Primary structure and expression studies of the dihydrofolate reductase gene (DFRI) of Saccharomyces cerevisiae

The nucleotide sequence of a genomic DNA fragment thought previously to contain the dihydrofolate reductase gene (DFR1) of Saccharomyces cerevisiae by genetic criteria was determined. This DNA fragment of 1784' basepairs contains a large open reading frame from position 800 to 1432, which encodes a enzyme with a predicted molecular weight of 24,229.8 Daltons. Analysis of the amino acid sequence of this protein revealed that the yeast polypep·tide contained 211 amino acids, compared to the 186 residues commonly found in the polypeptides of other eukaryotes. The difference in size of the gene product can be attributed mainly to an insert in the yeast gene. Within this region, several consensus sequences required for processing of yeast nuclear and class II mitochondrial introns were identified, but appear not sufficient for the RNA splicing. The primary structure of the yeast DHFR protein has considerable sequence homology with analogous polypeptides from other organisms, especially in the consensus residues involved in cofactor and/or inhibitor binding. Analysis of the nucleotide sequence also revealed the presence of a number of canonical sequences identified in yeast as having some function in the regulation of gene expression. These include UAS elements (TGACTC) required for tIle amino acid general control response, and "TATA H boxes as well as several consensus sequences thought to be required for transcriptional termination and polyadenylation. Analysis of the codon usage of the yeast DFRl coding region revealed a codon bias index of 0.0083. this valve very close to zero suggestes 3 that the gene is expressed at a relatively low level under normal physiological conditions. The information concerning the organization of the DFRl were used to construct a variety of fusions of its 5' regulatory region with the coding region of the lacZ gene of E. coli. Some of such fused genes encoded a fusion product that expressed in E.coli and/or in yeast under the control of the 5' regulatory elements of the DFR1. Further studies with these fusion constructions revealed that the beta-galactosidase activity encoded on multicopy plasmids was stimulated transiently by prior exposure of yeast host cells to UV light. This suggests that the yeast PFRl gene is indu.ced by UV light and nlay in1ply a novel function of DHFR protein in the cellular responses to DNA damage. Another novel f~ature of yeast DHFR was revealed during preliminary studies of a diploid strain containing a heterozygous DFRl null allele. The strain was constructed by insertion of a URA3 gene within the coding region of DFR1. Sporulation of this diploid revealed that meiotic products segregated 2:0 for uracil prototrophy when spore clones were germinated on medium supplemented with 5-formyltetrahydrofolate (folinic acid). This finding suggests that, in addition to its catalytic activity, the DFRl gene product nlay play some role in the anabolisln of folinic acid. Alternatively, this result may indicate that Ura+ haploid segregants were inviable and suggest that the enzyme has an essential cellular function in this species.

The relationship of individual, team and organizational learning in Ontario hospital clinical labratories

I t is generally accepted among scholars that individual learning and team learning contribute to the concept we refer to as organizational learning. However, a small number of quantitative and qualitative studies that have investigated their relationship reported contradicting results. This thesis investigated the relationship between individual learning, team learning, and organizational learning. A survey instrument was used to collect information on individual learning, team learning, and organizational learning. The study sample comprised of supervisors from the clinical laboratories in teaching hospitals and community hospitals in Ontario. The analyses utilized a linear regression to investigate the relationship between individual and team learning. The relationship between individual and organizational learning, and team and organizational learning were simultaneously investigated with canonical correlation and set correlation. T-test and multivariate analysis of variance were used to compare the differences in learning scores of respondents employed by laboratories in teaching and those employed by community hospitals. The study validated its tests results with 1,000 bootstrap replications. Results from this study suggest that there are moderate correlations between individual learning and team learning. The correlation individual learning and organizational learning and team learning and organizational learning appeared to be weak. The scores of the three learning levels show statistically significant differences between respondents from laboratories in teaching hospitals and respondents from community hospitals.

