scholarly journals Use of Centrality Metrics to Protect Wildlife Ecology and Habitat Connectivity Analysis

2021 ◽  
Author(s):  
Huriye Simten SÜTÜN
Keyword(s):  
Habitat Connectivity ◽  
Connectivity Analysis ◽  
Wildlife Ecology ◽  
Centrality Metrics

Habitat connectivity analysis for conservation implications in an urban area

2014 ◽  
Vol 34 (1) ◽  
pp. 44-52 ◽  
Author(s):  
Bin Xun ◽  
Deyong Yu ◽  
Yupeng Liu
Keyword(s):  
Urban Area ◽  
Habitat Connectivity ◽  
Connectivity Analysis ◽  
Conservation Implications

Habitat models and habitat connectivity analysis for butterflies and burnet moths – The example of Zygaena carniolica and Coenonympha arcania

2005 ◽  
Vol 126 (2) ◽  
pp. 247-259 ◽  
Author(s):  
Birgit Binzenhöfer ◽  
Boris Schröder ◽  
Barbara Strauss ◽  
Robert Biedermann ◽  
Josef Settele
Keyword(s):  
Habitat Connectivity ◽  
Connectivity Analysis ◽  
Habitat Models

scholarly journals How do African elephants utilize the landscape during wet season? A habitat connectivity analysis for Sioma Ngwezi landscape in Zambia

2021 ◽  
Author(s):  
Doubt Chibeya ◽  
Heather Wood ◽  
Sara Cousins ◽  
Kerryn Carter ◽  
Moses Amos Nyirenda ◽  
...  
Keyword(s):  
Habitat Connectivity ◽  
Connectivity Analysis ◽  
Wet Season ◽  
African Elephants

Impact of vigilance fluctuations on resting state fMRI functional connectivity analysis

2015 ◽  
Vol 48 (06) ◽  
Author(s):  
P Sämann ◽  
O Gruber ◽  
M Czisch
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Connectivity Analysis ◽  
Functional Connectivity Analysis

Reducing Human-Bear Conflicts: Bear-Resistant Trash Cans

EDIS ◽  
2017 ◽  
Vol 2017 (3) ◽  
Author(s):  
Ethan T. Noel ◽  
Elizabeth F. Pienaar ◽  
Mike Orlando
Keyword(s):  
Great Risk ◽  
Ursus Americanus ◽  
Black Bear ◽  
Food Sources ◽  
Vehicle Collisions ◽  
Wildlife Ecology ◽  
Human Food ◽  
Fact Sheet

The Florida black bear (Ursus americanus floridanus) is the only species of bear in Florida, with an estimated population of approximately 4,030 bears. Bears that eat garbage put themselves in danger. This 3-page fact sheet written by Ethan T. Noel, Elizabeth F. Pienaar, and and Mike Orlando and published by the Wildlife Ecology and Conservation Department explains how to secure human garbage from bears so that they don’t become reliant on human food sources, a condition that puts them at great risk of being killed from vehicle collisions, illegal shooting, or euthanasia.­http://edis.ifas.ufl.edu/uw429

Identifying Breast Cancer-induced gene perturbations and its application in guiding drug repurposing

2020 ◽  
Vol 15 ◽  
Author(s):  
Jujuan Zhuang ◽  
Shuang Dai ◽  
Lijun Zhang ◽  
Pan Gao ◽  
Yingmin Han ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differential Expression ◽  
Enrichment Analysis ◽  
Cancer Tissue ◽  
Connectivity Analysis ◽  
Cancer Drugs ◽  
Normal Tissues ◽  
Differential Gene ◽  
Differential Modules ◽  
Differential Connectivity

Background: Breast cancer is a complex disease with high prevalence in women, the molecular mechanisms of which are still unclear at present. Most transcriptomic studies on breast cancer focus on differential expression of each gene between tumor and the adjacent normal tissues, while the other perturbations induced by breast cancer including the gene regulation variations, the changes of gene modules and the pathways, which might be critical to the diagnosis, treatment and prognosis of breast cancer are more or less ignored. Objective: We presented a complete process to study breast cancer from multiple perspectives, including differential expression analysis, constructing gene co-expression networks, modular differential connectivity analysis, differential gene connectivity analysis, gene function enrichment analysis key driver analysis. In addition, we prioritized the related anti-cancer drugs based on enrichment analysis between differential expression genes and drug perturbation signatures. Methods: The RNA expression profiles of 1109 breast cancer tissue and 113 non-tumor tissues were downloaded from The Cancer Genome Atlas (TCGA) database. Differential expression of RNAs was identified using the “DESeq2” bioconductor package in R, and gene co-expression networks was constructed using the weighted gene co-expression network analysis (WGCNA). To compare the module changes and gene co-expression variations between tumor and the adjacent normal tissues, modular differential connectivity (MDC) analysis and differential gene connectivity analysis (DGCA) were performed. Results: Top differential genes like MMP11 and COL10A1 were known to be associated with breast cancer. And we found 23 modules in the tumor network had significantly different co-expression patterns. The top differential modules were enriched in Goterms related to breast cancer like MHC protein complex, leukocyte activation, regulation of defense response and so on. In addition, key genes like UBE2T driving the top differential modules were significantly correlated with the patients’ survival. Finally, we predicted some potential breast cancer drugs, such as Eribulin, Taxane, Cisplatin and Oxaliplatin. Conclusion: As an indication, this framework might be useful in understanding the molecular pathogenesis of diseases like breast cancer and inferring useful drugs for personalized medication

Functional Connectivity Analysis of Brain Default Mode Networks Using Hamiltonian Path

2017 ◽  
Vol 16 (1) ◽  
pp. 44-50 ◽  
Author(s):  
Zhuqing Jiao ◽  
Kai Ma ◽  
Huan Wang ◽  
Ling Zou ◽  
Jianbo Xiang
Keyword(s):  
Functional Connectivity ◽  
Hamiltonian Path ◽  
Connectivity Analysis ◽  
Default Mode ◽  
Functional Connectivity Analysis

scholarly journals How are network centrality metrics related to interest rates in the Mexican secured and unsecured interbank markets?

2021 ◽  
pp. 100893
Author(s):  
Isela-Elizabeth Tellez-Leon ◽  
Serafín Martínez-Jaramillo ◽  
Luis Escobar-Farfán ◽  
Ronald Hochreiter
Keyword(s):  
Interest Rates ◽  
Network Centrality ◽  
Interbank Markets ◽  
Centrality Metrics

Intra-specific variation in movement and habitat connectivity of a mobile predator revealed by acoustic telemetry and network analyses

2021 ◽  
Vol 168 (6) ◽  
Author(s):  
Mario Espinoza ◽  
Elodie J. I. Lédée ◽  
Amy F. Smoothey ◽  
Michelle R. Heupel ◽  
Victor M. Peddemors ◽  
...  
Keyword(s):  
Acoustic Telemetry ◽  
Habitat Connectivity ◽  
Network Analyses ◽  
Specific Variation
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