Construction of a Genome-Scale Metabolic Model of Arthrospira platensis NIES-39 and Metabolic Design for Cyanobacterial Bioproduction
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{"title"=>"Construction of a genome-scale metabolic model of arthrospira platensis nies-39 and metabolic design for cyanobacterial bioproduction", "type"=>"journal", "authors"=>[{"first_name"=>"Katsunori", "last_name"=>"Yoshikawa", "scopus_author_id"=>"9243734700"}, {"first_name"=>"Shimpei", "last_name"=>"Aikawa", "scopus_author_id"=>"54902422700"}, {"first_name"=>"Yuta", "last_name"=>"Kojima", "scopus_author_id"=>"54789000100"}, {"first_name"=>"Yoshihiro", "last_name"=>"Toya", "scopus_author_id"=>"34572737200"}, {"first_name"=>"Chikara", "last_name"=>"Furusawa", "scopus_author_id"=>"6603409269"}, {"first_name"=>"Akihiko", "last_name"=>"Kondo", "scopus_author_id"=>"7202350565"}, {"first_name"=>"Hiroshi", "last_name"=>"Shimizu", "scopus_author_id"=>"55230249300"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84955477248", "pui"=>"607909949", "pmid"=>"26640947", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0144430", "sgr"=>"84955477248"}, "id"=>"7e6faac6-9a84-3768-9dbc-d98fd1cc6515", "abstract"=>"Arthrospira (Spirulina) platensis is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of A. platensis NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, in silico knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(P)H dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source.", "link"=>"http://www.mendeley.com/research/construction-genomescale-metabolic-model-arthrospira-platensis-nies39-metabolic-design-cyanobacteria", "reader_count"=>23, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>2, "Researcher"=>5, "Student > Ph. D. Student"=>6, "Student > Master"=>1, "Other"=>2, "Student > Bachelor"=>2, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>2, "Researcher"=>5, "Student > Ph. D. Student"=>6, "Student > Master"=>1, "Other"=>2, "Student > Bachelor"=>2, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>4, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>5, "Agricultural and Biological Sciences"=>8, "Physics and Astronomy"=>2, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>8}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>1}}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/4302991"], "description"=>"<p>The simulated metabolic flux distribution under autotrophic conditions is shown (A). The values indicate the metabolic flux of each reaction. The flux was normalized to that of the reaction catalyzed by ribulose 1,5-bisphosphate carboxylase/oxygenase. Bold and dashed-bold arrows indicate the reactions whose activation or repression increased glycogen production, respectively, identified by flux response analysis. The reactions “DHAP + E4P ↔ S7P”, “DHAP + G3P ↔ F6P” and “G3P + S7P ↔ E4P + F6P” were undetermined, and the flux range of these reactions was calculated by flux variability analysis [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144430#pone.0144430.ref038\" target=\"_blank\">38</a>]. FRA results of the phosphoglucomutase reaction (G6P → G1P) (B) and phosphoenolpyruvate carboxylase reaction (PEP → Oxa) (C) are shown. The X-axis indicates the flux of the corresponding reaction and the Y-axis indicates the glycogen production rate excluding the flux for biomass production, as glycogen is a component of the biomass. The dotted line indicates the optimal flux of the corresponding reaction. The grey area in Fig 3(C) represents the solution space calculated by flux variability analysis. 2PG, glycerate-2-phosphate; 3PG, 3-phosphoglycerate; 6PGC, 6-phospho-gluconate; 6PGL, 6-phospho-glucono-1,5-lactone; AcCoA, acetyl-CoA; αKG, α-ketoglutarate; Cit, citrate; DHAP, dihydroxyacetone phosphate; E4P, erythrose-4-phosphate; F6P, fructose-6-phosphate; Fum, fumarate; G1P, glucose-1-phosphate; G3P, glyceraldehyde-3-phosphate; G6P, glucose-6-phosphate; Icit, isocitrate; Mal, malate; Oxa, oxalate; PEP, phosphoenolpyruvate; Pyr, pyruvate; R5P, ribose-5-phosphate; Ru5P, ribulose-5-phosphate; RuBP, ribulose-1,5-bisphosphate; S7P, sedoheptulose-7-phosphate; Suc, succinate; Sucsal, succinyl semialdehyde; Xu5P, xylulose-5-phosphate.