Improving Short DNA Sequence Alignment with Parallel Computing

Variations in different types of genomes have been found to be responsible for a large degree of physical diversity such as appearance and susceptibility to disease. Identification of genomic variations is difficult and can be facilitated through computational analysis of DNA sequences. Newly available technologies are able to sequence billions of DNA base pairs relatively quickly. These sequences can be used to identify variations within their specific genome but must be mapped to a reference sequence first. In order to align these sequences to a reference sequence, we require mapping algorithms that make use of approximate string matching and string indexing methods. To date, few mapping algorithms have been tailored to handle the massive amounts of output generated by newly available sequencing technologies. In otrder to handle this large amount of data, we modified the popular mapping software BWA to run in parallel using OpenMPI. Parallel BWA matches the efficiency of multithreaded BWA functions while providing efficient parallelism for BWA functions that do not currently support multithreading. Parallel BWA shows significant wall time speedup in comparison to multithreaded BWA on high-performance computing clusters, and will thus facilitate the analysis of genome sequencing data.

Behavioural and neural correlates of operant conditioning in Lymnaea stagnalis : role of previous experience during development /

The freshwater mollusc Lymnaea stagnalis was utilized in this study to further the understanding of how network properties change as a result of associative learning, and to determine whether or not this plasticity is dependent on previous experience during development. The respiratory and neural correlates of operant conditioning were first determined in normally reared Lymnaea. The same procedure was then applied to differentially reared Lymnaea, that is, animals that had never experienced aerial respiration during their development. The aim was to determine whether these animals would demonstrate the same responses to the training paradigm. In normally reared animals, a behavioural reduction in aerial respiration was accompanied by numerous changes within the neural network. Specifically, I provide evidence of changes at the level of the respiratory central pattern generator and the motor output. In the differentially reared animals, there was little behavioural data to suggest learning and memory. There were, however, significant differences in the network parameters, similar to those observed in normally reared animals. This demonstrated an effect of operant conditioning on differentially reared animals. In this thesis, I have identified additional correlates of operant conditioning in normally reared animals and provide evidence of associative learning in differentially reared animals. I conclude plasticity is not dependent on previous experience, but is rather ontogenetically programmed within the neural network.

Behavioural and neural correlates of emotion recognition as a function of psychopathic personality traits

Psychopathy is associated with well-known characteristics such as a lack of empathy and impulsive behaviour, but it has also been associated with impaired recognition of emotional facial expressions. The use of event-related potentials (ERPs) to examine this phenomenon could shed light on the specific time course and neural activation associated with emotion recognition processes as they relate to psychopathic traits. In the current study we examined the PI , N170, and vertex positive potential (VPP) ERP components and behavioural performance with respect to scores on the Self-Report Psychopathy (SRP-III) questionnaire. Thirty undergraduates completed two tasks, the first of which required the recognition and categorization of affective face stimuli under varying presentation conditions. Happy, angry or fearful faces were presented under with attention directed to the mouth, nose or eye region and varied stimulus exposure duration (30, 75, or 150 ms). We found that behavioural performance to be unrelated to psychopathic personality traits in all conditions, but there was a trend for the Nl70 to peak later in response to fearful and happy facial expressions for individuals high in psychopathic traits. However, the amplitude of the VPP was significantly negatively associated with psychopathic traits, but only in response to stimuli presented under a nose-level fixation. Finally, psychopathic traits were found to be associated with longer N170 latencies in response to stimuli presented under the 30 ms exposure duration. In the second task, participants were required to inhibit processing of irrelevant affective and scrambled face distractors while categorizing unrelated word stimuli as living or nonliving. Psychopathic traits were hypothesized to be positively associated with behavioural performance, as it was proposed that individuals high in psychopathic traits would be less likely to automatically attend to task-irrelevant affective distractors, facilitating word categorization. Thus, decreased interference would be reflected in smaller N170 components, indicating less neural activity associated with processing of distractor faces. We found that overall performance decreased in the presence of angry and fearful distractor faces as psychopathic traits increased. In addition, the amplitude of the N170 decreased and the latency increased in response to affective distractor faces for individuals with higher levels of psychopathic traits. Although we failed to find the predicted behavioural deficit in emotion recognition in Task 1 and facilitation effect in Task 2, the findings of increased N170 and VPP latencies in response to emotional faces are consistent wi th the proposition that abnormal emotion recognition processes may in fact be inherent to psychopathy as a continuous personality trait.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

The Terroir of Winter Hardiness: Investigation of Winter Hardiness, Water Metrics, and Yield of Riesling and Cabernet Franc in the Niagara Region Using Geomatic Technologies

