5,678 research outputs found

    Wellbeing Work-0ut: Utilisation and comparison of Green Exercise and Mindfulness-Based Stress Reduction as Workplace Interventions for staff at the University of Essex.

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    Wellbeing Work-0ut was a collaboration between the School of Sport, Rehabilitation and Exercise Sciences and OH at the University of Essex, using 0% of participant’s personal time. It aimed to compare the effects of three 8-week interventions: Circuit Training, Green Exercise and MBSR on stress (Primary Outcome), self-esteem, mood, general wellbeing and quality of life (Secondary Outcomes) in employees at the University of Essex. Employees (n=37; 9 males, 28 females; Mean Age = 42.8 years, SD = 10.7), either referred by Occupational Health for stress related issues (n=32) or responded to adverts to join the Wellbeing Work-0ut course (n=5) were assigned to their choice of intervention groups: Circuit Training (n=6), Green Exercise (n=16) or MBSR (n=15). Interventions were in 8-week blocks and ran twice, each time with a different group to increase sample size and better facilitate the interventions. This study used multi-methods (quantitative and qualitative), and a mixed-model design (between-subjects factor: intervention; within-subjects factor: time). Self-report questionnaires: PSS, POMS, RSE, SWEMWBS and SF-36 were taken Baseline, Mid and Post Intervention. Observational field notes were taken, transcribed, and reflexive thematic analysis was performed. Participants of all interventions experienced improved psychological outcomes over time, but there were no statistically significant interactions although two had large effect sizes (TMD and MCS - mental component of SF-36). Further, at timepoint 3, there were minimal clinically important differences on: PSS, SWEMWBS and PCS (physical component of SF-36) for Circuit Training; PSS, TMD, SWEMWBS and MCS for Green Exercise; PSS, RSE, TMD, SWEMWBS and MCS for MBSR. Reflexive thematic analysis gives deeper meaning and detail of participants’ positive experiences, enablers and barriers. Interventions overall are still relevant methods of reducing negative psychological health outcomes. Implications for more in-depth study and communication within businesses to make mental health services more prevalent, visible and accessible

    Advanced glycation end products and age-related diseases in the general population

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    In this thesis, epidemiological, nutritional, and gut microbiome related studies are presented to illustrate the relation of advanced glycation end products (AGEs) with age-related diseases. The studies are embedded in the Rotterdam Study, a cohort of the Dutch general population of middle-aged and elderly adults. The amount of skin AGEs measured as SAF was used as a representative of the long-term AGE burden. Chapter 1 gives an overview of the whole thesis (Section 1.1) and gives a brief introduction to AGEs and their implications in disease pathophysiology. Chapter 2 focuses on the interplay of AGEs in the skin and clinical and lifestyle factors, and Chapter 3 concerns the link of skin and dietary AGEs with age-related diseases. Chapter 4 discusses the interpretations and implications of the findings, major methodological considerations, and pressing questions for future research

    Beta-glucans to enhance adoptive therapy of anti-cancer T cells

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    Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques

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    The rapid growth of demanding applications in domains applying multimedia processing and machine learning has marked a new era for edge and cloud computing. These applications involve massive data and compute-intensive tasks, and thus, typical computing paradigms in embedded systems and data centers are stressed to meet the worldwide demand for high performance. Concurrently, the landscape of the semiconductor field in the last 15 years has constituted power as a first-class design concern. As a result, the community of computing systems is forced to find alternative design approaches to facilitate high-performance and/or power-efficient computing. Among the examined solutions, Approximate Computing has attracted an ever-increasing interest, with research works applying approximations across the entire traditional computing stack, i.e., at software, hardware, and architectural levels. Over the last decade, there is a plethora of approximation techniques in software (programs, frameworks, compilers, runtimes, languages), hardware (circuits, accelerators), and architectures (processors, memories). The current article is Part I of our comprehensive survey on Approximate Computing, and it reviews its motivation, terminology and principles, as well it classifies and presents the technical details of the state-of-the-art software and hardware approximation techniques.Comment: Under Review at ACM Computing Survey

