cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1629360.v2
title: Identification and characterization of microRNAs in Eucheuma denticulatum by high-throughput sequencing and bioinformatics analysis

description: <i>Eucheuma denticulatum</i>, an economically and industrially important red alga, is a valuable marine resource. Although microRNAs (miRNAs) play an essential role in gene post-transcriptional regulation, no research has been conducted to identify and characterize miRNAs in <i>E. denticulatum</i>. In this study, we identified 134 miRNAs (133 conserved miRNAs and one novel miRNA) from 2,997,135 small-RNA reads by high-throughput sequencing combined with bioinformatics analysis. BLAST searching against miRBase uncovered 126 potential miRNA families. A conservation and diversity analysis of predicted miRNA families in different plant species was performed by comparative alignment and homology searching. A total of 4 and 13 randomly selected miRNAs were respectively validated by northern blotting and stem-loop reverse transcription PCR, thereby demonstrating the reliability of the miRNA sequencing data. Altogether, 871 potential target genes were predicted using psRobot and TargetFinder. Target genes classification and enrichment were conducted based on Gene Ontology analysis. The functions of target gene products and associated metabolic pathways were predicted by Kyoto Encyclopedia of Genes and Genomes pathway analysis. A Cytoscape network was constructed to explore the interrelationships of miRNAs, miRNA-target genes and target genes. A large number of miRNAs with diverse target genes will play important roles for further understanding some essential biological processes in <i>E. denticulatum</i>. The uncovered information can serve as an important reference for the protection and utilization of this unique red alga in the future.
DOI: 10.6084/m9.figshare.1629360.v1
title: Identification and characterization of microRNAs in <i>Eucheuma denticulatum</i> by high-throughput sequencing and bioinformatics analysis

description: <i>Eucheuma denticulatum</i>, an economically and industrially important red alga, is a valuable marine resource. Although microRNAs (miRNAs) play an essential role in gene post-transcriptional regulation, no research has been conducted to identify and characterize miRNAs in <i>E. denticulatum</i>. In this study, we identified 134 miRNAs (133 conserved miRNAs and one novel miRNA) from 2,997,135 small-RNA reads by high-throughput sequencing combined with bioinformatics analysis. BLAST searching against miRBase uncovered 126 potential miRNA families. A conservation and diversity analysis of predicted miRNA families in different plant species was performed by comparative alignment and homology searching. A total of 4 and 13 randomly selected miRNAs were respectively validated by northern blotting and stem-loop reverse transcription PCR, thereby demonstrating the reliability of the miRNA sequencing data. Altogether, 871 potential target genes were predicted using psRobot and TargetFinder. Target genes classification and enrichment were conducted based on Gene Ontology analysis. The functions of target gene products and associated metabolic pathways were predicted by Kyoto Encyclopedia of Genes and Genomes pathway analysis. A Cytoscape network was constructed to explore the interrelationships of miRNAs, miRNA-target genes and target genes. A large number of miRNAs with diverse target genes will play important roles for further understanding some essential biological processes in <i>E. denticulatum</i>. The uncovered information can serve as an important reference for the protection and utilization of this unique red alga in the future.
DOI: 10.6084/m9.figshare.1629364
title: Forecasting the Impacts of Silver and Bighead Carp on the Lake Erie Food Web

