A constrained recursive least squares algorithm for adaptive combination of multiple models

In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters. Moreover an l1-norm constraint to the combination parameters is also applied with the aim to achieve sparsity of multiple models so that only a subset of models may be selected into the final model. Then a weighted l2-norm is applied as an approximation to the l1-norm term. As such at each time step, a closed solution of the model combination parameters is available. The contribution of this paper is to derive the proposed constrained recursive least squares algorithm that is computational efficient by exploiting matrix theory. The effectiveness of the approach has been demonstrated using both simulated and real time series examples.

Polymer-drug conjugates for combination anticancer therapy: investigating the mechanism of action.

We developed a family of polymer-drug conjugates carrying the combination of the anticancer agent epirubicin (EPI) and nitric oxide (NO). EPI-PEG-(NO)8, carrying the highest content of NO, displayed greater activity in Caco-2 cells while it decreased toxicity against endothelium cells and cardiomyocytes with respect to free EPI. FACS and confocal microscopy confirmed conjugates internalization. Light scattering showed formation of micelle whose size correlated with internalization rate. EPI-PEG-(NO)8 showed increased bioavailability in mice compared to free EPI.