plant science thesis

Theses and Dissertations

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UKnowledge > Martin-Gatton College of Agriculture, Food and Environment > Plant and Soil Sciences > Theses & Dissertations

Theses and Dissertations--Plant and Soil Sciences

Theses/dissertations from 2024 2024.

FROM CODE TO CROPS: HARNESSING BIOINFORMATICS AND ARTIFICIAL INTELLIGENCE (AI) IN AGRICULTURAL OMICS , Lakshay Anand

INTERACTIONS BETWEEN MULTIPLE STRESSORS, NANOMATERIALS AND A PATHOGEN, IN CAENORHABDITIS ELEGANS , Jarad Cochran

EVALUATION OF WINTER CEREAL COVER CROPS ACROSS NITROGEN MANAGEMENT STRAGIES FOR NO-TILL CORN PRODUCTION , Robert Nalley

THE EFFECTS OF POTASSIUM FERTILIZATION REGIME ON HIGH TUNNEL TOMATO PRODUCTION , Sapana Pandey

The roles of mRNA polyadenylation factors in plant growth and development , Lichun Zhou

Theses/Dissertations from 2023 2023

Understanding The Basis for Increased 2,4-D Tolerance in Red Clover (Trifolium pratense): Field Evaluations, Metabolism, and Gene Expression , Lucas Pinheiro de Araujo

MANAGEMENT AND CHARACTERIZATION OF ROOT-KNOT NEMATODE ( MELOIDOGYNE SPP.) IN KENTUCKY HIGH TUNNELS , Victoria Bajek

Nitrogen Behavior in a No-Tillage Agroecosystem Located in the Inner Bluegrass of Kentucky , Alec William Besinger Mr.

Building a Kentucky Baguette: Agronomic Traits, Bread Baking Quality Measurements, and Sensory Evaluation of Modern and Landrace Wheat Cultivars Grown Under Conventional and Organic Nitrogen Management , Bryan Brady

Improving Baking Quality of Soft Red Winter Wheat in Kentucky Through Breeding and Sulfur-Nitrogen Fertility Management , Maria Paula Castellari

Comparison of Botanical Composition Methods and Change Over Time in Kentucky Pastures , Echo Elizabeth Gotsick

EFFECTS OF FUNGICIDE PROGRAMS AND LOWER LEAF REMOVAL ON WRAPPER LEAF PRODUCTION IN CONNECTICUT BROADLEAF CIGAR WRAPPER TOBACCO , Caleb Haygan Perkins

MULTI-OMIC ANALYSIS OF VEGETATIVE PROPAGATION INDUCED PLANT REJUVENATION IN GRAPEVINES: IMPLICATIONS FOR THE GENERATION OF LOCALLY ADAPTED CULTIVARS USING EPIGENETICS , Tajbir Raihan

LATERAL SPACING OF SUBSURFACE POULTRY LITTER BANDS: EFFECT ON GASEOUS NITROGEN EMISSIONS, NUTRIENT UPTAKE, AND MAIZE YIELD , Jason R. Simmons

Winter Rye ( Secale cereale L.) Management and Production Profitability in Kentucky, and Heritability of Sensory Attributes , Elzbieta Szuleta

MOLECULAR ANALYSIS OF EPIGENETIC MEMORY OF STRESS ESTABLISHMENT AND LONG-TERM MAINTENANCE IN A PERENNIAL WOODY PLANT , Jia Wen Tan

EVALUATION OF CHEMICAL CONTROL OPTIONS, ENVIRONMENTAL FACTORS, AND MANAGEMENT PRACTICES ASSOCIATED WITH ANGULAR LEAF SPOT ( PSEUDOMONAS SYRINGAE PV. TABACI ) , Andrea Brooke Webb

Species-specific microsymbiont discrimination mediated by a Medicago receptor kinase , Xiaocheng Yu

Theses/Dissertations from 2022 2022

Understanding the cellular and physiological mechanisms of fertilization and early-stage seed development , Mohammad Foteh Ali

OPTIMIZING NITROGEN MANAGEMENT IN WINTER WHEAT PRODUCTION SYSTEMS FOR IMPROVED BREAD BAKING QUALITY , Ammar Sadiq Mahdi Al Zubade

Remote Sensing for Quantifying C3 and C4 Grass Ratios in Pastures , Jordyn Alyssa Bush

The Site Evaluation of Quercus alba Metabolites , Zachary Alexander Byrd

The role of class XI myosin in fertilization of Arabidopsis thaliana , Umma Fatema

Survey of Herbicide Resistance and Seed Fate of Italian Ryegrass ( Lolium perenne ssp. multiflorum ) in Kentucky , Amber Lynn Herman

Manipulating species diversity: environmental impacts in row crop, livestock, and grassland agroecosystems , Alayna A. Jacobs

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Home > CNS > Agriculture > PSIS_DISS

Plant and Soil Sciences Dissertations Collection

Dissertations from 2024 2024.

ROLE OF GLUTATHIONE DEGRADATION PATHWAY GENES FOR GLUTATHIONE HOMEOSTASIS AND TOXIC METAL TOLERANCE IN PLANTS , Gurpal Singh, Plant, Soil & Insect Sciences

Dissertations from 2023 2023

Using Cover Crops and Arbuscular Mycorrhizal (AM) Fungi to Enhance the Sustainability of Hardneck Garlic Production in the Northeast , Alexandra Smychkovich, Plant, Soil & Insect Sciences

THE ROLE OF NATURAL ORGANIC MATTER IN THE FORMATION OF SILVER NANOPARTICLES, AND AGGREGATION AND BIOLOGICAL RESPONSE OF NANOPLASTICS , Sicheng Xiong, Plant, Soil & Insect Sciences

Dissertations from 2019 2019

SMALL FRUIT PHENOLICS: PHENOLIC VARIATIONS AND RELATED HEALTH RELEVANCE , Susan Cheplick, Plant, Soil & Insect Sciences

Introducing faba bean as a new multi-purpose crop for Northeast U.S.A. , Fatemeh Etemadi, Plant, Soil & Insect Sciences

EFFECTS OF FERTILIZATION AND DRYING CONDITIONS ON THE QUALITY OF SELECTED CHINESE MEDICINAL PLANTS , Zoe Gardner, Plant, Soil & Insect Sciences

FACTORS INFLUENCING SPARTINA ALTERNIFLORA PRODUCTIVITY IN RELATIONSHIP TO ESTUARY INLET OPENING ELLISVILLE MARSH, PLYMOUTH, MA , Ellen K. Russell, Plant, Soil & Insect Sciences

Dissertations from 2018 2018

Integrated Transcriptomic and Metabolomic Approaches to Identify Genes and Gene Networks Involved in Lipid Biosynthesis in Camelina sativa (L.) Crantz Seeds , Hesham Abdullah, Plant, Soil & Insect Sciences

Dissertations from 2017 2017

Adsorption of Biomolecules on Carbon-Based Nanomaterial as Affected by Surface Chemistry and Ionic Strength , Peng Du, Plant, Soil & Insect Sciences

Investigation of Fungicide Resistance Mechanisms and Dynamics of the Multiple Fungicide Resistant Population in Sclerotinia homoeocarpa , Hyunkyu Sang, Plant, Soil & Insect Sciences

Dissertations from 2016 2016

Uptake and Accumulation of Engineered Nanomaterials by Agricultural Crops and Associated Risks in the Environment and Food Safety , Yingqing Deng, Plant, Soil & Insect Sciences

Evaluating the Role of Glutathione in Detoxification of Metal-Based Nanoparticles in Plants , Chuanxin Ma, Plant, Soil & Insect Sciences

Aggregation and Coagulation of C60 Fullerene as Affected by Natural Organic Matter and Ionic Strength , Hamid Mashayekhi, Plant, Soil & Insect Sciences

Dissertations from 2015 2015

Assessing Kiln-Produced Hardwood Biochar for Improving Soil Health in a Temperate Climate Agricultural Soil , Emily J. Cole, Plant, Soil & Insect Sciences

Cover Crop and Nitrogen Fertilizer Management for Potato Production in the Northeast , Emad Jahanzad, Plant, Soil & Insect Sciences

Evaluation of Leafy Green Species Popular Among Ethnic Groups for Production and Markets in the Northeastern USA , Ricardo A. Orellana, Plant, Soil & Insect Sciences

Effects of Overexpression of SAP12 and SAP13 in Providing Tolerance to Multiple Abiotic Stresses in Plants , Parul R. Tomar, Plant, Soil & Insect Sciences

Dissertations from 2014 2014

PRODUCTION, MARKETING, AND HANDLING PRACTICES TO EXPORT MCINTOSH APPLES TO CENTRAL AMERICAN MARKETS , Mildred L. Alvarado Herrera, Plant, Soil & Insect Sciences

Identification and Epidemiological Features of Important Fungal Species Causing Sooty Blotch on Apples in the Northeastern United States , Angela Marie Madeiras, Plant, Soil & Insect Sciences

Increasing Nutrient Density of Food Crops through Soil Fertility Management and Cultivar Selection , Md. J. Meagy, Plant, Soil & Insect Sciences

ASSESSING BEST MANAGEMENT PRACTICES FOR IMPROVING SWITCHGRASS ESTABLISHMENT AND PRODUCTION , Amir Sadeghpour, Plant, Soil & Insect Sciences

Dissertations from 2013 2013

Evaluation of a Split-Root Nutrition System to Optimize Nutrition of Basil , Ganisher Djurakulovich Abbasov, Plant, Soil & Insect Sciences

Understanding the Links Between Human Health and Climate Change: Agricultural Productivity and Allergenic Pollen Production of Timothy Grass(Phleum pratense L.) Under Future Predicted Levels of Carbon Dioxide and Ozone , Jennifer M. Albertine, Plant, Soil & Insect Sciences

Use of Flame Cultivation as a Nonchemical Weed Control In Cranberry Cultivation , Katherine M. Ghantous, Plant, Soil & Insect Sciences

Dissertations from 2012 2012

Management of Switchgrass for the Production Of Biofuel , Leryn Elise Gorlitsky, Plant, Soil & Insect Sciences

Functional characterization of members of plasma membrane intrinsic proteins subfamily and their involvement in metalloids transport in plants , Kareem A Mosa

Influence of Phosphate on the Adsorption/Desorption of Bovine Serum Albumin on Nano and Bulk Oxide Particles , Lei Song, Plant, Soil & Insect Sciences

Biological control of the ambermarked birch leafminer (Profenusa thomsoni) in Alaska , Anna L Soper

Dissertations from 2011 2011

Armillaria in Massachusetts Forests: Ecology, Species Distribution, and Population Structure, with an Emphasis on Mixed Oak Forests , Nicholas Justin Brazee, Plant, Soil & Insect Sciences

Functional characterization of stress associated proteins (SAPS) from arabidopsis , Anirudha R Dixit

Developing an Efficient Cover Cropping System for Maximum Nitrogen Recovery in Massachusetts , Ali Farsad, Plant, Soil & Insect Sciences

Bacterial Toxicity of Oxide Nanoparticles and Their Effects on Bacterial Surface Biomolecules , Wei Jiang, Plant, Soil & Insect Sciences

Population dynamics of the Hemlock Woolly Adelgid (Hemiptera: Adelgidae) , Annie F Paradis

Dissertations from 2010 2010

Influence of natural organic matter (NOM) and synthetic polyelectrolytes on colloidal behavior of metal oxide nanoparticles , Saikat Ghosh

Dissertations from 2009 2009

Proline-associated antioxidant enzyme response in cool-season turfgrasses under abiotic stress , Dipayan Sarkar

Dissertations from 2008 2008

Characterization of adsorbed organic matter on mineral surfaces , Seunghun Kang

Effects of fire mitigation on post-settlement ponderosa pine non-structural carbohydrate root reserves , Jonathan Thomas Parrott

Dissertations from 2007 2007

Soilless culture of moringa (Moringa oleifera Lam.) for the production of fresh biomass , George William Crosby

Effects of reducing phosphorus nutrition on plant growth and phosphorus leaching of containerized greenhouse crops , Roger A Gagne

Performance and microbial evaluation of an artificial wetland treatment system for simulation model development , Lesley A Spokas

Dissertations from 2006 2006

Structures and phenanthrene sorption behavior of plant cuticles and soil humic substances , Elizabeth Joy Johnson

Activity of primisulfuron and Alternaria helianthi as affected by leaf surface micro-morphology and surfactants , Debanjan Sanyal

Dissertations from 2005 2005

Patterns of predation by natural enemies of the banana weevil (Coleoptera: Curculionidae) in Indonesia and Uganda , Agnes Matilda Abera-Kanyamuhungu

Upright dieback disease of cranberry, Vaccinium macrocarpon Ait.: Causal agents and infection courts , Nora J Catlin

Myotropic peptide hormones and serotonin in the regulation of feeding in the adult blow fly Phormia regina, and the adult horse fly Tabanus nigrovittatus , Aaron T Haselton

Evaluation of organic turfgrass management and its environmental impact by dissolved organic matter , Kun Li

Dynamics of plum curculio, Conotrachelus nenuphar (Herbst.) (Coleoptera: Curculionidae), immigration into apple orchards , Jaime Cesar Pinero

Adult activity and host plant utilization in cranberry fruitworm, Acrobasis vaccinii Riley (Lepidoptera: Pyralidae) , Nagendra R Sharma

Characterization of humic substances and non-ideal phenanthrene sorption as affected by clay -humic interactions , Kaijun Wang

Investigations into mating disruption, delayed mating, and multiple mating in oriental beetle, Anomala orientalis (Waterhouse), Coleoptera: Scarabaeidae , Erik J Wenninger

Dissertations from 2004 2004

Phytoextraction of zinc from soils , Gretchen M Bryson

Identification of ethylene responsive genes that control flowering of Guzmania lingulata ‘Anita’ , Danijela Dukovski

Changes in soil quality under different agricultural management in Chinese Mollisols , Xiaobing Liu

Factors influencing colonization and establishment of plant species on cranberry bogs , Hilary A Sandler

Dissertations from 2003 2003

Thripinema nicklewoodi (Tylenchida: Allantonematidae), a potential biological control agent of Frankliniella occidentalis (Thysanoptera: Thripidae) , Un Taek Lim

Investigation into Listronotus maculicollis (Coleoptera: Curculionidae), a pest of highly maintained turfgrass , Nikki Lynn Rothwell

Dissertations from 2002 2002

Soil organic matter and metolachlor sorption characteristics as affected by soil management , Guangwei Ding

Manganese toxicity in marigold as affected by calcium and magnesium , Touria El-Jaoual Eaton

An investigation of the relationships between mineral nutrition and the phytoextraction of zinc by Indian mustard (Brassica juncea Czern.) , Russell Lawrence Hamlin

Vegetation patterns and associated hydrogeochemical cycles in a calcareous sloping fen of southwestern Massachusetts , Deborah Jane Picking

Dissertations from 2001 2001

Massachusetts agriculture and food self -sufficiency: An analysis of change from 1974 through 1997 , David Lee Holm

Study on heavy metal absorption by plants , Valtcho Demirov Jeliazkov

Absorption, translocation and metabolism of isoxaflutole by tolerant and susceptible plant species , Sanjay Kushwaha

Modeling ‘yield-population’ relationships in soybean , Jomol P Mathew

Evaluation of paper mill sludge as a soil amendment and as a component of topsoil mixtures , Tara A O'Brien

Towards development of optimal trap deployment strategies for apple maggot fly (Diptera: Tephritidae) behavioral control , Juan Antonio Rull Gabayet

Aspects of the behavior, ecology and evolution of a braconid parasitoid , Mark Stuart Sisterson

Herbicidal action of root -applied glufosinate -ammonium , Wenqi You

Dissertations from 2000 2000

A minimally invasive assay detects BRCA1 and BRCA2 protein truncations indicative of the presence of a germline mutation , Timothy John Byrne

Effects of composts on tomato growth and soil fertility , Yifan Hu

Olfactory and visual cues guiding plum curculios (Coleoptera: Curculionidae) to host plants , Tracy Christine Leskey

