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

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

Detection of Human Enteric Viruses in Freshwater from European Countries

The transmission of water-borne pathogens typically occurs by a faecal–oral route, through inhalation of aerosols, or by direct or indirect contact with contaminated water. Previous molecular-based studies have identified viral particles of zoonotic and human nature in surface waters. Contaminated water can lead to human health issues, and the development of rapid methods for the detection of pathogenic microorganisms is a valuable tool for the prevention of their spread. The aims of this work were to determine the presence and identity of representative human pathogenic enteric viruses in water samples from six European countries by quantitative polymerase chain reaction (q-PCR) and to develop two quantitative PCR methods for Adenovirus 41 and Mammalian Orthoreoviruses. A 2-year survey showed that Norovirus, Mammalian Orthoreovirus and Adenoviruses were the most frequently identified enteric viruses in the sampled surface waters. Although it was not possible to establish viability and infectivity of the viruses considered, the detectable presence of pathogenic viruses may represent a potential risk for human health. The methodology developed may aid in rapid detection of these pathogens for monitoring

Detection of Human Enteric Viruses in Freshwater from European Countries

The transmission of water-borne pathogens typically occurs by a faecal–oral route, through inhalation of aerosols, or by direct or indirect contact with contaminated water. Previous molecular-based studies have identified viral particles of zoonotic and human nature in surface waters. Contaminated water can lead to human health issues, and the development of rapid methods for the detection of pathogenic microorganisms is a valuable tool for the prevention of their spread. The aims of this work were to determine the presence and identity of representative human pathogenic enteric viruses in water samples from six European countries by quantitative polymerase chain reaction (q-PCR) and to develop two quantitative PCR methods for Adenovirus 41 and Mammalian Orthoreoviruses. A 2-year survey showed that Norovirus, Mammalian Orthoreovirus and Adenoviruses were the most frequently identified enteric viruses in the sampled surface waters. Although it was not possible to establish viability and infectivity of the viruses considered, the detectable presence of pathogenic viruses may represent a potential risk for human health. The methodology developed may aid in rapid detection of these pathogens for monitoring

Combining human perception and geometric restrictions for automatic pedestrian detection

[EN]Automatic detection systems do not perform as well as human observers, even on simple detection tasks. A potential solution to this problem is training vision systems on appropriate regions of interests (ROIs), in contrast to training on predefined and arbitrarily selected regions. Here we focus on detecting pedestrians in static scenes. Our aim is to answer the following question: Can automatic vision systems for pedestrian detection be improved by training them on perceptually-defined ROIs?

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?

Fast and accurate hand pose detection for human-robot interaction

[EN]Enabling natural human-robot interaction using computer vision based applications requires fast and accurate hand detection. However, previous works in this field assume different constraints, like a limitation in the number of detected gestures, because hands are highly complex objects difficult to locate. This paper presents an approach which integrates temporal coherence cues and hand detection based on wrists using a cascade classifier. With this approach, we introduce three main contributions: (1) a transparent initialization mechanism without user participation for segmenting hands independently of their gesture, (2) a larger number of detected gestures as well as a faster training phase than previous cascade classifier based methods and (3) near real-time performance for hand pose detection in video streams.

Modelling human behaviour and event detection from videos

Automatic analysis of human behaviour in large collections of videos is gaining interest, even more so with the advent of file sharing sites such as YouTube. However, challenges still exist owing to several factors such as inter- and intra-class variations, cluttered backgrounds, occlusion, camera motion, scale, view and illumination changes. This research focuses on modelling human behaviour for action recognition in videos. The developed techniques are validated on large scale benchmark datasets and applied on real-world scenarios such as soccer videos. Three major contributions are made. The first contribution is in the area of proper choice of a feature representation for videos. This involved a study of state-of-the-art techniques for action recognition, feature extraction processing and dimensional reduction techniques so as to yield the best performance with optimal computational requirements. Secondly, temporal modelling of human behaviour is performed. This involved frequency analysis and temporal integration of local information in the video frames to yield a temporal feature vector. Current practices mostly average the frame information over an entire video and neglect the temporal order. Lastly, the proposed framework is applied and further adapted to real-world scenario such as soccer videos. A dataset consisting of video sequences depicting events of players falling is created from actual match data to this end and used to experimentally evaluate the proposed framework.

