{"id":481,"date":"2019-08-18T15:25:59","date_gmt":"2019-08-18T19:25:59","guid":{"rendered":"https:\/\/scienceweb.clemson.edu\/chg\/?p=481"},"modified":"2025-07-30T10:21:05","modified_gmt":"2025-07-30T14:21:05","slug":"dr-christopher-steven-mcmahan-2","status":"publish","type":"post","link":"https:\/\/scienceweb.clemson.edu\/ihg\/dr-christopher-steven-mcmahan-2\/","title":{"rendered":"Dr. Christopher McMahan"},"content":{"rendered":"

[et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”4.16″ global_colors_info=”{}”][et_pb_row admin_label=”row” _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text admin_label=”Text” _builder_version=”4.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

Associate Professor, Department of Mathematical and Statistical Sciences<\/strong><\/p>\n

CURRICULUM VITAE<\/a><\/p>\n

Email: mcmaha2@clemson.edu
Phone: 864-656-4566\"\"<\/p>\n

Biosketch<\/strong><\/h2>\n

Currently, I am an Associate Professor in the School of Mathematical and Statistical Sciences at Clemson University. I completed my Bachelor’s degree majoring in mathematics, minoring in physics, at Austin Peay State University, Clarksville, TN. I then earned a master’s degree in mathematics at Western Kentucky University, Bowling Green, Kentucky. I then completed a doctoral degree in statistics at the University of South Carolina, Columbia, SC. After completing my Ph.D., I joined Clemson University as an assistant professor, and I was recently promoted to the rank of associate. In addition to my teaching responsibilities, I also consult with Biorealm and serve as a Visiting Professor at BINUS University.<\/p>\n

Research<\/strong><\/h2>\n

My research interests include, but are not limited to, categorical data analysis, group testing, survival data analysis, nonparametric methods, measurement error models, spatio-temporal modeling, statistical computing, Bayesian parametric\/nonparametric estimation, high dimensional regression techniques, epidemiology\/public health, statistical genetics, machine learning, and biomedical applications.<\/p>\n

Publications<\/strong><\/h2>\n

Gettings JR, Self SCW, McMahan CS<\/strong>, Brown DA, Nordone SK and Yabsley MJ. 2020. Local and regional temporal trends (2013-2019) of canine Ehrlichia<\/em> spp<\/em>. seroprevalence in the USA. Parasites and Vectors<\/em> 13:<\/strong> 153. doi: 10.1186\/s13071-020-04022-4.<\/p>\n

Gettings JR, Self SCW, McMahan CS<\/strong>, Brown DA, Nordone SK and Yabsley MJ. 2020. Regional and local temporal trends of\u00a0Borrelia burgdorferi<\/em>\u00a0and\u00a0Anaplasma<\/em>\u00a0spp. seroprevalence in domestic dogs: Contiguous United States 2013-2019. Front. Vet. Sci.<\/em> 7: <\/strong>561592. doi: 10.3389\/fvets.2020.561592.<\/p>\n

Hou P, Tebbs J, Wang D, McMahan C<\/strong> and Bilder C. 2020. Array testing with multiplex assays. Biostatistics<\/em> 21<\/strong>: 417-431. doi: 10.1093\/biostatistics\/kxy058.<\/p>\n

Joyner C, McMahan C,<\/strong> Baurley J and Pardamean B. 2020. A two-phase Bayesian methodology for the analysis of binary phenotypes in genome-wide association studies. Biom J<\/em> 62<\/strong>: 191-201. doi: 10.1002\/bimj.201900050<\/p>\n

Joyner CN, McMahan CS<\/strong>, Tebbs JM and Bilder CR. From mixed effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data. Biometrics<\/em> 76:<\/strong> 913-923. doi: 10.1111\/biom.13176.<\/p>\n

Sekhon R, Joyner C, Ackerman A, McMahan C,<\/strong> Cook D and Robertson D. 2020. Stalk bending strength is strongly associated with maize stalk lodging incidence across multiple Environments. Field Crops<\/em> 249<\/strong>: e107737. doi: 10.1016\/j.fcr.2020.107737.<\/p>\n

