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The Potential of Antimicrobial Peptides as Biocides
Garry Laverty,Sean P. Gorman,Brendan F. Gilmore
International Journal of Molecular Sciences , 2011, DOI: 10.3390/ijms12106566
Abstract: Antimicrobial peptides constitute a diverse class of naturally occurring antimicrobial molecules which have activity against a wide range of pathogenic microorganisms. Antimicrobial peptides are exciting leads in the development of novel biocidal agents at a time when classical antibiotics are under intense pressure from emerging resistance, and the global industry in antibiotic research and development stagnates. This review will examine the potential of antimicrobial peptides, both natural and synthetic, as novel biocidal agents in the battle against multi-drug resistant pathogen infections.
The In Vitro Susceptibility of Biofilm Forming Medical Device Related Pathogens to Conventional Antibiotics
Garry Laverty,Mahmoud Y. Alkawareek,Brendan F. Gilmore
Dataset Papers in Science , 2014, DOI: 10.1155/2014/250694
Abstract: Minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and minimum biofilm eradication concentration (MBEC) and kill kinetics were established for vancomycin, rifampicin, trimethoprim, gentamicin, and ciprofloxacin against the biofilm forming bacteria Staphylococcus epidermidis (ATCC 35984), Staphylococcus aureus (ATCC 29213), Methicillin Resistant Staphylococcus aureus (MRSA) (ATCC 43300), Pseudomonas aeruginosa (PAO1), and Escherichia coli (NCTC 8196). MICs and MBCs were determined via broth microdilution in 96-well plates. MBECs were studied using the Calgary Biofilm Device. Values obtained were used to investigate the kill kinetics of conventional antimicrobials against a range of planktonic and biofilm microorganisms over a period of 24 hours. Planktonic kill kinetics were determined at 4xMIC and biofilm kill kinetics at relative MBECs. Susceptibility of microorganisms varied depending on antibiotic selected and phenotypic form of bacteria. Gram-positive planktonic isolates were extremely susceptible to vancomycin (highest MBC: 7.81?mg?L?1: methicillin sensitive and resistant S. aureus) but no MBEC value was obtained against all biofilm pathogens tested (up to 1000?mg?L?1). Both gentamicin and ciprofloxacin displayed the broadest spectrum of activity with MIC and MBCs in the mg?L?1 range against all planktonic isolates tested and MBEC values obtained against all but S. epidermidis (ATCC 35984) and MRSA (ATCC 43300). 1. Introduction Medical device related infections present an increasing burden on health care systems with concomitant high rates of patient morbidity and mortality [1]. Their increased clinical presentation and associated problems are due mainly to the ability of microorganisms to form resistant biofilms at the biomaterial surface. Biofilms are heterogeneous by nature and contain a subpopulation of dormant persister cells that show tolerance to treatment by standard antimicrobial regimens [2]. In the presence of many standard antibiotics these persister cells reduce metabolic processes and uptake of nutrients and cease multiplying, only to become active again when the antibiotic is at subtherapeutic levels [3]. In order to be clinically successful in the treatment of a medical device infection, the antibiotic’s pharmacokinetic properties, rate of kill, and concentration must be assessed against a spectrum of relevant biofilm forming microorganisms. This study is set out to obtain an indication of the ability of currently prescribed antimicrobials to eradicate both planktonic and biofilm forms of these device
Eradication of Pseudomonas aeruginosa Biofilms by Atmospheric Pressure Non-Thermal Plasma
Mahmoud Y. Alkawareek, Qais Th. Algwari, Garry Laverty, Sean P. Gorman, William G. Graham, Deborah O'Connell, Brendan F. Gilmore
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0044289
Abstract: Bacteria exist, in most environments, as complex, organised communities of sessile cells embedded within a matrix of self-produced, hydrated extracellular polymeric substances known as biofilms. Bacterial biofilms represent a ubiquitous and predominant cause of both chronic infections and infections associated with the use of indwelling medical devices such as catheters and prostheses. Such infections typically exhibit significantly enhanced tolerance to antimicrobial, biocidal and immunological challenge. This renders them difficult, sometimes impossible, to treat using conventional chemotherapeutic agents. Effective alternative approaches for prevention and eradication of biofilm associated chronic and device-associated infections are therefore urgently required. Atmospheric pressure non-thermal plasmas are gaining increasing attention as a potential approach for the eradication and control of bacterial infection and contamination. To date, however, the majority of studies have been conducted with reference to planktonic bacteria and rather less attention has been directed towards bacteria in the biofilm mode of growth. In this study, the activity of a kilohertz-driven atmospheric pressure non-thermal plasma jet, operated in a helium oxygen mixture, against Pseudomonas aeruginosa in vitro biofilms was evaluated. Pseudomonas aeruginosa biofilms exhibit marked susceptibility to exposure of the plasma jet effluent, following even relatively short (~10′s s) exposure times. Manipulation of plasma operating conditions, for example, plasma operating frequency, had a significant effect on the bacterial inactivation rate. Survival curves exhibit a rapid decline in the number of surviving cells in the first 60 seconds followed by slower rate of cell number reduction. Excellent anti-biofilm activity of the plasma jet was also demonstrated by both confocal scanning laser microscopy and metabolism of the tetrazolium salt, XTT, a measure of bactericidal activity.
Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis  [PDF]
Yuanjun Guo, Kang Li, D. M. Laverty
Journal of Power and Energy Engineering (JPEE) , 2014, DOI: 10.4236/jpee.2014.24057
Abstract: In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events; however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly
auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.
