Adaptation to being at-risk for Huntington's disease and the availability of genetic testing: Application of a stress and coping model

This study examined the utility of a stress/coping model in explaining adaptation in two groups of people at-risk for Huntington's Disease (HD): those who have not approached genetic testing services (non-testees) and those who have engaged a testing service (testees). The aims were (1) to compare testees and non-testees on stress/coping variables, (2) to examine relations between adjustment and the stress/coping predictors in the two groups, and (3) to examine relations between the stress/coping variables and testees' satisfaction with their first counselling session. Participants were 44 testees and 40 non-testees who completed questionnaires which measured the stress/coping variables: adjustment (global distress, depression, health anxiety, social and dyadic adjustment), genetic testing concerns, testing context (HD contact, experience, knowledge), appraisal (control, threat, self-efficacy), coping strategies (avoidance, self-blame, wishful thinking, seeking support, problem solving), social support and locus of control. Testees also completed a genetic counselling session satisfaction scale. As expected, non-testees reported lower self-efficacy and control appraisals, higher threat and passive avoidant coping than testees. Overall, results supported the hypothesis that within each group poorer adjustment would be related to higher genetic testing concerns, contact with HD, threat appraisals, passive avoidant coping and external locus of control, and lower levels of positive experiences with HD, social support, internal locus of control, self-efficacy, control appraisals, problem solving, emotional approach and seeking social support coping. Session satisfaction scores were positively correlated with dyadic adjustment, problem solving and positive experience with HD, and inversely related to testing concerns, and threat and control appraisals. Findings support the utility of the stress/coping model in explaining adaptation in people who have decided not to seek genetic testing for HD and those who have decided to engage a genetic testing service.

Zero-non-zero patterned vector error correction modeling for I(2) cointegrated time series with applications in testing PPP and stock market relationships

Vector error-correction models (VECMs) have become increasingly important in their application to financial markets. Standard full-order VECM models assume non-zero entries in all their coefficient matrices. However, applications of VECM models to financial market data have revealed that zero entries are often a necessary part of efficient modelling. In such cases, the use of full-order VECM models may lead to incorrect inferences. Specifically, if indirect causality or Granger non-causality exists among the variables, the use of over-parameterised full-order VECM models may weaken the power of statistical inference. In this paper, it is argued that the zero–non-zero (ZNZ) patterned VECM is a more straightforward and effective means of testing for both indirect causality and Granger non-causality. For a ZNZ patterned VECM framework for time series of integrated order two, we provide a new algorithm to select cointegrating and loading vectors that can contain zero entries. Two case studies are used to demonstrate the usefulness of the algorithm in tests of purchasing power parity and a three-variable system involving the stock market.

Safety behaviors and dysfunctional beliefs about sleep: Testing a cognitive model of the maintenance of insomnia

Objective: Our aim was to determine if insomnia severity, dysfunctional beliefs about sleep, and depression predicted sleep-related safety behaviors. Method: Standard sleep-related measures (such as the Insomnia Severity Index; the Dysfunctional Beliefs About Sleep scale; the Depression, Anxiety, and Stress Scale; and the Sleep-Related Behaviors Questionnaire) were administered. Additionally, 14 days of sleep diary (Pittsburg Sleep Diary) data and actual use of sleep-related behaviors were collected. Results: Regression analysis revealed that dysfunctional beliefs about sleep predicted sleep-related safety behaviors. Insomnia severity did not predict sleep-related safety behaviors. Depression accounted for the greatest amount of unique variance in the prediction of safety behaviors, followed by dysfunctional beliefs. Exploratory analysis revealed that participants with higher levels of depression used more sleep-related behaviors and reported greater dysfunctional beliefs about their sleep. Conclusion: The findings underlie the significant influence that dysfunctional beliefs have on individuals' behaviors. Moreover, the results suggest that depression may need to be considered as an explicit component of cognitive-behavioral models of insomnia. (c) 2006 Elsevier Inc. All rights reserved.

