David Grass

3.9k total citations · 1 hit paper
44 papers, 3.0k citations indexed

About

David Grass is a scholar working on Molecular Biology, Genetics and Immunology. According to data from OpenAlex, David Grass has authored 44 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 11 papers in Genetics and 8 papers in Immunology. Recurrent topics in David Grass's work include Virus-based gene therapy research (7 papers), CRISPR and Genetic Engineering (7 papers) and Lipoproteins and Cardiovascular Health (4 papers). David Grass is often cited by papers focused on Virus-based gene therapy research (7 papers), CRISPR and Genetic Engineering (7 papers) and Lipoproteins and Cardiovascular Health (4 papers). David Grass collaborates with scholars based in United States, Finland and Austria. David Grass's co-authors include Nikolai Kiesel, Markus Aspelmeyer, Uroš Delić, Timo J. Nevalainen, Manuel Reisenbauer, Vladan Vuletić, Frederick C. de Beer, Kahan Dare, Mark E. Swanson and Stephen G. Young and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Physical Review Letters.

In The Last Decade

David Grass

43 papers receiving 2.9k citations

Hit Papers

Cooling of a levitated nanoparticle to the motional quant... 2020 2026 2022 2024 2020 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Grass United States 25 943 689 564 334 264 44 3.0k
Yibo Wang China 31 997 1.1× 215 0.3× 476 0.8× 205 0.6× 332 1.3× 216 3.3k
Jianyu Rao United States 37 1.3k 1.4× 216 0.3× 589 1.0× 206 0.6× 348 1.3× 146 4.4k
M. Casanova Spain 29 991 1.1× 414 0.6× 312 0.6× 232 0.7× 170 0.6× 79 3.4k
Keiichi Yamamoto Japan 36 1.5k 1.5× 156 0.2× 285 0.5× 281 0.8× 108 0.4× 252 4.0k
R. F. Bonner United States 37 3.2k 3.4× 239 0.3× 390 0.7× 169 0.5× 98 0.4× 100 7.7k
Volkmar Schulz Germany 40 1.0k 1.1× 699 1.0× 297 0.5× 60 0.2× 140 0.5× 256 5.7k
Hiroshi Yamashita Japan 31 745 0.8× 300 0.4× 231 0.4× 797 2.4× 106 0.4× 144 4.4k
Connie C. W. Hsia United States 36 772 0.8× 221 0.3× 971 1.7× 180 0.5× 152 0.6× 119 5.2k
Aihua Deng China 33 1.3k 1.3× 267 0.4× 201 0.4× 110 0.3× 234 0.9× 127 3.7k
J. Will Thompson United States 40 3.1k 3.2× 256 0.4× 426 0.8× 378 1.1× 103 0.4× 148 5.9k

Countries citing papers authored by David Grass

Since Specialization
Citations

This map shows the geographic impact of David Grass'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 David Grass with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Grass more than expected).

Fields of papers citing papers by David Grass

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Grass. 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 David Grass. The network helps show where David Grass may publish in the future.

