1,721,192 research outputs found
Alien Registration- Casey, Michael (Auburn, Androscoggin County)
Full text linkhttps://digitalmaine.com/alien_docs/30831/thumbnail.jp
An Information-Theoretic Definition of Cell Type
No full textIndividual cells are often classified into cell ‘types’ based on the expression of so-called marker genes. Such marker-based classification assumes that cells of a given type are (at least approximately) interchangeable with respect to the expression of their associated markers. This traditional approach to cellular classification has been disrupted by single-cell RNA-sequencing technologies, which are able to measure genome-wide gene expression across thousands of individual cells. While potentially providing a wealth of data for cellular classification, these technologies have revealed that cells ostensibly of the same type are often highly heterogeneous (i.e. not interchangeable) with respect to the expression of established marker genes.A myriad of single-cell clustering methods has recently been developed to overcome the issue of heterogeneity with respect to marker gene expression and identify cell types directly from single-cell expression data. These methods typically proceed via: (1) unsupervised identification of clusters from single-cell expression data sets; (2) mapping of identified clusters to known cell types based on the expression of previously established marker genes. However, this two-step cluster-based approach to cellular classification is less biologically intuitive than the traditional marker-based approach, involving substantial mathematical and biological assumptions regarding the nature of cell type.In this thesis, I formalise the traditional marker gene approach to cellular classification using notions from information theory, and show how this formalism can be applied to identifying cell types from single-cell RNA-sequencing data. Specifically, I develop a novel clustering method based on the assumption that cells of the same type should be minimally heterogeneous – i.e. approximately interchangeable – with respect to the measured expression of a set of genes. Thus, this work offers an intuitive, formal definition of cell type that unites the traditional and current approaches to cellular classification through the mathematics of information theory.<br/
Estimating cellular redundancy in networks of genetic expression
No full textNetworks of genetic expression can be modeled by hypergraphs with the additional structure that real coefficients are given to each vertex-edge incidence. The spectra, i.e. the multiset of the eigenvalues, of such hypergraphs, are known to encode structural information of the data. We show how these spectra can be used, in particular, in order to give an estimation of cellular redundancy, a novel measure of gene expression heterogeneity, of the network. We analyze some simulated and real data sets of gene expression for illustrating the new method proposed here.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Theory of cell fate
No full textCell fate decisions are controlled by complex intracellular molecular regulatory networks. Studies increasingly reveal the scale of this complexity: not only do cell fate regulatory networks contain numerous positive and negative feedback loops, they also involve a range of different kinds of nonlinear protein-protein and protein-DNA interactions. This inherent complexity and non-linearity makes cell fate decisions hard to understand using experiment and intuition alone. In this primer we will outline how tools from mathematics can be used to understand cell fate dynamics. We will briefly introduce some notions from dynamical systems theory, and discuss how they offer a framework within which to build a rigorous understanding of what we mean by a cell 'fate', and how cells change fate. We will also outline how modern experiments, particularly high-throughput single-cell experiments, are enabling us to test and explore the limits of these ideas, and build a better understanding of cellular identities
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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