Molecular diversity, cultivation, and improved detection by fluorescent in situ hybridization of a dominant group of human gut bacteria related to Roseburia spp. or Eubacterium rectale

Peer reviewed

Improving automatic pedestrian detection by means of human perception

[EN]Can automatic vision systems for pedestrian detection be improved by training them on perceptually-defined ROIs?

The Prevalence of Depression among patients and its detection by Primary Health Care Workers at Matawale Health Centre (Zomba)

Background Little information is available on the prevalence of depression in Malawi in primary health care settings and yet there is increased number of cases of depression presenting at tertiary level in severe form. Aim The aim of the study was to determine the prevalence of depression among patients and its detection by health care workers at a primary health care clinic in Zomba. Methods A cross-sectional survey was done among patients attending outpatient department at Matawale Health Centre, in Zomba from 1st July 2009 through to 31st July 2009. A total of 350 adults were randomly selected using systematic sampling. The “Self Reporting Questionnaire”, a questionnaire measuring social demographic factors and the Structured Clinical Interview for DSM-IV Axis I disorders Non-Patient Version (SCID-NP) were administered verbally to the participants. Findings The prevalence of depression among the patients attending the outpatients department was found to be 30.3% while detection rate of depression by clinician was 0%. Conclusion The results revealed the magnitude of depression which is prevalent in the primary health care clinic that goes undiagnosed and unmanaged. It is therefore recommended that primary health care providers do thorough assessments to address common mental disorders especially depression and they should be educated to recognise and manage depression appropriately at primary care level.

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.

Detection of respiratory viruses by real-time polymerase chain reaction in outpatients with acute respiratory infection

Viruses are the major contributors to the morbidity and mortality of upper and lower acute respiratory infections (ARIs) for all age groups. The aim of this study was to determine the frequencies for a large range of respiratory viruses using a sensitive molecular detection technique in specimens from outpatients of all ages with ARIs. Nasopharyngeal aspirates were obtained from 162 individuals between August 2007-August 2009. Twenty-three pathogenic respiratory agents, 18 respiratory viruses and five bacteria were investigated using multiplex real-time reverse transcriptase polymerase chain reaction (RT-PCR) and indirect immunofluorescence assay (IIF). Through IIF, 33 (20.4%) specimens with respiratory virus were recognised, with influenza virus representing over half of the positive samples. Through a multiplex real-time RT-PCR assay, 88 (54.3%) positive samples were detected; the most prevalent respiratory viral pathogens were influenza, human rhinovirus and respiratory syncytial virus (RSV). Six cases of viral co-detection were observed, mainly involving RSV. The use of multiplex real-time RT-PCR increased the viral detection by 33.9% and revealed a larger number of respiratory viruses implicated in ARI cases, including the most recently described respiratory viruses [human bocavirus, human metapneumovirus, influenza A (H1N1) pdm09 virus, human coronavirus (HCoV) NL63 and HCoV HKU1].

An embedded real-time red peach detection system based on an OV7670 camera, ARM Cortex-M4 processor and 3D Look-Up Tables

This work proposes the development of an embedded real-time fruit detection system for future automatic fruit harvesting. The proposed embedded system is based on an ARM Cortex-M4 (STM32F407VGT6) processor and an Omnivision OV7670 color camera. The future goal of this embedded vision system will be to control a robotized arm to automatically select and pick some fruit directly from the tree. The complete embedded system has been designed to be placed directly in the gripper tool of the future robotized harvesting arm. The embedded system will be able to perform real-time fruit detection and tracking by using a three-dimensional look-up-table (LUT) defined in the RGB color space and optimized for fruit picking. Additionally, two different methodologies for creating optimized 3D LUTs based on existing linear color models and fruit histograms were implemented in this work and compared for the case of red peaches. The resulting system is able to acquire general and zoomed orchard images and to update the relative tracking information of a red peach in the tree ten times per second.

The detection of Toxoplasma gondii by comparing cytology, histopathology, bioassay in mice, and the polymerase chain reaction (PCR)

The objective of this study was to compare the different methods of detecting Toxoplasma gondii in sheep tissue, tested serologically positive by the indirect immunofluorescent antibody test (IFAT). Brain, diaphragm, and blood samples were collected from 522 sheep slaughtered at the São Manuel abattoir, São Paulo State, Brazil. Brain and diaphragm samples from IFAT seropositive animals were digested by both trypsin and pepsin and then injected into mice. Part of the digested samples was used to prepare slides for Giemsa staining and in the polymerase chain reaction (PCR). Tissue fragments were fixed in formalin and examined using hematoxilin-eosin (HE). Forty of the sheep (7.7%) were IFAT positive. T. gondii was isolated in 23 (59.0%) of the 39 mice with pepsin-digested brain samples and in 27 (69.0%) of the 39 with trypsin-digested brain samples. Injection of diaphragm samples led to T. gondii isolation in 26 (66.7%) of the 39 pepsin-digested samples and 21 (53.8%) of the 39 trypsin-digested samples. Cytological and hystopathological examination of both brains and diaphragms was negative in all examined sheep. PCR was positive in 7 (17.9%) of the trypsin and 2 (5.1%) of the pepsin-digested samples, while 9 (23.1%) of the trypsin and 3 (7.7%) of the pepsin-digested samples showed T. gondii DNA. T. gondii isolation rate in mice (n = 34; 85.0%) was significantly higher than detection by PCR (n = 15; 37.5%). © 2001 Elsevier Science B.V.