Behavioral and ecological factors influencing oviposition of Acrobasis vaccinii (Riley) (Lepidoptera: Pyralidae), the cranberry fruitworm, with implications for pest management , Andrea Kent Osgood Rogers

The roles of midgut hormone and allatotropin in the adult black blow fly, Phormia regina Meigen (Diptera: Calliphoridae) , Meng-Ping Tu

The potential for reductive dechlorination under microwave extraction conditions , Steven Mark Wilkins

Dissertations from 1999 1999

Colorado potato beetle (Leptinotarsa decemlineata)(Say) dispersal and reproduction as potential factors in the development of resistance to Bacillus thuringiensis subsp. tenebrionis Cry3A toxin , Andrei Vladimirovitch Alyokhin

Fitness, survival and resistance management of the yellow fever mosquito, Aedes aegypti (L.) , Laura Catherine Harrington

Development of an effective behavioral technology for controlling fruit flies (Diptera: Tephritidae) , Xing Ping Hu

Selection of oviposition sites by Aedes aegypti: Behavior of gravid mosquitoes and mechanisms of attraction , Adam Sinclair Jones

Fate of isoxaflutole and its diketonitrile metabolite in soils as influenced by edaphic factors and soil types , Sowmya Mitra

The role of trans-sialidase on Trypanosoma cruzi parasite load in Rhodnius prolixus, impact of infection on triatomid behavior, and dispersal in a simulated field environment , Miwako Takano

Biology and behavior of Lymantria mathura Moore (Lepidoptera: Lymantriidae) , Marina A Zlotina

Dissertations from 1998 1998

Effects of light on endogenous seed abscisic acid levels and seed growth characteristics in soybean , Gurkirat Kaur Baath

Presence and enrichability of propanotrophs in subsoils , Jalal Ghaemghami

Effects of legume-cereal cover crop mixtures on nitrogen management in sweet corn , Francis Xavier Mangan

Retrospective analysis of epidemic eastern equine encephalomyelitis transmission in Massachusetts , Abelardo Carlos Moncayo

Development of enzyme-linked immunosorbent assays for the detection of mutagenic metabolites of the herbicide alachlor , Daniel M Tessier

Evaluation of Trichogramma ostriniae (Hymenoptera: Trichogrammatidae) as a biological control agent against the European corn borer, Ostrinia nubilalis (Lepidoptera: Pyralidae): Biological, behavioral and ecological perspectives , Baode Wang

Effect of benzyladenine on fruit thinning and its mode of action on 'McIntosh' apples , Rongcai Yuan

Dissertations from 1997 1997

Transmission of the gypsy moth nuclear polyhedrosis virus: Theory and experiment , Vincent D'Amico

Evaluating parasitoids (Hymenoptera: Aphelinidae) for biological control of Bemisia argentifolii (Homoptera: Aleyrodidae) on poinsettia , Mark Stephen Hoddle

Roles of phenolics, ethylene and fruit cuticle in scald development of apples (Malus domestica Borkh.) , Zhiguo Ju

Use of odors for in-flight orientation to the host and for host recognition by the parasitoid Brachymeria intermedia (Hymenoptera: Chalcididae) , Veronique Kerguelen

Interactions between two gypsy moth (Lymantria dispar L.) pathogens: Nuclear polyhedrosis virus and Entomophaga maimaiga (Entomophthorales: Zygomycetes) , Raksha Devi Malakar

Dissertations from 1996 1996

Genetic analysis of the breakdown of self-incompatibility in Lycopersicon peruvianum , Bindu Chawla

Dissertations from 1995 1995

The genetic structure of northeastern populations of the tachinid Compsilura concinnata (Meigen), an introduced parasitoid of exotic forest defoliators of North America , Vicente Sanchez

Chlorothalonil binding to dissolved humic substances isolated from a Massachusetts cranberry bog , Eric Scott Winkler

Dissertations from 1992 1992

Dispersal and diet of the Colorado potato beetle, Leptinotarsa decemlineata , Donald Charles Weber

Dissertations from 1991 1991

Potential effects of increased atmospheric carbon dioxide and climate change on thermal and water regimes affecting wheat and corn production in the Great Plains , Cynthia Rosenzweig

Dissertations from 1983 1983

VALIDATION OF BACTERIAL RETENTION BY MEMBRANE FILTRATION: A PROPOSED APPROACH FOR DETERMINING STERILITY ASSURANCE , TIMOTHY JAMES LEAHY

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M.S. & Ph.D. in Plant Science

Plant science graduate program.

biotech 10 Research Areas

today Fall, Spring, Summer, Winter Entry Terms

place Tucson COURSEWORK LOCATION

The Plant Science Graduate Program prepares you for careers focused on all aspects of plants, including the biological, climatic and other factors that affect them. World-class researchers will mentor you, and you’ll receive excellent support and opportunities to achieve at the highest level.

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About the M.S. & Ph.D. in Plant Science

The thesis-based M.S. in Plant Science prepares you for careers in government agencies, companies and academic institutions that require a solid background in plant science and experience with research techniques and experimental design.

The Ph.D. in Plant Science prepares you for research and leadership careers in crop improvement through genetics, genomics and advanced technology; with federal and state agencies involved in traditional and digital agriculture; and at academic institutions focused on basic and applied plant sciences.

Prospective graduate students should always feel free to reach out to faculty  whose research interests overlap with yours to ask if they anticipate accepting new students.

Coursework overview

As you prepare to conduct cutting-edge research in your field of interest, you will participate in seminars and take core required courses, such as:

• Advanced Plant Biology
• Principles of Plant Microbiology

With your advisor and advisory committee’s guidance, you will choose from an array of courses to bolster fundamental knowledge and expand your horizons, including these graduate-level courses on these topics:

• Applied Cyberinfrastructure Concepts
• Methods in Cell Biology and Genomics
• Mechanisms in Plant Development
• Plant Biochemistry and Metabolic Engineering
• Plant Genetics and Genomics
• Physiology of Plant Production under Controlled Environment
• Medicinal Plants

What to expect from the program

As a new thesis-M.S. student, you will rotate through one or two working research labs to let you and your prospective advisor assess how well you work together and to help you begin to build your professional network. Once you join a research lab, you and your advisor will develop a research project and you can expect an intense summer between your first and second year as you execute the projects that will form the basis of your thesis. Coursework typically is spread over two to three semesters.

As a new Ph.D. student, you will generally rotate through two to three labs, to expand the breadth of your training, ensure fit, and begin to build your professional network. As you settle in a lab, you and your advisor will begin developing a theme for your dissertation projects. You’ll also complete coursework in the first two years and prepare for your comprehensive exam. After passing the exam, your entire focus will be on research and the creation of new and relevant information and knowledge. Your advisor and advisory committee will help you on this journey to becoming a leader and expert in your chosen field.

Year-round funding generally is offered to Ph.D. and thesis-M.S. students making satisfactory progress, through a blend of research assistantships available from the major advisor and a limited number of teaching assistantships.

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M.S. and Ph.D. Theses

Winters, Noah P. Evolutionary and functional genetics of disease resistance in theobroma cacao and its wild relatives.   A Dissertation in Ecology Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy May 2022

Abstract: Plants have complex and dynamic immune systems that have evolved over millennia to help them resist pathogen invasion. Humans have worked to incorporate these evolved defenses into crops through breeding. However, many crop cultivars only harness a fraction of the overall genetic diversity available to a given species, or have such a long history of domestication that most diversity has been lost. Evaluating previously neglected germplasm for desirable traits, such as disease resistance, is therefore an essential step towards breeding crops that are adapted to both current and emerging threats. In this dissertation, we examine the evolution of defense response across populations of Theobroma cacao L. and wild species of Theobroma, with the goal of identifying genetic elements essential for protection against the cacao pathogen Phytophthora palmivora. < pdf >

Fister, A. S. (2016). Genomics of the theobroma cacao L. defense response (Order No. 10300628). Available From ProQuest Dissertations & Theses A&I. (1848683680). Retrieved from http://ezaccess.libraries.psu.edu/login?url=https://search-proquest-com.ezaccess.libraries.psu.edu/docview/1848683680?accountid=13158

Abstract : Theobroma cacao, the source of cocoa and a cash crop of global economic importance, suffers significant annual losses due to several pathogens. While study of the molecular mechanisms of defense in cacao has been limited, the recent sequencing of two cacao genomes has greatly expedited the ability to study genes and gene families with roles in defense. Here, the pathogenesis-related (PR) gene families were bioinformatically identified, and family size and gene organization were compared to other plant species, revealing significant conservation throughout higher monocots and dicots. Expression of the PR families was also analyzed using a whole genome microarray to measure transcriptomic regulation in leaves after treatment of cacao seedlings with two pathogens, identifying the induced PR genes within each family. We found significant overlap between the PR genes induced by the pathogens, and subsequent qRTPCR revealed up to 5000-fold induction of specific PR family members. Next, the regulation of the defense response in cacao by salicylic acid, a major defense hormone, was analyzed. The study focused on two genotypes, the broadly resistant Scavina 6 and the widely susceptible ICS1. First, treatment of leaves of two cacao genotypes with salicylic acid was shown to enhance resistance of both. Moreover, overexpression of TcNPR1, a master regulator of systemic acquired resistance, is also shown to enhance the defense response, supporting the importance of salicylic acid and its downstream targets in cacao immunity. Microarray analysis of the transcriptomic response to salicylic acid revealed genotype-specific responses to hormone treatment. ICS1 appeared to show a more canonical response to salicylic acid, with more PR genes induced, while Scavina 6 exhibited increased expression of chloroplastic and mitochondrial genes. It was hypothesized that this induction was linked to increased ROS production, and subsequent ROS staining experiments confirmed higher concentration of superoxide in salicylic acid-treated Scavina 6 leaf tissue. Third, a pilot study was performed to quantify genetic variability within defense genes. Using DNA samples representing three populations of cacao - Peruvian, Ecuadorian, and French Guianan - we amplified three genes involved in defense, two predicted to be more variable (cysteine-rich repeat secretory peptide 38 and a polygalacturonase inhibitor) and one predicted to harbor less polymorphism (pathogenesis-related 1). Population genetic analysis of variability suggested that the gene predicted to be more variable may be under diversifying selection, suggesting that they may directly interact with rapidly evolving pathogen proteins. The experiment validated previously described observations about the populations, in particular that the French Guianan population was less variable than the others. The study also supported the predictions regarding gene variability, indicating that our strategy for identifying genes with more variation appears to be applicable but will require further validation. The Guiltinan-Maximova lab developed a protocol for transient transformation of cacao leaf tissue, which has been applied to characterizing gene function in several published analyses. Here the highly efficient protocol is presented in full, along with data collected in a series of optimization experiments. We also use the protocol to demonstrate the effect of overexpression of a cacao chitinase after subsequent infection with Phytophthora mycelia. A preliminary study describing a strategy for selection of high-priority candidate genes for functional characterization is described. Six genes were cloned and overexpressed using the transient transformation protocol; and while the study showed the ability of our protocol to significantly increase transcript abundance of the gene of interest, it did not validate the role of any of the genes in defense by showing decreased susceptibility. This dissertation contributes to the study of genomics and molecular mechanisms of defense in four key ways: 1) 15 classes of defense genes are identified and their expression dynamics are characterized, 2) genotype-specific differences in defense response are identified, providing insight into different strategies for survival, 3) variability within defense genes is discovered, differentiating populations of cacao and providing evidence for diversifying selection, and 4) a rapid and efficient strategy for gene functional analysis, which will enhance future genetic analyses in cacao, is presented.

Zhang, Y. (2014). Functional genomics of theobroma cacao fatty acid biosynthesis: Convergence of fatty acid desaturation, embryo development, and defense signaling responses (Order No. 3690183). Available From ProQuest Dissertations & Theses A&I. (1658228179). Retrieved from http://ezaccess.libraries.psu.edu/login?url=https://search-proquest-com.ezaccess.libraries.psu.edu/docview/1658228179?accountid=13158

Abstract : Theobroma cacao L. (chocolate tree) is an important cash crop for 40-50 million farmers and their families in its tropical growing regions worldwide. Cocoa butter and cocoa powder extracted from cacao seeds provide the main raw ingredients for chocolate manufacturing, supporting a $80 billion global business. A unique fatty acid composition of cocoa butter makes its melting temperature close to the human body temperature, which is not only of particular importance for industrial uses, but also a valuable quality trait targeted by breeding programs. My Ph.D. dissertation focused mainly on the fatty acid biosynthesis pathway in cacao seeds. I identified a key desaturase gene TcSAD1 from a large stearoyl-acyl carrier protein-desaturase gene family in cacao that plays a crucial role in converting stearic acid (18:0, saturated fatty acid) into oleic acid (18:1, unsaturated fatty acid). The expression of TcSAD1 was highly correlated with the change of fatty acid composition during cacao seed development. The activity of TcSAD1 rescued all the Arabidopsis ssi2 (a fatty acid desaturase) related mutant phenotypes, further supporting its in vivo functions. The discovery of the critical function of TcSAD1 offers a new strategy for screening for novel genotypes with desirable fatty acid compositions, and for use in breeding programs, to help pyramid genes for quality traits such as cocoa butter content. Moreover, because of the significance of fatty acid biosynthesis and lipid accumulation during cacao seed development, to further explore the regulatory mechanism, I functionally characterized of a master regulator, TcLEC2 gene, which controls both zygotic and somatic embryo development of cacao. Transient overexpression of TcLEC2 induced the expression of a variety of seed specific genes in cacao leaves. Furthermore, functions of TcLEC2 were explored during somatic embryogenesis, which is an in vitro propagation system for cacao. My results suggested that the activity of TcLEC2 determines the embryogenic capacity of the cacao tissue explants and correlated with embryogenic capacity of cultured cells. Transgenic embryos overexpressing TcLEC2 produced a significantly higher number of embryos compared to non-transgenic embryos; however, most of these transgenic somatic embryos exhibited abnormal phenotypes, and the development normally ceased at globular stage. This discovery may have future applications in increasing the efficiency of cacao mass propagation programs. Notably, in addition to major storage compounds in cacao seeds, fatty acids also function as signals involved in defense responses. I found that the endogenous level of 18:1 was modulated by exogenous glycerol application. Glycerol application on cacao leaves increased the level of glycerol-3-phosphate and lowered the level of 18:1 through an acylation reaction, which further triggered the defense responses. 100mM glycerol was sufficient to induce the accumulation of ROS, activate the expression of a variety of pathogen-related genes, and confer enhanced resistance against fungal pathogen Phytophthora capsici. My results demonstrated the potential of foliar glycerol application to become an environmentally safe means to induce the plant defense responses and fight important plant diseases in the field. Together, my Ph.D. dissertation makes major contributions to three important research areas in cacao: (1) identification of the key gene regulating fatty acid composition in cocoa butter, (2) improvement of large-scale propagation system (somatic embryogenesis) of cacao, (3) enhancement of cacao foliar disease resistance. This thesis not only provides useful knowledge of the regulatory mechanisms of important quality traits at the molecular and genetic levels, but also demonstrates the potential of taking advantage of cacao genomic resources to accelerate cacao basic research and breeding programs. <pdf>

