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Community Ageing Research 75+ (CARE75+) REMOTE study: a remote model of recruitment and assessment of the health, well-being and social circumstances of older people
Get PDFYesThe Community Ageing Research 75+ (CARE75+) study is a longitudinal cohort study collecting extensive health and social data, with a focus on frailty, independence and quality of life in older age. CARE75+ was the first international experimental frailty research cohort designed using trial within cohorts (TwiCs) methodology, aligning epidemiological research with clinical trial evaluation of interventions to improve the health and well-being of older people. CARE75+ REMOTE is an extension of CARE75+ using a remote model that does not require face-to-face interactions for data collection in the current circumstances of a global pandemic and will provide an efficient, sustainable data collection model.
Methods and analysis Prospective cohort study using TwiCs. One thousand community-dwelling older people (≥75 years) will be recruited from UK general practices by telephone. Exclusions include: nursing home/care home residents; those with an estimated life expectancy of 3 months or less; and people receiving palliative care.
Data collection Assessments will be conducted by telephone, web-submission or postal questionnaire: baseline, 6 months, 12 months, 18 months, 24 months, 30 months and 36 months. Measures include activities of daily living, mood, health-related quality of life, comorbidities, medications, frailty, informal care, healthcare and social care service use. Consent will be sought for data linkage and invitations to additional studies (sub-studies).
Ethics and dissemination CARE75+ was approved by the National Research Ethics Service (NRES) Committee Yorkshire and the Humber—Bradford Leeds 10 October 2014 (14/YH/1120). CARE75+ REMOTE (amendment 13) was approved on the 18th November 2020. Consent is sought if an individual is willing to participate and has capacity to provide informed consent. Consultee assent is sought if an individual lacks capacity. Results will be disseminated in peer-reviewed scientific journals and conferences. Results will be summarised and disseminated to study participants via newsletters, local engagement events and on a bespoke website.NIHR Applied Research Collaboration (ARC) Yorkshire and Humber-https://www.arc-yh.nihr.ac.uk/ (study funding number NIHR200166), also supported by NIHR ARC South West Peninsula, ARC West Midlands and ARC North East and North Cumbri
Behaviours that prompt primary school teachers to adopt and implement physically active learning: a meta synthesis�of qualitative evidence
Get PDFYesPhysically active learning (PAL) - integration of movement within delivery of academic content - is a core component of many whole-of-school physical activity approaches. Yet, PAL intervention methods and strategies vary and frequently are not sustained beyond formal programmes. To improve PAL training, a more comprehensive understanding of the behavioural and psychological processes that influence teachers' adoption and implementation of PAL is required. To address this, we conducted a meta-synthesis to synthesise key stakeholders' knowledge of facilitators and barriers to teachers' implementing PAL in schools to improve teacher-focussed PAL interventions in primary (elementary) schools.
We conducted a meta-synthesis using a five-stage thematic synthesis approach to; develop a research purpose and aim, identify relevant articles, appraise studies for quality, develop descriptive themes and interpret and synthesise the literature. In the final stage, 14 domains from the Theoretical Domain Framework (TDF) were then aligned to the final analytical themes and subthemes.
We identified seven themes and 31 sub-themes from 25 eligible papers. Four themes summarised teacher-level factors: PAL benefits, teachers' beliefs about own capabilities, PAL teacher training, PAL delivery. One theme encompassed teacher and school-level factors: resources. Two themes reflected school and external factors that influence teachers' PAL behaviour: whole-school approach, external factors. Ten (of 14) TDF domains aligned with main themes and sub-themes: Knowledge, Skills, Social/Professional Role and Identity, Beliefs about Capabilities, Beliefs about Consequences, Reinforcement, Goals, Environmental Context and Resources, Social influences and Emotion.
