Bacterial and Archaeal Specific-Predation in the North Atlantic Basin

Stable isotope probing (SIP) was used to track prokaryotic and eukaryotic carbon uptake along a meridional transect (Long. 52°W) in the North Atlantic to assess if 13C-resource partitioning between bacteria and archaea and 13C-labeled eukaryotic predators could be detected. One-liter SIP microcosms were amended with 13C-acetate or 13C-urea and incubated for 48 h. Our data indicated archaea often outcompeted bacteria for 13C-urea while both archaea and bacteria could incorporate 13C-acetate. This 13C label could also be tracked into eukaryotic microbes. The largest number of 13C-labeled eukaryotic OTUs, and the greatest percentage of eukaryotic 13C signal, were observed in conjunction with both archaeal and bacterial 13C incorporation, suggesting that most eukaryotic predators do not distinguish between archaeal and bacterial prey. However, other 13C-eukaryotic OTUs were exclusively associated with either 13C-archaeal or 13C-bacterial OTUs. These archaeal-specific and bacterial-specific 13C-eukaryotic OTUs were related to known bactivorous predators including Ancyromonas, Amastigomonas, Cafeteria, and Caecitellus. Our SIP findings suggest both resource partitioning between bacteria and TACK (Thaumarchaeota, Aigarchaeota, Crenarchaeota, and Korarchaeota) archaea and selective predation by eukaryotic predators. Determining the equalizing mechanisms for co-existence in the marine environment can help map predator/prey interactions to better estimate carbon flow in the deep ocean

PADTUN - using semantic technologies in tunnel diagnosis and maintenance domain

YesA Decision Support System (DSS) in tunnelling domain deals with identifying pathologies based on disorders present in various tunnel portions and contextual factors affecting a tunnel. Another key area in diagnosing pathologies is to identify regions of interest (ROI). In practice, tunnel experts intuitively abstract regions of interest by selecting tunnel portions that are susceptible to the same types of pathologies with some distance approximation. This complex diagnosis process is often subjective and poorly scales across cases and transport structures. In this paper, we introduce PADTUN system, a working prototype of a DSS in tunnelling domain using semantic technologies. Ontologies are developed and used to capture tacit knowledge from tunnel experts. Tunnel inspection data are annotated with ontologies to take advantage of inferring capabilities offered by semantic technologies. In addition, an intelligent mechanism is developed to exploit abstraction and inference capabilities to identify ROI. PADTUN is developed in real-world settings offered by the NeTTUN EU Project and is applied in a tunnel diagnosis use case with Société Nationale des Chemins de Fer Français (SNCF), France. We show how the use of semantic technologies allows addressing the complex issues of pathology and ROI inferencing and matching experts’ expectations of decision support

A model for the creation of human-generated metadata within communities

This paper considers situations for which detailed metadata descriptions of learning resources are necessary, and focuses on human generation of such metadata. It describes a model which facilitates human production of good quality metadata by the development and use of structured vocabularies. Using examples, this model is applied to single and multiple communities of metadata creators. The approach for transferring vocabularies across communities is related to similar work on the use of ontologies to support the development of the semantic web. Notable conclusions from this work are the need to encourage collaboration between the metadata specialists, content authors and system designers, and the scope for using accurate and consistent metadata created for one context in another context by producing descriptions of the relationships between those contexts

Safety, tumor trafficking and immunogenicity of chimeric antigen receptor (CAR)-T cells specific for TAG-72 in colorectal cancer.

BackgroundT cells engineered to express chimeric antigen receptors (CARs) have established efficacy in the treatment of B-cell malignancies, but their relevance in solid tumors remains undefined. Here we report results of the first human trials of CAR-T cells in the treatment of solid tumors performed in the 1990s.MethodsPatients with metastatic colorectal cancer (CRC) were treated in two phase 1 trials with first-generation retroviral transduced CAR-T cells targeting tumor-associated glycoprotein (TAG)-72 and including a CD3-zeta intracellular signaling domain (CART72 cells). In trial C-9701 and C-9702, CART72 cells were administered in escalating doses up to 1010 total cells; in trial C-9701 CART72 cells were administered by intravenous infusion. In trial C-9702, CART72 cells were administered via direct hepatic artery infusion in patients with colorectal liver metastases. In both trials, a brief course of interferon-alpha (IFN-α) was given with each CART72 infusion to upregulate expression of TAG-72.ResultsFourteen patients were enrolled in C-9701 and nine in C-9702. CART72 manufacturing success rate was 100% with an average transduction efficiency of 38%. Ten patients were treated in CC-9701 and 6 in CC-9702. Symptoms consistent with low-grade, cytokine release syndrome were observed in both trials without clear evidence of on target/off tumor toxicity. Detectable, but mostly short-term (≤14 weeks), persistence of CART72 cells was observed in blood; one patient had CART72 cells detectable at 48 weeks. Trafficking to tumor tissues was confirmed in a tumor biopsy from one of three patients. A subset of patients had 111Indium-labeled CART72 cells injected, and trafficking could be detected to liver, but T cells appeared largely excluded from large metastatic deposits. Tumor biomarkers carcinoembryonic antigen (CEA) and TAG-72 were measured in serum; there was a precipitous decline of TAG-72, but not CEA, in some patients due to induction of an interfering antibody to the TAG-72 binding domain of humanized CC49, reflecting an anti-CAR immune response. No radiologic tumor responses were observed.ConclusionThese findings demonstrate the relative safety of CART72 cells. The limited persistence supports the incorporation of co-stimulatory domains in the CAR design and the use of fully human CAR constructs to mitigate immunogenicity

