Image reduction using fuzzy quantifiers

In this work we propose an image reduction algorith based on local reduction operators. We analyze the construction of weak local reduction operators by means of aggregation functions and we analyze the effect of several aggregation functions in image reduction with original and noisy images.

Dielectrophoresis of micro/nano particles using curved microelectrodes

Dielectrophoresis, the induced motion of polarisable particles in non-homogenous electric field, has been proven as a versatile mechanism to transport, immobilise, sort and characterise micro/nano scale particle in microfluidic platforms. The performance of dielectrophoretic (DEP) systems depend on two parameters: the configuration of microelectrodes designed to produce the DEP force and the operating strategies devised to employ this force in such processes. This work summarises the unique features of curved microelectrodes for the DEP manipulation of target particles in microfluidic systems. The curved microelectrodes demonstrate exceptional capabilities including (i) creating strong electric fields over a large portion of their structure, (ii) minimising electro-thermal vortices and undesired disturbances at their tips, (iii) covering the entire width of the microchannel influencing all passing particles, and (iv) providing a large trapping area at their entrance region, as evidenced by extensive numerical and experimental analyses. These microelectrodes have been successfully applied for a variety of engineering and biomedical applications including (i) sorting and trapping model polystyrene particles based on their dimensions, (ii) patterning carbon nanotubes to trap low-conductive particles, (iii) sorting live and dead cells based on their dielectric properties, (iv) real-time analysis of drug-induced cell death, and (v) interfacing tumour cells with environmental scanning electron microscopy to study their morphological properties. The DEP systems based on curved microelectrodes have a great potential to be integrated with the future lab-on-a-chip systems.

The burden of disease and injury in Australia 2003

The report measures mortality, disability, illness and injury arising from over 170 diseases and injuries. Burden of disease analysis gives a unique perspective on health

Granulocyte-macrophage colony-stimulating factor augments phagocytosis of mycobacterium avium complex by human immunodeficiency virus type 1-infected monocytes/macrophages in vitro and in vivo

The role of human immunodeficiency virus type 1 (HIV-1) infection on the ability of human monocytes/macrophages to phagocytose Mycobacterium avium complex (MAC) in vivo and in vitro and the effect of granulocyte-macrophage colony-stimulating factor (GM-CSF) on this function were investigated. By use of a flow cytometric assay to quantify phagocytosis, HIV-1 infection was found to impair the ability of monocyte-derived macrophages to phagocytose MAC in vitro, whereas GM-CSF significantly improved this defect. Phagocytosis was not altered by exposure to a mutant form of GM-CSF (E21R) binding only to the α chain of the GM-CSF receptor, suggesting that signaling by GM-CSF that leads to augmentation of phagocytosis is via the β chain of the receptor. In a patient with AIDS and disseminated multidrug-resistant MAC infection, GM-CSF treatment improved phagocytosis of MAC by peripheral blood monocytes and reduced bacteremia. These results imply that GM-CSF therapy may be useful in restoring antimycobacterial function by human monocytes/macrophages.

Visualizing and classifying data using a hybrid intelligent system

In this paper, a hybrid intelligent system that integrates the SOM (Self-Organizing Map) neural network, kMER (kernel-based Maximum Entropy learning Rule), and Probabilistic Neural Network (PNN) for data visualization and classification is proposed. The rationales of this Probabilistic SOM-kMER model are explained, and its applicability is demonstrated using two benchmark data sets. The results are analyzed and compared with those from a number of existing methods. Implication of the proposed hybrid system as a useful and usable data visualization and classification tool is discussed.

Using linear programming for weights identification of generalized bonferroni means in R

The generalized Bonferroni mean is able to capture some interaction effects between variables and model mandatory requirements. We present a number of weights identification algorithms we have developed in the R programming language in order to model data using the generalized Bonferroni mean subject to various preferences. We then compare its accuracy when fitting to the journal ranks dataset.