Withana Gamage PW, Chaudari M, McMahan CS<\/strong>, Kim EH and Kosorok MR. 2020. An extended proportional hazards model for interval-censored data subject to instantaneous failures. Lifetime Data Anal<\/em> 26<\/strong>: 158-182. doi: 10.1007\/s10985-019-09467-z.<\/p>\n

Bilder C, Tebbs J and McMahan C<\/strong>. 2019. Informative group testing for multiplex assays. Biometrics<\/em> 75:<\/strong> 278-288. doi: 10.1111\/biom.12988.<\/p>\n

Gregory K, Wang D and McMahan C<\/strong>. 2019. Adaptive elastic net regression with group testing data. Biometrics<\/em> 75:<\/strong> 13-23. doi: 10.1111\/biom.12973.<\/p>\n

Self SCW, Liu Y, Nordone SK, Yabsley MJ, Walden HS, Lund RB, Bowman DD, Carpenter C, McMahan CS<\/strong> and Gettings JR. 2019. Canine vector-borne disease: mapping and the accuracy of forecasting using big data from the veterinary community. Anim Health Res Rev <\/em>20<\/strong>: 47-60. doi: 10.1017\/S1466252319000045.<\/p>\n

Yang T, Gallagher CM and McMahan CS.<\/strong> 2019. A robust regression methodology via M-estimation. Communications in Statistics<\/em> 48:<\/strong> 1092-1107. doi: 10.1080\/03610926.2018.1423698.<\/p>\n

Withana Gamage P, McMahan C<\/strong>, Wang L, Tu W. 2018. A Gamma-frailty proportional hazards model for bivariate interval-censored data.\u00a0Computational\u00a0Statistics and Data Analysis<\/em> 128: 354-366.<\/p>\n

Lu M, McMahan C<\/strong>. 2018. A partially linear proportional hazards model for current status data. Biometrics<\/em> 74: 1240-1249.<\/p>\n

Watson Self S, McMahan C<\/strong>, Brown D, Lund R, Gettings J, Yabsley M. 2018. A large scale spatio-temporal binomial regression model for estimating seroprevalence trends. Environmetrics<\/em> 29: e2538.<\/p>\n

Chaudhari M, Kim E, Withana Gamage P, McMahan C<\/strong>, Kosorok M. 2018. Study design with staggered sampling times for evaluating sustained unresponsiveness to peanut sublingual immunotherapy. Statistics in Medicine<\/em> 37: 3944-3958.<\/p>\n

Baurley J, McMahan C<\/strong>, Ervin C, Pardamean B, Bergen A. 2018. Biosignature discovery for substance use disorders using statistical learning. Trends in Molecular Medicine<\/em> 24: 221-235.<\/p>\n

Wang D, McMahan C<\/strong>, Tebbs J, Bilder C. 2018.\u00a0Group testing case identification with biomarker information. Computational Statistics and Data Analysis<\/em> 122: 156-166.<\/p>\n

McMahan C<\/strong>, Baurley J, Bridges W, Joyner C, Fitra Kacamarga M, Lund R, Pardamean C, Paradmean\u00a0B. 2017.\u00a0 A Bayesian hierarchical model for identifying significant polygenic effects while controlling for confounding and repeated measures. Statistical Applications in Genetics and Molecular Biology<\/em> 16: 407-419.<\/p>\n

Warasi S, McMahan C<\/strong>, Tebbs J, Bilder C. 2017. Group testing regression models with dilution submodels. Statistics in Medicine<\/em> 36: 4860-4872.<\/p>\n

McMahan C<\/strong>, Tebbs J, Hanson T, Bilder C. 2017. Bayesian regression models for group testing data. Biometrics<\/em> 73: 1443-1452.<\/p>\n

Liu Y, Watson S, Lund R, Gettings J, Nordone S, Yabsley M, McMahan C<\/strong>. 2017. A Bayesian spatio-temporal model for forecasting Anaplasma<\/em> species seroprevalence in domestic dogs within the contiguous United States. PLoS One<\/em> 12: e0182028.<\/p>\n

