Evaluation of coated QCM for the detection of atmospheric ozone

A coated acoustic wave sensor has been developed to selectively detect atmospheric ozone. The selective detection has been assessed using a variety of coatings: beeswax, gallic acid, indigo carmine, polybutadiene, potassium iodide and sodium nitrite. Polybutadiene was the most sensitive with a limit of detection of 55 ppb. The sensitivity was improved by operating at higher harmonics and was shown to increase linearly with harmonic up to the 11th harmonic. This novel work shows that ozone detection can be improved by operating at the crystals' harmonic frequencies and in conjunction with a suitable flow rate, a potentially highly sensitive and fast response sensor can be created based on acoustic wave technolog

Gaussian process model based predictive control

Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark

Gaussian Process priors with uncertain inputs? Application to multiple-step ahead time series forecasting

We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. k-step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form y t = f(Yt-1 ,..., Yt-L ), the prediction of y at time t + k is based on the point estimates of the previous outputs. In this paper, we show how, using an analytical Gaussian approximation, we can formally incorporate the uncertainty about intermediate regressor values, thus updating the uncertainty on the current prediction

Diagnostic Care Pathways in Dementia.

OBJECTIVES: Increasing diagnostic rates of dementia is a national health priority; to meet this priority, improvement needs to be made to diagnostic services. It has been increasingly recognized that primary can play a significant role in the diagnostic journey for people with dementia, with some diagnostic services entirely located in primary care. This article reviews the extent of the involvement of primary care in diagnostic care pathways for people presenting with memory complaints within England, and presents examples of innovative approaches, which may be of interest to practitioners. METHOD: A rapid review was undertaken to identify articles outlining diagnostic care pathways for dementia involving primary care in England. RESULTS: Six articles relating to pathway evaluations and innovative approaches involving primary care were deemed suitable for inclusion in the review. CONCLUSIONS: The review found examples of diagnostic pathways and innovative practices being implemented in in primary care. These practices aligned to the strategic ambitions of the National Dementia Strategy. However, it was widely acknowledged that there is a need to improve postdiagnostic pathways; in particular, access to postdiagnostic support. This issue is being reflected in contemporary policy initiatives such as the Department of Health's 2016 Joint Declaration on postdiagnostic dementia care and support

Analysis of the Changes in the Oxidation of Brain Tissue Cytochrome-c-Oxidase in Traumatic Brain Injury Patients during Hypercapnoea A Broadband NIRS Study

Using broadband near-infrared spectroscopy (NIRS) and cerebral micro-dialysis (MD), we investigated cerebral cellular metabolism and mitochondrial redox states, following hypercapnoea in 6 patients with traumatic brain injury (TBI). In all patients hypercapnoea increased intracranial pressure and cerebral blood flow velocity measured with transcranial Doppler. Despite the likely increase in cerebral oxygen delivery, we did not see an increase in the oxidation status of cytochrome-c-oxidase [oxCCO] in every patient. Analysis of the NIRS data demonstrated two patterns of the changes; Group A (n = 4) showed an increase in [oxCCO] of 0.34(+/-0.34)mu M and Group B (n = 2) a decrease of 0.40(+/- 0.41)mu M. Although no obvious association was seen between the Delta[oxCCO] and the MD, measured changes in lactate and pyruvate concentrations. Further work using model informed data interpretation may be helpful in understanding the multimodal signals acquired in this heterogeneous patient group

A social composition view of team creativity: The role of member nationality-heterogeneous ties outside of the team

This is the author accepted manuscript. The final version is available from INFORMS (Institute for Operations Research and Management Sciences) via the DOI in this record. We sought to understand team member informal social network ties outside of the team as a way to achieve cognitive variation within the team, thereby facilitating creativity. Specifically, we take a configural perspective, which emphasizes individual team members and the heterogeneity and strength of their outside ties. We theorize that these characteristics of outside ties are important because they amend members' schemas and the team's cognitive architecture. Results of a study of 82 long-term MBA project teams suggest that both outside ties with nationality-heterogeneous individuals and weak outside ties independently facilitate team creativity. In addition, nationality-heterogeneous outside ties that are weak rather than strong are associated with higher team creative performance

T-cell immune adaptor SKAP1 regulates the induction of collagen-induced arthritis in mice

