Bayesian consensus clustering for multivariate longitudinal data

In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data.     

Plasmacytic cutaneous pathology: A review

We review the spectrum of cutaneous disorders associated with inflammatory and neoplastic plasmacytic pathology. Because plasma cells are derived from B‐lymphocytes our overview includes discussion of certain lymphoplasmacytic proliferations. It is structured along histopathological lines, addressing conditions characterized by (a) cutaneous plasma cell infiltrates, (b) deposits of plasma cell products or their derivatives in the skin and (c) miscellaneous, poorly understood cutaneous complications of plasmacytic disorders. Lesions arising primarily in the skin and those due to cutaneous involvement by multisystem disorders are addressed. The range includes a spectrum of tumefactive and circulatory manifestations. We highlight key clinical and pathological features of the different conditions and outline recent advances in our understanding of these entities. By emphasizing the dermatopathological characteristics of this spectrum of disorders we hope to hone the diagnostic accuracy of practitioners in the field.     

Predicting the initial spread of novel Asian origin influenza A viruses in the continental USA by wild waterfowl

Using data on waterfowl band recoveries, we identified spatially explicit hotspots of concentrated waterfowl movement to predict occurrence and spatial spread of a novel influenza A virus (clade 2.3.4.4) introduced from Asia by waterfowl from an initial outbreak in North America in November 2014. In response to the outbreak, the hotspots of waterfowl movement were used to help guide sampling for clade 2.3.4.4 viruses in waterfowl as an early warning for the US poultry industry during the outbreak . After surveillance sampling of waterfowl, we tested whether there was greater detection of clade 2.3.4.4 viruses inside hotspots. We found that hotspots defined using kernel density estimates of waterfowl band recoveries worked well in predicting areas with higher prevalence of the viruses in waterfowl. This approach exemplifies the value of ecological knowledge in predicting risk to agricultural security.