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At least 1,009 records · Page 56Linked to original sources
PubMed2000

The mutual influence of gaze and head orientation in the analysis of social attention direction.

Three experiments are reported that investigate the hypothesis that head orientation and gaze direction interact in the processing of another individual's direction of social attention. A Stroop-type interference paradigm was adopted, in which gaze and head cues were placed into conflict. In separate blocks of trials, participants were asked to make speeded keypress responses contingent on either the direction of gaze, or the orientation of the head displayed in a digitized photograph of a male face. In Experiments 1 and 2, head and gaze cues showed symmetrical interference effects. Compared with congruent arrangements, incongruent head cues slowed responses to gaze cues, and incongruent gaze cues slowed responses to head cues, suggesting that head and gaze are mutually influential in the analysis of social attention direction. This mutuality was also evident in a cross-modal version of the task (Experiment 3) where participants responded to spoken directional words whilst ignoring the head/gaze images. It is argued that these interference effects arise from the independent influences of gaze and head orientation on decisions concerning social attention direction.

Adult↗
PubMed2001

Statistical evaluation of PCDD/F emission data during solid waste combustion by fuzzy clustering techniques.

An advanced statistical analysis technique using the fuzzy clustering method was employed in this work, for the evaluation of PCDD/F emissions during solid waste combustion. In addition, this technique was applied for the assessment of the effect of an inhibitor (urea) on the toxic compound releases and on the various isomer distributions. Municipal solid wastes were combusted in a lab-scale reactor and the toxic gas emissions were measured at the unit outlet. Combustion tests of urea-fuel mixtures were classified in the same group, indicating that urea affected the formation mechanisms of toxic gases. Combustion tests of single fuel were not included in the same group. Furthermore, urea ability to modify the gas emissions pathways was not affected by the method of its addition to the fuel.

Air Pollution↗
PubMed1999

Application of a fuzzy pattern classifier to decision making in portal verification of radiotherapy.

With the large volume of electronic portal images acquired and stringent time constraints, it is no longer feasible to follow the convention whereby the radiation oncologist reviews and approves or rejects all portals. For that purpose we have developed a portal image classifier based on the fuzzy k-nearest neighbour (k-NN) algorithm. Each portal image is represented by a feature vector that consists of translational and rotational errors in the placement of radiation field borders that were measured in the portal image. Memberships in the acceptable portal class for the reference portal images within a training dataset were defined by a radiation oncologist expert. The fuzzy k-NN portal image classifier was trained and tested on a dataset of 328 portal images acquired during tangential irradiations of the breast. The memberships in the acceptable portal class produced by the fuzzy k-NN algorithm agreed very well with those defined by the expert. The linear correlation coefficient was equal to 0.89. Performance of the fuzzy k-NN classifier was also evaluated from the portal decision-making point of view using the measures of accuracy, sensitivity and specificity. The fuzzy k-NN portal classifier was capable of identifying almost all the truly unacceptable portals with an acceptably low false alarm rate.

Artificial Intelligence↗
PubMed2003

AI-guided parameter optimization in inverse treatment planning.

An artificial intelligence (AI)-guided inverse planning system was developed to optimize the combination of parameters in the objective function for intensity-modulated radiation therapy (IMRT). In this system, the empirical knowledge of inverse planning was formulated with fuzzy if-then rules, which then guide the parameter modification based on the on-line calculated dose. Three kinds of parameters (weighting factor, dose specification, and dose prescription) were automatically modified using the fuzzy inference system (FIS). The performance of the AI-guided inverse planning system (AIGIPS) was examined using the simulated and clinical examples. Preliminary results indicate that the expected dose distribution was automatically achieved using the AI-guided inverse planning system, with the complicated compromising between different parameters accomplished by the fuzzy inference technique. The AIGIPS provides a highly promising method to replace the current trial-and-error approach.

Algorithms↗
PubMed2004

Quantifying agitation in sedated ICU patients using heart rate and blood pressure.

Agitation is a significant problem in the intensive care unit (ICU), affecting 71% of sedated adult patients during 58% of ICU patient-days (Fraser and Riker 2001 NY Health-Syst. Pharm. 20 17-24). Subjective scale-based assessment methods are currently used to assess the level of patient agitation, but are limited in their accuracy and resolution. This research develops an objective agitation measurement method using heart rate variability (HRV), systolic blood pressure (BP) and blood pressure variability (BPV) data, processed by wavelet transforms and autoregressive signal processing. A fuzzy inference system (FIS) is developed to classify changes in these signals that correlate with observed patient agitation, and combine them into a final agitation level. Proof of concept clinical trials on 13 normal subjects and 5 ICU patients has been performed to verify the validity of this approach in comparison with agitation graded by nursing staff using the Riker sedation-agitation scale (SAS). Results show good correlation with medical staff assessment with no false positive results during calm periods. Clinically, this initial agitation measurement method promises the ability to consistently and objectively quantify patient agitation to enable better management of sedation and agitation through optimized drug delivery leading to reduced length of stay.

