Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa

Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use these models to explore the use of geographical features in predicting colorectal cancer survival curves for patients in the state of Iowa, spanning the years 1989 to 2012. Specifically, we compare model performance using a newly defined metric -- area between the curves (ABC) -- to assess (a) whether survival curves can be reasonably predicted for colorectal cancer patients in the state of Iowa, (b) whether geographical features improve predictive performance, and (c) whether a simple binary representation or richer, spectral clustering-based representation perform better. Our findings suggest that survival curves can be reasonably estimated on average, with predictive performance deviating at the five-year survival mark. We also find that geographical features improve predictive performance, and that the best performance is obtained using richer, spectral analysis-elicited features.Comment: 8 page

Beyond flows, fluids and networks: Social theory and the fetishism of the global informational economy

In this paper we critically explore some of the arguments made by those social theorists who claim that we live in a new global economy defined by informational and technological flows, fluids and networks. By recourse to Marx's concept of fetishism we argue that these theorists often fetishise the very social changes in the global economy they are trying to describe. As a result, they articulate a ‘flat ontology’ of concrete and contingent relations that mistakenly claims to capture the most important dynamics of global capitalism. We reject this approach, preferring instead to see global capitalism as a dialectical flux between concrete and more abstract processes. These critical points are developed by drawing on Marxism to explore how these social theorists often reproduce unhelpful dualisms in social theory, how they fetishise technology, how some of their arguments run parallel to a management justification logic of the market world and finally how they present a limited explanation of global finance

Mutational Analysis of EGFR and Related Signaling Pathway Genes in Lung Adenocarcinomas Identifies a Novel Somatic Kinase Domain Mutation in FGFR4

BACKGROUND: Fifty percent of lung adenocarcinomas harbor somatic mutations in six genes that encode proteins in the EGFR signaling pathway, i.e., EGFR, HER2/ERBB2, HER4/ERBB4, PIK3CA, BRAF, and KRAS. We performed mutational profiling of a large cohort of lung adenocarcinomas to uncover other potential somatic mutations in genes of this signaling pathway that could contribute to lung tumorigenesis. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed genomic DNA from a total of 261 resected, clinically annotated non-small cell lung cancer (NSCLC) specimens. The coding sequences of 39 genes were screened for somatic mutations via high-throughput dideoxynucleotide sequencing of PCR-amplified gene products. Mutations were considered to be somatic only if they were found in an independent tumor-derived PCR product but not in matched normal tissue. Sequencing of 9MB of tumor sequence identified 239 putative genetic variants. We further examined 22 variants found in RAS family genes and 135 variants localized to exons encoding the kinase domain of respective proteins. We identified a total of 37 non-synonymous somatic mutations; 36 were found collectively in EGFR, KRAS, BRAF, and PIK3CA. One somatic mutation was a previously unreported mutation in the kinase domain (exon 16) of FGFR4 (Glu681Lys), identified in 1 of 158 tumors. The FGFR4 mutation is analogous to a reported tumor-specific somatic mutation in ERBB2 and is located in the same exon as a previously reported kinase domain mutation in FGFR4 (Pro712Thr) in a lung adenocarcinoma cell line. CONCLUSIONS/SIGNIFICANCE: This study is one of the first comprehensive mutational analyses of major genes in a specific signaling pathway in a sizeable cohort of lung adenocarcinomas. Our results suggest the majority of gain-of-function mutations within kinase genes in the EGFR signaling pathway have already been identified. Our findings also implicate FGFR4 in the pathogenesis of a subset of lung adenocarcinomas