An advanced real-time predictive maintenance framework for large scale machine systems

This thesis introduces and develops a novel real-time predictive maintenance system to estimate the machine system parameters using the motion current signature. Recently, motion current signature analysis has been addressed as an alternative to the use of sensors for monitoring internal faults of a motor. A maintenance system based upon the analysis of motion current signature avoids the need for the implementation and maintenance of expensive motion sensing technology. By developing nonlinear dynamical analysis for motion current signature, the research described in this thesis implements a novel real-time predictive maintenance system for current and future manufacturing machine systems. A crucial concept underpinning this project is that the motion current signature contains infor­mation relating to the machine system parameters and that this information can be extracted using nonlinear mapping techniques, such as neural networks. Towards this end, a proof of con­cept procedure is performed, which substantiates this concept. A simulation model, TuneLearn, is developed to simulate the large amount of training data required by the neural network ap­proach. Statistical validation and verification of the model is performed to ascertain confidence in the simulated motion current signature. Validation experiment concludes that, although, the simulation model generates a good macro-dynamical mapping of the motion current signature, it fails to accurately map the micro-dynamical structure due to the lack of knowledge regarding performance of higher order and nonlinear factors, such as backlash and compliance. Failure of the simulation model to determine the micro-dynamical structure suggests the pres­ence of nonlinearity in the motion current signature. This motivated us to perform surrogate data testing for nonlinearity in the motion current signature. Results confirm the presence of nonlinearity in the motion current signature, thereby, motivating the use of nonlinear tech­niques for further analysis. Outcomes of the experiment show that nonlinear noise reduction combined with the linear reverse algorithm offers precise machine system parameter estimation using the motion current signature for the implementation of the real-time predictive maintenance system. Finally, a linear reverse algorithm, BJEST, is developed and applied to the motion current signature to estimate the machine system parameters.

Mixed-frequency testing of induction machines using inverters

This work has concentrated on the testing of induction machines to determine their temperature rise at full-load without mechanically coupling to a load machine. The achievements of this work are outlined as follows. 1. Four distinct categories of mixed-frequency test using an inverter have been identified by the author. The simulation results of these tests as well as the conventional 2-supply test have been analysed in detail. 2. Experimental work on mixed-frequency tests has been done on a small (4 kW) squirrel cage induction machine using a voltage source PWM inverter. Two out of the four categories of test suggested have been tested and the temperature rise results were found to be similar to the results of a direct loading test. Further, one of the categories of test proposed has been performed on a 3.3 kW slip-ring induction machine for the conformation of the rotor values. 3. A low current supply mixed-frequency test-rig has been proposed. For this purpose, a resonant bank was connected to the DC link of the inverter in order to maintain the exchange of power between the test machine and the resonant bank instead of between the main supply and the test machine. The resonant bank was then replaced with a special electro-mechanical energy storage unit. The current of the main power supply was then reduced in amplitude. 4. A variable inertia test for full load temperature rise testing of induction machines has been introduced. This test is purely mechanical in nature and does not require any electrical connection of the test machine to any other machine. It has the advantage of drawing very little net power from the supply.

Toward an atomistic understanding of the immune synapse:large-scale molecular dynamics simulation of a membrane-embedded TCR-pMHC-CD4 complex

T-cell activation requires interaction of T-cell receptors (TCR) with peptide epitopes bound by major histocompatibility complex (MHC) proteins. This interaction occurs at a special cell-cell junction known as the immune or immunological synapse. Fluorescence microscopy has shown that the interplay among one agonist peptide-MHC (pMHC), one TCR and one CD4 provides the minimum complexity needed to trigger transient calcium signalling. We describe a computational approach to the study of the immune synapse. Using molecular dynamics simulation, we report here on a study of the smallest viable model, a TCR-pMHC-CD4 complex in a membrane environment. The computed structural and thermodynamic properties are in fair agreement with experiment. A number of biomolecules participate in the formation of the immunological synapse. Multi-scale molecular dynamics simulations may be the best opportunity we have to reach a full understanding of this remarkable supra-macromolecular event at a cell-cell junction.

