Translational Contestation of Religious Concepts: A Case of Conversion Narratives

Drawing on a case study incorporating two opposing conversion narratives and their translations, thispaper explores the relationship between language, religion and translation. By examining linguistic,paralinguistic and paratextual framing techniques used by authors, translators and publishers, itdemonstrates that translation can be a powerful means of contesting fundamental concepts – such as “conversion”, “church” or “faith” – in the processes of safeguarding one’s own or challenging another’s religious identity, especially in the context of asymmetrical power relations.

Measuring workforce segregation: religious composition of private-sector employees at individual sites in Northern Ireland

In this paper I examine the scope of publicly available information on the religious composition of employees in private-sector companies in Northern Ireland. I highlight the unavailability of certain types of monitoring data and the impact of data aggregation at company as opposed to site level. Both oversights lead to underestimates of the extent of workplace segregation in Northern Ireland. The ability to provide more-coherent data on workplace segregation, by religion, in Northern Ireland is crucial in terms of advancing equality and other social-justice agendas. I argue that a more-accurate monitoring of religious composition of workplaces is part of an overall need to develop a spatial approach in which the importance of ethnically territorialised spaces in the reproduction of ethnosectarian disputation is understood.

A TMA De-Arraying Method for High Throughput Biomarker Discovery in Tissue Research

Background: Tissue MicroArrays (TMAs) represent a potential high-throughput platform for the analysis and discovery of tissue biomarkers. As TMA slides are produced manually and subject to processing and sectioning artefacts, the layout of TMA cores on the final slide and subsequent digital scan (TMA digital slide) is often disturbed making it difficult to associate cores with their original position in the planned TMA map. Additionally, the individual cores can be greatly altered and contain numerous irregularities such as missing cores, grid rotation and stretching. These factors demand the development of a robust method for de-arraying TMAs which identifies each TMA core, and assigns them to their appropriate coordinates on the constructed TMA slide.

Methodology: This study presents a robust TMA de-arraying method consisting of three functional phases: TMA core segmentation, gridding and mapping. The segmentation of TMA cores uses a set of morphological operations to identify each TMA core. Gridding then utilises a Delaunay Triangulation based method to find the row and column indices of each TMA core. Finally, mapping correlates each TMA core from a high resolution TMA whole slide image with its name within a TMAMap.

Conclusion: This study describes a genuine robust TMA de-arraying algorithm for the rapid identification of TMA cores from digital slides. The result of this de-arraying algorithm allows the easy partition of each TMA core for further processing. Based on a test group of 19 TMA slides (3129 cores), 99.84% of cores were segmented successfully, 99.81% of cores were gridded correctly and 99.96% of cores were mapped with their correct names via TMAMaps. The gridding of TMA cores were also extensively tested using a set of 113 pseudo slide (13,536 cores) with a variety of irregular grid layouts including missing cores, rotation and stretching. 100% of the cores were gridded correctly.

Implications for Powering Biomarker Discovery Studies

This study examined variations in gene expression between FFPE blocks within tumors of individual patients. Microarray data were used to measure tumor heterogeneity within and between patients and disease states. Data were used to determine the number of samples needed to power biomarker discovery studies. Bias and variation in gene expression were assessed at the intrapatient and interpatient levels and between adenocarcinoma and squamous samples. A mixed-model analysis of variance was fitted to gene expression data and model signatures to assess the statistical significance of observed variations within and between samples and disease states. Sample size analysis, adjusted for sample heterogeneity, was used to determine the number of samples required to support biomarker discovery studies. Variation in gene expression was observed between blocks taken from a single patient. However, this variation was considerably less than differences between histological characteristics. This degree of block-to-block variation still permits biomarker discovery using either macrodissected tumors or whole FFPE sections, provided that intratumor heterogeneity is taken into account. Failure to consider intratumor heterogeneity may result in underpowered biomarker studies that may result in either the generation of longer gene signatures or the inability to identify a viable biomarker. Moreover, the results of this study indicate that a single biopsy sample is suitable for applying a biomarker in nonsmall-cell lung cancer. © 2012 American Society for Investigative Pathology and the Association for Molecular Pathology.