Shi, Z. (2010). Functional analysis of non expressor of PR1 (NPR1) and its paralog NPR3 in theobroma cacao and arabidopsis thaliana (Order No. 3442953). Available From ProQuest Dissertations & Theses A&I. (853752677). Retrieved from http://ezaccess.libraries.psu.edu/login?url=https://search-proquest-com.ezaccess.libraries.psu.edu/docview/853752677?accountid=13158 Arabidopsis NON EXPRESSOR OF PR1 (NPR1) is a key transcription regulator of the salicylic acid (SA) mediated defense signaling pathway. The NPR gene family consists of NPR1 and five other NPR1-like genes in Arabidopsis. This research focuses on the functional analysis of an NPR1 ortholog from Theobroma cacao L. and characterization of one of the NPR1 paralogs, NPR3, in both Arabidopsis and cacao. To identify the function of NPR3 in Arabidopsis, I first examined the gene expression pattern of NPR3 and found it to be strongly expressed in developing flower tissues. Interestingly, an npr3 knockout mutant displayed enhanced resistance to Pseudomonas syringae tomato pv. DC3000 (P.s.t.) infection of immature flowers. Gene expression analysis also revealed increased basal and induced levels of PRI transcripts in npr3 developing flowers. To investigate the possible mechanism of NPR3-dependent negative regulation of defense response, I tested the physical interactions of NPR3 with both TGA2 and NPR1 in vivo, which suggests that NPR3 represses NPR1-dependent transcription by inhibiting the nuclear localization of NPR1 through direct binding to TGA2 and NPR1. To characterize the NPR1 ortholog from cacao, I isolated TcNPR1 gene from genotype of Scavina6, and demonstrated that it expresses constitutively in all the tested tissues. To functionally analyze this gene, a bacterial growth assay was carried out with npr1-2 transgenic lines overexpressing TcNPR1, and a reduced level of bacterial growth demonstrated that TcNPR1 can partially complement Arabidopsis the npr1-2 mutation. In addition, TcNPR1 was shown to translocate into nuclei upon SA treatment in a manner identical to Arabidopsis native NPR1. To further explore the NPR gene family in cacao, I identified a total of four NPR-like genes from the cacao genome, and phylogenetic analysis indicated that the duplications of three clades in this gene family occurred before the divergence of Arabidopsis and cacao. To identify the functional ortholog of Arabidopsis NPR3, I isolated a putative TcNPR3 gene and demonstrated that its expression level was higher in un-open flowers and older leaves, a pattern similar to Arabidopsis NPR3. A complementation test of TcNPR3 expressed in the Arabidopsis npr3-3 null mutant showed that TcNPR3 can functionally substitute for the Arabidopsis NPR3 gene, demonstrating that TcNPR3 is the functional ortholog of AtNPR3. To obtain the genome-wide transcriptional responses of SA treatment in cacao, I used microarray analysis to measure gene expression in two cacao genotypes (ICS1 and Scavina6), three leaf developmental stages (A, C and E) and two treatments (water and SA). After validating the microarray results with RT-PCR, I identified differentially expressed genes from all twenty-four pair-wise comparisons. Interestingly, chloroplast and mitochondrial genes are enriched in SA-induced Scavina6 but those genes are underrepresented in ICS1, suggesting that the oxidative burst and hypersensitive response during defense response may vary between the two genotypes. In all, this research will not only offer us the knowledge of defense response mechanism and signal transduction regulation in Arabidopsis and cacao, but also provide molecular tools for selecting cultivars with enhanced disease resistance for cacao breeders and farmers. <pdf>

Liu, Y. (2010). Molecular analysis of genes involved in the synthesis of proanthocyanidins in theobroma cacao (Order No. 3420238). Available From ProQuest Dissertations & Theses A&I. (750368282). Retrieved from http://ezaccess.libraries.psu.edu/login?url=https://search-proquest-com.ezaccess.libraries.psu.edu/docview/750368282?accountid=13158 The flavonoids catechin and epicatechin, and their polymerized oligomers, the proanthocyanidins (PAs, also called condensed tannins), accumulate to levels of up to 15% of the total weight of dry seeds of Theobroma cacao L. These compounds have been associated with several health benefits in humans including antioxidant activity, improvement of cardiovascular health and reduction of cholesterol levels. They also play important roles in pest and disease defense throughout the plant. This research focuses on molecularly dissecting the proanthocyanidin biosynthetic pathway of Theobroma cacao. To this end, I first isolated candidate genes from T.cacao (Tc) encoding key structural enzymes of this pathway which include, anthocyanidin reductase (ANR), leucoanthocyanidin dioxygenase (LDOX, also called anthocyanidin synthase, ANS) and leucoanthocyanidin reductase (LAR). I performed gene expression profiling of candidate TcANR, TcANS and TcLAR in various tissues through different developmental stages and also evaluated PA accumulation levels in those tissues. My results suggested that all PA candidate genes are co-regulated and positively correlated with PA synthesis. To functionally analyze the candidate genes, I used the model plants Arabidopsis and tobacco as expression platforms. Results from Arabidopsis mutant complementation tests and transgenic tobacco plants constitutively overexpressing cacao genes demonstrate that the candidate structural genes isolated from cacao are true ANS, ANR and LAR genes and all actively involved in PA synthesis in cacao. To further explore the transcriptional regulation of the PA synthesis pathway, I then isolated and characterized an R2R3 type MYB transcription factor TcMYBPA from cacao. I examined the spatial and temporal gene expression patterns of TcMYBPA in cacao and found it to be developmentally expressed in a manner consistent with its involvement in PAs as well as anthocyanin synthesis. Complementation test of TcMYBPA in Arabidopsis tt2 mutant suggested that TcMYBPA could functionally substitute Arabidopsis TT2 gene. Interestingly, except PA accumulation in seeds, I also observed an obvious increase of anthocyanidin accumulation in hypocotyls of transgenic Arabidopsis plants. This is consistent with gene expression analysis which showed that the entire PA pathway could be induced by overexpression of TcMYBPA gene, including DFR, LDOX (ANS) and BAN (ANR). Therefore I concluded that the isolated TcMYBPA gene encodes an R2R3 type MYB transcription factor and is involved in the regulation of both anthocyanin and PA synthesis in cacao. This research will not only offer us the knowledge of secondary metabolites production in cacao, but also provides molecular tools for breeding of cacao varieties with improved disease resistance and enhanced flavonoid profiles for nutritional and pharmaceutical applications. <pdf>

Miller, C. (2009). An integrated in vitro and greenhouse orthotropic clonal propagation system for Theobroma cacao L (United States -- Pennsylvania: The Pennsylvania State University), pp. 158. <pdf>

Xia, H. (2009). Structure and function of endosperm starch from maize mutants deficient in one or more starch-branching enzyme isoform activities (United States -- Pennsylvania: The Pennsylvania State University), pp. 261. <pdf>

Marelli, J. (2008). Solanum lycopersicum as a model system to study pathogenicity mechanisms of Moniliophthora perniciosa, the causal agent of witches' broom disease of Theobroma cacao (United States -- Pennsylvania: The Pennsylvania State University), pp. 178. <pdf>

Swanson, J.-D . (2005). Flower development in Theobroma cacao L.: An assessment of morphological and molecular conservation of floral development between Arabidopsis thaliana and Theobroma cacao (United States -- Pennsylvania: The Pennsylvania State University), pp. 201. <pdf>

Antunez de Mayolo, G. (2003). Genetic engineering of Theobroma cacao and molecular studies on cacao defense responses (United States -- Pennsylvania: The Pennsylvania State University), pp. 148. <pdf>

Cakirer, M.S. (2003). Color as an indicator of flavanol content in the fresh seeds of Theobroma cacao L. (The Pennsylvania State University). <pdf>

Tomscha, J.L. (2001). Phosphatase secretion mutants in Arabidopsis thaliana (United States -- Pennsylvania: The Pennsylvania State University), pp. 103. <pdf>

Traore, A. (2000). Somatic embryogenesis, embryo conversion, micropropagation and factors affecting genetic transformation of Theobroma cacao L (United States -- Pennsylvania: The Pennsylvania State University), pp. 135. <pdf>

Kim, K.-N. (1997). Molecular analysis of starch branching enzyme genes in maize (Zea mays L.) (United States -- Pennsylvania: The Pennsylvania State University), pp. 137. <pdf>

Maximova, S.N. (1997). Agrobacterium-mediated genetic transformation of apple (Malus domestica Borkh.) (United States -- Pennsylvania: The Pennsylvania State University), pp. 106. <pdf>

Gao, M. (1996). Molecular characterization of starch branching enzyme genes, Sbe1, Sbe2b and Sbe2a in maize (Zea mays L.) (United States -- Pennsylvania: The Pennsylvania State University), pp. 108. <pdf>

Fisher, D.K. (1995). Molecular genetic analysis of multiple isoforms of starch branching enzyme with emphasis on Zea mays L (United States -- Pennsylvania: The Pennsylvania State University), pp. 185.

Niu, X. (1995). DNA binding specificity and interactions with nucleosomal DNA of the plantbZIP protein EmBP-1 (United States -- Pennsylvania: The Pennsylvania State University), pp. 132.

Guiltinan, M.J. (1986). THE ISOLATION, CHARACTERIZATION AND INTERGENERIC TRANSFER OF TWO SOYBEAN (GLYCINE MAX L.) BETA-TUBULIN GENES (TI PLASMID) (United States -- California: University of California, Irvine), pp. 100. <pdf>

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Thesis Research

Plant Science graduate students conduct thesis research in the areas of agronomy, crop nutrition & soils, irrigation, horticulture, weed science, plant pathology, entomology, and mechanized agriculture.

You can review a list of recent thesis titles online .

Experiments may be conducted in the field, greenhouse, laboratory, or a combination thereof. Projects are developed in conjunction with a thesis advisor based on his/her area(s) of research expertise ( view faculty biographies ). In some cases, thesis research projects are developed under the direction of UC Cooperative Extension farm advisors, or with associated scientists at the University of California, Kearney Agricultural Experiment Station  or USDA-ARS laboratory , both located in Parlier, CA, 30-40 minutes from campus.

Upon application, students are encouraged to discuss their initial area of interest with the Graduate Coordinators who will then direct them to the appropriate faculty  or associated scientist. In the first semester, students develop a written thesis proposal which must be approved by the thesis chair and committee members prior to defending their thesis proposal during their second semester.

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Plant sciences articles from across Nature Portfolio

Plant sciences is the study of plants in all their forms and interactions using a scientific approach.

How cauliflower got its curd

For a variety of reasons, genetic understanding of the steps leading to domestication of the nutrient-rich edible arrested inflorescence of cauliflower — its curd — has proven relatively intractable. A genomic study now unravels the details.

• Alisdair R. Fernie
• Mustafa Bulut

Clonal gametes enable polyploid genome design

Many plant products eaten daily in human diets — such as potato or banana — are polyploid and are notoriously difficult to breed. In this study, the fusion of clonal gametes from distinct diploid tomato parents is used as a blueprint for the design of polyploid genomes in crops.

Nuclear pores beyond macromolecule channels

The nuclear pore is known as a large protein complex for the transport of macromolecules between the nucleus and the cytoplasm. Comprehensive proteomic analyses revealed a novel role of the nuclear pore complex as a platform for the coordinated regulation of the flow from transcription to translation.

• Sachihiro Matsunaga

Related Subjects

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Latest Research and Reviews

Shining a light on UV-fluorescent floral nectar after 50 years

• Brandi Zenchyzen
• John H. Acorn
• Jocelyn C. Hall

A conserved Pol II elongator SPT6L mediates Pol V transcription to regulate RNA-directed DNA methylation in Arabidopsis

How to facilitate the transcription of plant-specific RNA Pol V is largely unknown. Liu et al. find that a conserved RNA Pol II elongator, SPT6L, mediates DNA methylation by its association with Pol V and promoting the production of scaffold RNA.

Bio-stimulating effect of endophytic Aspergillus flavus AUMC 16068 and its respective ex-polysaccharides in lead stress tolerance of Triticum aestivum plant

• Hend A. EL-khawaga
• Abeer E. Mustafa
• Ghadir E. Daigham

Phylogenomics reveals the evolutionary origins of lichenization in chlorophyte algae

Lichen symbiosis between chlorophyte algae and fungi is a key player in ecosystems but our understanding of its evolution and genetic regulation in algae remains limited. This study finds that lichen symbiosis evolved at least three times in algae through gene family expansion and horizontal gene transfers

• Camille Puginier
• Cyril Libourel
• Jean Keller

Comparative analysis, diversification, and functional validation of plant nucleotide-binding site domain genes

• Athar Hussain
• Aqsa Anwer Khan
• Shahid Mansoor

Multispecies deep learning using citizen science data produces more informative plant community models

By modelling the distribution of the entire Swiss flora using deep learning and citizen science data, this study demonstrates a method that predicts flowering phenology and potentially dominant tree species more accurately than commonly used approaches. This approach could enable investigation of understudied aspects of ecology and refine our understanding of plant distributions.

• Philipp Brun
• Dirk N. Karger
• Niklaus E. Zimmermann

News and Comment

Selfish genes and mother trees.

Metaphors are excellent tools for explaining complicated concepts. But sometimes the concepts can become driven by the metaphors.

Two steps beyond

• Guillaume Tena

Coffee history under the genomic lens

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Plant Sciences

Dissertations and theses.

Resources listed in order of breadth and centrality to dissertation searching:

• Open Access Theses and Dissertations This link opens in a new window OATD.org aims to be the best possible resource for finding open access graduate theses and dissertations published around the world. Metadata (information about the theses) comes from over 1000 colleges, universities, and research institutions. OATD currently indexes 2,311,795 theses and dissertations.
• Networked Digital Library of Theses and Dissertations (NDLTD) This link opens in a new window The Networked Digital Library of Theses and Dissertations (NDLTD) is an international organization dedicated to promoting the adoption, creation, use, dissemination and preservation of electronic analogues to the traditional paper-based theses and dissertations. This website contains information about the initiative, how to set up Electronic Thesis and Dissertation (ETD) programmes, how to create and locate ETDs, and current research in digital libraries related to NDLTD and ETDs.
• Cybertesis : tesis electrónicas en línea This link opens in a new window Cybertesis.Net is a cooperative project between the Université de Montréal, the Université de Lyon2, the University of Chile and 32 universities in Europe, Africa and Chile that allows access to more than 27,000 full text theses and dissertations. Some institutions have opted to digitize theses dating back to the 1700s. [Coverage: 1700s-present]
• China Doctoral Dissertation & Masters’ Theses This link opens in a new window This database offers an unparalleled look into the academic research of China’s most prestigious institutions. CDMD is the most comprehensive, highest quality database of dissertations and theses from China, representing nearly 500 PhD-granting institutions and over 775 masters-granting institutions, including the Chinese Academy of Sciences, the Chinese Academy of Social Sciences, and the Chinese Academy of Agriculture, among others. The theses and dissertations are available in Chinese, with an interface in English.
• EThOS Beta Electronic Theses Online Service Open Access to UK theses This link opens in a new window The British Library is experiencing a major technology outage as a result of a cyber-attack. Access may be limited or unavailable at this time. Register for a free account to download theses. Almost-complete index of all doctoral level theses awarded by UK universities. You can uncover the latest cutting edge research inside the pages of UK PhD theses, immediately download over 300,000 theses or order many more through the unique EThOS digitisation on demand service. Among other services, EThOS allows one to search, select and in some/many cases download the full-text of items of interest free of charge.

University of California Dissertations

• See  Finding UC Davis Theses and Dissertations  for more information on locating and accessing UC Davis titles.
• Current Research @ University of California, Davis Searches all University of California, Davis dissertations published in the Dissertation Abstracts database.
• Proquest Dissertation Abstracts International/ Dissertations & Theses   (also known as ProQuest Digital Dissertations) The Dissertation Abstracts International (DAI) database is the authoritative source for finding doctoral dissertations and master’s theses. It also provides access to the full-text of all dissertations from University of California campuses since the late 1990s.
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• Last Updated: May 23, 2024 2:04 PM
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MPhil in Biological Science (Plant Sciences) by thesis

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The Department of Plant Sciences is an outstanding University Centre for research in plant and microbial sciences. It offers unrivalled research and training opportunities in the following areas of plant and microbial science:

• Cell function & responses to the environment
• Developmental biology & signalling
• Genetics & epigenetics
• Ecosystem function & conservation
• Evolution & diversity
• Microbiology & biotic interactions
• Plant pathology & epidemiology
• Systems & mathematical biology
• Enhancing photosynthesis
• Biotechnology & engineering

The Crop Science Centre is an alliance between the University of Cambridge’s Department of Plant Sciences and the crop research organisation NIAB. The Centre will serve as a global hub for crop science research and a base for collaborations with research partners around the world.