description: Nonindigenous bigheaded carps (Bighead Carp <i>Hypophthalmichthys nobilis</i> and Silver Carp <i>H. molitrix</i>; hereafter, “Asian carps” [AC]) threaten to invade and disrupt food webs and fisheries in the Laurentian Great Lakes through their high consumption of plankton. To quantify the potential effects of AC on the food web in Lake Erie, we developed an Ecopath with Ecosim (EwE) food web model and simulated four AC diet composition scenarios (high, low, and no detritus and low detritus with Walleye <i>Sander vitreus</i> and Yellow Perch <i>Perca flavescens</i> larvae) and two nutrient load scenarios (the 1999 baseline load and 2× the baseline [HP]). We quantified the uncertainty of the potential AC effects by coupling the EwE model with estimates of parameter uncertainty in AC production, consumption, and predator diets obtained using structured expert judgment. Our model projected mean ± SD AC equilibrium biomass ranging from 52 ± 34 to 104 ± 75 kg/ha under the different scenarios. Relative to baseline simulations without AC, AC invasion under all detrital diet scenarios decreased the biomass of most fish and zooplankton groups. The effects of AC in the HP scenario were similar to those in the detrital diet scenarios except that the biomasses of most Walleye and Yellow Perch groups were greater under HP because these fishes were buffered from competition with AC by increased productivity at lower trophic levels. Asian carp predation on Walleye and Yellow Perch larvae caused biomass declines among all Walleye and Yellow Perch groups. Large food web impacts of AC occurred in only 2% of the simulations, where AC biomass exceeded 200 kg/ha, resulting in biomass declines of zooplankton and planktivorous fish near the levels observed in the Illinois River. Our findings suggest that AC would affect Lake Erie's food web by competing with other planktivorous fishes and by providing additional prey for piscivores. Our methods provide a novel approach for including uncertainty into forecasts of invasive species' impacts on aquatic food webs. Received December 6, 2014; accepted July 15, 2015
DOI: 10.6084/m9.figshare.1629364.v1
title: Forecasting the Impacts of Silver and Bighead Carp on the Lake Erie Food Web

description: Nonindigenous bigheaded carps (Bighead Carp <i>Hypophthalmichthys nobilis</i> and Silver Carp <i>H. molitrix</i>; hereafter, “Asian carps” [AC]) threaten to invade and disrupt food webs and fisheries in the Laurentian Great Lakes through their high consumption of plankton. To quantify the potential effects of AC on the food web in Lake Erie, we developed an Ecopath with Ecosim (EwE) food web model and simulated four AC diet composition scenarios (high, low, and no detritus and low detritus with Walleye <i>Sander vitreus</i> and Yellow Perch <i>Perca flavescens</i> larvae) and two nutrient load scenarios (the 1999 baseline load and 2× the baseline [HP]). We quantified the uncertainty of the potential AC effects by coupling the EwE model with estimates of parameter uncertainty in AC production, consumption, and predator diets obtained using structured expert judgment. Our model projected mean ± SD AC equilibrium biomass ranging from 52 ± 34 to 104 ± 75 kg/ha under the different scenarios. Relative to baseline simulations without AC, AC invasion under all detrital diet scenarios decreased the biomass of most fish and zooplankton groups. The effects of AC in the HP scenario were similar to those in the detrital diet scenarios except that the biomasses of most Walleye and Yellow Perch groups were greater under HP because these fishes were buffered from competition with AC by increased productivity at lower trophic levels. Asian carp predation on Walleye and Yellow Perch larvae caused biomass declines among all Walleye and Yellow Perch groups. Large food web impacts of AC occurred in only 2% of the simulations, where AC biomass exceeded 200 kg/ha, resulting in biomass declines of zooplankton and planktivorous fish near the levels observed in the Illinois River. Our findings suggest that AC would affect Lake Erie's food web by competing with other planktivorous fishes and by providing additional prey for piscivores. Our methods provide a novel approach for including uncertainty into forecasts of invasive species' impacts on aquatic food webs. Received December 6, 2014; accepted July 15, 2015
DOI: 10.6084/m9.figshare.1629369
title: Identification of genes required for the spontaneous repair of partial-thickness cartilage defects in immature rats

description: <b><i>Purpose</i></b>: Our previous study showed that partial-thickness articular cartilage defects (PTCDs) created in immature rats spontaneously healed to resemble normal hyaline cartilage, but that of mature rats did not. To identify molecules involved in the spontaneous cartilage repair observed in this model, gene expression was compared between PTCD and sham-operated cartilage of immature and mature rats. <b><i>Materials and Methods</i></b>: Six sets of gene comparisons were made at 12, 24, and 48 hours after the creation of PTCDs in immature and mature rats using microarrays. All the genes upregulated in immature cartilage at 12 hours were selected for further analysis if their expression pattern was not irregular such that diminished at 24 hours and re-upregulated at 48 hours. Relationships among genes selected through the above steps were analyzed using Ingenuity Pathway Analysis (IPA) software. After deriving networks, important molecules were further narrowed down by location within a network. Genes were regarded as central if they had relationships with more than 10 molecules in a network. Protein localization in tissues was confirmed by immunohistochemistry. <b><i>Results</i></b>: Five networks were identified. Their functional annotations were gene expression, cell cycle, growth and proliferation, and cell signaling. Transforming growth factor-beta (TGF-β) was centrally located in the network with the highest IPA score and mothers against decapentaplegic homolog-3 (Smad3) were centrally located in the second highest ranking network. Phosphorylated Smad3 was detected in the nuclei of chondrocytes in immature cartilage. <b><i>Conclusions</i></b>: Our data suggest the possible importance of Smad3 in the TGF-β signaling in the spontaneous healing of PTCDs in immature rats.
DOI: 10.6084/m9.figshare.1629369.v1
title: Identification of genes required for the spontaneous repair of partial-thickness cartilage defects in immature rats

