cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5849547.v1
title: Supplementary Material for: Sex Differences in the Impact of <b><i>BDNF</i></b> Genotype on the Longitudinal Relationship between Physical Activity and Cognitive Performance

description: <b><i>Background:</i></b> Physical activity may preserve cognitive function in older adults, but benefits vary by sex and genetic factors. <b><i>Objective:</i></b> We tested the longitudinal association between physical activity and cognitive performance to de termine whether a common genetic polymorphism for brain-derived neurotrophic factor <i>(BDNF</i> Val66Met<i>)</i> moderated this effect. <b><i>Methods:</i></b> In a 12-year longitudinal population-based sample of older adults (<i>n</i> = 2,218), we used growth curve modeling to investigate whether the benefits of physical activity on cognitive preservation differed by <i>BDNF</i> genotype and sex across multiple cognitive domains including processing speed, attention, working memory, and episodic verbal memory. <b><i>Results:</i></b> The relationship between physical activity and cognitive performance was dependent on <i>BDNF</i> carrier status in males (Δχ<sup>2</sup> [Δdf] = 12.94 [4], <i>p</i> = 0.01), but not in females (Δχ<sup>2</sup> [Δdf] = 4.38 [4], <i>p</i> = 0.36). Cognition benefited from physical activity in male <i>BDNF</i> <i>met</i> noncarriers, but not <i>met</i> carriers, whereas cognition was not statistically significantly related to physical activity in females regardless of genotype. <b><i>Conclusion:</i></b> We observed longitudinal, but not cross-sectional, effects of physical activity on cognitive performance. Our study highlights the importance of longitudinal follow-up and consideration of sex differences in the relationships between physical activity, <i>BDNF</i> genotype, and cognitive decline. The findings contribute to understanding gene-lifestyle interactions in promoting cognitive health.
DOI: 10.6084/m9.figshare.5849532.v2
title: Implementation of machine-learning classification in remote sensing: an applied review

description: Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionality and to map classes with very complex characteristics. Nevertheless, implementing a machine-learning classification is not straightforward, and the literature provides conflicting advice regarding many key issues. This article therefore provides an overview of machine learning from an applied perspective. We focus on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and <i>k</i>-nearest neighbours (<i>k</i>-NN). Issues considered include the choice of algorithm, training data requirements, user-defined parameter selection and optimization, feature space impacts and reduction, and computational costs. We illustrate these issues through applying machine-learning classification to two publically available remotely sensed data sets.
DOI: 10.6084/m9.figshare.5849532.v1
title: Implementation of machine-learning classification in remote sensing: an applied review

description: Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionality and to map classes with very complex characteristics. Nevertheless, implementing a machine-learning classification is not straightforward, and the literature provides conflicting advice regarding many key issues. This article therefore provides an overview of machine learning from an applied perspective. We focus on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and <i>k</i>-nearest neighbours (<i>k</i>-NN). Issues considered include the choice of algorithm, training data requirements, user-defined parameter selection and optimization, feature space impacts and reduction, and computational costs. We illustrate these issues through applying machine-learning classification to two publically available remotely sensed data sets.
DOI: 10.6084/m9.figshare.5849520
title: Consensus QSAR modelling of SIRT1 activators using simplex representation of molecular structure

description: Hierarchical QSAR technology (HiT QSAR) was used for consensus QSAR modelling of 65 SIRT1 activators. Simplex representation of molecular structure (SiRMS) has been used for descriptor generation. The predictive QSAR models were developed using the partial least squares (PLS) method. The QSAR models were built up according to OECD principles. One hundred rounds of <i>Y</i>-scrambling were performed for each selected model to exclude chance correlations. A successful consensus model (<i>r</i><sup>2</sup> = 0.830, = 0.754) was obtained from the five best QSAR models. Leverage, ellipsoid and local tree domain of applicability (DA) approaches have been used for evaluation of the quality of predictions. Molecular fragments responsible for an increase and decrease of the activation properties have been determined by mechanistic interpretation of the developed QSAR model.
DOI: 10.6084/m9.figshare.5849520.v1
title: Consensus QSAR modelling of SIRT1 activators using simplex representation of molecular structure

