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Automated validation of polymerase chain reactions using amplicon melting curves.

PCR, the polymerase chain reaction, is a fundamental tool of molecular biology. Quantitative PCR is the gold-standard methodology for determination of DNA copy numbers, quantitating transcription, and numerous other applications. A major barrier to large-scale application of PCR for quantitative genomic analyses is the current requirement for manual validation of individual PCR reactions to ensure generation of a single product. This typically requires visual inspection either of gel electrophoreses or temperature dissociation ("melting") curves of individual PCR reactions - a time-consuming and costly process. Here we describe a robust computational solution to this fundamental problem. Using a training set of 10,080 reactions comprising multiple quantitative PCR reactions from each of 1,728 unique human genomic amplicons, we developed a support vector machine classifier capable of discriminating single-product PCR reactions with better than 99% accuracy. This approach has broad utility, and eliminates a major bottleneck to widespread application of PCR for high-throughput genomic applications.

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Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy.

A fully automated approach is presented for robust detection and classification of changes in longitudinal time-series of color retinal fundus images of diabetic retinopathy. The method is robust to: 1) spatial variations in illumination resulting from instrument limitations and changes both within, and between patient visits; 2) imaging artifacts such as dust particles; 3) outliers in the training data; 4) segmentation and alignment errors. Robustness to illumination variation is achieved by a novel iterative algorithm to estimate the reflectance of the retina exploiting automatically extracted segmentations of the retinal vasculature, optic disk, fovea, and pathologies. Robustness to dust artifacts is achieved by exploiting their spectral characteristics, enabling application to film-based, as well as digital imaging systems. False changes from alignment errors are minimized by subpixel accuracy registration using a 12-parameter transformation that accounts for unknown retinal curvature and camera parameters. Bayesian detection and classification algorithms are used to generate a color-coded output that is readily inspected. A multiobserver validation on 43 image pairs from 22 eyes involving nonproliferative and proliferative diabetic retinopathies, showed a 97% change detection rate, a 3% miss rate, and a 10% false alarm rate. The performance in correctly classifying the changes was 99.3%. A self-consistency metric, and an error factor were developed to measure performance over more than two periods. The average self consistency was 94% and the error factor was 0.06%. Although this study focuses on diabetic changes, the proposed techniques have broader applicability in ophthalmology.

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Description of a computerized adverse drug event monitor using a hospital information system.

To improve the detection and characterization of adverse drug events (ADEs) in hospitalized patients, a computerized adverse drug event monitor was developed. Computer programs were written to allow for voluntary as well as automated detection of adverse drug events using the HELP hospital information system, a large integrated hospital database containing computerized patient medical records and a knowledge base allowing for automated medical decisions. Programs were created to allow simple computer entry of potential adverse drug events by physicians, pharmacists, and nurses. Automated detection of potential adverse drug events relied on signals such as sudden medication stop orders, "antidote" orders, and selected abnormal laboratory values. Each day a list of all potential adverse drug events from these sources was generated and a pharmacist reviewed the medical records and interviewed healthcare personnel associated with patients identified as having potential adverse drug events. This process allowed for characterization of the event, causality assessment, and follow-up of the resulting clinical course by the pharmacist. The permanent storage of these results in the computerized patient medical record permits their future retrieval to prevent adverse drug events during subsequent hospital care. The authors conclude that fully integrated hospital systems will permit the further development and evaluation of computer-assisted methods for the detection of adverse drug events in hospitalized patients.

Adverse Drug Reaction Reporting Systems↗

Development of a controlled medical terminology: knowledge acquisition and knowledge representation.

The creation of controlled medical terminologies is a central challenge in the development of electronic patient records. In the T-Helper patient-record system, designed for the care of patients with HIV disease, the IVORY module allows health-care workers to compose textual progress notes by making selections from menus generated automatically from a controlled medical terminology. Construction of this IVORY terminology required extensive design sessions with a team of computer scientists and an expert physician. Refinement of the terminology was only possible when the design team could envision how the completed T-Helper system would be used in the context of clinical practice. Development of controlled medical terminologies is a significant problem in knowledge acquisition. Techniques used to acquire and represent clinical concepts for the purpose of building decision-support systems also are appropriate for the construction of controlled terminologies such as the one in T-Helper.

