Detection and differentiation of human polyomaviruses JC and BK by LightCycler PCR

Human polyomaviruses JC and BK may cause several clinical manifestations in immunocompromised hosts, including progressive multifocal leukoencephalopathy and hemorrhagic cystitis. Molecular detection by PCR is recognized as a sensitive and specific method for detecting human polyomaviruses in clinical samples. In this study, a real-time PCR assay using the LightCycler platform was evaluated and compared to an in-house PCR assay using a conventional detection method. A total of 122 urine specimens were tested, and human polyomavirus was detected in 49 specimens (40%) by both conventional PCR and LightCycler PCR. The remaining 73 specimens (60%) were found negative by both assays. For 46 of the 49 positive specimens, LightCycler PCR and conventional PCR identified the same polyomavirus type. These samples included 30 samples with JC virus (JCV), 14 samples with BK virus (BKV), and 2 samples in which both viruses were detected. In the remaining three samples, both JCV and BKV were detected by the conventional assay, but only JCV was detected by the LightCycler assay. The results of this study show that the LightCycler PCR assay displays sensitivity and specificity similar to those of a conventional PCR assay. These data, combined with its rapid turnaround time for results and decreased hands-on time, make the LightCycler PCR assay highly suitable for the rapid detection and differentiation of JCV and BKV in the clinical laboratory.

A real-time PCR assay for the detection of Neisseria gonorrhoeae by LightCycler

The detection of Neisseria gonorrhoeae by the polymerase chain reaction (PCR) is now recognized as a sensitive and specific method of diagnosing infection by the organism. In this Study 152 urine specimens were examined for N. gonorrhoeae by a real-time PCR method using the LightCycler platform and results were compared to an in-house PCR assay using an ELISA-based detection method. N. gonorrhoeae DNA was detected in 29 (19%) specimens by LightCycler PCR (LC-PCR) and in 31 (20%) specimens by the in house PCR method. The LightCycler assay proved to be specific and 94% sensitive when compared to the in house PCR method. These features combined with the rapid turn-around time for results makes the LC-PCR particularly suitable for the detection of N. gonorrhoeae in a routine clinical laboratory. (C) 2002 Elsevier Science Inc. All rights reserved.

Computer-aided recognition of dental implants in X-ray images

Dental implant recognition in patients without available records is a time-consuming and not straightforward task. The traditional method is a complete user-dependent process, where the expert compares a 2D X-ray image of the dental implant with a generic database. Due to the high number of implants available and the similarity between them, automatic/semi-automatic frameworks to aide implant model detection are essential. In this study, a novel computer-aided framework for dental implant recognition is suggested. The proposed method relies on image processing concepts, namely: (i) a segmentation strategy for semi-automatic implant delineation; and (ii) a machine learning approach for implant model recognition. Although the segmentation technique is the main focus of the current study, preliminary details of the machine learning approach are also reported. Two different scenarios are used to validate the framework: (1) comparison of the semi-automatic contours against implant’s manual contours of 125 X-ray images; and (2) classification of 11 known implants using a large reference database of 601 implants. Regarding experiment 1, 0.97±0.01, 2.24±0.85 pixels and 11.12±6 pixels of dice metric, mean absolute distance and Hausdorff distance were obtained, respectively. In experiment 2, 91% of the implants were successfully recognized while reducing the reference database to 5% of its original size. Overall, the segmentation technique achieved accurate implant contours. Although the preliminary classification results prove the concept of the current work, more features and an extended database should be used in a future work.

Land use / land cover change detection: an object oriented approach, Münster, Germany

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Evaluation of different methods for Plasmodia detection, in well defined population groups in an endemic area of Brazil

In Brazil, more than 500,000 new cases of malaria were notified in 1992. Plasmodium falciparum and P.vivax are the responsible species for 99.3% of the cases. For adequate treatment, precoce diagnosis is necessary. In this work, we present the results of the traditional Plasmodia detection method, thick blood film (TBF), and the results of alternative methods: Immunofluorescence assay (IFA) with polyclonal antibody and Quantitative Buffy Coat method (QBC)® in a well defined population groups. The analysis were done in relation to the presence or absence of malaria clinical symptoms. Also different classes of immunoglobulins anti-P.falciparum were quantified for the global analysis of the results, mainly in the discrepant results. We concluded that alternative methods are more sensitive than TBF and that the association of epidemiological, clinical and laboratory findings is necessary to define the presence of malaria.

Field experiments for marine casualty detection with autonomous surface vehicles

In this paper we present a set of field tests for detection of human in the water with an unmanned surface vehicle using infrared and color cameras. These experiments aimed to contribute in the development of victim target tracking and obstacle avoidance for unmanned surface vehicles operating in marine search and rescue missions. This research is integrated in the work conducted in the European FP7 research project Icarus aiming to develop robotic tools for large scale rescue operations. The tests consisted in the use of the ROAZ unmanned surface vehicle equipped with a precision GPS system for localization and both visible spectrum and IR cameras to detect the target. In the experimental setup, the test human target was deployed in the water wearing a life vest and a diver suit (thus having lower temperature signature in the body except hands and head) and was equipped with a GPS logger. Multiple target approaches were performed in order to test the system with different sun incidence relative angles. The experimental setup, detection method and preliminary results from the field trials performed in the summer of 2013 in Sesimbra, Portugal and in La Spezia, Italy are also presented in this work.