</p>", "links"=>[], "tags"=>["NAD", "NIES", "platensi", "growth rates", "flux balance analysis", "trophic conditions", "simulation results", "autotrophic production", "engineering strategies", "Decreased flux", "growth capabilities", "Metabolic simulation", "Cyanobacterial Bioproduction Arthrospira", "TCA cycle", "host strain", "ethanol production", "target molecule production", "nitrogen source", "Metabolic Design", "673 metabolites", "silico knockout simulation", "glycogen production", "novel strategies", "phosphoenolpyruvate reaction", "model"], "article_id"=>2648512, "categories"=>["Biochemistry", "Microbiology", "Genetics", "Physiology", "Ecology", "Biological Sciences not elsewhere classified"], "users"=>["Katsunori Yoshikawa", "Shimpei Aikawa", "Yuta Kojima", "Yoshihiro Toya", "Chikara Furusawa", "Akihiko Kondo", "Hiroshi Shimizu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0144430.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Flux_response_analysis_for_glycogen_production_/2648512", "title"=>"Flux response analysis for glycogen production.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-07 08:21:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/4303072"], "description"=>"<p>Detailed information is summarized in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144430#pone.0144430.s004\" target=\"_blank\">S4 Table</a>.</p>", "links"=>[], "tags"=>["NAD", "NIES", "platensi", "growth rates", "flux balance analysis", "trophic conditions", "simulation results", "autotrophic production", "engineering strategies", "Decreased flux", "growth capabilities", "Metabolic simulation", "Cyanobacterial Bioproduction Arthrospira", "TCA cycle", "host strain", "ethanol production", "target molecule production", "nitrogen source", "Metabolic Design", "673 metabolites", "silico knockout simulation", "glycogen production", "novel strategies", "phosphoenolpyruvate reaction", "model"], "article_id"=>2648548, "categories"=>["Biochemistry", "Microbiology", "Genetics", "Physiology", "Ecology", "Biological Sciences not elsewhere classified"], "users"=>["Katsunori Yoshikawa", "Shimpei Aikawa", "Yuta Kojima", "Yoshihiro Toya", "Chikara Furusawa", "Akihiko Kondo", "Hiroshi Shimizu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0144430.t004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_i_In_silico_i_knockout_simulation_for_ethanol_production_with_ammonium_as_nitrogen_source_/2648548", "title"=>"<i>In silico</i> knockout simulation for ethanol production with ammonium as nitrogen source.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-12-07 08:21:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/4303009"], "description"=>"<p>The number of unique metabolites was calculated by considering the metabolites present in more than one compartment as a single metabolite.</p>", "links"=>[], "tags"=>["NAD", "NIES", "platensi", "growth rates", "flux balance analysis", "trophic conditions", "simulation results", "autotrophic production", "engineering strategies", "Decreased flux", "growth capabilities", "Metabolic simulation", "Cyanobacterial Bioproduction Arthrospira", "TCA cycle", "host strain", "ethanol production", "target molecule production", "nitrogen source", "Metabolic Design", "673 metabolites", "silico knockout simulation", "glycogen production", "novel strategies", "phosphoenolpyruvate reaction", "model"], "article_id"=>2648521, "categories"=>["Biochemistry", "Microbiology", "Genetics", "Physiology", "Ecology", "Biological Sciences not elsewhere classified"], "users"=>["Katsunori Yoshikawa", "Shimpei Aikawa", "Yuta Kojima", "Yoshihiro Toya", "Chikara Furusawa", "Akihiko Kondo", "Hiroshi Shimizu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0144430.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Characteristics_of_the_reconstructed_metabolic_model_of_i_A_i_i_platensis_i_NIES_39_/2648521", "title"=>"Characteristics of the reconstructed metabolic model of <i>A</i>. <i>platensis</i> NIES-39.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-12-07 08:21:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/4303024"], "description"=>"<p>Comparison of the growth capabilities on the various organic substrates.