Smart homes, control and energy management:How do smart home technologies influence control over energy use and domestic life?

By introducing new ways of automatically and remotely controlling domestic environments smart technologies have the potential to significantly improve domestic energy management. It is argued that they will simplify users’ lives by allowing them to delegate aspects of decision-making and control - relating to energy management, security, leisure and entertainment etc. - to automated smart home systems. Whilst such technologically-optimistic visions are seductive to many, less research attention has so far been paid to how users interact with and make use of the advanced control functionality that smart homes provide within already complex everyday lives. What literature there is on domestic technology use and control, shows that control is a complex and contested concept. Far from merely controlling appliances, householders are also concerned about a wide range of broader understandings of control relating, for example, to control over security, independence, hectic schedules and even over other household members such as through parenting or care relationships. This paper draws on new quantitative and qualitative data from 4 homes involved in a smart home field trial that have been equipped with smart home systems that provide advanced control functionality over appliances and space heating. Quantitative data examines how householders have used the systems both to try and improve their energy efficiency but also for purposes such as enhanced security or scheduling appliances to align with lifestyles. Qualitative data (from in-depth interviews) explores how smart technologies have impacted upon, and were impacted by, broader understandings of control within the home. The paper concludes by proposing an analytical framework for future research on control in the smart home

Understanding Cytotoxicity and Cytostaticity in a High-Throughput Screening Collection.

While mechanisms of cytotoxicity and cytostaticity have been studied extensively from the biological side, relatively little is currently understood regarding areas of chemical space leading to cytotoxicity and cytostasis in large compound collections. Predicting and rationalizing potential adverse mechanism-of-actions (MoAs) of small molecules is however crucial for screening library design, given the link of even low level cytotoxicity and adverse events observed in man. In this study, we analyzed results from a cell-based cytotoxicity screening cascade, comprising 296 970 nontoxic, 5784 cytotoxic and cytostatic, and 2327 cytostatic-only compounds evaluated on the THP-1 cell-line. We employed an in silico MoA analysis protocol, utilizing 9.5 million active and 602 million inactive bioactivity points to generate target predictions, annotate predicted targets with pathways, and calculate enrichment metrics to highlight targets and pathways. Predictions identify known mechanisms for the top ranking targets and pathways for both phenotypes after review and indicate that while processes involved in cytotoxicity versus cytostaticity seem to overlap, differences between both phenotypes seem to exist to some extent. Cytotoxic predictions highlight many kinases, including the potentially novel cytotoxicity-related target STK32C, while cytostatic predictions outline targets linked with response to DNA damage, metabolism, and cytoskeletal machinery. Fragment analysis was also employed to generate a library of toxicophores to improve general understanding of the chemical features driving toxicity. We highlight substructures with potential kinase-dependent and kinase-independent mechanisms of toxicity. We also trained a cytotoxic classification model on proprietary and public compound readouts, and prospectively validated these on 988 novel compounds comprising difficult and trivial testing instances, to establish the applicability domain of models. The proprietary model performed with precision and recall scores of 77.9% and 83.8%, respectively. The MoA results and top ranking substructures with accompanying MoA predictions are available as a platform to assess screening collections.Biotechnology and Biological Sciences Research Council, AstraZenec

Understanding Cytotoxicity and Cytostaticity in a High-Throughput Screening Collection.

While mechanisms of cytotoxicity and cytostaticity have been studied extensively from the biological side, relatively little is currently understood regarding areas of chemical space leading to cytotoxicity and cytostasis in large compound collections. Predicting and rationalizing potential adverse mechanism-of-actions (MoAs) of small molecules is however crucial for screening library design, given the link of even low level cytotoxicity and adverse events observed in man. In this study, we analyzed results from a cell-based cytotoxicity screening cascade, comprising 296 970 nontoxic, 5784 cytotoxic and cytostatic, and 2327 cytostatic-only compounds evaluated on the THP-1 cell-line. We employed an in silico MoA analysis protocol, utilizing 9.5 million active and 602 million inactive bioactivity points to generate target predictions, annotate predicted targets with pathways, and calculate enrichment metrics to highlight targets and pathways. Predictions identify known mechanisms for the top ranking targets and pathways for both phenotypes after review and indicate that while processes involved in cytotoxicity versus cytostaticity seem to overlap, differences between both phenotypes seem to exist to some extent. Cytotoxic predictions highlight many kinases, including the potentially novel cytotoxicity-related target STK32C, while cytostatic predictions outline targets linked with response to DNA damage, metabolism, and cytoskeletal machinery. Fragment analysis was also employed to generate a library of toxicophores to improve general understanding of the chemical features driving toxicity. We highlight substructures with potential kinase-dependent and kinase-independent mechanisms of toxicity. We also trained a cytotoxic classification model on proprietary and public compound readouts, and prospectively validated these on 988 novel compounds comprising difficult and trivial testing instances, to establish the applicability domain of models. The proprietary model performed with precision and recall scores of 77.9% and 83.8%, respectively. The MoA results and top ranking substructures with accompanying MoA predictions are available as a platform to assess screening collections.Biotechnology and Biological Sciences Research Council, AstraZenec

