Binding of B‐cell maturation antigen to B‐cell activating factor induces survival of multiple myeloma cells by activating Akt and JNK signaling pathways

B‐cell maturation antigen (BCMA) is expressed on normal and malignant plasma cells and represents a potential target for therapeutic intervention. In this study, we characterized the mechanism underlying the protein kinase B (Akt) and c‐Jun N‐terminal kinase (JNK) pathways and BCMA interactions in regulating multiple myeloma (MM) cell survival. It was found that the expression levels of B cell‐activating factor (BAFF) and BCMA were increased in MM cells as compared with those in normal controls. The proliferation of U266 cells was induced by recombinant human BAFF (rhBAFF) and could also be decreased by BCMA siRNA. The expression of Bcl‐2 protein was up‐regulated, and Bax protein was down‐regulated after rhBAFF treatment, which could be reversed by BCMA siRNA. Similarly, the protein p‐JNK and p‐Akt were activated by rhBAFF and could be changed by BCMA siRNA. In addition, the BCMA mRNA and protein expression levels were decreased after treatment with Akt and JNK pathway inhibitors. These results suggest that Akt and JNK pathways are involved in the regulation of BCMA. A novel BAFF/BCMA signalling pathway in MM may be a new therapeutic target for MM. Copyright © 2016 John Wiley & Sons, Ltd.     

Robust Selection of the Most Discriminative Variables in the Dichotomous Problem with Application to Some Respiratory Disease Data

A robust method for selection of variables with the greatest discriminatory power is presented in the paper. The method deals with the two groups of data problem. An application of the method to some respiratory disease data and comparisons with classical procedures are given, also.     

Comparing Stochastically Restricted Linear Estimators in a Regression Model

Conditions for superiority of the minimum dispersion estimator over another with respect to the covariance matrix are derived when the vector parameter of a regression model is subject to competing stochastic restrictions. The restrictions may also consist both of a deterministic part and a stochastic part.     

Modelling HIV immune response and validation with clinical data

A system of ordinary differential equations is formulated to describe the pathogenesis of HIV infection, wherein certain features that have been shown to be important by recent experimental research are incorporated in the model. These include the role of CD4+memory cells that serve as a major reservoir of latently infected cells, a critical role for T-helper cells in the generation of CD8 memory cells capable of efficient recall response, and stimulation by antigens other than HIV. A stability analysis illustrates the capability of this model in admitting multiple locally asymptotically stable (locally a.s.) off-treatment equilibria. We show that this more biologically detailed model can exhibit the phenomenon of transient viremia experienced by some patients on therapy with viral load levels suppressed below the detection limit. We also show that the loss of CD4+T-cell help in the generation of CD8+memory cells leads to larger peak values for the viral load during transient viremia. Censored clinical data is used to obtain parameter estimates. We demonstrate that using a reduced set of 16 free parameters, obtained by fixing some parameters at their population averages, the model provides reasonable fits to the patient data and, moreover, that it exhibits good predictive capability. We further show that parameter values obtained for most clinical patients do not admit multiple locally a.s off-treatment equilibria. This suggests that treatment to move from a high viral load equilibrium state to an equilibrium state with a lower (or zero) viral load is not possible for these patients.     

Effect of pea bacterial blight (Pseudomonas syringae pv. pisi) on the yield of spring sown combining peas (Pisum sativum)

Field trials to examine the effect of pea bacterial blight (Pseudomonas syringae pv. pisi) (Psp) on the yield of combining peas were carried out at five sites (HRI Wellesbourne, ADAS Rosemaund, ADAS Terrington, PGRO, SASA East Craigs) in the UK in 1990, 1991 and 1992. Healthy seed, cv. Solara, and seed naturally infected with Psp Race 2 was sown in large plots (c. 200 m²) in the open or under nets to prevent bird damage by pigeons. Despite relatively low disease severity levels (< 15% leaf area) and separation by at least 12 m of cv. Consort (resistant to Race 2) between plots there was considerable spread of disease into plots sown with healthy seed. Regression analysis demonstrated a significant relationship between yield and disease. Of the disease measurements examined, disease severity on the leaves (stipules) at growth stage 208 was found to be the best predictor of yield. A model containing environmental and bird damage terms, in addition to disease, suggested that a yield loss of 0.98 t/ha would be expected for a disease severity score of 1, equivalent to 5% leaf area affected, at growth stage 208.     

Seemingly Unrelated Generalized Linear Models

By using deviance standardized residuals, the seemingly unrelated regression estimation procedure is extended to generalized linear models, and fitted by an iterative procedure. The matrix of cross products of standardized residuals is asymptotically multivariate normal, and can be used for further multivariate analyses and for hypothesis testing.     

Comparing multiple correspondence and principal component analyses with biomechanical signals. Example with turning the steering wheel

The purpose of this article is to compare Principal Component Analysis (PCA) and a much less used method, i.e. MCA (Multiple Correspondence Analysis) with data being first changed into membership values to fuzzy space windows. For such a comparison, data from an experimental study about turning the steering wheel is used. In a didactic perspective, this article only considers one multidimensional signal with 5 components: 3 linked to the steering wheel angle and hand positions and 2 to hand effort variables. A discussion weighs out the pros and the cons of both methods with criteria such as the possibility to show complex relational phenomena, the analysis/computing time or the information loss inherent to the averaging stage (in the perspective to analyze several hundreds of large multidimensional signals).