DNA damage induction in dry and hydrated DNA by synchrotron radiation

Radiation biophysics has sought to understand at a molecular level, the mechanisms through which ionizing radiations damage DNA, and other molecules within living cells. The complexity of lesions produced in the DNA by ionizing radiations is thought to depend on the amount of energy deposited at the site of each lesion. To study the relationship between the energy deposited and the damage produced, we have developed novel techniques for irradiating dry prasmid DNA, partially re-hydrated DNA and DNA in solution using monochromatic vacuum-UV synchrotron radiation. We have used photons in the energy range 7-150 eV, corresponding to the range of energies typically involved in the efficient production of DNA single-strand (SSB), and double-strand breaks (DSB) by ionizing radiation. The data show that both types of breaks are produced at all energies investigated (with, or without water present). Also, the energy dependence for DSB induction follows a similar trend to SSB induction but at a 20-30-fold reduced incidence, suggesting a common precursor for both types of damage. Preliminary studies where DNA has been irradiated in solution indicate a change in the shape of the dose-effect curve (from linear, to linear-quadratic for double-strand break induction) and a large increase in sensitivity due to the presence of water.

A review of dsb induction data for varying quality radiations

Purpose: This short review summarizes the data obtained with various techniques for measuring the yields of double strand breaks (dsb) produced by particle radiations of differing linear energy transfer (LET) in order to obtain relative biological effectiveness (RBE) values.

Improved conjugate gradient implementation for least squares support vector machines

As a promising method for pattern recognition and function estimation, least squares support vector machines (LS-SVM) express the training in terms of solving a linear system instead of a quadratic programming problem as for conventional support vector machines (SVM). In this paper, by using the information provided by the equality constraint, we transform the minimization problem with a single equality constraint in LS-SVM into an unconstrained minimization problem, then propose reduced formulations for LS-SVM. By introducing this transformation, the times of using conjugate gradient (CG) method, which is a greatly time-consuming step in obtaining the numerical solution, are reduced to one instead of two as proposed by Suykens et al. (1999). The comparison on computational speed of our method with the CG method proposed by Suykens et al. and the first order and second order SMO methods on several benchmark data sets shows a reduction of training time by up to 44%. (C) 2011 Elsevier B.V. All rights reserved.

A Fast Algorithm for Sparse Support Vector Machines for Mobile Computing Applications

This research presents a fast algorithm for projected support vector machines (PSVM) by selecting a basis vector set (BVS) for the kernel-induced feature space, the training points are projected onto the subspace spanned by the selected BVS. A standard linear support vector machine (SVM) is then produced in the subspace with the projected training points. As the dimension of the subspace is determined by the size of the selected basis vector set, the size of the produced SVM expansion can be specified. A two-stage algorithm is derived which selects and refines the basis vector set achieving a locally optimal model. The model expansion coefficients and bias are updated recursively for increase and decrease in the basis set and support vector set. The condition for a point to be classed as outside the current basis vector and selected as a new basis vector is derived and embedded in the recursive procedure. This guarantees the linear independence of the produced basis set. The proposed algorithm is tested and compared with an existing sparse primal SVM (SpSVM) and a standard SVM (LibSVM) on seven public benchmark classification problems. Our new algorithm is designed for use in the application area of human activity recognition using smart devices and embedded sensors where their sometimes limited memory and processing resources must be exploited to the full and the more robust and accurate the classification the more satisfied the user. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. This work builds upon a previously published algorithm specifically created for activity recognition within mobile applications for the EU Haptimap project [1]. The algorithms detailed in this paper are more memory and resource efficient making them suitable for use with bigger data sets and more easily trained SVMs.

Building Support Vector Machines in the Context of Regularised Least Squares

This paper formulates a linear kernel support vector machine (SVM) as a regularized least-squares (RLS) problem. By defining a set of indicator variables of the errors, the solution to the RLS problem is represented as an equation that relates the error vector to the indicator variables. Through partitioning the training set, the SVM weights and bias are expressed analytically using the support vectors. It is also shown how this approach naturally extends to Sums with nonlinear kernels whilst avoiding the need to make use of Lagrange multipliers and duality theory. A fast iterative solution algorithm based on Cholesky decomposition with permutation of the support vectors is suggested as a solution method. The properties of our SVM formulation are analyzed and compared with standard SVMs using a simple example that can be illustrated graphically. The correctness and behavior of our solution (merely derived in the primal context of RLS) is demonstrated using a set of public benchmarking problems for both linear and nonlinear SVMs.