Towards a programmable microfluidic valve: Formation dynamics of two-dimensional magnetic bead arrays in transient magnetic fields

Wittbracht F, Eickenberg B, Weddemann A, Hütten A. Towards a programmable microfluidic valve: Formation dynamics of two-dimensional magnetic bead arrays in transient magnetic fields. Journal of Applied Physics. 2011;109(11): 114503.The induction of dipolar coupling has proven to allow for the initiation of self-assembled, reconfigurable particle clusters of superparamagnetic microbeads suspended in a carrier liquid. The adjustment of the interplay between magnetic and hydrodynamic forces opens various possibilities for guiding strategies of these superstructures within microfluidic devices. In this work, the formation dynamics of such particle clusters under the influence of a rotating magnetic field are studied. Different agglomeration regimes are characterized by the dimensionality of the confined objects. The growth dynamics of the obtained agglomerates are analyzed quantitatively in order to deduce the microscopic growth mechanisms. The growth of two-dimensional clusters is governed by the addition of bead chains to previously formed agglomerates. Time scales for the cluster growth are characterized by the chain dissociation rate. Based on the experimental findings, we may conclude to a linear dependence of the chain dissociation rate on the rotation frequency of the applied magnetic field. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3582133

Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging.

Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity