Rodent models for resolving extremes of exercise and health

The extremes of exercise capacity and health are considered a complex interplay between genes and the environment. In general, the study of animal models has proven critical for deep mechanistic exploration that provides guidance for focused and hypothesis driven discovery in humans. Hypotheses underlying molecular mechanisms of disease, and gene/tissue function can be tested in rodents in order to generate sufficient evidence to resolve and progress our understanding of human biology. Here we provide examples of three alternative uses of rodent models that have been applied successfully to advance knowledge that bridges our understanding of the connection between exercise capacity and health status. Firstly we review the strong association between exercise capacity and all-cause morbidity and mortality in humans through artificial selection on low and high exercise performance in the rat and the consequent generation of the "energy transfer hypothesis". Secondly we review specific transgenic and knock-out mouse models that replicate the human disease condition and performance. This includes human glycogen storage diseases (McArdle and Pompe) and α-actinin-3 deficiency. Together these rodent models provide an overview of the advancements of molecular knowledge required for clinical translation. Continued study of these models in conjunction with human association studies will be critical to resolving the complex gene-environment interplay linking exercise capacity, health, and disease.

Subsethood-based Fuzzy Rule Models and their Application to Student Performance

K. Rasmani and Q. Shen. Subsethood-based Fuzzy Rule Models and their Application to Student Performance Classification. Proceedings of the 14th International Conference on Fuzzy Systems, pages 755-760, 2005.

Psychoanalysis and Performance

Kear, Adrian, Campbell, Patrick, Psychoanalysis and Performance (London: Routledge, 2001), pp.xiv+242 RAE2008

Recognition of Human Action Using Moment-Based Feature

The performance of different classification approaches is evaluated using a view-based approach for motion representation. The view-based approach uses computer vision and image processing techniques to register and process the video sequence. Two motion representations called Motion Energy Images and Motion History Image are then constructed. These representations collapse the temporal component in a way that no explicit temporal analysis or sequence matching is needed. Statistical descriptions are then computed using moment-based features and dimensionality reduction techniques. For these tests, we used 7 Hu moments, which are invariant to scale and translation. Principal Components Analysis is used to reduce the dimensionality of this representation. The system is trained using different subjects performing a set of examples of every action to be recognized. Given these samples, K-nearest neighbor, Gaussian, and Gaussian mixture classifiers are used to recognize new actions. Experiments are conducted using instances of eight human actions (i.e., eight classes) performed by seven different subjects. Comparisons in the performance among these classifiers under different conditions are analyzed and reported. Our main goals are to test this dimensionality-reduced representation of actions, and more importantly to use this representation to compare the advantages of different classification approaches in this recognition task.

Estimating Human Body Pose from a Single Image via the Specialized Mappings Architecture

A non-linear supervised learning architecture, the Specialized Mapping Architecture (SMA) and its application to articulated body pose reconstruction from single monocular images is described. The architecture is formed by a number of specialized mapping functions, each of them with the purpose of mapping certain portions (connected or not) of the input space, and a feedback matching process. A probabilistic model for the architecture is described along with a mechanism for learning its parameters. The learning problem is approached using a maximum likelihood estimation framework; we present Expectation Maximization (EM) algorithms for two different instances of the likelihood probability. Performance is characterized by estimating human body postures from low level visual features, showing promising results.

Specification of optimum holistic building environmental and energy performance information to support informed decision making