Our synthesis illustrates the inherent complexity required to change and sustain teachers' PAL behaviours. Initially, teachers must receive the training, resources and support to develop the capability to implement and adapt PAL. The PAL training programme should progress as teachers' build their experience and capability; content should be 'refreshed' and become more challenging over time. Subsequently, it is imperative to engage all levels of the school community for PAL to be fully integrated into a broader school system. Adequate resources, strong leadership and governance, an engaged activated community and political will are necessary to achieve this, and may not currently exist in most schools.European Union ERASMUS+ Strategic Partnership Fund as part of the Activating Classroom Teachers (ACTivate)- teachers on the move project (NO: 2019–1-NO01-KA203–060324
A Model-Based Reliability Analysis Method Using Bayesian Network
Get PDFYesBayesian Network (BN)-based methods are increasingly used
in system reliability analysis. While BNs enable to perform multiple analyses
based on a single model, the construction of robust BN models relies
either on the conversion from other intermediate system model structures
or direct analyst-led development based on experts input, both
requiring significant human effort. This article proposes an architecture
model-based approach for the direct generation of a BN model. Given
the architectural model of a system, a systematic bottom-up approach
is suggested, underpinned by failure behaviour models of components
composed based on interaction models to create a system-level failure
behaviour model. Interoperability and reusability of models are supported
by a library of component failure models. The approach was
illustrated with application to a case study of a steam boiler system.Bayesian Network (BN)-based methods are increasingly used
in system reliability analysis. While BNs enable to perform multiple analyses
based on a single model, the construction of robust BN models relies
either on the conversion from other intermediate system model structures
or direct analyst-led development based on experts input, both
requiring significant human effort. This article proposes an architecture
model-based approach for the direct generation of a BN model. Given
the architectural model of a system, a systematic bottom-up approach
is suggested, underpinned by failure behaviour models of components
composed based on interaction models to create a system-level failure
behaviour model. Interoperability and reusability of models are supported
by a library of component failure models. The approach was
illustrated with application to a case study of a steam boiler system
Overcoming wound healing complications following radiotherapy in human breast dermal fibroblasts, through the influence of preadipocytes from the stromal vascular fraction
No full textRadiotherapy has major therapeutic benefits for cancer patients, but ionizing
radiation causes damage of surrounding healthy tissues with poor wound
healing a common side effect. Therefore, further oncoplastic, reconstructive
surgery is challenging and often problematic. Current research models use
normal human dermal fibroblasts irradiated in vitro to mimic radiation
damage, but this is not comparable to ionising radiation and only measures
acute changes. Since radiotherapy may induce epigenetic changes leading
to alterations in dermal fibroblast phenotype, the first aim of this study was to
compare fibroblasts cultured from irradiated skin with non-irradiated skin. As
mesenchymal stem cells isolated from adipose tissue may offer beneficial
effects in the regenerative capacity of irradiated tissue, the second part of
this study was to compare those cultured from non-irradiated and irradiated
breast tissue.
Histological changes in the structural organisation of breast tissue in situ
from donors exposed to radiotherapy was compared to untreated breast.
Primary cultures of dermal fibroblasts from irradiated and non-irradiated
breast skin were established and comparisons quantitated in proliferation
(CyQuant), metabolism (Alamar Blue), migration (scratch-wound assay),
collagen production (Sircol), levels of proteases and protease inhibitors
(human protease/protease inhibitor array) and gene expression of COL1A1,
COL3A1, MMP1, MMP2, TIMP1 and PPAR-γ mRNA (qPCR). Cells from the
stromal vascular fraction (SVF) were cultured and characterised by
immunocytochemistry and compared to human preadipocytes sourced
commercially. The secretion of FGF, adiponectin and VEGF by the preadipocyte and the SVF mesenchymal cells was compared and the ability
of their secretome to modulate dermal fibroblast proliferation, metabolism
and migration was evaluated.
Radiotherapy caused extensive disorganisation of the reticular dermis and
flattening of the epidermal-dermal junction. Dermal fibroblasts cultured from
irradiated skin had a pronounced spindle shaped morphology with longer
thinner projections and took approximately twice as long to explant and grow.
They had a lower proliferative and higher basal metabolic rate and did not
respond to FGF-2. While they secreted similar amounts of total collagen they
demonstrated distinct differences in proteolytic enzyme and protease inhibitor
expression. This is the first report to culture cells from the SVF of irradiated
breast tissue. The cells expressed the preadipocyte markers CD10, CD73 and
CD105 and no CD45 (negative marker). SVF cells cultured displayed a typical
ASC fibroblastoid morphology. Analysis of the secretome identified the
presence of FGF, adiponectin and VEGF, while functional analysis
demonstrated a stimulatory effect on normal dermal fibroblast migration,
although irradiated dermal fibroblasts were unresponsive.
