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Robert Stevens

Publications and source records attributed to Robert Stevens.

5 recordsLinked to original sources
PubMed2003

Regulation of melanoma epitope-specific cytolytic T lymphocyte response by immature and activated dendritic cells, in vitro.

Dendritic cell (DC)-based immunization in cancer has proven to be a promising approach. However, just as DCs are crucial accessory cells in generating immune responses, they also seem to participate in tolerance induction, especially against peripheral "self" antigens. The bulk of the evidence that DCs present peripheral self antigens to induce tolerance has, however, come mostly from studies in transgenic animal models. A tolerogenic function of DCs for peripheral self antigens in a human model has not been critically examined. In this study using the Melan-A/MART-1(27-35) peptide as a model for self but melanoma-associated antigen-against which human hosts often harbor CD8(+) CTL precursors with high frequencies-we confirm that although immature dendritic cells (iDCs) are inefficient antigen presenting cells (APCs), fully activated DCs efficiently activate melanoma epitope-specific CD8(+) CTL precursors, in vitro. We, however, show that in a direct epitope presentation schema, iDCs neither delete nor anergize epitope-specific CD8(+) T cells in primary or secondary stimulation. Interestingly, iDCs and activated DCs can delete a large fraction of the epitope-specific CTLs on tertiary stimulation. The deletion is induced in an epitope-specific manner and through apoptosis. These observations, therefore, have implications on the DC-based cancer vaccine designs and are relevant in the inquiry into the role of DCs on tolerance induction.

Autoantigens↗
PubMed2002

OILing the way to machine understandable bioinformatics resources.

The complex questions and analyses posed by biologists, as well as the diverse data resources they develop, require the fusion of evidence from different, independently developed, and heterogeneous resources. The web, as an enabler for interoperability, has been an excellent mechanism for data publication and transportation. Successful exchange and integration of information, however, depends on a shared language for communication (a terminology) and a shared understanding of what the data means (an ontology). Without this kind of understanding, semantic heterogeneity remains a problem for both humans and machines. One means of dealing with heterogeneity in bioinformatics resources is through terminology founded upon an ontology. Bioinformatics resources tend to be rich in human readable and understandable annotation, with each resource using its own terminology. These resources are machine readable, but not machine understandable. Ontologies have a role in increasing this machine understanding, reducing the semantic heterogeneity between resources and thus promoting the flexible and reliable interoperation of bioinformatics resources. This paper describes a solution derived from the semantic web [a machine understandable world-wide web (WWW)], the ontology inference layer (OIL), as a solution for semantic bioinformatics resources. The nature of the heterogeneity problems are presented along with a description of how metadata from domain ontologies can be used to alleviate this problem. A companion paper in this issue gives an example of the development of a bio-ontology using OIL.

Algorithms↗
PubMed2002

Building a bioinformatics ontology using OIL.

This paper describes the initial stages of building an ontology of bioinformatics and molecular biology. The conceptualization is encoded using the ontology inference layer (OIL), a knowledge representation language that combines the modeling style of frame-based systems with the expressiveness and reasoning power of description logics (DLs). This paper is the second of a pair in this special issue. The first described the core of the OIL language and the need to use ontologies to deliver semantic bioinformatics resources. In this paper, the early stages of building an ontology component of a bioinformatics resource querying application are described. This ontology (TaO) holds the information about molecular biology represented in bioinformatics resources and the bioinformatics tasks performed over these resources. It, therefore, represents the metadata of the resources the application can query. It also manages the terminologies used in constructing the query plans used to retrieve instances from those external resources. The methodology used in this task capitalizes upon features of OIL-The conceptualization afforded by the frame-based view of OIL's syntax; the expressive power and reasoning of the logical formalism; and the ability to encode both handcrafted, hierarchies of concepts, as well as defining concepts in terms of their properties, which can then be used to establish a classification and infer relationships not encoded by the ontologist. This ability forms the basis of the methodology described here: For each portion of the TaO, a basic framework of concepts is asserted by the ontologist. Then, the properties of these concepts are defined by the ontologist and the logic's reasoning power used to reclassify and infer further relationships. This cycle of elaboration and refinement is iterated on each portion of the ontology until a satisfactory ontology has been created.

Algorithms↗
PubMed2002

The Midwives Act 1902: an historical landmark.

The 1902 Midwives Act had far-reaching implications for every midwife in England and Wales. This paper explores the legal aspects of the Act and what it meant for practising midwives then and now.

England↗