Political drivers such as the Kyoto protocol, the EU Energy Performance of Buildings Directive and the Energy end use and Services Directive have been implemented in response to an identified need for a reduction in human related CO2 emissions. Buildings account for a significant portion of global CO2 emissions, approximately 25-30%, and it is widely acknowledged by industry and research organisations that they operate inefficiently. In parallel, unsatisfactory indoor environmental conditions have proven to negatively impact occupant productivity. Legislative drivers and client education are seen as the key motivating factors for an improvement in the holistic environmental and energy performance of a building. A symbiotic relationship exists between building indoor environmental conditions and building energy consumption. However traditional Building Management Systems and Energy Management Systems treat these separately. Conventional performance analysis compares building energy consumption with a previously recorded value or with the consumption of a similar building and does not recognise the fact that all buildings are unique. Therefore what is required is a new framework which incorporates performance comparison against a theoretical building specific ideal benchmark. Traditionally Energy Managers, who work at the operational level of organisations with respect to building performance, do not have access to ideal performance benchmark information and as a result cannot optimally operate buildings. This thesis systematically defines Holistic Environmental and Energy Management and specifies the Scenario Modelling Technique which in turn uses an ideal performance benchmark. The holistic technique uses quantified expressions of building performance and by doing so enables the profiled Energy Manager to visualise his actions and the downstream consequences of his actions in the context of overall building operation. The Ideal Building Framework facilitates the use of this technique by acting as a Building Life Cycle (BLC) data repository through which ideal building performance benchmarks are systematically structured and stored in parallel with actual performance data. The Ideal Building Framework utilises transformed data in the form of the Ideal Set of Performance Objectives and Metrics which are capable of defining the performance of any building at any stage of the BLC. It is proposed that the union of Scenario Models for an individual building would result in a building specific Combination of Performance Metrics which would in turn be stored in the BLC data repository. The Ideal Data Set underpins the Ideal Set of Performance Objectives and Metrics and is the set of measurements required to monitor the performance of the Ideal Building. A Model View describes the unique building specific data relevant to a particular project stakeholder. The energy management data and information exchange requirements that underlie a Model View implementation are detailed and incorporate traditional and proposed energy management. This thesis also specifies the Model View Methodology which complements the Ideal Building Framework. The developed Model View and Rule Set methodology process utilises stakeholder specific rule sets to define stakeholder pertinent environmental and energy performance data. This generic process further enables each stakeholder to define the resolution of data desired. For example, basic, intermediate or detailed. The Model View methodology is applicable for all project stakeholders, each requiring its own customised rule set. Two rule sets are defined in detail, the Energy Manager rule set and the LEED Accreditor rule set. This particular measurement generation process accompanied by defined View would filter and expedite data access for all stakeholders involved in building performance. Information presentation is critical for effective use of the data provided by the Ideal Building Framework and the Energy Management View definition. The specifications for a customised Information Delivery Tool account for the established profile of Energy Managers and best practice user interface design. Components of the developed tool could also be used by Facility Managers working at the tactical and strategic levels of organisations. Informed decision making is made possible through specified decision assistance processes which incorporate the Scenario Modelling and Benchmarking techniques, the Ideal Building Framework, the Energy Manager Model View, the Information Delivery Tool and the established profile of Energy Managers. The Model View and Rule Set Methodology is effectively demonstrated on an appropriate mixed use existing ‘green’ building, the Environmental Research Institute at University College Cork, using the Energy Management and LEED rule sets. Informed Decision Making is also demonstrated using a prototype scenario for the demonstration building.

Rapid separation of biologically important low molecular weight compounds by microchip electrophoresis and ultra-fast performance liquid chromatography

The research work in this thesis reports rapid separation of biologically important low molecular weight compounds by microchip electrophoresis and ultrahigh liquid chromatography. Chapter 1 introduces the theory and principles behind capillary electrophoresis separation. An overview of the history, different modes and detection techniques coupled to CE is provided. The advantages of microchip electrophoresis are highlighted. Some aspects of metal complex analysis by capillary electrophoresis are described. Finally, the theory and different modes of the liquid chromatography technology are presented. Chapter 2 outlines the development of a method for the capillary electrophoresis of (R, S) Naproxen. Variable parameters of the separation were optimized (i.e. buffer concentration and pH, concentration of chiral selector additives, applied voltage and injection condition).The method was validated in terms of linearity, precision, and LOD. The optimized method was then transferred to a microchip electrophoresis system. Two different types of injection i.e. gated and pinched, were investigated. This microchip method represents the fastest reported chiral separation of Naproxen to date. Chapter 3 reports ultra-fast separation of aromatic amino acid by capillary electrophoresis using the short-end technique. Variable parameters of the separation were optimized and validated. The optimized method was then transferred to a microchip electrophoresis system where the separation time was further reduced. Chapter 4 outlines the use of microchip electrophoresis as an efficient tool for analysis of aluminium complexes. A 2.5 cm channel with linear imaging UV detection was used to separate and detect aluminium-dopamine complex and free dopamine. For the first time, a baseline, separation of aluminium dopamine was achieved on a 15 seconds timescale. Chapter 5 investigates a rapid, ultra-sensitive and highly efficient method for quantification of histamine in human psoriatic plaques using microdialysis and ultrahigh performance liquid chromatography with fluorescence detection. The method utilized a sub-two-micron packed C18 stationary phase. A fluorescent reagent, 4-(1-pyrene) butyric acid N-hydroxysuccinimide ester was conjugated to the primary and secondary amino moieties of histamine. The dipyrene-labeled histamine in human urine was also investigated by ultrahigh pressure liquid chromatography using a C18 column with 1.8 μm particle diameter. These methods represent one of the fastest reported separations to date of histamine using fluorescence detection.

