On the use of analytical expressions for the voltage distribution to analyze intracellular recordings

Different analytical expressions for the membrane potential distribution of membranes subject to synaptic noise have been proposed and can be very helpful in analyzing experimental data. However, all of these expressions are either approximations or limit cases, and it is not clear how they compare and which expression should be used in a given situation. In this note, we provide a comparison of the different approximations available, with an aim of delineating which expression is most suitable for analyzing experimental data.     

EvoMiner: frequent subtree mining in phylogenetic databases

The problem of mining collections of trees to identify common patterns, called frequent subtrees (FSTs), arises often when trying to interpret the results of phylogenetic analysis. FST mining generalizes the well-known maximum agreement subtree problem. Here we present EvoMiner, a new algorithm for mining frequent subtrees in collections of phylogenetic trees. EvoMiner is an Apriori-like levelwise method, which uses a novel phylogeny-specific constant-time candidate generation scheme, an efficient fingerprinting-based technique for downward closure, and a lowest-common-ancestor-based support counting step that requires neither costly subtree operations nor database traversal. Our algorithm achieves speedups of up to 100 times or more over Phylominer, the current state-of-the-art algorithm for mining phylogenetic trees. EvoMiner can also work in depth-first enumeration mode to use less memory at the expense of speed. We demonstrate the utility of FST mining as a way to extract meaningful phylogenetic information from collections of trees when compared to maximum agreement subtrees and majority-rule trees—two commonly used approaches in phylogenetic analysis for extracting consensus information from a collection of trees over a common leaf set.     

Involving the user Through Library 2.0

Libraries, as we know them today, can be defined by the term Library 1.0. This defines the way resources are kept on shelves or at a computer behind a login. These resources can be taken from a shelf, checked out by the library staff, taken home for a certain length of time and absorbed, and then returned to the library for someone else to avail of. Library 1.0 is a one-directional service that takes people to the information they require. Library 2.0 – or L2 as it is now more commonly addressed as – aims to take the information to the people by bringing the library service to the Internet and getting the users more involved by encouraging feedback participation. This paper presents an overview of Library 2.0 and introduces web 2.0 concepts.     

Computational mereology: A study of part-of relations for multimedia indexing

How to index or retrieve multimedia objects is by no means obvious, because the computer can retrieve right multimedia material only if it reasons about its contents. We show that it is possible to write formal specifications of this reasoning process using set theory and mereology. We discuss the theoretical consequences of trying to use mereology and set theory for multimedia indexing and retrieval. We re-examine the roles of mereology and set theory in knowledge representation. We conclude that both commonsense set theories and mereologies should play the role of constraining databases of arbitrary multimedia objects, e.g. video clips. But although both should be viewed as database constraints, we argue that part-of hierarchies should be used to encode relatively permanent background knowledge, elsewhere names thereferential level, while member-of hierarchies should describe arbitrary multimedia records. We also propose a language and a set of axioms, SetNM, for natural mereologies with sets. A multimedia indexing system can then be viewed as a particular SetNM theory.     

Toward Non Commutative Numerical Analysis: High Order Integration in Time

We review some methods for high precision time integration: it is not easy to ensure stability, precision and numerical efficiency at the same time. Operator splitting—when it works—can be a good way to satisfy all these constraints; in some cases, the order of the splitting schemes can be enhanced by extrapolation; nevertheless, the applicability of splitting is limited due to non commutativity. As an alternative to splitting, we introduce preconditioned Runge–Kutta (PRK) schemes: the preconditioning is included in the scheme, instead of being put aside for implementation. Examples of PRK schemes are given including the extrapolation of the residual smoothing scheme, and sufficient conditions for stability are described.     

Statistical analysis of manual segmentations of structures in medical images

The problem of extracting anatomical structures from medical images is both very important and difficult. In this paper we are motivated by a new paradigm in medical image segmentation, termed Citizen Science, which involves a volunteer effort from multiple, possibly non-expert, human participants. These contributors observe 2D images and generate their estimates of anatomical boundaries in the form of planar closed curves. The challenge, of course, is to combine these different estimates in a coherent fashion and to develop an overall estimate of the underlying structure. Treating these curves as random samples, we use statistical shape theory to generate joint inferences and analyze this data generated by the citizen scientists. The specific goals in this analysis are: (1) to find a robust estimate of the representative curve that provides an overall segmentation, (2) to quantify the level of agreement between segmentations, both globally (full contours) and locally (parts of contours), and (3) to automatically detect outliers and help reduce their influence in the estimation. We demonstrate these ideas using a number of artificial examples and real applications in medical imaging, and summarize their potential use in future scenarios.     

Distributed workflow mapping algorithm for maximized reliability under end-to-end delay constraint

A distributed scientific workflow mapping algorithm for maximized reliability under certain end-to-end delay (EED) bound is proposed. It is studied in a heterogeneous distributed computing environment, where computing node and communication link failures are inevitable. The mapping decision and the stored table information is distributed among various nodes in order to achieve scalability and robustness, which are especially important for large-scale distributed systems. This Distributed Reliability Maximization workflow mapping algorithm under End-to-end Delay constraint (dis-DRMED) considers both the maximum reliability and the minimum EED objectives in a two-step procedure. In the first step, a mapping algorithm combining iterative Critical Path search and Layer-based priority assigning techniques (CPL) is adopted to minimize the EED by focusing on the optimal allocation of tasks on the critical path. In the second step, tasks on noncritical paths are remapped to improve the overall execution reliability. Simulation results under various system setups demonstrated that dis-DRMED achieved considerably higher reliability values under the same EED constraint compared with some representative workflow mapping algorithms.     

Detecting image orientation based on low-level visual content

Accurately and automatically detecting image orientation is of great importance in intelligent image processing. In this paper, we present automatic image orientation detection algorithms based on both the luminance (structural) and chrominance (color) low-level content features. The statistical learning support vector machines (SVMs) are used in our approach as the classifiers. The different sources of the extracted image features, as well as the binary classification nature of SVM, require our system to be able to integrate the outputs from multiple classifiers. Both static combiner (averaging) and trainable combiner (also based on SVMs) are proposed and evaluated in this work. Furthermore, two rejection options (regular and re-enforced ambiguity rejections) are employed to improve orientation detection accuracy by sieving out images with low confidence values during the classification. Large amounts of experiments have been conducted on a database of more than 14,000 images to validate our approaches. Discussions and future directions for this work are also addressed at the end of the paper.     

Brain activity differentiates face and object processing in 6-month-old infants.

Event-related potentials were used to determine whether infants, like adults, show differences in spatial and temporal characteristics of brain activation during face and object recognition. Three aspects of visual processing were identified: (a) differentiation of face vs. object (P400 at occipital electrode was shorter latency for faces), (b) recognition of familiar identity (Nc, or negative component, at frontotemporal electrodes [FTEs] was of larger amplitude for familiar stimuli), and (c) encoding novelty (slow wave at FTEs was larger for unfamiliar stimuli). The topography of the Nc was influenced by category type: Effects of familiarity were limited to the midline and right anterior temporal electrodes for faces but extended to all temporal electrodes for objects. Results show that infants' experience with specific examples within categories and their general category knowledge influence the neural correlates of visual processing. (PsycINFO Database Record (c) 2010 APA, all rights reserved)