Soft tissue tumors: an overview

Review on Soft tissue tumors: an overview, with data on clinics, and the genes involved

t(5;9)(q14.1;p24) SSBP2/JAK2

Review on t(5;9)(q14.1;p24) SSBP2/JAK2, with data on clinics, and the genes implicated

t(16;21)(q24;q22) in therapy-related acute myelogenous leukemia arising from myelodysplastic syndrome

Case report of a translocation : t(16;21)(q24;q22) in therapy-related acute myelogenous leukemia arising from myelodysplastic syndrome

+13 or trisomy 13

Review on +13 or trisomy 13, with data on clinics, and the genes involved

A t(4;12)(q11;p13) in a patient with coincident CLL at the same time of AML diagnosis

Case report of a translocation : A t(4;12)(q11;p13) in a patient with coincident CLL at the same time of AML diagnosis

A de novo AML with a t(1;21)(p36;q22) in an elderly patient

Case report of a translocation : A de novo AML with a t(1;21)(p36;q22) in an elderly patient

Constraint Based Diagnosis Algorithms For Multiprocessors

Constraint-based diagnosis algorithms for multiprocessors A. Petri, P. Urban, J. Altmann, M. Dal Cin, E. Selenyi, K. Tilly, A. Pataricza In the latest years, new ideas appeared in system level diagnosis of multiprocessor systems. In contrary to the traditional diagnosis models (like PMC, BGM, etc.) which use strictly graph-oriented methods to determine the faulty components in a system, these new theories prefer AI-based algorithms, especially CSP methods. Syndrome decoding, the basic problem of self-diagnosis, can be easily transformed into constraints between the state of the tester and the tested components. Therefore, the diagnosis algorithm can be derived from a special constraint solving algorithm. The "benign" nature of the constraints (all their variables, representing the fault states of the components, have a very limited domain; the constraints are simple and similar to each other) reduces the algorithm's complexity so it can be converted to a powerful distributed diagnosis method with a minimal overhead. Experimental algorithms (using both centralized and distributed approach) were implemented for a Parsytec GC massively parallel multiprocessor system

Genome-Wide Analysis of Subependymomas Shows Underlying Chromosomal Copy Number Changes Involving Chromosomes 6, 7, 8 and 14 in a Proportion of Cases

Subependymomas (SE) are slow-growing brain tumors that tend to occur within the ventricles of middle-aged and elderly adults. The World Health Organization classifies these tumors within the ependymoma group. Previous limited analysis of this tumor type had not revealed significant underlying cytogenetic abnormalities