Empirical Evidence on Relationships between Ex Ante Innovation Pursuit and Post-M&A Performance in the Vietnamese M&A Industry, 2005-2012

This research aims to communicate new results of empirical investigations to learn about the relationship between determination of controlling an acquired firm’s capital, assets and brand versus its capability of innovation and ex post performance of the rising Vietnamese M&A industry in the 2005-2012 period. The analysis employs a categorical data sample, consisting of 212 M&A cases reported by various information sources, and performs a number of logistic regressions with significant results as follows. Firstly, the overall relationship between pre-M&A pursuit’s determination on acquiring resources and performance of the post-M&A performance is found significant. There exist profound effects of a ‘size matters’ strategy in M&A ex post performance. When there is an overwhelming ‘resources acquiring’ strategy, the innovation factor’s explanatory power becomes negligible. Secondly, for negative performance of post-M&A operations, the emphasis on both capital base and asset size, and the brand value at the time of the M&A pursuit is the major explanation in the post-M&A period. So does the absence of innovation as a goal in the pre-M&A period. These two insights together are useful in careful M&A planning. Lastly, expensive pre-M&A expenditures tend to adversely affect the post-M&A performance. As a general conclusion, this study shows that innovation can be an important factor to pursue in M&A transitions, together with the need to emphasize and find capable and willing human capital, rather than a capital base (equity or debt) and existing values of the acquired brands.

Screening the human exome: a comparison of whole genome and whole transcriptome sequencing.

BACKGROUND: There is considerable interest in the development of methods to efficiently identify all coding variants present in large sample sets of humans. There are three approaches possible: whole-genome sequencing, whole-exome sequencing using exon capture methods, and RNA-Seq. While whole-genome sequencing is the most complete, it remains sufficiently expensive that cost effective alternatives are important. RESULTS: Here we provide a systematic exploration of how well RNA-Seq can identify human coding variants by comparing variants identified through high coverage whole-genome sequencing to those identified by high coverage RNA-Seq in the same individual. This comparison allowed us to directly evaluate the sensitivity and specificity of RNA-Seq in identifying coding variants, and to evaluate how key parameters such as the degree of coverage and the expression levels of genes interact to influence performance. We find that although only 40% of exonic variants identified by whole genome sequencing were captured using RNA-Seq; this number rose to 81% when concentrating on genes known to be well-expressed in the source tissue. We also find that a high false positive rate can be problematic when working with RNA-Seq data, especially at higher levels of coverage. CONCLUSIONS: We conclude that as long as a tissue relevant to the trait under study is available and suitable quality control screens are implemented, RNA-Seq is a fast and inexpensive alternative approach for finding coding variants in genes with sufficiently high expression levels.

Coupling of beta2-adrenoceptor to Gi proteins and its physiological relevance in murine cardiac myocytes.

-Transgenic mouse models have been developed to manipulate beta-adrenergic receptor (betaAR) signal transduction. Although several of these models have altered betaAR subtypes, the specific functional sequelae of betaAR stimulation in murine heart, particularly those of beta2-adrenergic receptor (beta2AR) stimulation, have not been characterized. In the present study, we investigated effects of beta2AR stimulation on contraction, [Ca2+]i transient, and L-type Ca2+ currents (ICa) in single ventricular myocytes isolated from transgenic mice overexpressing human beta2AR (TG4 mice) and wild-type (WT) littermates. Baseline contractility of TG4 heart cells was increased by 3-fold relative to WT controls as a result of the presence of spontaneous beta2AR activation. In contrast, beta2AR stimulation by zinterol or isoproterenol plus a selective beta1-adrenergic receptor (beta1AR) antagonist CGP 20712A failed to enhance the contractility in TG4 myocytes, and more surprisingly, beta2AR stimulation was also ineffective in increasing contractility in WT myocytes. Pertussis toxin (PTX) treatment fully rescued the ICa, [Ca2+]i, and contractile responses to beta2AR agonists in both WT and TG4 cells. The PTX-rescued murine cardiac beta2AR response is mediated by cAMP-dependent mechanisms, because it was totally blocked by the inhibitory cAMP analog Rp-cAMPS. These results suggest that PTX-sensitive G proteins are responsible for the unresponsiveness of mouse heart to agonist-induced beta2AR stimulation. This was further corroborated by an increased incorporation of the photoreactive GTP analog [gamma-32P]GTP azidoanilide into alpha subunits of Gi2 and Gi3 after beta2AR stimulation by zinterol or isoproterenol plus the beta1AR blocker CGP 20712A. This effect to activate Gi proteins was abolished by a selective beta2AR blocker ICI 118,551 or by PTX treatment. Thus, we conclude that (1) beta2ARs in murine cardiac myocytes couple to concurrent Gs and Gi signaling, resulting in null inotropic response, unless the Gi signaling is inhibited; (2) as a special case, the lack of cardiac contractile response to beta2AR agonists in TG4 mice is not due to a saturation of cell contractility or of the cAMP signaling cascade but rather to an activation of beta2AR-coupled Gi proteins; and (3) spontaneous beta2AR activation may differ from agonist-stimulated beta2AR signaling.

Enhancement of cardiac function after adenoviral-mediated in vivo intracoronary beta2-adrenergic receptor gene delivery.

Exogenous gene delivery to alter the function of the heart is a potential novel therapeutic strategy for treatment of cardiovascular diseases such as heart failure (HF). Before gene therapy approaches to alter cardiac function can be realized, efficient and reproducible in vivo gene techniques must be established to efficiently transfer transgenes globally to the myocardium. We have been testing the hypothesis that genetic manipulation of the myocardial beta-adrenergic receptor (beta-AR) system, which is impaired in HF, can enhance cardiac function. We have delivered adenoviral transgenes, including the human beta2-AR (Adeno-beta2AR), to the myocardium of rabbits using an intracoronary approach. Catheter-mediated Adeno-beta2AR delivery produced diffuse multichamber myocardial expression, peaking 1 week after gene transfer. A total of 5 x 10(11) viral particles of Adeno-beta2AR reproducibly produced 5- to 10-fold beta-AR overexpression in the heart, which, at 7 and 21 days after delivery, resulted in increased in vivo hemodynamic function compared with control rabbits that received an empty adenovirus. Several physiological parameters, including dP/dtmax as a measure of contractility, were significantly enhanced basally and showed increased responsiveness to the beta-agonist isoproterenol. Our results demonstrate that global myocardial in vivo gene delivery is possible and that genetic manipulation of beta-AR density can result in enhanced cardiac performance. Thus, replacement of lost receptors seen in HF may represent novel inotropic therapy.

Human dimensions of bycatch reduction technology: Current assumptions and directions for future research

Bycatch reduction technology (BRT) modifies fishing gear to increase selectivity and avoid capture of non-target species, or to facilitate their non-lethal release. As a solution to fisheries-related mortality of non-target species, BRT is an attractive option; effectively implemented, BRT presents a technical 'fix' that can reduce pressure for politically contentious and economically detrimental interventions, such as fisheries closures. While a number of factors might contribute to effective implementation, our review of BRT literature finds that research has focused on technical design and experimental performance of individual technologies. In contrast, and with a few notable exceptions, research on the human and institutional context of BRT, and more specifically on how fishers respond to BRT, is limited. This is not to say that fisher attitudes are ignored or overlooked, but that incentives for fisher uptake of BRT are usually assumed rather than assessed or demonstrated. Three assumptions about fisher incentives dominate: (1) economic incentives will generate acceptance of BRT; (2) enforcement will generate compliance with BRT; and (3) 'participation' by fishers will increase acceptance and compliance, and overall support for BRT. In this paper, we explore evidence for and against these assumptions and situate our analysis in the wider social science literature on fisheries. Our goal is to highlight the need and suggest focal areas for further research. © Inter-Research 2008.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

BACKGROUND: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. RESULTS: Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. CONCLUSIONS: Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

Bioengineered human myobundles mimic clinical responses of skeletal muscle to drugs.

