Background Gastric dysplasia is classified as adenomatous/type I (intestinal phenotype) and foveolar or pyloric/ type II (gastric phenotype) according to morphological (architectural and cytological) features. The immunophenotypic classification of dysplasia, based on the expression of the mucins, CD10 and CDX2, recognizes the following immunophenotypes: intestinal (MUC2, CD10, and CDX2); gastric (MUC5AC and/or MUC6, absence of CD10, and absent or low expression of CDX2); hybrid (gastric and intestinal markers); and null.Methods Sixty-six cases of nonpolypoid epithelial dysplasia of the stomach were classified according to morphological features (histotype and grade) and immunophenotype. Immunohistochemical staining was performed with antibodies against MUC2, MUC5AC, MUC6, CD10, CDX2, chromogranin, synaptophysin, Ki-67, and TP53. HER2 alterations were analyzed by immunohistochemistry and silver-enhanced in situ hybridization.Results By conventional histology, dysplasia was classified as adenomatous/intestinal (n = 42; 64 %) and foveolar or pyloric/gastric (n = 24; 36 %) and graded as low grade (n = 37; 56 %) or high grade (n = 29; 44 %). Immunophenotypic classification showed intestinal (n = 22; 33.3 %), gastric (n = 25; 37.9 %), hybrid (n = 17; 25.8 %), or null (n = 2; 3.0 %) phenotypes. In 20 cases a coexistent intramucosal carcinoma was identified. The intestinal immunophenotype was shown to be significantly associated with low-grade dysplasia (p = 0.001), high expression of CDX2 (p = 0.015), TP53 (p = 0.034), synaptophysin (p = 0.003), and chromogranin (p \ 0.0001); the gastric immunophenotype was significantly associated with high-grade dysplasia (p = 0.001), high Ki-67 proliferative index (p = 0.05), and coexistence of intramucosal carcinoma (p = 0.013). HER2 amplification was observed in 3 cases, typed as gastric or hybrid. Conclusions Epithelial nonpolypoid dysplasia of the stomach with gastric immunophenotype shows features of biological aggressiveness and may represent the putative precursor lesion in a pathway of gastric carcinogenesis originated de novo from the native gastric mucosa, leading to gastric-type adenocarcinoma.

SOX2 is associated with gastric differentiation in incomplete IM and is lost in the progression to dysplasia, whereas CDX2 is acquired de novo in IM and maintained in dysplasia. This suggests that the balance between gastric and intestinal differentiation programmes impacts on the gastric carcinogenesis cascade progression.

Abstract. This paper presents the development of a bivalve farmer agent interacting with a realistic ecological simulation system. The purpose of the farmer agent is to determine the best combinations of bivalve seeding areas in a large region, maximizing the production without exceeding the total allowed seeding area. A system based on simulated annealing, tabu search, genetic algorithms and reinforcement learning, was developed to minimize the number of iterations required to unravel a semi-optimum solution by using customizable tactics. The farmer agent is part of a multi-agent system where several agents, representing human interaction with the coastal ecosystems, communicate with a realistic simulator developed especially for aquatic ecological simulations. The experiments performed revealed promising results in the field of optimization techniques and multi-agent systems applied to ecological simulations. The results obtained open many other possible uses of the simulation architecture with applications in industrial and ecological management problems, towards sustainable development.

In a recent past, face recognition was one of the most popular methods and successful application of image processing field which is widely used in security and biometric applications. The innovation of new approaches to face identification technologies is continuously subject to building much strong face recognition algorithms. Face recognition in real-time applications has been fast-growing challenging and interesting. The human face identification process is not trivial task especially different face lighting and poses are captured to be matched. In this study, the proposed method is tested using a benchmark ORL database that contains 400 images of 40 persons as the variant posse, lighting, etc. Discrete avelet Transform technique is applied on the ORL database to enhance the accuracy and the recognition rate. The best recognition rate result obtained is 99.25%, when tested using 9 training images and 1 testing image with cosine distance measurement. The recognition rate Increased when applying 2-level of DWT with the bior5.5 filter on training image database and the test image. For feature extraction and dimension reduction, PCA is used. Euclidean distance, Manhattan distance, and Cosine distance are Distance measures used for the matching process.

Face recognition has become an attractive field in computer based application development in the last few decades. That is because of the wide range of areas they used in. And because of the wide variations of faces, face recognition from the database images, real data, capture images and sensor images is challenging problem and limitation. Image processing, pattern recognition and computer vision are relevant subjects to face recognition field. The innovation of new approaches of face authentication technologies is continuous subject to build much strong face recognition algorithms. In this work, to identify a face, there are three major strategies for feature extractions are discussed. Appearance-based and Modelbased methods and hybrid techniques as feature extractions are discussed. Also, review of major person recognition research the characteristics of good face authentication applications, Classification, Distance measurements and face databases are discussed while the final suggested methods are presented. This research has six sections organized as follow: Section one is the introduction. Section two is dedicated to applications related to face recognition. In Section three, face recognition techniques are presented by details. Then, classification types are illustrated in Section four. In section five, standard face databases are presented. Finally, in Section six, the conclusion is presented followed by the list of references.