Protocol for the development of guidance for stakeholder engagement in health and healthcare guideline development and implementation

Stakeholder engagement has become widely accepted as a necessary component of guideline development and implementation. While frameworks for developing guidelines express the need for those potentially affected by guideline recommendations to be involved in their development, there is a lack of consensus on how this should be done in practice. Further, there is a lack of guidance on how to equitably and meaningfully engage multiple stakeholders. We aim to develop guidance for the meaningful and equitable engagement of multiple stakeholders in guideline development and implementation. METHODS: This will be a multi-stage project. The first stage is to conduct a series of four systematic reviews. These will (1) describe existing guidance and methods for stakeholder engagement in guideline development and implementation, (2) characterize barriers and facilitators to stakeholder engagement in guideline development and implementation, (3) explore the impact of stakeholder engagement on guideline development and implementation, and (4) identify issues related to conflicts of interest when engaging multiple stakeholders in guideline development and implementation. DISCUSSION: We will collaborate with our multiple and diverse stakeholders to develop guidance for multi-stakeholder engagement in guideline development and implementation. We will use the results of the systematic reviews to develop a candidate list of draft guidance recommendations and will seek broad feedback on the draft guidance via an online survey of guideline developers and external stakeholders. An invited group of representatives from all stakeholder groups will discuss the results of the survey at a consensus meeting which will inform the development of the final guidance papers. Our overall goal is to improve the development of guidelines through meaningful and equitable multi-stakeholder engagement, and subsequently to improve health outcomes and reduce inequities in health

A self-organized model for cell-differentiation based on variations of molecular decay rates

Systemic properties of living cells are the result of molecular dynamics governed by so-called genetic regulatory networks (GRN). These networks capture all possible features of cells and are responsible for the immense levels of adaptation characteristic to living systems. At any point in time only small subsets of these networks are active. Any active subset of the GRN leads to the expression of particular sets of molecules (expression modes). The subsets of active networks change over time, leading to the observed complex dynamics of expression patterns. Understanding of this dynamics becomes increasingly important in systems biology and medicine. While the importance of transcription rates and catalytic interactions has been widely recognized in modeling genetic regulatory systems, the understanding of the role of degradation of biochemical agents (mRNA, protein) in regulatory dynamics remains limited. Recent experimental data suggests that there exists a functional relation between mRNA and protein decay rates and expression modes. In this paper we propose a model for the dynamics of successions of sequences of active subnetworks of the GRN. The model is able to reproduce key characteristics of molecular dynamics, including homeostasis, multi-stability, periodic dynamics, alternating activity, differentiability, and self-organized critical dynamics. Moreover the model allows to naturally understand the mechanism behind the relation between decay rates and expression modes. The model explains recent experimental observations that decay-rates (or turnovers) vary between differentiated tissue-classes at a general systemic level and highlights the role of intracellular decay rate control mechanisms in cell differentiation.Comment: 16 pages, 5 figure

Demography and disorders of German Shepherd Dogs under primary veterinarycare in the UK

The German Shepherd Dog (GSD) has been widely used for a variety of working roles. However, concerns for the health and welfare of the GSD have been widely aired and there is evidence that breed numbers are now in decline in the UK. Accurate demographic and disorder data could assist with breeding and clinical prioritisation. The VetCompassTM Programme collects clinical data on dogs under primary veterinary care in the UK. This study included all VetCompassTM dogs under veterinary care during 2013. Demographic, mortality and clinical diagnosis data on GSDs were extracted and reported

Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries

Accurate prediction of the remaining useful life (RUL) in Lithium‐ion batteries (LiBs) is a key aspect of managing its health, in order to promote reliable and secure systems, and to reduce the need for unscheduled maintenance and costs. Recent work on RUL prediction has largely focused on refining the accuracy and reliability of the RUL prediction. The author introduces a new online RUL prediction for LiB using smooth particle filter (SPF)‐ based likelihood approximation method. The proposed algorithm can accurately estimate the unknown degradation model parameters and predict the degradation state by solving the optimisation problem at each iteration, rather than only taking a gradient step, that tends to lead to rapid convergence, avoids instability issues and improves predictive accuracy. From the experimental datasets published by Prognostics Centre of Excellence (PCoE) of NASA, a second order degradation model was created to explore the degradation of LiB, utilising non‐linear characteristics and non‐Gaussian capacity degradation. RUL prediction was tested with various predicted starting points to assess whether the amount of data and parameters' uncertainty influenced the accuracy of the prediction. Results show that the proposed prediction approach gives improved prediction accuracy and improves the convergence rate in comparison with the particle filter (PF) and other methods such as unscented particle filter (UPF). Since the maximum error of the SPF predicting approach is relatively small, RUL prediction in the best case at the prediction starting point consisting of 80 cycles is 127 cycles. The prediction relative error was approximately 0.024, and the absolute error of the proposed algorithm is around 2 cycles, which is lower than the PF (around 16 cycles). RUL prediction is close to 108 cycles and relative error is around 0.136, while the absolute error prediction is approximately 16

Time-Frequency Image Analysis and Transfer Learning for Capacity Prediction of Lithium-Ion Batteries

Energy storage is recognized as a key technology for enabling the transition to a low-carbon, sustainable future. Energy storage requires careful management, and capacity prediction of a lithium-ion battery (LIB) is an essential indicator in a battery management system for Electric Vehicles and Electricity Grid Management. However, present techniques for capacity prediction rely mainly on the quality of the features extracted from measured signals under strict operating conditions. To improve flexibility and accuracy, this paper introduces a new paradigm based on a multi-domain features time-frequency image (TFI) analysis and transfer deep learning algorithm, in order to extract diagnostic characteristics on the degradation inside the LIB. Continuous wavelet transform (CWT) is used to transfer the one-dimensional (1D) terminal voltage signals of the battery into 2D images (i.e., wavelet energy concentration). The generated TFIs are fed into the 2D deep learning algorithms to extract the features from the battery voltage images. The extracted features are then used to predict the capacity of the LIB. To validate the proposed technique, experimental data on LIB cells from the experimental datasets published by the Prognostics Center of Excellence (PCoE) NASA were used. The results show that the TFI analysis clearly visualised the degradation process of the battery due to its capability to extract different information on electrochemical features from the non-stationary and non-linear nature of the battery signal in both the time and frequency domains. AlexNet and VGG-16 transfer deep learning neural networks combined with stochastic gradient descent with momentum (SGDM) and adaptive data momentum (ADAM) optimization algorithms are examined to classify the obtained TFIs at different capacity values. The results reveal that the proposed scheme achieves 95.60% prediction accuracy, indicating good potential for the design of improved battery management systems

AMPK:a target for drugs and natural products with effects on both diabetes and cancer

The AMP-activated protein kinase (AMPK) is a highly conserved sensor of cellular energy that appears to have arisen at an early stage during eukaryotic evolution. In 2001 it was shown to be activated by metformin, currently the major drug for treatment for type 2 diabetes. Although the known metabolic effects of AMPK activation are consistent with the idea that it mediates some of the therapeutic benefits of metformin, as discussed below it now appears unlikely that AMPK is the sole target of the drug. AMPK is also activated by several natural plant products derived from traditional medicines, and the mechanisms by which they activate AMPK are discussed. One of these is salicylate, probably the oldest medicinal agent known to humankind. The salicylate prodrug salsalate has been shown to improve metabolic parameters in subjects with insulin resistance and prediabetes, and whether this might be mediated in part by AMPK is discussed. Interestingly, there is evidence that both metformin and aspirin provide some protection against development of cancer in humans, and whether AMPK might be involved in these effects is also discussed

Non-Invasive Real-Time Diagnosis of PMSM Faults Implemented in Motor Control Software for Mission Critical Applications

This paper presents a non-intrusive, real-time, online Condition Monitoring and FaultDiagnosis system for Permanent Magnet Synchronous Machines. The system utilizesonly the motor drive's built-in sensors, such as current and voltage sensors, to detectthree types of faults: inter-turn short circuit, partial demagnetization, and staticeccentricity. The proposed solution adopts a hardware-free approach, utilizingcurrent/voltage signature analysis to optimize cost-effectiveness. It requires a smallmemory and short execution time, allowing it to be implemented on a simple motorcontroller with limited memory and calculation power. The system is designed forcritical mission applications, and therefore, computation load, code size, memoryallocation, and run-time optimization are key focuses for real-time operation. Theproposed method has a high detection accuracy of 98%, is computationally efficient,and can accurately detect and classify the fault. The system provides immediateinsights into motor health without interrupting the drive operation