Electrocortical indices of cognitive control in working memory : exploring the effects of proactive interference, cognitive load, and aging

Cognitive control involves the ability to flexibly adjust cognitive processing in order to resist interference and promote goal-directed behaviour. Although frontal cortex is considered to be broadly involved in cognitive control, the mechanisms by which frontal brain areas implement control functions are unclear. Furthermore, aging is associated with reductions in the ability to implement control functions and questions remain as to whether unique cortical responses serve a compensatory role in maintaining maximal performance in later years. Described here are three studies in which electrophysiological data were recorded while participants performed modified versions of the standard Sternberg task. The goal was to determine how top-down control is implemented in younger adults and altered in aging. In study I, the effects of frequent stimulus repetition on the interference-related N450 were investigated in a Sternberg task with a small stimulus set (requiring extensive stimulus resampling) and a task with a large stimulus set (requiring no stimulus resampling).The data indicated that constant stimulus res amp ling required by employing small stimulus sets can undercut the effect of proactive interference on the N450. In study 2, younger and older adults were tested in a standard version of the Sternberg task to determine whether the unique frontal positivity, previously shown to predict memory impairment in older adults during a proactive interference task, would be associated with the improved performance when memory recognition could be aided by unambiguous stimulus familiarity. Here, results indicated that the frontal positivity was associated with poorer memory performance, replicating the effect observed in a more cognitively demanding task, and showing that stimulus familiarity does not mediate compensatory cortical activations in older adults. Although the frontal positivity could be interpreted to reflect maladaptive cortical activation, it may also reflect attempts at compensation that fail to fully ameliorate agerelated decline. Furthermore, the frontal positivity may be the result of older adults' reliance on late occurring, controlled processing in contrast to younger adults' ability to identify stimuli at very early stages of processing. In the final study, working memory load was manipulated in the proactive interference Sternberg task in order to investigate whether the N450 reflects simple interference detection, with little need for cognitive resources, or an active conflict resolution mechanism that requires executive resources to implement. Independent component analysis was used to isolate the effect of interference revealing that the canonical N450 was based on two dissociable cognitive control mechanisms: a left frontal negativity that reflects active interference resolution, , but requires executive resources to implement, and a right frontal negativity that reflects global response inhibition that can be relied on when executive resources are minimal but at the cost of a slowed response. Collectively, these studies advance understanding of the factors that influence younger and older adults' ability to satisfy goal-directed behavioural requirements in the face of interference and the effects of age-related cognitive decline.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

The Effects of Magnetic Dilution and Applied Pressure on Several Frustrated Spinels

The effects of magnetic dilution and applied pressure on frustrated spinels GeNi2O4, GeCo2O4, and NiAl2O4 are reported. Dilution was achieved by substitution of Mg2+ in place of magnetically active Co2+ and Ni2+ ions. Large values of the percolation thresholds were found in GeNi(2-x)MgxO4. Specifically, pc1 = 0.74 and pc2 = 0.65 in the sub-networks associated with the triangular and kagome planes, respectively. This anomalous behaviour may be explained by the kagome and triangular planes behaving as coupled networks, also know as a network of networks. In simulations of coupled lattices that form a network of networks, similar anomalous percolation threshold values have been found. In addition, at dilution levels above x=0.30, there is a T^2 dependency in the magnetic heat capacity which may indicate two dimensional spin glass behaviour. Applied pressures in the range of 0 GPa to 1.2 GPa yield a slight decrease in ordering temperature for both the kagome and triangular planes. In GeCo(2-x)MgxO4, the long range magnetic order is more robust with a percolation threshold of pc=0.448. Similar to diluted nickel germanate, at low temperatures, a T^2 magnetic heat capacity contribution is present which indicates a shift from a 3D ordered state to a 2D spin glass state in the presence of increased dilution. Dynamic magnetic susceptibility data indicate a change from canonical spin glass to a cluster glass behaviour. In addition, there is a non-linear increase in ordering temperature with applied pressure in the range P = 0 to 1.0 GPa. A spin glass ground state was observed in Ni(1-x)MgxAl2O4 for (x=0 to 0.375). Analysis of dynamic magnetic susceptibility data yield a characteristic time of tau* = 1.0x10^(-13) s, which is indicative of canonical spin glass behaviour. This is further corroborated by the linear behaviour of the magnetic specific heat contribution. However, the increasing frequency dependence of the freezing temperature suggests a trend towards spin cluster glass formation.