Artificial Intelligence↗

Contour tracking using a knowledge-based snake algorithm to construct three-dimensional pharyngeal bolus movement.

Videofluorography (VFG) using a barium-mixed bolus is in wide clinical use for assessing patients with swallowing disorders. VFG is usually done with both lateral (LA) and anterior-posterior (AP) views, most commonly in two separate sittings. A real-time, three-dimensional (3-D) representation of the evolution of a pharyngeal bolus and its volumetric information can potentially help clinicians analyze and visualize the kinematics of swallowing, dysphagia, and compensatory therapeutic strategies. Active contour models, also known as "Snakes," have been used to solve various image analysis and computer vision problems. We applied a Snake algorithm to automate in part the contour tracking and reconstruction of VFG images to visualize and quantitatively analyze the 3-D evolution of a pharyngeal bolus. To improve the accuracy of the Snake search, we provided the additional "knowledge" of the pharyngeal image itself, which served as an extra constraint to push the Snake curve toward the desired contour. VFG of pharyngeal bolus transport in a normal subject was recorded by using barium-mixed boluses (viscosity: 185 centipoise, density: 2.84 g/cc) with volumes of 5, 10, and 20 ml. The resulting LA and AP video images were digitally captured and matched frame by frame. The knowledge-based Snake search algorithm was used to generate Snake points to satisfy both internal (i.e., smoothness) and external (i.e., boundary fitting) constraints. Using these Snake points, we traced the 3-D bolus movement at each time instant, assuming elliptic geometry in the cross-section of the pharyngeal bolus. By concentrating the 3-D images for each time instant, we developed a 3-D movie representing pharyngeal bolus movement. The efficiency, reproducibility, and accuracy of this algorithm in tracing pharyngeal bolus boundaries and estimating front/tail velocities were assessed and found satisfactory. We conclude that 3-D pharyngeal bolus movement can be traced both accurately and efficiently by using a knowledge-based Snake search algorithm.

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On the analysis of single versus multiple channels of electromagnetic brain signals.

OBJECTIVE: When extracting information from electromagnetic (EM) brain function through recordings such as the electroencephalogram (EEG) it is often assumed that signal processing techniques must be applied to multiple simultaneous recordings in order to obtain useful results. However, sometimes only a single channel of EEG recording is available or desirable. In this paper we objectively assess a novel methodology which exploits only a single measurement channel to extract information of interest relatively independent of channel location (relative to the source of interest). METHODS: The method relies on a combination of a matrix of delay vectors constructed from the single channel measurement, along with constrained independent component analysis, which incorporates prior information into the process. MATERIALS: Here, we use synthetically generated seizure EEG, composed of real, normal multi-channel EEG onto which is superimposed synthetic epileptic "seizure-like" activity, at different signal-to-noise (SNR) levels, through an equivalent current dipole model. RESULTS: We show that the method can extract desired information from single channels with a reasonable accuracy even at very small SNR and from channels distant from the focus of the activity. This provides a powerful technique capable of extracting multiple sources underlying single channel recordings and will be useful in situations where only single channel EM recordings of brain function are desirable, such as would be the case in wearable or implantable recording devices.

Artificial Intelligence↗

Inversion recovery radial MRI with interleaved projection sets.