The research MPhil degree essentially follows the format of the PhD but is compressed into one year of full-time study or two years of part-time study. The work consists of research and courses as required under academic supervision. Applicants should contact a potential supervisor before proceeding with their MPhil application. You can browse the personal/group pages of the Research Group Leaders to check the details of their research.

The aim of the course is to provide Masters-level training in practical aspects of Plant Sciences, augmented by appropriate lecture courses delivered within the Department. 

The course provides training in a wide range of disciplines, which can include plant genetic engineering, plant development, plant molecular biology, plant biophysics, plant biochemistry, plant-microbe interactions, algal microbiology, plant ecology, crop biology, plant virology, plant epigenetics, epidemiology, plant taxonomy, plant physiology, eco-physiology and bioinformatics. 

Having identified a research area of interest and contacted the appropriate supervisor, the first stage in developing an application should be to draft an appropriate research summary of the training to be undertaken. 

MPhil students must submit a thesis for examination within the maximum period of their study.

All postgraduate students attend induction and safety training courses in the Department.

As well as undertaking their research, students will attend courses and lectures on some of the following: instrumentation, sequencing and database use, statistics, experimental design, analysing data, writing reports and a thesis, and how to give effective scientific presentations. Students are expected to take part in the Postgraduate School of Life Sciences' Researcher Development Programme.

Students receive termly reports on their work.

Learning Outcomes

The primary outcomes from successfully completing the MPhil include:

• specialist training in experimental or theoretical methods;
• an ability to analyse relevant literature and apply it to the development of innovative research;
• capacity to develop and apply data abstraction and analytical procedures with an appropriate level of statistical validation;
• independence in designing and conducting original research, and preparing that data in a format suitable for publication in peer-reviewed journals;
• enhanced organisational skills, in terms of time management, good laboratory practices, safety and planning of a specific programme of research.

MPhil candidate's are required to draft a project proposal four weeks after starting the course and deliver a seminar and prepare a thesis plan four months before their thesis submission deadline.  

As an MPhil student, you must keep a separate training log, in which you will record all seminars and lectures attended and given, training undertaken, the highlights of your research work, and your notes of discussions with your supervisor(s). This log will be quite distinct from your laboratory notebook(s) which should contain all the details of your research work. 

The Masters thesis has a word limit set at 20,000 words, exclusive of tables, footnotes, bibliography, and appendices.

The MPhil provides specialist training in scientific methodology relevant to the project subject area and based on the expertise of the supervisor and research group. This training also enables students from other scientific areas to proceed with a career in plant sciences and other allied areas. General training is also available and includes courses and lectures in instrumentation, sequencing and database use, statistics, experimental design, analysing data, writing reports and a thesis, and how to give effective scientific presentations. The training in research and preparation of the Masters thesis will provide an excellent foundation for those wishing to continue onto a PhD programme.  

On successfully passing the MPhil, students are welcome to apply to continue to a PhD. There is no automatic continuation from an MPhil to a PhD, and a new application must be made and a suitable supervisor must be identified. If a formal offer of admission to the PhD is made, this will usually be conditional and depend upon you meeting several conditions including your performance in the MPhil, as well as on providing evidence of your ability to fund your PhD studies. 

The Postgraduate Virtual Open Day usually takes place at the end of October. It’s a great opportunity to ask questions to admissions staff and academics, explore the Colleges virtually, and to find out more about courses, the application process and funding opportunities. Visit the  Postgraduate Open Day  page for more details.

See further the  Postgraduate Admissions Events  pages for other events relating to Postgraduate study, including study fairs, visits and international events.

Key Information

12 months full-time, 2 years part-time, study mode : research, master of philosophy, department of plant sciences, course - related enquiries, application - related enquiries, course on department website, dates and deadlines:, lent 2024 (closed).

Some courses can close early. See the Deadlines page for guidance on when to apply.

Easter 2024 (Closed)

Michaelmas 2024 (closed), easter 2025, funding deadlines.

These deadlines apply to applications for courses starting in Michaelmas 2024, Lent 2025 and Easter 2025.

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• URL: https://guides.library.ttu.edu/plant-and-soil-sciences

Plant Sciences Major

The Plant Sciences major is designed for students who are interested in a scientific understanding of how plants grow and develop in managed agricultural ecosystems and how plant products are utilized for food, fiber and environmental enhancement. Advances in science and technology have provided new insights and options for using plants to address the issues associated with providing renewable food, fiber and energy resources for a growing global population while minimizing adverse impacts on the natural environment. Graduates in Plant Sciences are able to apply their skills and knowledge to a diverse range of agricultural and environmental goals or pursue advanced degrees in plant sciences.

The Program.  The curriculum provides depth in the biological and physical sciences and a sound understanding of how plants obtain and utilize resources from their environment to sustain their growth and development. The influences of genetics, management systems and environmental inputs on crop development and productivity are emphasized along with the postharvest preservation and marketing of plant products. Students will develop an area of specialization with options in Crop Production, Plant Genetics and Breeding, or Postharvest Biology and Technology. An Individual option is also available to match specific subject matter or career goal interests in the plant sciences. All students gain practical experience through a combination of practical laboratory courses and internships. Students may also pursue an Honors thesis in their senior year.

Three specific options and one individualized option are offered in the Plant Sciences major. Each of these requires approximately 25-30 additional units of course work in the specified areas.

Choose one of six tracks as your area of specialization within the Plant Sciences major:

• Plant breeding, genetics & genomics Problem: Evolving pests and diseases, labor shortages, declining soil and water quality, rising temperatures and declining nutritional content threaten our food production systems and food security. You will: Use genetics, biotechnology and breeding techniques to develop new varieties of plants useful to farmers, ranchers, gardeners, foresters and consumers, while understanding the societal impacts of crop improvement.  
• Crop production & agroecology Problem: Global food production is growing, but the price is high: climate-warming gases, pollution to land and water, habitat destruction and loss of biodiversity. You will: Learn to see agriculture as a living web of diverse ecological interactions that is part of the solution to these problems. You’ll use core principles of plant biology and plant-environment interactions to design sustainable and resilient crop production systems in a global context. You’ll have options for further emphasis on plant nutrition, soils, pests and global food systems.  
• Plant informatics, sensing & data Problem: New, data-driven technologies in natural resource management are creating a need for trained professionals and researchers who can harness the exciting tools available to us. You will: Use drones, weather stations, satellites, artificial intelligence, machine learning and genetic information to model and monitor plant growth, nutrition, disease risk, pest development and scheduling to manage our natural resources.  
• Environmental horticulture & urban landscapes Problem: Climate, environmental and social changes threaten the livability of our cities and towns. We urgently need nature-based solutions to increase urban resilience and sustainability. You will: Learn the art and science of developing, producing and using plants that give us beauty, shade, resources and connection to the earth, ourselves and each other.  
• Ecological management & restoration Problem: Unbalanced management of natural ecosystems can impair the social, economic, and ecological services these landscapes provide to society. You will: Learn the ecology and management of non-farm environments to restore, maintain and improve their ability to nourish and protect us and the other creatures that live there, while balancing goals of land uses such as agricultural production with conservation objectives.  
• Crop quality & safety Problem: More than 1/3 of the food we produce gets wasted worldwide, while a billion people go hungry and more are poorly nourished. You will: Use basic and applied knowledge about plant sciences, engineering and economics to provide fresh, nutritious, safe, affordable, readily available and delicious food to all people.

General Catalog (publicly accessible)

Program Learning Outcomes:

Graduates of the Plant Sciences major should be able to:

I. Analyze how plants grow and develop

II. Identify plant characteristics and describe the role of the environment, genetics, evolution and breeding

III. Describe the movement of water, nutrients and energy through the biosphere and evaluate the impact of human management on these processes

IV. Critically evaluate options for sustainable plant management, including natural, urban and small and large scale production systems

V. Apply the scientific method and collect, manage, analyze and interpret data

VI. Communicate effectively in speaking and writing

VII. Work collaboratively in a team setting in both a leadership and team member role

VIII. Demonstrate personal and social responsibility

Career Alternatives.  Graduates from this program are prepared to pursue a wide range of careers, including various technical and management positions in agricultural and business enterprises, farming, or consulting; public, private, and non-profit agencies; Cooperative Extension; international development; teaching; or agricultural and environmental journalism and communication services. Graduates are qualified to pursue graduate studies in the natural and agricultural sciences, such as plant biology, genetics, breeding, horticulture, agronomy, biotechnology, ecology, environmental studies, pest management, education, or business management.

Plant Sciences Honors Thesis

The honors thesis in Plant Sciences can be an enriching experience during your undergrad program at UC Davis, as well as a competitive edge when applying for graduate schools, careers, and professional development trainings. Below is a listed sequence of courses for the Plant Sciences honors track, which should commence during Spring quarter of Junior year. Students who are already enrolled in the University Honors Program can also follow the sequence below during their 4th year of the program.

Plant Sciences Honors Thesis Course Sequence

• PLS 188 (3 units), Spring Quarter , preferably Junior year - Undergraduate Research Proposal Lecture/discussion— Lecture/discussion—3 hours. Prerequisite: upper division standing. Preparation and review of a scientific proposal. Problem definition, identification of objectives, literature survey, hypothesis generation, design of experiments, data analysis planning, proposal outline and preparation .
• PLS 189L (2-5 units), FWSp Quarters - Individual Research Laboratory—3-12 hours; discussion—1 hour. Prerequisite: course 188 and consent of instructor. Formulating experimental approaches to current questions in plant sciences ; performance of proposed experiments.
• PLS 194H (1-2 units), FWSpSu Quarters - Honors Thesis Independent Study—3-6 hours. Prerequisite: senior standing, over GPA of 3.250 or higher and consent of master adviser. Independent study of selected topics under the direction of a member or members of the staff. Completion will involve the writing of a senior thesis.

Major Advisor

Please contact [email protected] for advising support.

Faculty Advisor

Dan Potter 530-754-6141

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MSc and BSc thesis topics at HPP

Wageningen University students can do their BSc and/or MSc thesis in collaboration with the Horticulture and Product Physiology (HPP) group. Below you can find a variety of possible thesis subjects. On the subpage of each of the three research themes, you can find a list of possible thesis projects.

Interested in doing a BSc or MSc thesis at HPP? Please contact the HPP student coordinator Katharina Hanika. General information can also be found in the documents on the righthand side and thesis topics are also listed in the Internship and thesis projects database .

Photosynthesis

Photosynthesis is the driving force for most trophic systems on Earth. Basic research is conducted into understanding the regulation and limitations of photosynthesis. To do this, instrumentation that allows to look at the biophysical engine of photosynthesis inside the leaf, is developed and used. In addition, source/sink relationships are studied as optimal photosynthesis will only lead to optimal growth and quality if attention is paid to balancing the source and sink strength in plants.

Morphogenesis

The function and structure of a plant show strong interactions. These interactions are studied in an integrated way. The morphogenesis of plants is at least as important as leaf photosynthesis in determining plant growth. Furthermore, the morphology of the plant is a very important determinant of the quality of many horticultural (ornamental) plants.

Product physiology

The quality of a fruit, a flower, or a pot plant does not end when it leaves the grower. Neither does it start when it is bought by for instance a trading company. Quality is studied as a continuous process from cultivation through the post-harvest phase until the product is used by the consumers. Furthermore, in order to control product quality HPP not only aims to understand the processes underlying product quality but it also develops methodologies for monitoring product quality.

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Master Program Plant Sciences

The Faculty of Biology of the LMU Munich offers a comprehensive two-year-Master’s program in Plant Sciences for graduates with a background in biology (B.Sc. in biology or equivalent). The master’s program plant sciences at the LMU Biocenter links classical botanical disciplines to modern molecular methods and is oriented towards highly motivated and globally diverse next-generation researchers with a strong interest in molecular and adaptation mechanisms, and evolution of plants. The program is taught in English and it offers state-of-the-art education in four main topics: “Plant Molecular Biology” , “ Plant Cell Biology ”, “ Systematics ” and “ Biotic interactions of plants ”. Our aim is to offer our students comprehensive preparation for meeting future challenges in science-related professional careers within the field of plant sciences.

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Meta-Research Articles feature data-driven examinations of the methods, reporting, verification, and evaluation of scientific research.

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Assessing the evolution of research topics in a biological field using plant science as an example

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of America, Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, Michigan, United States of America, DOE-Great Lake Bioenergy Research Center, Michigan State University, East Lansing, Michigan, United States of America

Roles Conceptualization, Investigation, Project administration, Supervision, Writing – review & editing

Affiliation Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of America

• Shin-Han Shiu, 
• Melissa D. Lehti-Shiu

• Published: May 23, 2024
• https://doi.org/10.1371/journal.pbio.3002612
• Peer Review
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Scientific advances due to conceptual or technological innovations can be revealed by examining how research topics have evolved. But such topical evolution is difficult to uncover and quantify because of the large body of literature and the need for expert knowledge in a wide range of areas in a field. Using plant biology as an example, we used machine learning and language models to classify plant science citations into topics representing interconnected, evolving subfields. The changes in prevalence of topical records over the last 50 years reflect shifts in major research trends and recent radiation of new topics, as well as turnover of model species and vastly different plant science research trajectories among countries. Our approaches readily summarize the topical diversity and evolution of a scientific field with hundreds of thousands of relevant papers, and they can be applied broadly to other fields.

Citation: Shiu S-H, Lehti-Shiu MD (2024) Assessing the evolution of research topics in a biological field using plant science as an example. PLoS Biol 22(5): e3002612. https://doi.org/10.1371/journal.pbio.3002612

Academic Editor: Ulrich Dirnagl, Charite Universitatsmedizin Berlin, GERMANY

Received: October 16, 2023; Accepted: April 4, 2024; Published: May 23, 2024

Copyright: © 2024 Shiu, Lehti-Shiu. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The plant science corpus data are available through Zenodo ( https://zenodo.org/records/10022686 ). The codes for the entire project are available through GitHub ( https://github.com/ShiuLab/plant_sci_hist ) and Zenodo ( https://doi.org/10.5281/zenodo.10894387 ).

Funding: This work was supported by the National Science Foundation (IOS-2107215 and MCB-2210431 to MDL and SHS; DGE-1828149 and IOS-2218206 to SHS), Department of Energy grant Great Lakes Bioenergy Research Center (DE-SC0018409 to SHS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: BERT, Bidirectional Encoder Representations from Transformers; br, brassinosteroid; ccTLD, country code Top Level Domain; c-Tf-Idf, class-based Tf-Idf; ChatGPT, Chat Generative Pretrained Transformer; ga, gibberellic acid; LOWESS, locally weighted scatterplot smoothing; MeSH, Medical Subject Heading; SHAP, SHapley Additive exPlanations; SJR, SCImago Journal Rank; Tf-Idf, Term frequency-Inverse document frequency; UMAP, Uniform Manifold Approximation and Projection

Introduction

The explosive growth of scientific data in recent years has been accompanied by a rapidly increasing volume of literature. These records represent a major component of our scientific knowledge and embody the history of conceptual and technological advances in various fields over time. Our ability to wade through these records is important for identifying relevant literature for specific topics, a crucial practice of any scientific pursuit [ 1 ]. Classifying the large body of literature into topics can provide a useful means to identify relevant literature. In addition, these topics offer an opportunity to assess how scientific fields have evolved and when major shifts in took place. However, such classification is challenging because the relevant articles in any topic or domain can number in the tens or hundreds of thousands, and the literature is in the form of natural language, which takes substantial effort and expertise to process [ 2 , 3 ]. In addition, even if one could digest all literature in a field, it would still be difficult to quantify such knowledge.

