Cofilin immunolabelling correlates with depth of invasion in gastrointestinal endocrine cell tumors

Gastrointestinal endocrine cell tumors are a heterogeneous population of lesions believed to arise from neuroendocrine cells of the gastrointestinal mucosa. The current classification of these tumors is based on tumor size, microscopic features and clinical evidence of metastasis. Although diagnostic categories generally correlate with prognosis, molecular prognostic markers will be clinically useful adjuncts. Cofilin has been implicated in tumor invasion, and its immunolocalisation was studied in gastrointestinal endocrine cell tumors. The immunolocalisation of cofilin was studied by immunohistochemistry in 34 formalin-fixed, paraffin wax-embedded gastrointestinal endocrine cell tumors using a tissue microarray platform. A significant correlation was found between high cofilin immunolabelling and the depth of invasion (p

A Search for Line Shape and Depth Variations in 51 Pegasi and tau Bootis

Spectroscopic observations of 51 Pegasi and tau Bootis show no periodic changes in the shapes of their line profiles; these results for 51 Peg are in significant conflict with those reported by Gray & Hatzes. Our detection limits are small enough to rule out nonradial pulsations as the cause of the variability in tau Boo, but not in 51 Peg. The absence of line shape changes is consistent with these stars' radial velocity variability arising from planetary mass companions.

Flexible paleoclimate age-depth models using an autoregressive gamma process

Radiocarbon dating is routinely used in paleoecology to build chronolo- gies of lake and peat sediments, aiming at inferring a model that would relate the sediment depth with its age. We present a new approach for chronology building (called “Bacon”) that has received enthusiastic attention by paleoecologists. Our methodology is based on controlling core accumulation rates using a gamma autoregressive semiparametric model with an arbitrary number of subdivisions along the sediment. Using prior knowledge about accumulation rates is crucial and informative priors are routinely used. Since many sediment cores are currently analyzed, using different data sets and prior distributions, a robust (adaptive) MCMC is very useful. We use the t-walk (Christen and Fox, 2010), a self adjusting, robust MCMC sampling algorithm, that works acceptably well in many situations. Outliers are also addressed using a recent approach that considers a Student-t model for radiocarbon data. Two examples are presented here, that of a peat core and a core from a lake, and our results are compared with other approaches.