New programs for translating research to patient care: Lessons learned at the NIH Center for Accelerated Innovations at Cleveland Clinic.

The NIH Center for Accelerated Innovations at Cleveland Clinic (NCAI-CC) was funded by the National Heart Lung and Blood Institute (NHLBI) to support academic investigators in technology development and commercialization. NCAI-CC was one of three multi-institutional Centers established in the fall of 2013. The goal of each Center was to catalyze the growth of an ecosystem of commercialization within their affiliated institutions and regions by managing a program of funding and guiding translational project development and by delivering commercialization education programs to participating investigators. NCAI-CC created and managed such a funding program, ultimately supporting 75 different projects across seven separate academic institutions and developed tailored educational content following the National Science Foundation I-Corps™ curriculum and delivered the program to 79 teams from 12 institutions. We determined early on that in establishment and implementation of projects, it is important to support the teams and principal investigators throughout the program. The support includes a change in principal investigator mindset from specific aims orientation to goals and deliverables on projects. Our skills development efforts emphasized commercialization and a deep understanding of customer needs for new technology adoption. Here, we review our experiences, outcomes, and insights, including the challenges identified in program implementation.

Adaptive Confidence Bands for Nonparametric Regression Functions.

A new formulation for the construction of adaptive confidence bands in non-parametric function estimation problems is proposed. Confidence bands are constructed which have size that adapts to the smoothness of the function while guaranteeing that both the relative excess mass of the function lying outside the band and the measure of the set of points where the function lies outside the band are small. It is shown that the bands adapt over a maximum range of Lipschitz classes. The adaptive confidence band can be easily implemented in standard statistical software with wavelet support. Numerical performance of the procedure is investigated using both simulated and real datasets. The numerical results agree well with the theoretical analysis. The procedure can be easily modified and used for other nonparametric function estimation models.

Cognitive impairment in PDAPP mice depends on ApoE and ACT-catalyzed amyloid formation.

Biochemical and genetic studies indicate that the inflammatory proteins, apolipoprotein E (ApoE) and alpha(1)-antichymotrypsin (ACT) are important in the pathogenesis of Alzheimer's disease (AD). Using several lines of multiply transgenic/knockout mice we show here that murine ApoE and human ACT separately and synergistically facilitate both diffuse A beta immunoreactive and fibrillar amyloid deposition and thus also promote cognitive impairment in aged PDAPP(V717F) mice. The degree of cognitive impairment is highly correlated with the ApoE- and ACT-dependent hippocampal amyloid burden, with PDAPP mice lacking ApoE and ACT having little amyloid and little learning disability. A analysis of young mice before the onset of amyloid formation shows that steady-state levels of monomeric A beta peptide are unchanged by ApoE or ACT. These data suggest that the process or product of amyloid formation is more critical than monomeric A beta for the neurological decline in AD, and that the risk factors ApoE and ACT participate primarily in disease processes downstream of APP processing.

Use of multimetric statistical analysis to characterize and discriminate between the performance of four Alzheimer's transgenic mouse lines differing in Abeta deposition.

Behavioral assessment of genetically-manipulated mouse lines for Alzheimer's disease has become an important index for determining the efficacy of therapeutic interventions and examining disease pathogenesis. However, the potential for higher level statistical analyses to assist in these goals remains largely unexplored. The present study thus involved multimetric statistical analyses of behavioral and beta-amyloid (Abeta) deposition measures from four PDAPP-derived transgenic mouse lines that differ in extent of Abeta deposition. For all four lines, multiple behavioral measures obtained from a comprehensive task battery administered at 15-16 months of age were collectively examined by correlation, factor, and discriminant function analyses. In addition, both compact and total beta-amyloid (Abeta) histologic measures were determined from the same animals. Widespread intra- and inter-task correlations were evident, with impairment in all three water tasks (Morris maze, platform recognition, and radial arm water maze) correlating extensively with Abeta deposition in hippocampus and cerebral cortex. By elucidating the underlying relationships among measures, factor analysis revealed a single primary factor (Factor 1) that loaded most cognitive measures, particularly those for working memory and recognition. Abeta deposition measures loaded exclusively on this primary factor. In individual animals, only factor scores derived from this primary factor were correlated with Abeta deposition. Both of these findings again underscore the association between cognitive impairment and Abeta deposition. Finally, discriminant function analysis (step-wise forward method) was able to distinguish between all four AD transgenic lines based on behavioral performance alone, as well as when Abeta deposition measures were included. Our results demonstrate the utility of higher level, multimetric analysis of behavioral measures from AD transgenic mice. Analyses such as these will be very beneficial for the functional evaluation of therapeutic interventions against AD and for finding behavioral measures that can serve as predictors of pathology.