Russell B, Wang D, McMahan C<\/strong>. 2017. Spatially modeling the effects of meteorological drivers of PM2.<\/em>5<\/sub> in the Eastern United States via a local linear penalized quantile regression estimator. Environmetrics<\/em> 28: pii: e2448.<\/p>\n

Liu Y, McMahan C<\/strong>, Gallagher C. 2017. A general regression framework for the regression analysis of pooled biomarker assessments. Statistics in Medicine<\/em> 36: 2363-2377.<\/p>\n

Hou P, Tebbs J, Bilder C, McMahan C<\/strong>. 2017. Hierarchical group testing for multiple infections. Biometrics<\/em> 73: 656-665.<\/p>\n

Watson S, Liu Y, Lund R, Gettings J, Nordone S, McMahan C<\/strong>, Yabsley M. 2017. A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi<\/em>, causative agent of Lyme disease, in domestic dogs within the contiguous United States. PLoS One<\/em> 12: e0174428.<\/p>\n

Liu Y, Lund R, Nordone S, Yabsley M, McMahan C<\/strong>. 2017. A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Ehrlichia species in domestic dogs within the contiguous United States. Parasites and Vectors<\/em> 10: 138.<\/p>\n

Bowman D, Liu Y, McMahan C<\/strong>, Nordone S, Yabsley M, Lund R. 2016. Forecasting United States heartworm (Dirofilaria immitis<\/em>) prevalence in dogs. Parasites and Vectors<\/em> 9: \u00a0540.<\/p>\n

Sapp S, Weinstein S, McMahan C<\/strong>, Yabsley M. 2016. Variable infection dynamics in four Peromyscus species following experimental inoculation with Baylisascaris procyonis<\/em>. Journal of Parasitology<\/em> 102: 538-544.<\/p>\n

McDonald J, Gerard P, McMahan C<\/strong>, Schucany W. 2016. Exact-permutation based sign tests for clustered binary data via weighted and unweighted test statistics. Journal of Agricultural Biological and Environmental Statistics<\/em> 21: 698-712.<\/p>\n

Warasi S, Tebbs J, McMahan C<\/strong>, Bilder C. 2016. Estimating the prevalence of multiple diseases from two-stage hierarchical pooling. Statistics in Medicine<\/em> 35: 3851-3864.<\/p>\n

McMahan C<\/strong>, McLain A, Gallagher C, Schisterman E. 2016. Estimating covariate-adjusted measures of diagnostic accuracy based on pooled biomarker assessments. Biometrical Journal<\/em> 58: 944-961.<\/p>\n

McMahan C<\/strong>, Wang D, Beall M, Bowman D, Little S, Pithuia P, Sharp J, Stitch R, Yabsley M, Lund R. 2016. Factors associated with Anaplasma spp. seroprevalence among dogs in the United States. Parasites and Vectors<\/em> 9: 169.<\/p>\n

Wang L, McMahan C<\/strong>, Hudgens M, Qureshi Z. 2016. A flexible, computationally efficient method for fitting the proportional hazards model to interval-censored data. Biometrics<\/em> 72: 222-231.<\/p>\n

McMahan C<\/strong>, Tebbs J, Bilder C. 2016. Invited rejoinder to \u201cA note on the evaluation of group testing algorithms in the presence of misclassification.\u201d Biometrics<\/em> 72: 303-304.<\/p>\n

Link to Google Scholar<\/h3>\n

https:\/\/scholar.google.com\/citations?user=5OeAtdoAAAAJ&hl=en&oi=ao<\/a><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"

Associate Professor, Department of Mathematical and Statistical Sciences CURRICULUM VITAE Email: mcmaha2@clemson.eduPhone: 864-656-4566 Biosketch Currently, I am an Associate Professor in the School of Mathematical and Statistical Sciences at Clemson University. I completed my Bachelor’s degree majoring in mathematics, minoring in physics, at Austin Peay State University, Clarksville, TN. I then earned a master’s degree […]<\/p>\n","protected":false},"author":3,"featured_media":623,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"Associate Professor, Department of Mathematical and Statistical Sciences<\/strong>\r\n\r\nCURRICULUM VITAE<\/a>\r\n\r\nEmail: mcmaha2@clemson.edu\r\nPhone: 864-656-4566\"\"\r\n

