Stuart J. Newfeld

1.4k total citations
39 papers, 1.2k citations indexed

About

Stuart J. Newfeld is a scholar working on Molecular Biology, Cell Biology and Cellular and Molecular Neuroscience. According to data from OpenAlex, Stuart J. Newfeld has authored 39 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 8 papers in Cell Biology and 4 papers in Cellular and Molecular Neuroscience. Recurrent topics in Stuart J. Newfeld's work include TGF-β signaling in diseases (19 papers), Developmental Biology and Gene Regulation (18 papers) and Cancer-related gene regulation (10 papers). Stuart J. Newfeld is often cited by papers focused on TGF-β signaling in diseases (19 papers), Developmental Biology and Gene Regulation (18 papers) and Cancer-related gene regulation (10 papers). Stuart J. Newfeld collaborates with scholars based in United States, Finland and Italy. Stuart J. Newfeld's co-authors include Michael J Stinchfield, Norma T. Takaesu, Michael B. O’Connor, Sirio Dupont, Masafumi Inui, William M Gelbart, Joan Massagué, Guillermo Marqués, Ted Brummel and Liliana Attisano and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and PLoS ONE.

In The Last Decade

Stuart J. Newfeld

39 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Stuart J. Newfeld United States 16 1.0k 194 181 142 116 39 1.2k
Kazuya Hori Japan 14 954 0.9× 291 1.5× 105 0.6× 72 0.5× 109 0.9× 19 1.2k
Gisèle A. Deblandre United States 11 1.1k 1.1× 332 1.7× 119 0.7× 164 1.2× 181 1.6× 12 1.4k
Yongsok Kim United States 21 1.3k 1.2× 165 0.9× 344 1.9× 183 1.3× 188 1.6× 23 1.5k
Alexa Burger Switzerland 17 902 0.9× 223 1.1× 109 0.6× 140 1.0× 111 1.0× 30 1.2k
Anabel Herr United States 12 914 0.9× 225 1.2× 213 1.2× 91 0.6× 79 0.7× 13 1.1k
Liyun Sang United States 5 674 0.7× 189 1.0× 186 1.0× 152 1.1× 59 0.5× 5 913
Jacinta Caddy Australia 10 714 0.7× 288 1.5× 124 0.7× 164 1.2× 91 0.8× 11 977
Wan-Jin Lu United States 13 652 0.6× 116 0.6× 231 1.3× 76 0.5× 115 1.0× 16 841
Dennis J. Hazelett United States 19 1.0k 1.0× 196 1.0× 97 0.5× 257 1.8× 258 2.2× 33 1.5k
Steffen Biechele Canada 13 1.3k 1.3× 193 1.0× 251 1.4× 311 2.2× 138 1.2× 18 1.7k

Countries citing papers authored by Stuart J. Newfeld

Since Specialization
Citations

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

Fields of papers citing papers by Stuart J. Newfeld

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stuart J. Newfeld. 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 Stuart J. Newfeld. The network helps show where Stuart J. Newfeld may publish in the future.

