J. C. Matthews

2.4k total citations
81 papers, 1.9k citations indexed

About

J. C. Matthews is a scholar working on Agronomy and Crop Science, Molecular Biology and Nutrition and Dietetics. According to data from OpenAlex, J. C. Matthews has authored 81 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Agronomy and Crop Science, 21 papers in Molecular Biology and 16 papers in Nutrition and Dietetics. Recurrent topics in J. C. Matthews's work include Plant and fungal interactions (15 papers), Ruminant Nutrition and Digestive Physiology (15 papers) and Amino Acid Enzymes and Metabolism (13 papers). J. C. Matthews is often cited by papers focused on Plant and fungal interactions (15 papers), Ruminant Nutrition and Digestive Physiology (15 papers) and Amino Acid Enzymes and Metabolism (13 papers). J. C. Matthews collaborates with scholars based in United States, Canada and United Kingdom. J. C. Matthews's co-authors include D. L. Harmon, D. H. Smyth, J. A. Boling, K. E. Webb, Kendall C Swanson, Shengfa F. Liao, J. R. Strickland, B.W. McBride, W. R. Burris and Douglas B. DiRienzo and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and The Journal of Physiology.

In The Last Decade

J. C. Matthews

79 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
J. C. Matthews United States 26 618 617 341 325 306 81 1.9k
D.E. Bauman United States 25 971 1.6× 305 0.5× 531 1.6× 482 1.5× 41 0.1× 46 1.8k
Yingdong Ni China 24 308 0.5× 529 0.9× 224 0.7× 186 0.6× 56 0.2× 94 1.7k
R. W. Dougherty United States 24 401 0.6× 922 1.5× 159 0.5× 116 0.4× 68 0.2× 44 1.9k
D.R.E. Abayasekara United Kingdom 22 779 1.3× 333 0.5× 453 1.3× 449 1.4× 26 0.1× 58 2.1k
J.M. Dawson United Kingdom 20 242 0.4× 648 1.1× 223 0.7× 69 0.2× 115 0.4× 61 1.5k
Cristina Ortega‐Ferrusola Spain 34 459 0.7× 401 0.6× 362 1.1× 206 0.6× 26 0.1× 113 3.4k
Bernard Lebœuf France 31 969 1.6× 396 0.6× 739 2.2× 132 0.4× 26 0.1× 60 2.4k
Pascal Froment France 37 233 0.4× 1.2k 1.9× 400 1.2× 105 0.3× 40 0.1× 132 3.8k
D. B. Lindsay Slovakia 24 617 1.0× 291 0.5× 316 0.9× 204 0.6× 26 0.1× 59 1.6k
Mohsen Sharafi Iran 31 215 0.3× 393 0.6× 337 1.0× 436 1.3× 36 0.1× 134 3.0k

Countries citing papers authored by J. C. Matthews

Since Specialization
Citations

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

Fields of papers citing papers by J. C. Matthews

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of J. C. Matthews

This figure shows the co-authorship network connecting the top 25 collaborators of J. C. Matthews. A scholar is included among the top collaborators of J. C. Matthews 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 J. C. Matthews. J. C. Matthews 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
4.
Steele, M.A., Ousama AlZahal, S.L. Greenwood, J. C. Matthews, & B.W. McBride. (2013). Technical note: Use of laser capture microdissection for the localization of tissue-specific global gene expression in rumen papillae. Journal of Dairy Science. 96(12). 7748–7752. 7 indexed citations
5.
Xue, Yan, Shengfa F. Liao, J. R. Strickland, J. A. Boling, & J. C. Matthews. (2011). Bovine neuronal vesicular glutamate transporter activity is inhibited by ergovaline and other ergopeptines. Journal of Dairy Science. 94(7). 3331–3341. 12 indexed citations
7.
Liao, Shengfa F., E.S. Vanzant, D. L. Harmon, et al.. (2009). Ruminal and abomasal starch hydrolysate infusions selectively decrease the expression of cationic amino acid transporter mRNA by small intestinal epithelia of forage-fed beef steers. Journal of Dairy Science. 92(3). 1124–1135. 40 indexed citations
8.
Xue, Yan, Shengfa F. Liao, Susan Greenwood, et al.. (2009). Metabolic acidosis in sheep alters expression of renal and skeletal muscle amino acid enzymes and transporters1. Journal of Animal Science. 88(2). 707–717. 19 indexed citations
9.
Odongo, N. E., Susan Greenwood, M. M. Or-Rashid, et al.. (2008). Effects of nutritionally induced metabolic acidosis with or without glutamine infusion on acid-base balance, plasma amino acids, and plasma nonesterified fatty acids in sheep1. Journal of Animal Science. 87(3). 1077–1084. 8 indexed citations
10.
Liao, Shengfa F., E.S. Vanzant, D. L. Harmon, et al.. (2008). Basal Expression of Nucleoside Transporter mRNA Differs Among Small Intestinal Epithelia of Beef Steers and Is Differentially Altered by Ruminal or Abomasal Infusion of Starch Hydrolysate. Journal of Dairy Science. 91(4). 1570–1584. 12 indexed citations
11.
Greenwood, Susan, Tom Wright, J. P. Gilmore, et al.. (2007). Impact of metabolic acidosis on amino acid metabolism in lambs. Poultry Science. 86. 83–83. 1 indexed citations
12.
McLeod, K. R., et al.. (2007). Evidence for a role for glucagon-like peptide-2 (GLP-2) in ruminant animals. The FASEB Journal. 21(6). 1 indexed citations
13.
Odongo, N. E., Michael I. Lindinger, Ousama AlZahal, et al.. (2007). Effects of dietary strong acid anion challenge on regulation of acid-base balance in sheep1. Journal of Animal Science. 85(9). 2222–2229. 22 indexed citations
14.
Fan, Ming, et al.. (2004). Expression of apical membranel-glutamate transporters in neonatal porcine epithelial cells along the small intestinal crypt-villus axis. American Journal of Physiology-Gastrointestinal and Liver Physiology. 287(2). G385–G398. 71 indexed citations
16.
Swanson, Kendall C, et al.. (2001). Molecular identification of high-affinity glutamate transporters in sheep and cattle forestomach, intestine, liver, kidney, and pancreas.. Journal of Animal Science. 79(5). 1329–1329. 46 indexed citations
17.
Matthews, J. C., Mark Beveridge, Marc S. Malandro, et al.. (1998). Activity and protein localization of multiple glutamate transporters in gestationday 14vs.day 20rat placenta. American Journal of Physiology-Cell Physiology. 274(3). C603–C614. 59 indexed citations
18.
Matthews, J. C., Mark Beveridge, Marc S. Malandro, Michael S. Kilberg, & Donald A. Novak. (1998). Response of placental amino acid transport to gestational age and intrauterine growth retardation. Proceedings of The Nutrition Society. 57(2). 257–263. 5 indexed citations
19.
Matthews, J. C. & K. E. Webb. (1995). Absorption of L-carnosine, L-methionine, and L-methionylglycine by isolated sheep ruminal and omasal epithelial tissue1. Journal of Animal Science. 73(11). 3464–3475. 31 indexed citations
20.
Webb, K. E., Douglas B. DiRienzo, & J. C. Matthews. (1993). Recent Developments in Gastrointestinal Absorption and Tissue Utilization of Peptides: A Review. Journal of Dairy Science. 76(1). 351–361. 34 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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