Molecular characterisation of environmental enterococci derived from water samples and assessment of associated public health hazards

Enterococci are versatile Gram-positive bacteria that can survive under extreme conditions. Most enterococci are non-virulent and found in the gastrointestinal tract of humans and animals. Other strains are opportunistic pathogens that contribute to a large number of nosocomial infections globally. Epidemiological studies demonstrated a direct relationship between the density of enterococci in surface waters and the risk of swimmer-associated gastroenteritis. The distribution of infectious enterococcal strains from the hospital environment or other sources to environmental water bodies through sewage discharge or other means, could increase the prevalence of these strains in the human population. Environmental water quality studies may benefit from focusing on a subset of Enterococcus spp. that are consistently associated with sources of faecal pollution such as domestic sewage, rather than testing for the entire genus. E. faecalis and E. faecium are potentially good focal species for such studies, as they have been consistently identified as the dominant Enterococcus spp. in human faeces and sewage. On the other hand enterococcal infections are predominantly caused by E. faecalis and E. faecium. The characterisation of E. faecalis and E. faecium is important in studying their population structures, particularly in environmental samples. In developing and implementing rapid, robust molecular genotyping techniques, it is possible to more accurately establish the relationship between human and environmental enterococci. Of particular importance, is to determine the distribution of high risk enterococcal clonal complexes, such as E. faecium clonal complex 17 and E. faecalis clonal complexes 2 and 9 in recreational waters. These clonal complexes are recognized as particularly pathogenic enterococcal genotypes that cause severe disease in humans globally. The Pimpama-Coomera watershed is located in South East Queensland, Australia and was investigated in this study mainly because it is used intensively for agriculture and recreational purposes and has a strong anthropogenic impact. The primary aim of this study was to develop novel, universally applicable, robust, rapid and cost effective genotyping methods which are likely to yield more definitive results for the routine monitoring of E. faecalis and E. faecium, particularly in environmental water sources. To fullfill this aim, new genotyping methods were developed based on the interrogation of highly informative single nucleotide polymorphisms (SNPs) located in housekeeping genes of both E. faecalis and E. faecium. SNP genotyping was successfully applied in field investigations of the Coomera watershed, South-East Queensland, Australia. E. faecalis and E. faecium isolates were grouped into 29 and 23 SNP profiles respectively. This study showed the high longitudinal diversity of E. faecalis and E. faecium over a period of two years, and both human-related and human-specific SNP profiles were identified. Furthermore, 4.25% of E. faecium strains isolated from water was found to correspond to the important clonal complex-17 (CC17). Strains that belong to CC17 cause the majority of hospital outbreaks and clinical infections globally. Of the six sampling sites of the Coomera River, Paradise Point had the highest number of human-related and human-specific E. faecalis and E. faecium SNP profiles. The secondary aim of this study was to determine the antibiotic-resistance profiles and virulence traits associated with environmental E. faecalis and E. faecium isolates compared to human pathogenic E. faecalis and E. faecium isolates. This was performed to predict the potential health risks associated with coming into contact with these strains in the Coomera watershed. In general, clinical isolates were found to be more resistant to all the antibiotics tested compared to water isolates and they harbored more virulence traits. Multi-drug resistance was more prevalent in clinical isolates (71.18% of E. faecalis and 70.3 % of E. faecium) compared to water isolates (only 5.66 % E. faecium). However, tetracycline, gentamicin, ciprofloxacin and ampicillin resistance was observed in water isolates. The virulence gene esp was the most prevalent virulence determinant observed in clinical isolates (67.79% of E. faecalis and 70.37 % of E. faecium), and this gene has been described as a human-specific marker used for microbial source tracking (MST). The presence of esp in water isolates (16.36% of E. faecalis and 19.14% of E. faecium) could be indicative of human faecal contamination in these waterways. Finally, in order to compare overall gene expression between environmental and clinical strains of E. faecalis, a comparative gene hybridization study was performed. The results of this investigation clearly demonstrated the up-regulation of genes associated with pathogenicity in E. faecalis isolated from water. The expression study was performed at physiological temperatures relative to ambient temperatures. The up-regulation of virulence genes demonstrates that environmental strains of E. faecalis can pose an increased health risk which can lead to serious disease, particularly if these strains belong to the virulent CC17 group. The genotyping techniques developed in this study not only provide a rapid, robust and highly discriminatory tool to characterize E. faecalis and E. faecium, but also enables the efficient identification of virulent enterococci that are distributed in environmental water sources.