Co-authorship network of co-authors of Stuart J. Newfeld

This figure shows the co-authorship network connecting the top 25 collaborators of Stuart J. Newfeld. A scholar is included among the top collaborators of Stuart J. Newfeld 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 Stuart J. Newfeld. Stuart J. Newfeld 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.
Maizels, Rick M. & Stuart J. Newfeld. (2023). Convergent Evolution in a Murine Intestinal Parasite Rapidly Created the TGM Family of Molecular Mimics to Suppress the Host Immune Response. Genome Biology and Evolution. 15(9). 2 indexed citations
2.
Newfeld, Stuart J., et al.. (2023). dSmad2 differentially regulates dILP2 and dILP5 in insulin producing and circadian pacemaker cells in unmated adult females. PLoS ONE. 18(1). e0280529–e0280529. 1 indexed citations
3.
4.
Newfeld, Stuart J. & Michael B. O’Connor. (2022). New aspects of TGF-β superfamily signaling in development and disease (2022 FASEB meeting review). PubMed. 11. 36–36. 1 indexed citations
5.
Han, Tae Hee, et al.. (2020). Selective Disruption of Synaptic BMP Signaling by a Smad Mutation Adjacent to the Highly Conserved H2 Helix. Genetics. 216(1). 159–175. 4 indexed citations
6.
Takaesu, Norma T., et al.. (2018). CORLExpression in theDrosophilaCentral Nervous System Is Regulated by Stage Specific Interactions of Intertwined Activators and Repressors. G3 Genes Genomes Genetics. 8(7). 2527–2536. 6 indexed citations
7.
Wisotzkey, Robert G., et al.. (2016). lolal Is an Evolutionarily New Epigenetic Regulator of dpp Transcription during Dorsal–Ventral Axis Formation. Molecular Biology and Evolution. 33(10). 2621–2632. 5 indexed citations
8.
Wisotzkey, Robert G., et al.. (2014). New Gene Evolution in the Bonus-TIF1-γ/TRIM33 Family Impacted the Architecture of the Vertebrate Dorsal–Ventral Patterning Network. Molecular Biology and Evolution. 31(9). 2309–2321. 9 indexed citations
9.
Dupont, Sirio, Masafumi Inui, & Stuart J. Newfeld. (2012). Regulation of TGF‐β signal transduction by mono‐ and deubiquitylation of Smads. FEBS Letters. 586(14). 1913–1920. 33 indexed citations
10.
Wisotzkey, Robert G., et al.. (2012). Hippo Pathway Phylogenetics Predicts Monoubiquitylation of Salvador and Merlin/Nf2. PLoS ONE. 7(12). e51599–e51599. 2 indexed citations
11.
Stinchfield, Michael J, et al.. (2010). The Sno Oncogene Antagonizes Wingless Signaling during Wing Development in Drosophila. PLoS ONE. 5(7). e11619–e11619. 9 indexed citations
12.
Wisotzkey, Robert G., et al.. (2008). Lysine Conservation and Context in TGFβ and Wnt Signaling Suggest New Targets and General Themes for Posttranslational Modification. Journal of Molecular Evolution. 67(4). 323–333. 13 indexed citations
13.
Takaesu, Norma T., et al.. (2007). A combinatorial enhancer recognized by Mad, TCF and Brinker first activates then represses dpp expression in the posterior spiracles of Drosophila. Developmental Biology. 313(2). 829–843. 9 indexed citations
14.
15.
Bartholin, Laurent, et al.. (2003). Drosophila TGIF Proteins Are Transcriptional Activators. Molecular and Cellular Biology. 23(24). 9262–9274. 36 indexed citations
16.
Wisotzkey, Robert G., Aaron N. Johnson, Norma T. Takaesu, & Stuart J. Newfeld. (2003). α/β Hydrolase2, a Predicated Gene Adjacent to Mad in Drosophila melanogaster , Belongs to a New Global Multigene Family and Is Associated with Obesity. Journal of Molecular Evolution. 56(3). 351–361. 7 indexed citations
17.
Johnson, Aaron N. & Stuart J. Newfeld. (2002). The TGF-β Family: Signaling Pathways, Developmental Roles, and Tumor Suppressor Activities. The Scientific World JOURNAL. 2. 892–925. 14 indexed citations
18.
Takaesu, Norma T., et al.. (2002). Posterior spiracle specific GAL4 lines: New reagents for developmental biology and respiratory physiology. genesis. 34(1-2). 16–18. 12 indexed citations
19.
Newfeld, Stuart J., Richard W. Padgett, Seth D. Findley, et al.. (1997). Molecular Evolution at the decapentaplegic Locus in Drosophila. Genetics. 145(2). 297–309. 16 indexed citations
20.
Brummel, Ted, Vern Twombly, Guillermo Marqués, et al.. (1994). Characterization and relationship of dpp receptors encoded by the saxophone and thick veins genes in Drosophila. Cell. 78(2). 251–261. 279 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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