Comparison of Antibodies to Detect Uroplakin in Urothelial Carcinomas

Immunohistochemistry for Uroplakin (UP) II and III is used to determine urothelial origin of carcinomas of unknown primary site and are especially valuable to differentiate urothelial carcinomas (UCs) from lung squamous cell carcinomas and prostate carcinomas. In the Nordic immunohistochemical Quality Control assessment scheme, only 45% of the participants obtained a sufficient staining result for UP. Primary antibodies (Abs) against UPII were most successful with a pass rate of 86%. No Abs against UPIII provided sufficient staining results. A comparative study was carried out on a larger cohort of tissue samples with optimized methods for the UPII mouse monoclonal antibody (mmAb) clone BC21, UPIII mmAb clone AU-1, and rabbit monoclonal antibody (rmAb) clone SP73 to evaluate the performance in a standardized way. Tissue microarrays containing 58 UCs, 111 non-UCs, and 20 normal tissues were included. The UP stains were evaluated by using H-score. Based on H-scores, samples were categorized as high-expressor (150 to 300), moderate-expressor (10 to 149), low-expressor (1 to 9), and negative (150 for the UPII Ab. The 2 UPIII Abs gave an analytical specificity of 100% compared with 97% for the UPII Ab being positive in 2 ovarian carcinomas and 1 cervical squamous cell carcinoma

Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data

Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), and the results point towards the conclusion that FC exhibits dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a non-parametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted in Bayesian statistical modeling we use the predictive likelihood to investigate if the model can discriminate between a motor task and rest both within and across subjects. We further investigate what drives dynamic states using the model on the entire data collated across subjects and task/rest. We find that the number of states extracted are driven by subject variability and preprocessing differences while the individual states are almost purely defined by either task or rest. This questions how we in general interpret dynamic FC and points to the need for more research on what drives dynamic FC.Comment: 8 pages, 1 figure. Presented at the Machine Learning and Interpretation in Neuroimaging Workshop (MLINI-2015), 2015 (arXiv:1605.04435