description: <b><i>Purpose</i></b>: Our previous study showed that partial-thickness articular cartilage defects (PTCDs) created in immature rats spontaneously healed to resemble normal hyaline cartilage, but that of mature rats did not. To identify molecules involved in the spontaneous cartilage repair observed in this model, gene expression was compared between PTCD and sham-operated cartilage of immature and mature rats. <b><i>Materials and Methods</i></b>: Six sets of gene comparisons were made at 12, 24, and 48 hours after the creation of PTCDs in immature and mature rats using microarrays. All the genes upregulated in immature cartilage at 12 hours were selected for further analysis if their expression pattern was not irregular such that diminished at 24 hours and re-upregulated at 48 hours. Relationships among genes selected through the above steps were analyzed using Ingenuity Pathway Analysis (IPA) software. After deriving networks, important molecules were further narrowed down by location within a network. Genes were regarded as central if they had relationships with more than 10 molecules in a network. Protein localization in tissues was confirmed by immunohistochemistry. <b><i>Results</i></b>: Five networks were identified. Their functional annotations were gene expression, cell cycle, growth and proliferation, and cell signaling. Transforming growth factor-beta (TGF-β) was centrally located in the network with the highest IPA score and mothers against decapentaplegic homolog-3 (Smad3) were centrally located in the second highest ranking network. Phosphorylated Smad3 was detected in the nuclei of chondrocytes in immature cartilage. <b><i>Conclusions</i></b>: Our data suggest the possible importance of Smad3 in the TGF-β signaling in the spontaneous healing of PTCDs in immature rats.
DOI: 10.6084/m9.figshare.1632715
title: Variable Selection in a Log–Linear Birnbaum–Saunders Regression Model for High-Dimensional Survival Data via the Elastic-Net and Stochastic EM

description: The Birnbaum–Saunders (BS) distribution is broadly used to model failure times in reliability and survival analysis. In this article, we propose a simultaneous parameter estimation and variable selection procedure in a log–linear BS regression model for high-dimensional survival data. To deal with censored survival data, we iteratively run a combination of the stochastic EM algorithm (SEM) and variable selection procedure to generate pseudo-complete data and select variables until convergence. Treating pseudo-complete data as uncensored data via SEM makes it possible to incorporate iterative penalized least squares and simplify computation. We demonstrate the efficacy of our method using simulated and real datasets.
DOI: 10.6084/m9.figshare.1632715.v2
title: Variable Selection in a Log–Linear Birnbaum–Saunders Regression Model for High-Dimensional Survival Data via the Elastic-Net and Stochastic EM

description: The Birnbaum–Saunders (BS) distribution is broadly used to model failure times in reliability and survival analysis. In this article, we propose a simultaneous parameter estimation and variable selection procedure in a log–linear BS regression model for high-dimensional survival data. To deal with censored survival data, we iteratively run a combination of the stochastic EM algorithm (SEM) and variable selection procedure to generate pseudo-complete data and select variables until convergence. Treating pseudo-complete data as uncensored data via SEM makes it possible to incorporate iterative penalized least squares and simplify computation. We demonstrate the efficacy of our method using simulated and real datasets.
DOI: 10.6084/m9.figshare.1632715.v1
title: Variable Selection in a Log-linear Birnbaum-Saunders Regression Model for High-dimensional Survival Data via the Elastic-Net and Stochastic EM

