67 resultados para patterns detection and recognition


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Leptospirosis is a global zoonotic disease. Pathogenic Leptospira species, the causative agent of leptospirosis, colonize the renal tubules of chronically infected maintenance hosts such as dogs, rats and cattle. Maintenance hosts typically remain clinically asymptomatic and shed leptospires into the environment via urine. In contrast, accidental hosts such as humans can suffer severe acute forms of the disease. Infection results from direct contact with infected urine or indirectly, through contaminated water sources. In this study, a quantitative real-time PCR specific for lipL32 was designed to detect the urinary shedding of leptospires from dogs. The sensitivity and specificity of the assay was evaluated using both a panel of pathogenic Leptospira species and clinical microbial isolates, and samples of urine collected from experimentally infected rats and non-infected controls. The lower limit of detection was approximately 3 genome equivalents per reaction. The assay was applied to canine urine samples collected from local dog sanctuaries and the University Veterinary Hospital (UVH) at University College Dublin. Of 525 canine urine samples assayed, 37 were positive, indicating a prevalence of urinary shedding of leptospires of 7.05%. These results highlight the need to provide effective canine vaccination strategies and raise public health awareness.

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SETTING Drug resistance threatens tuberculosis (TB) control, particularly among human immunodeficiency virus (HIV) infected persons. OBJECTIVE To describe practices in the prevention and management of drug-resistant TB under antiretroviral therapy (ART) programs in lower-income countries. DESIGN We used online questionnaires to collect program-level data on 47 ART programs in Southern Africa (n = 14), East Africa (n = 8), West Africa (n = 7), Central Africa (n = 5), Latin America (n = 7) and the Asia-Pacific (n = 6 programs) in 2012. Patient-level data were collected on 1002 adult TB patients seen at 40 of the participating ART programs. RESULTS Phenotypic drug susceptibility testing (DST) was available in 36 (77%) ART programs, but was only used for 22% of all TB patients. Molecular DST was available in 33 (70%) programs and was used in 23% of all TB patients. Twenty ART programs (43%) provided directly observed therapy (DOT) during the entire course of treatment, 16 (34%) during the intensive phase only, and 11 (23%) did not follow DOT. Fourteen (30%) ART programs reported no access to second-line anti-tuberculosis regimens; 18 (38%) reported TB drug shortages. CONCLUSIONS Capacity to diagnose and treat drug-resistant TB was limited across ART programs in lower-income countries. DOT was not always implemented and drug supplies were regularly interrupted, which may contribute to the global emergence of drug resistance.

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Ovine foot rot caused by Dichelobacter nodosus is affecting sheep worldwide. The current diagnostic methods are difficult and cumbersome. Here, we present a competitive real-time PCR based on allelic discrimination of the protease genes aprV2 and aprB2. This method allows direct detection and differentiation of virulent and benign D. nodosus from interdigital skin swabs in a single test. Clinically affected sheep harbored high loads of only virulent strains, whereas healthy sheep had lower loads of predominantly benign strains.

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BACKGROUND Flavobacterium psychrophilum is the agent of Bacterial Cold Water Disease and Rainbow Trout Fry Syndrome, two diseases leading to high mortality. Pathogen detection is mainly carried out using cultures and more rapid and sensitive methods are needed. RESULTS We describe a qPCR technique based on the single copy gene β' DNA-dependent RNA polymerase (rpoC). Its detection limit was 20 gene copies and the quantification limit 103 gene copies per reaction. Tests on spiked spleens with known concentrations of F. psychrophilum (106 to 101 cells per reaction) showed no cross-reactions between the spleen tissue and the primers and probe. Screening of water samples and spleens from symptomless and infected fishes indicated that the pathogen was already present before the outbreaks, but F. psychrophilum was only quantifiable in spleens from diseased fishes. CONCLUSIONS This qPCR can be used as a highly sensitive and specific method to detect F. psychrophilum in different sample types without the need for culturing. qPCR allows a reliable detection and quantification of F. psychrophilum in samples with low pathogen densities. Quantitative data on F. psychrophilum abundance could be useful to investigate risk factors linked to infections and also as early warning system prior to potential devastating outbreak.