</p>", "links"=>[], "tags"=>["NAD", "NIES", "platensi", "growth rates", "flux balance analysis", "trophic conditions", "simulation results", "autotrophic production", "engineering strategies", "Decreased flux", "growth capabilities", "Metabolic simulation", "Cyanobacterial Bioproduction Arthrospira", "TCA cycle", "host strain", "ethanol production", "target molecule production", "nitrogen source", "Metabolic Design", "673 metabolites", "silico knockout simulation", "glycogen production", "novel strategies", "phosphoenolpyruvate reaction", "model"], "article_id"=>2648527, "categories"=>["Biochemistry", "Microbiology", "Genetics", "Physiology", "Ecology", "Biological Sciences not elsewhere classified"], "users"=>["Katsunori Yoshikawa", "Shimpei Aikawa", "Yuta Kojima", "Yoshihiro Toya", "Chikara Furusawa", "Akihiko Kondo", "Hiroshi Shimizu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0144430.t002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Comparison_of_the_growth_capabilities_on_the_various_organic_substrates_/2648527", "title"=>"Comparison of the growth capabilities on the various organic substrates.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-12-07 08:21:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/4302946", "https://ndownloader.figshare.com/files/4302949", "https://ndownloader.figshare.com/files/4302958", "https://ndownloader.figshare.com/files/4302961"], "description"=>"<div><p><i>Arthrospira</i> (<i>Spirulina</i>) <i>platensis</i> is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of <i>A</i>. <i>platensis</i> NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, <i>in silico</i> knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(P)H dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source.</p></div>", "links"=>[], "tags"=>["NAD", "NIES", "platensi", "growth rates", "flux balance analysis", "trophic conditions", "simulation results", "autotrophic production", "engineering strategies", "Decreased flux", "growth capabilities", "Metabolic simulation", "Cyanobacterial Bioproduction Arthrospira", "TCA cycle", "host strain", "ethanol production", "target molecule production", "nitrogen source", "Metabolic Design", "673 metabolites", "silico knockout simulation", "glycogen production", "novel strategies", "phosphoenolpyruvate reaction", "model"], "article_id"=>2648497, "categories"=>["Biochemistry", "Microbiology", "Genetics", "Physiology", "Ecology", "Biological Sciences not elsewhere classified"], "users"=>["Katsunori Yoshikawa", "Shimpei Aikawa", "Yuta Kojima", "Yoshihiro Toya", "Chikara Furusawa", "Akihiko Kondo", "Hiroshi Shimizu"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0144430.s001", "https://dx.doi.org/10.1371/journal.pone.0144430.s002", "https://dx.doi.org/10.1371/journal.pone.0144430.s003", "https://dx.doi.org/10.1371/journal.pone.0144430.s004"], "stats"=>{"downloads"=>1, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Construction_of_a_Genome_Scale_Metabolic_Model_of_i_Arthrospira_platensis_i_NIES_39_and_Metabolic_Design_for_Cyanobacterial_Bioproduction/2648497", "title"=>"Construction of a Genome-Scale Metabolic Model of <i>Arthrospira platensis</i> NIES-39 and Metabolic Design for Cyanobacterial Bioproduction", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-12-07 08:21:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/4303048"], "description"=>"<p>Detailed information is summarized in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144430#pone.0144430.s004\" target=\"_blank\">S4 Table</a>.</p>", "links"=>[], "tags"=>["NAD", "NIES", "platensi", "growth rates", "flux balance analysis", "trophic conditions", "simulation results", "autotrophic production", "engineering strategies", "Decreased flux", "growth capabilities", "Metabolic simulation", "Cyanobacterial Bioproduction Arthrospira", "TCA cycle", "host strain", "ethanol production", "target molecule production", "nitrogen source", "Metabolic Design", "673 metabolites", "silico knockout simulation", "glycogen production", "novel strategies", "phosphoenolpyruvate reaction", "model"], "article_id"=>2648533, "categories"=>["Biochemistry", "Microbiology", "Genetics", "Physiology", "Ecology", "Biological Sciences not elsewhere classified"], "users"=>["Katsunori Yoshikawa", "Shimpei Aikawa", "Yuta Kojima", "Yoshihiro Toya", "Chikara Furusawa", "Akihiko Kondo", "Hiroshi Shimizu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0144430.t003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_i_In_silico_i_knockout_simulation_for_ethanol_production_with_nitrate_as_nitrogen_source_/2648533", "title"=>"<i>In silico</i> knockout simulation for ethanol production with nitrate as nitrogen source.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-12-07 08:21:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/4302970"], "description"=>"<p>The specific growth rate (solid line) and glycogen production rate (dashed line) at each nitrate uptake rate are shown. The dotted line indicates the optimal uptake rate for biomass production. The grey area represents the solution space of the glycogen production rate calculated by flux variability analysis [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144430#pone.0144430.ref038\" target=\"_blank\">38</a>], as the glycogen production rate was undetermined.