Active ageing : developing a quantitative multi-dimensional measure

With estimates that two billion of the world’s population will be 65 years or older by 2050, ensuring that older people ‘age well’ is an international priority. To date, however, there is significant disagreement and debate about how to define and measure ‘ageing well’, with no consensus on either terminology or measurement. Thus, this chapter describes the research rationale, methodology and findings of the Australian Active Ageing Study (Triple A Study), which surveyed 2620 older Australians to identify significant contributions to quality of life for older people: work, learning, social participation, spirituality, emotional wellbeing, health, and life events. Exploratory factor analyses identified eight distinct elements (grouped into four key concepts) which appear to define ‘active ageing’ and explained 55% of the variance: social and life participation (25%), emotional health (22%), physical health and functioning (4%) and security (4%). These findings highlight the importance of understanding and supporting the social and emotional dimensions of ageing, as issues of social relationships, life engagement and emotional health dominated the factor structure. Our intension is that this paper will prompt informed debate and discussion on defining and measuring active ageing, facilitating exploration and understanding of the complexity of issues that intertwine, converge and enhance the ageing experience.

Non-invasive, quantitative analysis of drug mixtures in containers using spatially offset Raman spectroscopy (SORS) and multivariate statistical analysis

In this paper, spatially offset Raman spectroscopy (SORS) is demonstrated for non-invasively investigating the composition of drug mixtures inside an opaque plastic container. The mixtures consisted of three components including a target drug (acetaminophen or phenylephrine hydrochloride) and two diluents (glucose and caffeine). The target drug concentrations ranged from 5% to 100%. After conducting SORS analysis to ascertain the Raman spectra of the concealed mixtures, principal component analysis (PCA) was performed on the SORS spectra to reveal trends within the data. Partial least squares (PLS) regression was used to construct models that predicted the concentration of each target drug, in the presence of the other two diluents. The PLS models were able to predict the concentration of acetaminophen in the validation samples with a root-mean-square error of prediction (RMSEP) of 3.8% and the concentration of phenylephrine hydrochloride with an RMSEP of 4.6%. This work demonstrates the potential of SORS, used in conjunction with multivariate statistical techniques, to perform non-invasive, quantitative analysis on mixtures inside opaque containers. This has applications for pharmaceutical analysis, such as monitoring the degradation of pharmaceutical products on the shelf, in forensic investigations of counterfeit drugs, and for the analysis of illicit drug mixtures which may contain multiple components.

Navigational traffic conflict technique: a proactive approach to quantitative measurement of collision risks in port waters

Navigational safety analysis relying on collision statistics is often hampered because of low number of observations. A promising alternative approach that overcomes this problem is proposed in this paper. By analyzing critical vessel interactions this approach proactively measures collision risk in port waters. The proposed method is illustrated for quantitative measurement of collision risks in Singapore port fairways, and validated by examining correlations between the measured risks with those perceived by pilots. This method is an ethically appealing alternative to the collision-based analysis for fast, reliable and effective safety assessment, thus possesses great potential for managing collision risks in port waters.

Proteomics Approaches in the Identification of Molecular Signatures of Mesenchymal Stem Cells

Mesenchymal stem cells (MSCs) are undifferentiated, multi-potent stem cells with the ability to renew. They can differentiate into many types of terminal cells, such as osteoblasts, chondrocytes, adipocytes, myocytes, and neurons. These cells have been applied in tissue engineering as the main cell type to regenerate new tissues. However, a number of issues remain concerning the use of MSCs, such as cell surface markers, the determining factors responsible for their differentiation to terminal cells, and the mechanisms whereby growth factors stimulate MSCs. In this chapter, we will discuss how proteomic techniques have contributed to our current knowledge and how they can be used to address issues currently facing MSC research. The application of proteomics has led to the identification of a special pattern of cell surface protein expression of MSCs. The technique has also contributed to the study of a regulatory network of MSC differentiation to terminal differentiated cells, including osteocytes, chondrocytes, adipocytes, neurons, cardiomyocytes, hepatocytes, and pancreatic islet cells. It has also helped elucidate mechanisms for growth factor–stimulated differentiation of MSCs. Proteomics can, however, not reveal the accurate role of a special pathway and must therefore be combined with other approaches for this purpose. A new generation of proteomic techniques have recently been developed, which will enable a more comprehensive study of MSCs. Keywords

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Qualitative and quantitative analysis of six sigma in service organizations

