The Wikipedia Image Retrieval Task

The wikipedia image retrieval task at ImageCLEF provides a testbed for the system-oriented evaluation of visual information retrieval from a collection of Wikipedia images. The aim is to investigate the effectiveness of retrieval approaches that exploit textual and visual evidence in the context of a large and heterogeneous collection of images that are searched for by users with diverse information needs. This chapter presents an overview of the available test collections, summarises the retrieval approaches employed by the groups that participated in the task during the 2008 and 2009 ImageCLEF campaigns, provides an analysis of the main evaluation results, identifies best practices for effective retrieval, and discusses open issues

Overview of the wikipediaMM task at ImageCLEF 2008

The wikipediaMM task provides a testbed for the system-oriented evaluation of ad-hoc retrieval from a large collection of Wikipedia images. It became a part of the ImageCLEF evaluation campaign in 2008 with the aim of investigating the use of visual and textual sources in combination for improving the retrieval performance. This paper presents an overview of the task¿s resources, topics, assessments, participants' approaches, and main results

CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology

Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple-to-use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257).EU 7th Framework Programme (BIOLEDGE Contract No: 289126 to S. G. O and J. R), BBSRC (BRIC2.2 to S. G. O. and N. K. H. S.), Synthetic Biology Research Initiative Cambridge (SynBioFund to D. D., A. C. C. and J. M. L. D.

小学校理科において自然事象を科学的に説明し理解を深める児童の育成―見通しと振り返りを充実させて―

The ability for a host to recognize infection is critical for virus clearance and often begins with induction of inflammation. The PB1-F2 of pathogenic influenza A viruses (IAV) contributes to the pathophysiology of infection, although the mechanism for this is unclear. The NLRP3-inflammasome has been implicated in IAV pathogenesis, but whether IAV virulence proteins can be activators of the complex is unknown. We investigated whether PB1-F2-mediated activation of the NLRP3-inflammasome is a mechanism contributing to overt inflammatory responses to IAV infection. We show PB1-F2 induces secretion of pyrogenic cytokine IL-1β by activating the NLRP3-inflammasome, contributing to inflammation triggered by pathogenic IAV. Compared to infection with wild-type virus, mice infected with reverse engineered PB1-F2-deficient IAV resulted in decreased IL-1β secretion and cellular recruitment to the airways. Moreover, mice exposed to PB1-F2 peptide derived from pathogenic IAV had enhanced IL-1β secretion compared to mice exposed to peptide derived from seasonal IAV. Implicating the NLRP3-inflammasome complex specifically, we show PB1-F2 derived from pathogenic IAV induced IL-1β secretion was Caspase-1-dependent in human PBMCs and NLRP3-dependent in mice. Importantly, we demonstrate PB1-F2 is incorporated into the phagolysosomal compartment, and upon acidification, induces ASC speck formation. We also show that high molecular weight aggregated PB1-F2, rather than soluble PB1-F2, induces IL-1β secretion. Furthermore, NLRP3-deficient mice exposed to PB1-F2 peptide or infected with PB1-F2 expressing IAV were unable to efficiently induce the robust inflammatory response as observed in wild-type mice. In addition to viral pore forming toxins, ion channel proteins and RNA, we demonstrate inducers of NLRP3-inflammasome activation may include disordered viral proteins, as exemplified by PB1-F2, acting as host pathogen 'danger' signals. Elucidating immunostimulatory PB1-F2 mediation of NLRP3-inflammasome activation is a major step forward in our understanding of the aetiology of disease attributable to exuberant inflammatory responses to IAV infection

Overview of the wikipediaMM task at ImageCLEF 2009.

ImageCLEF's wikipediaMM task provides a testbed for the system-oriented evaluation of multimedia information retrieval from a collection of Wikipedia images. The aim is to investigate retrieval approaches in the context of a large and heterogeneous collection of images (similar to those encountered on the Web) that are searched for by users with diverse information needs. This paper presents an overview of the resources, topics, and assessments of the wikipediaMM task at ImageCLEF 2009, summarises the retrieval approaches employed by the participating groups, and provides a first analysis of the main evaluation results

Overview of the wikipedia retrieval task at ImageCLEF 2010

ImageCLEF's Wikipedia Retrieval task provides a testbed for the system-oriented evaluation of multimedia information retrieval from a collection of Wikipedia images. The aim is to investigate retrieval approaches in the context of a large and heterogeneous collection of images (similar to those encountered on the Web) that are searched for by users with diverse information needs. This paper presents an overview of the resources, topics, and assessments of the Wikipedia Retrieval task at ImageCLEF 2010, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results