    The Sparse Abstract Machine

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    We propose the Sparse Abstract Machine (SAM), an abstract machine model for targeting sparse tensor algebra to reconfigurable and fixed-function spatial dataflow accelerators. SAM defines a streaming dataflow abstraction with sparse primitives that encompass a large space of scheduled tensor algebra expressions. SAM dataflow graphs naturally separate tensor formats from algorithms and are expressive enough to incorporate arbitrary iteration orderings and many hardware-specific optimizations. We also present Custard, a compiler from a high-level language to SAM that demonstrates SAM's usefulness as an intermediate representation. We automatically bind from SAM to a streaming dataflow simulator. We evaluate the generality and extensibility of SAM, explore the performance space of sparse tensor algebra optimizations using SAM, and show SAM's ability to represent dataflow hardware.Comment: 18 pages, 17 figures, 3 table

    The Omics basis of human health: investigating plasma proteins and their genetic effects on complex traits

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    Over the past decade, the advancements in technology and the growing amount of identified genetic variants have led to a high number of important discoveries in the field of precision medicine concerning human biology and pathophysiology. However, it became evident that genomics alone could not properly explain the onset and regulation of the specific molecular mechanisms of certain phenotypes. Studying omics helped complement this gap in genetic research, providing detailed information on the quantification of molecules that are involved in structural and functional processes in the organism. Specifically, protein production, levels, and regulation are dynamic and change during the course of one’s lifetime. This information has proven fundamental to understanding how certain proteins affect complex phenotypes such as neurological and psychiatric disorders. In this thesis, I describe the three groups of analyses I conducted over the course of my doctoral programme on different sets of blood plasma proteins and over a broad range of neurological, psychiatric, cardiovascular, and electrophysiology phenotypes. The underlying mechanisms that trigger the onset of psychiatric and neurological conditions are often not limited to the nervous system, but rather stem from multi-system molecular triggers. The first part of the work I carried out aims at investigating the frequent co-occurrence and comorbidity of neurological and cardiovascular phenotypes by conducting a genome-wide association (GWA) meta-analysis of 183 neurology-related blood proteins on data from over 12000 individuals. The second part concerns the bivariate and multivariate analyses conducted on 276 cardiology and inflammatory proteins, while the third illustrates the contribution to consortia focussed on heart rate and electrophysiology. Results from the second and third parts of the work provided information that played an important role in understanding a part of the genetic mechanisms of the complex traits of interest. Overall, the results presented in this thesis strongly support the notion that proteomics is an important tool to be used to study complex traits and drug discovery and development should focus on targeting protein synthesis and regulation. Furthermore, the results also support the notion that complex diseases involve more than one biological system, and in order to gain a better understanding of human pathology, it is fundamental to study the causes and effects across the entire organism

    Cerebrovascular dysfunction in cerebral small vessel disease

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    INTRODUCTION: Cerebral small vessel disease (SVD) is the cause of a quarter of all ischaemic strokes and is postulated to have a role in up to half of all dementias. SVD pathophysiology remains unclear but cerebrovascular dysfunction may be important. If confirmed many licensed medications have mechanisms of action targeting vascular function, potentially enabling new treatments via drug repurposing. Knowledge is limited however, as most studies assessing cerebrovascular dysfunction are small, single centre, single imaging modality studies due to the complexities in measuring cerebrovascular dysfunctions in humans. This thesis describes the development and application of imaging techniques measuring several cerebrovascular dysfunctions to investigate SVD pathophysiology and trial medications that may improve small blood vessel function in SVD. METHODS: Participants with minor ischaemic strokes were recruited to a series of studies utilising advanced MRI techniques to measure cerebrovascular dysfunction. Specifically MRI scans measured the ability of different tissues in the brain to change blood flow in response to breathing carbon dioxide (cerebrovascular reactivity; CVR) and the flow and pulsatility through the cerebral arteries, venous sinuses and CSF spaces. A single centre observational study optimised and established feasibility of the techniques and tested associations of cerebrovascular dysfunctions with clinical and imaging phenotypes. Then a randomised pilot clinical trial tested two medications’ (cilostazol and isosorbide mononitrate) ability to improve CVR and pulsatility over a period of eight weeks. The techniques were then expanded to include imaging of blood brain barrier permeability and utilised in multi-centre studies investigating cerebrovascular dysfunction in both sporadic and monogenetic SVDs. RESULTS: Imaging protocols were feasible, consistently being completed with usable data in over 85% of participants. After correcting for the effects of age, sex and systolic blood pressure, lower CVR was associated with higher white matter hyperintensity volume, Fazekas score and perivascular space counts. Lower CVR was associated with higher pulsatility of blood flow in the superior sagittal sinus and lower CSF flow stroke volume at the foramen magnum. Cilostazol and isosorbide mononitrate increased CVR in white matter. The CVR, intra-cranial flow and pulsatility techniques, alongside blood brain barrier permeability and microstructural integrity imaging were successfully employed in a multi-centre observational study. A clinical trial assessing the effects of drugs targeting blood pressure variability is nearing completion. DISCUSSION: Cerebrovascular dysfunction in SVD has been confirmed and may play a more direct role in disease pathogenesis than previously established risk factors. Advanced imaging measures assessing cerebrovascular dysfunction are feasible in multi-centre studies and trials. Identifying drugs that improve cerebrovascular dysfunction using these techniques may be useful in selecting candidates for definitive clinical trials which require large sample sizes and long follow up periods to show improvement against outcomes of stroke and dementia incidence and cognitive function