The concept of Six Sigma was initiated in the 1980s by Motorola. Since then it has been implemented in several manufacturing and service organizations. Till now Six Sigma implementation is mostly limited to healthcare and financial services in private sector. Its implementation is now gradually picking up in services such as call center, education, construction and related engineering etc. in private as well as public sector. Through a literature review, a questionnaire survey, and multiple case study approach the paper develops a conceptual framework to facilitate widening the scope of Six Sigma implementation in service organizations. Using grounded theory methodology, this study develops theory for Six Sigma implementation in service organizations. The study involves a questionnaire survey and case studies to understand and build a conceptual framework. The survey was conducted in service organizations in Singapore and exploratory in nature. The case studies involved three service organizations which implemented Six Sigma. The objective is to explore and understand the issues highlighted by the survey and the literature. The findings confirm the inclusion of critical success factors, critical-to-quality characteristics, and set of tools and techniques as observed from the literature. In case of key performance indicator, there are different interpretations about it in literature and also by industry practitioners. Some literature explain key performance indicator as performance metrics whereas some feel it as key process input or output variables, which is similar to interpretations by practitioners of Six Sigma. The response of not relevant and unknown to us as reasons for not implementing Six Sigma shows the need for understanding specific requirements of service organizations. Though much theoretical description is available about Six Sigma, but there has been limited rigorous academic research on it. This gap is far more pronounced about Six Sigma implementation in service organizations, where the theory is not mature enough. Identifying this need, the study contributes by going through theory building exercise and developing a conceptual framework to understand the issues involving its implementation in service organizations.

Proteomics science & society : the role of knowledge translation in moving towards clinical applications

Background: Outside the mass-spectrometer, proteomics research does not take place in a vacuum. It is affected by policies on funding and research infrastructure. Proteomics research both impacts and is impacted by potential clinical applications. It provides new techniques & clinically relevant findings, but the possibilities for such innovations (and thus the perception of the potential for the field by funders) are also impacted by regulatory practices and the readiness of the health sector to incorporate proteomics-related tools & findings. Key to this process is how knowledge is translated. Methods: We present preliminary results from a multi-year social science project, funded by the Canadian Institutes of Health Research, on the processes and motivations for knowledge translation in the health sciences. The proteomics case within this wider study uses qualitative methods to examine the interplay between proteomics science and regulatory and policy makers regarding clinical applications of proteomics. Results: Adopting an interactive format to encourage conference attendees’ feedback, our poster focuses on deficits in effective knowledge translation strategies from the laboratory to policy, clinical, & regulatory arenas. An analysis of the interviews conducted to date suggests five significant choke points: the changing priorities of funding agencies; the complexity of proteomics research; the organisation of proteomics research; the relationship of proteomics to genomics and other omics sciences; and conflict over the appropriate role of standardisation. Conclusion: We suggest that engagement with aspects of knowledge translation, such as those mentioned above, is crucially important for the eventual clinical application ofproteomics science on any meaningful scale.

Quantitative studies of lower motor neuron degeneration in amyotrophic lateral sclerosis : Evidence for exponential decay of motor unit numbers and greatest rate of loss at the site of onset

Objective: To use our Bayesian method of motor unit number estimation (MUNE) to evaluate lower motor neuron degeneration in ALS. Methods: In subjects with ALS we performed serial MUNE studies. We examined the repeatability of the test and then determined whether the loss of MUs was fitted by an exponential or Weibull distribution. Results: The decline in motor unit (MU) numbers was well-fitted by an exponential decay curve. We calculated the half life of MUs in the abductor digiti minimi (ADM), abductor pollicis brevis (APB) and/or extensor digitorum brevis (EDB) muscles. The mean half life of the MUs of ADM muscle was greater than those of the APB or EDB muscles. The half-life of MUs was less in the ADM muscle of subjects with upper limb than in those with lower limb onset. Conclusions: The rate of loss of lower motor neurons in ALS is exponential, the motor units of the APB decay more quickly than those of the ADM muscle and the rate of loss of motor units is greater at the site of onset of disease. Significance: This shows that the Bayesian MUNE method is useful in following the course and exploring the clinical features of ALS. 2012 International Federation of Clinical Neurophysiology.

Prostate cancer biomarkers and the emerging proteomic techniques used in biomarker research

Prostate cancer (CaP) is the second leading cause of cancer-related deaths in North American males and the most common newly diagnosed cancer in men world wide. Biomarkers are widely used for both early detection and prognostic tests for cancer. The current, commonly used biomarker for CaP is serum prostate specific antigen (PSA). However, the specificity of this biomarker is low as its serum level is not only increased in CaP but also in various other diseases, with age and even body mass index. Human body fluids provide an excellent resource for the discovery of biomarkers, with the advantage over tissue/biopsy samples of their ease of access, due to the less invasive nature of collection. However, their analysis presents challenges in terms of variability and validation. Blood and urine are two human body fluids commonly used for CaP research, but their proteomic analyses are limited both by the large dynamic range of protein abundance making detection of low abundance proteins difficult and in the case of urine, by the high salt concentration. To overcome these challenges, different techniques for removal of high abundance proteins and enrichment of low abundance proteins are used. Their applications and limitations are discussed in this review. A number of innovative proteomic techniques have improved detection of biomarkers. They include two dimensional differential gel electrophoresis (2D-DIGE), quantitative mass spectrometry (MS) and functional proteomic studies, i.e., investigating the association of post translational modifications (PTMs) such as phosphorylation, glycosylation and protein degradation. The recent development of quantitative MS techniques such as stable isotope labeling with amino acids in cell culture (SILAC), isobaric tags for relative and absolute quantitation (iTRAQ) and multiple reaction monitoring (MRM) have allowed proteomic researchers to quantitatively compare data from different samples. 2D-DIGE has greatly improved the statistical power of classical 2D gel analysis by introducing an internal control. This chapter aims to review novel CaP biomarkers as well as to discuss current trends in biomarker research from two angles: the source of biomarkers (particularly human body fluids such as blood and urine), and emerging proteomic approaches for biomarker research.

