On Regulatory and Organizational Constraints in Visualization Design and Evaluation

Problem-based visualization research provides explicit guidance toward identifying and designing for the needs of users, but absent is more concrete guidance toward factors external to a user's needs that also have implications for visualization design and evaluation. This lack of more explicit guidance can leave visualization researchers and practitioners vulnerable to unforeseen constraints beyond the user's needs that can affect the validity of evaluations, or even lead to the premature termination of a project. Here we explore two types of external constraints in depth, regulatory and organizational constraints, and describe how these constraints impact visualization design and evaluation. By borrowing from techniques in software development, project management, and visualization research we recommend strategies for identifying, mitigating, and evaluating these external constraints through a design study methodology. Finally, we present an application of those recommendations in a healthcare case study. We argue that by explicitly incorporating external constraints into visualization design and evaluation, researchers and practitioners can improve the utility and validity of their visualization solution and improve the likelihood of successful collaborations with industries where external constraints are more present.Comment: 9 pages, 2 figures, presented at BELIV workshop associated with IEEE VIS 201

Assessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria

BACKGROUND: Identification of a bacterial protein's subcellular localization (SCL) is important for genome annotation, function prediction and drug or vaccine target identification. Subcellular fractionation techniques combined with recent proteomics technology permits the identification of large numbers of proteins from distinct bacterial compartments. However, the fractionation of a complex structure like the cell into several subcellular compartments is not a trivial task. Contamination from other compartments may occur, and some proteins may reside in multiple localizations. New computational methods have been reported over the past few years that now permit much more accurate, genome-wide analysis of the SCL of protein sequences deduced from genomes. There is a need to compare such computational methods with laboratory proteomics approaches to identify the most effective current approach for genome-wide localization characterization and annotation. RESULTS: In this study, ten subcellular proteome analyses of bacterial compartments were reviewed. PSORTb version 2.0 was used to computationally predict the localization of proteins reported in these publications, and these computational predictions were then compared to the localizations determined by the proteomics study. By using a combined approach, we were able to identify a number of contaminants and proteins with dual localizations, and were able to more accurately identify membrane subproteomes. Our results allowed us to estimate the precision level of laboratory subproteome studies and we show here that, on average, recent high-precision computational methods such as PSORTb now have a lower error rate than laboratory methods. CONCLUSION: We have performed the first focused comparison of genome-wide proteomic and computational methods for subcellular localization identification, and show that computational methods have now attained a level of precision that is exceeding that of high-throughput laboratory approaches. We note that analysis of all cellular fractions collectively is required to effectively provide localization information from laboratory studies, and we propose an overall approach to genome-wide subcellular localization characterization that capitalizes on the complementary nature of current laboratory and computational methods

PSORTdb: a protein subcellular localization database for bacteria

Information about bacterial subcellular localization (SCL) is important for protein function prediction and identification of suitable drug/vaccine/diagnostic targets. PSORTdb (http://db.psort.org/) is a web-accessible database of SCL for bacteria that contains both information determined through laboratory experimentation and computational predictions. The dataset of experimentally verified information (∼2000 proteins) was manually curated by us and represents the largest dataset of its kind. Earlier versions have been used for training SCL predictors, and its incorporation now into this new PSORTdb resource, with its associated additional annotation information and dataset version control, should aid researchers in future development of improved SCL predictors. The second component of this database contains computational analyses of proteins deduced from the most recent NCBI dataset of completely sequenced genomes. Analyses are currently calculated using PSORTb, the most precise automated SCL predictor for bacterial proteins. Both datasets can be accessed through the web using a very flexible text search engine, a data browser, or using BLAST, and the entire database or search results may be downloaded in various formats. Features such as GO ontologies and multiple accession numbers are incorporated to facilitate integration with other bioinformatics resources. PSORTdb is freely available under GNU General Public License

