cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3563001
title: Bioinformatics exploration of PAK1 (P21-activated kinase-1) revealed potential network gene elements in breast invasive carcinoma

description: P21-activated kinase-1 (PAK1) is an enzyme associated with multiple metabolic networks and different types of cancers. Hence, there is a need to study the global network map of PAK1 to understand its role and regulatory mechanisms by means of its significant molecular interactive partners. This will help to explore its global biological functions in breast cancer. In view of this, we obtained the gene expression data-sets of PAK1 from The Cancer Genome Atlas-cBioportal and GeneCards databases and found that 91 PAK1-related genes are associated with breast cancer. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway investigations of 91 genes via Database for Annotation Visualization and Integrated Discovery bioinformatics resource revealed that, PAK1 being a major kinase is associated with several metabolic pathways and involved in phosphorylation, signal transduction, apoptosis, biosynthesis and majorly cancer-related cell signalling pathways. The PAK1 interaction network derived from STRING and Cytoscape revealed that the genes Signal-Transducer-and-Activator-of-Transcription-3 (STAT3), Cyclin-D1 (CCND1), Mitogen-activated protein kinase-1 (MAPK1), Ras-Homolog-Family-Member-A (RHOA) and Catenin-beta-1 have high degrees of interaction where CCND1, MAPK1 and RHOA have direct interaction with PAK1. Finally, the global expression map of PAK1 and its related genes was derived as topological frame that helped to explore and investigate PAK1 interactions. Further, the molecular modelling studies of PAK1 with its major interacting partners RHOA and STAT3 helped to explore the key interactive residues of PAK1 structure. This information can be used to develop novel therapeutic and control strategies against breast cancer.
DOI: 10.6084/m9.figshare.3563001.v1
title: Bioinformatics exploration of PAK1 (P21-activated kinase-1) revealed potential network gene elements in breast invasive carcinoma

description: P21-activated kinase-1 (PAK1) is an enzyme associated with multiple metabolic networks and different types of cancers. Hence, there is a need to study the global network map of PAK1 to understand its role and regulatory mechanisms by means of its significant molecular interactive partners. This will help to explore its global biological functions in breast cancer. In view of this, we obtained the gene expression data-sets of PAK1 from The Cancer Genome Atlas-cBioportal and GeneCards databases and found that 91 PAK1-related genes are associated with breast cancer. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway investigations of 91 genes via Database for Annotation Visualization and Integrated Discovery bioinformatics resource revealed that, PAK1 being a major kinase is associated with several metabolic pathways and involved in phosphorylation, signal transduction, apoptosis, biosynthesis and majorly cancer-related cell signalling pathways. The PAK1 interaction network derived from STRING and Cytoscape revealed that the genes Signal-Transducer-and-Activator-of-Transcription-3 (STAT3), Cyclin-D1 (CCND1), Mitogen-activated protein kinase-1 (MAPK1), Ras-Homolog-Family-Member-A (RHOA) and Catenin-beta-1 have high degrees of interaction where CCND1, MAPK1 and RHOA have direct interaction with PAK1. Finally, the global expression map of PAK1 and its related genes was derived as topological frame that helped to explore and investigate PAK1 interactions. Further, the molecular modelling studies of PAK1 with its major interacting partners RHOA and STAT3 helped to explore the key interactive residues of PAK1 structure. This information can be used to develop novel therapeutic and control strategies against breast cancer.
DOI: 10.6084/m9.figshare.3562998
title: Appendix B. Pearson correlation coefficients between community-weighted biological trait categories expressed as percentages and the stress intensity.

description: Pearson correlation coefficients between community-weighted biological trait categories expressed as percentages and the stress intensity.
DOI: 10.6084/m9.figshare.3562998.v1
title: Appendix B. Pearson correlation coefficients between community-weighted biological trait categories expressed as percentages and the stress intensity.

description: Pearson correlation coefficients between community-weighted biological trait categories expressed as percentages and the stress intensity.
DOI: 10.6084/m9.figshare.3562995
title: Appendix C. Mean absolute Pearson correlation coefficients between trait categories and the stress intensity for each trait.

description: Mean absolute Pearson correlation coefficients between trait categories and the stress intensity for each trait.
DOI: 10.6084/m9.figshare.3562995.v1
title: Appendix C. Mean absolute Pearson correlation coefficients between trait categories and the stress intensity for each trait.

