The quality of the mother-child relationship during collaborative problem solving /

The quality of the mother-child relationship was examined in relation to joint planning, maternal teaching strategies, maternal emotional support, mutual positive affect and attachment security. Fifty-five grade five children and their mothers participated in a laboratory session comprised of various activities and completed questionnaires to evaluate attachment security. Joint planning and social problem solving were assessed observationally during an origami task. Problem solving effectiveness was unrelated to maternal teaching strategies, maternal encouragement and mutual positive affect. A marginally significant relationship was found between maternal encouragement and active child participation. Attachment security was found to be significantly related to sharing of responsibility during local planning, but only for child autonomous performance. An examination of conditional probabilities revealed that mutual positive affect did not increase the likelihood of subsequent mother-child dyadic regulation. However, mutual positive affect was found to be significantly related to both active child participation and dyadic regulation. The hypothesis predicting a mediational model was not supported. The implications of these findings in the theoretical and empirical literature were considered and suggestions for future research were made.

Decision analysis of the effectiveness of lung cancer screening using autofluorescence bronchoscopy and computed tomography

Background: Lung cancer (LC) is the leading cause of cancer death in the developed world. Most cancers are associated with tobacco smoking. A primary hope for reducing lung cancer has been prevention of smoking and successful smoking cessation programs. To date, these programs have not been as successful as anticipated. Objective: The aim of the current study was to evaluate whether lung cancer screening combining low dose computed tomography with autofluorescence bronchoscopy (combined CT & AFB) is superior to CT or AFB screening alone in improving lung cancer specific survival. In addition, the extent of improvement and ideal conditions for combined CT & AFB screening were evaluated. Methods: We applied decision analysis and Monte Carlo simulation modeling using TreeAge Software to evaluate our study aims. Histology- and stage specific probabilities of lung cancer 5-year survival proportions were taken from Surveillance and Epidemiologic End Results (SEER) Registry data. Screeningassociated data was taken from the US NCI Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO), National Lung Screening Trial (NLST), and US NCI Lung Screening Study (LSS), other relevant published data and expert opinion. Results: Decision Analysis - Combined CT and AFB was the best approach at Improving 5-year survival (Overall Expected Survival (OES) in the entire screened population was 0.9863) and in lung cancer patients only (Lung Cancer Specific Expected Survival (LOSES) was 0.3256). Combined screening was slightly better than CT screening alone (OES = 0.9859; LCSES = 0.2966), and substantially better than AFB screening alone (OES = 0.9842; LCSES = 0.2124), which was considerably better than no screening (OES = 0.9829; LCSES = 0.1445). Monte Carlo simulation modeling revealed that expected survival in the screened population and lung cancer patients is highest when screened using CT and combined CT and AFB. CT alone and combined screening was substantially better than AFB screening alone or no screening. For LCSES, combined CT and AFB screening is significantly better than CT alone (0.3126 vs. 0.2938, p< 0.0001). Conclusions: Overall, these analyses suggest that combined CT and AFB is slightly better than CT alone at improving lung cancer survival, and both approaches are substantially better than AFB screening alone or no screening.

The role of molecular, clinical and socioeconomic factors in the long-term survival of axillary node negative breast cancer patients

The overall objective of this study was to investigate factors associated with long-term survival in axillary node negative (ANN) breast cancer patients. Clinical and biological factors included stage, histopathologic grade, p53 mutation, Her-2/neu amplification, estrogen receptor status (ER), progesterone receptor status (PR) and vascular invasion. Census derived socioeconomic (SES) indicators included median individual and household income, proportions of university educated individuals, housing type, "incidence" of low income and an indicator of living in an affluent neighbourhood. The effects of these measures on breast cancer-specific survival and competing cause survival were investigated. A cohort study examining survival among axillary node negative (ANN) breast cancer patients in the greater Toronto area commenced in 1 989. Patients were followed up until death, lost-to-follow up or study termination in 2004. Data were collected from several sources measuring patient demographics, clinical factors, treatment, recurrence of disease and survival. Census level SES data were collected using census geo-coding of patient addresses' at the time of diagnosis. Additional survival data were acquired from the Ontario Cancer Registry to enhance and extend the observation period of the study. Survival patterns were examined using KaplanMeier and life table procedures. Associations were examined using log-rank and Wilcoxon tests of univariate significance. Multivariate survival analyses were perfonned using Cox proportional hazards models. Analyses were stratified into less than and greater than 5 year survival periods to observe whether known markers of short-tenn survival were also associated with reductions in long-tenn survival among breast cancer patients. The 15 year survival probabilities in this cohort were: for breast cancerspecific survival 0.88, competing causes survival 0.89 and for overall survival 0.78. Estrogen receptor (ER) and progesterone receptor (PR) status (Hazard Ratio (HR) ERIPR- versus ER+/PR+, 8.15,95% CI, 4.74, 14.00), p53 mutation (HR, 3.88, 95% CI, 2.00, 7.53) and Her-2 amplification (HR, 2.66, 95% CI, 1.36, 5.19) were associated with significant reductions in short-tenn breast cancer-specific survival «5 years following diagnosis), however, not with long-term survival in univariate analyses. Stage, histopathologic grade and ERiPR status were the clinicallbiologieal factors that were associated with short-term breast cancer specific survival in multivariate results. Living in an affluent neighbourhood (top quintile of median household income compared to the rest of the population) was associated with the largest significant increase in long-tenn breast cancer-specific survival after adjustment for stage, histopathologic grade and treatment (HR, 0.36, 95% CI, 0.12, 0.89).

A game-theoretic network formation model

We study the dynamics of a game-theoretic network formation model that yields large-scale small-world networks. So far, mostly stochastic frameworks have been utilized to explain the emergence of these networks. On the other hand, it is natural to seek for game-theoretic network formation models in which links are formed due to strategic behaviors of individuals, rather than based on probabilities. Inspired by Even-Dar and Kearns (2007), we consider a more realistic model in which the cost of establishing each link is dynamically determined during the course of the game. Moreover, players are allowed to put transfer payments on the formation of links. Also, they must pay a maintenance cost to sustain their direct links during the game. We show that there is a small diameter of at most 4 in the general set of equilibrium networks in our model. Unlike earlier model, not only the existence of equilibrium networks is guaranteed in our model, but also these networks coincide with the outcomes of pairwise Nash equilibrium in network formation. Furthermore, we provide a network formation simulation that generates small-world networks. We also analyze the impact of locating players in a hierarchical structure by constructing a strategic model, where a complete b-ary tree is the seed network.

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.