Rule-based knowledge aggregation for large-scale protein sequence analysis of influenza A viruses

Background: The explosive growth of biological data provides opportunities for new statistical and comparative analyses of large information sets, such as alignments comprising tens of thousands of sequences. In such studies, sequence annotations frequently play an essential role, and reliable results depend on metadata quality. However, the semantic heterogeneity and annotation inconsistencies in biological databases greatly increase the complexity of aggregating and cleaning metadata. Manual curation of datasets, traditionally favoured by life scientists, is impractical for studies involving thousands of records. In this study, we investigate quality issues that affect major public databases, and quantify the effectiveness of an automated metadata extraction approach that combines structural and semantic rules. We applied this approach to more than 90,000 influenza A records, to annotate sequences with protein name, virus subtype, isolate, host, geographic origin, and year of isolation. Results: Over 40,000 annotated Influenza A protein sequences were collected by combining information from more than 90,000 documents from NCBI public databases. Metadata values were automatically extracted, aggregated and reconciled from several document fields by applying user-defined structural rules. For each property, values were recovered from ≥88.8% of records, with accuracy exceeding 96% in most cases. Because of semantic heterogeneity, each property required up to six different structural rules to be combined. Significant quality differences between databases were found: GenBank documents yield values more reliably than documents extracted from GenPept. Using a simple set of semantic rules and a reasoner, we reconstructed relationships between sequences from the same isolate, thus identifying 7640 isolates. Validation of isolate metadata against a simple ontology highlighted more than 400 inconsistencies, leading to over 3,000 property value corrections. Conclusion: To overcome the quality issues inherent in public databases, automated knowledge aggregation with embedded intelligence is needed for large-scale analyses. Our results show that user-controlled intuitive approaches, based on combination of simple rules, can reliably automate various curation tasks, reducing the need for manual corrections to approximately 5% of the records. Emerging semantic technologies possess desirable features to support today's knowledge aggregation tasks, with a potential to bring immediate benefits to this field

Using Ontario's "Telehealth" health telephone helpline as an early-warning system: a study protocol

BACKGROUND: The science of syndromic surveillance is still very much in its infancy. While a number of syndromic surveillance systems are being evaluated in the US, very few have had success thus far in predicting an infectious disease event. Furthermore, to date, the majority of syndromic surveillance systems have been based primarily in emergency department settings, with varying levels of enhancement from other data sources. While research has been done on the value of telephone helplines on health care use and patient satisfaction, very few projects have looked at using a telephone helpline as a source of data for syndromic surveillance, and none have been attempted in Canada. The notable exception to this statement has been in the UK where research using the national NHS Direct system as a syndromic surveillance tool has been conducted. METHODS/DESIGN: The purpose of our proposed study is to evaluate the effectiveness of Ontario's telephone nursing helpline system as a real-time syndromic surveillance system, and how its implementation, if successful, would have an impact on outbreak event detection in Ontario. Using data collected retrospectively, all "reasons for call" and assigned algorithms will be linked to a syndrome category. Using different analytic methods, normal thresholds for the different syndromes will be ascertained. This will allow for the evaluation of the system's sensitivity, specificity and positive predictive value. The next step will include the prospective monitoring of syndromic activity, both temporally and spatially. DISCUSSION: As this is a study protocol, there are currently no results to report. However, this study has been granted ethical approval, and is now being implemented. It is our hope that this syndromic surveillance system will display high sensitivity and specificity in detecting true outbreaks within Ontario, before they are detected by conventional surveillance systems. Future results will be published in peer-reviewed journals so as to contribute to the growing body of evidence on syndromic surveillance, while also providing an non US-centric perspective

Resource description framework technologies in chemistry

ISSN:1758-294

A unified framework for managing provenance information in translational research

