A deconvolution procedure for use in extracting information in average evoked response EEG signals.
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Audiometry↗
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Results for “information extraction”
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Information extraction from contralateral cues in the detection of signals of uncertain frequency.
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Auditory Perception↗
Optimal frequency ranges for extracting information on cardiovascular autonomic control from the blood pressure and pulse interval spectrograms in mice.
The analysis of blood pressure (BP) and heart rate (HR) variability by spectral methods has proven a useful tool in many animal species for the assessment of the vagal and sympathetic contributions to oscillations of BP and HR. Continuous BP measurements obtained in mice by telemetry were used to characterize the spectral bandwidths of autonomic relevance by using an approach with no a priori. The paradigm was based on the autonomic blockades obtained with conventional drugs (atropine, prazosin, atenolol). The spectral changes were estimated in all of the combinations of spectral bandwidths. The effect of hydralazine was also tested using the same systematic analysis, to detect the zones of sympathetic activation resulting reflexly from the vasodilatory action of the drug. Two zones of interest in the study of the autonomic control of BP and HR were observed. The first zone covered the 0.15-0.60 Hz range of the systolic BP spectrum and corresponds to the low-frequency zone (or Mayer waves). This zone reflects sympathetic control since the power spectral density of this zone was significantly reduced with alpha1-adrenoceptor blockade (prazosin), while it was significantly amplified as a result of a reflex sympathetic activation (hydralazine). The second zone covered the 2.5-5.0 Hz range of the pulse interval spectrum and corresponded to the high-frequency zone (respiratory sinus arrhythmia) under vagal control (blocked by atropine). These zones are recommended for testing the autonomic control of circulation in mice.
Animals↗
Haplotype parsing: methods for extracting information from human genetic variations.
While the shared consensus genetic sequence of our species contains a great deal of information about our common biology, there is also much to be learned from the subtle genetic variations across our species. These variations are believed to be generally of little or no direct functional significance and predominantly reflect the chance accumulation of small genetic changes since our emergence as a species. Therefore, they carry little useful information when observed in a single individual. When tallied across a whole population though, these chance mutations can teach us a great deal about our evolutionary history and the patterns of inheritance in particular individuals. In particular, frequently observed patterns of single nucleotide polymorphisms (SNPs) in a population can identify segments of chromosome that have been passed down largely intact through long stretches of our evolution. Finding these frequently conserved chromosomal segments, or haplotypes, and developing methods to identify haplotype patterns in particular individuals, will in turn help us to identify those particular segments that carry genetic factors influencing risk for many common human diseases. To make the best use of this data, we will need to develop new models for the encoding of information in genome variations--the "language of genetic variation"--and new algorithms for fitting datasets to those models. This article surveys past work by the author and colleagues on this problem, utilising computational methods for locating frequent patterns in haploid sequence data, and "parsing" sequences so as to optimally explain them given the knowledge of the general population structure. The author's recent work in this area has been compiled into a set of computational tools available at http://www-2.cs.cmu.edu/~russells/software/hapmotif.html.
Algorithms↗
A system designed to extract information on seizures.
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Electroencephalography↗
URINEX: URological INformation EXtraction; an automated history-taking system for urological diseases.
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Computers↗
Probabilistic inference in computer-aided screening for cervical cancer: an event covering approach to information extraction and decision rule formulation.
A pattern analysis approach called event covering is introduced and applied to the problem of determining the significance of measured morphological attributes of cervical cells in an automated screening process. The approach is shown to be effective in deciding which characteristics at the event (outcome) level are of little or no diagnostic value, and in formulating sets of predictive rules using the most pertinent ones.
Adult↗
Computational characterization of behavioral response of medaka (Oryzias latipes) treated with diazinon.
