Assessing the Reproducibility of Clustering of Molecular Dynamics Conformations on Self-Organizing Maps

The goal of most clustering algorithms is to find the optimal number of clusters (i.e. fewest number of clusters). However, analysis of molecular conformations of biological macromolecules obtained from computer simulations may benefit from a larger array of clusters. The Self-Organizing Map (SOM) clustering method has the advantage of generating large numbers of clusters, but often gives ambiguous results. In this work, SOMs have been shown to be reproducible when the same conformational dataset is independently clustered multiple times (~100), with the help of the Cramérs V-index (C_v). The ability of C_v to determine which SOMs are reproduced is generalizable across different SOM source codes. The conformational ensembles produced from MD (molecular dynamics) and REMD (replica exchange molecular dynamics) simulations of the penta peptide Met-enkephalin (MET) and the 34 amino acid protein human Parathyroid Hormone (hPTH) were used to evaluate SOM reproducibility. The training length for the SOM has a huge impact on the reproducibility. Analysis of MET conformational data definitively determined that toroidal SOMs cluster data better than bordered maps due to the fact that toroidal maps do not have an edge effect. For the source code from MATLAB, it was determined that the learning rate function should be LINEAR with an initial learning rate factor of 0.05 and the SOM should be trained by a sequential algorithm. The trained SOMs can be used as a supervised classification for another dataset. The toroidal 10×10 hexagonal SOMs produced from the MATLAB program for hPTH conformational data produced three sets of reproducible clusters (27%, 15%, and 13% of 100 independent runs) which find similar partitionings to those of smaller 6×6 SOMs. The χ^2 values produced as part of the C_v calculation were used to locate clusters with identical conformational memberships on independently trained SOMs, even those with different dimensions. The χ^2 values could relate the different SOM partitionings to each other.

Conformational analysis of antibody binding site using EDMA methods /

Euclidean distance matrix analysis (EDMA) methods are used to distinguish whether or not significant difference exists between conformational samples of antibody complementarity determining region (CDR) loops, isolated LI loop and LI in three-loop assembly (LI, L3 and H3) obtained from Monte Carlo simulation. After the significant difference is detected, the specific inter-Ca distance which contributes to the difference is identified using EDMA.The estimated and improved mean forms of the conformational samples of isolated LI loop and LI loop in three-loop assembly, CDR loops of antibody binding site, are described using EDMA and distance geometry (DGEOM). To the best of our knowledge, it is the first time the EDMA methods are used to analyze conformational samples of molecules obtained from Monte Carlo simulations. Therefore, validations of the EDMA methods using both positive control and negative control tests for the conformational samples of isolated LI loop and LI in three-loop assembly must be done. The EDMA-I bootstrap null hypothesis tests showed false positive results for the comparison of six samples of the isolated LI loop and true positive results for comparison of conformational samples of isolated LI loop and LI in three-loop assembly. The bootstrap confidence interval tests revealed true negative results for comparisons of six samples of the isolated LI loop, and false negative results for the conformational comparisons between isolated LI loop and LI in three-loop assembly. Different conformational sample sizes are further explored by combining the samples of isolated LI loop to increase the sample size, or by clustering the sample using self-organizing map (SOM) to narrow the conformational distribution of the samples being comparedmolecular conformations. However, there is no improvement made for both bootstrap null hypothesis and confidence interval tests. These results show that more work is required before EDMA methods can be used reliably as a method for comparison of samples obtained by Monte Carlo simulations.

Investigating the effects of arousal state on cognitive performance in individuals with and without mild head injury

We examined the cognitive and emotional sequelae following mild head injury (MHI; e.g., concussion) in high-functioning individuals and whether persons with MHI pre~ent, both physiologically and via self-report, in a manner different from (i.e., underaroused) that of persons who have no history of head injury. We also investigated the effect arousal state ~as on the cognitive performance of this population. Using a quasiexperimental research design (N = 91), we examined changes in attention, working memory, and cognitive flexibility (subtests ofthe WAIS-III, 1997,WMS-III, 1997, & DKEFS, 2002) as a function of manipulated arousal (i.e., induced psychosocial stress/activation; reduced activation/relaxation). In addition to self-reported arousal and state anxiety (State-Trait Anxiety Inventory; Speilberger, 1983a) measures, physiological indices of arousal state (i.e., electrodermal responsivity, heart rate, and respiration activity) were recorded (via Polygraph Professional Suite, 2008) across a 2.5 hour interval while completing various cognitive tasks. Students also completed the Post-concussive Symptom Checklist (Gouvier et aI., 1992). The results demonstrate that university students who report a history ofMHI (i.e., "altered state of consciousness") experience significantly lower levels of anxiety, were physiologically underaroused, and were less responsive to stressors in their environment, compared to their non-~HI cohorts. As expected, cognitive flexibility (but not other neuropsychological measures of cognition) was advantaged with increased stress, and disadvantaged with reduced stress, in persons with reported MHI, but not for those without reported MHI which provided limited support for our hypothesis. Further, university students who had no complaints related to their previous MHI endorsed a greater number of traditional post-concussive symptoms in terms of intensity, duration and frequency as compared to students who did not report a MHI. The underarousal in traumatic brain injury has been associated with (ventromedial prefrontal cortex) VMPFC disruption and may be implicated in MHI generally. Students who report sustaining a previous MHI may be less able to physiologically respond and/or cognitively appraise, stressful experiences as compared to their no-MHI cohort and experience persistent, long-lasting consequences despite the subtle nature of a history of head injury.

An examination of the role of personality and self-regulation in the gambling behaviours of late adolescents and emerging adults

The purposes of this study were: a) to examine the role of personality and selfregulation in the gambling behaviour participation of late adolescents and emerging adults. In particular, the present study examined i f certain personality traits were more prevalent in high-risk gamblers than in young people considered low or at-risk gamblers; and, b) to examine i f the ability to self-regulate helped distinguish differences among the three groups of gamblers (low-risk, at-risk, and high-risk gamblers). A sample of late adolescents and emerging adults (N = 100) attending Brock University, completed a survey that assessed current gambling behaviour (both frequency and consequence experience), personality, self-esteem, and self-regulation. It was found that high-risk gamblers had lower scores on the personality dimensions Emotionality, Conscientiousness (especially on its Prudence facet), and Honesty-Humility (especially on its Fairness, Greed Avoidance, and Modesty facets) than at-risk or low-risk gamblers and higher scores on impulsive sensation seeking and impulsivity than at-risk or low-risk gamblers. Similarly, high-risk gamblers reported lower levels of self-regulation than both at-risk and low-risk gamblers. The findings from this study support past research which suggests that young people who gamble at problematic levels differ on many personality traits and often have more difficulty self-regulating than young people who do not participate at problematic levels. Findings may aid in the development of intervention and prevention programs that utilize specific self-regulation techniques with a young gambling population.

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).