement for the diagnosis and management of ocular tuberculosis (TB) draws on the expertise of both TB and and ophthalmic specialists to outline the current understanding of disease pathogenesis, diagnosis and management in adults. Published literature lacks high-quality evidence to inform clinical practice and there is also a paucity of data from animal models to elucidate mechanisms of disease. However, in order to improve and standardise patient care, this statement provides consensus points with the currently available data and agreed best practice

Caring for Individuals with Dementia and Cognitive Impairment, Not Dementia: Findings from the Aging, Demographics, and Memory Study

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106975/1/j.1532-5415.2010.03304.x.pd

Assessment of Dentally Related Function in Individuals with Cognitive Impairment: The Dental Activities Test

To develop and validate the Dental Activities Test (DAT), a clinical tool for measuring dentally-related function in cognitively-impaired older adults

Brain morphometric features predict depression symptom phenotypes in late-life depression using a deep learning model

Objectives Our objective was to use deep learning models to identify underlying brain regions associated with depression symptom phenotypes in late-life depression (LLD). Participants Diagnosed with LLD (N = 116) and enrolled in a prospective treatment study.DesignCross-sectional.MeasurementsStructural magnetic resonance imaging (sMRI) was used to predict five depression symptom phenotypes from the Hamilton and MADRS depression scales previously derived from factor analysis: (1) Anhedonia, (2) Suicidality, (3) Appetite, (4) Sleep Disturbance, and (5) Anxiety. Our deep learning model was deployed to predict each factor score via learning deep feature representations from 3D sMRI patches in 34 a priori regions-of-interests (ROIs). ROI-level prediction accuracy was used to identify the most discriminative brain regions associated with prediction of factor scores representing each of the five symptom phenotypes. Results Factor-level results found significant predictive models for Anxiety and Suicidality factors. ROI-level results suggest the most LLD-associated discriminative regions in predicting all five symptom factors were located in the anterior cingulate and orbital frontal cortex. Conclusions We validated the effectiveness of using deep learning approaches on sMRI for predicting depression symptom phenotypes in LLD. We were able to identify deep embedded local morphological differences in symptom phenotypes in the brains of those with LLD, which is promising for symptom-targeted treatment of LLD. Future research with machine learning models integrating multimodal imaging and clinical data can provide additional discriminative information

Drama without drama: The late rise of scripted TV formats

This article revisits the history of TV formats - concepts of TV shows that are licensed for local adaptations – focusing on scripted entertainment. While the TV format revolution of the 1990s bypassed scripted formats, they have been catching up in recent years. This paper analyses both the reasons for this late rise and the factors behind the recent growth. It argues that the adaptation of scripted formats is more complex and risks remain higher than for other genres. The underlying economics of their production and distribution also differs from non-scripted formats. Stars came together when demand for drama increased worldwide, Hollywood studios began to mine their catalogues, new exporters and scripted genres emerged, and knowledge transfer techniques improved. Finally, this paper analyses the significance of the rise of scripted entertainment in the global TV format trading system

Identification of MCI individuals using structural and functional connectivity networks

Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimer’s disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity