cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1289352.v1
title: Combined biofouling and scaling in membrane feed channels: a new modeling approach

description: A mathematical model was developed for combined fouling due to biofilms and mineral precipitates in membrane feed channels with spacers. Finite element simulation of flow and solute transport in two-dimensional geometries was coupled with a particle-based approach for the development of a composite (cells and crystals) foulant layer. Three fouling scenarios were compared: biofouling only, scaling only and combined fouling. Combined fouling causes a quicker flux decline than the summed flux deterioration when scaling and biofouling act independently. The model results indicate that the presence of biofilms leads to more mineral formation due to: (1) an enhanced degree of saturation for salts next to the membrane and within the biofilm; and (2) more available surface for nucleation to occur. The impact of biofilm in accelerating gypsum precipitation depends on the composition of the feed water (eg the presence of NaCl) and the kinetics of crystal nucleation and growth. Interactions between flow, solute transport and biofilm-induced mineralization are discussed.
DOI: 10.6084/m9.figshare.1289355.v3
title: Blood diagnostic biomarkers for major depressive disorder using multiplex DNA methylation profiles: discovery and validation

description: Aberrant DNA methylation in the blood of patients with major depressive disorder (MDD) has been reported in several previous studies. However, no comprehensive studies using medication-free subjects with MDD have been conducted. Furthermore, the majority of these previous studies has been limited to the analysis of the CpG sites in CpG islands (CGIs) in the gene promoter regions. The main aim of the present study is to identify DNA methylation markers that distinguish patients with MDD from non-psychiatric controls. Genome-wide DNA methylation profiling of peripheral leukocytes was conducted in two set of samples, a discovery set (20 medication-free patients with MDD and 19 controls) and a replication set (12 medication-free patients with MDD and 12 controls), using Infinium HumanMethylation450 BeadChips. Significant diagnostic differences in DNA methylation were observed at 363 CpG sites in the discovery set. All of these loci demonstrated lower DNA methylation in patients with MDD than in the controls, and most of them (85.7%) were located in the CGIs in the gene promoter regions. We were able to distinguish patients with MDD from the control subjects with high accuracy in the discriminant analysis using the top DNA methylation markers. We also validated these selected DNA methylation markers in the replication set. Our results indicate that multiplex DNA methylation markers may be useful for distinguishing patients with MDD from non-psychiatric controls.
DOI: 10.6084/m9.figshare.1289355.v5
title: Blood diagnostic biomarkers for major depressive disorder using multiplex DNA methylation profiles: discovery and validation

description: Aberrant DNA methylation in the blood of patients with major depressive disorder (MDD) has been reported in several previous studies. However, no comprehensive studies using medication-free subjects with MDD have been conducted. Furthermore, the majority of these previous studies has been limited to the analysis of the CpG sites in CpG islands (CGIs) in the gene promoter regions. The main aim of the present study is to identify DNA methylation markers that distinguish patients with MDD from non-psychiatric controls. Genome-wide DNA methylation profiling of peripheral leukocytes was conducted in two set of samples, a discovery set (20 medication-free patients with MDD and 19 controls) and a replication set (12 medication-free patients with MDD and 12 controls), using Infinium HumanMethylation450 BeadChips. Significant diagnostic differences in DNA methylation were observed at 363 CpG sites in the discovery set. All of these loci demonstrated lower DNA methylation in patients with MDD than in the controls, and most of them (85.7%) were located in the CGIs in the gene promoter regions. We were able to distinguish patients with MDD from the control subjects with high accuracy in the discriminant analysis using the top DNA methylation markers. We also validated these selected DNA methylation markers in the replication set. Our results indicate that multiplex DNA methylation markers may be useful for distinguishing patients with MDD from non-psychiatric controls.
DOI: 10.6084/m9.figshare.1289350.v1
title: Gaussian Job Archive for C14H14N2OS

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1289355.v4
title: Blood diagnostic biomarkers for major depressive disorder using multiplex DNA methylation profiles: discovery and validation

description: Aberrant DNA methylation in the blood of patients with major depressive disorder (MDD) has been reported in several previous studies. However, no comprehensive studies using medication-free subjects with MDD have been conducted. Furthermore, the majority of these previous studies has been limited to the analysis of the CpG sites in CpG islands (CGIs) in the gene promoter regions. The main aim of the present study is to identify DNA methylation markers that distinguish patients with MDD from non-psychiatric controls. Genome-wide DNA methylation profiling of peripheral leukocytes was conducted in two set of samples, a discovery set (20 medication-free patients with MDD and 19 controls) and a replication set (12 medication-free patients with MDD and 12 controls), using Infinium HumanMethylation450 BeadChips. Significant diagnostic differences in DNA methylation were observed at 363 CpG sites in the discovery set. All of these loci demonstrated lower DNA methylation in patients with MDD than in the controls, and most of them (85.7%) were located in the CGIs in the gene promoter regions. We were able to distinguish patients with MDD from the control subjects with high accuracy in the discriminant analysis using the top DNA methylation markers. We also validated these selected DNA methylation markers in the replication set. Our results indicate that multiplex DNA methylation markers may be useful for distinguishing patients with MDD from non-psychiatric controls.
DOI: 10.6084/m9.figshare.1289335
title: Gaussian Job Archive for C14H14N2OS

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1289330
title: Gaussian Job Archive for C14H14N2OS

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1289331
title: Gaussian Job Archive for C14H14N2OS

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1289331.v1
title: Gaussian Job Archive for C14H14N2OS

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1289335.v1
title: Gaussian Job Archive for C14H14N2OS

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1289330.v1
title: Gaussian Job Archive for C14H14N2OS

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1289320
title: Phytosociological analysis of the <i>Genista</i> sp. pl. garrigues of the <i>Cisto-Lavanduletea</i> and <i>Rosmarinetea officinalis</i> classes in the South-Tyrrhenian area (Mediterranean Region)

description: The study reviews the <i>Genista</i> sp. pl. garrigues ascribable to the classes <i>Cisto-Lavanduletea</i> (acidophilous communities on volcanics and quartzarenites) and <i>Rosmarinetea officinalis</i> (basiphilous communities on calcareous and calcareous-dolomite substrates), in the southern territories of the Italo-Tyrrhenian Province. The floristic-synecological characterization of the identified coenoses led to the description and/or redefinition of various new syntaxa. They are distributed in the two new suballiances <i>Calicotomo villosae–Genistenion tyrrhenae</i> and <i>Genisto aristatae–Calicotomenion infestae</i> of the class <i>Cisto-Lavanduletea</i> (<i>Lavanduletalia stoechadis</i> and <i>Calicotomo villosae–Genistion tyrrhenae</i>), and in the alliance <i>Cisto eriocephali–Ericion multiflorae</i> of the class <i>Rosmarinetea officinalis</i> (<i>Rosmarinetalia officinalis</i>), with two new described associations.
DOI: 10.6084/m9.figshare.1289323.v1
title: iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach

description: Information about the interactions of drug compounds with proteins in cellular networking is very important for drug development. Unfortunately, all the existing predictors for identifying drug–protein interactions were trained by a skewed benchmark data-set where the number of non-interactive drug–protein pairs is overwhelmingly larger than that of the interactive ones. Using this kind of highly unbalanced benchmark data-set to train predictors would lead to the outcome that many interactive drug–protein pairs might be mispredicted as non-interactive. Since the minority interactive pairs often contain the most important information for drug design, it is necessary to minimize this kind of misprediction. In this study, we adopted the neighborhood cleaning rule and synthetic minority over-sampling technique to treat the skewed benchmark datasets and balance the positive and negative subsets. The new benchmark datasets thus obtained are called the optimized benchmark datasets, based on which a new predictor called iDrug-Target was developed that contains four sub-predictors: iDrug-GPCR, iDrug-Chl, iDrug-Ezy, and iDrug-NR, specialized for identifying the interactions of drug compounds with GPCRs (G-protein-coupled receptors), ion channels, enzymes, and NR (nuclear receptors), respectively. Rigorous cross-validations on a set of experiment-confirmed datasets have indicated that these new predictors remarkably outperformed the existing ones for the same purpose. To maximize users’ convenience, a public accessible Web server for iDrug-Target has been established at http://www.jci-bioinfo.cn/iDrug-Target/, by which users can easily get their desired results. It has not escaped our notice that the aforementioned strategy can be widely used in many other areas as well.
DOI: 10.6084/m9.figshare.1289319
title: Gaussian Job Archive for C14H14N2OS