</p>", "links"=>[], "tags"=>["NAD", "NIES", "platensi", "growth rates", "flux balance analysis", "trophic conditions", "simulation results", "autotrophic production", "engineering strategies", "Decreased flux", "growth capabilities", "Metabolic simulation", "Cyanobacterial Bioproduction Arthrospira", "TCA cycle", "host strain", "ethanol production", "target molecule production", "nitrogen source", "Metabolic Design", "673 metabolites", "silico knockout simulation", "glycogen production", "novel strategies", "phosphoenolpyruvate reaction", "model"], "article_id"=>2648503, "categories"=>["Biochemistry", "Microbiology", "Genetics", "Physiology", "Ecology", "Biological Sciences not elsewhere classified"], "users"=>["Katsunori Yoshikawa", "Shimpei Aikawa", "Yuta Kojima", "Yoshihiro Toya", "Chikara Furusawa", "Akihiko Kondo", "Hiroshi Shimizu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0144430.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Effect_of_nitrate_uptake_rate_on_growth_and_glycogen_production_/2648503", "title"=>"Effect of nitrate uptake rate on growth and glycogen production.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-07 08:21:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/4302982"], "description"=>"<p>Growth and intracellular glycogen (A) together with the concentrations of organic acids in the culture medium (B) were summarized. Closed circle, OD<sub>750</sub>; open circle, glycogen content; square, acetate; diamond, lactate; triangle, pyruvate. Error bars represent the standard deviation of triplicate experiments.</p>", "links"=>[], "tags"=>["NAD", "NIES", "platensi", "growth rates", "flux balance analysis", "trophic conditions", "simulation results", "autotrophic production", "engineering strategies", "Decreased flux", "growth capabilities", "Metabolic simulation", "Cyanobacterial Bioproduction Arthrospira", "TCA cycle", "host strain", "ethanol production", "target molecule production", "nitrogen source", "Metabolic Design", "673 metabolites", "silico knockout simulation", "glycogen production", "novel strategies", "phosphoenolpyruvate reaction", "model"], "article_id"=>2648509, "categories"=>["Biochemistry", "Microbiology", "Genetics", "Physiology", "Ecology", "Biological Sciences not elsewhere classified"], "users"=>["Katsunori Yoshikawa", "Shimpei Aikawa", "Yuta Kojima", "Yoshihiro Toya", "Chikara Furusawa", "Akihiko Kondo", "Hiroshi Shimizu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0144430.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Culture_profile_under_nitrogen_depletion_conditions_/2648509", "title"=>"Culture profile under nitrogen depletion conditions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-07 08:21:41"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"11", "full-text"=>"13", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
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  • {"unique-ip"=>"10", "full-text"=>"11", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
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  • {"unique-ip"=>"23", "full-text"=>"31", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"3", "cited-by"=>"1", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"6", "full-text"=>"4", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"10", "full-text"=>"11", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"7", "full-text"=>"5", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"17", "full-text"=>"16", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"8", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"11", "full-text"=>"9", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"12", "full-text"=>"14", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"6", "full-text"=>"9", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"19", "full-text"=>"19", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
  • {"unique-ip"=>"21", "full-text"=>"39", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"18", "full-text"=>"18", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"11", "full-text"=>"15", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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