963 resultados para automatic target detection
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The promoter of the bean PAL2 gene (encoding phenylalanine ammonia-lyase; EC 4.3.1.5) is a model for studies of tissue-restricted gene expression in plants. Petal epidermis is one of the tissues in which this promoter is activated in tobacco. Previous work suggested that a major factor establishing the pattern of PAL2 expression in tobacco petals is the tissue distribution of a protein closely related to Myb305, which is a Myb-like transcriptional activator from snapdragon. In the present work, we show that Myb305 expression in tobacco leaves causes ectopic activation of the PAL2 promoter. To achieve Myb305 expression in planta, a viral expression vector was used. This approach combines the utility of transient assays with the possibility of direct biochemical detection of the introduced factor and may have wider application for studying the function of plant transcription factors.
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The challenge of the Human Genome Project is to increase the rate of DNA sequence acquisition by two orders of magnitude to complete sequencing of the human genome by the year 2000. The present work describes a rapid detection method using a two-dimensional optical wave guide that allows measurement of real-time binding or melting of a light-scattering label on a DNA array. A particulate label on the target DNA acts as a light-scattering source when illuminated by the evanescent wave of the wave guide and only the label bound to the surface generates a signal. Imaging/visual examination of the scattered light permits interrogation of the entire array simultaneously. Hybridization specificity is equivalent to that obtained with a conventional system using autoradiography. Wave guide melting curves are consistent with those obtained in the liquid phase and single-base discrimination is facile. Dilution experiments showed an apparent lower limit of detection at 0.4 nM oligonucleotide. This performance is comparable to the best currently known fluorescence-based systems. In addition, wave guide detection allows manipulation of hybridization stringency during detection and thereby reduces DNA chip complexity. It is anticipated that this methodology will provide a powerful tool for diagnostic applications that require rapid cost-effective detection of variations from known sequences.
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We have developed a technique for isolating DNA markers tightly linked to a target region that is based on RLGS, named RLGS spot-bombing (RLGS-SB). RLGS-SB allows us to scan the genome of higher organisms quickly and efficiently to identify loci that are linked to either a target region or gene of interest. The method was initially tested by analyzing a C57BL/6-GusS mouse congenic strain. We identified 33 variant markers out of 10,565 total loci in a 4.2-centimorgan (cM) interval surrounding the Gus locus in 4 days of laboratory work. The validity of RLGS-SB to find DNA markers linked to a target locus was also tested on pooled DNA from segregating backcross progeny by analyzing the spot intensity of already mapped RLGS loci. Finally, we used RLGS-SB to identify DNA markers closely linked to the mouse reeler (rl) locus on chromosome 5 by phenotypic pooling. A total of 31 RLGS loci were identified and mapped to the target region after screening 8856 loci. These 31 loci were mapped within 11.7 cM surrounding rl. The average density of RLGS loci located in the rl region was 0.38 cM. Three loci were closely linked to rl showing a recombination frequency of 0/340, which is < 1 cM from rl. Thus, RLGS-SB provides an efficient and rapid method for the detection and isolation of polymorphic DNA markers linked to a trait or gene of interest.
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This paper describes a module for the prediction of emotions in text chats in Spanish, oriented to its use in specific-domain text-to-speech systems. A general overview of the system is given, and the results of some evaluations carried out with two corpora of real chat messages are described. These results seem to indicate that this system offers a performance similar to other systems described in the literature, for a more complex task than other systems (identification of emotions and emotional intensity in the chat domain).
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Rock mass characterization requires a deep geometric understanding of the discontinuity sets affecting rock exposures. Recent advances in Light Detection and Ranging (LiDAR) instrumentation currently allow quick and accurate 3D data acquisition, yielding on the development of new methodologies for the automatic characterization of rock mass discontinuities. This paper presents a methodology for the identification and analysis of flat surfaces outcropping in a rocky slope using the 3D data obtained with LiDAR. This method identifies and defines the algebraic equations of the different planes of the rock slope surface by applying an analysis based on a neighbouring points coplanarity test, finding principal orientations by Kernel Density Estimation and identifying clusters by the Density-Based Scan Algorithm with Noise. Different sources of information —synthetic and 3D scanned data— were employed, performing a complete sensitivity analysis of the parameters in order to identify the optimal value of the variables of the proposed method. In addition, raw source files and obtained results are freely provided in order to allow to a more straightforward method comparison aiming to a more reproducible research.