Artificial Intelligence↗
PubMed2005

An intelligent ventilation and oxygenation management system in neonatal intensive care using fuzzy trend template fitting.

This paper describes an open loop feedback intelligent system for neonatal intensive care management. The system provides a tool enabling the user to make the final decision to accept or reject the advice given. The system collects 18 parameters from the bedside monitor and ventilator using a Medical Information Bus (MIB) system. Comparison between the system's recommendations and seven clinical users (three doctors and four nurses) actions was made during monitoring of seven neonates with gestation age of 27-31 weeks for 124.13 h (mu=17.7329, sigma=5.3843 h, range=10.40-23.85 h). The validation process compared the recommendations triggered by the system with the user feedback (agree, disagree, wait). The system made 191 recommendations in total, 33 of which (17%) were for ventilation and 158 (83%) for oxygenation. The clinician agreed with the system ventilation decisions in 30 occasions (91%) and in 148 occasions for the system oxygenation decisions (94%). The overall percentage of the agreement between the system and the clinician was 93%.

Artificial Intelligence↗
PubMed2003

Research use of the AIDA www.2aida.org diabetes software simulation program: a review-part 1. decision support testing and neural network training.

The purpose of this two-part review is to overview research use of the AIDA diabetes software simulator. AIDA is a diabetes computer program that permits the interactive simulation of plasma insulin and blood glucose profiles for teaching, demonstration, and self-learning purposes. It has been made freely available, without charge, on the Internet as a noncommercial contribution to continuing diabetes education. Since its launch in 1996 over 300,000 visits have been logged at the main AIDA Website-www.2aida.org-and over 60,000 copies of the AIDA program have been downloaded free-of-charge. This review describes research projects and ventures, undertaken for the most part by other research workers in the diabetes computing field, that have made use of the freeware AIDA software. Relevant research work was identified in three main ways: (i) by personal (e-mail/written) communications from researchers, (ii) via the ISI Web of Science citation database to identify published articles that referred to AIDA-related papers, and (iii) via searches on the Internet. In a number of cases research students who had sought advice about AIDA, and diabetes computing in general, provided copies of their research dissertations/theses upon the completion of their projects. The two reviews highlight some of the many and varied research projects that have made use of the AIDA diabetes simulation software to date. A wide variety of diabetes computing topics have been addressed. In Part 1 of the review, these range from testing decision support prototypes to training artificial neural networks. In Part 2 of the review, issues surrounding dietary assessments, developing new diabetes models, and performance monitoring of closed-loop insulin delivery devices are considered. Overall, research projects making use of AIDA have been identified in Australia, Italy, South Korea, the United Kingdom, and the United States. These reviews confirm an unexpected but useful benefit of distributing medical software, like AIDA, for free via the Internet-demonstrating how it is possible to have a synergistic benefit with other researchers-facilitating their own research projects in related medical fields. The reviews highlight a variety of these projects that have benefited from the free availability of the AIDA diabetes software simulator. In a number of cases these other research projects simply would not have been possible without unrestricted access to the AIDA software and/or technical descriptions of its workings. In addition, some specific common themes begin to emerge from the research ventures that have been reviewed. These include the use of simulated blood glucose data from the AIDA program for preliminary computerlab-based testing of other decision support prototypes. Issues surrounding such use of simulated data for separate prototype testing are discussed further in Part 2 of the review.

Computer Simulation↗
PubMed1998

Transcription regulatory region analysis using signal detection and fuzzy clustering.

MOTIVATION: Presently available programs for the recognition of potential transcription factor binding sites in genomic sequences generally yield a huge amount of output. These output lists have to be filtered to obtain biologically significant elements, which is highly laborious work to be done manually. RESULTS: We developed a strategy for systematic verification and improvement of the underlying profiles, and for their contextual analysis by a fuzzy clustering approach using non-redundant libraries of search profiles as a prerequisite. AVAILABILITY: The tools mentioned in the paper are available upon request. CONTACT: ewi@gbf.de

Algorithms↗
PubMed2002

A novel approach to local reliability of sequence alignments.

MOTIVATION: The pairwise alignment of biological sequences obtained from an algorithm will in general contain both correct and incorrect parts. Hence, to allow for a valid interpretation of the alignment, the local trustworthiness of the alignment has to be quantified. RESULTS: We present a novel approach that attributes a reliability index to every pair of residues, including gapped regions, in the optimal alignment of two protein sequences. The method is based on a fuzzy recast of the dynamic programming algorithm for sequence alignment in terms of mean field annealing. An extensive evaluation with structural reference alignments not only shows that the probability for a pair of residues to be correctly aligned grows consistently with increasing reliability index, but moreover demonstrates that the value of the reliability index can directly be translated into an estimate of the probability for a correct alignment.