Effective discrimination of acoustic emission source signals for structural health monitoring

Acoustic emission (AE) is the phenomenon where stress waves are generated due to rapid release of energy within a material caused by sources such as crack initiation or growth. AE technique involves recording the stress waves by means of sensors and subsequent analysis of the recorded signals to gather information about the nature of the source. Though AE technique is one of the popular non destructive evaluation (NDE) techniques for structural health monitoring of mechanical, aerospace and civil structures; several challenges still exist in successful application of this technique. Presence of spurious noise signals can mask genuine damage‐related AE signals; hence a major challenge identified is finding ways to discriminate signals from different sources. Analysis of parameters of recorded AE signals, comparison of amplitudes of AE wave modes and investigation of uniqueness of recorded AE signals have been mentioned as possible criteria for source differentiation. This paper reviews common approaches currently in use for source discrimination, particularly focusing on structural health monitoring of civil engineering structural components such as beams; and further investigates the applications of some of these methods by analyzing AE data from laboratory tests.

Health monitoring of buildings during construction and service stages using vibration characteristics

Columns and walls in buildings are subjected to a number of load increments during the construction and service stages. The combination of these load increments and poor quality construction can cause defects in these structural components. In addition, defects can also occur due to accidental or deliberate actions by users of the building during construction and service stages. Such defects should be detected early so that remedial measures can be taken to improve life time serviceability and performance of the building. This paper uses micro and macro model upgrading methods during construction and service stages of a building based on the mass and stiffness changes to develop a comprehensive procedure for locating and detecting defects in columns and walls of buildings. Capabilities of the procedure are illustrated through examples.

Assessment of “change in flexibility method” in structural health monitoring systems

The modern structural diagnosis process is rely on vibration characteristics to assess safer serviceability level of the structure. This paper examines the potential of change in flexibility method to use in damage detection process and two main practical constraints associated with it. The first constraint addressed in this paper is reduction in number of data acquisition points due to limited number of sensors. Results conclude that accuracy of the change in flexibility method is influenced by the number of data acquisition points/sensor locations in real structures. Secondly, the effect of higher modes on damage detection process has been studied. This addresses the difficulty of extracting higher order modal data with available sensors. Four damage indices have been presented to identify their potential of damage detection with respect to different locations and severity of damage. A simply supported beam with two degrees of freedom at each node is considered only for a single damage cases throughout the paper.

Analysis of acoustic emission data for accurate damage assessment for structural health monitoring applications

Structural health monitoring (SHM) refers to the procedure used to assess the condition of structures so that their performance can be monitored and any damage can be detected early. Early detection of damage and appropriate retrofitting will aid in preventing failure of the structure and save money spent on maintenance or replacement and ensure the structure operates safely and efficiently during its whole intended life. Though visual inspection and other techniques such as vibration based ones are available for SHM of structures such as bridges, the use of acoustic emission (AE) technique is an attractive option and is increasing in use. AE waves are high frequency stress waves generated by rapid release of energy from localised sources within a material, such as crack initiation and growth. AE technique involves recording these waves by means of sensors attached on the surface and then analysing the signals to extract information about the nature of the source. High sensitivity to crack growth, ability to locate source, passive nature (no need to supply energy from outside, but energy from damage source itself is utilised) and possibility to perform real time monitoring (detecting crack as it occurs or grows) are some of the attractive features of AE technique. In spite of these advantages, challenges still exist in using AE technique for monitoring applications, especially in the area of analysis of recorded AE data, as large volumes of data are usually generated during monitoring. The need for effective data analysis can be linked with three main aims of monitoring: (a) accurately locating the source of damage; (b) identifying and discriminating signals from different sources of acoustic emission and (c) quantifying the level of damage of AE source for severity assessment. In AE technique, the location of the emission source is usually calculated using the times of arrival and velocities of the AE signals recorded by a number of sensors. But complications arise as AE waves can travel in a structure in a number of different modes that have different velocities and frequencies. Hence, to accurately locate a source it is necessary to identify the modes recorded by the sensors. This study has proposed and tested the use of time-frequency analysis tools such as short time Fourier transform to identify the modes and the use of the velocities of these modes to achieve very accurate results. Further, this study has explored the possibility of reducing the number of sensors needed for data capture by using the velocities of modes captured by a single sensor for source localization. A major problem in practical use of AE technique is the presence of sources of AE other than crack related, such as rubbing and impacts between different components of a structure. These spurious AE signals often mask the signals from the crack activity; hence discrimination of signals to identify the sources is very important. This work developed a model that uses different signal processing tools such as cross-correlation, magnitude squared coherence and energy distribution in different frequency bands as well as modal analysis (comparing amplitudes of identified modes) for accurately differentiating signals from different simulated AE sources. Quantification tools to assess the severity of the damage sources are highly desirable in practical applications. Though different damage quantification methods have been proposed in AE technique, not all have achieved universal approval or have been approved as suitable for all situations. The b-value analysis, which involves the study of distribution of amplitudes of AE signals, and its modified form (known as improved b-value analysis), was investigated for suitability for damage quantification purposes in ductile materials such as steel. This was found to give encouraging results for analysis of data from laboratory, thereby extending the possibility of its use for real life structures. By addressing these primary issues, it is believed that this thesis has helped improve the effectiveness of AE technique for structural health monitoring of civil infrastructures such as bridges.

Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS)

The serviceability and safety of bridges are crucial to people’s daily lives and to the national economy. Every effort should be taken to make sure that bridges function safely and properly as any damage or fault during the service life can lead to transport paralysis, catastrophic loss of property or even casualties. Nonetheless, aggressive environmental conditions, ever-increasing and changing traffic loads and aging can all contribute to bridge deterioration. With often constrained budget, it is of significance to identify bridges and bridge elements that should be given higher priority for maintenance, rehabilitation or replacement, and to select optimal strategy. Bridge health prediction is an essential underpinning science to bridge maintenance optimization, since the effectiveness of optimal maintenance decision is largely dependent on the forecasting accuracy of bridge health performance. The current approaches for bridge health prediction can be categorised into two groups: condition ratings based and structural reliability based. A comprehensive literature review has revealed the following limitations of the current modelling approaches: (1) it is not evident in literature to date that any integrated approaches exist for modelling both serviceability and safety aspects so that both performance criteria can be evaluated coherently; (2) complex system modelling approaches have not been successfully applied to bridge deterioration modelling though a bridge is a complex system composed of many inter-related bridge elements; (3) multiple bridge deterioration factors, such as deterioration dependencies among different bridge elements, observed information, maintenance actions and environmental effects have not been considered jointly; (4) the existing approaches are lacking in Bayesian updating ability to incorporate a variety of event information; (5) the assumption of series and/or parallel relationship for bridge level reliability is always held in all structural reliability estimation of bridge systems. To address the deficiencies listed above, this research proposes three novel models based on the Dynamic Object Oriented Bayesian Networks (DOOBNs) approach. Model I aims to address bridge deterioration in serviceability using condition ratings as the health index. The bridge deterioration is represented in a hierarchical relationship, in accordance with the physical structure, so that the contribution of each bridge element to bridge deterioration can be tracked. A discrete-time Markov process is employed to model deterioration of bridge elements over time. In Model II, bridge deterioration in terms of safety is addressed. The structural reliability of bridge systems is estimated from bridge elements to the entire bridge. By means of conditional probability tables (CPTs), not only series-parallel relationship but also complex probabilistic relationship in bridge systems can be effectively modelled. The structural reliability of each bridge element is evaluated from its limit state functions, considering the probability distributions of resistance and applied load. Both Models I and II are designed in three steps: modelling consideration, DOOBN development and parameters estimation. Model III integrates Models I and II to address bridge health performance in both serviceability and safety aspects jointly. The modelling of bridge ratings is modified so that every basic modelling unit denotes one physical bridge element. According to the specific materials used, the integration of condition ratings and structural reliability is implemented through critical failure modes. Three case studies have been conducted to validate the proposed models, respectively. Carefully selected data and knowledge from bridge experts, the National Bridge Inventory (NBI) and existing literature were utilised for model validation. In addition, event information was generated using simulation to demonstrate the Bayesian updating ability of the proposed models. The prediction results of condition ratings and structural reliability were presented and interpreted for basic bridge elements and the whole bridge system. The results obtained from Model II were compared with the ones obtained from traditional structural reliability methods. Overall, the prediction results demonstrate the feasibility of the proposed modelling approach for bridge health prediction and underpin the assertion that the three models can be used separately or integrated and are more effective than the current bridge deterioration modelling approaches. The primary contribution of this work is to enhance the knowledge in the field of bridge health prediction, where more comprehensive health performance in both serviceability and safety aspects are addressed jointly. The proposed models, characterised by probabilistic representation of bridge deterioration in hierarchical ways, demonstrated the effectiveness and pledge of DOOBNs approach to bridge health management. Additionally, the proposed models have significant potential for bridge maintenance optimization. Working together with advanced monitoring and inspection techniques, and a comprehensive bridge inventory, the proposed models can be used by bridge practitioners to achieve increased serviceability and safety as well as maintenance cost effectiveness.

Respiratory health effects of diesel particulate matter

Particulate matter (PM) emissions involve a complex mixture of solid and liquid particles suspended in a gas, where it is noted that PM emissions from diesel engines are a major contributor to the ambient air pollution problem. Whilst epidemiological studies have shown a link between increased ambient PM emissions and respiratory morbidity and mortality, studies of this design are not able to identify the PM constituents responsible for driving adverse respiratory health effects. This review explores in detail the physico-chemical properties of diesel particulate matter (DPM), and identifies the constituents of this pollution source that are responsible for the development of respiratory disease. In particular, this review shows that the DPM surface area and adsorbed organic compounds play a significant role in manifesting chemical and cellular processes that if sustained can lead to the development of adverse respiratory health effects. The mechanisms of injury involved included: inflammation, innate and acquired immunity, and oxidative stress. Understanding the mechanisms of lung injury from DPM will enhance efforts to protect at-risk individuals from the harmful respiratory effects of air pollutants.

