Education-Oriented People-to-People Association Network (E-PAN)

It is still a question of debate whether neutrophils, often found in the tumor microenvironment, mediate tumor-promoting or rather tumor-inhibiting activities. The present study focuses on the involvement of neutrophils in high grade serous ovarian cancer (HGSOC). Macroscopic features classify two types of peritoneal tumor spread in HGSOC. Widespread and millet sized lesions characterize the miliary type, while non-miliary metastases are larger and associated with better prognosis. Multi-omics and FACS data were generated from ascites samples. Integrated data analysis demonstrates a significant increase of neutrophil extracellular trap (NET)-associated molecules in non-miliary ascites samples. A co-association network analysis performed with the ascites data further revealed a striking correlation between NETosis-associated metabolites and several eicosanoids. The congruence of data generated from primary neutrophils with ascites analyses indicates the predominance of NADPH oxidase 2 (NOX)-independent NETosis. NETosis is associated with protein S100A8/A9 release. An increase of the S100A8/CRP abundance ratio was found to correlate with favorable survival of HGSOC patients. The analysis of additional five independent proteome studies with regard to S100A8/CRP ratios confirmed this observation. In conclusion, NET formation seems to relate with better cancer patient outcome.

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Isolation of a novel association network in a polymer gel

Microscopy Research and Technique ◽

10.1002/jemt.1070310515 ◽

1995 ◽

Vol 31 (5) ◽

pp. 467-468 ◽

Cited By ~ 1

Author(s):

John R. Ilzhoefer ◽

Valerie M. Knowlton ◽

Richard J. Spontak

Keyword(s):

Association Network ◽

Polymer Gel ◽

Novel Association

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An association network reveals co-regulation of carbon and nitrogen metabolism-related parameters in durum wheat grown under different environmental conditions

New Biotechnology ◽

10.1016/j.nbt.2015.10.079 ◽

2016 ◽

Vol 33 (3) ◽

pp. 414 ◽

Cited By ~ 3

Author(s):

Rubén Vicente ◽

Rafael Martínez-Carrasco ◽

Pilar Pérez ◽

Rosa Morcuende

Keyword(s):

Durum Wheat ◽

Nitrogen Metabolism ◽

Environmental Conditions ◽

Association Network ◽

Carbon And Nitrogen ◽

Carbon And Nitrogen Metabolism

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A Co-Association Network Analysis of the Genetic Determination of Pig Conformation, Growth and Fatness

PLoS ONE ◽

10.1371/journal.pone.0114862 ◽

2014 ◽

Vol 9 (12) ◽

pp. e114862 ◽

Cited By ~ 16

Author(s):

Anna Puig-Oliveras ◽

Maria Ballester ◽

Jordi Corominas ◽

Manuel Revilla ◽

Jordi Estellé ◽

...

Keyword(s):

Network Analysis ◽

Association Network ◽

Genetic Determination

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Networks in the mind – what communities reveal about the structure of the lexicon

Open Linguistics ◽

10.1515/opli-2021-0012 ◽

2021 ◽

Vol 7 (1) ◽

pp. 181-199

Author(s):

László Kovács ◽

András Bóta ◽

László Hajdu ◽

Miklós Krész

Keyword(s):

Word Association ◽

Mental Lexicon ◽

Association Network ◽

Community Structures ◽

Associative Structure ◽

The Mind ◽

Agglutinative Language

Abstract The mental lexicon stores words and information about words. The lexicon is seen by many researchers as a network, where lexical units are nodes and the different links between the units are connections. Based on the analysis of a word association network, in this article we show that different kinds of associative connections exist in the mental lexicon. Our analysis is based on a word association database from the agglutinative language Hungarian. We use communities – closely knit groups – of the lexicon to provide evidence for the existence and coexistence of different connections. We search for communities in the database using two different algorithms, enabling us to see the overlapping (a word belongs to multiple communities) and non-overlapping (a word belongs to only one community) community structures. Our results show that the network of the lexicon is organized by semantic, phonetic, syntactic and grammatical connections, but encyclopedic knowledge and individual experiences are also shaping the associative structure. We also show that words may be connected not just by one, but more types of connections at the same time.

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Predicting Diagnostic Gene Biomarkers for Non-Small-Cell Lung Cancer

BioMed Research International ◽

10.1155/2016/3952494 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8

Author(s):

Bin Liang ◽

Yang Shao ◽

Fei Long ◽

Shu-Juan Jiang

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Association Network ◽

Small Cell Lung ◽

Functional Association ◽

Functional Association Network ◽

Gene Modules

Lung cancer is the primary reason for death due to cancer worldwide, and non-small-cell lung cancer (NSCLC) is the most common subtype of lung cancer. Most patients die from complications of NSCLC due to poor diagnosis. In this paper, we aimed to predict gene biomarkers that may be of use for diagnosis of NSCLC by integrating differential gene expression analysis with functional association network analysis. We first constructed an NSCLC-specific functional association network by combining gene expression correlation with functional association. Then, we applied a network partition algorithm to divide the network into gene modules and identify the most NSCLC-specific gene modules based on their differential expression pattern in between normal and NSCLC samples. Finally, from these modules, we identified genes that exhibited the most impact on the expression of their functionally associated genes in between normal and NSCLC samples and predicted them as NSCLC biomarkers. Literature review of the top predicted gene biomarkers suggested that most of them were already considered critical for development of NSCLC.

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How a Word of a Text Selects the Related Words in a Human Association Network

Cognitive Approach to Natural Language Processing ◽

10.1016/b978-1-78548-253-3.50003-2 ◽

2017 ◽

pp. 41-62

Author(s):

Wiesław Lubaszewski ◽

Izabela Gatkowska ◽

Maciej Godny

Keyword(s):

Association Network ◽

Human Association

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Structure at every scale: A semantic network account of the similarities between very unrelated concepts

10.31234/osf.io/s3k79 ◽

2019 ◽

Author(s):

Simon De Deyne ◽

Danielle Navarro ◽

Amy Perfors ◽

Gert Storms

Keyword(s):

Word Association ◽

Semantic Network ◽

Activation Mechanism ◽

Spreading Activation ◽

Association Network ◽

Semantic System

Similarity plays an important role in organizing the semantic system. However, given that similarity cannot be defined on purely logical grounds, it is important to understand how people perceive similarities between different entities. Despite this, the vast majority of studies focus on measuring similarity between very closely related items. When considering concepts that are very weakly related, little is known. In this article, we present 4 experiments showing that there are reliable and systematic patterns in how people evaluate the similarities between very dissimilar entities. We present a semantic network account of these similarities showing that a spreading activation mechanism defined over a word association network naturally makes correct predictions about weak similarities, whereas, though simpler, models based on direct neighbors between word pairs derived using the same network cannot.

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