description: The Birnbaum-Saunders (BS) distribution is broadly used to model failure times in reliability and survival analysis. In this article, we propose a simultaneous parameter estimation and variable selection procedure in a log-linear BS regression model for high-dimensional survival data. To deal with censored survival data, we iteratively run a combination of the stochastic EM algorithm (SEM) and variable selection procedure to generate pseudo-complete data and select variables until convergence. Treating pseudo-complete data as uncensored data via SEM makes it possible to incorporate iterative penalized least squares and simplify computation. We demonstrate the efficacy of our method using simulated and real data sets.
DOI: 10.6084/m9.figshare.1632766
title: The tectonic setting of early Permian bimodal volcanism in central Inner Mongolia: continental rift, post-collisional extension, or active continental margin?

description: ABSTRACTThe petrology, geochronology, and geochemistry of the early Permian volcanic rocks from Houtoumiao area, south Xiwuqi County in central Inner Mongolia of China, are studied to elucidate the early Permian tectonic setting of the region. The volcanic rocks, which are interbedded with sandstone, feature both mafic and felsic compositions and show a bimodal nature. Zircon U–Pb dating reveals that the volcanic rocks formed at 274–278 Ma, similar to the ages of bimodal magmatism in neighbouring areas. The mafic rocks are composed of tholeiitic basalt, basaltic andesite, basaltic trachyandesite, and trachyandesite. They are rich in Th, U, and LILEs, depleted in HFSEs Nb, Ta, and Ti, and have positive ε<sub>Nd</sub>(t) values (+3.6 to +7.9). Geochemical analyses indicate that the mafic rocks originated from metasomatized lithospheric mantle. The felsic volcanic rocks are mainly rhyolite, with minor trachyte and dacite. They have different evolutionary tendencies of major elements, chondrite-normalized REE patterns, and isotopic compositions from the mafic volcanic rocks, which preclude formation by fractional crystallization of mafic melts. The ε<sub>Nd</sub>(t) values of the felsic rocks are similar to those of the Carboniferous Baolidao arc rocks in the region. It is suggested that Permian felsic melts originated from the partial melting of Carboniferous juvenile arc-related rocks. By comparison with typical Cenozoic bimodal volcanism associated with several tectonic settings, including rift, post-collisional setting, back-arc basin, and the Basin and Range, USA, the bimodal volcanic rocks in central Inner Mongolia display similar petrological and geochemical characteristics to the rocks from back-arc basin and the Basin and Range, USA. Based on the analysis of regional geological data, it is inferred that the early Permian bimodal volcanic rocks in the study area formed on an extensional continental margin of the Siberian palaeoplate after late Carboniferous subduction–accretion.
DOI: 10.6084/m9.figshare.1632766.v1
title: The tectonic setting of early Permian bimodal volcanism in central Inner Mongolia: continental rift, post-collisional extension, or active continental margin?

description: ABSTRACTThe petrology, geochronology, and geochemistry of the early Permian volcanic rocks from Houtoumiao area, south Xiwuqi County in central Inner Mongolia of China, are studied to elucidate the early Permian tectonic setting of the region. The volcanic rocks, which are interbedded with sandstone, feature both mafic and felsic compositions and show a bimodal nature. Zircon U–Pb dating reveals that the volcanic rocks formed at 274–278 Ma, similar to the ages of bimodal magmatism in neighbouring areas. The mafic rocks are composed of tholeiitic basalt, basaltic andesite, basaltic trachyandesite, and trachyandesite. They are rich in Th, U, and LILEs, depleted in HFSEs Nb, Ta, and Ti, and have positive ε<sub>Nd</sub>(t) values (+3.6 to +7.9). Geochemical analyses indicate that the mafic rocks originated from metasomatized lithospheric mantle. The felsic volcanic rocks are mainly rhyolite, with minor trachyte and dacite. They have different evolutionary tendencies of major elements, chondrite-normalized REE patterns, and isotopic compositions from the mafic volcanic rocks, which preclude formation by fractional crystallization of mafic melts. The ε<sub>Nd</sub>(t) values of the felsic rocks are similar to those of the Carboniferous Baolidao arc rocks in the region. It is suggested that Permian felsic melts originated from the partial melting of Carboniferous juvenile arc-related rocks. By comparison with typical Cenozoic bimodal volcanism associated with several tectonic settings, including rift, post-collisional setting, back-arc basin, and the Basin and Range, USA, the bimodal volcanic rocks in central Inner Mongolia display similar petrological and geochemical characteristics to the rocks from back-arc basin and the Basin and Range, USA. Based on the analysis of regional geological data, it is inferred that the early Permian bimodal volcanic rocks in the study area formed on an extensional continental margin of the Siberian palaeoplate after late Carboniferous subduction–accretion.
DOI: 10.6084/m9.figshare.1632806
title: Analysis and prediction of drug–drug interaction by minimum redundancy maximum relevance and incremental feature selection