In the last several years, there has been a quantum leap in natural language processing approaches due to the feasibility of building complex deep learning models with highly flexible architectures [ 4 , 5 ]. The development of large language models such as Bidirectional Encoder Representations from Transformers (BERT; [ 6 ]) and Chat Generative Pretrained Transformer (ChatGPT; [ 7 ]) has enabled the analysis, generation, and modeling of natural language texts in a wide range of applications. The success of these applications is, in large part, due to the feasibility of considering how the same words are used in different contexts when modeling natural language [ 6 ]. One such application is topic modeling, the practice of establishing statistical models of semantic structures underlying a document collection. Topic modeling has been proposed for identifying scientific hot topics over time [ 1 ], for example, in synthetic biology [ 8 ], and it has also been applied to, for example, automatically identify topical scenes in images [ 9 ] and social network topics [ 10 ], discover gene programs highly correlated with cancer prognosis [ 11 ], capture “chromatin topics” that define cell-type differences [ 12 ], and investigate relationships between genetic variants and disease risk [ 13 ]. Here, we use topic modeling to ask how research topics in a scientific field have evolved and what major changes in the research trends have taken place, using plant science as an example.

Plant science corpora allow classification of major research topics

Plant science, broadly defined, is the study of photosynthetic species, their interactions with biotic/abiotic environments, and their applications. For modeling plant science topical evolution, we first identified a collection of plant science documents (i.e., corpus) using a text classification approach. To this end, we first collected over 30 million PubMed records and narrowed down candidate plant science records by searching for those with plant-related terms and taxon names (see Materials and methods ). Because there remained a substantial number of false positives (i.e., biomedical records mentioning plants in passing), a set of positive plant science examples from the 17 plant science journals with the highest numbers of plant science publications covering a wide range of subfields and a set of negative examples from journals with few candidate plant science records were used to train 4 types of text classification models (see Materials and methods ). The best text classification model performed well (F1 = 0.96, F1 of a naïve model = 0.5, perfect model = 1) where the positive and negative examples were clearly separated from each other based on prediction probability of the hold-out testing dataset (false negative rate = 2.6%, false positive rate = 5.2%, S1A and S1B Fig ). The false prediction rate for documents from the 17 plant science journals annotated with the Medical Subject Heading (MeSH) term “Plants” in NCBI was 11.7% (see Materials and methods ). The prediction probability distribution of positive instances with the MeSH term has an expected left-skew to lower values ( S1C Fig ) compared with the distributions of all positive instances ( S1A Fig ). Thus, this subset with the MeSH term is a skewed representation of articles from these 17 major plant science journals. To further benchmark the validity of the plant science records, we also conducted manual annotation of 100 records where the false positive and false negative rates were 14.6% and 10.6%, respectively (see Materials and methods ). Using 12 other plant science journals not included as positive examples as benchmarks, the false negative rate was 9.9% (see Materials and methods ). Considering the range of false prediction rate estimates with different benchmarks, we should emphasize that the model built with the top 17 plant science journals represents a substantial fraction of plant science publications but with biases. Applying the model to the candidate plant science record led to 421,658 positive predictions, hereafter referred to as “plant science records” ( S1D Fig and S1 Data ).

To better understand how the models classified plant science articles, we identified important terms from a more easily interpretable model (Term frequency-Inverse document frequency (Tf-Idf) model; F1 = 0.934) using Shapley Additive Explanations [ 14 ]; 136 terms contributed to predicting plant science records (e.g., Arabidopsis, xylem, seedling) and 138 terms contributed to non-plant science record predictions (e.g., patients, clinical, mice; Tf-Idf feature sheet, S1 Data ). Plant science records as well as PubMed articles grew exponentially from 1950 to 2020 ( Fig 1A ), highlighting the challenges of digesting the rapidly expanding literature. We used the plant science records to perform topic modeling, which consisted of 4 steps: representing each record as a BERT embedding, reducing dimensionality, clustering, and identifying the top terms by calculating class (i.e., topic)-based Tf-Idf (c-Tf-Idf; [ 15 ]). The c-Tf-Idf represents the frequency of a term in the context of how rare the term is to reduce the influence of common words. SciBERT [ 16 ] was the best model among those tested ( S2 Data ) and was used for building the final topic model, which classified 372,430 (88.3%) records into 90 topics defined by distinct combinations of terms ( S3 Data ). The topics contained 620 to 16,183 records and were named after the top 4 to 5 terms defining the topical areas ( Fig 1B and S3 Data ). For example, the top 5 terms representing the largest topic, topic 61 (16,183 records), are “qtl,” “resistance,” “wheat,” “markers,” and “traits,” which represent crop improvement studies using quantitative genetics.

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(A) Numbers of PubMed (magenta) and plant science (green) records between 1950 and 2020. (a, b, c) Coefficients of the exponential function, y = ae b . Data for the plot are in S1 Data . (B) Numbers of documents for the top 30 plant science topics. Each topic is designated by an index number (left) and the top 4–6 terms with the highest cTf-Idf values (right). Data for the plot are in S3 Data . (C) Two-dimensional representation of the relationships between plant science records generated by Uniform Manifold Approximation and Projection (UMAP, [ 17 ]) using SciBERT embeddings of plant science records. All topics panel: Different topics are assigned different colors. Outlier panel: UMAP representation of all records (gray) with outlier records in red. Blue dotted circles: areas with relatively high densities indicating topics that are below the threshold for inclusion in a topic. In the 8 UMAP representations on the right, records for example topics are in red and the remaining records in gray. Blue dotted circles indicate the relative position of topic 48.

https://doi.org/10.1371/journal.pbio.3002612.g001

Records with assigned topics clustered into distinct areas in a two-dimensional (2D) space ( Fig 1C , for all topics, see S4 Data ). The remaining 49,228 outlier records not assigned to any topic (11.7%, middle panel, Fig 1C ) have 3 potential sources. First, some outliers likely belong to unique topics but have fewer records than the threshold (>500, blue dotted circles, Fig 1C ). Second, some of the many outliers dispersed within the 2D space ( Fig 1C ) were not assigned to any single topic because they had relatively high prediction scores for multiple topics ( S2 Fig ). These likely represent studies across subdisciplines in plant science. Third, some outliers are likely interdisciplinary studies between plant science and other domains, such as chemistry, mathematics, and physics. Such connections can only be revealed if records from other domains are included in the analyses.

Topical clusters reveal closely related topics but with distinct key term usage

Related topics tend to be located close together in the 2D representation (e.g., topics 48 and 49, Fig 1C ). We further assessed intertopical relationships by determining the cosine similarities between topics using cTf-Idfs ( Figs 2A and S3 ). In this topic network, some topics are closely related and form topic clusters. For example, topics 25, 26, and 27 collectively represent a more general topic related to the field of plant development (cluster a , lower left in Fig 2A ). Other topic clusters represent studies of stress, ion transport, and heavy metals ( b ); photosynthesis, water, and UV-B ( c ); population and community biology (d); genomics, genetic mapping, and phylogenetics ( e , upper right); and enzyme biochemistry ( f , upper left in Fig 2A ).

(A) Graph depicting the degrees of similarity (edges) between topics (nodes). Between each topic pair, a cosine similarity value was calculated using the cTf-Idf values of all terms. A threshold similarity of 0.6 was applied to illustrate the most related topics. For the full matrix presented as a heatmap, see S4 Fig . The nodes are labeled with topic index numbers and the top 4–6 terms. The colors and width of the edges are defined based on cosine similarity. Example topic clusters are highlighted in yellow and labeled a through f (blue boxes). (B, C) Relationships between the cTf-Idf values (see S3 Data ) of the top terms for topics 26 and 27 (B) and for topics 25 and 27 (C) . Only terms with cTf-Idf ≥ 0.6 are labeled. Terms with cTf-Idf values beyond the x and y axis limit are indicated by pink arrows and cTf-Idf values. (D) The 2D representation in Fig 1C is partitioned into graphs for different years, and example plots for every 5-year period since 1975 are shown. Example topics discussed in the text are indicated. Blue arrows connect the areas occupied by records of example topics across time periods to indicate changes in document frequencies.

https://doi.org/10.1371/journal.pbio.3002612.g002

Topics differed in how well they were connected to each other, reflecting how general the research interests or needs are (see Materials and methods ). For example, topic 24 (stress mechanisms) is the most well connected with median cosine similarity = 0.36, potentially because researchers in many subfields consider aspects of plant stress even though it is not the focus. The least connected topics include topic 21 (clock biology, 0.12), which is surprising because of the importance of clocks in essentially all aspects of plant biology [ 18 ]. This may be attributed, in part, to the relatively recent attention in this area.

Examining topical relationships and the cTf-Idf values of terms also revealed how related topics differ. For example, topic 26 is closely related to topics 27 and 25 (cluster a on the lower left of Fig 2A ). Topics 26 and 27 both contain records of developmental process studies mainly in Arabidopsis ( Fig 2B ); however, topic 26 is focused on the impact of light, photoreceptors, and hormones such as gibberellic acids (ga) and brassinosteroids (br), whereas topic 27 is focused on flowering and floral development. Topic 25 is also focused on plant development but differs from topic 27 because it contains records of studies mainly focusing on signaling and auxin with less emphasis on Arabidopsis ( Fig 2C ). These examples also highlight the importance of using multiple top terms to represent the topics. The similarities in cTf-Idfs between topics were also useful for measuring the editorial scope (i.e., diverse, or narrow) of journals publishing plant science papers using a relative topic diversity measure (see Materials and methods ). For example, Proceedings of the National Academy of Sciences , USA has the highest diversity, while Theoretical and Applied Genetics has the lowest ( S4 Fig ). One surprise is the relatively low diversity of American Journal of Botany , which focuses on plant ecology, systematics, development, and genetics. The low diversity is likely due to the relatively larger number of cellular and molecular science records in PubMed, consistent with the identification of relatively few topical areas relevant to studies at the organismal, population, community, and ecosystem levels.

Investigation of the relative prevalence of topics over time reveals topical succession

We next asked whether relationships between topics reflect chronological progression of certain subfields. To address this, we assessed how prevalent topics were over time using dynamic topic modeling [ 19 ]. As shown in Fig 2D , there is substantial fluctuation in where the records are in the 2D space over time. For example, topic 44 (light, leaves, co, synthesis, photosynthesis) is among the topics that existed in 1975 but has diminished gradually since. In 1985, topic 39 (Agrobacterium-based transformation) became dense enough to be visualized. Additional examples include topics 79 (soil heavy metals), 42 (differential expression), and 82 (bacterial community metagenomics), which became prominent in approximately 2005, 2010, and 2020, respectively ( Fig 2D ). In addition, animating the document occupancy in the 2D space over time revealed a broad change in patterns over time: Some initially dense areas became sparse over time and a large number of topics in areas previously only loosely occupied at the turn of the century increased over time ( S5 Data ).

While the 2D representations reveal substantial details on the evolution of topics, comparison over time is challenging because the number of plant science records has grown exponentially ( Fig 1A ). To address this, the records were divided into 50 chronological bins each with approximately 8,400 records to make cross-bin comparisons feasible ( S6 Data ). We should emphasize that, because of the way the chronological bins were split, the number of records for each topic in each bin should be treated as a normalized value relative to all other topics during the same period. Examining this relative prevalence of topics across bins revealed a clear pattern of topic succession over time (one topic evolved into another) and the presence of 5 topical categories ( Fig 3 ). The topics were categorized based on their locally weighted scatterplot smoothing (LOWESS) fits and ordered according to timing of peak frequency ( S7 and S8 Data , see Materials and methods ). In Fig 3 , the relative decrease in document frequency does not mean that research output in a topic is dwindling. Because each row in the heatmap is normalized based on the minimum and maximum values within each topic, there still can be substantial research output in terms of numbers of publications even when the relative frequency is near zero. Thus, a reduced relative frequency of a topic reflects only a below-average growth rate compared with other topical areas.

(A-E) A heat map of relative topic frequency over time reveals 5 topical categories: (A) stable, (B) early, (C) transitional, (D) sigmoidal, and (E) rising. The x axis denotes different time bins with each bin containing a similar number of documents to account for the exponential growth of plant science records over time. The sizes of all bins except the first are drawn to scale based on the beginning and end dates. The y axis lists different topics denoted by the label and top 4 to 5 terms. In each cell, the prevalence of a topic in a time bin is colored according to the min-max normalized cTf-Idf values for that topic. Light blue dotted lines delineate different decades. The arrows left of a subset of topic labels indicate example relationships between topics in topic clusters. Blue boxes with labels a–f indicate topic clusters, which are the same as those in Fig 2 . Connecting lines indicate successional trends. Yellow circles/lines 1 – 3: 3 major transition patterns. The original data are in S5 Data .

https://doi.org/10.1371/journal.pbio.3002612.g003

The first topical category is a stable category with 7 topics mostly established before the 1980s that have since remained stable in terms of prevalence in the plant science records (top of Fig 3A ). These topics represent long-standing plant science research foci, including studies of plant physiology (topics 4, 58, and 81), genetics (topic 61), and medicinal plants (topic 53). The second category contains 8 topics established before the 1980s that have mostly decreased in prevalence since (the early category, Fig 3B ). Two examples are physiological and morphological studies of hormone action (topic 45, the second in the early category) and the characterization of protein, DNA, and RNA (topic 18, the second to last). Unlike other early topics, topic 78 (paleobotany and plant evolution studies, the last topic in Fig 3B ) experienced a resurgence in the early 2000s due to the development of new approaches and databases and changes in research foci [ 20 ].

The 33 topics in the third, transitional category became prominent in the 1980s, 1990s, or even 2000s but have clearly decreased in prevalence ( Fig 3C ). In some cases, the early and the transitional topics became less prevalent because of topical succession—refocusing of earlier topics led to newer ones that either show no clear sign of decrease (the sigmoidal category, Fig 3D ) or continue to increase in prevalence (the rising category, Fig 3E ). Consistent with the notion of topical succession, topics within each topic cluster ( Fig 2 ) were found across topic categories and/or were prominent at different time periods (indicated by colored lines linking topics, Fig 3 ). One example is topics in topic cluster b (connected with light green lines and arrows, compare Figs 2 and 3 ); the study of cation transport (topic 47, the third in the transitional category), prominent in the 1980s and early 1990s, is connected to 5 other topics, namely, another transitional topic 29 (cation channels and their expression) peaking in the 2000s and early 2010s, sigmoidal topics 24 and 28 (stress response, tolerance mechanisms) and 30 (heavy metal transport), which rose to prominence in mid-2000s, and the rising topic 42 (stress transcriptomic studies), which increased in prevalence in the mid-2010s.

The rise and fall of topics can be due to a combination of technological or conceptual breakthroughs, maturity of the field, funding constraints, or publicity. The study of transposable elements (topic 62) illustrates the effect of publicity; the rise in this field coincided with Barbara McClintock’s 1983 Nobel Prize but not with the publication of her studies in the 1950s [ 21 ]. The reduced prevalence in early 2000 likely occurred in part because analysis of transposons became a central component of genome sequencing and annotation studies, rather than dedicated studies. In addition, this example indicates that our approaches, while capable of capturing topical trends, cannot be used to directly infer major papers leading to the growth of a topic.

Three major topical transition patterns signify shifts in research trends

Beyond the succession of specific topics, 3 major transitions in the dynamic topic graph should be emphasized: (1) the relative decreasing trend of early topics in the late 1970s and early 1980s; (2) the rise of transitional topics in late 1980s; and (3) the relative decreasing trend of transitional topics in the late 1990s and early 2000s, which coincided with a radiation of sigmoidal and rising topics (yellow circles, Fig 3 ). The large numbers of topics involved in these transitions suggest major shifts in plant science research. In transition 1, early topics decreased in relative prevalence in the late 1970s to early 1980s, which coincided with the rise of transitional topics over the following decades (circle 1, Fig 3 ). For example, there was a shift from the study of purified proteins such as enzymes (early topic 48, S5A Fig ) to molecular genetic dissection of genes, proteins, and RNA (transitional topic 35, S5B Fig ) enabled by the wider adoption of recombinant DNA and molecular cloning technologies in late 1970s [ 22 ]. Transition 2 (circle 2, Fig 3 ) can be explained by the following breakthroughs in the late 1980s: better approaches to create transgenic plants and insertional mutants [ 23 ], more efficient creation of mutant plant libraries through chemical mutagenesis (e.g., [ 24 ]), and availability of gene reporter systems such as β-glucuronidase [ 25 ]. Because of these breakthroughs, molecular genetics studies shifted away from understanding the basic machinery to understanding the molecular underpinnings of specific processes, such as molecular mechanisms of flower and meristem development and the action of hormones such as auxin (topic 27, S5C Fig ); this type of research was discussed as a future trend in 1988 [ 26 ] and remains prevalent to this date. Another example is gene silencing (topic 12), which became a focal area of study along with the widespread use of transgenic plants [ 27 ].