    Tools for efficient Deep Learning

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    In the era of Deep Learning (DL), there is a fast-growing demand for building and deploying Deep Neural Networks (DNNs) on various platforms. This thesis proposes five tools to address the challenges for designing DNNs that are efficient in time, in resources and in power consumption. We first present Aegis and SPGC to address the challenges in improving the memory efficiency of DL training and inference. Aegis makes mixed precision training (MPT) stabler by layer-wise gradient scaling. Empirical experiments show that Aegis can improve MPT accuracy by at most 4\%. SPGC focuses on structured pruning: replacing standard convolution with group convolution (GConv) to avoid irregular sparsity. SPGC formulates GConv pruning as a channel permutation problem and proposes a novel heuristic polynomial-time algorithm. Common DNNs pruned by SPGC have maximally 1\% higher accuracy than prior work. This thesis also addresses the challenges lying in the gap between DNN descriptions and executables by Polygeist for software and POLSCA for hardware. Many novel techniques, e.g. statement splitting and memory partitioning, are explored and used to expand polyhedral optimisation. Polygeist can speed up software execution in sequential and parallel by 2.53 and 9.47 times on Polybench/C. POLSCA achieves 1.5 times speedup over hardware designs directly generated from high-level synthesis on Polybench/C. Moreover, this thesis presents Deacon, a framework that generates FPGA-based DNN accelerators of streaming architectures with advanced pipelining techniques to address the challenges from heterogeneous convolution and residual connections. Deacon provides fine-grained pipelining, graph-level optimisation, and heuristic exploration by graph colouring. Compared with prior designs, Deacon shows resource/power consumption efficiency improvement of 1.2x/3.5x for MobileNets and 1.0x/2.8x for SqueezeNets. All these tools are open source, some of which have already gained public engagement. We believe they can make efficient deep learning applications easier to build and deploy.Open Acces

    Re-evaluation of the risks to public health related to the presence of bisphenol A (BPA) in foodstuffs

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    Publisher Copyright: © 2023 European Food Safety Authority. EFSA Journal published by Wiley-VCH GmbH on behalf of European Food Safety Authority.In 2015, EFSA established a temporary tolerable daily intake (t-TDI) for BPA of 4 μg/kg body weight (bw) per day. In 2016, the European Commission mandated EFSA to re-evaluate the risks to public health from the presence of BPA in foodstuffs and to establish a tolerable daily intake (TDI). For this re-evaluation, a pre-established protocol was used that had undergone public consultation. The CEP Panel concluded that it is Unlikely to Very Unlikely that BPA presents a genotoxic hazard through a direct mechanism. Taking into consideration the evidence from animal data and support from human observational studies, the immune system was identified as most sensitive to BPA exposure. An effect on Th17 cells in mice was identified as the critical effect; these cells are pivotal in cellular immune mechanisms and involved in the development of inflammatory conditions, including autoimmunity and lung inflammation. A reference point (RP) of 8.2 ng/kg bw per day, expressed as human equivalent dose, was identified for the critical effect. Uncertainty analysis assessed a probability of 57–73% that the lowest estimated Benchmark Dose (BMD) for other health effects was below the RP based on Th17 cells. In view of this, the CEP Panel judged that an additional uncertainty factor (UF) of 2 was needed for establishing the TDI. Applying an overall UF of 50 to the RP, a TDI of 0.2 ng BPA/kg bw per day was established. Comparison of this TDI with the dietary exposure estimates from the 2015 EFSA opinion showed that both the mean and the 95th percentile dietary exposures in all age groups exceeded the TDI by two to three orders of magnitude. Even considering the uncertainty in the exposure assessment, the exceedance being so large, the CEP Panel concluded that there is a health concern from dietary BPA exposure.Peer reviewe
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