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Moderate resolution remote sensing data, as provided by MODIS, can be used to detect and map active or past wildfires from daily records of suitable combinations of reflectance bands. The objective of the present work was to develop and test simple algorithms and variations for automatic or semiautomatic detection of burnt areas from time series data of MODIS biweekly vegetation indices for a Mediterranean region. MODIS-derived NDVI 250m time series data for the Valencia region, East Spain, were subjected to a two-step process for the detection of candidate burnt areas, and the results compared with available fire event records from the Valencia Regional Government. For each pixel and date in the data series, a model was fitted to both the previous and posterior time series data. Combining drops between two consecutive points and 1-year average drops, we used discrepancies or jumps between the pre and post models to identify seed pixels, and then delimitated fire scars for each potential wildfire using an extension algorithm from the seed pixels. The resulting maps of the detected burnt areas showed a very good agreement with the perimeters registered in the database of fire records used as reference. Overall accuracies and indices of agreement were very high, and omission and commission errors were similar or lower than in previous studies that used automatic or semiautomatic fire scar detection based on remote sensing. This supports the effectiveness of the method for detecting and mapping burnt areas in the Mediterranean region.
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Abdominal Aortic Aneurism is a disease related to a weakening in the aortic wall that can cause a break in the aorta and the death. The detection of an unusual dilatation of a section of the aorta is an indicative of this disease. However, it is difficult to diagnose because it is necessary image diagnosis using computed tomography or magnetic resonance. An automatic diagnosis system would allow to analyze abdominal magnetic resonance images and to warn doctors if any anomaly is detected. We focus our research in magnetic resonance images because of the absence of ionizing radiation. Although there are proposals to identify this disease in magnetic resonance images, they need an intervention from clinicians to be precise and some of them are computationally hard. In this paper we develop a novel approach to analyze magnetic resonance abdominal images and detect the lumen and the aortic wall. The method combines different algorithms in two stages to improve the detection and the segmentation so it can be applied to similar problems with other type of images or structures. In a first stage, we use a spatial fuzzy C-means algorithm with morphological image analysis to detect and segment the lumen; and subsequently, in a second stage, we apply a graph cut algorithm to segment the aortic wall. The obtained results in the analyzed images are pretty successful obtaining an average of 79% of overlapping between the automatic segmentation provided by our method and the aortic wall identified by a medical specialist. The main impact of the proposed method is that it works in a completely automatic way with a low computational cost, which is of great significance for any expert and intelligent system.
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Object tracking with subpixel accuracy is of fundamental importance in many fields since it provides optimal performance at relatively low cost. Although there are many theoretical proposals that lead to resolution increments of several orders of magnitude, in practice this resolution is limited by the imaging systems. In this paper we propose and demonstrate through simple numerical models a realistic limit for subpixel accuracy. The final result is that maximum achievable resolution enhancement is connected with the dynamic range of the image, i.e., the detection limit is 1/2∧(nr.bits). The results here presented may aid in proper design of superresolution experiments in microscopy, surveillance, defense, and other fields.
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Texas Department of Transportation, Austin
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Techniques for improving the signal to clutter ratio of an. ultra-wideband SAR designed to detect small mine-like objects in the surface of the ground were investigated. In particular, images were collected using different bistatic antenna configurations in an attempt to decorrelate the clutter with respect to the targets. The images were converted to a reference depression angle, summed, and then converted to ground coordinates. The resulting target strengths were then compared with the amplitude distribution of the ground clutter to show the improvement obtained. While some improvement was demonstrated, this was for the relatively easy scenario of targets on the surface partially obscured by grass. Detection based on thresholding the raw RF signal (the bipolar response) rather than the envelope (baseband I-2 + Q(2)) was also considered to further enhance target-to-clutter ratios.
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Molecular tools for the species-specific detection of Gluconacetobacter sacchari, Gluconacetobacter diazotrophicus, and Gluconacetobacter liquefaciens from the pink sugarcane mealybug (PSMB) Saccharicoccus sacchari Cockerell (Homiptera: Pseudococcidae) were developed and used in polymerase chain reactions (PCR) and in fluorescence in situ hybridizations (FISH) to better understand the microbial diversity and the numerical significance of the acetic acid bacteria in the PSMB microenvironment. The presence of these species in the PSMB occurred over a wide range of sites, but not in all sites in sugarcane-growing areas of Queensland, Australia, and was variable over time. Molecular probes for use in FISH were also designed for the three acetic acid bacterial species, and shown to be specific only for the target species. Use of these probes in FISH of squashed whole mealybugs indicated that these acetic acid bacteria species represent only a small proportion of the microbial population of the PSMB. Despite the detection of Glac. sacchari, Glac. diazotrophicus, and Glac. liquefaciens by PCR from different mealybugs isolated at various times and from various sugarcane-growing areas in Queensland, Australia, these bacteria do not appear to be significant commensals in the PSMB environment.
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Background: This paper describes SeqDoC, a simple, web-based tool to carry out direct comparison of ABI sequence chromatograms. This allows the rapid identification of single nucleotide polymorphisms (SNPs) and point mutations without the need to install or learn more complicated analysis software. Results: SeqDoC produces a subtracted trace showing differences between a reference and test chromatogram, and is optimised to emphasise those characteristic of single base changes. It automatically aligns sequences, and produces straightforward graphical output. The use of direct comparison of the sequence chromatograms means that artefacts introduced by automatic base-calling software are avoided. Homozygous and heterozygous substitutions and insertion/deletion events are all readily identified. SeqDoC successfully highlights nucleotide changes missed by the Staden package 'tracediff' program. Conclusion: SeqDoC is ideal for small-scale SNP identification, for identification of changes in random mutagenesis screens, and for verification of PCR amplification fidelity. Differences are highlighted, not interpreted, allowing the investigator to make the ultimate decision on the nature of the change.