Nanoparticles in European cities and associated health impacts

Atmospheric nanoparticles are one of those pollutants currently unregulated through ambient air quality standards. The aim of this chapter is to assess the environmental and health impacts of atmospheric nanoparticles in European environments. The chapter begins with the conventional information on the origin of atmospheric nanoparticles, followed by their physical and chemical characteristics. A brief overview of recently published review articles on this topic is then presented to guide those readers interested in exploring any specific aspect of nanoparticles in greater detail. A further section reports a summary of recently published studies on atmospheric nanoparticles in European cities. This covers a total of about 45 sampling locations in 30 different cities within 15 European countries for quantifying levels of roadside and urban background particle number concentrations (PNCs). Average PNCs at roadside and urban background sites were found to be 3.82±3.25 ×104 cm–3 and 1.63±0.82 ×104 cm–3, respectively, giving a roadside to background PNC ratio of ~2.4. Engineered nanoparticles are one of the key emerging categories of airborne nanoparticles, especially for the indoor environments. Their ambient concentrations may increase in future due to widespread use of nanotechnology integrated products. Evaluation of their sources and probable impacts on air quality and human health are briefly discussed in the following section. Respiratory deposition doses received by the public exposed to roadside PNCs in numerous European locations are then estimated. These were found to be in the 1.17–7.56 1010 h–1 range over the studied roadside European locations. The following section discusses the potential framework for airborne nanoparticle regulations in Europe and, in addition, the existing control measures to limit nanoparticle emissions at source. The chapter finally concludes with a synthesis of the topic areas covered and highlights important areas for further work.

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.

Traffic-related fine and ultrafine particle exposures of professional drivers and illness : An opportunity to better link exposure science and epidemiology to address an occupational hazard?

Exposures to traffic-related air pollution (TRAP) can be particularly high in transport microenvironments (i.e. in and around vehicles) despite the short durations typically spent there. There is a mounting body of evidence that suggests that this is especially true for fine (b2.5 μm) and ultrafine (b100 nm, UF) particles. Professional drivers, who spend extended periods of time in transport microenvironments due to their job, may incur exposures markedly higher than already elevated non-occupational exposures. Numerous epidemiological studies have shown a raised incidence of adverse health outcomes among professional drivers, and exposure to TRAP has been suggested as one of the possible causal factors. Despite this, data describing the range and determinants of occupational exposures to fine and UF particles are largely conspicuous in their absence. Such information could strengthen attempts to define the aetiology of professional drivers' illnesses as it relates to traffic combustion-derived particles. In this article, we suggest that the drivers' occupational fine and UF particle exposures are an exemplar case where opportunities exist to better link exposure science and epidemiology in addressing questions of causality. The nature of the hazard is first introduced, followed by an overview of the health effects attributable to exposures typical of transport microenvironments. Basic determinants of exposure and reduction strategies are also described, and finally the state of knowledge is briefly summarised along with an outline of the main unanswered questions in the topic area.

Impact of the social networking applications for health information management for patients and physicians

Most social network users hold more than one social network account and utilize them in different ways depending on the digital context. For example, friendly chat on Facebook, professional discussion on LinkedIn, and health information exchange on PatientsLikeMe. Thus many web users need to manage many disparate profiles across many distributed online sources. Maintaining these profiles is cumbersome, time consuming, inefficient, and leads to lost opportunity. In this paper we propose a framework for multiple profile management of online social networks and showcase a demonstrator utilising an open source platform. The result of the research enables a user to create and manage an integrated profile and share/synchronise their profiles with their social networks. A number of use cases were created to capture the functional requirements and describe the interactions between users and the online services. An innovative application of this project is in public health informatics. We utilize the prototype to examine how the framework can benefit patients and physicians. The framework can greatly enhance health information management for patients and more importantly offer a more comprehensive personal health overview of patients to physicians.

Positive impacts of environmental characteristics on health and wellbeing in health-care facilities : a review

Well-designed indoor environments can support people’s health and welfare. In this literature review, we identify the environmental features that affect human health and wellbeing. Environmental characteristics found to influence health outcomes and/or wellbeing included: environmental safety; indoor air quality (e.g. odour and temperature); sound and noise; premises and interior design (e.g. construction materials, viewing nature and experiencing nature, windows versus no windows, light, colours, unit layout and placement of the furniture, the type of room, possibilities to control environmental elements, environmental complexity and sensory simulations, cleanliness, ergonomics and accessibility, ‛‛wayfinding’’); art, and music, among others. Indoor environments that incorporate healing elements can, for instance, reduce anxiety, lower blood pressure, lessen pain and shorten hospital stays.