Declaring a tuberculosis outbreak over with genomic epidemiology

We report an updated method for inferring the time at which an infectious disease was transmitted between persons from a time-labelled pathogen genome phylogeny. We applied the method to 48 Mycobacterium tuberculosis genomes as part of a real-time public health outbreak investigation, demonstrating that although active tuberculosis (TB) cases were diagnosed through 2013, no transmission events took place beyond mid-2012. Subsequent cases were the result of progression from latent TB infection to active disease, and not recent transmission. This evolutionary genomic approach was used to declare the outbreak over in January 2015

Declaring a tuberculosis outbreak over with genomic epidemiology

We report an updated method for inferring the time at which an infectious disease was transmitted between persons from a time-labelled pathogen genome phylogeny. We applied the method to 48 Mycobacterium tuberculosis genomes as part of a real-time public health outbreak investigation, demonstrating that although active tuberculosis (TB) cases were diagnosed through 2013, no transmission events took place beyond mid-2012. Subsequent cases were the result of progression from latent TB infection to active disease, and not recent transmission. This evolutionary genomic approach was used to declare the outbreak over in January 2015

Systems-Level Comparison of Host-Responses Elicited by Avian H5N1 and Seasonal H1N1 Influenza Viruses in Primary Human Macrophages

Human disease caused by highly pathogenic avian influenza (HPAI) H5N1 can lead to a rapidly progressive viral pneumonia leading to acute respiratory distress syndrome. There is increasing evidence from clinical, animal models and in vitro data, which suggests a role for virus-induced cytokine dysregulation in contributing to the pathogenesis of human H5N1 disease. The key target cells for the virus in the lung are the alveolar epithelium and alveolar macrophages, and we have shown that, compared to seasonal human influenza viruses, equivalent infecting doses of H5N1 viruses markedly up-regulate pro-inflammatory cytokines in both primary cell types in vitro. Whether this H5N1-induced dysregulation of host responses is driven by qualitative (i.e activation of unique host pathways in response to H5N1) or quantitative differences between seasonal influenza viruses is unclear. Here we used microarrays to analyze and compare the gene expression profiles in primary human macrophages at 1, 3, and 6 h after infection with H5N1 virus or low-pathogenic seasonal influenza A (H1N1) virus. We found that host responses to both viruses are qualitatively similar with the activation of nearly identical biological processes and pathways. However, in comparison to seasonal H1N1 virus, H5N1 infection elicits a quantitatively stronger host inflammatory response including type I interferon (IFN) and tumor necrosis factor (TNF)-α genes. A network-based analysis suggests that the synergy between IFN-β and TNF-α results in an enhanced and sustained IFN and pro-inflammatory cytokine response at the early stage of viral infection that may contribute to the viral pathogenesis and this is of relevance to the design of novel therapeutic strategies for H5N1 induced respiratory disease

InnateDB: facilitating systems-level analyses of the mammalian innate immune response

Although considerable progress has been made in dissecting the signaling pathways involved in the innate immune response, it is now apparent that this response can no longer be productively thought of in terms of simple linear pathways. InnateDB (www.innatedb.ca) has been developed to facilitate systems-level analyses that will provide better insight into the complex networks of pathways and interactions that govern the innate immune response. InnateDB is a publicly available, manually curated, integrative biology database of the human and mouse molecules, experimentally verified interactions and pathways involved in innate immunity, along with centralized annotation on the broader human and mouse interactomes. To date, more than 3500 innate immunity-relevant interactions have been contextually annotated through the review of 1000 plus publications. Integrated into InnateDB are novel bioinformatics resources, including network visualization software, pathway analysis, orthologous interaction network construction and the ability to overlay user-supplied gene expression data in an intuitively displayed molecular interaction network and pathway context, which will enable biologists without a computational background to explore their data in a more systems-oriented manner

Cross-Lineage Influenza B and Heterologous Influenza A Antibody Responses in Vaccinated Mice: Immunologic Interactions and B/Yamagata Dominance

