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Institution

University of New Mexico

EducationAlbuquerque, New Mexico, United States
About: University of New Mexico is a education organization based out in Albuquerque, New Mexico, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 28870 authors who have published 64767 publications receiving 2578371 citations. The organization is also known as: UNM & Universitatis Novus Mexico.


Papers
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Journal ArticleDOI
TL;DR: Public Participation in Scientific Research (PPSR) as discussed by the authors ) is a popular term for participatory action research and citizen science, and it has been widely used in the literature.
Abstract: Members of the public participate in scientific research in many different contexts, stemming from traditions as varied as participatory action research and citizen science. Particularly in conservation and natural resource management contexts, where research often addresses complex social-ecological questions, the emphasis on and nature of this participation can significantly affect both the way that projects are designed and the outcomes that projects achieve. We review and integrate recent work in these and other fields, which has converged such that we propose the term public participation in scientific research (PPSR) to discuss initiatives from diverse fields and traditions. We describe three predominant models of PPSR and call upon case studies suggesting that—regardless of the research context—project outcomes are influenced by (1) the degree of public participation in the research process and (2) the quality of public participation as negotiated during project design. To illustrate relationships between the quality of participation and outcomes, we offer a framework that considers how scientific and public interests are negotiated for project design toward multiple, integrated goals. We suggest that this framework and models, used in tandem, can support deliberate design of PPSR efforts that will enhance their outcomes for scientific research, individual participants, and social-ecological systems.

1,016 citations

Journal ArticleDOI
TL;DR: The Wiener index W is the sum of distances between all pairs of vertices of a (connected) graph as discussed by the authors, defined as the distance between all vertices in a graph.
Abstract: The Wiener index W is the sum of distances between all pairs of vertices of a (connected) graph. The paper outlines the results known for W of trees: methods for computation of W and combinatorial expressions for W for various classes of trees, the isomorphism–discriminating power of W, connections between W and the center and centroid of a tree, as well as between W and the Laplacian eigenvalues, results on the Wiener indices of the line graphs of trees, on trees extremal w.r.t. W, and on integers which cannot be Wiener indices of trees. A few conjectures and open problems are mentioned, as well as the applications of W in chemistry, communication theory and elsewhere.

1,015 citations

Journal ArticleDOI
02 Nov 2017-Nature
TL;DR: A genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry finds that heritability of Breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2–5-fold enriched relative to the genome- wide average.
Abstract: Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10-8. The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.

1,014 citations

Journal ArticleDOI
TL;DR: The interaction of phonological properties of lexical patterns with frequency and the interaction of type and token frequency are shown to influence degree of productivity in three models of morphological storage and processing.
Abstract: Three models of morphological storage and processing are compared: the dual-processing model of Pinker, Marcus and colleagues, the connectionist model of Marchman, Plunkett, Seidenberg and others, and the network model of Bybee and Langacker. In line with predictions made in the latter two frameworks, type frequency of a morphological pattern is shown to be important in determining productivity. In addition, the paper considers the nature of lexical schemas in the network model, which are of two types: source-oriented and product-oriented. The interaction of phonological properties of lexical patterns with frequency and the interaction of type and token frequency are shown to influence degree of productivity. Data are drawn from English, German, Arabic and Hausa.

1,010 citations

Journal ArticleDOI
TL;DR: It is concluded that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting and bioengineering.
Abstract: Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

1,010 citations


Authors

Showing all 29120 results

NameH-indexPapersCitations
Bruce S. McEwen2151163200638
David Miller2032573204840
Jing Wang1844046202769
Paul M. Thompson1832271146736
David A. Weitz1781038114182
David R. Williams1782034138789
John A. Rogers1771341127390
George F. Koob171935112521
John D. Minna169951106363
Carlos Bustamante161770106053
Lewis L. Lanier15955486677
Joseph Wang158128298799
John E. Morley154137797021
Fabian Walter14699983016
Michael F. Holick145767107937
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202390
2022595
20213,060
20203,048
20192,779
20182,729