Molecular study of the perforin gene in familial hematological malignancies

Perforin gene (PRF1) mutations have been identified in some patients diagnosed with the familial form of hemophagocytic lymphohistiocytosis (HLH) and in patients with lymphoma. The aim of the present study was to determine whether patients with a familial aggregation of hematological malignancies harbor germline perforin gene mutations. For this purpose, 81 unrelated families from Tunisia and France with aggregated hematological malignancies were investigated. The variants detected in the PRF1 coding region amounted to 3.7% (3/81). Two of the three variants identified were previously described: the p.Ala91Val pathogenic mutation and the p.Asn252Ser polymorphism. A new p.Ala 211Val missense substitution was identified in two related Tunisian patients. In order to assess the pathogenicity of this new variation, bioinformatic tools were used to predict its effects on the perforin protein structure and at the mRNA level. The segregation of the mutant allele was studied in the family of interest and a control population was screened. The fact that this variant was not found to occur in 200 control chromosomes suggests that it may be pathogenic. However, overexpression of mutated PRF1 in rat basophilic leukemia cells did not affect the lytic function of perforin differently from the wild type protein

Towards Higher-Order Zeroing Neural Network Dynamics for Solving Time-Varying Algebraic Riccati Equations

One of the most often used approaches for approximating various matrix equation problems is the hyperpower family of iterative methods with arbitrary convergence order, whereas the zeroing neural network (ZNN) is a type of neural dynamics intended for handling time-varying problems. A family of ZNN models that correlate with the hyperpower iterative methods is defined on the basis of the analogy that was discovered. These models, known as higher-order ZNN models (HOZNN), can be used to find real symmetric solutions of time-varying algebraic Riccati equations. Furthermore, a noise-handling HOZNN (NHOZNN) class of dynamical systems is introduced. The traditional ZNN and HOZNN dynamic flows are compared theoretically and numerically

Impact of liquidity on stock returns: an empirical investigation of the Tunisian stock market

This paper investigates the return-liquidity relationship on one Middle East and North Africa frontier market, the Tunisian Stock Exchange (TSE). The findings provide evidence that there is a significant and positive premium for companies with high price impact and low trading frequency. However, Tunisian investors appreciate more low spread stocks. We show, also, a non-linear relation between potential delays of execution and stock returns. In addition, we find that Tunisian investors require a premium to compensate past cumulative illiquidity risk (high price impact, low turnover and high potential delay of execution) over the prior three to 12 months and to compensate past cumulative spread over 12 months. We point out also that these effects are seasonal.return, liquidity, Tunisian stock market,