The behavior of indicator specimens in response to sub-lethal doses of toxic substances has been used to detect contamination in aquatic ecosystems. Changes in the movement behaviors of medaka (Oryzias latipes) were analyzed after being treated with diazinon at a concentration of 0.1 mg/l. The movement tracks of medaka were continuously recorded in two-dimension by a digital image processing system both before and after the treatments. Subsequently, two computational methods--two-dimensional fast Fourier transform (2D FFT) and self-organizing map (SOM), were implemented to extract information from the movement data. The differences in the shapes of the movement tracks before and after the treatments were clearly manifested through 2D FFT. The short-distance, irregular turnings in the movement tracks observed after the treatments in the time domain were characteristically transformed to circular or ellipsoidal patterns in the frequency domain. The amplitudes of 2D FFT were efficiently classified by SOM, demonstrating the effects of the different treatments. To evaluate the feasibility of information extraction by 2D FFT, SOM was similarly carried out on the parameters (speed, meander, stop duration, etc.) conventionally used for characterizing the movement tracks. 2D FFT was more efficient in information extraction from the movement data than the parameters. The 2D FFT and SOM were useful as computational methods for automatically detecting response behaviors of indicator specimens exposed to toxic chemicals in aquatic ecosystems.
Animals↗
Brain mechanisms for extracting spatial information from smell.
Forty years ago, von Békésy demonstrated that the spatial source of an odorant is determined by comparing input across nostrils, but it is unknown how this comparison is effected in the brain. To address this, we delivered odorants to the left or right of the nose, and contrasted olfactory left versus right localization with olfactory identification during brain imaging. We found nostril-specific responses in primary olfactory cortex that were predictive of the accuracy of left versus right localization, thus providing a neural substrate for the behavior described by von Békésy. Additionally, left versus right localization preferentially engaged a portion of the superior temporal gyrus previously implicated in visual and auditory localization, suggesting that localization information extracted from smell was then processed in a convergent brain system for spatial representation of multisensory inputs.
Adult↗
Stereology in the extraction of information from images.
Some important aspects for information extraction by stereology from images in surgical and experimental pathology are discussed. The relationship between stereology and morphometry is briefly discussed, with the most important conditions for stereologic analysis in pathology pointed out. The possibilities, limits and problems inherent in stereologic and morphometric analysis in pathology are explained in two examples: so-called "small airways disease" and tight junctions of hepatocytes during physiologic choleresis.
Adult↗
Egomotion and relative depth map from optical flow.
When an observer moves in a 3D world, optical flow fields are generated on his retina. We argue that such an observer can in principle compute the parameters of his egomotion, and following this, the relative depth map of the stationary environment solely from the instantaneous positional velocity fields (IPVF). Moreover, we argue that in the stationary world, this analysis can be done locally, and is not dependent on global properties of the optical flow under the imposed ocnstraints (smoothness of the egomotion path, rigidity of objects, temporal continuity of perception). To investigate the method, and to analyze its performance, a computer model has been constructed whch simulates an observer moving through a 3D world of stationary rectangular planes at different depths and orientations. The results suggest that the method offers a reasonable and computationally feasible means of extracting information about egomotion and surface layout from optical flows, under certain circumstances. We discuss some issues related to extending the analysis to the case of a rigid world of moving objects, and some issues related to the status of information extractable from optical flows with respect to other sources of information.
Computers↗
LSAT: learning about alternative transcripts in MEDLINE.
MOTIVATION: Generation of alternative transcripts from the same gene is an important biological event due to their contribution in creating functional diversity in eukaryotes. In this work, we choose the task of extracting information around this complex topic using a two-step procedure involving machine learning and information extraction. RESULTS: In the first step, we trained a classifier that inductively learns to identify sentences about physiological transcript diversity from the MEDLINE abstracts. Using a large hand-built corpus, we compared the sentence classification performance of various text categorization methods. Support vector machines (SVMs) followed by the maximum entropy classifier outperformed other methods for the sentence classification task. The SVM with the radial basis function kernel and optimized parameters achieved Fbeta-measure of 91% during the 4-fold cross validation and of 74% when applied to all sentences in more than 12 million abstracts of MEDLINE. In the second step, we identified eight frequently present semantic categories in the sentences and performed a limited amount of semantic role labeling. The role labeling step also achieved very high Fbeta-measure for all eight categories. AVAILABILITY: The results of our two-step procedure are summarized in the LSAT database of alternative transcripts. LSAT is available at http://www.bork.embl.de/LSAT CONTACT: shah@embl.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Benchmarking↗
Measuring trauma and health status in refugees: a critical review.
