Grid data mining for outcome prediction in intensive care medicine

This paper introduces a distributed data mining approach suited to grid computing environments based on a supervised learning classifier system. Specific Classifier and Majority Voting methods for Distributed Data Mining (DDM) are explored and compared with the Centralized Data Mining (CDM) approach. Experimental tests were conducted considering a real world data set from the intensive care medicine in order to predict the outcome of the patients. The results demonstrate that the performance of the DDM methods are better than the CDM method.Fundação para a Ciência e a Tecnologia (FCT

Cutting into perceptions : investigating men's understanding of protection - through medical male circumcision for HIV prevention, in Durban, KwaZulu-Natal.

Thesis (M.Soc.Sc.)-University of KwaZulu-Natal, Durban, 2012.Three recent Randomised Controlled Trials (RCTs) have been able to deduce that Medical Male Circumcision (MMC) can reduce a heterosexual man’s chances of acquiring HIV through vaginal sexual intercourse by approximately 60% (Auvert et al. 2005; Gray et al. 2007; Bailey et al. 2007). In 2010, based on WHO recommendations, South Africa commenced a nationwide roll-out of MMC services. However, in the wake of these findings have come concerns that decreases in men’s perceived risk of contracting HIV could spark increases in risky sexual behaviour (risk compensation), in turn, driving up HIV incidence as opposed to abating it (Cassell et al. 2006). Accordingly, the World Health Organisation has identified social change communication as one of the ten key elements critical to the success of a wide scale MMC roll out (WHO & UNAIDS, 2010). Aside from creating demand, the role of MMC health communication efforts in crafting messages delineating the scope of MMC’s protective ability is paramount; especially in South Africa, a country hamstrung by a weak public health sector that can ill afford any regression in the fight against HIV and AIDS. This thesis provides a small-scale qualitative study that investigates both the motivating and discouraging factors impacting on men’s choices to undergo MMC, as well as exploring how and what ‘key messages’ of Medical Male Circumcision media and information initiatives are being received. In this way, my study hopes to bring insight into not only risk compensation associated with MMC, but also to provide a glimpse into the condition of health communication for MMC in the South African context

Grid data mining by means of learning classifier systems and distributed model induction

This paper introduces a distributed data mining approach suited to grid computing environments based on a supervised learning classifier system. Different methods of merging data mining models generated at different distributed sites are explored. Centralized Data Mining (CDM) is a conventional method of data mining in distributed data. In CDM, data that is stored in distributed locations have to be collected and stored in a central repository before executing the data mining algorithm. CDM method is reliable; however it is expensive (computational, communicational and implementation costs are high). Alternatively, Distributed Data Mining (DDM) approach is economical but it has limitations in combining local models. In DDM, the data mining algorithm has to be executed at each one of the sites to induce a local model. Those induced local models are collected and combined to form a global data mining model. In this work six different tactics are used for constructing the global model in DDM: Generalized Classifier Method (GCM); Specific Classifier Method (SCM); Weighed Classifier Method (WCM); Majority Voting Method (MVM); Model Sampling Method (MSM); and Centralized Training Method (CTM). Preliminary experimental tests were conducted with two synthetic data sets (eleven multiplexer and monks3) and a real world data set (intensive care medicine). The initial results demonstrate that the performance of DDM methods is competitive when compared with the CDM methods.Fundação para a Ciência e a Tecnologia (FCT

Concise review: can stem cells be used to treat or model Alzheimer’s disease

Abstract Alzheimer disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States alone. AD patients suffer from progressive neurodegeneration that gradually impairs their memory, ability to learn, and carry out daily activities. Unfortunately, current therapies for AD are largely palliative and several promising drug candidates have failed in recent clinical trials. There is therefore an urgent need to improve our understanding of AD pathogenesis, create innovative and predictive models, and develop new and effective therapies. In this review we will discuss the potential of stem cells to aid in these challenging endeavors. Because of the widespread nature of AD pathology, cell replacement strategies have been viewed as an incredibly challenging and unlikely treatment approach. Yet, recent work shows that transplantation of neural stem cells (NSCs) can improve cognition, reduce neuronal loss, and enhance synaptic plasticity in animal models of AD. Interestingly, the mechanisms that mediate these effects appear to involve neuroprotection and trophic support rather than neuronal replacement. Stem cells may also offer a powerful new approach to model and study AD. Patient-derived induced pluriptotent stem cells (iPSCs), for example, may help to advance our understanding of disease mechanisms. Likewise, studies of human embryonic and neural stem cells are helping to decipher the normal functions of AD-related genes; revealing intriguing roles in neural development

Seprase: An overview of an important matrix serine protease

Seprase or Fibroblast Activation Protein (FAP) is an integral membrane serine peptidase, which has been shown to have gelatinase activity. Seprase has a dual function in tumour progression. The proteolytic activity of Seprase has been shown to promote cell invasiveness towards the ECM and also to support tumour growth and proliferation. Seprase appears to act as a proteolytically active 170-kDa dimer, consisting of two 97- kDa subunits. It is a member of the group type II integral serine proteases, which includes dipeptidyl peptidase IV (DPPIV/CD26) and related type II transmembrane prolyl serine peptidases, which exert their mechanisms of action on the cell surface. DPPIV and Seprase exhibit multiple functions due to their abilities to form complexes with each other and to interact with other membrane-associated molecules. Localisation of these protease complexes at cell surface protrusions, called invadopodia, may have a prominent role in processing soluble factors and in the degradation of extracellular matrix components that are essential to the cellular migration and matrix invasion that occur during tumour invasion, metastasis and angiogenesis