description: Hierarchical QSAR technology (HiT QSAR) was used for consensus QSAR modelling of 65 SIRT1 activators. Simplex representation of molecular structure (SiRMS) has been used for descriptor generation. The predictive QSAR models were developed using the partial least squares (PLS) method. The QSAR models were built up according to OECD principles. One hundred rounds of <i>Y</i>-scrambling were performed for each selected model to exclude chance correlations. A successful consensus model (<i>r</i><sup>2</sup> = 0.830, = 0.754) was obtained from the five best QSAR models. Leverage, ellipsoid and local tree domain of applicability (DA) approaches have been used for evaluation of the quality of predictions. Molecular fragments responsible for an increase and decrease of the activation properties have been determined by mechanistic interpretation of the developed QSAR model.
DOI: 10.6084/m9.figshare.5849502
title: Supplementary Material for: Rs1520220 and Rs2195239 Polymorphisms of IGF-1 Gene Associated with Histopathological Grades in IgA Nephropathy in Northwestern Chinese Han Population

description: <b><i>Background/Aims:</i></b> Insulin-like growth factor-1 (IGF-1) plays important roles in cellular proliferation, differentiation, and growth. Previous studies showed that single-nucleotide polymorphisms (SNPs) of IGF-1 are associated with various diseases. This case-control study aimed to examine the relationship between IGF-1 polymorphisms and IgA nephropathy (IgAN) risk in a Chinese Han population. <b><i>Methods:</i></b> We recruited 351 IgAN patients and 310 healthy controls from Northwestern China. Sequenom MassARRAY was utilized to examine the genotypes of two common IGF-1 SNPs (rs1520220 and rs2195239). Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated by the Chi square test to evaluate the associations between IGF-1 and IgAN. <b><i>Results:</i></b> Our study demonstrated that IGF-1 gene rs1520220 and rs2195239 polymorphisms did not confer susceptibility to IgAN. We found no correlation between gender, blood pressure, proteinuria, eGFR, and IgAN in both SNPs. However, the rs1520220 and rs2195239 variants were correlated with M1 and E1 in patients with IgAN (M0/M1: CC vs. CG+GG: OR = 1.62, <i>P</i> = 0.04; E0/E1: CC vs. CG+GG: OR = 1.95, <i>P</i> = 0.004; GG vs. GC+CC: OR = 1.90, <i>P</i> = 0.004, respectively). <b><i>Conclusion:</i></b> These results indicate that IGF-1 gene polymorphisms play crucial roles in the histopathological progression of IgAN in the Chinese Han population.
DOI: 10.6084/m9.figshare.5849493
title: Multi-targeted directed ligands for Alzheimer’s disease: design of novel lead coumarin conjugates

description: Alzheimer’s Disease (AD) is a neurodegenerative disease characterized by central nervous system insults with progressive cognitive (memory, attention) and non-cognitive (anxiety, depression) impairments. Pathophysiological events affect predominantly cholinergic neuronal loss and dysfunctions of the dopaminergic system. The aim of the current study was to design multi-targeted directed lead structures based on the coumarin scaffold with inhibitory properties at two key enzymes in disease relevant systems, i.e. acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B). Conventional and microwave synthetic methods were utilized to synthesize coumarin scaffold-based novel morpholino, piperidino, thiophene and erucic acid conjugates. Biological assays indicated that the coumarin–morpholine ether conjugate BPR 10 was the most potent hMAO-B inhibitor. The coumarin–piperidine conjugates BPR 13 and BPR 12 were the most potent inhibitors of eeAChE at 100 μM and 1 μM, respectively. Molecular modelling studies were conducted with Accelrys<sup>®</sup> Discovery Studio<sup>®</sup> V3.1.1 utilising the published hMAO-B (2V61) and hAChE (4EY7) crystal structures. Compound BPR 10 occupies both the entrance and substrate cavities of the active site of MAO-B. BPR 13 resides in both the peripheral anionic site (PAS) and the catalytic anionic site (CAS) of hAChE. This study demonstrated that the coumarin scaffold serves as a promising pharmacophore for MTDLs design.
DOI: 10.6084/m9.figshare.5849493.v1
title: Multi-targeted directed ligands for Alzheimer’s disease: design of novel lead coumarin conjugates