A comparative meta-analysis of the prevalence of exercise addiction in adults with and without indicated eating disorders

Background: Exercise addiction is associated with multiple adverse outcomes and can be classified as co-occurring with an eating disorder, or a primary condition with no indication of eating disorders. We conducted a meta-analysis exploring the prevalence of exercise addiction in adults with and without indicated eating disorders. Methods: A systematic review of major databases and grey literature was undertaken from inception to 30/04/2019. Studies reporting prevalence of exercise addiction with and without indicated eating disorders in adults were identified. A random effect meta-analysis was undertaken, calculating odds ratios for exercise addiction with versus without indicated eating disorders. Results: Nine studies with a total sample of 2140 participants (mean age = 25.06; 70.6% female) were included. Within these, 1732 participants did not show indicated eating disorders (mean age = 26.4; 63.0% female) and 408 had indicated eating disorders (mean age = 23.46; 79.2% female). The odds ratio for exercise addiction in populations with versus without indicated eating disorders was 3.71 (95%CI 2.00-6.89; I2 = 81; p=<0.001). Exercise addiction prevalence in both populations differed according to the measurement instrument used. Discussion: Exercise addiction occurs more than three and a half times as often as a comorbidity to an eating disorder than in people without an indicated eating disorder. The creation of a measurement tool able to identify exercise addiction risk in both populations would benefit researchers and practitioners by easily classifying samples

Exercise addiction in athletes: Comparing two assessment instruments and willingness to stop exercise after medical advice

Exercise is overwhelmingly beneficial for physical and mental health, but for some people exercise addiction (EA) can develop and negatively impact an individual. This study sought to (a) compare the latent structure of two instruments assessing EA and (b) examine differences in attitudes toward stopping exercise, if required to on medical grounds, among exercise-addicted and non-addicted athletes. In a cross-sectional study, 1,011 athletes competing at different levels completed an anonymous on-line survey. The survey contained Exercise Dependence Scale-Revised (EDS-R), Exercise Addiction Inventory (EAI), and questions on adherence to medical prescriptions to stop exercise. We tested the latent structure of EDS-R and EAI with multigroup confirmatory factor analyses (CFA), across gender and competition level. Finally, we measured the difference of athletes' attitudes toward stopping exercise, if prescribed by a physician. Both instruments showed good fit indexes, even across gender. CFAs on EAI scores showed some violations of measurement invariance across competition level (ΔCFI = .03; ΔRMSEA = .02). On the contrary, CFAs on EDS-R scores did not show invariance violations across competition level (ΔCFI = <.01; ΔRMSEA = <.01). Finally, athletes who reached thresholds for exercise addiction, by means of EDS-R, were more prone to not follow medical prescriptions to cease exercise, independently of the competition level. These results suggest that athletes' answers on the EDS-R seem to be less affected by competition level, compared to EAI. Moreover, EDS-R outcomes could be used to identify individuals who may be unlikely to cease exercise for medical reasons, independently of their competition level

Promoting physical activity in vulnerable adults “at risk” of homelessness: A Randomised Controlled Trial Protocol

Introduction: People who are homeless, or at risk of homelessness, have substantially poorer health. Sustained and regular participation in physical activity is beneficial for both mental and physical health. Limited data suggests that levels of physical activity in the homeless and those at risk of homelessness are low, and access to community-based exercise is limited or non-existent for this population. Nonetheless, exercise programs for the homeless could provide a feasible and scalable intervention for providing beneficial effects on physical and mental health in this population. The primary aim of this studyis to evaluate the impact of a group exercise intervention on activity levels in people who are homeless or at risk of homelessness in central London, UK. The secondary aim is to evaluate the impact of the intervention on mental and physical health outcomes. Method and analysis: A 2-arm, individually randomised controlled trial in people who are homeless and those vulnerable and at risk of homelessness in central London, UK. Participants will be recruited through a London-based homeless charity, Single Homeless Project. Following baseline assessments and allocation to intervention (exercise classes) or control (usual care), participants will be followed up at 3, 6, 9 and 12 months. The primary outcomes will be change in objective physical activity. The secondary outcomes will includechange in fitness assessments and mental health parameters. Changes in drug use and alcohol dependency will also be explored. Ethics and dissemination: Ethical approval was obtained through the Anglia Ruskin University Department of Sport and Exercise Sciences Research Ethics Committee. Results of this study will be disseminated through peer-reviewed publications and scientific presentations

