Multiple Myeloma : an update on disease biology and therapy

Multiple myeloma is a malignancy of immunoglobulin producing plasma cells. Clinical features include bone pain due to lytic bone lesions or pathological fractures, anemia, symptomatic hypercalcemia, renal insufficiency, recurrent infections and amyloidosis. In the last few years, there have been considerable advances in the understanding of the biology of this disease. While multiple myeloma is biologically diverse, several oncogenes are activated in this illness. In addition, the role of the bone marrow microenvironment to support the growth and survival of the malignant cells has been well described. In this review, we discuss recent developments in the molecular pathogenesis of myeloma. These recent observations are being translated into novel therapeutic approaches that target both the tumor cell as well as the stroma. Current therapeutic strategies are discussed.peer-reviewe

The Universal Equation to Price All Civil Judgments

David Cook discusses methods for valuing civil judgments

Panel III: Implications of the New Telecommunications Legislation

We present a method that employs a tree-based Neural Network (NN) for performing classification. The novel mechanism, apart from incorporating the information provided by unlabeled and labeled instances, re-arranges the nodes of the tree as per the laws of Adaptive Data Structures (ADSs). Particularly, we investigate the Pattern Recognition (PR) capabilities of the Tree-Based Topology-Oriented SOM (TTOSOM) when Conditional Rotations (CONROT) [8] are incorporated into the learning scheme. The learning methodology inherits all the properties of the TTOSOM-based classifier designed in [4]. However, we now augment it with the property that frequently accessed nodes are moved closer to the root of the tree. Our experimental results show that on average, the classification capabilities of our proposed strategy are reasonably comparable to those obtained by some of the state-of-the-art classification schemes that only use labeled instances during the training phase. The experiments also show that improved levels of accuracy can be obtained by imposing trees with a larger number of nodes

A Note on Visualizing Response Transformations in Regression

A new graphical method for assessing parametric transformations of the response in linear regression is given. Simply regress the response variable Y on the predictors and find the fitted values. Then dynamically plot the transformed response Y(λ) against those fitted values by varying the transformation parameter λ until the plot is linear. The method can also be used to assess the success of numerical response transformation methods and to discover influential observations. Modifications using robust estimators can be used as well

Modifying the Einstein Equations off the Constraint Hypersuface

A new technique is presented for modifying the Einstein evolution equations off the constraint hypersurface. With this approach the evolution equations for the constraints can be specified freely. The equations of motion for the gravitational field variables are modified by the addition of terms that are linear and nonlocal in the constraints. These terms are obtained from solutions of the linearized Einstein constraints.Comment: 4 pages, 1 figure, uses REVTe