The annually reformulated trivalent inactivated influenza vaccine (TIV) includes both influenza A/subtypes (H3N2 and H1N1) but only one of two influenza B/lineages (Yamagata or Victoria). In a recent series of clinical trials to evaluate prime-boost response across influenza B/lineages, influenza-naïve infants and toddlers originally primed with two doses of 2008–09 B/Yamagata-containing TIV were assessed after two doses of B/Victoria-containing TIV administered in the subsequent 2009–10 and 2010–11 seasons. In these children, the Victoria-containing vaccines strongly recalled antibody to the initiating B/Yamagata antigen but induced only low B/Victoria antibody responses. To further evaluate this unexpected pattern of cross-lineage vaccine responses, we conducted additional immunogenicity assessment in mice. In the current study, mice were primed with two doses of 2008–09 Yamagata-containing TIV and subsequently boosted with two doses of 2010–11 Victoria-containing TIV (Group-Yam/Vic). With the same vaccines, we also assessed the reverse order of two-dose Victoria followed by two-dose Yamagata immunization (Group-Vic/Yam). The Group-Yam/Vic mice showed strong homologous responses to Yamagata antigen. However, as previously reported in children, subsequent doses of Victoria antigen substantially boosted Yamagata but induced only low antibody response to the immunizing Victoria component. The reverse order of Group-Vic/Yam mice also showed low homologous responses to Victoria but subsequent heterologous immunization with even a single dose of Yamagata antigen induced substantial boost response to both lineages. For influenza A/H3N2, homologous responses were comparably robust for the differing TIV variants and even a single follow-up dose of the heterologous strain, regardless of vaccine sequence, substantially boosted antibody to both strains. For H1N1, two doses of 2008–09 seasonal antigen significantly blunted response to two doses of the 2010–11 pandemic H1N1 antigen. Immunologic interactions between influenza viruses considered antigenically distant and in particular the cross-lineage influenza B and dominant Yamagata boost responses we have observed in both human and animal studies warrant further evaluation

Association between the 2008–09 Seasonal Influenza Vaccine and Pandemic H1N1 Illness during Spring–Summer 2009: Four Observational Studies from Canada

In three case-control studies and a household transmission cohort, Danuta Skowronski and colleagues find an association between prior seasonal flu vaccination and increased risk of 2009 pandemic H1N1 flu

Mycobacterium tuberculosis lineage 4 comprises globally distributed and geographically restricted sublineages

Generalist and specialist species differ in the breadth of their ecological niches. Little is known about the niche width of obligate human pathogens. Here we analyzed a global collection of Mycobacterium tuberculosis lineage 4 clinical isolates, the most geographically widespread cause of human tuberculosis. We show that lineage 4 comprises globally distributed and geographically restricted sublineages, suggesting a distinction between generalists and specialists. Population genomic analyses showed that, whereas the majority of human T cell epitopes were conserved in all sublineages, the proportion of variable epitopes was higher in generalists. Our data further support a European origin for the most common generalist sublineage. Hence, the global success of lineage 4 reflects distinct strategies adopted by different sublineages and the influence of human migration.We thank S. Lecher, S. Li and J. Zallet for technical support. Calculations were performed at the sciCORE scientific computing core facility at the University of Basel. This work was supported by the Swiss National Science Foundation (grants 310030_166687 (S.G.) and 320030_153442 (M.E.) and Swiss HIV Cohort Study grant 740 to L.F.), the European Research Council (309540-EVODRTB to S.G.), TB-PAN-NET (FP7-223681 to S.N.), PathoNgenTrace projects (FP7-278864-2 to S.N.), SystemsX.ch (S.G.), the German Center for Infection Research (DZIF; S.N.), the Novartis Foundation (S.G.), the Natural Science Foundation of China (91631301 to Q.G.), and the National Institute of Allergy and Infectious Diseases (5U01-AI069924-05) of the US National Institutes of Health (M.E.)