description: Mean absolute Pearson correlation coefficients between trait categories and the stress intensity for each trait.
DOI: 10.6084/m9.figshare.3562992
title: Appendix D. Procedure used to calculate functional niche features.

description: Procedure used to calculate functional niche features.
DOI: 10.6084/m9.figshare.3562992.v1
title: Appendix D. Procedure used to calculate functional niche features.

description: Procedure used to calculate functional niche features.
DOI: 10.6084/m9.figshare.3562986
title: Appendix A. Tables giving all experimental species and detailed information on their status, seed origin, years planted into the experiment, and GenBank accession numbers, results from phylogenetic generalized least squares (PGLS) ANOVA testing the effects of plant status (native, non-invasive exotic, or invasive) and phylogeny on insect herbivory and mammal browsing, results from ANCOVA testing the effects of plant status and geographic spread (at three spatial scales) or time on herbivory...

description: Tables giving all experimental species and detailed information on their status, seed origin, years planted into the experiment, and GenBank accession numbers, results from phylogenetic generalized least squares (PGLS) ANOVA testing the effects of plant status (native, non-invasive exotic, or invasive) and phylogeny on insect herbivory and mammal browsing, results from ANCOVA testing the effects of plant status and geographic spread (at three spatial scales) or time on herbivory and browsing, and a figure giving the best scoring ML phylogenetic tree.
DOI: 10.6084/m9.figshare.3562986.v1
title: Appendix A. Tables giving all experimental species and detailed information on their status, seed origin, years planted into the experiment, and GenBank accession numbers, results from phylogenetic generalized least squares (PGLS) ANOVA testing the effects of plant status (native, non-invasive exotic, or invasive) and phylogeny on insect herbivory and mammal browsing, results from ANCOVA testing the effects of plant status and geographic spread (at three spatial scales) or time on herbivory...

description: Tables giving all experimental species and detailed information on their status, seed origin, years planted into the experiment, and GenBank accession numbers, results from phylogenetic generalized least squares (PGLS) ANOVA testing the effects of plant status (native, non-invasive exotic, or invasive) and phylogeny on insect herbivory and mammal browsing, results from ANCOVA testing the effects of plant status and geographic spread (at three spatial scales) or time on herbivory and browsing, and a figure giving the best scoring ML phylogenetic tree.
DOI: 10.6084/m9.figshare.3562983
title: Appendix B. Describes the statistical methods and results for ANOVA models testing the effects of plant status and family on herbivory and browsing.

description: Describes the statistical methods and results for ANOVA models testing the effects of plant status and family on herbivory and browsing.
DOI: 10.6084/m9.figshare.3562983.v1
title: Appendix B. Describes the statistical methods and results for ANOVA models testing the effects of plant status and family on herbivory and browsing.

description: Describes the statistical methods and results for ANOVA models testing the effects of plant status and family on herbivory and browsing.
DOI: 10.6084/m9.figshare.3562980
title: Appendix A. Statistical results from the ‘sympatric vs. allopatric’ tests for local adaption.

description: Statistical results from the ‘sympatric vs. allopatric’ tests for local adaption.
DOI: 10.6084/m9.figshare.3562980.v1
title: Appendix A. Statistical results from the ‘sympatric vs. allopatric’ tests for local adaption.

description: Statistical results from the ‘sympatric vs. allopatric’ tests for local adaption.
DOI: 10.6084/m9.figshare.3562977
title: Appendix A. Detailed description of the methods.

description: Detailed description of the methods.
DOI: 10.6084/m9.figshare.3562977.v1
title: Appendix A. Detailed description of the methods.

description: Detailed description of the methods.
DOI: 10.6084/m9.figshare.3562974
title: Appendix B. Explanation and definition of all a priori models.

description: Explanation and definition of all a priori models.
DOI: 10.6084/m9.figshare.3562974.v1
title: Appendix B. Explanation and definition of all a priori models.

description: Explanation and definition of all a priori models.
DOI: 10.6084/m9.figshare.3562971
title: Supplement 1. Weather summary statistics for all data sets.

description: File List Supplement1.csv (MD5: dcbd9df604beab366cc4ca5ce57bbbb5) Description Summary statistics (mean, minimum, maximum, median, and standard deviation) for each weather variable at (A) Cedar Creek (Bethel, MN; 1985–2003), (B) Konza Prairie (Manhattan, KS; 1984–2011) used for analysis of the fire frequency experiment, (C) Konza Prairie (1994–2011) used for analyses of the grazing and season of burn experiments, (D) College Pasture (Hays, KS; 1934–1972), and E) Shortgrass Steppe (Nunn, CO; 1992–2008). CV = coefficient of variation, sum = summer, spr = spring, win = winter, t_1 = season preceding growing season (spring and summer) when plants were sampled, precipitation (PPT) given in mm, temperature (TEMP) in °C.
DOI: 10.6084/m9.figshare.3562971.v1
title: Supplement 1. Weather summary statistics for all data sets.

description: File List Supplement1.csv (MD5: dcbd9df604beab366cc4ca5ce57bbbb5) Description Summary statistics (mean, minimum, maximum, median, and standard deviation) for each weather variable at (A) Cedar Creek (Bethel, MN; 1985–2003), (B) Konza Prairie (Manhattan, KS; 1984–2011) used for analysis of the fire frequency experiment, (C) Konza Prairie (1994–2011) used for analyses of the grazing and season of burn experiments, (D) College Pasture (Hays, KS; 1934–1972), and E) Shortgrass Steppe (Nunn, CO; 1992–2008). CV = coefficient of variation, sum = summer, spr = spring, win = winter, t_1 = season preceding growing season (spring and summer) when plants were sampled, precipitation (PPT) given in mm, temperature (TEMP) in °C.
DOI: 10.6084/m9.figshare.3562968
title: Supplement 2. Complete AICc results for five nitrogen fertilization treatments at Cedar Creek Long-Term Ecological Research (LTER) site (Bethel, MN, USA).

description: File List Supplement2.csv (MD5: 32a51cff7bf470d9b2608dd010c90acc) Description Model selection results from analysis of the relationship between a priori 1- and 2-variable weather models and native, exotic, and total plant species (A) richness and (B) Shannon diversity in each annual nitrogen fertilization treatment at Cedar Creek (1985–2003, <i>N</i> = 19). <i>R</i>² = least square means regression coefficient, K = # of model parameters including intercept and first order autoregressive parameter, LL = log likelihood, AICc = Akaike Information Criterion corrected for small samples size, DAICC = difference in AICc value between given model and model with lowest AICc, wi = Akaike weights (measure of relative importance of a given model compared to all other models in the model set). Treatment (TRT): 0 = true control (no N-fertilizer or micronutrients added), 1 = 0 g·N·m<sup>-2</sup>·yr<sup>-1</sup> + micronutrients, 2 = 1.02 g·N·m<sup>-2</sup> yr<sup>-1</sup> + micronutrients, 5 = 5.44 g·N·m<sup>-2</sup> yr<sup>-1</sup> + micronutrients, 8 = 27.2 g N·m<sup>-2</sup>·yr<sup>-1</sup> + micronutrients. CV = coefficient of variation, sum = summer, spr = spring, win = winter, PPT = precipitation, TEMP = temperature, t_1 = season preceding growing season (spring and summer) when plants were sampled. Ecologically meaningful models (EMMs) were those with DAICC &lt; 2 and <i>R</i>² &gt; 0.30. Model # corresponds to a priori models as described in Appendix B.
DOI: 10.6084/m9.figshare.3562968.v1
title: Supplement 2. Complete AICc results for five nitrogen fertilization treatments at Cedar Creek Long-Term Ecological Research (LTER) site (Bethel, MN, USA).

description: File List Supplement2.csv (MD5: 32a51cff7bf470d9b2608dd010c90acc) Description Model selection results from analysis of the relationship between a priori 1- and 2-variable weather models and native, exotic, and total plant species (A) richness and (B) Shannon diversity in each annual nitrogen fertilization treatment at Cedar Creek (1985–2003, <i>N</i> = 19). <i>R</i>² = least square means regression coefficient, K = # of model parameters including intercept and first order autoregressive parameter, LL = log likelihood, AICc = Akaike Information Criterion corrected for small samples size, DAICC = difference in AICc value between given model and model with lowest AICc, wi = Akaike weights (measure of relative importance of a given model compared to all other models in the model set). Treatment (TRT): 0 = true control (no N-fertilizer or micronutrients added), 1 = 0 g·N·m<sup>-2</sup>·yr<sup>-1</sup> + micronutrients, 2 = 1.02 g·N·m<sup>-2</sup> yr<sup>-1</sup> + micronutrients, 5 = 5.44 g·N·m<sup>-2</sup> yr<sup>-1</sup> + micronutrients, 8 = 27.2 g N·m<sup>-2</sup>·yr<sup>-1</sup> + micronutrients. CV = coefficient of variation, sum = summer, spr = spring, win = winter, PPT = precipitation, TEMP = temperature, t_1 = season preceding growing season (spring and summer) when plants were sampled. Ecologically meaningful models (EMMs) were those with DAICC &lt; 2 and <i>R</i>² &gt; 0.30. Model # corresponds to a priori models as described in Appendix B.
DOI: 10.6084/m9.figshare.3562965
title: Supplement 3. Complete relative importance values for five nitrogen fertilization treatments at Cedar Creek.