description: Alzheimer’s Disease (AD) is a neurodegenerative disease characterized by central nervous system insults with progressive cognitive (memory, attention) and non-cognitive (anxiety, depression) impairments. Pathophysiological events affect predominantly cholinergic neuronal loss and dysfunctions of the dopaminergic system. The aim of the current study was to design multi-targeted directed lead structures based on the coumarin scaffold with inhibitory properties at two key enzymes in disease relevant systems, i.e. acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B). Conventional and microwave synthetic methods were utilized to synthesize coumarin scaffold-based novel morpholino, piperidino, thiophene and erucic acid conjugates. Biological assays indicated that the coumarin–morpholine ether conjugate BPR 10 was the most potent hMAO-B inhibitor. The coumarin–piperidine conjugates BPR 13 and BPR 12 were the most potent inhibitors of eeAChE at 100 μM and 1 μM, respectively. Molecular modelling studies were conducted with Accelrys<sup>®</sup> Discovery Studio<sup>®</sup> V3.1.1 utilising the published hMAO-B (2V61) and hAChE (4EY7) crystal structures. Compound BPR 10 occupies both the entrance and substrate cavities of the active site of MAO-B. BPR 13 resides in both the peripheral anionic site (PAS) and the catalytic anionic site (CAS) of hAChE. This study demonstrated that the coumarin scaffold serves as a promising pharmacophore for MTDLs design.
DOI: 10.6084/m9.figshare.5849490
title: Prediction of therapeutic potency of tacrine derivatives as BuChE inhibitors from quantitative structure–activity relationship modelling

description: Numerous studies show that tacrine derivatives exhibit increased inhibitory activity against butyrylcholinesterase (BuChE) and acetylcholinesterase (AChE). However, the screening assays for currently available BuChE inhibitors are expensive, time consuming and dependent on the inhibitory compound. It is therefore desirable to develop alternative methods to facilitate the screening of these derivatives in the early phase of drug discovery. In order to develop robust predictive models, three regression methods were chosen in this study: multiple linear regression (MLR), support vector regression (SVR) and multilayer perceptron network (MLP). Eight relevant descriptors were selected on a dataset of 151 molecules using a method based on genetic algorithms. Internal and external validation strategies play an important role. Also, to check the robustness of the selected models, all available validation strategies were used, and all criteria used to validate these models revealed the superiority of the SVR model. The statistical parameters obtained with the SVR model were RMSE = 0.197, <i>r</i><sup>2</sup> = 0.969 and <i>Q</i><sup>2</sup> = 0.964 for the training set, and <i>r</i><sup>2</sup> = 0.906 and <i>Q</i><sup>2</sup> = 0.891 for the test set. Therefore, the model developed in this study provides an excellent prediction of the inhibitory concentration of tacrine derivatives.
DOI: 10.6084/m9.figshare.5849490.v1
title: Prediction of therapeutic potency of tacrine derivatives as BuChE inhibitors from quantitative structure–activity relationship modelling