description: Drug–drug interaction (DDI) defines a situation in which one drug affects the activity of another when both are administered together. DDI is a common cause of adverse drug reactions and sometimes also leads to improved therapeutic effects. Therefore, it is of great interest to discover novel DDIs according to their molecular properties and mechanisms in a robust and rigorous way. This paper attempts to predict effective DDIs using the following properties: (1) chemical interaction between drugs; (2) protein interactions between the targets of drugs; and (3) target enrichment of KEGG pathways. The data consisted of 7323 pairs of DDIs collected from the DrugBank and 36,615 pairs of drugs constructed by randomly combining two drugs. Each drug pair was represented by 465 features derived from the aforementioned three categories of properties. The random forest algorithm was adopted to train the prediction model. Some feature selection techniques, including minimum redundancy maximum relevance and incremental feature selection, were used to extract key features as the optimal input for the prediction model. The extracted key features may help to gain insights into the mechanisms of DDIs and provide some guidelines for the relevant clinical medication developments, and the prediction model can give new clues for identification of novel DDIs.
DOI: 10.6084/m9.figshare.1632806.v2
title: Analysis and prediction of drug–drug interaction by minimum redundancy maximum relevance and incremental feature selection

description: Drug–drug interaction (DDI) defines a situation in which one drug affects the activity of another when both are administered together. DDI is a common cause of adverse drug reactions and sometimes also leads to improved therapeutic effects. Therefore, it is of great interest to discover novel DDIs according to their molecular properties and mechanisms in a robust and rigorous way. This paper attempts to predict effective DDIs using the following properties: (1) chemical interaction between drugs; (2) protein interactions between the targets of drugs; and (3) target enrichment of KEGG pathways. The data consisted of 7323 pairs of DDIs collected from the DrugBank and 36,615 pairs of drugs constructed by randomly combining two drugs. Each drug pair was represented by 465 features derived from the aforementioned three categories of properties. The random forest algorithm was adopted to train the prediction model. Some feature selection techniques, including minimum redundancy maximum relevance and incremental feature selection, were used to extract key features as the optimal input for the prediction model. The extracted key features may help to gain insights into the mechanisms of DDIs and provide some guidelines for the relevant clinical medication developments, and the prediction model can give new clues for identification of novel DDIs.
DOI: 10.6084/m9.figshare.1632806.v1
title: Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection

description: : Drug-drug interaction (DDI) defines a situation in which one drug affects the activity of another when both are administered together. DDI is a common cause of adverse drug reactions and sometimes also leads to improved therapeutic effects. Therefore, it is of great interest to discover novel DDIs according to their molecular properties and mechanisms in a robust and rigorous way. This paper attempts to predict effective DDIs using the following properties: (1) chemical interaction between drugs; (2) protein interactions between the targets of drugs; and (3) target enrichment of KEGG pathways. The data consisted of 7,323 pairs of DDIs collected from the DrugBank and 36,615 pairs of drugs constructed by randomly combining two drugs. Each drug pair was represented by 465 features derived from the aforementioned three categories of properties. The random forest algorithm was adopted to train the prediction model. Some feature selection techniques, including minimum redundancy maximum relevance (mRMR) and incremental feature selection (IFS), were used to extract key features as the optimal input for the prediction model. The extracted key features may help to gain insights into the mechanisms of DDIs and provide some guidelines for the relevant clinical medication developments, and the prediction model can give new clues for identification of novel DDIs.