Detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data

In this paper we discuss the detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data. The sensing units contained either lipase or glucose oxidase immobilized in layer-by-layer (LbL) films deposited onto interdigitated electrodes. The optimization consisted in identifying which part of the electrical response and combination of sensing units yielded the best distinguishing ability. It is shown that complete separation can be obtained for a range of concentrations of glucose and triglyceride when the interactive document map (IDMAP) technique is used to project the data into a two-dimensional plot. Most importantly, the optimization procedure can be extended to other types of biosensors, thus increasing the versatility of analysis provided by tailored molecular architectures exploited with various detection principles. (C) 2012 Elsevier B.V. All rights reserved.

GEDI: a user-friendly toolbox for analysis of large-scale gene expression data

Abstract Background Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, and software implementations that require advanced computer programming skills. Researchers from other areas may experience difficulties when they attempting to use those methods in their research. Here we present an user-friendly toolbox which allows large-scale gene expression analysis to be carried out by biomedical researchers with limited programming skills. Results Here, we introduce an user-friendly toolbox called GEDI (Gene Expression Data Interpreter), an extensible, open-source, and freely-available tool that we believe will be useful to a wide range of laboratories, and to researchers with no background in Mathematics and Computer Science, allowing them to analyze their own data by applying both classical and advanced approaches developed and recently published by Fujita et al. Conclusion GEDI is an integrated user-friendly viewer that combines the state of the art SVR, DVAR and SVAR algorithms, previously developed by us. It facilitates the application of SVR, DVAR and SVAR, further than the mathematical formulas present in the corresponding publications, and allows one to better understand the results by means of available visualizations. Both running the statistical methods and visualizing the results are carried out within the graphical user interface, rendering these algorithms accessible to the broad community of researchers in Molecular Biology.

Seeing beyond reading: a survey on visual text analytics

We review recent visualization techniques aimed at supporting tasks that require the analysis of text documents, from approaches targeted at visually summarizing the relevant content of a single document to those aimed at assisting exploratory investigation of whole collections of documents.Techniques are organized considering their target input materialeither single texts or collections of textsand their focus, which may be at displaying content, emphasizing relevant relationships, highlighting the temporal evolution of a document or collection, or helping users to handle results from a query posed to a search engine.We describe the approaches adopted by distinct techniques and briefly review the strategies they employ to obtain meaningful text models, discuss how they extract the information required to produce representative visualizations, the tasks they intend to support and the interaction issues involved, and strengths and limitations. Finally, we show a summary of techniques, highlighting their goals and distinguishing characteristics. We also briefly discuss some open problems and research directions in the fields of visual text mining and text analytics.

A Simpler and More Accurate AUTO-HDS Framework for Clustering and Visualization of Biological Data

In [1], the authors proposed a framework for automated clustering and visualization of biological data sets named AUTO-HDS. This letter is intended to complement that framework by showing that it is possible to get rid of a user-defined parameter in a way that the clustering stage can be implemented more accurately while having reduced computational complexity

Identical sequence patterns in the ends of exons and introns of human protein-coding genes

Intron splicing is one of the most important steps involved in the maturation process of a pre-mRNA. Although the sequence profiles around the splice sites have been studied extensively, the levels of sequence identity between the exonic sequences preceding the donor sites and the intronic sequences preceding the acceptor sites has not been examined as thoroughly. In this study we investigated identity patterns between the last 15 nucleotides of the exonic sequence preceding the 5' splice site and the intronic sequence preceding the 3' splice site in a set of human protein-coding genes that do not exhibit intron retention. We found that almost 60% of consecutive exons and introns in human protein-coding genes share at least two identical nucleotides at their 3' ends and, on average, the sequence identity length is 2.47 nucleotides. Based on our findings we conclude that the 3' ends of exons and introns tend to have longer identical sequences within a gene than when being taken from different genes. Our results hold even if the pairs are non-consecutive in the transcription order. (C) 2012 Elsevier Ltd. All rights reserved.