Evaluation of five commercial methods for the extraction and purification of DNA from human faecal samples for downstream molecular detection of the enteric protozoan parasites Cryptosporidium spp., Giardia duodenalis, and Entamoeba spp

High quality, pure DNA is required for ensuring reliable and reproducible results in molecular diagnosis applications. A number of in-house and commercial methods are available for the extraction and purification of genomic DNA from faecal material, each one offering a specific combination of performance, cost-effectiveness, and easiness of use that should be conveniently evaluated in function of the pathogen of interest. In this comparative study the marketed kits QIAamp DNA stool mini (Qiagen), SpeedTools DNA extraction (Biotools), DNAExtract-VK (Vacunek), PowerFecal DNA isolation (MoBio), and Wizard magnetic DNA purification system (Promega Corporation) were assessed for their efficacy in obtaining DNA of the most relevant enteric protozoan parasites associated to gastrointestinal disease globally. A panel of 113 stool specimens of clinically confirmed patients with cryptosporidiosis (n = 29), giardiasis (n = 47) and amoebiasis by Entamoeba histolytica (n = 3) or E. dispar (n = 10) and apparently healthy subjects (n = 24) were used for this purpose. Stool samples were aliquoted in five sub-samples and individually processed by each extraction method evaluated. Purified DNA samples were subsequently tested in PCR-based assays routinely used in our laboratory. The five compared methods yielded amplifiable amounts of DNA of the pathogens tested, although performance differences were observed among them depending on the parasite and the infection burden. Methods combining chemical, enzymatic and/or mechanical lysis procedures at temperatures of at least 56 °C were proven more efficient for the release of DNA from Cryptosporidium oocysts.

Detection of human interchromosomal trans-splicing in sequence databanks.

Trans-splicing is a common phenomenon in nematodes and kinetoplastids, and it has also been reported in other organisms, including humans. Up to now, all in silico strategies to find evidence of trans-splicing in humans have required that the candidate sequences follow the consensus splicing site rules (spliceosome-mediated mechanism). However, this criterion is not supported by the best human experimental evidence, which, except in a single case, do not follow canonical splicing sites. Moreover, recent findings describe a novel alternative tRNA mediated trans-splicing mechanism, which prescinds the spliceosome machinery. In order to answer the question, ?Are there hybrid mRNAs in sequence databanks, whose characteristics resemble those of the best human experimental evidence??, we have developed a methodology that successfully identified 16 hybrid mRNAs which might be instances of interchromosomal trans-splicing. Each hybrid mRNA is formed by a trans-spliced region (TSR), which was successfully mapped either onto known genes or onto a human endogenous retrovirus (HERV-K) transcript which supports their transcription. The existence of these hybrid mRNAs indicates that trans-splicing may be more widespread than believed. Furthermore, non-canonical splice site patterns suggest that infrequent splicing sites may occur under special conditions, or that an alternative trans-splicing mechanism is involved. Finally, our candidates are supposedly from normal tissue, and a recent study has reported that trans-splicing may occur not only in malignant tissues, but in normal tissues as well. Our methodology can be applied to 5'-UTR, coding sequences and 3'-UTR in order to find new candidates for a posteriori experimental confirmation.

Adaptive unsupervised learning of human actions

Automatic detection of suspicious activities in CCTV camera feeds is crucial to the success of video surveillance systems. Such a capability can help transform the dumb CCTV cameras into smart surveillance tools for fighting crime and terror. Learning and classification of basic human actions is a precursor to detecting suspicious activities. Most of the current approaches rely on a non-realistic assumption that a complete dataset of normal human actions is available. This paper presents a different approach to deal with the problem of understanding human actions in video when no prior information is available. This is achieved by working with an incomplete dataset of basic actions which are continuously updated. Initially, all video segments are represented by Bags-Of-Words (BOW) method using only Term Frequency-Inverse Document Frequency (TF-IDF) features. Then, a data-stream clustering algorithm is applied for updating the system's knowledge from the incoming video feeds. Finally, all the actions are classified into different sets. Experiments and comparisons are conducted on the well known Weizmann and KTH datasets to show the efficacy of the proposed approach.

A nested real-time PCR assay has an increased sensitivity suitable for detection of viruses in aerosol studies

Aims: Influenza is commonly spread by infectious aerosols; however, detection of viruses in aerosols is not sensitive enough to confirm the characteristics of virus aerosols. The aim of this study was to develop an assay for respiratory viruses sufficiently sensitive to be used in epidemiological studies. Method: A two-step, nested real-time PCR assay was developed for MS2 bacteriophage, and for influenza A and B, parainfluenza 1 and human respiratory syncytial virus. Outer primer pairs were designed to nest each existing real-time PCR assay. The sensitivities of the nested real-time PCR assays were compared to those of existing real-time PCR assays. Both assays were applied in an aerosol study to compare their detection limits in air samples. Conclusions: The nested real-time PCR assays were found to be several logs more sensitive than the real-time PCR assays, with lower levels of virus detected at lower Ct values. The nested real-time PCR assay successfully detected MS2 in air samples, whereas the real-time assay did not. Significance and Impact of the Study: The sensitive assays for respiratory viruses will permit further research using air samples from naturally generated virus aerosols. This will inform current knowledge regarding the risks associated with the spread of viruses through aerosol transmission.