CONTEXT: Refugees experience multiple traumatic events and have significant associated health problems, but data about refugee trauma and health status are often conflicting and difficult to interpret. OBJECTIVES: To assess the characteristics of the literature on refugee trauma and health, to identify and evaluate instruments used to measure refugee trauma and health status, and to recommend improvements. DATA SOURCES: MEDLINE, PsychInfo, Health and PsychoSocial Instruments, CINAHL, and Cochrane Systematic Reviews (searched through OVID from the inception of each database to October 2001), and the New Mexico Refugee Project database. STUDY SELECTION: Key terms and combination operators were applied to identify English-language publications evaluating measurement of refugee trauma and/or health status. DATA EXTRACTION: Information extracted for each article included author; year of publication; primary focus; type (empirical, review, or descriptive); and type/name and properties of instrument(s) included. Articles were excluded from further analyses if they were review or descriptive, were not primarily about refugee health status or trauma, or were only about infectious diseases. Instruments were then evaluated according to 5 criteria (purpose, construct definition, design, developmental process, reliability and validity) as described in the published literature. DATA SYNTHESIS: Of 394 publications identified, 183 were included for further analyses of their characteristics; 91 (49.7%) included quantitative data but did not evaluate measurement properties of instruments used in refugee research, 78 (42.6%) reported on statistical relationships between measures (presuming validity), and 14 (7.7%) were only about statistical properties of instruments. In these 183 publications, 125 different instruments were used; of these, 12 were developed in refugee research. None of these instruments fully met all 5 evaluation criteria, 3 met 4 criteria, and 5 met only 1 of the criteria. Another 8 standard instruments were designed and developed in nonrefugee populations but adapted for use in refugee research; of these, 2 met all 5 criteria and 6 met 4 criteria. CONCLUSIONS: The majority of articles about refugee trauma or health are either descriptive or include quantitative data from instruments that have limited or untested validity and reliability in refugees. Primary limitations to accurate measurement in refugee research are the lack of theoretical bases to instruments and inattention to using and reporting sound measurement principles.
Health Status↗
Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex.
Traditional (univariate) analysis of functional MRI (fMRI) data relies exclusively on the information contained in the time course of individual voxels. Multivariate analyses can take advantage of the information contained in activity patterns across space, from multiple voxels. Such analyses have the potential to greatly expand the amount of information extracted from fMRI data sets. In the present study, multivariate statistical pattern recognition methods, including linear discriminant analysis and support vector machines, were used to classify patterns of fMRI activation evoked by the visual presentation of various categories of objects. Classifiers were trained using data from voxels in predefined regions of interest during a subset of trials for each subject individually. Classification of subsequently collected fMRI data was attempted according to the similarity of activation patterns to prior training examples. Classification was done using only small amounts of data (20 s worth) at a time, so such a technique could, in principle, be used to extract information about a subject's percept on a near real-time basis. Classifiers trained on data acquired during one session were equally accurate in classifying data collected within the same session and across sessions separated by more than a week, in the same subject. Although the highest classification accuracies were obtained using patterns of activity including lower visual areas as input, classification accuracies well above chance were achieved using regions of interest restricted to higher-order object-selective visual areas. In contrast to typical fMRI data analysis, in which hours of data across many subjects are averaged to reveal slight differences in activation, the use of pattern recognition methods allows a subtle 10-way discrimination to be performed on an essentially trial-by-trial basis within individuals, demonstrating that fMRI data contain far more information than is typically appreciated.