Target Tracking in UWB Multistatic Radars

Detection, localization and tracking of non-collaborative objects moving inside an area is of great interest to many surveillance applications. An ultra- wideband (UWB) multistatic radar is considered as a good infrastructure for such anti-intruder systems, due to the high range resolution provided by the UWB impulse-radio and the spatial diversity achieved with a multistatic configuration. Detection of targets, which are typically human beings, is a challenging task due to reflections from unwanted objects in the area, shadowing, antenna cross-talks, low transmit power, and the blind zones arised from intrinsic peculiarities of UWB multistatic radars. Hence, we propose more effective detection, localization, as well as clutter removal techniques for these systems. However, the majority of the thesis effort is devoted to the tracking phase, which is an essential part for improving the localization accuracy, predicting the target position and filling out the missed detections. Since UWB radars are not linear Gaussian systems, the widely used tracking filters, such as the Kalman filter, are not expected to provide a satisfactory performance. Thus, we propose the Bayesian filter as an appropriate candidate for UWB radars. In particular, we develop tracking algorithms based on particle filtering, which is the most common approximation of Bayesian filtering, for both single and multiple target scenarios. Also, we propose some effective detection and tracking algorithms based on image processing tools. We evaluate the performance of our proposed approaches by numerical simulations. Moreover, we provide experimental results by channel measurements for tracking a person walking in an indoor area, with the presence of a significant clutter. We discuss the existing practical issues and address them by proposing more robust algorithms.

Scene Reconstruction And Understanding By RGB-D Sensors

This thesis investigates interactive scene reconstruction and understanding using RGB-D data only. Indeed, we believe that depth cameras will still be in the near future a cheap and low-power 3D sensing alternative suitable for mobile devices too. Therefore, our contributions build on top of state-of-the-art approaches to achieve advances in three main challenging scenarios, namely mobile mapping, large scale surface reconstruction and semantic modeling. First, we will describe an effective approach dealing with Simultaneous Localization And Mapping (SLAM) on platforms with limited resources, such as a tablet device. Unlike previous methods, dense reconstruction is achieved by reprojection of RGB-D frames, while local consistency is maintained by deploying relative bundle adjustment principles. We will show quantitative results comparing our technique to the state-of-the-art as well as detailed reconstruction of various environments ranging from rooms to small apartments. Then, we will address large scale surface modeling from depth maps exploiting parallel GPU computing. We will develop a real-time camera tracking method based on the popular KinectFusion system and an online surface alignment technique capable of counteracting drift errors and closing small loops. We will show very high quality meshes outperforming existing methods on publicly available datasets as well as on data recorded with our RGB-D camera even in complete darkness. Finally, we will move to our Semantic Bundle Adjustment framework to effectively combine object detection and SLAM in a unified system. Though the mathematical framework we will describe does not restrict to a particular sensing technology, in the experimental section we will refer, again, only to RGB-D sensing. We will discuss successful implementations of our algorithm showing the benefit of a joint object detection, camera tracking and environment mapping.

Homography-based ground plane detection using a single on-board camera

This study presents a robust method for ground plane detection in vision-based systems with a non-stationary camera. The proposed method is based on the reliable estimation of the homography between ground planes in successive images. This homography is computed using a feature matching approach, which in contrast to classical approaches to on-board motion estimation does not require explicit ego-motion calculation. As opposed to it, a novel homography calculation method based on a linear estimation framework is presented. This framework provides predictions of the ground plane transformation matrix that are dynamically updated with new measurements. The method is specially suited for challenging environments, in particular traffic scenarios, in which the information is scarce and the homography computed from the images is usually inaccurate or erroneous. The proposed estimation framework is able to remove erroneous measurements and to correct those that are inaccurate, hence producing a reliable homography estimate at each instant. It is based on the evaluation of the difference between the predicted and the observed transformations, measured according to the spectral norm of the associated matrix of differences. Moreover, an example is provided on how to use the information extracted from ground plane estimation to achieve object detection and tracking. The method has been successfully demonstrated for the detection of moving vehicles in traffic environments.

Simultaneous detection and differentiation of human Polyomaviruses JC and BK by a rapid and sensitive PCR-ELAHA assay and a survey of the JCV subtypes within an Australian population

Human polyomaviruses JCV and BKV can cause several clinical manifestations in immunocompromised hosts, including progressive multifocal leukoencephalopathy (PML) and haemorrhagic cystitis. Molecular detection by polymerase chain reaction (PCR) is recognised as a sensitive and specific method for detecting human polyomaviruses in clinical samples. In this study, we developed a PCR assay using a single primer pair to amplify a segment of the VP1 gene of JCV and BKV. An enzyme linked amplicon hybridisation assay (ELAHA) using species-specific biotinylated oligonucleotide probes was used to differentiate between JCV and BKV. This assay (VP1-PCR-ELAHA) was evaluated and compared to a PCR assay targeting the human polyomavirus T antigen gene (pol-PCR). DNA sequencing was used to confirm the polyomavirus species identified by the VP1-PCR-ELAHA and to determine the subtype of each JCV isolate. A total of 297 urine specimens were tested and human polyomavirus was detected in 105 specimens (35.4%) by both PCR assays. The differentiation of JCV and BKV by the VP1-PCR-ELAHA showed good agreement with the results of DNA sequencing. Further, DNA sequencing of the JCV positive specimens showed the most prevalent JCV subtype in our cohort was 2a (27%) followed by 1b (20%), 1a (15%), 2c (14%), 4 (14%) and 2b (10%). The results of this study show that the VP1-PCR-ELAHA is a sensitive, specific and rapid method for detecting and differentiating human polyomaviruses JC and BK and is highly suitable for routine use in the clinical laboratory. (C) 2004 Wiley-Liss, Inc.