M3G: Maximum Margin Microarray Gridding

<p>Abstract</p> <p>Background</p> <p>Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.</p> <p>Methods</p> <p>In this paper we propose M³G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots.</p> <p>Results</p> <p>The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M³G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels.</p> <p>Conclusions</p> <p>The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots.</p

Negotiating the Meaning of Team Expertise: A Firefighter Team’s Epistemic Denial

In this case study, we report how a team of firefighters critiqued one of its member’s decisions to facilitate learning and process improvement. The study is supported by 500+ hr of ethnographic observations, documents, and 11 retrospective interviews, which captured how the team’s talk about the member’s decision shaped their interpretations of their own and others’ expertise—interpretations that ironically undermined learning. Constant comparative analysis revealed that these firefighters positioned themselves as experts by crediting either personal experience or technical knowledge and then discrediting the alternative way of knowing. We labeled this process epistemic denial. The process of epistemic denial was rooted in identity concern; specifically, veteran team members relied on personal experience and newer members relied on technical information gained from training to assert their expertise, and to devalue others’ expertise. The article concludes with recommendations for avoiding problems associated with epistemic denial in high-reliability teams.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Trust-Building in a Patient Forum: The Interplay of Professional and Personal Expertise

Online discussion forums for patients offer the benefits of community but the risks of misinformation. A physician-moderated forum may help to mitigate this tension. How do both the professional expertise of a physician moderator and the personal, experiential expertise of patients contribute to trust in a forum? A rhetorical analysis of a year of postings in an online Parkinson’s community reveals that both forms of expertise were trusted, demonstrating the possibility for them to complement each other. This study illustrates the broader ways trust is established in patient communities and offers implications for technical communicators as forum designers or moderators