description: File List Supplement3.csv (MD5: d527703cfbd627caee9063fa3f25ddd3) Description Relative importance values (RIVs) of weather variables for native, exotic, and total species (A) richness and (B) Shannon diversity (H') for Cedar Creek by annual nitrogen (g N·m<sup>-2</sup>·yr<sup>-1</sup>) fertilization treatments. RIVs are calculated as the sum of Akaike weights (wi) across all models in which a given variable appeared for each nitrogen fertilization treatment. See Appendix B for description of all a priori models and Supplement 2 for all results of AICc analyses. CV = coefficient of variation, sum = summer, spr = spring, win = winter, PPT = precipitation, TEMP = temperature, t_1 = season preceding growing season (spring and summer) when plants were sampled, mn = micronutrients.
DOI: 10.6084/m9.figshare.3562965.v1
title: Supplement 3. Complete relative importance values for five nitrogen fertilization treatments at Cedar Creek.

description: File List Supplement3.csv (MD5: d527703cfbd627caee9063fa3f25ddd3) Description Relative importance values (RIVs) of weather variables for native, exotic, and total species (A) richness and (B) Shannon diversity (H') for Cedar Creek by annual nitrogen (g N·m<sup>-2</sup>·yr<sup>-1</sup>) fertilization treatments. RIVs are calculated as the sum of Akaike weights (wi) across all models in which a given variable appeared for each nitrogen fertilization treatment. See Appendix B for description of all a priori models and Supplement 2 for all results of AICc analyses. CV = coefficient of variation, sum = summer, spr = spring, win = winter, PPT = precipitation, TEMP = temperature, t_1 = season preceding growing season (spring and summer) when plants were sampled, mn = micronutrients.
DOI: 10.6084/m9.figshare.3562962
title: Supplement 4. Complete AICc results for three experiments at Konza Prairie LTER site (Manhattan, KS, USA).

description: File List Supplement4.csv (MD5: 9c2a02597ef066d593745194c0a92bb0) Description Model selection results from analysis of the relationship between a priori 1- and 2-variable weather models and total species (A) richness and (B) Shannon diversity in each fire frequency treatment (Experiment = FRI, 1984–2011, <i>N</i> = 28), grazing treatment (Experiment = Graze, 1994–2011, <i>N</i> = 18), and season of burn treatment (Experiment = SB, 1994–2011, <i>N</i> = 18) at Konza Prairie LTER. <i>R</i>² = least square means regression coefficient, K = # of model parameters including intercept and first order autoregressive parameter, LL = log likelihood, AICc = Akaike Information Criterion corrected for small samples size, DAICC = difference in AICc value between given model and model with lowest AICc, wi = Akaike weights (measure of relative importance of a given model compared to all other models in the model set). Treatments are as follows: 1 = annually burned, 4 = burned once every 4 years, 20 = burned once every 20 years, GR = bison grazed, UG = ungrazed, FAL = fall-burned, WIN = winter-burned, SPR = spring-burned, SUM = summer burned. CV = coefficient of variation, sum = summer, spr = spring, win = winter, PPT = precipitation, TEMP = temperature, t_1 = season preceding growing season (spring and summer) when plants were sampled. Ecologically meaningful models (EMMs) were those with DAICC &lt; 2 and <i>R</i>² &gt; 0.30. Model # corresponds to a priori models as described in Appendix B.
DOI: 10.6084/m9.figshare.3562962.v1
title: Supplement 4. Complete AICc results for three experiments at Konza Prairie LTER site (Manhattan, KS, USA).