Radiotherapy induces long term, detrimental changes in breast skin. This is
the first quantitative characterisation of dermal fibroblasts and mesenchymal
cells from the SVF, subjected to ionising radiation in situ. Changes in their
phenotype that alter their function will impact on wound healing. Further
characterisation of these cells may explain their dysfunctional behaviour, and
lead to therapies to reverse or reduce this deleterious side-effect and
significantly improve treatments facilitating wound healing following radiation
injury.Plastic Surgery and Burns Research uni
Reconstructing the childhood diet of individuals buried with the Pictish monastic community at Portmahomack
No full textNoThis research aims to reconstruct the childhood diets (aged 9–10 years) of the individuals buried during the active years of the Pictish monastic community (hereafter referred to as PMC) from early medieval (7th–11th century) Portmahomack in north-east Scotland, using 13C and 15N isotopes. Dietary reconstructions were achieved by isotope analysis of δ13C and δ15N on the tooth root apex from permanent first molars (M1) of 26 adult male individuals. The results indicate that the individuals in PMC predominantly consumed terrestrial C3 resources during childhood, with a rich terrestrial protein diet and some marine resource consumption. Statistically significant differences were observed between childhood and adulthood diets (the latter derived from previous research), suggesting that when these individuals were children, they consumed more marine protein than in later years as adults. This is true for all individuals, whether or not they spent significant time in Portmahomack during their childhoods. This is the most extensive study of the childhood diet of individuals from the PMC and so makes a significant contribution to augmenting information on diet and lifestyles in Pictish Scotland
Investigation of the sediment transport capacity in vegetated open channel flow
No full textNoThe suspended sediment transport capacity is important for estimating the suspended load concentration and the ecological environment of the river. So far, few studies have been conducted to investigate the suspended sediment transport capacity in the vegetated sediment-laden flow. In this study, a new formula is derived to predict the sediment transport capacity in a vegetated flow by considering the absolute value of the energy loss between the sediment-laden flow and the clear water flow. Finally, the formula is expressed in a practical form by using the logarithmic matching method
The Automated Prediction of Solar Flares from SDO Images Using Deep Learning
No full textYesIn the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS)
Isostructurality of quinoxaline crystal phases: The interplay of weak hydrogen bonds and halogen bonding
No full textYesTailoring the physical properties of molecular crystals though the construction of solid solutions requires the existence of isostructural crystals. Simple substitutions of a given molecular framework can give a range of different crystal structures. A set of quinoxaline derivatives, C8H4N2(C6H4X)2,Q3,3′X2, has been investigated (X = F, Cl, Br, I and Me) where kinetic factors generated a set of isostructural crystals for the lighter halogens (F, Cl, Br) alone. Computational analysis shows that the stabilising interactions are maximal for Cl, while DSC studies demonstrate the existence of more stable polymorphs for both F and Br containing systems. Steric factors appear to have a lower contribution than the balance of weaker hydrogen and halogen bonding shown by the Me and I containing systems displaying different packing driven by CH⋯N/CH⋯π bonds and I⋯I bonds respectively
The nature of the self, self‑regulation and moral action: implications from the Confucian relational self and Buddhist non‑self
Get PDFYesThe concept of the self and its relation to moral action is complex and subject to varying interpretations, not only between
different academic disciplines but also across time and space. This paper presents empirical evidence from a cross-cultural study on the Buddhist and Confucian notions of self in SMEs in Vietnam and Taiwan. The study employs Hwang’s Mandala Model of the Self, and its extension into Shiah’s non-self-model, to interpret how these two Eastern philosophical representations of the self, the Confucian relational self and Buddhist non-self, can lead to moral action. By demonstrating the
strengths of the model, emphasizing how social and cultural influences constrain the individual self and promote the social person leading to moral action, the paper extends understanding of the self with empirical evidence of the mechanisms involved in organizational context
Brazil’s Maria da Penha domestic violence police patrols: A second-response innovation in preventing re-victimization
Get PDFyesThis article examines an innovative domestic violence intervention: some 300 ‘second-response’ police patrols set up since 2015 by military police forces and municipal guards in cities around Brazil. They enforce court-issued protection orders by paying repeat visits to women at high risk, referring them to support services, and ensuring abusers stay away. Drawing on interviews with officers who founded or now lead these patrols, and on local-level police data and studies, the article analyses their origins and modus operandi, and evaluates their impacts on victims, abusers, the community, and internal police force culture. Available evidence shows that victims enrolled in these programmes are much less likely to suffer repeated assault or feminicide than those who are not. The article examines how this intervention fits with the other elements of local protection networks and compares these patrols to second-response police interventions developed elsewher