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Detection of point mutations or single nucleotide polymorphisms (SNPs) is important in relation to disease susceptibility or detection in pathogens of mutations determining drug resistance or host range. There is an emergent need for rapid detection methods amenable to point-of-care applications. The purpose of this study was to reduce to practice a novel method for SNP detection and to demonstrate that this technology can be used downstream of nucleic acid amplification. The authors used a model system to develop an oligonucleotide-based SNP detection system on nitrocellulose lateral flow strips. To optimize the assay they used cloned sequences of the herpes simplex virus-1 (HSV-1) DNA polymerase gene into which they introduced a point mutation. The assay system uses chimeric polymerase chain reaction (PCR) primers that incorporate hexameric repeat tags ("hexapet tags"). The chimeric sequences allow capture of amplified products to predefined positions on a lateral flow strip. These "hexapet" sequences have minimal cross-reactivity and allow specific hybridization-based capture of the PCR products at room temperature onto lateral flow strips that have been striped with complementary hexapet tags. The allele-specific amplification was carried out with both mutant and wild-type primer sets present in the PCR mix ("competitive" format). The resulting PCR products carried a hexapet tag that corresponded with either a wild-type or mutant sequence. The lateral flow strips are dropped into the PCR reaction tube, and mutant sequence and wild-type sequences diffuse along the strip and are captured at the corresponding position on the strip. A red line indicative of a positive reaction is visible after 1 minute. Unlike other systems that require separate reactions and strips for each target sequence, this system allows multiplex PCR reactions and multiplex detection on a single strip or other suitable substrates. Unambiguous visual discrimination of a point mutation under room temperature hybridization conditions was achieved with this model system in 10 minutes after PCR. The authors have developed a capture-based hybridization method for the detection and discrimination of HSV-1 DNA polymerase genes that contain a single nucleotide change. It has been demonstrated that the hexapet oligonucleotides can be adapted for hybridization on the lateral flow strip platform for discrimination of SNPs. This is the first step in demonstrating SNP detection on lateral flow using the hexapet oligonucleotide capture system. It is anticipated that this novel system can be widely used in point-of-care settings.
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Automatic signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. The proposed approach is based on fuzzy modeling that employs the Takagi-Sugeno (TS) model. Signature verification and forgery detection are carried out using angle features extracted from box approach. Each feature corresponds to a fuzzy set. The features are fuzzified by an exponential membership function involved in the TS model, which is modified to include structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect moods. The membership functions constitute weights in the TS model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. We have also derived two TS models by considering a rule for each input feature in the first formulation (Multiple rules) and by considering a single rule for all input features in the second formulation. In this work, we have found that TS model with multiple rules is better than TS model with single rule for detecting three types of forgeries; random, skilled and unskilled from a large database of sample signatures in addition to verifying genuine signatures. We have also devised three approaches, viz., an innovative approach and two intuitive approaches using the TS model with multiple rules for improved performance. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
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Background. Genital ulcer disease (GUD) is commonly caused by pathogens for which suitable therapies exist, but clinical and laboratory diagnoses may be problematic. This collaborative project was undertaken to address the need for a rapid, economical, and sensitive approach to the detection and diagnosis of GUD using noninvasive techniques to sample genital ulcers. Methods. The genital ulcer disease multiplex polymerase chain reaction (GUMP) was developed as an inhouse nucleic acid amplification technique targeting serious causes of GUD, namely, herpes simplex viruses (HSVs), Haemophilus ducreyi, Treponema pallidum, and Klebsiella species. In addition, the GUMP assay included an endogenous internal control. Amplification products from GUMP were detected by enzyme linked amplicon hybridization assay (ELAHA). Results. GUMP-ELAHA was sensitive and specific in detecting a target microbe in 34.3% of specimens, including 1 detection of HSV-1, three detections of HSV-2, and 18 detections of T. pallidum. No H. ducreyi has been detected in Australia since 1998, and none was detected here. No Calymmatobacterium ( Klebsiella) granulomatis was detected in the study, but there were 3 detections during ongoing diagnostic use of GUMP-ELAHA in 2004 and 2005. The presence of C. granulomatis was confirmed by restriction enzyme digestion and nucleotide sequencing of the 16S rRNA gene for phylogenetic analysis. Conclusions. GUMP-ELAHA permitted comprehensive detection of common and rare causes of GUD and incorporated noninvasive sampling techniques. Data obtained by using GUMP-ELAHA will aid specific treatment of GUD and better define the prevalence of each microbe among at-risk populations with a view to the eradication of chancroid and donovanosis in Australia.