Improved detection and tracking of objects in surveillance video

Surveillance networks are typically monitored by a few people, viewing several monitors displaying the camera feeds. It is then very difficult for a human operator to effectively detect events as they happen. Recently, computer vision research has begun to address ways to automatically process some of this data, to assist human operators. Object tracking, event recognition, crowd analysis and human identification at a distance are being pursued as a means to aid human operators and improve the security of areas such as transport hubs. The task of object tracking is key to the effective use of more advanced technologies. To recognize an event people and objects must be tracked. Tracking also enhances the performance of tasks such as crowd analysis or human identification. Before an object can be tracked, it must be detected. Motion segmentation techniques, widely employed in tracking systems, produce a binary image in which objects can be located. However, these techniques are prone to errors caused by shadows and lighting changes. Detection routines often fail, either due to erroneous motion caused by noise and lighting effects, or due to the detection routines being unable to split occluded regions into their component objects. Particle filters can be used as a self contained tracking system, and make it unnecessary for the task of detection to be carried out separately except for an initial (often manual) detection to initialise the filter. Particle filters use one or more extracted features to evaluate the likelihood of an object existing at a given point each frame. Such systems however do not easily allow for multiple objects to be tracked robustly, and do not explicitly maintain the identity of tracked objects. This dissertation investigates improvements to the performance of object tracking algorithms through improved motion segmentation and the use of a particle filter. A novel hybrid motion segmentation / optical flow algorithm, capable of simultaneously extracting multiple layers of foreground and optical flow in surveillance video frames is proposed. The algorithm is shown to perform well in the presence of adverse lighting conditions, and the optical flow is capable of extracting a moving object. The proposed algorithm is integrated within a tracking system and evaluated using the ETISEO (Evaluation du Traitement et de lInterpretation de Sequences vidEO - Evaluation for video understanding) database, and significant improvement in detection and tracking performance is demonstrated when compared to a baseline system. A Scalable Condensation Filter (SCF), a particle filter designed to work within an existing tracking system, is also developed. The creation and deletion of modes and maintenance of identity is handled by the underlying tracking system; and the tracking system is able to benefit from the improved performance in uncertain conditions arising from occlusion and noise provided by a particle filter. The system is evaluated using the ETISEO database. The dissertation then investigates fusion schemes for multi-spectral tracking systems. Four fusion schemes for combining a thermal and visual colour modality are evaluated using the OTCBVS (Object Tracking and Classification in and Beyond the Visible Spectrum) database. It is shown that a middle fusion scheme yields the best results and demonstrates a significant improvement in performance when compared to a system using either mode individually. Findings from the thesis contribute to improve the performance of semi-automated video processing and therefore improve security in areas under surveillance.

Automatic tracking, super-resolution and recognition of human faces from surveillance video

Identifying an individual from surveillance video is a difficult, time consuming and labour intensive process. The proposed system aims to streamline this process by filtering out unwanted scenes and enhancing an individual's face through super-resolution. An automatic face recognition system is then used to identify the subject or present the human operator with likely matches from a database. A person tracker is used to speed up the subject detection and super-resolution process by tracking moving subjects and cropping a region of interest around the subject's face to reduce the number and size of the image frames to be super-resolved respectively. In this paper, experiments have been conducted to demonstrate how the optical flow super-resolution method used improves surveillance imagery for visual inspection as well as automatic face recognition on an Eigenface and Elastic Bunch Graph Matching system. The optical flow based method has also been benchmarked against the ``hallucination'' algorithm, interpolation methods and the original low-resolution images. Results show that both super-resolution algorithms improved recognition rates significantly. Although the hallucination method resulted in slightly higher recognition rates, the optical flow method produced less artifacts and more visually correct images suitable for human consumption.

Vision-based detection and tracking of aerial targets for UAV collision avoidance

Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of the sensors involved (as opposed to radar). This paper describes the development and evaluation of a vision-based collision detection algorithm suitable for fixed-wing aerial robotics. The system was evaluated using highly realistic vision data of the moments leading up to a collision. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400m to about 900m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning of between 8-10 seconds ahead of impact, which approaches the 12.5 second response time recommended for human pilots. We make use of the enormous potential of graphic processing units to achieve processing rates of 30Hz (for images of size 1024-by- 768). Currently, integration in the final platform is under way.