<p>Abstract</p> <p>Background</p> <p>A critical aspect of the NIH <it>Translational Research </it>roadmap, which seeks to accelerate the delivery of "bench-side" discoveries to patient's "bedside," is the management of the <it>provenance </it>metadata that keeps track of the origin and history of data resources as they traverse the path from the bench to the bedside and back. A comprehensive provenance framework is essential for researchers to verify the quality of data, reproduce scientific results published in peer-reviewed literature, validate scientific process, and associate trust value with data and results. Traditional approaches to provenance management have focused on only partial sections of the translational research life cycle and they do not incorporate "domain semantics", which is essential to support domain-specific querying and analysis by scientists.</p> <p>Results</p> <p>We identify a common set of challenges in managing provenance information across the <it>pre-publication </it>and <it>post-publication </it>phases of data in the translational research lifecycle. We define the semantic provenance framework (SPF), underpinned by the Provenir upper-level provenance ontology, to address these challenges in the four stages of provenance metadata:</p> <p>(a) Provenance <b>collection </b>- during data generation</p> <p>(b) Provenance <b>representation </b>- to support interoperability, reasoning, and incorporate domain semantics</p> <p>(c) Provenance <b>storage </b>and <b>propagation </b>- to allow efficient storage and seamless propagation of provenance as the data is transferred across applications</p> <p>(d) Provenance <b>query </b>- to support queries with increasing complexity over large data size and also support knowledge discovery applications</p> <p>We apply the SPF to two exemplar translational research projects, namely the Semantic Problem Solving Environment for <it>Trypanosoma cruzi </it>(<it>T.cruzi </it>SPSE) and the Biomedical Knowledge Repository (BKR) project, to demonstrate its effectiveness.</p> <p>Conclusions</p> <p>The SPF provides a unified framework to effectively manage provenance of translational research data during pre and post-publication phases. This framework is underpinned by an upper-level provenance ontology called Provenir that is extended to create domain-specific provenance ontologies to facilitate provenance interoperability, seamless propagation of provenance, automated querying, and analysis.</p

Molecular approaches to the analysis of deformed wing virus replication and pathogenesis in the honey bee, Apis mellifera

<p>Abstract</p> <p>Background</p> <p>For years, the understanding of the pathogenetic mechanisms that underlie honey bee viral diseases has been severely hindered because of the lack of a cell culture system for virus propagation. As a result, it is very imperative to develop new methods that would permit the <it>in vitro </it>pathogenesis study of honey bee viruses. The identification of virus replication is an important step towards the understanding of the pathogenesis process of viruses in their respective hosts. In the present study, we developed a strand-specific RT-PCR-based method for analysis of Deformed Wing Virus (DWV) replication in honey bees and in honey bee parasitic mites, <it>Varroa Destructor</it>.</p> <p>Results</p> <p>The results shows that the method developed in our study allows reliable identification of the virus replication and solves the problem of falsely-primed cDNA amplifications that commonly exists in the current system. Using TaqMan real-time quantitative RT-PCR incorporated with biotinylated primers and magnetic beads purification step, we characterized the replication and tissue tropism of DWV infection in honey bees. We provide evidence for DWV replication in the tissues of wings, head, thorax, legs, hemolymph, and gut of honey bees and also in Varroa mites.</p> <p>Conclusion</p> <p>The strategy reported in the present study forms a model system for studying bee virus replication, pathogenesis and immunity. This study should be a significant contribution to the goal of achieving a better understanding of virus pathogenesis in honey bees and to the design of appropriate control measures for bee populations at risk to virus infections.</p

e-Science and biological pathway semantics

<p>Abstract</p> <p>Background</p> <p>The development of e-Science presents a major set of opportunities and challenges for the future progress of biological and life scientific research. Major new tools are required and corresponding demands are placed on the high-throughput data generated and used in these processes. Nowhere is the demand greater than in the semantic integration of these data. Semantic Web tools and technologies afford the chance to achieve this semantic integration. Since pathway knowledge is central to much of the scientific research today it is a good test-bed for semantic integration. Within the context of biological pathways, the BioPAX initiative, part of a broader movement towards the standardization and integration of life science databases, forms a necessary prerequisite for its successful application of e-Science in health care and life science research. This paper examines whether BioPAX, an effort to overcome the barrier of disparate and heterogeneous pathway data sources, addresses the needs of e-Science.</p> <p>Results</p> <p>We demonstrate how BioPAX pathway data can be used to ask and answer some useful biological questions. We find that BioPAX comes close to meeting a broad range of e-Science needs, but certain semantic weaknesses mean that these goals are missed. We make a series of recommendations for re-modeling some aspects of BioPAX to better meet these needs.</p> <p>Conclusion</p> <p>Once these semantic weaknesses are addressed, it will be possible to integrate pathway information in a manner that would be useful in e-Science.</p