Co-authorship network of co-authors of David Grass

This figure shows the co-authorship network connecting the top 25 collaborators of David Grass. A scholar is included among the top collaborators of David Grass 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 David Grass. David Grass is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Warren, Warren S., et al.. (2024). Noninvasive identification of carbon-based black pigments with pump-probe microscopy. Science Advances. 10(50). eadp0005–eadp0005.
2.
Debiossac, Maxime, et al.. (2022). Nonequilibrium Control of Thermal and Mechanical Changes in a Levitated System. Physical Review Letters. 128(7). 70601–70601. 18 indexed citations
3.
Delić, Uroš, Manuel Reisenbauer, Kahan Dare, et al.. (2020). Cooling of a levitated nanoparticle to the motional quantum ground state. Science. 367(6480). 892–895. 395 indexed citations breakdown →
4.
Fischer, Martin C., et al.. (2020). Low-cost measurement of face mask efficacy for filtering expelled droplets during speech. Science Advances. 6(36). 204 indexed citations
5.
Liu, Edison T., et al.. (2017). Of mice and CRISPR. EMBO Reports. 18(2). 187–193. 27 indexed citations
6.
Kiesel, Nikolai, et al.. (2013). Cavity cooling of an optically levitated submicron particle. Proceedings of the National Academy of Sciences. 110(35). 14180–14185. 205 indexed citations
7.
Grass, David, James Ross, J. R. Barbour, et al.. (2009). Airborne particulate metals in the New York City subway: A pilot study to assess the potential for health impacts. Environmental Research. 110(1). 1–11. 80 indexed citations
8.
Pettigrew, L. Creed, Mark S. Kindy, Stephen W. Scheff, et al.. (2008). Focal cerebral ischemia in the TNFalpha-transgenic rat. Journal of Neuroinflammation. 5(1). 47–47. 50 indexed citations
9.
Hayward, Michael D., Beverly K. Jones, Arman Saparov, et al.. (2007). An extensive phenotypic characterization of the hTNFα transgenic mice. BMC Physiology. 7(1). 13–13. 52 indexed citations
10.
Zhu, Lingyun, et al.. (2004). Non-invasive imaging of GFAP expression after neuronal damage in mice. Neuroscience Letters. 367(2). 210–212. 85 indexed citations
11.
Huhtinen, Heikki, Juha Grönroos, J Uksila, et al.. (2004). Experimental Helicobacter felis Infection in Transgenic Mice Expressing Human Group IIA Phospholipase A2. Helicobacter. 9(5). 408–416. 8 indexed citations
12.
Laine, V., Allan Rajamäki, David Grass, & Timo J. Nevalainen. (2000). Neutrophil Response of Transgenic Mice Expressing Human Group IIA Phospholipase A2 in Bacterial Infections. Scandinavian Journal of Immunology. 52(4). 362–368. 14 indexed citations
13.
Laine, V., et al.. (2000). mRNA differential display of acute-phase proteins in experimentalEscherichia coli infection. Electrophoresis. 21(14). 2957–2968. 6 indexed citations
14.
Tietge, Uwe J.F., Cyrille Maugeais, William J. Cain, et al.. (2000). Overexpression of Secretory Phospholipase A2 Causes Rapid Catabolism and Altered Tissue Uptake of High Density Lipoprotein Cholesteryl Ester and Apolipoprotein A-I. Journal of Biological Chemistry. 275(14). 10077–10084. 148 indexed citations
15.
Laine, V., David Grass, & Timo J. Nevalainen. (2000). Resistance of Transgenic Mice Expressing Human Group II Phospholipase A2 toEscherichia coliInfection. Infection and Immunity. 68(1). 87–92. 73 indexed citations
16.
Grass, David, et al.. (1999). Protection by Group II Phospholipase A2 Against Staphylococcus aureus. The Journal of Immunology. 162(12). 7402–7408. 131 indexed citations
17.
Nevalainen, Timo J., V. Laine, & David Grass. (1997). Expression of Human Group II Phospholipase A2 in Transgenic Mice. Journal of Histochemistry & Cytochemistry. 45(8). 1109–1119. 19 indexed citations
18.
Grass, David, et al.. (1996). Expression of human group II PLA2 in transgenic mice results in epidermal hyperplasia in the absence of inflammatory infiltrate.. Journal of Clinical Investigation. 97(10). 2233–2241. 150 indexed citations
19.
Young, Stephen G., Robert V. Farese, Vincenzo Pierotti, et al.. (1994). Transgenic mice expressing human apoB100 and apoB48. Current Opinion in Lipidology. 5(2). 94–101. 16 indexed citations
20.
Linton, MacRae F., Robert V. Farese, Giulia Chiesa, et al.. (1993). Transgenic mice expressing high plasma concentrations of human apolipoprotein B100 and lipoprotein(a).. Journal of Clinical Investigation. 92(6). 3029–3037. 201 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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