Existing in vitro models of human skeletal muscle cannot recapitulate the organization and function of native muscle, limiting their use in physiological and pharmacological studies. Here, we demonstrate engineering of electrically and chemically responsive, contractile human muscle tissues ('myobundles') using primary myogenic cells. These biomimetic constructs exhibit aligned architecture, multinucleated and striated myofibers, and a Pax7(+) cell pool. They contract spontaneously and respond to electrical stimuli with twitch and tetanic contractions. Positive correlation between contractile force and GCaMP6-reported calcium responses enables non-invasive tracking of myobundle function and drug response. During culture, myobundles maintain functional acetylcholine receptors and structurally and functionally mature, evidenced by increased myofiber diameter and improved calcium handling and contractile strength. In response to diversely acting drugs, myobundles undergo dose-dependent hypertrophy or toxic myopathy similar to clinical outcomes. Human myobundles provide an enabling platform for predictive drug and toxicology screening and development of novel therapeutics for muscle-related disorders.

Improving Indwelling Glucose Sensor Performance: Porous, Dexamethasone-Releasing Coatings that Modulate the Foreign Body Response

Inflammation and the formation of an avascular fibrous capsule have been identified as the key factors controlling the wound healing associated failure of implantable glucose sensors. Our aim is to guide advantageous tissue remodeling around implanted sensor leads by the temporal release of dexamethasone (Dex), a potent anti-inflammatory agent, in combination with the presentation of a stable textured surface.

First, Dex-releasing polyurethane porous coatings of controlled pore size and thickness were fabricated using salt-leaching/gas-foaming technique. Porosity, pore size, thickness, drug release kinetics, drug loading amount, and drug bioactivity were evaluated. In vitro sensor functionality test were performed to determine if Dex-releasing porous coatings interfered with sensor performance (increased signal attenuation and/or response times) compared to bare sensors. Drug release from coatings monitored over two weeks presented an initial fast release followed by a slower release. Total release from coatings was highly dependent on initial drug loading amount. Functional in vitro testing of glucose sensors deployed with porous coatings against glucose standards demonstrated that highly porous coatings minimally affected signal strength and response rate. Bioactivity of the released drug was determined by monitoring Dex-mediated, dose-dependent apoptosis of human peripheral blood derived monocytes in culture.

The tissue modifying effects of Dex-releasing porous coatings were accessed by fully implanting Tygon® tubing in the subcutaneous space of healthy and diabetic rats. Based on encouraging results from these studies, we deployed Dex-releasing porous coatings from the tips of functional sensors in both diabetic and healthy rats. We evaluated if the tissue modifying effects translated into accurate, maintainable and reliable sensor signals in the long-term. Sensor functionality was accessed by continuously monitoring glucose levels and performing acute glucose challenges at specified time points.

Sensors treated with porous Dex-releasing coatings showed diminished inflammation and enhanced vascularization of the tissue surrounding the implants in healthy rats. Functional sensors with Dex-releasing porous coatings showed enhanced sensor sensitivity over a 21-day period when compared to controls. Enhanced sensor sensitivity was accompanied with an increase in sensor signal lag and MARD score. These results indicated that Dex-loaded porous coatings were able to elicit a favorable tissue response, and that such tissue microenvironment could be conducive towards extending the performance window of glucose sensors in vivo.

The diabetic pilot animal study showed differences in wound healing patters between healthy and diabetic subjects. Diabetic rats showed lower levels of inflammation and vascularization of the tissue surrounding implants when compared to their healthy counterparts. Also, functional sensors treated with Dex-releasing porous coatings did not show enhanced sensor sensitivity over a 21-day period. Moreover, increased in sensor signal lag and MARD scores were present in porous coated sensors regardless of Dex-loading when compared to bare implants. These results suggest that the altered wound healing patterns presented in diabetic tissues may lead to premature sensor failure when compared to sensors implanted in healthy rats.