Identities and transformational experiences for quantitative problem solving : gender comparisons of first-year university science students

Women are underrepresented in science, technology, engineering and mathematics (STEM) areas in university settings; however this may be the result of attitude rather than aptitude. There is widespread agreement that quantitative problem-solving is essential for graduate competence and preparedness in science and other STEM subjects. The research question addresses the identities and transformative experiences (experiential, perception, & motivation) of both male and female university science students in quantitative problem solving. This study used surveys to investigate first-year university students’ (231 females and 198 males) perceptions of their quantitative problem solving. Stata (statistical analysis package version 11) analysed gender differences in quantitative problem solving using descriptive and inferential statistics. Males perceived themselves with a higher mathematics identity than females. Results showed that there was statistical significance (p<0.05) between the genders on 21 of the 30 survey items associated with transformative experiences. Males appeared to have a willingness to be involved in quantitative problem solving outside their science coursework requirements. Positive attitudes towards STEM-type subjects may need to be nurtured in females before arriving in the university setting (e.g., high school or earlier). Females also need equitable STEM education opportunities such as conversations or activities outside school with family and friends to develop more positive attitudes in these fields.

Quantitative evaluation of matching methods and validity measures for stereo vision

The authors present a qualitative and quantitative comparison of various similarity measures that form the kernel of common area-based stereo-matching systems. The authors compare classical difference and correlation measures as well as nonparametric measures based on the rank and census transforms for a number of outdoor images. For robotic applications, important considerations include robustness to image defects such as intensity variation and noise, the number of false matches, and computational complexity. In the absence of ground truth data, the authors compare the matching techniques based on the percentage of matches that pass the left-right consistency test. The authors also evaluate the discriminatory power of several match validity measures that are reported in the literature for eliminating false matches and for estimating match confidence. For guidance applications, it is essential to have and estimate of confidence in the three-dimensional points generated by stereo vision. Finally, a new validity measure, the rank constraint, is introduced that is capable of resolving ambiguous matches for rank transform-based matching.

Quantitative approaches to comparing communication patterns on Twitter

To date, the available literature mainly discusses Twitter activity patterns in the context of individual case studies, while comparative research on a large number of communicative events and their dynamics and patterns is missing. By conducting a comparative study of more than 40 different cases (covering topics such as elections, natural disasters, corporate crises, and televised events) we identify a number of distinct types of discussion that can be observed on Twitter. Drawing on a range of communicative metrics, we show that thematic and contextual factors influence the usage of different communicative tools available to Twitter users, such as original tweets, @replies, retweets, and URLs. Based on this first analysis of the overall metrics of Twitter discussions, we also demonstrate stable patterns in the use of Twitter in the context of major topics and events.

Comparison of antigen detection and quantitative PCR in the detection of chlamydial infection in koalas (Phascolarctos cinereus)

The gold standard method for detecting chlamydial infection in domestic and wild animals is PCR, but the technique is not suited to testing animals in the field when a rapid diagnosis is frequently required. The objective of this study was to compare the results of a commercially available enzyme immunoassay test for Chlamydia against a quantitative Chlamydia pecorum-specific PCR performed on swabs collected from the conjunctival sac, nasal cavity and urogenital sinuses of naturally infected koalas (Phascolarctos cinereus). The level of agreement for positive results between the two assays was low (43.2%). The immunoassay detection cut-off was determined as approximately 400 C. pecorum copies, indicating that the test was sufficiently sensitive to be used for the rapid diagnosis of active chlamydial infections.

Quantitative assessment of fault tolerant precision timing for electricity substations

Advanced substation applications, such as synchrophasors and IEC 61850-9-2 sampled value process buses, depend upon highly accurate synchronizing signals for correct operation. The IEEE 1588 Precision Timing Protocol (PTP) is the recommended means of providing precise timing for future substations. This paper presents a quantitative assessment of PTP reliability using Fault Tree Analysis. Two network topologies are proposed that use grandmaster clocks with dual network connections and take advantage of the Best Master Clock Algorithm (BMCA) from IEEE 1588. The cross-connected grandmaster topology doubles reliability, and the addition of a shared third grandmaster gives a nine-fold improvement over duplicated grandmasters. The performance of BMCA mediated handover of the grandmaster role during contingencies in the timing system was evaluated experimentally. The 1 µs performance requirement of sampled values and synchrophasors are met, even during network or GPS antenna outages. Slave clocks are shown to synchronize to the backup grandmaster in response to degraded performance or loss of the main grandmaster. Slave disturbances are less than 350 ns provided the grandmaster reference clocks are not offset from one another. A clear understanding of PTP reliability and the factors that affect availability will encourage the adoption of PTP for substation time synchronization.