Research data supporting "A Tool for Exploiting Complex Adaptive Evolution to Optimise Protocols for Biological Experiments"

CamOptimus is a tool for applying Genetic Algorithm (GA) to solve multi-parametric optimisation problems and Symbolic Regression (SR) to obtain models using the data generated during optimisation procedure to investigate the effect of individual parameters on the system of interest. The source code for the compiled software, and the Graphical User Interface (GUI) of the application are available under free licensing (GNU General Public License v3.0). The user manual is supplied in the compressed folder. $\textbf{Important information}$: access to the files for this software has been restricted as they are out of date. The software is available on Github, where updated documentation and new releases are available. $\href{https://github.com/DuyguD}{\text{https://github.com/DuyguD}}$.The University of Cambridge Synthetic Biology Research Initiative [SynBio Fund: CamOptimus] to DD, ACC, JML

A boosting approach to multiview classification with cooperation

International audienceNowadays in numerous fields such as bioinformatics or multimedia, data may be described using many different sets of features (or views) which carry either global or local information. Many learning tasks make use of these competitive views in order to improve overall predictive power of classifiers through fusion-based methods. Usually, these approaches rely on a weighted combination of classifiers (or selected descriptions), where classifiers are learnt independently the ones from the others. One drawback of these methods is that the classifier learnt on one view does not communicate its lack to the other views. In other words, learning algorithms do not cooperate although they are trained on the same objects. This paper deals with a novel approach to integrate multiview information within an iterative learning scheme, where the classifier learnt on one view is allowed to somehow communicate its performances to the other views. The proposed algorithm, named Mumbo, is based on boosting. Within the boosting scheme, Mumbo maintains one distribution of examples on each view, and at each round, it learns one weak classifier on each view. Within a view, the distribution of examples evolves both with the ability of the dedicated classifier to deal with examples of the corresponding features space, and with the ability of classifiers in other views to process the same examples within their own description spaces. Hence, the principle is to slightly remove the hard examples from the learning space of one view, while their weights get higher in the other views. This way, we expect that examples are urged to be processed by the most appropriate views, when possible. At the end of the iterative learning process, a final classifier is computed by a weighted combination of selected weak classifiers. Such an approach is merely useful when some examples detected as outliers in a view -- for instance because of noise -- are quite probabilisticaly regular hence informative within some other view. This paper provides the Mumbo algorithm in a multiclass and multiview setting, based on recent advances in theoretical boosting. The boosting properties of Mumbo are proven, as well as a some results on its generalization capabilities. Several experimental results are reported which point out that complementary views may actually cooperate under some assumptions

Rapid evolution of the PB1-F2 virulence protein expressed by human seasonal H3N2 influenza viruses reduces inflammatory responses to infection

Influenza A virus (IAV) PB1-F2 protein has been linked to viral virulence. Strains of the H3N2 subtype historically express full-length PB1-F2 proteins but during the 2010-2011 influenza seasons, nearly half of the circulating H3N2 IAVs encoded truncated PB1-F2 protein. Using a panel of reverse engineered H3N2 IAVs differing only in the origin of the PB1 gene segment, we found that only the virus encoding the avian-derived 1968 PB1 gene matching the human pandemic strain enhanced cellular infiltrate into the alveolar spaces of infected mice. We linked this phenomenon to expression of full-length PB1-F2 protein encompassing critical "inflammatory" residues

Induction of memory cytotoxic T cells to influenza A virus and subsequent viral clearance is not modulated by PB1-F2-dependent inflammasome activation

Expression of the viral virulence protein PB1-F2 during infection has been linked to NLRP3 inflammasome complex activation in macrophages and induction of early inflammatory events enhancing immunopathology during influenza disease. We sought to determine whether PB1-F2-specific NLRP3 inflammasome activation influenced the magnitude and/or robustness of the CD8(+) T-cell responses specific for conserved viral antigens and subsequent virus elimination. Using murine heterosubtypic viral infection models, we showed that mice infected with virus unable to produce PB1-F2 protein showed no deficit in the overall magnitude and functional memory responses of CD8(+) T cells established during the effector phase compared with those infected with wild-type PB1-F2-expressing virus and were equally capable of mounting robust recall responses. These data indicate that while expression of PB1-F2 protein can induce inflammatory events, the capacity to generate memory CD8(+) T cells specific for immunodominant viral epitopes remains uncompromised