The radial trajectory has found applications in cardiac imaging because of its resilience to undersampling and motion artifacts. Recent work has shown that interleaved and weighted radial imaging can produce images with multiple contrasts from a single data set. This feature was investigated for inversion recovery imaging of scar using a radial technique. The 2D radial imaging method was modified to acquire quadruply interleaved projection sets within each acquisition window of the cardiac cycle. These data were reconstructed using k-space weightings that used a smaller segment of the acquisition window for the central k-space data, the determinant of image contrast. This method generates four images with different T1 weightings. The novel approach was compared with noninterleaved radial imaging, interleaved radial without weightings, and Cartesian imaging in simulations, phantoms, and seven subjects with clinical myocardial infarction. The results show that during a typical acquisition window after an inversion pulse, magnetization changes rapidly. The interleaved acquisition provided better image quality than the noninterleaved radial acquisition. Interleaving with weighting provided better quality when the inversion time (TI) was shorter than optimal; otherwise, interleaving without weighting was superior. These methods enable a radial trajectory to be employed in conjunction with preparation pulses for viability imaging.

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Metabolomic networks in plants: Transitions from pattern recognition to biological interpretation.

Nowadays techniques for non-targeted metabolite profiling allow for the generation of huge amounts of relevant data essential for the construction of dynamic metabolomic networks. Thus, metabolomics, besides transcriptomics or proteomics, provides a major tool for the characterization of postgenomic processes. In this work, we introduce comparative correlation analysis as a complementary approach to characterize the physiological states of various organs of diverse plant species with focus on specific participation of metabolites in different reaction networks. The correlations observed are induced by diminutive fluctuations in environmental conditions, which propagate through the system and induce specific patterns depending on the genomic background. In order to examine this hypothesis, numeric examples of such fluctuations are computed and compared with experimentally obtained metabolite data.

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Technical quality evaluation of EEG recording based on electroencephalographers' knowledge.

The aim of this study is to develop a technical quality evaluation system of electroencephalogram (EEG) recording in order to acquire technically satisfactory EEG records, which may contribute to the accuracy improvement of EEG interpretation. In our developed system, the evaluation of EEG recording comprises the detection of technical artifacts and physiological status, which indicates the recording status objectively. In addition, the caution signals to users are generated in the system according to the undesired status detected. The information displayed to users includes the updated EEG records and instant evaluation results. Two examples of evaluation results are introduced in this paper, illustrating unsatisfactory records and artifact free records, respectively. The experimental results are proposed to verify the effectiveness of the technical quality evaluation of EEG recording. The implementation of the technical quality evaluation of EEG recording is helpful to acquire technically satisfactory EEG records, which may improve the accuracy of results in both the visual and the automatic EEG interpretation, and ease the laborious work of EEG technicians in the recording progress.

Adult↗

Finding differentially expressed genes for pattern generation.

MOTIVATION: It is important to consider finding differentially expressed genes in a dataset of microarray experiments for pattern generation. RESULTS: We developed two methods which are mainly based on the q-values approach; the first is a direct extension of the q-values approach, while the second uses two approaches: q-values and maximum-likelihood. We present two algorithms for the second method, one for error minimization and the other for confidence bounding. Also, we show how the method called Patterns from Gene Expression (PaGE) (Grant et al., 2000) can benefit from q-values. Finally, we conducted some experiments to demonstrate the effectiveness of the proposed methods; experimental results on a selected dataset (BRCA1 vs BRCA2 tumor types) are provided. CONTACT: alhajj@cpsc.ucalgary.ca.

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START: an automated tool for serial analysis of chromatin occupancy data.

UNLABELLED: The serial analysis of chromatin occupancy technique (SACO) promises to become a widely used method for the unbiased genome-wide experimental identification of loci bound by a transcription factor of interest. We describe the first web-based automatic tool, termed sequence tag analysis and reporting tool (START), for processing SACO data generated by experiments performed for the yeast, fruit fly, mouse, rat or human genomes. The program uses as input sequences of inserts from a SACO library from which it extracts all SACO tags, maps them to genomic locations and annotates them. START returns detailed information about these tags including the genes, the genomic elements and the miRNA precursors found in their vicinity, and makes use of the MAPPER database to identify putative transcription factor binding sites located close to the tags. AVAILABILITY: The program is available at http://bio.chip.org/start/. SUPPLEMENTARY INFORMATION: SUPPLEMENTARY INFORMATION is available at http://bio.chip.org/doc/start/START-supplementary.pdf

Artificial Intelligence↗

Supervisory control of mobile sensor networks: math formulation, simulation, and implementation.

