The locus of legitimate interpretation in Big Data sciences : Lessons for computational social science from -omic biology and high-energy physics

This paper argues that analyses of the ways in which Big Data has been enacted in other academic disciplines can provide us with concepts that will help understand the application of Big Data to social questions. We use examples drawn from our Science and Technology Studies (STS) analyses of -omic biology and high energy physics to demonstrate the utility of three theoretical concepts: (i) primary and secondary inscriptions, (ii) crafted and found data, and (iii) the locus of legitimate interpretation. These help us to show how the histories, organisational forms, and power dynamics of a field lead to different enactments of big data. The paper suggests that these concepts can be used to help us to understand the ways in which Big Data is being enacted in the domain of the social sciences, and to outline in general terms the ways in which this enactment might be different to that which we have observed in the ‘hard’ sciences. We contend that the locus of legitimate interpretation of Big Data biology and physics is tightly delineated, found within the disciplinary institutions and cultures of these disciplines. We suggest that when using Big Data to make knowledge claims about ‘the social’ the locus of legitimate interpretation is more diffuse, with knowledge claims that are treated as being credible made from other disciplines, or even by those outside academia entirely

Why Functional Pre-Erythrocytic and Bloodstage Malaria Vaccines Fail: A Meta-Analysis of Fully Protective Immunizations and Novel Immunological Model

Background: Clinically protective malaria vaccines consistently fail to protect adults and children in endemic settings, and at best only partially protect infants. Methodology/Principal Findings: We identify and evaluate 1916 immunization studies between 1965-February 2010, and exclude partially or nonprotective results to find 177 completely protective immunization experiments. Detailed reexamination reveals an unexpectedly mundane basis for selective vaccine failure: live malaria parasites in the skin inhibit vaccine function. We next show published molecular and cellular data support a testable, novel model where parasite-host interactions in the skin induce malaria-specific regulatory T cells, and subvert early antigen-specific immunity to parasite-specific immunotolerance. This ensures infection and tolerance to reinfection. Exposure to Plasmodium-infected mosquito bites therefore systematically triggers immunosuppression of endemic vaccine-elicited responses. The extensive vaccine trial data solidly substantiate this model experimentally. Conclusions/Significance: We conclude skinstage-initiated immunosuppression, unassociated with bloodstage parasites, systematically blocks vaccine function in the field. Our model exposes novel molecular and procedural strategies to significantly and quickly increase protective efficacy in both pipeline and currently ineffective malaria vaccines, and forces fundamental reassessment of central precepts determining vaccine development. This has major implications fo

Oncoprotein metastasis and its suppression revisited

<p>Abstract</p> <p>The past two decades have witnessed an increasing appreciation of the role of the tumor microenvironment, of genetic and epigenetic alterations in normal cells adjacent to tumors and of the migration of normal cells with aberrant intrinsic properties in cancer pathophysiology. Aside from these insights, a novel concept termed "oncoprotein metastasis" (OPM) has recently been advanced and proposed to reflect protein-based neoplastic phenomena that might occur even before any modifications relating to the morphology, location or (epi)genetic outfit of cells during the malignant process. Here, evidence is presented that supports the OPM perception and thus should contribute not only to further rethink the definition of a normal cell, but also the treatment of cancer disease in the years to come.</p