Generation and performance of localised surface plasmons utilising nano-scale structured multi-layered thin films deposited upon D-shaped optical fiber

A new generation of surface plasmonic optical fibre sensors is fabricated using multiple coatings deposited on a lapped section of a single mode fibre. Post-deposition UV laser irradiation using a phase mask produces a nano-scaled surface relief grating structure, resembling nano-wires. The overall length of the individual corrugations is approximately 14 μm with an average full width half maximum of 100 nm. Evidence is presented to show that these surface structures result from material compaction created by the silicon dioxide and germanium layers in the multi-layered coating and the surface topology is capable of supporting localised surface plasmons. The coating compaction induces a strain gradient into the D-shaped optical fibre that generates an asymmetric periodic refractive index profile which enhances the coupling of the light from the core of the fibre to plasmons on the surface of the coating. Experimental data are presented that show changes in spectral characteristics after UV processing and that the performance of the sensors increases from that of their pre-UV irradiation state. The enhanced performance is illustrated with regards to change in external refractive index and demonstrates high spectral sensitivities in gaseous and aqueous index regimes ranging up to 4000 nm/RIU for wavelength and 800 dB/RIU for intensity. The devices generate surface plasmons over a very large wavelength range, (visible to 2 μm) depending on the polarization state of the illuminating light. © 2013 SPIE.

Range of confidence scale and consumer knowledge calibration

The study examined the effect of range of a confidence scale on consumer knowledge calibration, specifically whether a restricted range scale (25%- 100%) leads to difference in calibration compared to a full range scale (0%-100%), for multiple-choice questions. A quasi-experimental study using student participants (N = 434) was employed. Data were collected from two samples; in the first sample (N = 167) a full range confidence scale was used, and in the second sample (N = 267) a restricted range scale was used. No differences were found between the two scales on knowledge calibration. Results from studies of knowledge calibration employing restricted range and full range confidence scales are thus comparable. © Psychological Reports 2014.

Characterization of human mesenchymal stem cells from multiple donors and the implications for large scale bioprocess development

Cell-based therapies have the potential to contribute to global healthcare, whereby the use of living cells and tissues can be used as medicinal therapies. Despite this potential, many challenges remain before the full value of this emerging field can be realized. The characterization of input material for cell-based therapy bioprocesses from multiple donors is necessary to identify and understand the potential implications of input variation on process development. In this work, we have characterized bone marrow derived human mesenchymal stem cells (BM-hMSCs) from multiple donors and discussed the implications of the measurable input variation on the development of autologous and allogeneic cell-based therapy manufacturing processes. The range of cumulative population doublings across the five BM-hMSC lines over 30 days of culture was 5.93, with an 18.2% range in colony forming efficiency at the end of the culture process and a 55.1% difference in the production of interleukin-6 between these cell lines. It has been demonstrated that this variation results in a range in the process time between these donor hMSC lines for a hypothetical product of over 13 days, creating potential batch timing issues when manufacturing products from multiple patients. All BM-hMSC donor lines demonstrated conformity to the ISCT criteria but showed a difference in cell morphology. Metabolite analysis showed that hMSCs from the different donors have a range in glucose consumption of 26.98 pmol cell−1 day−1, Lactate production of 29.45 pmol cell−1 day−1 and ammonium production of 1.35 pmol cell−1 day−1, demonstrating the extent of donor variability throughout the expansion process. Measuring informative product attributes during process development will facilitate progress towards consistent manufacturing processes, a critical step in the translation cell-based therapies.

