Dace Klimanis

598 total citations
9 papers, 509 citations indexed

About

Dace Klimanis is a scholar working on Molecular Biology, Neurology and Immunology. According to data from OpenAlex, Dace Klimanis has authored 9 papers receiving a total of 509 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 3 papers in Neurology and 3 papers in Immunology. Recurrent topics in Dace Klimanis's work include Neuroinflammation and Neurodegeneration Mechanisms (3 papers), Ubiquitin and proteasome pathways (2 papers) and Sphingolipid Metabolism and Signaling (2 papers). Dace Klimanis is often cited by papers focused on Neuroinflammation and Neurodegeneration Mechanisms (3 papers), Ubiquitin and proteasome pathways (2 papers) and Sphingolipid Metabolism and Signaling (2 papers). Dace Klimanis collaborates with scholars based in United States, United Kingdom and China. Dace Klimanis's co-authors include John M. Hallenbeck, Irene Ginis, Yongshan Mou, Yang-ja Lee, José Greenspon, Jie Liu, Rama K. Jaiswal, Dragan Maric, Sungyoung Auh and Maria Spatz and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and Brain Research.

In The Last Decade

Dace Klimanis

9 papers receiving 495 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Dace Klimanis United States 9 319 120 83 69 67 9 509
Janet E. Holley United Kingdom 10 172 0.5× 90 0.8× 105 1.3× 31 0.4× 69 1.0× 15 425
Michelle D. Ashley United States 8 229 0.7× 50 0.4× 47 0.6× 51 0.7× 153 2.3× 10 444
Xuejun He China 12 274 0.9× 122 1.0× 54 0.7× 56 0.8× 34 0.5× 29 525
Longlong Luo China 10 244 0.8× 194 1.6× 135 1.6× 37 0.5× 17 0.3× 15 518
Dallas Boodhoo United States 11 154 0.5× 69 0.6× 132 1.6× 59 0.9× 68 1.0× 21 395
Malene Ambjørn Denmark 9 145 0.5× 106 0.9× 124 1.5× 80 1.2× 20 0.3× 10 493
Rainer Akkermann Germany 11 131 0.4× 77 0.6× 175 2.1× 34 0.5× 73 1.1× 14 439
Maria Podbielska Poland 16 331 1.0× 80 0.7× 184 2.2× 22 0.3× 134 2.0× 20 599
Carmen Picón Spain 10 167 0.5× 163 1.4× 142 1.7× 31 0.4× 192 2.9× 15 510
Shengyi Peng China 10 379 1.2× 50 0.4× 50 0.6× 45 0.7× 29 0.4× 11 608

Countries citing papers authored by Dace Klimanis

Since Specialization
Citations

This map shows the geographic impact of Dace Klimanis's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dace Klimanis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dace Klimanis more than expected).

Fields of papers citing papers by Dace Klimanis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dace Klimanis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dace Klimanis. The network helps show where Dace Klimanis may publish in the future.

Co-authorship network of co-authors of Dace Klimanis

This figure shows the co-authorship network connecting the top 25 collaborators of Dace Klimanis. A scholar is included among the top collaborators of Dace Klimanis based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dace Klimanis. Dace Klimanis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
1.
Lee, Yang-ja, Joshua D. Bernstock, Dace Klimanis, & John M. Hallenbeck. (2018). Akt Protein Kinase, miR-200/miR-182 Expression and Epithelial-Mesenchymal Transition Proteins in Hibernating Ground Squirrels. Frontiers in Molecular Neuroscience. 11. 22–22. 9 indexed citations
2.
3.
Lee, Yang-ja, Yongshan Mou, Dace Klimanis, Joshua D. Bernstock, & John M. Hallenbeck. (2014). Global SUMOylation is a molecular mechanism underlying hypothermia-induced ischemic tolerance. Frontiers in Cellular Neuroscience. 8. 416–416. 34 indexed citations
4.
Lee, Yang-ja, Yongshan Mou, Dragan Maric, et al.. (2011). Elevated Global SUMOylation in Ubc9 Transgenic Mice Protects Their Brains against Focal Cerebral Ischemic Damage. PLoS ONE. 6(10). e25852–e25852. 98 indexed citations
5.
Hillion, Jöelle, et al.. (2006). Involvement of Akt in Preconditioning-Induced Tolerance to Ischemia in PC12 Cells. Journal of Cerebral Blood Flow & Metabolism. 26(10). 1323–1331. 36 indexed citations
6.
Takahashi, Kenzo, Irene Ginis, Dace Klimanis, et al.. (2004). Glucosylceramide Synthase Activity and Ceramide Levels are Modulated during Cerebral Ischemia after Ischemic Preconditioning. Journal of Cerebral Blood Flow & Metabolism. 24(6). 623–627. 34 indexed citations
7.
Ginis, Irene, Rama K. Jaiswal, Dace Klimanis, et al.. (2002). TNF-α–Induced Tolerance to Ischemic Injury Involves Differential Control of NF-κB Transactivation: The Role of NF-κB Association with p300 Adaptor. Journal of Cerebral Blood Flow & Metabolism. 22(2). 142–152. 118 indexed citations
8.
Zimmermann, Carolin, Irene Ginis, Kazuhide Furuya, et al.. (2001). Lipopolysaccharide-induced ischemic tolerance is associated with increased levels of ceramide in brain and in plasma. Brain Research. 895(1-2). 59–65. 75 indexed citations
9.
McBride, O. Wesley, David C. Swan, Steven R. Tronick, et al.. (1983). Regional chromosomal localization of N-ras, K-ras-1, K-ras-2 andmyboncogenes in human cells. Nucleic Acids Research. 11(23). 8221–8236. 72 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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