Transition 3 is the most drastic: A large number of transitional, sigmoidal, and rising topics became prevalent nearly simultaneously at the turn of the century (circle 3, Fig 3 ). This period also coincides with a rapid increase in plant science citations ( Fig 1A ). The most notable breakthroughs included the availability of the first plant genome in 2000 [ 28 ], increasing ease and reduced cost of high-throughput sequencing [ 29 ], development of new mass spectrometry–based platforms for analyzing proteins [ 30 ], and advancements in microscopic and optical imaging approaches [ 31 ]. Advances in genomics and omics technology also led to an increase in stress transcriptomics studies (42, S5D Fig ) as well as studies in many other topics such as epigenetics (topic 11), noncoding RNA analysis (13), genomics and phylogenetics (80), breeding (41), genome sequencing and assembly (60), gene family analysis (23), and metagenomics (82 and 55).

In addition to the 3 major transitions across all topics, there were also transitions within topics revealed by examining the top terms for different time bins (heatmaps, S5 Fig ). Taken together, these observations demonstrate that knowledge about topical evolution can be readily revealed through topic modeling. Such knowledge is typically only available to experts in specific areas and is difficult to summarize manually, as no researcher has a command of the entire plant science literature.

Analysis of taxa studied reveals changes in research trends

Changes in research trends can also be illustrated by examining changes in the taxa being studied over time ( S9 Data ). There is a strong bias in the taxa studied, with the record dominated by research models and economically important taxa ( S6 Fig ). Flowering plants (Magnoliopsida) are found in 93% of records ( S6A Fig ), and the mustard family Brassicaceae dominates at the family level ( S6B Fig ) because the genus Arabidopsis contributes to 13% of plant science records ( Fig 4A ). When examining the prevalence of taxa being studied over time, clear patterns of turnover emerged similar to topical succession ( Figs 4B , S6C, and S6D ; Materials and methods ). Given that Arabidopsis is mentioned in more publications than other species we analyzed, we further examined the trends for Arabidopsis publications. The increase in the normalized number (i.e., relative to the entire plant science corpus) of Arabidopsis records coincided with advocacy of its use as a model system in the late 1980s [ 32 ]. While it remains a major plant model, there has been a decrease in overall Arabidopsis publications relative to all other plant science publications since 2011 (blue line, normalized total, Fig 4C ). Because the same chronological bins, each with same numbers of records, from the topic-over-time analysis ( Fig 3 ) were used, the decrease here does not mean that there were fewer Arabidopsis publications—in fact, the number of Arabidopsis papers has remained steady since 2011. This decrease means that Arabidopsis-related publications represent a relatively smaller proportion of plant science records. Interestingly, this decrease took place much earlier (approximately 2005) and was steeper in the United States (red line, Fig 4C ) than in all countries combined (blue line, Fig 4C ).

(A) Percentage of records mentioning specific genera. (B) Change in the prevalence of genera in plant science records over time. (C) Changes in the normalized numbers of all records (blue) and records from the US (red) mentioning Arabidopsis over time. The lines are LOWESS fits with fraction parameter = 0.2. (D) Topical over (red) and under (blue) representation among 5 genera with the most plant science records. LLR: log 2 likelihood ratios of each topic in each genus. Gray: topic-species combination not significantly enriched at the 5% level based on enrichment p -values adjusted for multiple testing with the Benjamini–Hochberg method [ 33 ]. The data used for plotting are in S9 Data . The statistics for all topics are in S10 Data .

https://doi.org/10.1371/journal.pbio.3002612.g004

Assuming that the normalized number of publications reflects the relative intensity of research activities, one hypothesis for the relative decrease in focus on Arabidopsis is that advances in, for example, plant transformation, genetic manipulation, and genome research have allowed the adoption of more previously nonmodel taxa. Consistent with this, there was a precipitous increase in the number of genera being published in the mid-90s to early 2000s during which approaches for plant transgenics became established [ 34 ], but the number has remained steady since then ( S7A Fig ). The decrease in the proportion of Arabidopsis papers is also negatively correlated with the timing of an increase in the number of draft genomes ( S7B Fig and S9 Data ). It is plausible that genome availability for other species may have contributed to a shift away from Arabidopsis. Strikingly, when we analyzed US National Science Foundation records, we found that the numbers of funded grants mentioning Arabidopsis ( S7C Fig ) have risen and fallen in near perfect synchrony with the normalized number of Arabidopsis publication records (red line, Fig 4C ). This finding likely illustrates the impact of funding on Arabidopsis research.

By considering both taxa information and research topics, we can identify clear differences in the topical areas preferred by researchers using different plant taxa ( Fig 4D and S10 Data ). For example, studies of auxin/light signaling, the circadian clock, and flowering tend to be carried out in Arabidopsis, while quantitative genetic studies of disease resistance tend to be done in wheat and rice, glyphosate research in soybean, and RNA virus research in tobacco. Taken together, joint analyses of topics and species revealed additional details about changes in preferred models over time, and the preferred topical areas for different taxa.

Countries differ in their contributions to plant science and topical preference

We next investigated whether there were geographical differences in topical preference among countries by inferring country information from 330,187 records (see Materials and methods ). The 10 countries with the most records account for 73% of the total, with China and the US contributing to approximately 18% each ( Fig 5A ). The exponential growth in plant science records (green line, Fig 1A ) was in large part due to the rapid rise in annual record numbers in China and India ( Fig 5B ). When we examined the publication growth rates using the top 17 plant science journals, the general patterns remained the same ( S7D Fig ). On the other hand, the US, Japan, Germany, France, and Great Britain had slower rates of growth compared with all non-top 10 countries. The rapid increase in records from China and India was accompanied by a rapid increase in metrics measuring journal impact ( Figs 5C and S8 and S9 Data ). For example, using citation score ( Fig 5C , see Materials and methods ), we found that during a 22-year period China (dark green) and India (light green) rapidly approached the global average (y = 0, yellow), whereas some of the other top 10 countries, particularly the US (red) and Japan (yellow green), showed signs of decrease ( Fig 5C ). It remains to be determined whether these geographical trends reflect changes in priority, investment, and/or interest in plant science research.

(A) Numbers of plant science records for countries with the 10 highest numbers. (B) Percentage of all records from each of the top 10 countries from 1980 to 2020. (C) Difference in citation scores from 1999 to 2020 for the top 10 countries. (D) Shown for each country is the relationship between the citation scores averaged from 1999 to 2020 and the slope of linear fit with year as the predictive variable and citation score as the response variable. The countries with >400 records and with <10% missing impact values are included. Data used for plots (A–D) are in S11 Data . (E) Correlation in topic enrichment scores between the top 10 countries. PCC, Pearson’s correlation coefficient, positive in red, negative in blue. Yellow rectangle: countries with more similar topical preferences. (F) Enrichment scores (LLR, log likelihood ratio) of selected topics among the top 10 countries. Red: overrepresentation, blue: underrepresentation. Gray: topic-country combination that is not significantly enriched at the 5% level based on enrichment p -values adjusted for multiple testing with the Benjamini–Hochberg method (for all topics and plotting data, see S12 Data ).

https://doi.org/10.1371/journal.pbio.3002612.g005

Interestingly, the relative growth/decline in citation scores over time (measured as the slope of linear fit of year versus citation score) was significantly and negatively correlated with average citation score ( Fig 5D ); i.e., countries with lower overall metrics tended to experience the strongest increase in citation scores over time. Thus, countries that did not originally have a strong influence on plant sciences now have increased impact. These patterns were also observed when using H-index or journal rank as metrics ( S8 Fig and S11 Data ) and were not due to increased publication volume, as the metrics were normalized against numbers of records from each country (see Materials and methods ). In addition, the fact that different metrics with different caveats and assumptions yielded consistent conclusions indicates the robustness of our observations. We hypothesize that this may be a consequence of the ease in scientific communication among geographically isolated research groups. It could also be because of the prevalence of online journals that are open access, which makes scientific information more readily accessible. Or it can be due to the increasing international collaboration. In any case, the causes for such regression toward the mean are not immediately clear and should be addressed in future studies.

We also assessed how the plant research foci of countries differ by comparing topical preference (i.e., the degree of enrichment of plant science records in different topics) between countries. For example, Italy and Spain cluster together (yellow rectangle, Fig 5E ) partly because of similar research focusing on allergens (topic 0) and mycotoxins (topic 54) and less emphasis on gene family (topic 23) and stress tolerance (topic 28) studies ( Fig 5F , for the fold enrichment and corrected p -values of all topics, see S12 Data ). There are substantial differences in topical focus between countries ( S9 Fig ). For example, research on new plant compounds associated with herbal medicine (topic 69) is a focus in China but not in the US, but the opposite is true for population genetics and evolution (topic 86) ( Fig 5F ). In addition to revealing how plant science research has evolved over time, topic modeling provides additional insights into differences in research foci among different countries, which are informative for science policy considerations.

In this study, topic modeling revealed clear transitions among research topics, which represent shifts in research trends in plant sciences. One limitation of our study is the bias in the PubMed-based corpus. The cellular, molecular, and physiological aspects of plant sciences are well represented, but there are many fewer records related to evolution, ecology, and systematics. Our use of titles/abstracts from the top 17 plant science journals as positive examples allowed us to identify papers we typically see in these journals, but this may have led to us missing “outlier” articles, which may be the most exciting. Another limitation is the need to assign only one topic to a record when a study is interdisciplinary and straddles multiple topics. Furthermore, a limited number of large, inherently heterogeneous topics were summarized to provide a more concise interpretation, which undoubtedly underrepresents the diversity of plant science research. Despite these limitations, dynamic topic modeling revealed changes in plant science research trends that coincide with major shifts in biological science. While we were interested in identifying conceptual advances, our approach can identify the trend but the underlying causes for such trends, particularly key records leading to the growth in certain topics, still need to be identified. It also remains to be determined which changes in research trends lead to paradigm shifts as defined by Kuhn [ 35 ].

The key terms defining the topics frequently describe various technologies (e.g., topic 38/39: transformation, 40: genome editing, 59: genetic markers, 65: mass spectrometry, 69: nuclear magnetic resonance) or are indicative of studies enabled through molecular genetics and omics technologies (e.g., topic 8/60: genome, 11: epigenetic modifications, 18: molecular biological studies of macromolecules, 13: small RNAs, 61: quantitative genetics, 82/84: metagenomics). Thus, this analysis highlights how technological innovation, particularly in the realm of omics, has contributed to a substantial number of research topics in the plant sciences, a finding that likely holds for other scientific disciplines. We also found that the pattern of topic evolution is similar to that of succession, where older topics have mostly decreased in relative prevalence but appear to have been superseded by newer ones. One example is the rise of transcriptome-related topics and the correlated, reduced focus on regulation at levels other than transcription. This raises the question of whether research driven by technology negatively impacts other areas of research where high-throughput studies remain challenging.

One observation on the overall trends in plant science research is the approximately 10-year cycle in major shifts. One hypothesis is related to not only scientific advances but also to the fashion-driven aspect of science. Nonetheless, given that there were only 3 major shifts and the sample size is small, it is difficult to speculate as to why they happened. By analyzing the country of origin, we found that China and India have been the 2 major contributors to the growth in the plant science records in the last 20 years. Our findings also show an equalizing trend in global plant science where countries without a strong plant science publication presence have had an increased impact over the last 20 years. In addition, we identified significant differences in research topics between countries reflecting potential differences in investment and priorities. Such information is important for discerning differences in research trends across countries and can be considered when making policy decisions about research directions.

Materials and methods

Collection and preprocessing of a candidate plant science corpus.

For reproducibility purposes, a random state value of 20220609 was used throughout the study. The PubMed baseline files containing citation information ( ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/ ) were downloaded on November 11, 2021. To narrow down the records to plant science-related citations, a candidate citation was identified as having, within the titles and/or abstracts, at least one of the following words: “plant,” “plants,” “botany,” “botanical,” “planta,” and “plantarum” (and their corresponding upper case and plural forms), or plant taxon identifiers from NCBI Taxonomy ( https://www.ncbi.nlm.nih.gov/taxonomy ) or USDA PLANTS Database ( https://plants.sc.egov.usda.gov/home ). Note the search terms used here have nothing to do with the values of the keyword field in PubMed records. The taxon identifiers include all taxon names including and at taxonomic levels below “Viridiplantae” till the genus level (species names not used). This led to 51,395 search terms. After looking for the search terms, qualified entries were removed if they were duplicated, lacked titles and/or abstracts, or were corrections, errata, or withdrawn articles. This left 1,385,417 citations, which were considered the candidate plant science corpus (i.e., a collection of texts). For further analysis, the title and abstract for each citation were combined into a single entry. Text was preprocessed by lowercasing, removing stop-words (i.e., common words), removing non-alphanumeric and non-white space characters (except Greek letters, dashes, and commas), and applying lemmatization (i.e., grouping inflected forms of a word as a single word) for comparison. Because lemmatization led to truncated scientific terms, it was not included in the final preprocessing pipeline.

Definition of positive/negative examples

Upon closer examination, a large number of false positives were identified in the candidate plant science records. To further narrow down citations with a plant science focus, text classification was used to distinguish plant science and non-plant science articles (see next section). For the classification task, a negative set (i.e., non-plant science citations) was defined as entries from 7,360 journals that appeared <20 times in the filtered data (total = 43,329, journal candidate count, S1 Data ). For the positive examples (i.e., true plant science citations), 43,329 plant science citations (positive examples) were sampled from 17 established plant science journals each with >2,000 entries in the filtered dataset: “Plant physiology,” “Frontiers in plant science,” “Planta,” “The Plant journal: for cell and molecular biology,” “Journal of experimental botany,” “Plant molecular biology,” “The New phytologist,” “The Plant cell,” “Phytochemistry,” “Plant & cell physiology,” “American journal of botany,” “Annals of botany,” “BMC plant biology,” “Tree physiology,” “Molecular plant-microbe interactions: MPMI,” “Plant biology,” and “Plant biotechnology journal” (journal candidate count, S1 Data ). Plant biotechnology journal was included, but only 1,894 records remained after removal of duplicates, articles with missing info, and/or withdrawn articles. The positive and negative sets were randomly split into training and testing subsets (4:1) while maintaining a 1:1 positive-to-negative ratio.

Text classification based on Tf and Tf-Idf

Instead of using the preprocessed text as features for building classification models directly, text embeddings (i.e., representations of texts in vectors) were used as features. These embeddings were generated using 4 approaches (model summary, S1 Data ): Term-frequency (Tf), Tf-Idf [ 36 ], Word2Vec [ 37 ], and BERT [ 6 ]. The Tf- and Tf-Idf-based features were generated with CountVectorizer and TfidfVectorizer, respectively, from Scikit-Learn [ 38 ]. Different maximum features (1e4 to 1e5) and n-gram ranges (uni-, bi-, and tri-grams) were tested. The features were selected based on the p- value of chi-squared tests testing whether a feature had a higher-than-expected value among the positive or negative classes. Four different p- value thresholds were tested for feature selection. The selected features were then used to retrain vectorizers with the preprocessed training texts to generate feature values for classification. The classification model used was XGBoost [ 39 ] with 5 combinations of the following hyperparameters tested during 5-fold stratified cross-validation: min_child_weight = (1, 5, 10), gamma = (0.5, 1, 1.5, 2.5), subsample = (0.6, 0.8, 1.0), colsample_bytree = (0.6, 0.8, 1.0), and max_depth = (3, 4, 5). The rest of the hyperparameters were held constant: learning_rate = 0.2, n_estimators = 600, objective = binary:logistic. RandomizedSearchCV from Scikit-Learn was used for hyperparameter tuning and cross-validation with scoring = F1-score.

