Sediment genotoxicity and its relationship to the frequency of chironomid labial plate deformities

Sediment samples were taken from seven locations in the WeIland River in December 1986 and April 1987. The DMSO extracts of these sediment samples showed a significant (p

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.

Genetic Analysis of Taste in Homo Sapiens

There are many known taste receptors specific to each taste attribute. This thesis examines the relationship between single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) in known taste and taste pathway receptors TAS2R38, Gustin, and TRPM5 and for PROP (6-n-propylthiouracil) taster status (PTS), thermal taster status (TTS), and orosensory sensation intensity ratings. PTS is a proxy for general taste responsiveness, and the ability to taste PROP classifies individuals into three phenotypes: super (PST), medium (PMT), and non-tasters (PNT). Another taste phenotype, also serving as a proxy for general taste responsiveness, is TTS, classifying individuals as thermal tasters (TTs) or thermal non-tasters (TnTs). DNA extractions from buccal cells obtained from 60 individuals were performed and analysis of TAS2R38, Gustin, and TRPM5 variations were conducted through Polymerase Chain Reaction (PCR), sequencing for SNPs, and upQMPSF for CNV analysis of TRPM5. Among the SNPs and CNVs studied, only TAS2R38 was found to be significantly associated with PTS and intensity ratings for sweet, bitter, and sour taste as well as astringency. However, not all PROP phenotypic differences can be explained by the variations at these three SNP sites in TAS2R38, suggesting the involvement of additional genes. No association was found between TTS and TAS2R38 or Gustin, confirming that PTS and TTS are not genetically associated. The examined TRPM5 SNPs and CNVs did not correlate with TTS. Therefore, further research is necessary into other factors contributing to PTS and TTS.

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.