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1289323.v2
title: iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach

description: Information about the interactions of drug compounds with proteins in cellular networking is very important for drug development. Unfortunately, all the existing predictors for identifying drug–protein interactions were trained by a skewed benchmark data-set where the number of non-interactive drug–protein pairs is overwhelmingly larger than that of the interactive ones. Using this kind of highly unbalanced benchmark data-set to train predictors would lead to the outcome that many interactive drug–protein pairs might be mispredicted as non-interactive. Since the minority interactive pairs often contain the most important information for drug design, it is necessary to minimize this kind of misprediction. In this study, we adopted the neighborhood cleaning rule and synthetic minority over-sampling technique to treat the skewed benchmark datasets and balance the positive and negative subsets. The new benchmark datasets thus obtained are called the optimized benchmark datasets, based on which a new predictor called iDrug-Target was developed that contains four sub-predictors: iDrug-GPCR, iDrug-Chl, iDrug-Ezy, and iDrug-NR, specialized for identifying the interactions of drug compounds with GPCRs (G-protein-coupled receptors), ion channels, enzymes, and NR (nuclear receptors), respectively. Rigorous cross-validations on a set of experiment-confirmed datasets have indicated that these new predictors remarkably outperformed the existing ones for the same purpose. To maximize users’ convenience, a public accessible Web server for iDrug-Target has been established at http://www.jci-bioinfo.cn/iDrug-Target/, by which users can easily get their desired results. It has not escaped our notice that the aforementioned strategy can be widely used in many other areas as well.
DOI: 10.6084/m9.figshare.1289320.v1
title: Phytosociological analysis of the <i>Genista</i> sp. pl. garrigues of the <i>Cisto</i>-<i>Lavanduletea</i> and <i>Rosmarinetea</i><i>officinalis</i> classes in the South-Tyrrhenian area (Mediterranean Region)

description: The study reviews the <i>Genista</i> sp. pl. garrigues ascribable to the classes <i>Cisto</i>-<i>Lavanduletea</i> (acidophilous communities on volcanics and quartzarenites) and <i>Rosmarinetea</i><i>officinalis</i> (basiphilous communities on calcareous and calcareous-dolomite substrates), in the southern territories of the Italo-Tyrrhenian Province. The floristic-synecological characterization of the identified coenoses led to the description and/or redefinition of various new syntaxa. They are distributed in the two new suballiances <i>Calicotomo</i><i>villosae</i>-<i>Genistenion</i><i>tyrrhenae</i> and <i>Genisto</i><i>aristatae</i>-<i>Calicotomenion</i><i>infestae</i> of the class <i>Cisto</i>-<i>Lavanduletea</i> (<i>Lavanduletalia</i><i>stoechadis</i>, <i>Calicotomo</i><i>villosae</i>-<i>Genistion</i><i>tyrrhenae</i>), and in the alliance <i>Cisto</i><i>eriocephali</i>-<i>Ericion</i><i>multiflorae</i> of the class <i>Rosmarinetea</i><i>officinalis</i> (<i>Rosmarinetalia</i><i>officinalis</i>), with two new described associations.
DOI: 10.6084/m9.figshare.1289319.v1
title: Gaussian Job Archive for C14H14N2OS

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1289320.v2
title: Phytosociological analysis of the <i>Genista</i> sp. pl. garrigues of the <i>Cisto</i>-<i>Lavanduletea</i> and <i>Rosmarinetea</i><i>officinalis</i> classes in the South-Tyrrhenian area (Mediterranean Region)

