Eric Harley

Inhibition of Nucleoside Transport

In many types of animal cells, the movement of nucleoside molecules across the plasma membrane is a specific, transporter-mediated process [1, 2]. The existence of the nucleoside transport mechanism has been recognized from the kinetic properties of nucleoside fluxes, but nucleoside transporter elements of the plasma membrane have not yet been visualized or isolated, and their properties have only been studied indirectly. The potent, tightly bound inhibitor of nucleoside transport, nitrobenzylthioinosine (NBMPR),1 has been a valuable probe of transporter function and biology, enabling, for example, quantitation of transporter elements on cells [3] and providing an approach to their isolation from plasma membrane preparations [4]. This chapter discusses the nucleoside transport-inhibitory properties of NBMPR, N6-nitrobenzyldeoxyadenosine, and certain of their congeners, and of dilazep, a coronary vasodilator.

Measurement and Inhibition of Membrane Transport of Adenosine

The effects of adenosine on a variety of physiological processes in cells and tissues have been widely interpreted in terms of regulatory roles for adenosine. These effects are mediated by the presence of adenosine (or certain related compounds) on extracellular adenosine receptors on the responsive cells. An understanding of such apparent regulatory actions of adenosine will require not only knowledge of the biochemical events linking receptor occupancy and the cellular response, but also an understanding of the source and delivery of adenosine molecules to receptors and of their clearance from the immediate vicinity of the receptors. Cellular utilization appears to be a principal means of clearing of extracellular adenosine from the vicinity of receptors. Because adenosine and other physiological nucleosides leave and enter cells mainly by way of nucleoside-specific transport mechanisms, transport is a primary step both in the formation and in the disposition of extracellular adenosine. This chapter is concerned with the measurement of adenosine transport in cell suspensions and with the potent inhibition of this process by several agents. Because of the emphasis of this volume on methodology, the material presented is selective rather than comprehensive.

Uniform integration of genome mapping data using intersection graphs

MOTIVATION The methods for analyzing overlap data are distinct from those for analyzing probe data, making integration of the two forms awkward. Conversion of overlap data to probe-like data elements would facilitate comparison and uniform integration of overlap data and probe data using software developed for analysis of STS data. RESULTS We show that overlap data can be effectively converted to probe-like data elements by extracting maximal sets of mutually overlapping clones. We call these sets virtual probes, since each set determines a site in the genome corresponding to the region which is common among the clones of the set. Finding the virtual probes is equivalent to finding the maximal cliques of a graph. We modify a known maximal-clique algorithm such that it finds all virtual probes in a large dataset within minutes. We illustrate the algorithm by converting fingerprint and Alu-PCR overlap data to virtual probes. The virtual probes are then analyzed using double-linkage intersection graphs and structure graphs to show that methods designed for STS data are also applicable to overlap data represented as virtual probes. Next we show that virtual probes can produce a uniform integration of different kinds of mapping data, in particular STS probe data and fingerprint and Alu-PCR overlap data. The integrated virtual probes produce longer double-linkage contigs than STS probes alone, and in conjunction with structure graphs they facilitate the identification and elimination of anomalies. Thus, the virtual-probe technique provides: (i) a new way to examine overlap data; (ii) a basis on which to compare overlap data and probe data using the same systems and standards; and (iii) a unique and useful way to uniformly integrate overlap data with probe data.

Revealing hidden interval graph structure in STS-content data

MOTIVATION STS-content data for genomic mapping contain numerous errors and anomalies resulting in cross-links among distant regions of the genome. Identification of contigs within the data is an important and difficult problem. RESULTS This paper introduces a graph algorithm which creates a simplified view of STS-content data. The shape of the resulting structure graph provides a quality check - coherent data produce a straight line, while anomalous data produce branches and loops. In the latter case, it is sometimes possible to disentangle the various paths into subsets of the data covering contiguous regions of the genome, i.e. contigs. These straight subgraphs can then be analyzed in standard ways to construct a physical map. A theoretical basis for the method is presented along with examples of its application to current STS data from human genome centers. AVAILABILITY Freely available on request.

Attempts to verify written English

The English language offers a complex and ambiguous grammar that is readily understood by its natural users, but at times can be difficult to grasp by beginners/learners, and no less, by machines. This paper discusses research and implementations of several techniques towards algorithmically analyzing and verifying the grammatical correctness of sentences in written English.

Estimation of the number of cliques in a random graph

This paper examines methods for predicting and estimating the number of maximal cliques in a random graph. A clique is a subgraph where each vertex is connected to every other vertex in the subgraph. A maximal clique is a clique which is not a proper subgraph of another clique. There are many algorithms that enumerate all maximal cliques in a graph, but since the task can take exponential time, there are practical limits on the size of the input. In this paper, we examine three methods that could be used to estimate the number of cliques in a random graph. One method is based on sampling, another on probability arguments and the third uses curve fitting. We compare the methods for accuracy and efficiency.

Tracking a Person with Pre-recorded Image Database and a Pan, Tilt, and Zoom Camera

This paper proposes a novel tracking strategy that can robustly track a person or other object within a fixed environment using a pan, tilt, and zoom camera with the help of a pre-recorded image database. We define a set called the Minimum Camera Parameter Settings (MCPS) which contains just enough camera states as required to survey the environment for the target. This set of states is used to facilitate tracking and segmentation. The idea is to store a background image of the environment for every camera state in MCPS, thus creating an image database. During tracking camera movements are restricted to states in MCPS. Scanning for the target and segmentation of the target from the background are simplified as each current image can be compared with the corresponding pre-recorded background image.

Classification and Generation of Grammatical Errors

The misuse of grammar is a common and natural nuisance, and a strategy for automatically detecting mistakes in grammatical syntax is warranted. This research defines and implements a unique approach that combines machine-learning and statistical natural language processing techniques. Several important methods are established: (1) the automated and systematic generation of grammatical errors and parallel error corpora; (2) the definition and extraction of over 150 features of a sentence; and (3) the application of various machine-learning classification algorithms on extracted feature data, in order to classify and predict the presence of grammatical errors in a sentence.

Protein structural class prediction using predicted secondary structure and hydropathy profile

Protein structural class prediction is a significant classification problem in the domain of bioinformatics. Knowledge of protein structural classes contributes to an understanding of protein folding patterns, and this has made research in predicting structural classes a major topic of interest. In this paper, some newly developed features extracted from secondary structure sequence and hydropathy sequence are used to classify proteins into one of the four major structural classes: all-α, all-β, α/β and α+β. The prediction accuracy using these features compares favourably with some existing successful methods. We use Support Vector Machines (SVM), since this learning method has well-known efficiency in solving this classification problem. On a standard dataset (25PDB), the proposed system has an overall accuracy of 89% with as few as 22 features, whereas the previous best performing method had an accuracy of 88% using 2510 features.

Detection function and its application in visual tracking

This paper introduces the concept of detection function for assessing recognition algorithms. A detection function specifies the probability that a particular recognition algorithm will detect the target, given the camera viewing direction and angle size and the target position. The detection function thus determines the region of space in which the target can be detected with high probability using certain specified camera parameters. For this reason, it provides a natural language for discussion of the task of tracking an object moving in 3D using a camera with adjustable pan, tilt, and zoom. Most previous studies on visual tracking involve the use of a camera with fixed viewing direction and viewing angle size. We advocate, however, an algorithm wherein these camera parameters are actively controlled to keep the target in the field of view and to maintain its image quality. In this paper we study geometrical issues related to the detection function and describe a novel tracking algorithm.