Autonomous fault detection and diagnosis for permanent magnet synchronous motors using combined variational mode decomposition, the Hilbert-Huang transform, and a convolutional neural network

The continuous and online monitoring of the condition of electrical machines is key to their safe operation. This study introduces a novel fault detection and diagnosis technique for continuous monitoring of faults in permanent magnet synchronous motors (PMSM). The proposed method relies solely on built-in sensors (stator phase currents only) to detect three types of faults: inter-turn short circuit, partial demagnetisation, and static eccentricity. Our fault detection and diagnosis strategy was developed by combining variational mode decomposition (VMD), the Hilbert-Huang transform (HHT) and a convolutional neural network (CNN). The VMD is first applied to the stator phase current signals to analyse the characteristic behaviour of the current signals by decomposing the current signals into several intrinsic mode functions. The intrinsic mode functions of the healthy and faulty signals are compared, and that with the frequency shift characteristics is selected. HHT is then applied to extract the fault feature by calculating the instantaneous frequency. Finally, the instantaneous frequency feature is fed into the CNN, which is designed to detect and classify motor faults. Experimental results clearly show that the variation of the instantaneous frequency of the PMSM, working at different operating states, can be utilised for condition monitoring and fault detection

AMPK:a nutrient and energy sensor that maintains energy homeostasis

AMP-activated protein kinase (AMPK) is a crucial cellular energy sensor. Once activated by falling energy status, it promotes ATP production by increasing the activity or expression of proteins involved in catabolism while conserving ATP by switching off biosynthetic pathways. AMPK also regulates metabolic energy balance at the whole-body level. For example, it mediates the effects of agents acting on the hypothalamus that promote feeding and entrains circadian rhythms of metabolism and feeding behaviour. Finally, recent studies reveal that AMPK conserves ATP levels through the regulation of processes other than metabolism, such as the cell cycle and neuronal membrane excitability

Identification of methylated deoxyadenosines in vertebrates reveals diversity in DNA modifications.

Methylation of cytosine deoxynucleotides generates 5-methylcytosine (m(5)dC), a well-established epigenetic mark. However, in higher eukaryotes much less is known about modifications affecting other deoxynucleotides. Here, we report the detection of N(6)-methyldeoxyadenosine (m(6)dA) in vertebrate DNA, specifically in Xenopus laevis but also in other species including mouse and human. Our methylome analysis reveals that m(6)dA is widely distributed across the eukaryotic genome and is present in different cell types but is commonly depleted from gene exons. Thus, direct DNA modifications might be more widespread than previously thought.M.J.K. was supported by the Long-Term Human Frontiers Fellowship (LT000149/2010-L), the Medical Research Council grant (G1001690), and by the Isaac Newton Trust Fellowship (R G76588). The work was sponsored by the Biotechnology and Biological Sciences Research Council grant BB/M022994/1 (J.B.G. and M.J.K.). The Gurdon laboratory is funded by the grant 101050/Z/13/Z (J.B.G.) from the Wellcome Trust, and is supported by the Gurdon Institute core grants, namely by the Wellcome Trust Core Grant (092096/Z/10/Z) and by the Cancer Research UK Grant (C6946/A14492). C.R.B. and G.E.A. are funded by the Wellcome Trust Core Grant. We are grateful to D. Simpson and R. Jones-Green for preparing X. laevis eggs and oocytes, F. Miller for providing us with M. musculus tissue, T. Dyl for X. laevis eggs and D. rerio samples, and to Gurdon laboratory members for their critical comments. We thank U. Ruether for providing us with M. musculus kidney DNA (Entwicklungs- und Molekularbiologie der Tiere, Heinrich Heine Universitaet Duesseldorf, Germany). We also thank J. Ahringer, S. Jackson, A. Bannister and T. Kouzarides for critical input and advice, M. Sciacovelli and E. Gaude for suggestions.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nsmb.314