Detection of parvovirus B19 infection in formalin-fixed and paraffin-embedded placenta and fetal tissues

Parvovirus B19 infection was first discovered in 1975 and it is implicated in fetal death from hydrops fetalis the world over. Diagnosis is usually made through histological identification of the intranuclear inclusion in placenta and fetal organs. However, these cells may be scarce or uncharacteristic, making definitive diagnosis difficult. We analyzed histologically placentas and fetal organs from 34 cases of non-immune hydrops fetalis, stained with Hematoxylin and Eosin (HE) and submitted to immunohistochemistry and polymerase chain reaction (PCR). Of 34 tissue samples, two (5.9%) presented typical intranuclear inclusion in circulating normoblasts seen in Hematoxylin and Eosin stained sections, confirmed by immunohistochemistry and PCR. However, PCR of fetal organs was negative in one case in which the placenta PCR was positive. We concluded that parvovirus B19 infection frequency is similar to the literature and that immunohistochemistry was the best detection method. It is highly specific and sensitive, preserves the morphology and reveals a larger number of positive cells than does HE with the advantage of showing cytoplasmic and nuclear positivity, making it more reliable. Although PCR is more specific and sensitive in fresh or ideally fixed material it is not so in formalin-fixed paraffin-embedded tissues, frequently the only one available in such cases.

Gold nanoprobes for the detection of mutations associated with antibiotic resistance in Mycobacterium tuberculosis complex

Dissertação para obtenção do Grau de Mestre em Genética Molecular e Biomedicina

Concurrency detection on finish-to-start activity precedence networks

We explore the finish-to-start precedence relations of project activities used in scheduling problems. From these relations, we devise a method to identify groups of activities that could execute concurrently, i.e. activities in the same group can all execute in parallel. The method derives a new set of relations to describe the concurrency. Then, it is represented by an undirected graph and the maximal cliques problem identifies the groups. We provide a running example with a project from our previous studies in resource constrained project cost minimization together with an example application on the concurrency detection method: the evaluation of the resource stress.

Clustering and hot spot detection in socio-economic spatio-temporal data

Distribution of socio-economic features in urban space is an important source of information for land and transportation planning. The metropolization phenomenon has changed the distribution of types of professions in space and has given birth to different spatial patterns that the urban planner must know in order to plan a sustainable city. Such distributions can be discovered by statistical and learning algorithms through different methods. In this paper, an unsupervised classification method and a cluster detection method are discussed and applied to analyze the socio-economic structure of Switzerland. The unsupervised classification method, based on Ward's classification and self-organized maps, is used to classify the municipalities of the country and allows to reduce a highly-dimensional input information to interpret the socio-economic landscape. The cluster detection method, the spatial scan statistics, is used in a more specific manner in order to detect hot spots of certain types of service activities. The method is applied to the distribution services in the agglomeration of Lausanne. Results show the emergence of new centralities and can be analyzed in both transportation and social terms.

Automatic landmarks detection in breast reconstruction aesthetic assessment.

This paper addresses a fully automatic landmarks detection method for breast reconstruction aesthetic assessment. The set of landmarks detected are the supraesternal notch (SSN), armpits, nipples, and inframammary fold (IMF). These landmarks are commonly used in order to perform anthropometric measurements for aesthetic assessment. The methodological approach is based on both illumination and morphological analysis. The proposed method has been tested with 21 images. A good overall performance is observed, although several improvements must be achieved in order to refine the detection of nipples and SSNs.

Multi-modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 data fusion contest

The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.

An Interval Intelligent-based Approach for Fault Detection and Modelling

Not considered in the analytical model of the plant, uncertainties always dramatically decrease the performance of the fault detection task in the practice. To cope better with this prevalent problem, in this paper we develop a methodology using Modal Interval Analysis which takes into account those uncertainties in the plant model. A fault detection method is developed based on this model which is quite robust to uncertainty and results in no false alarm. As soon as a fault is detected, an ANFIS model is trained in online to capture the major behavior of the occurred fault which can be used for fault accommodation. The simulation results understandably demonstrate the capability of the proposed method for accomplishing both tasks appropriately

Geometry-based demosaicking

Demosaicking is a particular case of interpolation problems where, from a scalar image in which each pixel has either the red, the green or the blue component, we want to interpolate the full-color image. State-of-the-art demosaicking algorithms perform interpolation along edges, but these edges are estimated locally. We propose a level-set-based geometric method to estimate image edges, inspired by the image in-painting literature. This method has a time complexity of O(S) , where S is the number of pixels in the image, and compares favorably with the state-of-the-art algorithms both visually and in most relevant image quality measures.

Improved detection of Rhodococcus coprophilus with a new quantitative PCR assay.

Agricultural practices, such as spreading liquid manure or the utilisation of land as animal pastures, can result in faecal contamination of water resources. Rhodococcus coprophilus is used in microbial source tracking to indicate animal faecal contamination in water. Methods previously described for detecting of R. coprophilus in water were neither sensitive nor specific. Therefore, the aim of this study was to design and validate a new quantitative polymerase chain reaction (qPCR) to improve the detection of R. coprophilus in water. The new PCR assay was based on the R. coprophilus 16S rRNA gene. The validation showed that the new approach was specific and sensitive for deoxyribunucleic acid from target host species. Compared with other PCR assays tested in this study, the detection limit of the new qPCR was between 1 and 3 log lower. The method, including a filtration step, was further validated and successfully used in a field investigation in Switzerland. Our work demonstrated that the new detection method is sensitive and robust to detect R. coprophilus in surface and spring water. Compared with PCR assays that are available in the literature or to the culture-dependent method, the new molecular approach improves the detection of R. coprophilus.