This paper uses a novel discrete-event controller (DEC) for the coordination of cooperating heterogeneous wireless sensor networks (WSNs) containing both unattended ground sensors (UGSs) and mobile sensor robots. The DEC sequences the most suitable tasks for each agent and assigns sensor resources according to the current perception of the environment. A matrix formulation makes this DEC particularly useful for WSN, where missions change and sensor agents may be added or may fail. WSN have peculiarities that complicate their supervisory control. Therefore, this paper introduces several new tools for DEC design and operation, including methods for generating the required supervisory matrices based on mission planning, methods for modifying the matrices in the event of failed nodes, or nodes entering the network, and a novel dynamic priority assignment weighting approach for selecting the most appropriate and useful sensors for a given mission task. The resulting DEC represents a complete dynamical description of the WSN system, which allows a fast programming of deployable WSN, a computer simulation analysis, and an efficient implementation. The DEC is actually implemented on an experimental wireless-sensor-network prototyping system. Both simulation and experimental results are presented to show the effectiveness and versatility of the developed control architecture.

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Beta edge strands in protein structure prediction and aggregation.

It is well established that recognition between exposed edges of beta-sheets is an important mode of protein-protein interaction and can have pathological consequences; for instance, it has been linked to the aggregation of proteins into a fibrillar structure, which is associated with a number of predominantly neurodegenerative disorders. A number of protective mechanisms have evolved in the edge strands of beta-sheets, preventing the aggregation and insolubility of most natural beta-sheet proteins. Such mechanisms are unfavorable in the interior of a beta-sheet. The problem of distinguishing edge strands from central strands based on sequence information alone is important in predicting residues and mutations likely to be involved in aggregation, and is also a first step in predicting folding topology. Here we report support vector machine (SVM) and decision tree methods developed to classify edge strands from central strands in a representative set of protein domains. Interestingly, rules generated by the decision tree method are in close agreement with our knowledge of protein structure and are potentially useful in a number of different biological applications. When trained on strands from proteins of known structure, using structure-based (Dictionary of Secondary Structure in Proteins) strand assignments, both methods achieved mean cross-validated, prediction accuracies of approximately 78%. These accuracies were reduced when strand assignments from secondary structure prediction were used. Further investigation of this effect revealed that it could be explained by a significant reduction in the accuracy of standard secondary structure prediction methods for edge strands, in comparison with central strands.

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Medical knowledge reengineering--converting major portions of the UMLS into a terminological knowledge base.

We describe a semi-automatic knowledge engineering approach for converting the human anatomy and pathology portion of the UMLS metathesaurus into a terminological knowledge base. Particular attention is paid to the proper representation of part-whole hierarchies, which complement taxonomic ones as a major hierarchy-forming principle for anatomical knowledge. Our approach consists of four steps. First, concept definitions are automatically generated from the metathesaurus, with LOOM as the target language. Second, integrity checking of the emerging taxonomic and partonomic hierarchies is automatically carried out by the terminological classifier. Third, terminological cycles and inconsistencies are manually eliminated and, in the last step, the knowledge base built this way is incrementally refined by a medical expert. Our experiments were run on a terminological knowledge base which is composed of 164,000 concepts and 76,000 relations. Empirical evidence for the lack of logical consistency, adequacy and improper granularity of the UMLS knowledge source is given, and finally, assessments of what kind of efforts are needed to render the formal target representation structures complete and empirically adequate.

Anatomy↗

Reduction of stimulus artifact in somatosensory evoked potentials: segmented versus subthreshold training.

