Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network

This study reports a statistical analysis of monthly sunspot number time series and observes non homogeneity and asymmetry within it. Using Mann-Kendall test a linear trend is revealed. After identifying stationarity within the time series we generate autoregressive AR(p) and autoregressive moving average (ARMA(p,q)). Based on minimization of AIC we find 3 and 1 as the best values of p and q respectively. In the next phase, autoregressive neural network (AR-NN(3)) is generated by training a generalized feedforward neural network (GFNN). Assessing the model performances by means of Willmott's index of second order and coefficient of determination, the performance of AR-NN(3) is identified to be better than AR(3) and ARMA(3,1).Comment: 17 pages, 4 figure

Immunohistochemical Typing of Adenocarcinomas of the Pancreatobiliary System Improves Diagnosis and Prognostic Stratification

<div><p>Background & Aims</p><p>Adenocarcinomas of the pancreatobiliary system are currently classified by their primary anatomical location. In particular, the pathological diagnosis of intrahepatic cholangiocarcinoma is still considered as a diagnosis of exclusion of metastatic adenocarcinoma. Periampullary cancers have been previously classified according to the histological type of differentiation (pancreatobiliary, intestinal), but overlapping morphological features hinder their differential diagnosis. We performed an integrative immunohistochemical analysis of pancreato-biliary tumors to improve their diagnosis and prediction of outcome.</p><p>Methods</p><p>This was a retrospective observational cohort study on patients with adenocarcinoma of the pancreatobiliary system who underwent diagnostic core needle biopsy or surgical resection at a tertiary referral center. 409 tumor samples were analyzed with up to 27 conventional antibodies used in diagnostic pathology. Immunohistochemical scoring system was the percentage of stained tumor cells. Bioinformatic analysis, internal validation, and survival analysis were performed.</p><p>Results</p><p>Hierarchical clustering and differential expression analysis identified three immunohistochemical tumor types (extrahepatic pancreatobiliary, intestinal, and intrahepatic cholangiocarcinoma) and the discriminant markers between them. Among patients who underwent surgical resection of their primary tumor with curative intent, the intestinal type showed an adjusted hazard ratio of 0.19 for overall survival (95% confidence interval 0.05–0.72; p value = 0.014) compared to the extrahepatic pancreatobiliary type.</p><p>Conclusions</p><p>Integrative immunohistochemical classification of adenocarcinomas of the pancreatobiliary system results in a characteristic immunohistochemical profile for intrahepatic cholangiocarcinoma and intestinal type adenocarcinoma, which helps in distinguishing them from metastatic and pancreatobiliary type adenocarcinoma, respectively. A diagnostic immunohistochemical panel and additional extended panels of discriminant markers are proposed as guidance for their pathological diagnosis.</p></div