The Polynomial Method Augmented by Supervised Training for Hand-Printed Character Recognition

We present a pattern recognition algorithm for hand-printed characters, based on a combination of the classical least squares method and a neural-network-type supervised training algorithm. Characters are mapped, nonlinearly, to feature vectors using selected quadratic polynomilas of the given pixels. We use a method for extracting an equidistributed subsample of all possible quadratic features. This method creates pattern classifiers with accuracy competitive to feed-forward systems trained using back propagation; however back propagation training takes longer by a factor of ten to fifty. (This makes our system particularly attractive for experimentation with other forms of feature representation, other character sets, etc.) The resulting classifier runs much faster in use than the back propagation trained systems, because all arithmetic is done using bit and integer operations

Genetic Algorithm Selection of Features for Hand-printed Character Identification

We have constructed a linear discriminator for hand-printed character recognition that uses a (binary) vector of 1,500 features based on an equidistributed collection of products of pixel pairs. This classifier is competitive with other techniques, but faster to train and to run for classification. However, the 1,500-member feature set clearly contains many redundant (overlapping or useless) members, anda significantly smaller set would be very desirable (e.g., for faster training, a faster and smaller application program, and a smaller system suitable for hardware implementation). A system using the small set of features should also be better at generalization, since fewer features are less likely to allow a system to memorize noise in the training data. Several approaches to using a genetic algorithm to search for effective small subsets of features have been tried, and we have successfully derived a 300-element set of features and built a classifier whose performance is as good on our training and testing set as the system using the full set

Using quasirandom numbers in neural networks

We present a novel training algorithm for a feed forward neural network with a single hidden layer of nodes (i.e., two layers of connection weights). Our algorithm is capable of training networks for hard problems, such as the classic two-spirals problem. The weights in the first layer are determined using a quasirandom number generator. These weights are frozen---they are never modified during the training process. The second layer of weights is trained as a simple linear discriminator using methods such as the pseudo-inverse, with possible iterations. We also study the problem of reducing the hidden layer: pruning low-weight nodes and a genetic algorithm search for good subsets

Use of the MicroSiM (µSiM) Barrier Tissue Platform for Modeling the Blood-Brain Barrier.

The microSiM (µSiM) is a membrane-based culture platform for modeling the blood-brain barrier (BBB). Unlike conventional membrane-based platforms, the µSiM provides experimentalists with new capabilities, including live cell imaging, unhindered paracrine signaling between 'blood' and 'brain' chambers, and the ability to directly image immunofluorescence without the need for the extraction/remounting of membranes. Here we demonstrate the basic use of the platform to establish monoculture (endothelial cells) and co-culture (endothelial cells and pericytes) models of the BBB using ultrathin nanoporous silicon-nitride membranes. We demonstrate compatibility with both primary cell cultures and human induced pluripotent stem cell (hiPSC) cultures. We provide methods for qualitative analysis of BBB models via immunofluorescence staining and demonstrate the use of the µSiM for the quantitative assessment of barrier function in a small molecule permeability assay. The methods provided should enable users to establish their barrier models on the platform, advancing the use of tissue chip technology for studying human tissues

The neck region of the C-type lectin DC-SIGN regulates its surface spatiotemporal organization and virus-binding capacity on antigen presenting cells

The C-type lectin DC-SIGN expressed on dendritic cells (DCs) facilitates capture and internalization of a plethora of different pathogens. Although it is known that DC-SIGN organizes in nanoclusters at the surface of DCs, the molecular mechanisms responsible for this well defined nanopatterning and role in viral binding remain enigmatic. By combining biochemical and advanced biophysical techniques, including optical superresolution and single particle tracking, we demonstrate that DC-SIGN intrinsic nanoclustering strictly depends on its molecular structure. DC-SIGN nanoclusters exhibited free, Brownian diffusion on the cell membrane. Truncation of the extracellular neck region, known to abrogate tetramerization, significantly reduced nanoclustering and concomitantly increased lateral diffusion. Importantly, DC-SIGN nanocluster dissolution exclusively compromised binding to nanoscale size pathogens. Monte Carlo simulations revealed that heterogeneity on nanocluster density and spatial distribution confers broader binding capabilities to DC-SIGN. As such, our results underscore a direct relationship between spatial nanopatterning, driven by intermolecular interactions between the neck regions, and receptor diffusion to provide DC-SIGN with the exquisite ability to dock pathogens at the virus length scale. Insight into how virus receptors are organized prior to virus binding and how they assemble into functional platforms for virus docking is helpful to develop novel strategies to prevent virus entry and infectio

Ultrathin Silicon Membranes for Wearable Dialysis

The development of wearable or implantable technologies that replace center-based hemodialysis (HD) hold promise to improve outcomes and quality of life for patients with ESRD. A prerequisite for these technologies is the development of highly efficient membranes that can achieve high toxin clearance in small-device formats. Here we examine the application of the porous nanocrystalline silicon (pnc-Si) to HD. pnc-Si is a molecularly thin nanoporous membrane material that is orders of magnitude more permeable than conventional HD membranes. Material developments have allowed us to dramatically increase the amount of active membrane available for dialysis on pnc-Si chips. By controlling pore sizes during manufacturing, pnc-Si membranes can be engineered to pass middle-molecular-weight protein toxins while retaining albumin, mimicking the healthy kidney. A microfluidic dialysis device developed with pnc-Si achieves urea clearance rates that confirm that the membrane offers no resistance to urea passage. Finally, surface modifications with thin hydrophilic coatings are shown to block cell and protein adhesion

The polynomial method augmented by supervised training for hand printed character recognition

We present a pattern recognition algorithm for hand-printed characters, based on a combination of the classical least squares method and a neural-network-type supervised training algorithm. Characters are mapped, nonlinearly, to feature vectors using selected quadratic polynomials of the given pixels. We use a method for extracting an equidistributed subsample of all possible quadratic features. This method creates pattern classifiers with accuracy competitive to feed-forward systems trained using back propagation; however back propagation training takes longer by a factor of ten to fifty. (This makes our system particularly attractive for experimentation with other forms of feature representation, other character sets, etc.) The resulting classifier runs much faster in use than the back propagation trained systems, because all arithmetic is done using bit and integer operations. 1 Background The augmented polynomial method is a system for classification of hand printed characters and digits such as ZIP codes, and the characters written on tax forms. This method extend