Landmark-based registration using radial basis functions (RBF) is an efficient and mathematically transparent method for the registration of medical images. To ensure invertibility and diffeomorphism of the RBF-based vector field, various regularization schemes have been suggested. Here, we report a novel analytic method of RBF regularization and demonstrate its power for Gaussian RBF. Our anal...

Radial Basis Functions (RBF) interpolation theory is briefly introduced at the “application level” including some basic principles and computational issues. The RBF interpolation is convenient for un-ordered data sets in n-dimensional space, in general. This approach is convenient especially for a higher dimension N 2 conversion to ordered data set, e.g. using tessellation, is computationally v...

We have examined the role of the renal sympathetic nerves in the renal blood flow (RBF) response to hemorrhage in seven conscious rabbits. Hemorrhage was produced by blood withdrawal at 1.35 ml ⋅ min-1 ⋅ kg-1for 20 min while RBF and renal sympathetic nerve activity (RSNA) were simultaneously measured. Hemorrhage was associated with a gradual increase in RSNA and decrease in RBF from the 4th min...

During Drosophila eye development, cell proliferation is coordinated with differentiation. Immediately posterior to the morphogenetic furrow, cells enter a synchronous round of S phase called second mitotic wave. We have examined the role of RBF, the Drosophila RB family homolog, in cell cycle progression in the second mitotic wave. RBF-280, a mutant form of RBF that has four putative cdk phosp...

Interpolation or approximation of scattered data is very often task in engineering problems. The Radial Basis Functions (RBF) interpolation is convenient for scattered (un-ordered) data sets in k-dimensional space, in general. This approach is convenient especially for a higher dimension k > 2 as the conversion to an ordered data set, e.g. using tessellation, is computationally very expensive. ...

An effective technique for identifying nonlinear discrete-time systems using raisedcosine radial basis function (RBF) networks is presented. Raised-cosine RBF networks are bounded-input bounded-output stable systems, and the network output is a continuously differentiable function of the past input and the past output. The evaluation speed of an n-dimensional raised-cosine RBF network is high b...

Radial basis function(RBF) methods have been actively developed in the last decades. The advantages of RBF methods are that these methods are mesh-free and yield high order accuracy if the function is smooth enough. The RBF approximation for discontinuous problems, however, deteriorates its high order accuracy due to the Gibbs phenomenon. With the Gibbs phenomenon in the RBF approximation, the ...

We previously identified Rbf as an activator for biofilm formation on polystyrene surfaces in Staphylococcus aureus strain 8325-4. However, strain 8325-4 contains genetic mutations that may affect biofilm formation. To extend the observation to other strains, we used strain Newman, a weak biofilm producer, and strain UAMS-1, an osteomyelitis clinical strain, in this study. We found that mutatio...

Meshfree methods based on radial basis function (RBF) approximation are of interest for numerical solution of partial differential equations (PDEs) because they are flexible with respect to geometry, they can provide high order convergence, they allow for local refinement, and they are easy to implement in higher dimensions. For global RBF methods, one of the major disadvantages is the computat...