Current therapeutic strategies to mitigate the eNOS dysfunction in ischaemic stroke

Impairment of endothelial nitric oxide synthase (eNOS) activity is implicated in the pathogenesis of endothelial dysfunction in many diseases including ischaemic stroke. The modulation of eNOS during and/or following ischaemic injury often represents a futile compensatory mechanism due to a significant decrease in nitric oxide (NO) bioavailability coupled with dramatic increases in the levels of reactive oxygen species that further neutralise NO. However, applications of a number of therapeutic agents alone or in combination have been shown to augment eNOS activity under a variety of pathological conditions by potentiating the expression and/or activity of Akt/eNOS/NO pathway components. The list of these therapeutic agents include NO donors, statins, angiotensin-converting enzyme inhibitors, calcium channel blockers, phosphodiesterase-3 inhibitors, aspirin, dipyridamole and ellagic acid. While most of these compounds exhibit anti-platelet properties and are able to up-regulate eNOS expression in endothelial cells and platelets, others suppress eNOS uncoupling and tetrahydrobiopterin (an eNOS stabiliser) oxidation. As the number of therapeutic molecules that modulate the expression and activity of eNOS increases, further detailed research is required to reveal their mode of action in preventing and/or reversing the endothelial dysfunction.

Extended Tree Augmented Naive Classifier

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.

STC-1 Cells

Gastrointestinal hormones such as cholecystokinin (CCK), glucagon like peptide 1 (GLP-1), and peptide YY (PYY) play an important role in suppressing hunger and controlling food intake. These satiety hormones are secreted from enteroendocrine cells present throughout the intestinal tract. The intestinal secretin tumor cell line (STC-1) possesses many features of native intestinal enteroendocrine cells. As such, STC-1 cells are routinely used in screening platforms to identify foods or compounds that modulate secretion of gastrointestinal hormones in vitro. This chapter describes this intestinal cell model focussing on it’s applications, advantages and limitations. A general protocol is provided for challenging STC-1 cells with test compounds.

Automatically Annotating Structured Web Data Using a SVM-Based Multiclass Classifier

In this paper, we propose a new learning approach to Web data annotation, where a support vector machine-based multiclass classifier is trained to assign labels to data items. For data record extraction, a data section re-segmentation algorithm based on visual and content features is introduced to improve the performance of Web data record extraction. We have implemented the proposed approach and tested it with a large set of Web query result pages in different domains. Our experimental results show that our proposed approach is highly effective and efficient.

Technological Learning in Developing Countries

Technological learning refers to the learning processes involved in improving the productive capabilities of an enterprise, sector or economy to enable it to produce higher quality goods or services with increasing levels of efficiency. Approaches to the study of technological learning include case studies of particular countries, sectors and firms; measures of export sophistication; and composite indicators of innovation and competitiveness. The present review draws on these approaches to provide an overview of the policies and practices that have been successful in different regions (East-Asia and Latin America) ; contexts (import substitution and liberalization) ; sectors (pulp and paper, IT services, electronics and passenger cars); and firms (Embrear and Lenovo). While it is clear that there is strong complementarity between domestic technological capability and the ability to absorb foreign technology, there is no simple policy recipe which is appropriate for all times, industries or places. Technological learning builds on and is shaped by what is already known. It requires time, space and resources all of which are influenced by the wider domestic and international context. The current international context is challenging but countries and firms have to find ways of moving forward despite the limited strategy space.

Animal models of diabetic retinopathy

Diabetic retinopathy (DR) is a major cause of visual impairment worldwide. The precise pathogenesis of this diabetic complication remains ill-defined and this is reflected in the limited options for preventing development and progression of this disease. The value of animal models to understand and treat human disease is well recognised and this chapter focuses on the range of in vivo model systems that are available for studying DR. These models have been developed over many decades and utilised to aid our understanding of what causes DR, about how microvascular and neural lesions develop and to provide evidence for key cellular and molecular mechanisms that drive this pathology. A wide range of animal models of DR are currently available, each with advantages and disadvantages that need to be understood and evaluated for their scientific and clinical value. As transgenic and imaging technology improves, more models will be developed and they will continue to play a critical role in the development of new therapeutic approaches to DR by providing robust, preclinical evidence prior to clinical trial.

Replication of the association of GLT6D1 with aggressive periodontitis in a Sudanese population

BACKGROUND: Susceptibility to aggressive periodontitis (AgP) is influenced by genetic as well as environmental factors. Studies linking gene variants to AgP have been mainly centred in developed countries with limited data from Africa.
AIM: To investigate whether previously reported candidate gene associations with AgP could be replicated in a population from Sudan.


METHODS: The investigation was a case-control design. Cases with AgP (n = 132) and controls (n = 136) were identified from patients attending the Periodontal Department in Khartoum Dental Hospital. Genotyping was performed using the Sequenom MassARRAY iPLEX platform. Analysis focused on gene variants with a minor allele frequency (MAF) > 25% in the Sudanese subjects that had previously been reported to be associated with AgP.


RESULTS: One candidate gene rs1537415 (GLT6D1) was significantly associated with AgP, OR = 1.50 (95% CI 1.04-2.17), p = 0.0295 (increasing to p = 0.09 after correction for multiple testing). The association strengthened to OR = 1.56 (95% CI 1.15-2.16), p = 0.0042 when the controls were supplemented with data from the Hap map for the Yoruba in Ibadan (n = 147) and remained significant (p = 0.013) after correction for multiple testing.