Biosketch<\/strong><\/h3>\r\nCurrently, I am an Associate Professor in the School of Mathematical and Statistical Sciences at Clemson University. I completed my Bachelor's degree majoring in mathematics, minoring in physics, at Austin Peay State University, Clarksville, TN. I then earned a master's degree in mathematics at Western Kentucky University, Bowling Green, Kentucky. I then completed a doctoral degree in statistics at the University of South Carolina, Columbia, SC. After completing my Ph.D., I joined Clemson University as an assistant professor, and I was recently promoted to the rank of associate. In addition to my teaching responsibilities, I also consult with Biorealm and serve as a Visiting Professor at BINUS University.\r\n

Research<\/strong><\/h3>\r\nMy research interests include, but are not limited to, categorical data analysis, group testing, survival data analysis, nonparametric methods, measurement error models, spatio-temporal modeling, statistical computing, Bayesian parametric\/nonparametric estimation, high dimensional regression techniques, epidemiology\/public health, statistical genetics, machine learning, and biomedical applications.\r\n

Publications<\/strong><\/h3>\r\nGettings JR, Self SCW, McMahan CS<\/strong>, Brown DA, Nordone SK and Yabsley MJ. 2020. Local and regional temporal trends (2013-2019) of canine Ehrlichia<\/em> spp<\/em>. seroprevalence in the USA. Parasites and Vectors<\/em> 13:<\/strong> 153. doi: 10.1186\/s13071-020-04022-4.\r\n\r\nGettings JR, Self SCW, McMahan CS<\/strong>, Brown DA, Nordone SK and Yabsley MJ. 2020. Regional and local temporal trends of\u00a0Borrelia burgdorferi<\/em>\u00a0and\u00a0Anaplasma<\/em>\u00a0spp. seroprevalence in domestic dogs: Contiguous United States 2013-2019. Front. Vet. Sci.<\/em> 7: <\/strong>561592. doi: 10.3389\/fvets.2020.561592.\r\n\r\nHou P, Tebbs J, Wang D, McMahan C<\/strong> and Bilder C. 2020. Array testing with multiplex assays. Biostatistics<\/em> 21<\/strong>: 417-431. doi: 10.1093\/biostatistics\/kxy058.\r\n\r\nJoyner C, McMahan C,<\/strong> Baurley J and Pardamean B. 2020. A two-phase Bayesian methodology for the analysis of binary phenotypes in genome-wide association studies. Biom J<\/em> 62<\/strong>: 191-201. doi: 10.1002\/bimj.201900050\r\n\r\nJoyner CN, McMahan CS<\/strong>, Tebbs JM and Bilder CR. From mixed effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data. Biometrics<\/em> 76:<\/strong> 913-923. doi: 10.1111\/biom.13176.\r\n\r\nSekhon R, Joyner C, Ackerman A, McMahan C,<\/strong> Cook D and Robertson D. 2020. Stalk bending strength is strongly associated with maize stalk lodging incidence across multiple Environments. Field Crops<\/em> 249<\/strong>: e107737. doi: 10.1016\/j.fcr.2020.107737.\r\n\r\nWithana Gamage PW, Chaudari M, McMahan CS<\/strong>, Kim EH and Kosorok MR. 2020. An extended proportional hazards model for interval-censored data subject to instantaneous failures. Lifetime Data Anal<\/em> 26<\/strong>: 158-182. doi: 10.1007\/s10985-019-09467-z.\r\n\r\nBilder C, Tebbs J and McMahan C<\/strong>. 2019. Informative group testing for multiplex assays. Biometrics<\/em> 75:<\/strong> 278-288. doi: 10.1111\/biom.12988.\r\n\r\nGregory K, Wang D and McMahan C<\/strong>. 2019. Adaptive elastic net regression with group testing data. Biometrics<\/em> 75:<\/strong> 13-23. doi: 10.1111\/biom.12973.\r\n\r\nSelf SCW, Liu Y, Nordone SK, Yabsley MJ, Walden HS, Lund RB, Bowman DD, Carpenter C, McMahan CS<\/strong> and Gettings JR. 2019. Canine vector-borne disease: mapping and the accuracy of forecasting using big data from the veterinary community. Anim Health Res Rev <\/em>20<\/strong>: 47-60. doi: 10.1017\/S1466252319000045.\r\n\r\nYang T, Gallagher CM and McMahan CS.<\/strong> 2019. A robust regression methodology via M-estimation. Communications in Statistics<\/em> 48:<\/strong> 1092-1107. doi: 10.1080\/03610926.2018.1423698.\r\n\r\nWithana Gamage P, McMahan C<\/strong>, Wang L, Tu W. 2018. A Gamma-frailty proportional hazards model for bivariate interval-censored data.\u00a0Computational\u00a0Statistics and Data Analysis<\/em> 128: 354-366.\r\n\r\nLu M, McMahan C<\/strong>. 2018. A partially linear proportional hazards model for current status data. Biometrics<\/em> 74: 1240-1249.\r\n\r\nWatson Self S, McMahan C<\/strong>, Brown D, Lund R, Gettings J, Yabsley M. 2018. A large scale spatio-temporal binomial regression model for estimating seroprevalence trends. Environmetrics<\/em> 29: e2538.