Because the Tf-Idf model had a relatively high model performance and was relatively easy to interpret (terms are frequency-based, instead of embedding-based like those generated by Word2Vec and BERT), the Tf-Idf model was selected as input to SHapley Additive exPlanations (SHAP; [ 14 ]) to assess the importance of terms. Because the Tf-Idf model was based on XGBoost, a tree-based algorithm, the TreeExplainer module in SHAP was used to determine a SHAP value for each entry in the training dataset for each Tf-Idf feature. The SHAP value indicates the degree to which a feature positively or negatively affects the underlying prediction. The importance of a Tf-Idf feature was calculated as the average SHAP value of that feature among all instances. Because a Tf-Idf feature is generated based on a specific term, the importance of the Tf-Idf feature indicates the importance of the associated term.

Text classification based on Word2Vec

The preprocessed texts were first split into train, validation, and test subsets (8:1:1). The texts in each subset were converted to 3 n-gram lists: a unigram list obtained by splitting tokens based on the space character, or bi- and tri-gram lists built with Gensim [ 40 ]. Each n-gram list of the training subset was next used to fit a Skip-gram Word2Vec model with vector_size = 300, window = 8, min_count = (5, 10, or 20), sg = 1, and epochs = 30. The Word2Vec model was used to generate word embeddings for train, validate, and test subsets. In the meantime, a tokenizer was trained with train subset unigrams using Tensorflow [ 41 ] and used to tokenize texts in each subset and turn each token into indices to use as features for training text classification models. To ensure all citations had the same number of features (500), longer texts were truncated, and shorter ones were zero-padded. A deep learning model was used to train a text classifier with an input layer the same size as the feature number, an attention layer incorporating embedding information for each feature, 2 bidirectional Long-Short-Term-Memory layers (15 units each), a dense layer (64 units), and a final, output layer with 2 units. During training, adam, accuracy, and sparse_categorical_crossentropy were used as the optimizer, evaluation metric, and loss function, respectively. The training process lasted 30 epochs with early stopping if validation loss did not improve in 5 epochs. An F1 score was calculated for each n-gram list and min_count parameter combination to select the best model (model summary, S1 Data ).

Text classification based on BERT models

Two pretrained models were used for BERT-based classification: DistilBERT (Hugging face repository [ 42 ] model name and version: distilbert-base-uncased [ 43 ]) and SciBERT (allenai/scibert-scivocab-uncased [ 16 ]). In both cases, tokenizers were retrained with the training data. BERT-based models had the following architecture: the token indices (512 values for each token) and associated masked values as input layers, pretrained BERT layer (512 × 768) excluding outputs, a 1D pooling layer (768 units), a dense layer (64 units), and an output layer (2 units). The rest of the training parameters were the same as those for Word2Vec-based models, except training lasted for 20 epochs. Cross-validation F1-scores for all models were compared and used to select the best model for each feature extraction method, hyperparameter combination, and modeling algorithm or architecture (model summary, S1 Data ). The best model was the Word2Vec-based model (min_count = 20, window = 8, ngram = 3), which was applied to the candidate plant science corpus to identify a set of plant science citations for further analysis. The candidate plant science records predicted as being in the positive class (421,658) by the model were collectively referred to as the “plant science corpus.”

Plant science record classification

In PubMed, 1,384,718 citations containing “plant” or any plant taxon names (from the phylum to genus level) were considered candidate plant science citations. To further distinguish plant science citations from those in other fields, text classification models were trained using titles and abstracts of positive examples consisting of citations from 17 plant science journals, each with >2,000 entries in PubMed, and negative examples consisting of records from journals with fewer than 20 entries in the candidate set. Among 4 models tested the best model (built with Word2Vec embeddings) had a cross validation F1 of 0.964 (random guess F1 = 0.5, perfect model F1 = 1, S1 Data ). When testing the model using 17,330 testing set citations independent from the training set, the F1 remained high at 0.961.

We also conducted another analysis attempting to use the MeSH term “Plants” as a benchmark. Records with the MeSH term “Plants” also include pharmaceutical studies of plants and plant metabolites or immunological studies of plants as allergens in journals that are not generally considered plant science journals (e.g., Acta astronautica , International journal for parasitology , Journal of chromatography ) or journals from local scientific societies (e.g., Acta pharmaceutica Hungarica , Huan jing ke xue , Izvestiia Akademii nauk . Seriia biologicheskaia ). Because we explicitly labeled papers from such journals as negative examples, we focused on 4,004 records with the “Plants” MeSH term published in the 17 plant science journals that were used as positive instances and found that 88.3% were predicted as the positive class. Thus, based on the MeSH term, there is an 11.7% false prediction rate.

We also enlisted 5 plant science colleagues (3 advanced graduate students in plant biology and genetic/genome science graduate programs, 1 postdoctoral breeder/quantitative biologist, and 1 postdoctoral biochemist/geneticist) to annotate 100 randomly selected abstracts as a reviewer suggested. Each record was annotated by 2 colleagues. Among 85 entries where the annotations are consistent between annotators, 48 were annotated as negative but with 7 predicted as positive (false positive rate = 14.6%) and 37 were annotated as positive but with 4 predicted as negative (false negative rate = 10.8%). To further benchmark the performance of the text classification model, we identified another 12 journals that focus on plant science studies to use as benchmarks: Current opinion in plant biology (number of articles: 1,806), Trends in plant science (1,723), Functional plant biology (1,717), Molecular plant pathology (1,573), Molecular plant (1,141), Journal of integrative plant biology (1,092), Journal of plant research (1,032), Physiology and molecular biology of plants (830), Nature plants (538), The plant pathology journal (443). Annual review of plant biology (417), and The plant genome (321). Among the 12,611 candidate plant science records, 11,386 were predicted as positive. Thus, there is a 9.9% false negative rate.

Global topic modeling

BERTopic [ 15 ] was used for preliminary topic modeling with n-grams = (1,2) and with an embedding initially generated by DistilBERT, SciBERT, or BioBERT (dmis-lab/biobert-base-cased-v1.2; [ 44 ]). The embedding models converted preprocessed texts to embeddings. The topics generated based on the 3 embeddings were similar ( S2 Data ). However, SciBERT-, BioBERT-, and distilBERT-based embedding models had different numbers of outlier records (268,848, 293,790, and 323,876, respectively) with topic index = −1. In addition to generating the fewest outliers, the SciBERT-based model led to the highest number of topics. Therefore, SciBERT was chosen as the embedding model for the final round of topic modeling. Modeling consisted of 3 steps. First, document embeddings were generated with SentenceTransformer [ 45 ]. Second, a clustering model to aggregate documents into clusters using hdbscan [ 46 ] was initialized with min_cluster_size = 500, metric = euclidean, cluster_selection_method = eom, min_samples = 5. Third, the embedding and the initialized hdbscan model were used in BERTopic to model topics with neighbors = 10, nr_topics = 500, ngram_range = (1,2). Using these parameters, 90 topics were identified. The initial topic assignments were conservative, and 241,567 records were considered outliers (i.e., documents not assigned to any of the 90 topics). After assessing the prediction scores of all records generated from the fitted topic models, the 95-percentile score was 0.0155. This score was used as the threshold for assigning outliers to topics: If the maximum prediction score was above the threshold and this maximum score was for topic t , then the outlier was assigned to t . After the reassignment, 49,228 records remained outliers. To assess if some of the outliers were not assigned because they could be assigned to multiple topics, the prediction scores of the records were used to put records into 100 clusters using k- means. Each cluster was then assessed to determine if the outlier records in a cluster tended to have higher prediction scores across multiple topics ( S2 Fig ).

Topics that are most and least well connected to other topics

The most well-connected topics in the network include topic 24 (stress mechanisms, median cosine similarity = 0.36), topic 42 (genes, stress, and transcriptomes, 0.34), and topic 35 (molecular genetics, 0.32, all t test p -values < 1 × 10 −22 ). The least connected topics include topic 0 (allergen research, median cosine similarity = 0.12), topic 21 (clock biology, 0.12), topic 1 (tissue culture, 0.15), and topic 69 (identification of compounds with spectroscopic methods, 0.15; all t test p- values < 1 × 10 −24 ). Topics 0, 1, and 69 are specialized topics; it is surprising that topic 21 is not as well connected as explained in the main text.

Analysis of documents based on the topic model

Topical diversity among top journals with the most plant science records

Using a relative topic diversity measure (ranging from 0 to 10), we found that there was a wide range of topical diversity among 20 journals with the largest numbers of plant science records ( S3 Fig ). The 4 journals with the highest relative topical diversities are Proceedings of the National Academy of Sciences , USA (9.6), Scientific Reports (7.1), Plant Physiology (6.7), and PLOS ONE (6.4). The high diversities are consistent with the broad, editorial scopes of these journals. The 4 journals with the lowest diversities are American Journal of Botany (1.6), Oecologia (0.7), Plant Disease (0.7), and Theoretical and Applied Genetics (0.3), which reflects their discipline-specific focus and audience of classical botanists, ecologists, plant pathologists, and specific groups of geneticists.

Dynamic topic modeling

The codes for dynamic modeling were based on _topic_over_time.py in BERTopics and modified to allow additional outputs for debugging and graphing purposes. The plant science citations were binned into 50 subsets chronologically (for timestamps of bins, see S5 Data ). Because the numbers of documents increased exponentially over time, instead of dividing them based on equal-sized time intervals, which would result in fewer records at earlier time points and introduce bias, we divided them into time bins of similar size (approximately 8,400 documents). Thus, the earlier time subsets had larger time spans compared with later time subsets. If equal-size time intervals were used, the numbers of documents between the intervals would differ greatly; the earlier time points would have many fewer records, which may introduce bias. Prior to binning the subsets, the publication dates were converted to UNIX time (timestamp) in seconds; the plant science records start in 1917-11-1 (timestamp = −1646247600.0) and end in 2021-1-1 (timestamp = 1609477201). The starting dates and corresponding timestamps for the 50 subsets including the end date are in S6 Data . The input data included the preprocessed texts, topic assignments of records from global topic modeling, and the binned timestamps of records. Three additional parameters were set for topics_over_time, namely, nr_bin = 50 (number of bins), evolution_tuning = True, and global_tuning = False. The evolution_tuning parameter specified that averaged c-Tf-Idf values for a topic be calculated in neighboring time bins to reduce fluctuation in c-Tf-Idf values. The global_tuning parameter was set to False because of the possibility that some nonexisting terms could have a high c-Tf-Idf for a time bin simply because there was a high global c-Tf-Idf value for that term.

The binning strategy based on similar document numbers per bin allowed us to increase signal particularly for publications prior to the 90s. This strategy, however, may introduce more noise for bins with smaller time durations (i.e., more recent bins) because of publication frequencies (there can be seasonal differences in the number of papers published, biased toward, e.g., the beginning of the year or the beginning of a quarter). To address this, we examined the relative frequencies of each topic over time ( S7 Data ), but we found that recent time bins had similar variances in relative frequencies as other time bins. We also moderated the impact of variation using LOWESS (10% to 30% of the data points were used for fitting the trend lines) to determine topical trends for Fig 3 . Thus, the influence of the noise introduced via our binning strategy is expected to be minimal.

Topic categories and ordering

The topics were classified into 5 categories with contrasting trends: stable, early, transitional, sigmoidal, and rising. To define which category a topic belongs to, the frequency of documents over time bins for each topic was analyzed using 3 regression methods. We first tried 2 forecasting methods: recursive autoregressor (the ForecasterAutoreg class in the skforecast package) and autoregressive integrated moving average (ARIMA implemented in the pmdarima package). In both cases, the forecasting results did not clearly follow the expected trend lines, likely due to the low numbers of data points (relative frequency values), which resulted in the need to extensively impute missing data. Thus, as a third approach, we sought to fit the trendlines with the data points using LOWESS (implemented in the statsmodels package) and applied additional criteria for assigning topics to categories. When fitting with LOWESS, 3 fraction parameters (frac, the fraction of the data used when estimating each y-value) were evaluated (0.1, 0.2, 0.3). While frac = 0.3 had the smallest errors for most topics, in situations where there were outliers, frac = 0.2 or 0.1 was chosen to minimize mean squared errors ( S7 Data ).

The topics were classified into 5 categories based on the slopes of the fitted line over time: (1) stable: topics with near 0 slopes over time; (2) early: topics with negative (<−0.5) slopes throughout (with the exception of topic 78, which declined early on but bounced back by the late 1990s); (3) transitional: early positive (>0.5) slopes followed by negative slopes at later time points; (4) sigmoidal: early positive slopes followed by zero slopes at later time points; and (5) rising: continuously positive slopes. For each topic, the LOWESS fits were also used to determine when the relative document frequency reached its peak, first reaching a threshold of 0.6 (chosen after trial and error for a range of 0.3 to 0.9), and the overall trend. The topics were then ordered based on (1) whether they belonged to the stable category or not; (2) whether the trends were decreasing, stable, or increasing; (3) the time the relative document frequency first reached 0.6; and (4) the time that the overall peak was reached ( S8 Data ).

Taxa information

To identify a taxon or taxa in all plant science records, NCBI Taxonomy taxdump datasets were downloaded from the NCBI FTP site ( https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/new_taxdump/ ) on September 20, 2022. The highest-level taxon was Viridiplantae, and all its child taxa were parsed and used as queries in searches against the plant science corpus. In addition, a species-over-time analysis was conducted using the same time bins as used for dynamic topic models. The number of records in different time bins for top taxa are in the genus, family, order, and additional species level sheet in S9 Data . The degree of over-/underrepresentation of a taxon X in a research topic T was assessed using the p -value of a Fisher’s exact test for a 2 × 2 table consisting of the numbers of records in both X and T, in X but not T, in T but not X, and in neither ( S10 Data ).

For analysis of plant taxa with genome information, genome data of taxa in Viridiplantae were obtained from the NCBI Genome data-hub ( https://www.ncbi.nlm.nih.gov/data-hub/genome ) on October 28, 2022. There were 2,384 plant genome assemblies belonging to 1,231 species in 559 genera (genome assembly sheet, S9 Data ). The date of the assembly was used as a proxy for the time when a genome was sequenced. However, some species have updated assemblies and have more recent data than when the genome first became available.

Taxa being studied in the plant science records

Flowering plants (Magnoliopsida) are found in 93% of records, while most other lineages are discussed in <1% of records, with conifers and related species being exceptions (Acrogynomsopermae, 3.5%, S6A Fig ). At the family level, the mustard (Brassicaceae), grass (Poaceae), pea (Fabaceae), and nightshade (Solanaceae) families are in 51% of records ( S6B Fig ). The prominence of the mustard family in plant science research is due to the Brassica and Arabidopsis genera ( Fig 4A ). When examining the prevalence of taxa being studied over time, clear patterns of turnovers emerged ( Figs 4B , S6C, and S6D ). While the study of monocot species (Liliopsida) has remained steady, there was a significant uptick in the prevalence of eudicot (eudicotyledon) records in the late 90s ( S6C Fig ), which can be attributed to the increased number of studies in the mustard, myrtle (Myrtaceae), and mint (Lamiaceae) families among others ( S6D Fig ). At the genus level, records mentioning Gossypium (cotton), Phaseolus (bean), Hordeum (wheat), and Zea (corn), similar to the topics in the early category, were prevalent till the 1980s or 1990s but have mostly decreased in number since ( Fig 4B ). In contrast, Capsicum , Arabidopsis , Oryza , Vitus , and Solanum research has become more prevalent over the last 20 years.