description: File List Supplement4.csv (MD5: 9c2a02597ef066d593745194c0a92bb0) Description Model selection results from analysis of the relationship between a priori 1- and 2-variable weather models and total species (A) richness and (B) Shannon diversity in each fire frequency treatment (Experiment = FRI, 1984–2011, <i>N</i> = 28), grazing treatment (Experiment = Graze, 1994–2011, <i>N</i> = 18), and season of burn treatment (Experiment = SB, 1994–2011, <i>N</i> = 18) at Konza Prairie LTER. <i>R</i>² = least square means regression coefficient, K = # of model parameters including intercept and first order autoregressive parameter, LL = log likelihood, AICc = Akaike Information Criterion corrected for small samples size, DAICC = difference in AICc value between given model and model with lowest AICc, wi = Akaike weights (measure of relative importance of a given model compared to all other models in the model set). Treatments are as follows: 1 = annually burned, 4 = burned once every 4 years, 20 = burned once every 20 years, GR = bison grazed, UG = ungrazed, FAL = fall-burned, WIN = winter-burned, SPR = spring-burned, SUM = summer burned. CV = coefficient of variation, sum = summer, spr = spring, win = winter, PPT = precipitation, TEMP = temperature, t_1 = season preceding growing season (spring and summer) when plants were sampled. Ecologically meaningful models (EMMs) were those with DAICC &lt; 2 and <i>R</i>² &gt; 0.30. Model # corresponds to a priori models as described in Appendix B.
DOI: 10.6084/m9.figshare.3562959
title: Supplement 5. Complete relative importance values for Konza Prairie.

description: File List Supplement5.csv (MD5: cc3c36117e8b123bb98e25cbc84719c2) Description Relative importance values (RIVs) of weather variables for total species (A) richness and (B) Shannon diversity (H') from the Konza Prairie fire return interval experiment, bison grazing experiment, and season of burn experiment. RIVs are calculated as the sum of Akaike weights (<i>wi</i>) across all models in which a given variable appeared for each fire return interval, bison grazing, or season of burn. See Appendix B for description of all a priori models and Supplement 4 for all results of AICc analyses. CV = coefficient of variation, sum = summer, spr = spring, win = winter, PPT = precipitation, TEMP = temperature, t_1 = season preceding growing season (spring and summer) when plants were sampled.
DOI: 10.6084/m9.figshare.3562959.v1
title: Supplement 5. Complete relative importance values for Konza Prairie.

description: File List Supplement5.csv (MD5: cc3c36117e8b123bb98e25cbc84719c2) Description Relative importance values (RIVs) of weather variables for total species (A) richness and (B) Shannon diversity (H') from the Konza Prairie fire return interval experiment, bison grazing experiment, and season of burn experiment. RIVs are calculated as the sum of Akaike weights (<i>wi</i>) across all models in which a given variable appeared for each fire return interval, bison grazing, or season of burn. See Appendix B for description of all a priori models and Supplement 4 for all results of AICc analyses. CV = coefficient of variation, sum = summer, spr = spring, win = winter, PPT = precipitation, TEMP = temperature, t_1 = season preceding growing season (spring and summer) when plants were sampled.
DOI: 10.6084/m9.figshare.3562956
title: Supplement 6. Complete AICc results for the College Pasture site (Hays, KS, USA).

description: File List Supplement6.csv (MD5: e3f159b1ef6d3783b663e3c70bbd2371) Description Model selection results from analysis of the relationship between weather models and total plant species (A) richness and (B) diversity in each plant assemblage at College Pasture (1934–1972, <i>N</i> = 39). <i>R</i>² = least-square means regression coefficient, K = # of model parameters including intercept and first order autoregressive parameter, LL = log likelihood, AICc = Akaike Information Criterion corrected for small samples size, DAICc = difference in AICc value between given model and model with lowest AICc, wi = Akaike weights (measure of relative importance of a given model compared to all other models in the model set). Treatments (TRT) are as follows: No_bb = ungrazed big bluestem assemblage, No_et = ungrazed ecotone assemblage, No_lb = ungrazed little bluestem assemblage, No_sg = ungrazed shortgrass assemblage, Yes_sg = cattle-grazed shortgrass assemblage. CV = coefficient of variation, sum = summer, spr = spring, win = winter, PPT = precipitation, TEMP = temperature, t_1 = season preceding growing season (spring and summer) when plants were sampled. Ecologically meaningful models (EMMs) were those with DAICc &lt; 2 and <i>R</i>² &gt; 0.30. Model # corresponds to a priori models as described in Appendix B.