Grapevine winter hardiness is a key factor in vineyard success in many cool climate wine regions. Winter hardiness may be governed by a myriad of factors in addition to extreme weather conditions – e.g. soil factors (texture, chemical composition, moisture, drainage), vine water status, and yield– that are unique to each site. It was hypothesized that winter hardiness would be influenced by certain terroir factors , specifically that vines with low water status [more negative leaf water potential (leaf ψ)] would be more winter hardy than vines with high water status (more positive leaf ψ). Twelve different vineyard blocks (six each of Riesling and Cabernet franc) throughout the Niagara Region in Ontario, Canada were chosen. Data were collected during the growing season (soil moisture, leaf ψ), at harvest (yield components, berry composition), and during the winter (bud LT50, bud survival). Interpolation and mapping of the variables was completed using ArcGIS 10.1 (ESRI, Redlands, CA) and statistical analyses (Pearson’s correlation, principal component analysis, multilinear regression) were performed using XLSTAT. Clear spatial trends were observed in each vineyard for soil moisture, leaf ψ, yield components, berry composition, and LT50. Both leaf ψ and berry weight could predict the LT50 value, with strong positive correlations being observed between LT50 and leaf ψ values in eight of the 12 vineyard blocks. In addition, vineyards in different appellations showed many similarities (Niagara Lakeshore, Lincoln Lakeshore, Four Mile Creek, Beamsville Bench). These results suggest that there is a spatial component to winter injury, as with other aspects of terroir, in the Niagara region.

APPLICATION OF GEOMATICS TECHNOLOGIES TO CHARACTERIZE SPATIAL VARIABILITY AT STRATUS VINEYARDS

Vineyards vary over space and time, making geomatics technologies ideally suited to study terroir. This study applied geomatics technologies - GPS, remote sensing and GIS - to characterize the spatial variability at Stratus Vineyards in the Niagara Region. The concept of spatial terroir was used to visualize, monitor and analyze the spatial and temporal variability of variables that influence grape quality. Spatial interpolation and spatial autocorrelation were used to measure the pattern demonstrated by soil moisture, leaf water potential, vine vigour, soil composition and grape composition on two Cabernet Franc blocks and one Chardonnay block. All variables demonstrated some spatial variability within and between the vineyard block and over time. Soil moisture exhibited the most significant spatial clustering and was temporally stable. Geomatics technologies provided valuable spatial information related to the natural spatial variability at Stratus Vineyards and can be used to inform and influence vineyard management decisions.

Students’ Use and Perceptions of Social Networking Technologies: Connections to Reading, Reading Ability, and Self-Perception

Abstract This study was undertaken to examine traditional forms of literacy and the newest form of literacy: technology. Students who have trouble reading traditional forms of literacy tend to have lower self-esteem. This research intended to explore if students with reading difficulties and, therefore, lower self-esteem, could use Social Networking Technologies including text messaging, Facebook, email, blogging, MySpace, or Twitter to help improve their self-esteem, in a field where spelling mistakes and grammatical errors are commonplace, if not encouraged. A collective case study was undertaken based on surveys, individual interviews, and gathered documents from 3 students 9-13 years old. The data collected in this study were analyzed and interpreted using qualitative methods. These cases were individually examined for themes, which were then analyzed across the cases to examine points of convergence and divergence in the data. The research found that students with reading difficulties do not necessarily have poor self-esteem, as prior research has suggested (Carr, Borkowski, & Maxwell, 1991; Feiler, & Logan, 2007; Meece, Wigfield, & Eccles, 1990; Pintirch & DeGroot, 1990; Pintrich & Garcia, 1991). All of the participants who had reading difficulties, were found both through interviews and the CFSEI-3 self-esteem test (Battle, 2002) to have average self-esteem, although their parents all stated that their child felt poorly about their academic abilities. The research also found that using Social Networking Technologies helped improve the self-esteem of the majority of the participants both socially and academically.

How health organizations can apply scenario based planning and analytic technologies to make strategic consequential decisions in a time of uncertainty

The magnitude of the cervical cancer problem, coupled with the potential for prevention with recent technological advances, made it imperative to step back and reassess strategic options for dealing with cervical cancer screening in Kenya. The purpose of this qualitative study was: 1) to explore the extent to which the Participatory Action Research (PAR) methodology and the Scenario Based Planning (SBP) method, with the application of analytics, could enable strategic, consequential, informed decision making, and 2) to determine how influential Kenyan decision makers could apply SBP with analytic tools and techniques to make strategic, consequential decisions regarding the implementation of a Cervical Self Sampling Program (CSSP) in both urban and rural settings. The theoretical paradigm for this study was action research; it was experiential, practical, and action oriented, and resulted in co-created knowledge that influenced study participants’ decision making. Action Africa Help International (AAHI) and Brock University collaborated with Local Decision Influencing Participants (LDIP’s) to develop innovative strategies on how to implement the CSSP. SBP tools, along with traditional approaches to data collection and analysis, were applied to collect, visualize and analyze predominately qualitative data. Outputs from the study included: a) a generic implementation scenario for a CSSP (along with scenarios unique to urban and rural settings), and b) 10 strategic directions and 22 supporting implementation strategies that address the variables of: 1) technical viability, 2) political support, 3) affordability, 4) logistical feasibility, 5) social acceptability, and 6) transformation/sustainability. In addition, study participants’ capacity to effectively engage in predictive/prescriptive strategic decision making was strengthened.