Linda Stern

Plasma Lipidomic Analysis of Stable and Unstable Coronary Artery Disease

Objective—Traditional risk factors for coronary artery disease (CAD) fail to adequately distinguish patients who have atherosclerotic plaques susceptible to instability from those who have more benign forms. Using plasma lipid profiling, this study aimed to provide insight into disease pathogenesis and evaluate the potential of lipid profiles to assess risk of future plaque instability. Methods and Results—Plasma lipid profiles containing 305 lipids were measured on 220 individuals (matched healthy controls, n=80; stable angina, n=60; unstable coronary syndrome, n=80) using electrospray-ionisation tandem mass spectrometry. ReliefF feature selection coupled with an L2-regularized logistic regression based classifier was used to create multivariate classification models which were verified via 3-fold cross-validation (1000 repeats). Models incorporating both lipids and traditional risk factors provided improved classification of unstable CAD from stable CAD (C-statistic=0.875, 95% CI 0.874–0.877) compared with models containing only traditional risk factors (C-statistic=0.796, 95% CI 0.795–0.798). Many of the lipids identified as discriminatory for unstable CAD displayed an association with disease acuity (severity), suggesting that they are antecedents to the onset of acute coronary syndrome. Conclusion—Plasma lipid profiling may contribute to a new approach to risk stratification for unstable CAD.

Sequence complexity for biological sequence analysis

A new statistical model for DNA considers a sequence to be a mixture of regions with little structure and regions that are approximate repeats of other subsequences, i.e. instances of repeats do not need to match each other exactly. Both forward- and reverse-complementary repeats are allowed. The model has a small number of parameters which are fitted to the data. In general there are many explanations for a given sequence and how to compute the total probability of the data given the model is shown. Computer algorithms are described for these tasks. The model can be used to compute the information content of a sequence, either in total or base by base. This amounts to looking at sequences from a data-compression point of view and it is argued that this is a good way to tackle intelligent sequence analysis in general.

Comparative analysis of long DNA sequences by per element information content using different contexts

BackgroundFeatures of a DNA sequence can be found by compressing the sequence under a suitable model; good compression implies low information content. Good DNA compression models consider repetition, differences between repeats, and base distributions. From a linear DNA sequence, a compression model can produce a linear information sequence. Linear space complexity is important when exploring long DNA sequences of the order of millions of bases. Compressing a sequence in isolation will include information on self-repetition. Whereas compressing a sequence Y in the context of another X can find what new information X gives about Y. This paper presents a methodology for performing comparative analysis to find features exposed by such models.ResultsWe apply such a model to find features across chromosomes of Cyanidioschyzon merolae. We present a tool that provides useful linear transformations to investigate and save new sequences. Various examples illustrate the methodology, finding features for sequences alone and in different contexts. We also show how to highlight all sets of self-repetition features, in this case within Plasmodium falciparum chromosome 2.ConclusionThe methodology finds features that are significant and that biologists confirm. The exploration of long information sequences in linear time and space is fast and the saved results are self documenting.

Discovering patterns in Plasmodium falciparum genomic DNA

A method has been developed for discovering patterns in DNA sequences. Loosely based on the well-known Lempel Ziv model for text compression, the model detects repeated sequences in DNA. The repeats can be forward or inverted, and they need not be exact. The method is particularly useful for detecting distantly related sequences, and for finding patterns in sequences of biased nucleotide composition, where spurious patterns are often observed because the bias leads to coincidental nucleotide matches. We show here the utility of the method by applying it to genomic sequences of Plasmodium falciparum. A single scan of chromosomes 2 and 3 of P. falciparum, using our method and no other a priori information about the sequences, reveals regions of low complexity in both telomeric and central regions, long repeats in the subtelomeric regions, and shorter repeat areas in dense coding regions. Application of the method to a recently sequenced contig of chromosome 10 that has a particularly biased base composition detects a long internal repeat more readily than does the conventional dot matrix plot. Space requirements are linear, so the method can be used on large sequences. The observed repeat patterns may be related to large-scale chromosomal organization and control of gene expression. The method has general application in detecting patterns of potential interest in newly sequenced genomic material.

Visual representations for recursive algorithms

We have developed a framework for pedagogically-oriented animations, designed to help students learn new algorithms. Recursive sorting and searching algorithms pose a particular challenge, as it can be difficult to find visual representations that help students develop a mental model of how the recursion proceeds. Relatively complex representations, such as thumbnail sketches or explicitly showing the function stack along with the data structure are appropriate for some algorithms, while simpler representations suffice for others. We have found it useful to classify recursive algorithms according to the way they navigate through a data structure and manipulate data items within it, sometimes with further subdivision according to the kind of recursion. Within each category there are common strategies for visual representation. While there may be no single, general way to represent recursive algorithms, classification is a useful guide to picking an appropriate strategy when animating recursive algorithms.

You can lead a horse to water: how students really use pedagogical software

A great deal of effort is expended creating multimedia systems to help students learn. Some amount of effort is spent evaluating learning outcomes for students who have used these systems. Yet very little effort is spent examining how students actually use the software or how learning outcomes are related to system design. In a study involving direct observation of university students as they used pedagogical software, it was found that students develop their own strategies for learning with software and that these strategies are not necessarily those predicted by software designers and educators. Systematic field observation led to a more comprehensive view of how students were interacting with the software.

A rapid method for illustrating features in both coding and non-coding regions of a genome

A bitmap display of the Fourier spectra has been developed which allows convenient whole chromosome scanning for genes and other features. Use of a limited sliding window gives rapid visualization and localization of coding regions in the chromosomes, as well as non-coding features such as repetitive DNA. The method works particularly well on organisms with a skewed base composition, to provide an overview of genomic features.

Replication of epistatic DNA loci in two case-control GWAS studies using OPE algorithm

Background One of the limiting factors of current genome-wide association studies (GWAS) is the inability of current methods to comprehensively examine SNP interactions for a reasonable sized dataset. It is hypothesised that this limitation is one of the reasons that GWAS studies have not been able to have a greater impact [1,2]. Many current methods for handling interactions are computationally expensive and do not scale to entire studies. Those methods that do scale often achieve this by pruning their datasets in some manner. This is commonly done by considering only those SNPs that show strong marginal effects, despite the fact that a strongly interacting pair may consist of SNPs with low effects individually.

Supporting a diverse group of casual tutors and demonstrators: one size doesn't fit all

A trainiigandsupportprogramfor casualstatlinvolvedin undergraduate teaching in the Department of Computer Science at The University of hfelbourne has evolved over several years. The program was axpanded in 1997, when its major components were a large group induction session at the start of the year and small group discussion sessions midway through each semester. Overall, casual staif, most of whom are students in the department, reported that the various components of the program were useful. Differences were noted, however, across d.iEerentcohorts of casual stail We report our iindings, speculate on the reasons for the diiYerences found, and describe changes designed to make the program more inclusive in the future.

Teaching AI algorithms using animations reinforced by interactive exercises

This paper describes our experience using laboratories in the teaching of an undergraduate subject in artificial intelligence (AI). The presentation of key AI algorithms in lectures was replaced by a highly structured set of exercises undertaken by students in supervised laboratory sessions. The exercises, which were the students’ first contact with the algorithms, used a graphic animation of the algorithm, followed by active problem solving using a computer implementation of the same algorithm. As an environment for introducing students to new material, this laboratorybased approach encouraged the students to engage deeply with the material from the start. The sessions were very popular as evidenced by responses from student surveys, and anecdotal evidence suggests that the material was learned better. We suggest that a similar approach may be effective in other areas of computer science and in other disciplines.