A comparison of two distributed large-volume measurement systems:the mobile spatial co-ordinate measuring system and the indoor global positioning system

Advances in the area of industrial metrology have generated new technologies that are capable of measuring components with complex geometry and large dimensions. However, no standard or best-practice guides are available for the majority of such systems. Therefore, these new systems require appropriate testing and verification in order for the users to understand their full potential prior to their deployment in a real manufacturing environment. This is a crucial stage, especially when more than one system can be used for a specific measurement task. In this paper, two relatively new large-volume measurement systems, the mobile spatial co-ordinate measuring system (MScMS) and the indoor global positioning system (iGPS), are reviewed. These two systems utilize different technologies: the MScMS is based on ultrasound and radiofrequency signal transmission and the iGPS uses laser technology. Both systems have components with small dimensions that are distributed around the measuring area to form a network of sensors allowing rapid dimensional measurements to be performed in relation to large-size objects, with typical dimensions of several decametres. The portability, reconfigurability, and ease of installation make these systems attractive for many industries that manufacture large-scale products. In this paper, the major technical aspects of the two systems are briefly described and compared. Initial results of the tests performed to establish the repeatability and reproducibility of these systems are also presented. © IMechE 2009.

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society.

Bioactive sol-gel glasses at the atomic scale:the complementary use of advanced probe and computer modeling methods

Sol-gel-synthesized bioactive glasses may be formed via a hydrolysis condensation reaction, silica being introduced in the form of tetraethyl orthosilicate (TEOS), and calcium is typically added in the form of calcium nitrate. The synthesis reaction proceeds in an aqueous environment; the resultant gel is dried, before stabilization by heat treatment. These materials, being amorphous, are complex at the level of their atomic-scale structure, but their bulk properties may only be properly understood on the basis of that structural insight. Thus, a full understanding of their structure-property relationship may only be achieved through the application of a coherent suite of leading-edge experimental probes, coupled with the cogent use of advanced computer simulation methods. Using as an exemplar a calcia-silica sol-gel glass of the kind developed by Larry Hench, in the memory of whom this paper is dedicated, we illustrate the successful use of high-energy X-ray and neutron scattering (diffraction) methods, magic-angle spinning solid-state NMR, and molecular dynamics simulation as components to a powerful methodology for the study of amorphous materials.

Optimising the diagnostic performance of the Geriatric Depression Scale

The aim of this work is to empirically generate a shortened version of the Geriatric Depression Scale (GDS), with the intention of maximising the diagnostic performance in the detection of depression compared with previously GDS validated versions, while optimizing the size of the instrument. A total of 233 individuals (128 from a Day Hospital, 105 randomly selected from the community) aged 60 or over completed the GDS and other measures. The 30 GDS items were entered in the Day Hospital sample as independent variables in a stepwise logistic regression analysis predicting diagnosis of Major Depression. A final solution of 10 items was retained, which correctly classified 97.4% of cases. The diagnostic performance of these 10 GDS items was analysed in the random sample with a receiver operating characteristic (ROC) curve. Sensitivity (100%), specificity (97.2%), positive (81.8%) and negative (100%) predictive power, and the area under the curve (0.994) were comparable with values for GDS-30 and higher compared with GDS-15, GDS-10 and GDS-5. In addition, the new scale proposed had excellent fit when testing its unidimensionality with CFA for categorical outcomes (e.g., CFI=0.99). The 10-item version of the GDS proposed here, the GDS-R, seems to retain the diagnostic performance for detecting depression in older adults of the GDS-30 items, while increasing the sensitivity and predictive values relative to other shortened versions.

Segmental body composition determination through a bioelectrical very large scale integration (VLSI) system

The purpose of this investigation was to develop new techniques to generate segmental assessments of body composition based on Segmental Bioelectrical Impedance Analysis (SBIA). An equally important consideration was the design, simulation, development, and the software and hardware integration of the SBIA system. This integration was carried out with a Very Large Scale Integration (VLSI) Field Programmable Gate Array (FPGA) microcontroller that analyzed the measurements obtained from segments of the body, and provided full body and segmental Fat Free Mass (FFM) and Fat Mass (FM) percentages. Also, the issues related to the estimate of the body's composition in persons with spinal cord injury (SCI) were addressed and investigated. This investigation demonstrated that the SBIA methodology provided accurate segmental body composition measurements. Disabled individuals are expected to benefit from these SBIA evaluations, as they are non-invasive methods, suitable for paralyzed individuals. The SBIA VLSI system may replace bulky, non flexible electronic modules attached to human bodies. ^