930 resultados para false negative rate


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High resolution descriptions of plant distribution have utility for many ecological applications but are especially useful for predictive modeling of gene flow from transgenic crops. Difficulty lies in the extrapolation errors that occur when limited ground survey data are scaled up to the landscape or national level. This problem is epitomized by the wide confidence limits generated in a previous attempt to describe the national abundance of riverside Brassica rapa (a wild relative of cultivated rapeseed) across the United Kingdom. Here, we assess the value of airborne remote sensing to locate B. rapa over large areas and so reduce the need for extrapolation. We describe results from flights over the river Nene in England acquired using Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) imagery, together with ground truth data. It proved possible to detect 97% of flowering B. rapa on the basis of spectral profiles. This included all stands of plants that occupied >2m square (>5 plants), which were detected using single-pixel classification. It also included very small populations (<5 flowering plants, 1-2m square) that generated mixed pixels, which were detected using spectral unmixing. The high detection accuracy for flowering B. rapa was coupled with a rather large false positive rate (43%). The latter could be reduced by using the image detections to target fieldwork to confirm species identity, or by acquiring additional remote sensing data such as laser altimetry or multitemporal imagery.

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This paper presents a new face verification algorithm based on Gabor wavelets and AdaBoost. In the algorithm, faces are represented by Gabor wavelet features generated by Gabor wavelet transform. Gabor wavelets with 5 scales and 8 orientations are chosen to form a family of Gabor wavelets. By convolving face images with these 40 Gabor wavelets, the original images are transformed into magnitude response images of Gabor wavelet features. The AdaBoost algorithm selects a small set of significant features from the pool of the Gabor wavelet features. Each feature is the basis for a weak classifier which is trained with face images taken from the XM2VTS database. The feature with the lowest classification error is selected in each iteration of the AdaBoost operation. We also address issues regarding computational costs in feature selection with AdaBoost. A support vector machine (SVM) is trained with examples of 20 features, and the results have shown a low false positive rate and a low classification error rate in face verification.

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We have discovered a novel approach of intrusion detection system using an intelligent data classifier based on a self organizing map (SOM). We have surveyed all other unsupervised intrusion detection methods, different alternative SOM based techniques and KDD winner IDS methods. This paper provides a robust designed and implemented intelligent data classifier technique based on a single large size (30x30) self organizing map (SOM) having the capability to detect all types of attacks given in the DARPA Archive 1999 the lowest false positive rate being 0.04 % and higher detection rate being 99.73% tested using full KDD data sets and 89.54% comparable detection rate and 0.18% lowest false positive rate tested using corrected data sets.

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Background: Changes in cellular phenotype result from underlying changes in mRNA transcription and translation. Endothelin-1 stimulates cardiomyocyte hypertrophy with associated changes in mRNA/protein expression and an increase in the rate of protein synthesis. Insulin also increases the rate of translation but does not promote overt cardiomyocyte hypertrophy. One mechanism of translational regulation is through 5' terminal oligopyrimidine tracts (TOPs) that, in response to growth stimuli, promote mRNA recruitment to polysomes for increased translation. TOP mRNAs include those encoding ribosomal proteins, but the full panoply remains to be established. Here, we used microarrays to compare the effects of endothelin-1 and insulin on the global transcriptome of neonatal rat cardiomyocytes, and on mRNA recruitment to polysomes (i.e. the translatome). Results: Globally, endothelin-1 and insulin (1 h) promoted >1.5-fold significant (false discovery rate < 0.05) changes in expression of 341 and 38 RNAs, respectively. For these transcripts with this level of change there was little evidence of translational regulation. However, 1336 and 712 RNAs had >1.25-fold significant changes in expression in total and/or polysomal RNA induced by endothelin-1 or insulin, respectively, of which ~35% of endothelin-1-responsive and ~56% of insulin-responsive transcripts were translationally regulated. Of mRNAs for established proteins recruited to polysomes in response to insulin, 49 were known TOP mRNAs with a further 15 probable/possible TOP mRNAs, but 49 had no identifiable TOP sequences or other consistent features in the 5' untranslated region. Conclusions: Endothelin-1, rather than insulin, substantially affects global transcript expression to promote cardiomyocyte hypertrophy. Effects on RNA recruitment to polysomes are subtle, with differential effects of endothelin-1 and insulin on specific transcripts. Furthermore, although insulin promotes recruitment of TOP mRNAs to cardiomyocyte polysomes, not all recruited mRNAs are TOP mRNAs.

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A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, classifying 89% of flooded pixels correctly, with an associated false positive rate of 6%. Of the urban water pixels visible to TerraSAR-X, 75% were correctly detected, with a false positive rate of 24%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 57% and 18% respectively.

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Left inferior frontal gyrus (IFG) is a critical neural substrate for the resolution of proactive interference (PI) in working memory. We hypothesized that left IFG achieves this by controlling the influence of familiarity- versus recollection-based information about memory probes. Consistent with this idea, we observed evidence for an early (200 msec)-peaking signal corresponding to memory probe familiarity and a late (500 msec)-resolving signal corresponding to full accrual of trial-related contextual ("recollection-based") information. Next, we applied brief trains of repetitive transcranial magnetic stimulation (rTMS) time locked to these mnemonic signals, to left IFG and to a control region. Only early rTMS of left IFG produced a modulation of the false alarm rate for high-PI probes. Additionally, the magnitude of this effect was predicted by individual differences in susceptibility to PI. These results suggest that left IFG-based control may bias the influence of familiarity- and recollection-based signals on recognition decisions.