CONCLUSION: The study independently replicated the finding that rs1537415, a variant in glycosyl transferase gene GLT6D1, is associated with AgP and provided the first report of genetic associations with AgP in a Sudanese population.

Game-­theoretic Resource Allocation with Real-­time Probabilistic Surveillance Information

Game-theoretic security resource allocation problems have generated significant interest in the area of designing and developing security systems. These approaches traditionally utilize the Stackelberg game model for security resource scheduling in order to improve the protection of critical assets. The basic assumption in Stackelberg games is that a defender will act first, then an attacker will choose their best response after observing the defender’s strategy commitment (e.g., protecting a specific asset). Thus, it requires an attacker’s full or partial observation of a defender’s strategy. This assumption is unrealistic in real-time threat recognition and prevention. In this paper, we propose a new solution concept (i.e., a method to predict how a game will be played) for deriving the defender’s optimal strategy based on the principle of acceptable costs of minimax regret. Moreover, we demonstrate the advantages of this solution concept by analyzing its properties.

Incentive Mechanisms and Their Impact on Project Performance

Good performance characterizes project success and value for money. However, performance problems are not uncommon in project management. Incentivization is generally recognized as a strategy of addressing performance problems. This chapter aims to explore incentive mechanisms and their impact on project performance. It is mainly based on the use of incentives in construction and engineering projects. The same principles apply to project management in other industry sectors. Incentivization can be used in such performance areas as time, cost, quality, safety and environment. A client has different ways of incentivizing his contractor’s performance, e.g. (1) a single incentive or multiple incentives; and (2) incentives or disincentives or a combination of both. The establishment of incentive mechanisms proves to have a significant potential for relationship development, process enhancement and performance improvement. In order to ensure the success of incentive mechanisms, both contractors and clients need to make extra efforts. As a result, a link is developed among incentive mechanisms, project management system and project performance.

Fusion of Static and Temporal Information for Threat Evaluation in Sensor Networks

In many CCTV and sensor network based intelligent surveillance systems, a number of attributes or criteria are used to individually evaluate the degree of potential threat of a suspect. The outcomes for these attributes are in general from analytical algorithms where data are often pervaded with uncertainty and incompleteness. As a result, such individual threat evaluations are often inconsistent, and individual evaluations can change as time elapses. Therefore, integrating heterogeneous threat evaluations with temporal influence to obtain a better overall evaluation is a challenging issue. So far, this issue has rarely be considered by existing event reasoning frameworks under uncertainty in sensor network based surveillance. In this paper, we first propose a weighted aggregation operator based on a set of principles that constraints the fusion of individual threat evaluations. Then, we propose a method to integrate the temporal influence on threat evaluation changes. Finally, we demonstrate the usefulness of our system with a decision support event modeling framework using an airport security surveillance scenario.

Rational Partial Choice Functions and Their Application to Belief Revision.

Necessary and sufficient conditions for choice functions to be rational have been intensively studied in the past. However, in these attempts, a choice function is completely specified. That is, given any subset of options, called an issue, the best option over that issue is always known, whilst in real-world scenarios, it is very often that only a few choices are known instead of all. In this paper, we study partial choice functions and investigate necessary and sufficient rationality conditions for situations where only a few choices are known. We prove that our necessary and sufficient condition for partial choice functions boils down to the necessary and sufficient conditions for complete choice functions proposed in the literature. Choice functions have been instrumental in belief revision theory. That is, in most approaches to belief revision, the problem studied can simply be described as the choice of possible worlds compatible with the input information, given an agent’s prior belief state. The main effort has been to devise strategies in order to infer the agents revised belief state. Our study considers the converse problem: given a collection of input information items and their corresponding revision results (as provided by an agent), does there exist a rational revision operation used by the agent and a consistent belief state that may explain the observed results?

Uncertainty in the Cloud: An Angel-Daemon Approach to Modelling Performance

Uncertainty profiles are used to study the effects of contention within cloud and service-based environments. An uncertainty profile provides a qualitative description of an environment whose quality of service (QoS) may fluctuate unpredictably. Uncertain environments are modelled by strategic games with two agents; a daemon is used to represent overload and high resource contention; an angel is used to represent an idealised resource allocation situation with no underlying contention. Assessments of uncertainty profiles are useful in two ways: firstly, they provide a broad understanding of how environmental stress can effect an application’s performance (and reliability); secondly, they allow the effects of introducing redundancy into a computation to be assessed

The Robustness of Periodic Orchestrations in Uncertain Evolving Environments

A framework for assessing the robustness of long-duration repetitive orchestrations in uncertain evolving environments is proposed. The model assumes that service-based evaluation environments are stable over short time-frames only; over longer periods service-based environments evolve as demand fluctuates and contention for shared resources varies. The behaviour of a short-duration orchestration E in a stable environment is assessed by an uncertainty profile U and a corresponding zero-sum angel-daemon game Γ(U) [2]. Here the angel-daemon approach is extended to assess evolving environments by means of a subfamily of stochastic games. These games are called strategy oblivious because their transition probabilities are strategy independent. It is shown that the value of a strategy oblivious stochastic game is well defined and that it can be computed by solving a linear system. Finally, the proposed stochastic framework is used to assess the evolution of the Gabrmn IT system.