Box Drawings for Learning with Imbalanced Data

The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes. We propose two machine learning algorithms to handle highly imbalanced classification problems. The classifiers constructed by both methods are created as unions of parallel axis rectangles around the positive examples, and thus have the benefit of being interpretable. The first algorithm uses mixed integer programming to optimize a weighted balance between positive and negative class accuracies. Regularization is introduced to improve generalization performance. The second method uses an approximation in order to assist with scalability. Specifically, it follows a \textit{characterize then discriminate} approach, where the positive class is characterized first by boxes, and then each box boundary becomes a separate discriminative classifier. This method has the computational advantages that it can be easily parallelized, and considers only the relevant regions of feature space

Reinforcement versus Fluidization in Cytoskeletal Mechanoresponsiveness

Every adherent eukaryotic cell exerts appreciable traction forces upon its substrate. Moreover, every resident cell within the heart, great vessels, bladder, gut or lung routinely experiences large periodic stretches. As an acute response to such stretches the cytoskeleton can stiffen, increase traction forces and reinforce, as reported by some, or can soften and fluidize, as reported more recently by our laboratory, but in any given circumstance it remains unknown which response might prevail or why. Using a novel nanotechnology, we show here that in loading conditions expected in most physiological circumstances the localized reinforcement response fails to scale up to the level of homogeneous cell stretch; fluidization trumps reinforcement. Whereas the reinforcement response is known to be mediated by upstream mechanosensing and downstream signaling, results presented here show the fluidization response to be altogether novel: it is a direct physical effect of mechanical force acting upon a structural lattice that is soft and fragile. Cytoskeletal softness and fragility, we argue, is consistent with early evolutionary adaptations of the eukaryotic cell to material properties of a soft inert microenvironment

Dependence of cancer cell adhesion kinetics on integrin ligand surface density measured by a high-throughput label-free resonant waveguide grating biosensor

A novel high-throughput label-free resonant waveguide grating (RWG) imager biosensor, the Epic® BenchTop (BT), was utilized to determine the dependence of cell spreading kinetics on the average surface density (vRGD) of integrin ligand RGD-motifs. vRGD was tuned over four orders of magnitude by co-adsorbing the biologically inactive PLL-g-PEG and the RGD-functionalized PLL-g-PEG-RGD synthetic copolymers from their mixed solutions onto the sensor surface. Using highly adherent human cervical tumor (HeLa) cells as a model system, cell adhesion kinetic data of unprecedented quality were obtained. Spreading kinetics were fitted with the logistic equation to obtain the spreading rate constant (r) and the maximum biosensor response (Δλmax), which is assumed to be directly proportional to the maximum spread contact area (Amax). r was found to be independent of the surface density of integrin ligands. In contrast, Δλmax increased with increasing RGD surface density until saturation at high densities. Interpreting the latter behavior with a simple kinetic mass action model, a 2D dissociation constant of 1753 ± 243 μm−2 (corresponding to a 3D dissociation constant of ~30 μM) was obtained for the binding between RGD-specific integrins embedded in the cell membrane and PLL-g-PEG-RGD. All of these results were obtained completely noninvasively without using any labels

Magnetically-actuated microposts stimulate axon growth

New strategies to promote neuronal regeneration should aim to increase the speed of axonal elongation. Biochemical signaling is a key factor in axon growth, but recent discoveries have shown that mechanical force, through a process referred to as stretch growth, can significantly influence the elongation rate. Here, we develop a method to apply forces to primary hippocampal neurons from mice using magnetic microposts that actuate in response to an external magnetic field. Neurons are cultured onto these microposts and subjected to an average displacement of 0.2 μm at a frequency of 5 Hz. We find that the mechanical stimulation promotes an increase in the length of the axons compared to control conditions. In addition, there is an increase in the density of microtubules and in the amount of cisternae of the endoplasmic reticulum, providing evidence that stretch growth is accompanied by a mass addition to the neurite. Together, these results indicate that magnetically-actuated microposts can accelerate the rate of axon growth, paving the way for future applications in neuronal regeneration. Video abstract: [Figure presented