Algorithms↗
PubMed2002

Analysis of expression profile using fuzzy adaptive resonance theory.

MOTIVATION: It is well understood that the successful clustering of expression profiles give beneficial ideas to understand the functions of uncharacterized genes. In order to realize such a successful clustering, we investigate a clustering method based on adaptive resonance theory (ART) in this report. RESULTS: We apply Fuzzy ART as a clustering method for analyzing the time series expression data during sporulation of Saccharomyces cerevisiae. The clustering result by Fuzzy ART was compared with those by other clustering methods such as hierarchical clustering, k-means algorithm and self-organizing maps (SOMs). In terms of the mathematical validations, Fuzzy ART achieved the most reasonable clustering. We also verified the robustness of Fuzzy ART using noised data. Furthermore, we defined the correctness ratio of clustering, which is based on genes whose temporal expressions are characterized biologically. Using this definition, it was proved that the clustering ability of Fuzzy ART was superior to other clustering methods such as hierarchical clustering, k-means algorithm and SOMs. Finally, we validate the clustering results by Fuzzy ART in terms of biological functions and evidence. AVAILABILITY: The software is available at http//www.nubio.nagoya-u.ac.jp/proc/index.html

Algorithms↗
PubMed2003

Simulating complex traits influenced by genes with fuzzy-valued effects in pedigreed populations.

MOTIVATION: Methods involving fuzzy theory have been rarely applied to genetics. We present an open platform for experimentation with fuzzy numbers as a tool to represent imprecise phenotypes in genetic modeling. RESULTS: A C++ library for simulation of genetic information transmission is introduced. The study of genetic linkage was its first goal, though a design so general as possible has been meant. Fuzzy-valued phenotypes are handled by means of fuzzy numbers. AVAILABILITY: ftp://carleos.etsiig.uniovi.es/pub/falin ftp://fisher.ciencias.uniovi.es/pub/falin ftp://bellman.ciencias.uniovi.es/pub/falin Licensed under the GNU General Public License version 2 (see http://www.gnu.org/licenses/gpl.html).

Algorithms↗
PubMed2003

The fuzzy polynucleotide space: basic properties.

MOTIVATION: Any triplet codon may be regarded as a 12-dimensional fuzzy code. Sufficient information about a particular sequence may not be available in certain situations. The investigator will be confronted with imprecise sequences, yet want to make comparisons of sequences. Fuzzy polynucleotides can be compared by using geometrical interpretation of fuzzy sets as points in a hypercube. RESULTS: We introduce the space of fuzzy polynucleotides and a means of measuring dissimilitudes between them. We establish mathematical principles to measure dissimilarities between fuzzy polynucleotides and present several examples in this metric space. We calculate the frequencies of the nucleotides at the three base sites of a codon in the coding sequences of Escherichia coli K-12 and Mycobacterium tuberculosis H37Rv, and consider them as points in that fuzzy space. We compute the distance between the genomes of E.coli and M.tuberculosis.

Algorithms↗
PubMed2003

Sensitive pattern discovery with 'fuzzy' alignments of distantly related proteins.

MOTIVATION: Evolutionary comparison leads to efficient functional characterisation of hypothetical proteins. Here, our goal is to map specific sequence patterns to putative functional classes. The evolutionary signal stands out most clearly in a maximally diverse set of homologues. This diversity, however, leads to a number of technical difficulties. The targeted patterns-as gleaned from structure comparisons-are too sparse for statistically significant signals of sequence similarity and accurate multiple sequence alignment. RESULTS: We address this problem by a fuzzy alignment model, which probabilistically assigns residues to structurally equivalent positions (attributes) of the proteins. We then apply multivariate analysis to the 'attributes x proteins' matrix. The dimensionality of the space is reduced using non-negative matrix factorization. The method is general, fully automatic and works without assumptions about pattern density, minimum support, explicit multiple alignments, phylogenetic trees, etc. We demonstrate the discovery of biologically meaningful patterns in an extremely diverse superfamily related to urease.

Algorithms↗
PubMed2003

Fuzzy C-means method for clustering microarray data.

MOTIVATION: Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes. RESULTS: A major problem in applying the FCM method for clustering microarray data is the choice of the fuzziness parameter m. We show that the commonly used value m = 2 is not appropriate for some data sets, and that optimal values for m vary widely from one data set to another. We propose an empirical method, based on the distribution of distances between genes in a given data set, to determine an adequate value for m. By setting threshold levels for the membership values, genes which are tigthly associated to a given cluster can be selected. Using a yeast cell cycle data set as an example, we show that this selection increases the overall biological significance of the genes within the cluster. AVAILABILITY: Supplementary text and Matlab functions are available at http://www-igbmc.u-strasbg.fr/fcm/

Algorithms↗
PubMed2004

Prediction of protein subcellular locations using fuzzy k-NN method.

MOTIVATION: Protein localization data are a valuable information resource helpful in elucidating protein functions. It is highly desirable to predict a protein's subcellular locations automatically from its sequence. RESULTS: In this paper, fuzzy k-nearest neighbors (k-NN) algorithm has been introduced to predict proteins' subcellular locations from their dipeptide composition. The prediction is performed with a new data set derived from version 41.0 SWISS-PROT databank, the overall predictive accuracy about 80% has been achieved in a jackknife test. The result demonstrates the applicability of this relative simple method and possible improvement of prediction accuracy for the protein subcellular locations. We also applied this method to annotate six entirely sequenced proteomes, namely Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Oryza sativa, Arabidopsis thaliana and a subset of all human proteins. AVAILABILITY: Supplementary information and subcellular location annotations for eukaryotes are available at http://166.111.30.65/hying/fuzzy_loc.htm

Algorithms↗
PubMed2004

Fuzzy J-Means and VNS methods for clustering genes from microarray data.

MOTIVATION: In the interpretation of gene expression data from a group of microarray experiments that include samples from either different patients or conditions, special consideration must be given to the pleiotropic and epistatic roles of genes, as observed in the variation of gene coexpression patterns. Crisp clustering methods assign each gene to one cluster, thereby omitting information about the multiple roles of genes. RESULTS: Here, we present the application of a local search heuristic, Fuzzy J-Means, embedded into the variable neighborhood search metaheuristic for the clustering of microarray gene expression data. We show that for all the datasets studied this algorithm outperforms the standard Fuzzy C-Means heuristic. Different methods for the utilization of cluster membership information in determining gene coregulation are presented. The clustering and data analyses were performed on simulated datasets as well as experimental cDNA microarray data for breast cancer and human blood from the Stanford Microarray Database. AVAILABILITY: The source code of the clustering software (C programming language) is freely available from Nabil.Belacel@nrc-cnrc.gc.ca

Algorithms↗
PubMed2004

Extraction of fluorescent cell puncta by adaptive fuzzy segmentation.

MOTIVATION: The discrimination and measurement of fluorescent-labeled vesicles using microscopic analysis of fixed cells presents a challenge for biologists interested in quantifying the abundance, size and distribution of such vesicles in normal and abnormal cellular situations. In the specific application reported here, we were interested in quantifying changes to the population of a major organelle, the peroxisome, in cells from normal control patients and from patients with a defect in peroxisome biogenesis. In the latter, peroxisomes are present as larger vesicular structures with a more restricted cytoplasmic distribution. Existing image processing methods for extracting fluorescent cell puncta do not provide useful results and therefore, there is a need to develop some new approaches for dealing with such a task effectively. RESULTS: We present an effective implementation of the fuzzy c-means algorithm for extracting puncta (spots), representing fluorescent-labeled peroxisomes, which are subject to low contrast. We make use of the quadtree partition to enhance the fuzzy c-means based segmentation and to disregard regions which contain no target objects (peroxisomes) in order to minimize considerable time taken by the iterative process of the fuzzy c-means algorithm. We finally isolate touching peroxisomes by an aspect-ratio criterion. The proposed approach has been applied to extract peroxisomes contained in several sets of color images and the results are superior to those obtained from a number of standard techniques for spot extraction. AVAILABILITY: Image data and computer codes written in Matlab are available upon request from the first author.

Algorithms↗
PubMed2004

Reliability analysis of microarray data using fuzzy c-means and normal mixture modeling based classification methods.

MOTIVATION: A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. Therefore, the elimination of unreliable signal intensities will enhance reproducibility and reliability of gene expression ratios produced from microarray data. In this study, we applied fuzzy c-means (FCM) and normal mixture modeling (NMM) based classification methods to separate microarray data into reliable and unreliable signal intensity populations. RESULTS: We compared the results of FCM classification with those of classification based on NMM. Both approaches were validated against reference sets of biological data consisting of only true positives and true negatives. We observed that both methods performed equally well in terms of sensitivity and specificity. Although a comparison of the computation times indicated that the fuzzy approach is computationally more efficient, other considerations support the use of NMM for the reliability analysis of microarray data. AVAILABILITY: The classification approaches described in this paper and sample microarray data are available as Matlab( TM ) (The MathWorks Inc., Natick, MA) programs (mfiles) and text files, respectively, at http://rc.kfshrc.edu.sa/bssc/staff/MusaAsyali/Downloads.asp. The programs can be run/tested on many different computer platforms where Matlab is available. CONTACT: asyali@kfshrc.edu.sa.

Algorithms↗