\r\n\r\nChaudhari M, Kim E, Withana Gamage P, McMahan C<\/strong>, Kosorok M. 2018. Study design with staggered sampling times for evaluating sustained unresponsiveness to peanut sublingual immunotherapy. Statistics in Medicine<\/em> 37: 3944-3958.\r\n\r\nBaurley J, McMahan C<\/strong>, Ervin C, Pardamean B, Bergen A. 2018. Biosignature discovery for substance use disorders using statistical learning. Trends in Molecular Medicine<\/em> 24: 221-235.\r\n\r\nWang D, McMahan C<\/strong>, Tebbs J, Bilder C. 2018.\u00a0Group testing case identification with biomarker information. Computational Statistics and Data Analysis<\/em> 122: 156-166.\r\n\r\nMcMahan C<\/strong>, Baurley J, Bridges W, Joyner C, Fitra Kacamarga M, Lund R, Pardamean C, Paradmean\u00a0B. 2017.\u00a0 A Bayesian hierarchical model for identifying significant polygenic effects while controlling for confounding and repeated measures. Statistical Applications in Genetics and Molecular Biology<\/em> 16: 407-419.\r\n\r\nWarasi S, McMahan C<\/strong>, Tebbs J, Bilder C. 2017. Group testing regression models with dilution submodels. Statistics in Medicine<\/em> 36: 4860-4872.\r\n\r\nMcMahan C<\/strong>, Tebbs J, Hanson T, Bilder C. 2017. Bayesian regression models for group testing data. Biometrics<\/em> 73: 1443-1452.\r\n\r\nLiu Y, Watson S, Lund R, Gettings J, Nordone S, Yabsley M, McMahan C<\/strong>. 2017. A Bayesian spatio-temporal model for forecasting Anaplasma<\/em> species seroprevalence in domestic dogs within the contiguous United States. PLoS One<\/em> 12: e0182028.\r\n\r\nRussell B, Wang D, McMahan C<\/strong>. 2017. Spatially modeling the effects of meteorological drivers of PM2.<\/em>5<\/sub> in the Eastern United States via a local linear penalized quantile regression estimator. Environmetrics<\/em> 28: pii: e2448.\r\n\r\nLiu Y, McMahan C<\/strong>, Gallagher C. 2017. A general regression framework for the regression analysis of pooled biomarker assessments. Statistics in Medicine<\/em> 36: 2363-2377.\r\n\r\nHou P, Tebbs J, Bilder C, McMahan C<\/strong>. 2017. Hierarchical group testing for multiple infections. Biometrics<\/em> 73: 656-665.\r\n\r\nWatson S, Liu Y, Lund R, Gettings J, Nordone S, McMahan C<\/strong>, Yabsley M. 2017. A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi<\/em>, causative agent of Lyme disease, in domestic dogs within the contiguous United States. PLoS One<\/em> 12: e0174428.\r\n\r\nLiu Y, Lund R, Nordone S, Yabsley M, McMahan C<\/strong>. 2017. A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Ehrlichia species in domestic dogs within the contiguous United States. Parasites and Vectors<\/em> 10: 138.\r\n\r\nBowman D, Liu Y, McMahan C<\/strong>, Nordone S, Yabsley M, Lund R. 2016. Forecasting United States heartworm (Dirofilaria immitis<\/em>) prevalence in dogs. Parasites and Vectors<\/em> 9: \u00a0540.\r\n\r\nSapp S, Weinstein S, McMahan C<\/strong>, Yabsley M. 2016. Variable infection dynamics in four Peromyscus species following experimental inoculation with Baylisascaris procyonis<\/em>. Journal of Parasitology<\/em> 102: 538-544.\r\n\r\nMcDonald J, Gerard P, McMahan C<\/strong>, Schucany W. 2016. Exact-permutation based sign tests for clustered binary data via weighted and unweighted test statistics. Journal of Agricultural Biological and Environmental Statistics<\/em> 21: 698-712.\r\n\r\nWarasi S, Tebbs J, McMahan C<\/strong>, Bilder C. 2016. Estimating the prevalence of multiple diseases from two-stage hierarchical pooling. Statistics in Medicine<\/em> 35: 3851-3864.\r\n\r\nMcMahan C<\/strong>, McLain A, Gallagher C, Schisterman E. 2016. Estimating covariate-adjusted measures of diagnostic accuracy based on pooled biomarker assessments. Biometrical Journal<\/em> 58: 944-961.\r\n\r\nMcMahan C<\/strong>, Wang D, Beall M, Bowman D, Little S, Pithuia P, Sharp J, Stitch R, Yabsley M, Lund R. 2016. Factors associated with Anaplasma spp. seroprevalence among dogs in the United States. Parasites and Vectors<\/em> 9: 169.\r\n\r\nWang L, McMahan C<\/strong>, Hudgens M, Qureshi Z. 2016. A flexible, computationally efficient method for fitting the proportional hazards model to interval-censored data. Biometrics<\/em> 72: 222-231.\r\n\r\nMcMahan C<\/strong>, Tebbs J, Bilder C. 2016. Invited rejoinder to \u201cA note on the evaluation of group testing algorithms in the presence of misclassification.\u201d Biometrics<\/em> 72: 303-304.\r\n

