Progress in Whole Genome Sequencing (WGS) will soon allow a large number of individuals to have their genome fully sequenced. This lays the foundations to improve modern healthcare, enabling a new era of personalized medicine where diagnosis and treatment is tailored to the patient's genetic makeup. It also allows individuals motivated by personal curiosity to have access to their genetic information, and use it, e.g., to trace their ancestry. However, the very same progress also amplifies a number of ethical and privacy concerns, that stem from the unprecedented sensitivity of genomic information and that are not well studied. This paper presents an exploratory ethnographic study of users' perception of privacy and ethical issues with WGS, as well as their attitude toward different WGS programs. We report on a series of semi-structured interviews, involving 16 participants, and analyze the results both quantitatively and qualitatively. Our analysis shows that users exhibit common trust concerns and fear of discrimination, and demand to retain strict control over their genetic information. Finally, we highlight the need for further research in the area and follow-up studies that build on our initial findings.
Also, prior work analyzed the issue of labor discrimination in relationship to disease predisposition, mostly from the legal standpoint. We refer to the work by @cite_39 for details, along with a review of well-known rulings.
@cite_39
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The article introduces digital image restoration to the reader who is just beginning in this field, and provides a review and analysis for the reader who may already be well-versed in image restoration. The perspective on the topic is one that comes primarily from work done in the field of signal processing. Thus, many of the techniques and works cited relate to classical signal processing approaches to estimation theory, filtering, and numerical analysis. In particular, the emphasis is placed primarily on digital image restoration algorithms that grow out of an area known as "regularized least squares" methods. It should be noted, however, that digital image restoration is a very broad field, as we discuss, and thus contains many other successful approaches that have been developed from different perspectives, such as optics, astronomy, and medical imaging, just to name a few. In the process of reviewing this topic, we address a number of very important issues in this field that are not typically discussed in the technical literature. Code ownership establishes a chain of responsibility for modules in large software systems. Although prior work uncovers a link between code ownership heuristics and software quality, these heuristics rely solely on the authorship of code changes. In addition to authoring code changes, developers also make important contributions to a module by reviewing code changes. Indeed, recent work shows that reviewers are highly active in modern code review processes, often suggesting alternative solutions or providing updates to the code changes. In this paper, we complement traditional code ownership heuristics using code review activity. Through a case study of six releases of the large Qt and OpenStack systems, we find that: (1) 67 --86 of developers did not author any code changes for a module, but still actively contributed by reviewing 21 --39 of the code changes, (2) code ownership heuristics that are aware of reviewing activity share a relationship with software quality, and (3) the proportion of reviewers without expertise shares a strong, increasing relationship with the likelihood of having post-release defects. Our results suggest that reviewing activity captures an important aspect of code ownership, and should be included in approximations of it in future studies. LifeLines provide a general visualization environment for personal histories that can be applied to medical and court records, professional histories and other types of biographical data. A one screen overview shows multiple facets of the records. Aspects, for example medical conditions or legal cases, are displayed as individual time lines, while icons indicate discrete events, such as physician consultations or legal reviews. Line color and thickness illustrate relationships or significance, rescaling tools and filters allow users to focus on part of the information. LifeLines reduce the chances of missing information, facilitate spotting anomalies and trends, streamline access to details, while remaining tailorable and easily transferable between applications. The paper describes the use of LifeLines for youth records of the Maryland Department of Juvenile Justice and also for medical records. User's feedback was collected using a Visual Basic prototype for the youth record. Techniques to deal with complex records are reviewed and issues of a standard personal record format are discussed. The observation that disease associated proteins often interact with each other has fueled the development of network-based approaches to elucidate the molecular mechanisms of human disease. Such approaches build on the assumption that protein interaction networks can be viewed as maps in which diseases can be identified with localized perturbation within a certain neighborhood. The identification of these neighborhoods, or disease modules, is therefore a prerequisite of a detailed investigation of a particular pathophenotype. While numerous heuristic methods exist that successfully pinpoint disease associated modules, the basic underlying connectivity patterns remain largely unexplored. In this work we aim to fill this gap by analyzing the network properties of a comprehensive corpus of 70 complex diseases. We find that disease associated proteins do not reside within locally dense communities and instead identify connectivity significance as the most predictive quantity. This quantity inspires the design of a novel Disease Module Detection (DIAMOnD) algorithm to identify the full disease module around a set of known disease proteins. We study the performance of the algorithm using well-controlled synthetic data and systematically validate the identified neighborhoods for a large corpus of diseases.