Health, Justice, and Fairness

In this study I will endeavor to show that the American system of health care violates any conception of distributive justice understood as equality of opportunity. This system fails to provide equal access through a lack of universal insurance, a consumer driven conception of quality, and a system wide focus on cost control, leaving millions of Americans exposed to the ravages of disease. However, if health is understood as an antecedent for one's ability to function across a number of categories that have been objectively deemed as vital to engage in a life that is fully human than the commitment our nation has to the protection of fair equality of opportunity, established by our adoption of a Rawlsian conception of justice, necessitates a revision of our nation's conception of quality to encapsulate health outcomes as well as the advent of a system of universal coverage. Quality care will come to be understood as care that returns to the patient the ability to function across those categories of functioning that illness has jeopardized, and this conception of quality will precipitate system wide reform geared at the creation of positive health outcomes. This paper will articulate this argument by reconstructing and synthesizing precepts from the contemporary philosophical sources and then applying these to the practical workings of our healthcare system, while concurrently demonstrating that a system of distributive justice is compatible with the creation of a universal system of healthcare.

Utilization of Probabilistic Models in Short Read Assembly from Second-Generation Sequencing

With the advent of cheaper and faster DNA sequencing technologies, assembly methods have greatly changed. Instead of outputting reads that are thousands of base pairs long, new sequencers parallelize the task by producing read lengths between 35 and 400 base pairs. Reconstructing an organism’s genome from these millions of reads is a computationally expensive task. Our algorithm solves this problem by organizing and indexing the reads using n-grams, which are short, fixed-length DNA sequences of length n. These n-grams are used to efficiently locate putative read joins, thereby eliminating the need to perform an exhaustive search over all possible read pairs. Our goal was develop a novel n-gram method for the assembly of genomes from next-generation sequencers. Specifically, a probabilistic, iterative approach was utilized to determine the most likely reads to join through development of a new metric that models the probability of any two arbitrary reads being joined together. Tests were run using simulated short read data based on randomly created genomes ranging in lengths from 10,000 to 100,000 nucleotides with 16 to 20x coverage. We were able to successfully re-assemble entire genomes up to 100,000 nucleotides in length.