Safety and Pharmacokinetics of Dapivirine Delivery From Matrix and Reservoir Intravaginal Rings to HIV-Negative Women

Vaginal microbicides for the prevention of HIV transmission may be an important option for protecting women from infection. Incorporation of dapivirine, a lead candidate nonnucleoside reverse transcriptase inhibitor, into intravaginal rings (IVRs) for sustained mucosal delivery may increase microbicide product adherence and efficacy compared with conventional vaginal formulations. Twentyfour
healthy HIV-negative women 18–35 years of age were randomly assigned (1:1:1) to dapivirine matrix IVR, dapivirine reservoir IVR, or placebo IVR. Dapivirine concentrations were measured in plasma
and vaginal fluid samples collected at sequential time points over the 33-day study period (28 days of IVR use, 5 days of follow-up). Safety was assessed by pelvic/colposcopic examinations, clinical laboratory tests, and adverse events. Both IVR types were safe and well tolerated with similar adverse events observed in the placebo and dapivirine groups. Dapivirine from both IVR types was successfully distributed throughout the lower genital tract at concentrations over 4 logs greater than the EC50 against wild-type HIV-1 (LAI) in MT4 cells. Maximum concentration (Cmax) and area under the concentration–time curve (AUC) values were significantly higher with the matrix than reservoir IVR. Mean plasma concentrations of dapivirine were ,2 ng/mL. These findings suggest that IVR delivery of microbicides is a viable option meriting further study.

A new Jacobian matrix for optimal learning of single-layer neural networks

This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples.

Comment on "Electron collisional excitation of argon-like Ni XI using the Breit-Pauli R-matrix method" [Eur. Phys. J. D 42, 235-241 (2007)] - Electron impact excitation of Ni XI

In a recent paper, Verma et al. [Eur. Phys. J. D 42, 235 (2007)] have reported results for energy levels, radiative rates, collision strengths, and effective collision strengths for transitions among the lowest 17 levels of the (1s(2)2s(2)2p(6))3s(2)3p(6), 3s(2)3p(5)3d and 3s3p(6)3d configurations of Ni XI. They adopted the CIV3 and R-matrix codes for the generation of wavefunctions and the scattering process, respectively. In this paper, through two independent calculations performed with the fully relativistic DARC (along with GRASP) and FAC codes, we demonstrate that their results are unreliable. New data are presented and their accuracy is assessed.

2-D R-matrix propagation: A large scale electron scattering simulation dominated by the multiplication of dynamically changing matrices

We examine the computational aspects of propagating a global R-matrix, R, across sub-regions in a 2-D plane. This problem originates in the large scale simulation of electron collisions with atoms and ions at intermediate energies. The propagation is dominated by matrix multiplications which are complicated because of the dynamic nature of R, which changes the designations of its rows and columns and grows in size as the propagation proceeds. The use of PBLAS to solve this problem on distributed memory HPC machines is the main focus of the paper.

Custom coprocessor based matrix algorithms for image and signal processing

Matrix algorithms are important in many types of applications including image and signal processing. A close examination of the algorithms used in these, and related, applications reveals that many of the fundamental actions involve matrix algorithms such as matrix multiplication. This paper presents an investigation into the design and implementation of different matrix algorithms such as matrix operations, matrix transforms and matrix decompositions using a novel custom coprocessor system for MATrix algorithms based on Reconfigurable Computing (RCMAT). The proposed RCMAT architectures are scalable, modular and require less area and time complexity with reduced latency when compared with existing structures.

Ultrafast Laser-Driven Excitation Dynamics in Ne: An Ab Initio Time-Dependent R-Matrix Approach

An attosecond pump-probe scheme that combines the use of a free-electron laser pulse with an ultrashort pulse is applied in order to explore the ultrafast excitation dynamics in Ne. We describe the multielectron dynamics using a new nonperturbative time-dependent R-matrix theory. This theory enables the interaction of ultrashort light fields with multielectron atoms and atomic ions to be determined from first principles. By probing the emission of an inner 2s electron from Ne we are also able to study the bound state population dynamics during the free-electron laser pulse.

Electron impact excitation collision strengths for neon-like Ni XIX calculated using the relativistic R-matrix method

In a recent paper [Pramana - J. Phys. 64, 129 (2005)] results have been presented for electron impact excitation collision strengths for transitions among the fine-structure levels of the 2s(2)2p(6) and 2s(2)2p(5)3s configurations of Ni XIX. In this paper we demonstrate through an independent calculation with the relativistic R-matrix code that those results are unreliable and the conclusions drawn are invalid.

Time-dependent R -matrix theory for ultrafast atomic processes

We describe an ab initio nonperturbative time-dependent R-matrix theory for ultrafast atomic processes. This theory enables investigations of the interaction of few-femtosecond and -attosecond pulse lasers with complex multielectron atoms and atomic ions. A derivation and analysis of the basic equations are given, which propagate the atomic wave function in the presence of the laser field forward in time in the internal and external R-matrix regions. To verify the accuracy of the approach, we investigate two-photon ionization of Ne irradiated by an intense laser pulse and compare current results with those obtained using the R-matrix Floquet method and an alternative time-dependent method. We also verify the capability of the current approach by applying it to the study of two-dimensional momentum distributions of electrons ejected from Ne due to irradiation by a sequence of 2 as light pulses in the presence of a 780 nm laser field.