Adult↗
Nuclear magnetic resonance relaxation in nucleic acid fragments: models for internal motion.
A variety of models incorporating internal motion, which can be used to extract information from nuclear magnetic resonance relaxation studies of deoxyribonucleic acid fragments, are formulated. Illustrative analyses of some recent multinuclear relaxation data are presented. Special emphasis is placed on determining whether the information extracted is unique. It is shown that the data are consistent with several physical pictures of the internal motion. However, all the models we have considered imply the existence of large-amplitude internal motions on the nanosecond time scale.
DNA↗
Visual search strategy, selective attention, and expertise in soccer.
This research examined the relationship between visual search strategy, selective attention, and expertise in soccer. Experienced (n = 12) and less experienced (n = 12) soccer players moved in response to filmed offensive sequences. Experiment 1 examined differences in search strategy between the two groups, using an eye movement registration system. Experienced players demonstrated superior anticipation in 3-on-3 and 1-on-1 soccer simulations. There were no differences in search strategy in 3-on-3 situations. In 1-on-1 simulations, the experienced players had a higher search rate, involving more fixations of shorter duration, and fixated for longer on the hip region, indicating that this area was important in anticipating an opponent's movements. Experiment 2 examined the relationship between visual fixation and selective attention, using a spatial occlusion approach. In 3-on-3 situations, masking information "pick up" from areas other than the ball or ball passer had a more detrimental effect on the experienced players' performances, suggesting differences in selective attention. In 1-on-1 situations, occluding an oncoming dribbler's head and shoulders, hips, or lower leg and ball region did not affect the experienced players' performances more than the less experienced group. The disparities in search strategy observed in Experiment 1 did not directly relate to differences in information extraction. Experiment 3 used concurrent verbal reports to indicate where participants extracted information from while viewing 3-on-3 sequences. Experienced players spent less time attending to the ball or ball passer and more time on other areas of the display. Findings highlight the advantages of integrating eye movements with more direct measures of selective attention.
Attention↗
Two methods for isolating the lung area of a CT scan for density information.
Extracting density information from irregularly shaped tissue areas of CT scans requires automated methods when many scans are involved. We describe two computer methods that automatically isolate the lung area of a CT scan. Each starts from a single, operator specified point in the lung. The first method follows the steep density gradient boundary between lung and adjacent tissues; this tracking method is useful for estimating the overall density and total area of lung in a scan because all pixels within the lung area are available for statistical sampling. The second method finds all contiguous pixels of lung that are within the CT number range of air to water and are not a part of strong density gradient edges; this method is useful for estimating density and area of the lung parenchyma. Structures within the lung area that are surrounded by strong density gradient edges, such as large blood vessels, airways and nodules, are excluded from the lung sample while lung areas with diffuse borders, such as an area of mild or moderate edema, are retained. Both methods were tested on scans from an animal model of pulmonary edema and were found to be effective in isolating normal and diseased lungs. These methods are also suitable for isolating other organ areas of CT scans that are bounded by density gradient edges.
Animals↗
MEDSYNDIKATE--a natural language system for the extraction of medical information from findings reports.
MEDSYNDIKATE is a natural language processor, which automatically acquires medical information from findings reports. In the course of text analysis their contents is transferred to conceptual representation structures, which constitute a corresponding text knowledge base. MEDSYNDIKATE is particularly adapted to deal properly with text structures, such as various forms of anaphoric reference relations spanning several sentences. The strong demands MEDSYNDIKATE poses on the availability of expressive knowledge sources are accounted for by two alternative approaches to acquire medical domain knowledge (semi)automatically. We also present data for the information extraction performance of MEDSYNDIKATE in terms of the semantic interpretation of three major syntactic patterns in medical documents.
Confidence Intervals↗