Geographical information for the plant science corpus

The geographical information (country) of authors in the plant science corpus was obtained from the address (AD) fields of first authors in Medline XML records accessible through the NCBI EUtility API ( https://www.ncbi.nlm.nih.gov/books/NBK25501/ ). Because only first author affiliations are available for records published before December 2014, only the first author’s location was considered to ensure consistency between records before and after that date. Among the 421,658 records in the plant science corpus, 421,585 had Medline records and 421,276 had unique PMIDs. Among the records with unique PMIDs, 401,807 contained address fields. For each of the remaining records, the AD field content was split into tokens with a “,” delimiter, and the token likely containing geographical info (referred to as location tokens) was selected as either the last token or the second to last token if the last token contained “@” indicating the presence of an email address. Because of the inconsistency in how geographical information was described in the location tokens (e.g., country, state, city, zip code, name of institution, and different combinations of the above), the following 4 approaches were used to convert location tokens into countries.

The first approach was a brute force search where full names and alpha-3 codes of current countries (ISO 3166–1), current country subregions (ISO 3166–2), and historical country (i.e., country that no longer exists, ISO 3166–3) were used to search the address fields. To reduce false positives using alpha-3 codes, a space prior to each code was required for the match. The first approach allowed the identification of 361,242, 16,573, and 279,839 records with current country, historical country, and subregion information, respectively. The second method was the use of a heuristic based on common address field structures to identify “location strings” toward the end of address fields that likely represent countries, then the use of the Python pycountry module to confirm the presence of country information. This approach led to 329,025 records with country information. The third approach was to parse first author email addresses (90,799 records), recover top-level domain information, and use country code Top Level Domain (ccTLD) data from the ISO 3166 Wikipedia page to define countries (72,640 records). Only a subset of email addresses contains country information because some are from companies (.com), nonprofit organizations (.org), and others. Because a large number of records with address fields still did not have country information after taking the above 3 approaches, another approach was implemented to query address fields against a locally installed Nominatim server (v.4.2.3, https://github.com/mediagis/nominatim-docker ) using OpenStreetMap data from GEOFABRIK ( https://www.geofabrik.de/ ) to find locations. Initial testing indicated that the use of full address strings led to false positives, and the computing resource requirement for running the server was high. Thus, only location strings from the second approach that did not lead to country information were used as queries. Because multiple potential matches were returned for each query, the results were sorted based on their location importance values. The above steps led to an additional 72,401 records with country information.

Examining the overlap in country information between approaches revealed that brute force current country and pycountry searches were consistent 97.1% of the time. In addition, both approaches had high consistency with the email-based approach (92.4% and 93.9%). However, brute force subregion and Nominatim-based predictions had the lowest consistencies with the above 3 approaches (39.8% to 47.9%) and each other. Thus, a record’s country information was finalized if the information was consistent between any 2 approaches, except between the brute force subregion and Nominatim searches. This led to 330,328 records with country information.

Topical and country impact metrics

To determine annual country impact, impact scores were determined in the same way as that for annual topical impact, except that values for different countries were calculated instead of topics ( S8 Data ).

Topical preferences by country

To determine topical preference for a country C , a 2 × 2 table was established with the number of records in topic T from C , the number of records in T but not from C , the number of non- T records from C , and the number of non- T records not from C . A Fisher’s exact test was performed for each T and C combination, and the resulting p -values were corrected for multiple testing with the Bejamini–Hochberg method (see S12 Data ). The preference of T in C was defined as the degree of enrichment calculated as log likelihood ratio of values in the 2 × 2 table. Topic 5 was excluded because >50% of the countries did not have records for this topic.

The top 10 countries could be classified into a China–India cluster, an Italy–Spain cluster, and remaining countries (yellow rectangles, Fig 5E ). The clustering of Italy and Spain is partly due to similar research focusing on allergens (topic 0) and mycotoxins (topic 54) and less emphasis on gene family (topic 23) and stress tolerance (topic 28) studies ( Figs 5F and S9 ). There are also substantial differences in topical focus between countries. For example, plant science records from China tend to be enriched in hyperspectral imaging and modeling (topic 9), gene family studies (topic 23), stress biology (topic 28), and research on new plant compounds associated with herbal medicine (topic 69), but less emphasis on population genetics and evolution (topic 86, Fig 5F ). In the US, there is a strong focus on insect pest resistance (topic 75), climate, community, and diversity (topic 83), and population genetics and evolution but less focus on new plant compounds. In summary, in addition to revealing how plant science research has evolved over time, topic modeling provides additional insights into differences in research foci among different countries.

Supporting information

S1 fig. plant science record classification model performance..

(A–C) Distributions of prediction probabilities (y_prob) of (A) positive instances (plant science records), (B) negative instances (non-plant science records), and (C) positive instances with the Medical Subject Heading “Plants” (ID = D010944). The data are color coded in blue and orange if they are correctly and incorrectly predicted, respectively. The lower subfigures contain log10-transformed x axes for the same distributions as the top subfigure for better visualization of incorrect predictions. (D) Prediction probability distribution for candidate plant science records. Prediction probabilities plotted here are available in S13 Data .

https://doi.org/10.1371/journal.pbio.3002612.s001

S2 Fig. Relationships between outlier clusters and the 90 topics.

(A) Heatmap demonstrating that some outlier clusters tend to have high prediction scores for multiple topics. Each cell shows the average prediction score of a topic for records in an outlier cluster. (B) Size of outlier clusters.

https://doi.org/10.1371/journal.pbio.3002612.s002

S3 Fig. Cosine similarities between topics.

(A) Heatmap showing cosine similarities between topic pairs. Top-left: hierarchical clustering of the cosine similarity matrix using the Ward algorithm. The branches are colored to indicate groups of related topics. (B) Topic labels and names. The topic ordering was based on hierarchical clustering of topics. Colored rectangles: neighboring topics with >0.5 cosine similarities.

https://doi.org/10.1371/journal.pbio.3002612.s003

S4 Fig. Relative topical diversity for 20 journals.

The 20 journals with the most plant science records are shown. The journal names were taken from the journal list in PubMed ( https://www.nlm.nih.gov/bsd/serfile_addedinfo.html ).

https://doi.org/10.1371/journal.pbio.3002612.s004

S5 Fig. Topical frequency and top terms during different time periods.

(A-D) Different patterns of topical frequency distributions for example topics (A) 48, (B) 35, (C) 27, and (D) 42. For each topic, the top graph shows the frequency of topical records in each time bin, which are the same as those in Fig 3 (green line), and the end date for each bin is indicated. The heatmap below each line plot depicts whether a term is among the top terms in a time bin (yellow) or not (blue). Blue dotted lines delineate different decades (see S5 Data for the original frequencies, S6 Data for the LOWESS fitted frequencies and the top terms for different topics/time bins).

https://doi.org/10.1371/journal.pbio.3002612.s005

S6 Fig. Prevalence of records mentioning different taxonomic groups in Viridiplantae.

(A, B) Percentage of records mentioning specific taxa at the ( A) major lineage and (B) family levels. (C, D) The prevalence of taxon mentions over time at the (C) major lineage and (E) family levels. The data used for plotting are available in S9 Data .

https://doi.org/10.1371/journal.pbio.3002612.s006

S7 Fig. Changes over time.

(A) Number of genera being mentioned in plant science records during different time bins (the date indicates the end date of that bin, exclusive). (B) Numbers of genera (blue) and organisms (salmon) with draft genomes available from National Center of Biotechnology Information in different years. (C) Percentage of US National Science Foundation (NSF) grants mentioning the genus Arabidopsis over time with peak percentage and year indicated. The data for (A–C) are in S9 Data . (D) Number of plant science records in the top 17 plant science journals from the USA (red), Great Britain (GBR) (orange), India (IND) (light green), and China (CHN) (dark green) normalized against the total numbers of publications of each country over time in these 17 journals. The data used for plotting can be found in S11 Data .

https://doi.org/10.1371/journal.pbio.3002612.s007

S8 Fig. Change in country impact on plant science over time.

(A, B) Difference in 2 impact metrics from 1999 to 2020 for the 10 countries with the highest number of plant science records. (A) H-index. (B) SCImago Journal Rank (SJR). (C, D) Plots show the relationships between the impact metrics (H-index in (C) , SJR in (D) ) averaged from 1999 to 2020 and the slopes of linear fits with years as the predictive variable and impact metric as the response variable for different countries (A3 country codes shown). The countries with >400 records and with <10% missing impact values are included. The data used for plotting can be found in S11 Data .

https://doi.org/10.1371/journal.pbio.3002612.s008

S9 Fig. Country topical preference.

Enrichment scores (LLR, log likelihood ratio) of topics for each of the top 10 countries. Red: overrepresentation, blue: underrepresentation. The data for plotting can be found in S12 Data .

https://doi.org/10.1371/journal.pbio.3002612.s009

S1 Data. Summary of source journals for plant science records, prediction models, and top Tf-Idf features.

Sheet–Candidate plant sci record j counts: Number of records from each journal in the candidate plant science corpus (before classification). Sheet—Plant sci record j count: Number of records from each journal in the plant science corpus (after classification). Sheet–Model summary: Model type, text used (txt_flag), and model parameters used. Sheet—Model performance: Performance of different model and parameter combinations on the validation data set. Sheet–Tf-Idf features: The average SHAP values of Tf-Idf (Term frequency-Inverse document frequency) features associated with different terms. Sheet–PubMed number per year: The data for PubMed records in Fig 1A . Sheet–Plant sci record num per yr: The data for the plant science records in Fig 1A .

https://doi.org/10.1371/journal.pbio.3002612.s010

S2 Data. Numbers of records in topics identified from preliminary topic models.

Sheet–Topics generated with a model based on BioBERT embeddings. Sheet–Topics generated with a model based on distilBERT embeddings. Sheet–Topics generated with a model based on SciBERT embeddings.

https://doi.org/10.1371/journal.pbio.3002612.s011

S3 Data. Final topic model labels and top terms for topics.

Sheet–Topic label: The topic index and top 10 terms with the highest cTf-Idf values. Sheets– 0 to 89: The top 50 terms and their c-Tf-Idf values for topics 0 to 89.

https://doi.org/10.1371/journal.pbio.3002612.s012

S4 Data. UMAP representations of different topics.

For a topic T , records in the UMAP graph are colored red and records not in T are colored gray.

https://doi.org/10.1371/journal.pbio.3002612.s013

S5 Data. Temporal relationships between published documents projected onto 2D space.

The 2D embedding generated with UMAP was used to plot document relationships for each year. The plots from 1975 to 2020 were compiled into an animation.

https://doi.org/10.1371/journal.pbio.3002612.s014

S6 Data. Timestamps and dates for dynamic topic modeling.

Sheet–bin_timestamp: Columns are: (1) order index; (2) bin_idx–relative positions of bin labels; (3) bin_timestamp–UNIX time in seconds; and (4) bin_date–month/day/year. Sheet–Topic frequency per timestamp: The number of documents in each time bin for each topic. Sheets–LOWESS fit 0.1/0.2/0.3: Topic frequency per timestamp fitted with the fraction parameter of 0.1, 0.2, or 0.3. Sheet—Topic top terms: The top 5 terms for each topic in each time bin.

https://doi.org/10.1371/journal.pbio.3002612.s015

S7 Data. Locally weighted scatterplot smoothing (LOWESS) of topical document frequencies over time.

There are 90 scatter plots, one for each topic, where the x axis is time, and the y axis is the document frequency (blue dots). The LOWESS fit is shown as orange points connected with a green line. The category a topic belongs to and its order in Fig 3 are labeled on the top left corner. The data used for plotting are in S6 Data .

https://doi.org/10.1371/journal.pbio.3002612.s016

S8 Data. The 4 criteria used for sorting topics.

Peak: the time when the LOWESS fit of the frequencies of a topic reaches maximum. 1st_reach_thr: the time when the LOWESS fit first reaches a threshold of 60% maximal frequency (peak value). Trend: upward (1), no change (0), or downward (−1). Stable: whether a topic belongs to the stable category (1) or not (0).

https://doi.org/10.1371/journal.pbio.3002612.s017

S9 Data. Change in taxon record numbers and genome assemblies available over time.

Sheet–Genus: Number of records mentioning a genus during different time periods (in Unix timestamp) for the top 100 genera. Sheet–Genus: Number of records mentioning a family during different time periods (in Unix timestamp) for the top 100 families. Sheet–Genus: Number of records mentioning an order during different time periods (in Unix timestamp) for the top 20 orders. Sheet–Species levels: Number of records mentioning 12 selected taxonomic levels higher than the order level during different time periods (in Unix timestamp). Sheet–Genome assembly: Plant genome assemblies available from NCBI as of October 28, 2022. Sheet–Arabidopsis NSF: Absolute and normalized numbers of US National Science Foundation funded proposals mentioning Arabidopsis in proposal titles and/or abstracts.

https://doi.org/10.1371/journal.pbio.3002612.s018

S10 Data. Taxon topical preference.

Sheet– 5 genera LLR: The log likelihood ratio of each topic in each of the top 5 genera with the highest numbers of plant science records. Sheets– 5 genera: For each genus, the columns are: (1) topic; (2) the Fisher’s exact test p -value (Pvalue); (3–6) numbers of records in topic T and in genus X (n_inT_inX), in T but not in X (n_inT_niX), not in T but in X (n_niT_inX), and not in T and X (n_niT_niX) that were used to construct 2 × 2 tables for the tests; and (7) the log likelihood ratio generated with the 2 × 2 tables. Sheet–corrected p -value: The 4 values for generating LLRs were used to conduct Fisher’s exact test. The p -values obtained for each country were corrected for multiple testing.

https://doi.org/10.1371/journal.pbio.3002612.s019

S11 Data. Impact metrics of countries in different years.

Sheet–country_top25_year_count: number of total publications and publications per year from the top 25 countries with the most plant science records. Sheet—country_top25_year_top17j: number of total publications and publications per year from the top 25 countries with the highest numbers of plant science records in the 17 plant science journals used as positive examples. Sheet–prank: Journal percentile rank scores for countries (3-letter country codes following https://www.iban.com/country-codes ) in different years from 1999 to 2020. Sheet–sjr: Scimago Journal rank scores. Sheet–hidx: H-Index scores. Sheet–cite: Citation scores.

https://doi.org/10.1371/journal.pbio.3002612.s020

S12 Data. Topical enrichment for the top 10 countries with the highest numbers of plant science publications.

Sheet—Log likelihood ratio: For each country C and topic T, it is defined as log((a/b)/(c/d)) where a is the number of papers from C in T, b is the number from C but not in T, c is the number not from C but in T, d is the number not from C and not in T. Sheet: corrected p -value: The 4 values, a, b, c, and d, were used to conduct Fisher’s exact test. The p -values obtained for each country were corrected for multiple testing.

https://doi.org/10.1371/journal.pbio.3002612.s021

S13 Data. Text classification prediction probabilities.

This compressed file contains the PubMed ID (PMID) and the prediction probabilities (y_pred) of testing data with both positive and negative examples (pred_prob_testing), plant science candidate records with the MeSH term “Plants” (pred_prob_candidates_with_mesh), and all plant science candidate records (pred_prob_candidates_all). The prediction probability was generated using the Word2Vec text classification models for distinguishing positive (plant science) and negative (non-plant science) records.

https://doi.org/10.1371/journal.pbio.3002612.s022

Acknowledgments

We thank Maarten Grootendorst for discussions on topic modeling. We also thank Stacey Harmer, Eva Farre, Ning Jiang, and Robert Last for discussion on their respective research fields and input on how to improve this study and Rudiger Simon for the suggestion to examine differences between countries. We also thank Mae Milton, Christina King, Edmond Anderson, Jingyao Tang, Brianna Brown, Kenia Segura Abá, Eleanor Siler, Thilanka Ranaweera, Huan Chen, Rajneesh Singhal, Paulo Izquierdo, Jyothi Kumar, Daniel Shiu, Elliott Shiu, and Wiggler Catt for their good ideas, personal and professional support, collegiality, fun at parties, as well as the trouble they have caused, which helped us improve as researchers, teachers, mentors, and parents.

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