Link to Google Scholar<\/h3>\r\nhttps:\/\/scholar.google.com\/citations?user=5OeAtdoAAAAJ&hl=en&oi=ao<\/a>","_et_gb_content_width":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[12],"tags":[],"class_list":["post-481","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-faculty"],"_links":{"self":[{"href":"https:\/\/scienceweb.clemson.edu\/ihg\/wp-json\/wp\/v2\/posts\/481","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/scienceweb.clemson.edu\/ihg\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/scienceweb.clemson.edu\/ihg\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/scienceweb.clemson.edu\/ihg\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/scienceweb.clemson.edu\/ihg\/wp-json\/wp\/v2\/comments?post=481"}],"version-history":[{"count":0,"href":"https:\/\/scienceweb.clemson.edu\/ihg\/wp-json\/wp\/v2\/posts\/481\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/scienceweb.clemson.edu\/ihg\/wp-json\/wp\/v2\/media\/623"}],"wp:attachment":[{"href":"https:\/\/scienceweb.clemson.edu\/ihg\/wp-json\/wp\/v2\/media?parent=481"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/scienceweb.clemson.edu\/ihg\/wp-json\/wp\/v2\/categories?post=481"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/scienceweb.clemson.edu\/ihg\/wp-json\/wp\/v2\/tags?post=481"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}