An Examination of Male and Female Monthly Employment Rates over Time in Canada and the United States Using Hidden Markov Probability Models  [PDF]
William H. Laverty, Ivan W. Kelly
Open Journal of Statistics (OJS) , 2018, DOI: 10.4236/ojs.2018.85055
Abstract: In this paper, we will illustrate the use and power of Hidden Markov models in analyzing multivariate data over time. The data used in this study was obtained from the Organization for Economic Co-operation and Development (OECD. Stat database url: https://stats.oecd.org/) and encompassed monthly data on the employment rate of males and females in Canada and the United States (aged 15 years and over; seasonally adjusted from January 1995 to July 2018). Two different underlying patterns of trends in employment over the 23 years observation period were uncovered.
Using Excel to Explore the Effects of Assumption Violations on One-Way Analysis of Variance (ANOVA) Statistical Procedures  [PDF]
William Laverty, Ivan Kelly
Open Journal of Statistics (OJS) , 2019, DOI: 10.4236/ojs.2019.94031
Abstract: To understand any statistical tool requires not only an understanding of the relevant computational procedures but also an awareness of the assumptions upon which the procedures are based, and the effects of violations of these assumptions. In our earlier articles (Laverty, Miket, & Kelly [1]) and (Laverty & Kelly, [2] [3]) we used Microsoft Excel to simulate both a Hidden Markov model and heteroskedastic models showing different realizations of these models and the performance of the techniques for identifying the underlying hidden states using simulated data. The advantage of using Excel is that the simulations are regenerated when the spreadsheet is recalculated allowing the user to observe the performance of the statistical technique under different realizations of the data. In this article we will show how to use Excel to generate data from a one-way ANOVA (Analysis of Variance) model and how the statistical methods behave both when the fundamental assumptions of the model hold and when these assumptions are violated. The purpose of this article is to provide tools for individuals to gain an intuitive understanding of these violations using this readily available program.
Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations  [PDF]
Kang Li, Yuanjun Guo, David Laverty, Haibo He, Minrui Fei
Energy and Power Engineering (EPE) , 2013, DOI: 10.4236/epe.2013.54B185

This paper presents a preliminary study of developing a novel distributed adaptive real-time learning framework for wide area monitoring of power systems integrated with distributed generations using synchrophasor technology. The framework comprises distributed agents (synchrophasors) for autonomous local condition monitoring and fault detection, and a central unit for generating global view for situation awareness and decision making. Key technologies that can be integrated into this hierarchical distributed learning scheme are discussed to enable real-time information extraction and knowledge discovery for decision making, without explicitly accumulating and storing all raw data by the central unit. Based on this, the configuration of a wide area monitoring system of power systems using synchrophasor technology, and the functionalities for locally installed open-phasor-measurement-units (OpenPMUs) and a central unit are presented. Initial results on anti-islanding protection using the proposed approach are given to illustrate the effectiveness.

Obesity, Chronic Disease, and Economic Growth: A Case for “Big Picture” Prevention
Garry Egger
Advances in Preventive Medicine , 2011, DOI: 10.4061/2011/149158
Abstract: The discovery of a form of chronic, low-grade systemic inflammation (“metaflammation”) linked with obesity, but also associated with several lifestyle-related behaviours not necessarily causing obesity, suggests a re-consideration of obesity as a direct cause of chronic disease and a search for the main drivers—or cause of causes. Factors contributing to this are considered here within an environmental context, leading to the conclusion that humans have an immune reaction to aspects of the modern techno-industrial environment, to which they have not fully adapted. It is suggested that economic growth—beyond a point—leads to increases in chronic diseases and climate change and that obesity is a signal of these problems. This is supported by data from Sweden over 200 years, as well as “natural” experiments in disrupted economies like Cuba and Nauru, which have shown a positive health effect with economic downturns. The effect is reflected both in human health and environmental problems such as climate change, thus pointing to the need for greater cross-disciplinary communication and a concept shift in thinking on prevention if economic growth is to continue to benefit human health and well-being. 1. Introduction Obesity is currently pandemic, as are many of the chronic diseases often associated with this (e.g., type 2 diabetes) [1]. However, attributing the rise in chronic diseases to obesity does little to explain the true aetiology of the problem—the “cause of the causes” [2], which lies in more distal determining factors. This is indicated by recent findings that suggest a more complicated aetiological role for obesity than just a simple weight-disease association. The discovery of a form of low-grade systemic inflammation associated with obesity [3], as well as with other lifestyle and environmental factors (e.g., aspects of nutrition, inactivity, inadequate sleep, stress, depression, excessive alcohol intake, smoking, etc. [4, 5]) only some of which are linked to obesity, suggests that obesity may be just a marker of a type of environment and accompanying human lifestyle, which is mediated by aspects of the modern industrial environment to which humans have had little time to adapt. Furthermore, it has been shown, using the metaphor of inflammation, that this environment, is a driver not just of biological, but of ecological “disease,” manifest in excessive greenhouse gas emissions and potential climate change, as well as obesity and chronic disease [6]. In the current paper, which is proposed as a forum for a broader discussion in prevention, this
Introductory Editorial
Garry Walsh
Clinical Medicine Reviews in Oncology , 2012,
Introductory Editorial
Garry Walsh
Clinical Medicine Reviews in Women's Health , 2012,
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