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SUMMARY There is interest in the potential of companion animal surveillance to provide data to improve pet health and to provide early warning of environmental hazards to people. We implemented a companion animal surveillance system in Calgary, Alberta and the surrounding communities. Informatics technologies automatically extracted electronic medical records from participating veterinary practices and identified cases of enteric syndrome in the warehoused records. The data were analysed using time-series analyses and a retrospective space-time permutation scan statistic. We identified a seasonal pattern of reports of occurrences of enteric syndromes in companion animals and four statistically significant clusters of enteric syndrome cases. The cases within each cluster were examined and information about the animals involved (species, age, sex), their vaccination history, possible exposure or risk behaviour history, information about disease severity, and the aetiological diagnosis was collected. We then assessed whether the cases within the cluster were unusual and if they represented an animal or public health threat. There was often insufficient information recorded in the medical record to characterize the clusters by aetiology or exposures. Space-time analysis of companion animal enteric syndrome cases found evidence of clustering. Collection of more epidemiologically relevant data would enhance the utility of practice-based companion animal surveillance.

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In early pregnancy, abortion can be induced by blocking the actions of progesterone receptors (PR). However, the PR antagonist, mifepristone (RU38486), is rather unselective in clinical use because it also cross-reacts with other nuclear receptors. Since the ligand-binding domain of human progesterone receptor (hPR) and androgen receptor (hAR) share 54% identity, we hypothesized that derivatives of dihydrotestosterone (DHT), the cognate ligand for hAR, might also regulate the hPR. Compounds designed and synthesized in our laboratory were investigated for their affinities for hPRB, hAR, glucocorticoid receptor (hGRα) and mineralocorticoid receptor (hMR), using whole cell receptor competitive binding assays. Agonistic and antagonistic activities were characterized by reporter assays. Nuclear translocation was monitored using cherry-hPRB and GFP-hAR chimeric receptors. Cytostatic properties and apoptosis were tested on breast cancer cells (MCF7, T-47D). One compound presented a favorable profile with an apparent neutral hPRB antagonistic function, a selective cherry-hPRB nuclear translocation and a cytostatic effect. 3D models of human PR and AR with this ligand were constructed to investigate the molecular basis of selectivity. Our data suggest that these novel DHT-derivatives provide suitable templates for the development of new selective steroidal hPR antagonists.

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In this paper, we propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. To detect landmarks, we estimate the displacements from some randomly sampled image patches to the (unknown) landmark positions, and then we integrate these predictions via a voting scheme. Our key contribution is a new algorithm for estimating these displacements. Different from other methods where each image patch independently predicts its displacement, we jointly estimate the displacements from all patches together in a data driven way, by considering not only the training data but also geometric constraints on the test image. The displacements estimation is formulated as a convex optimization problem that can be solved efficiently. Finally, we use the sparse shape composition model as the a priori information to regularize the landmark positions and thus generate the segmented shape contour. We validate our method on X-ray image datasets of three different anatomical structures: complete femur, proximal femur and pelvis. Experiments show that our method is accurate and robust in landmark detection, and, combined with the shape model, gives a better or comparable performance in shape segmentation compared to state-of-the art methods. Finally, a preliminary study using CT data shows the extensibility of our method to 3D data.

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Methods for tracking an object have generally fallen into two groups: tracking by detection and tracking through local optimization. The advantage of detection-based tracking is its ability to deal with target appearance and disappearance, but it does not naturally take advantage of target motion continuity during detection. The advantage of local optimization is efficiency and accuracy, but it requires additional algorithms to initialize tracking when the target is lost. To bridge these two approaches, we propose a framework for unified detection and tracking as a time-series Bayesian estimation problem. The basis of our approach is to treat both detection and tracking as a sequential entropy minimization problem, where the goal is to determine the parameters describing a target in each frame. To do this we integrate the Active Testing (AT) paradigm with Bayesian filtering, and this results in a framework capable of both detecting and tracking robustly in situations where the target object enters and leaves the field of view regularly. We demonstrate our approach on a retinal tool tracking problem and show through extensive experiments that our method provides an efficient and robust tracking solution.