description: The study reviews the <i>Genista</i> sp. pl. garrigues ascribable to the classes <i>Cisto</i>-<i>Lavanduletea</i> (acidophilous communities on volcanics and quartzarenites) and <i>Rosmarinetea</i><i>officinalis</i> (basiphilous communities on calcareous and calcareous-dolomite substrates), in the southern territories of the Italo-Tyrrhenian Province. The floristic-synecological characterization of the identified coenoses led to the description and/or redefinition of various new syntaxa. They are distributed in the two new suballiances <i>Calicotomo</i><i>villosae</i>–<i>Genistenion</i><i>tyrrhenae</i> and <i>Genisto</i><i>aristatae</i>–<i>Calicotomenion</i><i>infestae</i> of the class <i>Cisto</i>-<i>Lavanduletea</i> (<i>Lavanduletalia</i><i>stoechadis</i> and <i>Calicotomo</i><i>villosae</i>–<i>Genistion</i><i>tyrrhenae</i>), and in the alliance <i>Cisto</i><i>eriocephali</i>–<i>Ericion</i><i>multiflorae</i> of the class <i>Rosmarinetea</i><i>officinalis</i> (<i>Rosmarinetalia</i><i>officinalis</i>), with two new described associations.
DOI: 10.6084/m9.figshare.1289323.v3
title: iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach

description: Information about the interactions of drug compounds with proteins in cellular networking is very important for drug development. Unfortunately, all the existing predictors for identifying drug–protein interactions were trained by a skewed benchmark data-set where the number of non-interactive drug–protein pairs is overwhelmingly larger than that of the interactive ones. Using this kind of highly unbalanced benchmark data-set to train predictors would lead to the outcome that many interactive drug–protein pairs might be mispredicted as non-interactive. Since the minority interactive pairs often contain the most important information for drug design, it is necessary to minimize this kind of misprediction. In this study, we adopted the neighborhood cleaning rule and synthetic minority over-sampling technique to treat the skewed benchmark datasets and balance the positive and negative subsets. The new benchmark datasets thus obtained are called the optimized benchmark datasets, based on which a new predictor called iDrug-Target was developed that contains four sub-predictors: iDrug-GPCR, iDrug-Chl, iDrug-Ezy, and iDrug-NR, specialized for identifying the interactions of drug compounds with GPCRs (G-protein-coupled receptors), ion channels, enzymes, and NR (nuclear receptors), respectively. Rigorous cross-validations on a set of experiment-confirmed datasets have indicated that these new predictors remarkably outperformed the existing ones for the same purpose. To maximize users’ convenience, a public accessible Web server for iDrug-Target has been established at http://www.jci-bioinfo.cn/iDrug-Target/, by which users can easily get their desired results. It has not escaped our notice that the aforementioned strategy can be widely used in many other areas as well.
DOI: 10.6084/m9.figshare.1289320.v3
title: Phytosociological analysis of the <i>Genista</i> sp. pl. garrigues of the <i>Cisto</i>-<i>Lavanduletea</i> and <i>Rosmarinetea</i><i>officinalis</i> classes in the South-Tyrrhenian area (Mediterranean Region)

description: The study reviews the <i>Genista</i> sp. pl. garrigues ascribable to the classes <i>Cisto</i>-<i>Lavanduletea</i> (acidophilous communities on volcanics and quartzarenites) and <i>Rosmarinetea</i><i>officinalis</i> (basiphilous communities on calcareous and calcareous-dolomite substrates), in the southern territories of the Italo-Tyrrhenian Province. The floristic-synecological characterization of the identified coenoses led to the description and/or redefinition of various new syntaxa. They are distributed in the two new suballiances <i>Calicotomo</i><i>villosae</i>–<i>Genistenion</i><i>tyrrhenae</i> and <i>Genisto</i><i>aristatae</i>–<i>Calicotomenion</i><i>infestae</i> of the class <i>Cisto</i>-<i>Lavanduletea</i> (<i>Lavanduletalia</i><i>stoechadis</i> and <i>Calicotomo</i><i>villosae</i>–<i>Genistion</i><i>tyrrhenae</i>), and in the alliance <i>Cisto</i><i>eriocephali</i>–<i>Ericion</i><i>multiflorae</i> of the class <i>Rosmarinetea</i><i>officinalis</i> (<i>Rosmarinetalia</i><i>officinalis</i>), with two new described associations.
DOI: 10.6084/m9.figshare.1289323.v4
title: iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach

description: Information about the interactions of drug compounds with proteins in cellular networking is very important for drug development. Unfortunately, all the existing predictors for identifying drug–protein interactions were trained by a skewed benchmark data-set where the number of non-interactive drug–protein pairs is overwhelmingly larger than that of the interactive ones. Using this kind of highly unbalanced benchmark data-set to train predictors would lead to the outcome that many interactive drug–protein pairs might be mispredicted as non-interactive. Since the minority interactive pairs often contain the most important information for drug design, it is necessary to minimize this kind of misprediction. In this study, we adopted the neighborhood cleaning rule and synthetic minority over-sampling technique to treat the skewed benchmark datasets and balance the positive and negative subsets. The new benchmark datasets thus obtained are called the optimized benchmark datasets, based on which a new predictor called iDrug-Target was developed that contains four sub-predictors: iDrug-GPCR, iDrug-Chl, iDrug-Ezy, and iDrug-NR, specialized for identifying the interactions of drug compounds with GPCRs (G-protein-coupled receptors), ion channels, enzymes, and NR (nuclear receptors), respectively. Rigorous cross-validations on a set of experiment-confirmed datasets have indicated that these new predictors remarkably outperformed the existing ones for the same purpose. To maximize users’ convenience, a public accessible Web server for iDrug-Target has been established at http://www.jci-bioinfo.cn/iDrug-Target/, by which users can easily get their desired results. It has not escaped our notice that the aforementioned strategy can be widely used in many other areas as well.
DOI: 10.6084/m9.figshare.1289323.v5
title: iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach

description: Information about the interactions of drug compounds with proteins in cellular networking is very important for drug development. Unfortunately, all the existing predictors for identifying drug–protein interactions were trained by a skewed benchmark data-set where the number of non-interactive drug–protein pairs is overwhelmingly larger than that of the interactive ones. Using this kind of highly unbalanced benchmark data-set to train predictors would lead to the outcome that many interactive drug–protein pairs might be mispredicted as non-interactive. Since the minority interactive pairs often contain the most important information for drug design, it is necessary to minimize this kind of misprediction. In this study, we adopted the neighborhood cleaning rule and synthetic minority over-sampling technique to treat the skewed benchmark datasets and balance the positive and negative subsets. The new benchmark datasets thus obtained are called the optimized benchmark datasets, based on which a new predictor called iDrug-Target was developed that contains four sub-predictors: iDrug-GPCR, iDrug-Chl, iDrug-Ezy, and iDrug-NR, specialized for identifying the interactions of drug compounds with GPCRs (G-protein-coupled receptors), ion channels, enzymes, and NR (nuclear receptors), respectively. Rigorous cross-validations on a set of experiment-confirmed datasets have indicated that these new predictors remarkably outperformed the existing ones for the same purpose. To maximize users’ convenience, a public accessible Web server for iDrug-Target has been established at http://www.jci-bioinfo.cn/iDrug-Target/, by which users can easily get their desired results. It has not escaped our notice that the aforementioned strategy can be widely used in many other areas as well.
DOI: 10.6084/m9.figshare.1289320.v4
title: Phytosociological analysis of the <i>Genista</i> sp. pl. garrigues of the <i>Cisto</i>-<i>Lavanduletea</i> and <i>Rosmarinetea officinalis</i> classes in the South-Tyrrhenian area (Mediterranean Region)

description: The study reviews the <i>Genista</i> sp. pl. garrigues ascribable to the classes <i>Cisto</i>-<i>Lavanduletea</i> (acidophilous communities on volcanics and quartzarenites) and <i>Rosmarinetea officinalis</i> (basiphilous communities on calcareous and calcareous-dolomite substrates), in the southern territories of the Italo-Tyrrhenian Province. The floristic-synecological characterization of the identified coenoses led to the description and/or redefinition of various new syntaxa. They are distributed in the two new suballiances <i>Calicotomo villosae</i>–<i>Genistenion tyrrhenae</i> and <i>Genisto aristatae</i>–<i>Calicotomenion infestae</i> of the class <i>Cisto</i>-<i>Lavanduletea</i> (<i>Lavanduletalia stoechadis</i> and <i>Calicotomo villosae</i>–<i>Genistion tyrrhenae</i>), and in the alliance <i>Cisto eriocephali</i>–<i>Ericion multiflorae</i> of the class <i>Rosmarinetea officinalis</i> (<i>Rosmarinetalia officinalis</i>), with two new described associations.