Ensemble transfer learning for Alzheimer's disease diagnosis.

There is considerable interest in developing inexpensive, nonintrusive diagnostic tests for Alzheimer's disease (AD), in the hope these tests will facilitate early diagnosis and support design of effective treatments. While tests based on blood-borne biomarkers such as microRNAs have shown promise, work to date indicates these tests often do not generalize well: diagnostic models derived for one patient group are not accurate when applied to a new cohort. This paper presents a novel ensemble-based transfer learning methodology which induces models that provide accurate diagnoses across distinct patient groups without retraining.

Imbibition in porous membranes of complex shape: quasi-stationary flow in thin rectangular segments.

The sustained liquid flow of a typical lateral flow assay can be mimicked by two-dimensional shaped, thin porous membranes, specifically rectangular membranes appended to circular sectors. In designing these fan-shaped devices, we have been aided by analytical equations and finite-element simulations. We show both mathematically and experimentally how a continuous increase in unwetted pore volume causes a deviation from traditional imbibition, and leads to quasi-stationary flow in the rectangular element.