SKAP1 is an immune cell adaptor that couples the T-cell receptor with the ‘inside-out’ signalling pathway for LFA-1 mediated adhesion in T-cells. A connection of SKAP1 to the regulation of an autoimmune disorder has not previously been reported. In this study, we show that Skap1-deficient (skap1-/-) mice are highly resistant to the induction of collagen-induced arthritis (CIA), both in terms of incidence or severity. Skap1-/- T-cells were characterised by a selective reduction in the presence IL-17+ (Th17) in response to CII peptide and a marked reduction of joint infiltrating T-cells in Skap1-/- mice. SKAP1 therefore represents a novel connection to Th17 producing T-cells and is new potential target in the therapeutic intervention in autoimmune and inflammatory diseases

Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications

Modern scientific instruments produce vast amounts of data, which can overwhelm the processing ability of computer systems. Lossy compression of data is an intriguing solution, but comes with its own drawbacks, such as potential signal loss, and the need for careful optimization of the compression ratio. In this work, we focus on a setting where this problem is especially acute: compressive sensing frameworks for interferometry and medical imaging. We ask the following question: can the precision of the data representation be lowered for all inputs, with recovery guarantees and practical performance? Our first contribution is a theoretical analysis of the normalized Iterative Hard Thresholding (IHT) algorithm when all input data, meaning both the measurement matrix and the observation vector are quantized aggressively. We present a variant of low precision normalized {IHT} that, under mild conditions, can still provide recovery guarantees. The second contribution is the application of our quantization framework to radio astronomy and magnetic resonance imaging. We show that lowering the precision of the data can significantly accelerate image recovery. We evaluate our approach on telescope data and samples of brain images using CPU and FPGA implementations achieving up to a 9x speed-up with negligible loss of recovery quality.Comment: 19 pages, 5 figures, 1 table, in IEEE Transactions on Signal Processin

Cytochrome c oxidase response to changes in cerebral oxygen delivery in the adult brain shows higher brain-specificity than haemoglobin

The redox state of cerebral mitochondrial cytochrome c oxidase monitored with near-infrared spectroscopy (Δ[oxCCO]) is a signal with strong potential as a non-invasive, bedside biomarker of cerebral metabolic status. We hypothesised that the higher mitochondrial density of brain compared to skin and skull would lead to evidence of brain-specificity of the Δ[oxCCO] signal when measured with a multi-distance near-infrared spectroscopy (NIRS) system. Measurements of Δ[oxCCO] as well as of concentration changes in oxygenated (Δ[HbO2]) and deoxygenated haemoglobin (Δ[HHb]) were taken at multiple source-detector distances during systemic hypoxia and hypocapnia (decrease in cerebral oxygen delivery), and hyperoxia and hypercapnia (increase in cerebral oxygen delivery) from 15 adult healthy volunteers. Increasing source-detector spacing is associated with increasing light penetration depth and thus higher sensitivity to cerebral changes. An increase in Δ[oxCCO] was observed during the challenges that increased cerebral oxygen delivery and the opposite was observed when cerebral oxygen delivery decreased. A consistent pattern of statistically significant increasing amplitude of the Δ[oxCCO] response with increasing light penetration depth was observed in all four challenges, a behaviour that was distinctly different from that of the haemoglobin chromophores, which did not show this statistically significant depth gradient. This depth-dependence of the Δ[oxCCO] signal corroborates the notion of higher concentrations of CCO being present in cerebral tissue compared to extracranial components and highlights the value of NIRS-derived Δ[oxCCO] as a brain-specific signal of cerebral metabolism, superior in this aspect to haemoglobin

Generalized information reuse for optimization under uncertainty with non-sample average estimators

In optimization under uncertainty for engineering design, the behavior of the system outputs due to uncertain inputs needs to be quantified at each optimization iteration, but this can be computationally expensive. Multi-fidelity techniques can significantly reduce the computational cost of Monte Carlo sampling methods for quantifying the effect of uncertain inputs, but existing multi-fidelity techniques in this context apply only to Monte Carlo estimators that can be expressed as a sample average, such as estimators of statistical moments. Information reuse is a particular multi-fidelity method that treats previous optimization iterations as lower-fidelity models. This work generalizes information reuse to be applicable to quantities with non-sample average estimators. The extension makes use of bootstrapping to estimate the error of estimators and the covariance between estimators at different fidelities. Specifically, the horsetail matching metric and quantile function are considered as quantities whose estimators are not sample-averages. In an optimization under uncertainty for an acoustic horn design problem, generalized information reuse demonstrated computational savings of over 60% compared to regular Monte Carlo sampling