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A polymerase chain reaction (PCR) for the specific detection of the gene sequence, sefA, encoded by all isolates of Salmonella enteritidis, was developed. The PCR could detect as few as four S enteritidis washed bacterial cells but egg contents inhibited the PCR. Eggs spiked with 50 S enteritidis bacterial cells were homogenised, inoculated into buffered peptone water and grown at 37 degrees C for 16 hours, when the PCR was successful. A positive internal control was developed to differentiate between true and false negative PCR results for the detection of S enteritidis. In a limited trial of the egg handling procedures and the PCR, one of 250 chickens' eggs from retail outlets was found to be contaminated with S enteritidis.

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Purpose: To quantify to what extent the new registration method, DARTEL (Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra), may reduce the smoothing kernel width required and investigate the minimum group size necessary for voxel-based morphometry (VBM) studies. Materials and Methods: A simulated atrophy approach was employed to explore the role of smoothing kernel, group size, and their interactions on VBM detection accuracy. Group sizes of 10, 15, 25, and 50 were compared for kernels between 0–12 mm. Results: A smoothing kernel of 6 mm achieved the highest atrophy detection accuracy for groups with 50 participants and 8–10 mm for the groups of 25 at P < 0.05 with familywise correction. The results further demonstrated that a group size of 25 was the lower limit when two different groups of participants were compared, whereas a group size of 15 was the minimum for longitudinal comparisons but at P < 0.05 with false discovery rate correction. Conclusion: Our data confirmed DARTEL-based VBM generally benefits from smaller kernels and different kernels perform best for different group sizes with a tendency of smaller kernels for larger groups. Importantly, the kernel selection was also affected by the threshold applied. This highlighted that the choice of kernel in relation to group size should be considered with care.

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We propose and demonstrate a fully probabilistic (Bayesian) approach to the detection of cloudy pixels in thermal infrared (TIR) imagery observed from satellite over oceans. Using this approach, we show how to exploit the prior information and the fast forward modelling capability that are typically available in the operational context to obtain improved cloud detection. The probability of clear sky for each pixel is estimated by applying Bayes' theorem, and we describe how to apply Bayes' theorem to this problem in general terms. Joint probability density functions (PDFs) of the observations in the TIR channels are needed; the PDFs for clear conditions are calculable from forward modelling and those for cloudy conditions have been obtained empirically. Using analysis fields from numerical weather prediction as prior information, we apply the approach to imagery representative of imagers on polar-orbiting platforms. In comparison with the established cloud-screening scheme, the new technique decreases both the rate of failure to detect cloud contamination and the false-alarm rate by one quarter. The rate of occurrence of cloud-screening-related errors of >1 K in area-averaged SSTs is reduced by 83%. Copyright © 2005 Royal Meteorological Society.

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Various fall-detection solutions have been previously proposed to create a reliable surveillance system for elderly people with high requirements on accuracy, sensitivity and specificity. In this paper, an enhanced fall detection system is proposed for elderly person monitoring that is based on smart sensors worn on the body and operating through consumer home networks. With treble thresholds, accidental falls can be detected in the home healthcare environment. By utilizing information gathered from an accelerometer, cardiotachometer and smart sensors, the impacts of falls can be logged and distinguished from normal daily activities. The proposed system has been deployed in a prototype system as detailed in this paper. From a test group of 30 healthy participants, it was found that the proposed fall detection system can achieve a high detection accuracy of 97.5%, while the sensitivity and specificity are 96.8% and 98.1% respectively. Therefore, this system can reliably be developed and deployed into a consumer product for use as an elderly person monitoring device with high accuracy and a low false positive rate.

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The performance of the rapid slide agglutination test, with and without 2-mercaptoethanol (RSAT and 2ME-RSAT) and agar gel immunodiffusion test (AGID) was evaluated for the diagnosis of brucellosis in naturally infected dogs. The microbiological culture, PCR and clinical parameters were used as reference. A total of 167 dogs were clinically examined and tested by blood culture, culture of semen/vaginal swab and PCR in blood and semen/vaginal swab. According to the results observed the 167 dogs were divided into three groups: Brucella canis infected dogs (Group 1). B. canis non-infected dogs (Group 2) and dogs with suspected brucellosis (Group 3). The dogs were then tested by RSAT, 2ME-RSAT and AGID. Groups 1 and 2 were used to calculate the diagnostic sensitivity and specificity of the serological tests and the results observed in Group 3 were also discussed. The diagnostic sensitivity of RSAT, 2ME-RSAT and AGID was respectively 70.58%, 31.76%, and 52.94%. The diagnostic specificity of RSAT, 2ME-RSAT and AGID was respectively 83.34%, 100%, and 100%. In dogs with suspected brucellosis 15% were RSAT positive, none was 2ME-RSAT positive and 5% were AGID positive. Although the serological tests are the most commonly used methods for brucellosis diagnosis, a significant proportion of false-negative results were observed highlighting the importance of the direct methods of diagnosis, like blood culture and PCR to improve the diagnosis of canine brucellosis. (c) 2008 Elsevier Ltd. All rights reserved.

