103 research outputs found

    Label-free segmentation of co-cultured cells on a nanotopographical gradient

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    The function and fate of cells is influenced by many different factors, one of which is surface topography of the support culture substrate. Systematic studies of nanotopography and cell response have typically been limited to single cell types and a small set of topographical variations. Here, we show a radical expansion of experimental throughput using automated detection, measurement, and classification of co-cultured cells on a nanopillar array where feature height changes continuously from planar to 250 nm over 9 mm. Individual cells are identified and characterized by more than 200 descriptors, which are used to construct a set of rules for label-free segmentation into individual cell types. Using this approach we can achieve label-free segmentation with 84% confidence across large image data sets and suggest optimized surface parameters for nanostructuring of implant devices such as vascular stents

    On location estimation for LARCH processes

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    AbstractWe consider location estimation when the error process is a stationary LARCH process with long memory in the second moments. The asymptotic distribution of the sample mean and nonlinear M-estimators of the location parameter are derived. Essential assumptions for obtaining asymptotic normality with n-12-rate of convergence are symmetry of the innovation distribution and skew-symmetry of the ψ-function

    Three-dimensional elliptic grid generation technique with application to turbomachinery cascades

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    Described is a numerical method for generating 3-D grids for turbomachinery computational fluid dynamic codes. The basic method is general and involves the solution of a quasi-linear elliptic partial differential equation via pointwise relaxation with a local relaxation factor. It allows specification of the grid point distribution on the boundary surfaces, the grid spacing off the boundary surfaces, and the grid orthogonality at the boundary surfaces. A geometry preprocessor constructs the grid point distributions on the boundary surfaces for general turbomachinery cascades. Representative results are shown for a C-grid and an H-grid for a turbine rotor. Two appendices serve as user's manuals for the basic solver and the geometry preprocessor

    Discovering Jewish Studies Collections in Academic Libraries: A Practical Guide

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    The U.S. colleges and universities offering non-sectarian educational programs in Jewish Studies rely on the support of their academic libraries for research materials and library services. For college libraries which use Library of Congress Classification scheme, it is a common practice to integrate studies resources into their general library collections. Since Jewish Studies sources span a vast number of subjects within all major disciplines, shelving integration leads to the dispersion of all relevant sources and such dispersion in turn leads to a variety of problems for library professionals and library users. For collection development librarians the problems range from lack of information about collection\u27s size, strengths or weaknesses, and for library users interested in browsing the collection, dispersion of subjects creates a major roadblock. This practical guide aims at providing a solution to such problems. By identifying all relevant Library of Congress call numbers and the corresponding Library of Congress subject headings, the guide offers a simplified access to Jewish Studies sources in general library collections. It is arranged by four major discipline: Arts & Humanities, Social Sciences, Sciences, and General Works & Bibliographies. Within each discipline, specific LC call number ranges and corresponding subjects are listed. The subjects are further subdivided and precisely identified. The guide will assist collection development librarians, library liaisons, grants and fundraising professionals and especially the Jewish Studies faculty and students, in identifying and locating relevant sources

    Families of artinian and low dimensional determinantal rings

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    Let GradAlg(H) be the scheme parameterizing graded quotients of R=k[x_0,...,x_n] with Hilbert function H (it is a subscheme of the Hilbert scheme of P^n if we restrict to quotients of positive dimension, see definition below). A graded quotient A=R/I of codimension c is called standard determinantal if the ideal I can be generated by the t by t minors of a homogeneous t by (t+c-1) matrix (f_{ij}). Given integers a_0\le a_1\le ...\le a_{t+c-2} and b_1\le ...\le b_t, we denote by W_s(\underline{b};\underline{a}) the stratum of GradAlg(H) of determinantal rings where f_{ij} \in R are homogeneous of degrees a_j-b_i. In this paper we extend previous results on the dimension and codimension of W_s(\underline{b};\underline{a}) in GradAlg(H) to {\it artinian determinantal rings}, and we show that GradAlg(H) is generically smooth along W_s(\underline{b};\underline{a}) under some assumptions. For zero and one dimensional determinantal schemes we generalize earlier results on these questions. As a consequence we get that the general element of a component W of the Hilbert scheme of P^n is glicci provided W contains a standard determinantal scheme satisfying some conditions. We also show how certain ghost terms disappear under deformation while other ghost terms remain and are present in the minimal resolution of a general element of GradAlg(H).Comment: Postprint replacing preprint. 29 pages. Online 26.May 2017 in Journal of Pure and Applied Algebr

    Sparse production networks

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    Firm-to-firm connections in domestic and international production networks play a fundamental role in economic outcomes. Firm heterogeneity and the sparse nature of firm-to-firm connections implicitly discipline network structure. We find that a large group of well-established statistical relationships are not useful in improving our understanding of production networks. We propose an "elementary" model for production networks based on random matching and firm heterogeneity and characterize the families of statistics and data generating processes that may raise underidentification concerns in more complex models. The elementary model is a useful benchmark in developing "instructive" statistics and informing model construction and selection
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