508 research outputs found

    Box Drawings for Learning with Imbalanced Data

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    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

    Remote Manipulation of Droplets on a Flexible Magnetically Responsive Film

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    The manipulation of droplets is used in a wide range of applications, from lab-on-a-chip devices to bioinspired functional surfaces. Although a variety of droplet manipulation techniques have been proposed, active, fast and reversible manipulation of pure discrete droplets remains elusive due to the technical limitations of previous techniques. Here, we describe a novel technique that enables active, fast, precise and reversible control over the position and motion of a pure discrete droplet with only a permanent magnet by utilizing a magnetically responsive flexible film possessing actuating hierarchical pillars on the surface. This magnetically responsive surface shows reliable actuating capabilities with immediate field responses and maximum tilting angles of ???90??. Furthermore, the magnetic responsive film exhibits superhydrophobicity regardless of tilting angles of the actuating pillars. Using this magnetically responsive film, we demonstrate active and reversible manipulation of droplets with a remote magnetic force.open0

    Field-Effect Flow Control in Microfluidics

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    Lab-on-chip (LOC) devices have miniaturized routine laboratory processes for automated, high-throughput chemical analysis. Separations of biomolecules, including protein and DNA, have been performed with high efficiencies in LOC devices, but the need for improved fluid flow control, i.e. pumping and valves, remains a significant challenge for next-generation systems. This dissertation explores the development of novel flow-control technology in polymer microfluidic networks for the realization of inexpensive, next-generation LOC devices. In the microchannels, electroosmotic flow (EOF) is used to electro-kinetically move the fluid with a longitudinal electric field. To modulate the EOF velocity, the technique of field-effect flow control (FEFC) is employed. In FEFC, a second electric field is applied through the microchannel wall to influence the surface charge at the fluid-microchannel interface for independent control of the EOF. Presented in this work is the first demonstration of FEFC in a polymer network. The microchannel walls were composed of Parylene C (1 - 2 um thick), which is an inexpensive, chemical vapor deposited polymer. In this work, FEFC modulated the EOF velocity from 240% to 60% of its original value in a microchannel that was 40 um in height, 100 um in width, and 2 cm long. The next research phase integrated FEFC technology into microfluidic networks with microchannels in the second and third dimensions. At the T-intersection of three microchannels, FEFC established different EOF pumping rates in the two main microchannels. The different flow rates induced pressure pumping in the third, field-free microchannel with ultra-low flow rate control (-2.0 nL/min to 2.0 nL/min). Moreover, adjusting the secondary electric fields enabled bi-directional flow control for the induced pumping in the 2D and 3D field-free microchannels. To improve the microfluidic networks, an electro-fluid flow model was developed to describe the induced pressure and FEFC phenomenon. The model closely predicted the experimentally obtained flow rates in the field-free microchannel. Additionally, the study of multiple gate electrodes along the same microchannel showed that FEFC has only a local effect over the EOF, but revealed that position and size of the electrodes influence the EOF control in the microfluidic network

    Nanotechnology for Cell–Substrate Interactions

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    In the pursuit to understand the interaction between cells and their underlying substrates, the life sciences are beginning to incorporate micro- and nanotechnology-based tools to probe and measure cells. The development of these tools portends endless possibilities for new insights into the fundamental relationships between cells and their surrounding microenvironment that underlie the physiology of human tissue. Here, we review techniques and tools that have been used to study how a cell responds to the physical factors in its environment. We also discuss unanswered questions that could be addressed by these approaches to better elucidate the molecular processes and mechanical forces that dominate the interactions between cells and their physical scaffolds

    The consequence of substrates of large- scale rigidity on actin network tension in adherent cells

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    International audienceThere is compelling evidence that substrate stiffness affects cell adhesion as well as cytoskeleton organization and contractile activity. This work was designed to study the cytoskeletal contractile activity of cells plated on microposts of different stiffness using a numerical model simulating the intracellular tension of individual cells. We allowed cells to adhere onto micropost substrates of various rigidities and used experimental traction force data to infer cell contractility using a numerical model. The model discriminates between the influence of substrate stiffness on cell tension and shows that higher substrate stiffness leads to an increase in intracellular tension. The strength of this model is its ability to calculate the mechanical state of each cell in accordance to its individual cytoskeletal structure. This is achieved by regenerating a numerical cytoskeleton base

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

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    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
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