Flow Visualization with Quantified Spatial and Temporal Errors Using Edge Maps

Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Traditional analysis and visualization techniques rely primarily on computing streamlines through numerical integration. The inherent numerical errors of such approaches are usually ignored, leading to inconsistencies that cause unreliable visualizations and can ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with maps from the triangle boundaries to themselves. This representation, called edge maps, permits a concise description of flow behaviors and is equivalent to computing all possible streamlines at a user defined error threshold. Independent of this error streamlines computed using edge maps are guaranteed to be consistent up to floating point precision, enabling the stable extraction of features such as the topological skeleton. Furthermore, our representation explicitly stores spatial and temporal errors which we use to produce more informative visualizations. This work describes the construction of edge maps, the error quantification, and a refinement procedure to adhere to a user defined error bound. Finally, we introduce new visualizations using the additional information provided by edge maps to indicate the uncertainty involved in computing streamlines and topological structures.

Modeling and Identification of an Open-frame Underwater Vehicle: The Yaw Motion Dynamics

A semi-autonomous unmanned underwater vehicle (UUV), named LAURS, is being developed at the Laboratory of Sensors and Actuators at the University of Sao Paulo. The vehicle has been designed to provide inspection and intervention capabilities in specific missions of deep water oil fields. In this work, a method of modeling and identification of yaw motion dynamic system model of an open-frame underwater vehicle is presented. Using an on-board low cost magnetic compass sensor the method is based on the utilization of an uncoupled 1-DOF (degree of freedom) dynamic system equation and the application of the integral method which is the classical least squares algorithm applied to the integral form of the dynamic system equations. Experimental trials with the actual vehicle have been performed in a test tank and diving pool. During these experiments, thrusters responsible for yaw motion are driven by sinusoidal voltage signal profiles. An assessment of the feasibility of the method reveals that estimated dynamic system models are more reliable when considering slow and small sinusoidal voltage signal profiles, i.e. with larger periods and with relatively small amplitude and offset.

Modelling shared attention through relational reinforcement learning

Shared attention is a type of communication very important among human beings. It is sometimes reserved for the more complex form of communication being constituted by a sequence of four steps: mutual gaze, gaze following, imperative pointing and declarative pointing. Some approaches have been proposed in Human-Robot Interaction area to solve part of shared attention process, that is, the most of works proposed try to solve the first two steps. Models based on temporal difference, neural networks, probabilistic and reinforcement learning are methods used in several works. In this article, we are presenting a robotic architecture that provides a robot or agent, the capacity of learning mutual gaze, gaze following and declarative pointing using a robotic head interacting with a caregiver. Three learning methods have been incorporated to this architecture and a comparison of their performance has been done to find the most adequate to be used in real experiment. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human in a controlled environment. The experimental results show that the robotic head is able to produce appropriate behavior and to learn from sociable interaction.

Topology Verification for Isosurface Extraction

The broad goals of verifiable visualization rely on correct algorithmic implementations. We extend a framework for verification of isosurfacing implementations to check topological properties. Specifically, we use stratified Morse theory and digital topology to design algorithms which verify topological invariants. Our extended framework reveals unexpected behavior and coding mistakes in popular publicly available isosurface codes.

An algorithm for the strip packing problem using collision free region and exact fitting placement

The irregular shape packing problem is approached. The container has a fixed width and an open dimension to be minimized. The proposed algorithm constructively creates the solution using an ordered list of items and a placement heuristic. Simulated annealing is the adopted metaheuristic to solve the optimization problem. A two-level algorithm is used to minimize the open dimension of the container. To ensure feasible layouts, the concept of collision free region is used. A collision free region represents all possible translations for an item to be placed and may be degenerated. For a moving item, the proposed placement heuristic detects the presence of exact fits (when the item is fully constrained by its surroundings) and exact slides (when the item position is constrained in all but one direction). The relevance of these positions is analyzed and a new placement heuristic is proposed. Computational comparisons on benchmark problems show that the proposed algorithm generated highly competitive solutions. Moreover, our algorithm updated some best known results. (C) 2012 Elsevier Ltd. All rights reserved.

SMAC multi-hop mesh routing protocol using IEEE 802.15.4

This paper discusses some aspects related to Wireless Sensor Networks over the IEEE 802.15.4 standard, and proposes, for the very first time, a mesh network topology with geographic routing integrated to the open Freescale protocol (SMAC - Simple Medium Access Control). For this is proposed the SMAC routing protocol. Before this work the SMAC protocol was suitable to perform one hop communications only. However, with the developed mechanisms, it is possible to use multi-hop communication. Performance results from the implemented protocol are presented and analyzed in order to define important requirements for wireless sensor networks, such as robustness, self-healing property and low latency. (c) 2011 Elsevier Ltd. All rights reserved.

SOAP processing performance and enhancement

The web services (WS) technology provides a comprehensive solution for representing, discovering, and invoking services in a wide variety of environments, including Service Oriented Architectures (SOA) and grid computing systems. At the core of WS technology lie a number of XML-based standards, such as the Simple Object Access Protocol (SOAP), that have successfully ensured WS extensibility, transparency, and interoperability. Nonetheless, there is an increasing demand to enhance WS performance, which is severely impaired by XML's verbosity. SOAP communications produce considerable network traffic, making them unfit for distributed, loosely coupled, and heterogeneous computing environments such as the open Internet. Also, they introduce higher latency and processing delays than other technologies, like Java RMI and CORBA. WS research has recently focused on SOAP performance enhancement. Many approaches build on the observation that SOAP message exchange usually involves highly similar messages (those created by the same implementation usually have the same structure, and those sent from a server to multiple clients tend to show similarities in structure and content). Similarity evaluation and differential encoding have thus emerged as SOAP performance enhancement techniques. The main idea is to identify the common parts of SOAP messages, to be processed only once, avoiding a large amount of overhead. Other approaches investigate nontraditional processor architectures, including micro-and macrolevel parallel processing solutions, so as to further increase the processing rates of SOAP/XML software toolkits. This survey paper provides a concise, yet comprehensive review of the research efforts aimed at SOAP performance enhancement. A unified view of the problem is provided, covering almost every phase of SOAP processing, ranging over message parsing, serialization, deserialization, compression, multicasting, security evaluation, and data/instruction-level processing.

Class-specific metrics for multidimensional data projection applied to CBIR

Content-based image retrieval is still a challenging issue due to the inherent complexity of images and choice of the most discriminant descriptors. Recent developments in the field have introduced multidimensional projections to burst accuracy in the retrieval process, but many issues such as introduction of pattern recognition tasks and deeper user intervention to assist the process of choosing the most discriminant features still remain unaddressed. In this paper, we present a novel framework to CBIR that combines pattern recognition tasks, class-specific metrics, and multidimensional projection to devise an effective and interactive image retrieval system. User interaction plays an essential role in the computation of the final multidimensional projection from which image retrieval will be attained. Results have shown that the proposed approach outperforms existing methods, turning out to be a very attractive alternative for managing image data sets.

Dynamic Features for Iris Recognition

The human eye is sensitive to visible light. Increasing illumination on the eye causes the pupil of the eye to contract, while decreasing illumination causes the pupil to dilate. Visible light causes specular reflections inside the iris ring. On the other hand, the human retina is less sensitive to near infra-red (NIR) radiation in the wavelength range from 800 nm to 1400 nm, but iris detail can still be imaged with NIR illumination. In order to measure the dynamic movement of the human pupil and iris while keeping the light-induced reflexes from affecting the quality of the digitalized image, this paper describes a device based on the consensual reflex. This biological phenomenon contracts and dilates the two pupils synchronously when illuminating one of the eyes by visible light. In this paper, we propose to capture images of the pupil of one eye using NIR illumination while illuminating the other eye using a visible-light pulse. This new approach extracts iris features called "dynamic features (DFs)." This innovative methodology proposes the extraction of information about the way the human eye reacts to light, and to use such information for biometric recognition purposes. The results demonstrate that these features are discriminating features, and, even using the Euclidean distance measure, an average accuracy of recognition of 99.1% was obtained. The proposed methodology has the potential to be "fraud-proof," because these DFs can only be extracted from living irises.

A survey of evolutionary algorithms for decision-tree induction

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.