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Sampling protocols for detecting Salmonella on poultry differ among various countries. In the United States, the U.S. Department of Agriculture Food Safety and Inspection Service dictates that whole broiler carcasses should be rinsed with 400 ml of 1% buffered peptone water, whereas in the European Union 25-g samples composed of neck skin from three carcasses are evaluated. The purpose of this study was to evaluate a whole carcass rinse (WCR) and a neck skin excision (NS) procedure for Salmonella and Escherichia coli isolation from the same broiler carcass. Carcasses were obtained from three broiler processing plants. The skin around the neck area was aseptically removed and bagged separately from the carcass, and microbiological analysis was performed. The corresponding carcass was bagged and a WCR sample was evaluated. No significant difference (alpha <= 0.05) in Salmonella prevalence was found between the samples processed by the two methods, but both procedures produced many false-negative Salmonella results. Prechill, 37% (66 carcasses), 28% (50 carcasses), and 51% (91 carcasses) of the 180 carcasses examined were positive for Salmonella by WCR, NS, and both procedures combined, respectively. Postchill, 3% (5 carcasses), 7% (12 carcasses), and 10% (17 carcasses) of the 177 carcasses examined were positive for Salmonella by the WCR, NS, and combination of both procedures, respectively. Prechill, E. coli plus coliform counts were 3.0 and 2.6 log CFU/ml by the WCR and NS methods, respectively. Postchill. E. coli plus coliform counts were 1.7 and 1.4 log CFU/ml by the WCR and NS methods, respectively.

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Only a small fraction of spectra acquired in LC-MS/MS runs matches peptides from target proteins upon database searches. The remaining, operationally termed background, spectra originate from a variety of poorly controlled sources and affect the throughput and confidence of database searches. Here, we report an algorithm and its software implementation that rapidly removes background spectra, regardless of their precise origin. The method estimates the dissimilarity distance between screened MS/MS spectra and unannotated spectra from a partially redundant background library compiled from several control and blank runs. Filtering MS/MS queries enhanced the protein identification capacity when searches lacked spectrum to sequence matching specificity. In sequence-similarity searches it reduced by, on average, 30-fold the number of orphan hits, which were not explicitly related to background protein contaminants and required manual validation. Removing high quality background MS/MS spectra, while preserving in the data set the genuine spectra from target proteins, decreased the false positive rate of stringent database searches and improved the identification of low-abundance proteins.

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Objectives: To assess the value of computerised decision support in the management of chronic respiratory disease by comparing agreement between three respiratory specialists, general practitioners (care coordinators), and decision support software.
Methods: Care guidelines for two chronic obstructive pulmonary disease projects of the SA HealthPlus Coordinated Care Trial were formulated. Decision support software, Care Plan On-Line (CPOL), was created to represent the intent of these guidelines via automated attention flags to appear in patients' electronic medical records. For a random sample of 20 patients with care plans, decisions about the use of nine additional services (eg,.smoking cessation, pneumococcal vaccination) were compared between the respiratory specialists, the patients' GPs and the CPOL attention flags.
Results: Agreement among the specialists was at the lower end of moderate (intraclass correlation coefficient [ICC], 0.48; 95% CI, 0.39-0.56), with a 20% rate of contradictory decisions. Agreement with recommendations of specialists was moderate to poor for GPs (le, 0.49; 95% CI, 0.33-0.66) and moderate to good for CPOL (K, 0.72; 95% CI, 0.55-0.90). CPOL agreement with GPs was moderate to poor (le, 0.41; 95% CI, 0.24-0.58). GPs were less likely than specialists or CPOL to decide in favour of an additional service (P< 0.001). CPOL was 87% accurate as an indicator of specialist decisions. It gave a 16% false-positive rate according to specialist decisions, and flagged 61% of decisions where GPs said No and specialists said Yes.
Conclusions: Automated decision support may provide GPs with improved access to the intent of guidelines; however, further investigation is required.

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A system that can automatically detect nodules within lung images may assist expert radiologists in interpreting the abnormal patterns as nodules in 2D CT lung images. A system is presented that can automatically identify nodules of various sizes within lung images. The pattern classification method is employed to develop the proposed system. A random forest ensemble classifier is formed consisting of many weak learners that can grow decision trees. The forest selects the decision that has the most votes. The developed system consists of two random forest classifiers connected in a series fashion. A subset of CT lung images from the LIDC database is employed. It consists of 5721 images to train and test the system. There are 411 images that contained expert- radiologists identified nodules. Training sets consisting of nodule, non-nodule, and false-detection patterns are constructed. A collection of test images are also built. The first classifier is developed to detect all nodules. The second classifier is developed to eliminate the false detections produced by the first classifier. According to the experimental results, a true positive rate of 100%, and false positive rate of 1.4 per lung image are achieved.