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BACKGROUND New psychoactive substances (NPS) have become increasingly prevalent and are sold in internet shops as 'bath salts' or 'research chemicals' and comprehensive bioanalytical methods are needed for their detection. METHODOLOGY We developed and validated a method using LC and MS/MS to quantify 56 NPS in blood and urine, including amphetamine derivatives, 2C compounds, aminoindanes, cathinones, piperazines, tryptamines, dissociatives and others. Instrumentation included a Synergi Polar-RP column (Phenomenex) and a 3200 QTrap mass spectrometer (AB Sciex). Run time was 20 min. CONCLUSION A novel method is presented for the unambiguous identification and quantification of 56 NPS in blood and urine samples in clinical and forensic cases, e.g., intoxications or driving under the influence of drugs.

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Protein screening/detection is an essential tool in many laboratories. Owing to the relatively large time investments that are required by standard protocols, the development of methods with higher throughput while maintaining an at least comparable signal-to-noise ratio is highly beneficial in many research areas. This chapter describes how cold microwave technology can be used to enhance the rate of molecular interactions and provides protocols for dot blots, Western blots, and ELISA procedures permitting a completion of all incubation steps (blocking and antibody steps) within 24-45 min.

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While forms of ethics based upon authenticity and recognition are holding sway in contemporary philosophical debates (Ferrara, Honneth, Fraser, etc.), many of the implications of both processes – conceptual, moral, political – are still insufficiently reflected upon. The talk will offer a “critique” (in the Kantian sense) of both, based upon an analysis of the “semiotics” of authenticity and the resulting perpetuation of a regime of authority of experts, as well as commenting upon the striking absence of the realm of literature and the arts from this debate, except in some references to a rather abstract notion of Aesthetics. It will also critically revaluate the concept of agency implicit in an ethics of authenticity and recognition.

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Low self-referential thoughts are associated with better concentration, which leads to deeper encoding and increases learning and subsequent retrieval. There is evidence that being engaged in externally rather than internally focused tasks is related to low neural activity in the default mode network (DMN) promoting open mind and the deep elaboration of new information. Thus, reduced DMN activity should lead to enhanced concentration, comprehensive stimulus evaluation including emotional categorization, deeper stimulus processing, and better long-term retention over one whole week. In this fMRI study, we investigated brain activation preceding and during incidental encoding of emotional pictures and on subsequent recognition performance. During fMRI, 24 subjects were exposed to 80 pictures of different emotional valence and subsequently asked to complete an online recognition task one week later. Results indicate that neural activity within the medial temporal lobes during encoding predicts subsequent memory performance. Moreover, a low activity of the default mode network preceding incidental encoding leads to slightly better recognition performance independent of the emotional perception of a picture. The findings indicate that the suppression of internally-oriented thoughts leads to a more comprehensive and thorough evaluation of a stimulus and its emotional valence. Reduced activation of the DMN prior to stimulus onset is associated with deeper encoding and enhanced consolidation and retrieval performance even one week later. Even small prestimulus lapses of attention influence consolidation and subsequent recognition performance. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.

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Diet-related chronic diseases severely affect personal and global health. However, managing or treating these diseases currently requires long training and high personal involvement to succeed. Computer vision systems could assist with the assessment of diet by detecting and recognizing different foods and their portions in images. We propose novel methods for detecting a dish in an image and segmenting its contents with and without user interaction. All methods were evaluated on a database of over 1600 manually annotated images. The dish detection scored an average of 99% accuracy with a .2s/image run time, while the automatic and semi-automatic dish segmentation methods reached average accuracies of 88% and 91% respectively, with an average run time of .5s/image, outperforming competing solutions.