Calculation of the optical conductivity for the Anderson impurity model using the slave boson technique

The optical conductivity of the Anderson impurity mode l has been calculated by emp l oying the slave boson technique and an expansion in powers of l i N, where N is the d egeneracy o f the f electron level . This method has been used to find the effective mass of the conduction electrons for temperatures above and below the Kondo tempera ture. For low temperatures, the mass enhancement is f ound to be large while a t high t emperatures, the mass enhancement is sma ll. The conductivity i s f ound to be Drude like with frequency dependent effective mass and scattering time for low independent effective mass and temperatures and scattering time f requency for high t emperatures. The behavior of both the effective mass and the conductivity is in qualitative agreement with experimental r esul t s .

Decoding algorithms using side-effect machines

Bioinformatics applies computers to problems in molecular biology. Previous research has not addressed edit metric decoders. Decoders for quaternary edit metric codes are finding use in bioinformatics problems with applications to DNA. By using side effect machines we hope to be able to provide efficient decoding algorithms for this open problem. Two ideas for decoding algorithms are presented and examined. Both decoders use Side Effect Machines(SEMs) which are generalizations of finite state automata. Single Classifier Machines(SCMs) use a single side effect machine to classify all words within a code. Locking Side Effect Machines(LSEMs) use multiple side effect machines to create a tree structure of subclassification. The goal is to examine these techniques and provide new decoders for existing codes. Presented are ideas for best practices for the creation of these two types of new edit metric decoders.

Mode of action of FMRFamide-like peptide on drosophila body wall muscle conractions

Neuropeptides are the largest group of signalling chemicals that can convey the information from the brain to the cells of all tissues. DPKQDFMRFamide, a member of one of the largest families of neuropeptides, FMRFamide-like peptides, has modulatory effects on nerve-evoked contractions of Drosophila body wall muscles (Hewes et aI.,1998) which are at least in part mediated by the ability of the peptide to enhance neurotransmitter release from the presynaptic terminal (Hewes et aI., 1998, Dunn & Mercier., 2005). However, DPKQDFMRFamide is also able to act directly on Drosophila body wall muscles by inducing contractions which require the influx of extracellular Ca 2+ (Clark et aI., 2008). The present study was aimed at identifying which proteins, including the membrane-bound receptor and second messenger molecules, are involved in mechanisms mediating this myotropic effect of the peptide. DPKQDFMRFamide induced contractions were reduced by 70% and 90%, respectively, in larvae in which FMRFamide G-protein coupled receptor gene (CG2114) was silenced either ubiquitously or specifically in muscle tissue, when compared to the response of the control larvae in which the expression of the same gene was not manipulated. Using an enzyme immunoassay (EIA) method, it was determined that at concentrations of 1 ~M- 0.01 ~M, the peptide failed to increase cAMP and cGMP levels in Drosophila body wall muscles. In addition, the physiological effect of DPKQDFMRFamide at a threshold dose was not potentiated by 3-lsobutyl-1-methylxanthine, a phosphodiesterase inhibitor, nor was the response to 1 ~M peptide blocked or reduced by inhibitors of cAMP-dependent or cGMP-dependent protein kinases. The response to DPKQDFMRFamide was not affected in the mutants of the phosholipase C-~ (PLC~) gene (norpA larvae) or IP3 receptor mutants, which suggested that the PLC-IP3 pathway is not involved in mediat ing the peptide's effects. Alatransgenic flies lacking activity of calcium/calmodul in-dependent protein kinase (CamKII showed an increase in muscle tonus following the application of 1 JlM DPKQDFMRFamide similar to the control larvae. Heat shock treatment potentiated the response to DPKQDFMRFamide in both ala1 and control flies by approximately 150 and 100 % from a non heat-shocked larvae, respectively. Furthermore, a CaMKII inhibitor, KN-93, did not affect the ability of peptide to increase muscle tonus. Thus, al though DPKQDFMRFamide acts through a G-protein coupled FMRFamide receptor, it does not appear to act via cAMP, cGMP, IP3, PLC or CaMKl1. The mechanism through which the FMRFamide receptor acts remains to be determined.

Hyperspectral Mineral Identification using SVM and SOM

Remote sensing techniques involving hyperspectral imagery have applications in a number of sciences that study some aspects of the surface of the planet. The analysis of hyperspectral images is complex because of the large amount of information involved and the noise within that data. Investigating images with regard to identify minerals, rocks, vegetation and other materials is an application of hyperspectral remote sensing in the earth sciences. This thesis evaluates the performance of two classification and clustering techniques on hyperspectral images for mineral identification. Support Vector Machines (SVM) and Self-Organizing Maps (SOM) are applied as classification and clustering techniques, respectively. Principal Component Analysis (PCA) is used to prepare the data to be analyzed. The purpose of using PCA is to reduce the amount of data that needs to be processed by identifying the most important components within the data. A well-studied dataset from Cuprite, Nevada and a dataset of more complex data from Baffin Island were used to assess the performance of these techniques. The main goal of this research study is to evaluate the advantage of training a classifier based on a small amount of data compared to an unsupervised method. Determining the effect of feature extraction on the accuracy of the clustering and classification method is another goal of this research. This thesis concludes that using PCA increases the learning accuracy, and especially so in classification. SVM classifies Cuprite data with a high precision and the SOM challenges SVM on datasets with high level of noise (like Baffin Island).

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.

Instagram, Social Media, and the "Like": Exploring Virtual Identity's Role in 21st Century Students' New Socialization Experience

Personal technologies and social media use have changed the socialization experience of our 21st century learners. As learners have a new, embodied, virtual identity that is an omnipresent force within their social interactions, this study sought to examine how virtual identity influences student relationships both within and outside of a school context. This study also explored how personal technologies and social media use have influenced learners’ perceptions of their own 21st century learning. Using a qualitative inquiry, purposeful sampling was employed to recruit 6 participants between the ages of 15 to 19 to examine their social networking site use and education experience. Data were collected from single, one-on-one semi-structured interviews in which participants discussed their experiences using social media. Data were also collected from the teens’ personal Instagram accounts, and a personal reflexive researcher’s journal was kept for triangulation of data. Open and axial coding strategies alongside constant comparative methods were used to analyze data. Participants shared how they and their peers use social media, the pressures and expectations from other users, social media’s influence on peer relationships, and how social media influences their choices in the physical realm. All 6 participants explained that their teachers do not talk to them about their social media use, and even offered critiques of the school system itself and its inability to prepare students for the new realities of a digital world. This study concludes that while social media is very influential on students’ socialization, educators should be more concerned about the lack of guidance and support that students receive in school in terms of appropriate social media use and the navigation of virtual identity.