A new approach to stimulus artifact cancellation is introduced, which attempts to model the process of stimulus artifact generation. This is done by training an estimator with multiple exemplars of the stimulus artifact at levels below the threshold of evoked response stimulation. Two estimators are formulated: one using a dynamic neural network and another using a linear estimator. The performance of these new approaches is compared to the segmented training approach, which has been previously demonstrated to be one of the most capable methods available. Performance assessment is carried out using a novel metric introduced in this paper, which focuses upon the relevant portion of the recorded waveform. The new cancellation schemes show distinct performance advantages over the segmented training approach.

Adult↗

Automated 3-D reconstruction of the surface of live early-stage amphibian embryos.

Although three-dimensional (3-D) reconstructions of the surfaces of live embyos are vital to understanding embryo development, morphogenetic tissue movements and other factors have prevented the automation of this task. Here, we report an integrated set of software algorithms that overcome these challenges, making it possible to completely automate the reconstruction of embryo surfaces and other textured surfaces from multiview images. The process involves: 1) building accurate point correspondences using a robust deformable template block matching algorithm; 2) removing outliers using fundamental matrix calculations in conjunction with a RANSAC algorithm; 3) generating 3-D point clouds using a bundle adjustment algorithm that includes camera position and distortion corrections; 4) meshing the point clouds into triangulated surfaces using a Tight Cocone algorithm that produces water tight models; 5) refining surfaces using midpoint insertion and Laplacian smoothing algorithms; and 6) repeating these steps until a measure of convergence G, the rms difference between successive reconstructions, is below a specified threshold. Reconstructions were made of 2.2-mm diameter, neurulation-stage axolotl (amphibian) embryos using 44 multiview images collected with a robotic microscope. A typical final model (sixth iteration) contained 3787 points and 7562 triangles and had an error measure of G = 5.9 microm.

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Using fuzzy sets to analyze putative correlates between age, blood type, gender and/or race with bacterial infection.

Previous studies have suggested that the demographic variables of age and blood type may serve as "risk factors" for infection by specific bacterial species. Since both demographic variables and bacterial species are defined using generally accepted parameters, they constitute highly suitable variables for the generation of a fuzzy logic program. A prospective study was therefore undertaken to examine the influence of age, blood type, gender and race on bacterial infection rates using a real database generated from 187 bacteremic patients admitted to Albert Einstein Medical Center. A fuzzy logic program was created using 155 randomly selected patients' data with four input (demographic variables) and four output classes (infections with "staphylococci", "streptococci", "Escherichia coli" or "non-E. coli gram negative rods (non-E.coli GNR)"). To see whether bacterial infection could be predicted based on demographic data alone, the program was tested using the remaining 32 patients' data. The program was able to correctly determine the bacterial output group of 27 of 32 randomly selected patients, giving an overall correlation of 84.38%. This study suggests that the direct correlation of demographic variables with a predisposition to bacterial infection allow the design of an intelligent medical system, which shows great future potential as a powerful diagnostic tool for all physicians.

Adult↗

Knowledge-based framework for hypothesis formation in biochemical networks.

MOTIVATION: The current knowledge about biochemical networks is largely incomplete. Thus biologists constantly need to revise or extend existing knowledge. The revision and/or extension are first formulated as theoretical hypotheses, then verified experimentally. Recently, biological data have been produced in great volumes and in diverse formats. It is a major challenge for biologists to process these data to reason about hypotheses. Many computer-aided systems have been developed to assist biologists in undertaking this challenge. The majority of the systems help in finding 'pattern' in data and leave the reasoning to biologists. A few systems have tried to automate the reasoning process of hypothesis formation. These systems generate hypotheses from a knowledge base and given observations. A main drawback of these knowledge-based systems is the knowledge representation formalisms they use. These formalisms are mostly monotonic and are now known to be not quite suitable for knowledge representation, especially in dealing with the inherently incomplete knowledge about biochemical networks. RESULTS: We present a knowledge-based framework for hypothesis formation for biochemical networks. The framework has been implemented by extending BioSigNet-RR-a knowledge based system that supports elaboration-tolerant representation and non-monotonic reasoning. Features of the extended system are illustrated by a case study of the p53 signal network. AVAILABILITY: http://www.biosignet.org

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