Correction of a urea cycle defect after ex vivo gene editing of human hepatocytes

Ornithine transcarbamylase deficiency (OTCD) is a monogenic disease of ammonia metabolism in hepatocytes. Severe disease is frequently treated by orthotopic liver transplantation. An attractive approach is the correction of a patient's own cells to regenerate the liver with gene-repaired hepatocytes. This study investigates the efficacy and safety of ex vivo correction of primary human hepatocytes. Hepatocytes isolated from an OTCD patient were genetically corrected ex vivo, through the deletion of a mutant intronic splicing site achieving editing efficiencies >60% and the restoration of the urea cycle in vitro. The corrected hepatocytes were transplanted into the liver of FRGN mice and repopulated to high levels (>80%). Animals transplanted and liver repopulated with genetically edited patient hepatocytes displayed normal ammonia, enhanced clearance of an ammonia challenge and OTC enzyme activity, as well as lower urinary orotic acid when compared to mice repopulated with unedited patient hepatocytes. Gene expression was shown to be similar between mice transplanted with unedited or edited patient hepatocytes. Finally, a genome-wide screening by performing CIRCLE-seq and deep sequencing of >70 potential off-targets revealed no unspecific editing. Overall analysis of disease phenotype, gene expression, and possible off-target editing indicated that the gene editing of a severe genetic liver disease was safe and effective. Keywords: CRISPR; FRGN; ex vivo; genome editing; hepatocyte transplantation; liver-humanized mouse; primary hepatocytes; urea cycle disorder

Climate emergency summit III:nature-based solutions report

An RSGS &amp; SNH report from the Climate Summit held in April 2020"The Climate Emergency is the result of burning fossils fuels and changes in the way we use the land that short-circuit global carbon and nitrogen cycles. To remain within safe climate limits (1.5-2°C), the remaining carbon budget for all people, and for all time, is now so small that stopping fossil fuel use, while essential, will not by itself address the problem. Changing the way we use the land and sea is now essential. Nature-based solutions are vital to creating a safe operating space for humanity. "Extract from the foreword by Dr Clive Mitchell, Outcome Manager: People and Nature, Scottish Natural Heritage. The report has 45 contributors for a variety of institutions

The Role of Practice Research Networks (PRN) in the Development and Implementation of Evidence: The Northern Improving Access to Psychological Therapies PRN Case Study

Practice research networks (PRNs) can support the implementation of evidence based practice in routine services and generate practice based evidence. This paper describes the structure, processes and learning from a new PRN in the Improving Access to Psychological Therapies programme in England, in relation to an implementation framework and using one study as a case example. Challenges related to: ethics and governance processes; communications with multiple stakeholders; competing time pressures and linking outcome data. Enablers included: early tangible outputs and impact; a collaborative approach; engaging with local research leads; clarity of processes; effective dissemination; and committed leadership

Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data.

Telomere length is a risk factor in disease and the dynamics of telomere length are crucial to our understanding of cell replication and vitality. The proliferation of whole genome sequencing represents an unprecedented opportunity to glean new insights into telomere biology on a previously unimaginable scale. To this end, a number of approaches for estimating telomere length from whole-genome sequencing data have been proposed. Here we present Telomerecat, a novel approach to the estimation of telomere length. Previous methods have been dependent on the number of telomeres present in a cell being known, which may be problematic when analysing aneuploid cancer data and non-human samples. Telomerecat is designed to be agnostic to the number of telomeres present, making it suited for the purpose of estimating telomere length in cancer studies. Telomerecat also accounts for interstitial telomeric reads and presents a novel approach to dealing with sequencing errors. We show that Telomerecat performs well at telomere length estimation when compared to leading experimental and computational methods. Furthermore, we show that it detects expected patterns in longitudinal data, repeated measurements, and cross-species comparisons. We also apply the method to a cancer cell data, uncovering an interesting relationship with the underlying telomerase genotype