description: Numerous studies show that tacrine derivatives exhibit increased inhibitory activity against butyrylcholinesterase (BuChE) and acetylcholinesterase (AChE). However, the screening assays for currently available BuChE inhibitors are expensive, time consuming and dependent on the inhibitory compound. It is therefore desirable to develop alternative methods to facilitate the screening of these derivatives in the early phase of drug discovery. In order to develop robust predictive models, three regression methods were chosen in this study: multiple linear regression (MLR), support vector regression (SVR) and multilayer perceptron network (MLP). Eight relevant descriptors were selected on a dataset of 151 molecules using a method based on genetic algorithms. Internal and external validation strategies play an important role. Also, to check the robustness of the selected models, all available validation strategies were used, and all criteria used to validate these models revealed the superiority of the SVR model. The statistical parameters obtained with the SVR model were RMSE = 0.197, <i>r</i><sup>2</sup> = 0.969 and <i>Q</i><sup>2</sup> = 0.964 for the training set, and <i>r</i><sup>2</sup> = 0.906 and <i>Q</i><sup>2</sup> = 0.891 for the test set. Therefore, the model developed in this study provides an excellent prediction of the inhibitory concentration of tacrine derivatives.
DOI: 10.6084/m9.figshare.5849484
title: Molecular activity prediction by means of supervised subspace projection based ensembles of classifiers

description: This paper proposes a method for molecular activity prediction in QSAR studies using ensembles of classifiers constructed by means of two supervised subspace projection methods, namely nonparametric discriminant analysis (NDA) and hybrid discriminant analysis (HDA). We studied the performance of the proposed ensembles compared to classical ensemble methods using four molecular datasets and eight different models for the representation of the molecular structure. Using several measures and statistical tests for classifier comparison, we observe that our proposal improves the classification results with respect to classical ensemble methods. Therefore, we show that ensembles constructed using supervised subspace projections offer an effective way of creating classifiers in cheminformatics.
DOI: 10.6084/m9.figshare.5849484.v1
title: Molecular activity prediction by means of supervised subspace projection based ensembles of classifiers

description: This paper proposes a method for molecular activity prediction in QSAR studies using ensembles of classifiers constructed by means of two supervised subspace projection methods, namely nonparametric discriminant analysis (NDA) and hybrid discriminant analysis (HDA). We studied the performance of the proposed ensembles compared to classical ensemble methods using four molecular datasets and eight different models for the representation of the molecular structure. Using several measures and statistical tests for classifier comparison, we observe that our proposal improves the classification results with respect to classical ensemble methods. Therefore, we show that ensembles constructed using supervised subspace projections offer an effective way of creating classifiers in cheminformatics.
DOI: 10.6084/m9.figshare.5849433
title: Supplementary Material for: A Pooled Analysis of Pharmacokinetic Variability Information for Common Probe Substrates Used in Drug-Drug Interaction Studies

description: Sample size estimates for drug-drug interaction (DDI) studies are often based on variability information from the literature or from historical studies, but small sample sizes in these sources may limit the precision of the estimates obtained. This project aimed to create an intra-subject variability library of the pharmacokinetic (PK) exposure parameters, area under the curve, and maximum plasma concentration, for probes commonly used in DDI studies. Data from 66 individual DDI studies in healthy subjects relating to 18 common probe substrates were pooled to increase the effective sample size for the identified probes by 1.5- to 9-fold, with corresponding improvements in precision of the intra-subject PK variability estimates in this library. These improved variability estimates will allow better assessment of the sample sizes needed for DDI studies in future.
DOI: 10.6084/m9.figshare.5849433.v1
title: Supplementary Material for: A Pooled Analysis of Pharmacokinetic Variability Information for Common Probe Substrates Used in Drug-Drug Interaction Studies

description: Sample size estimates for drug-drug interaction (DDI) studies are often based on variability information from the literature or from historical studies, but small sample sizes in these sources may limit the precision of the estimates obtained. This project aimed to create an intra-subject variability library of the pharmacokinetic (PK) exposure parameters, area under the curve, and maximum plasma concentration, for probes commonly used in DDI studies. Data from 66 individual DDI studies in healthy subjects relating to 18 common probe substrates were pooled to increase the effective sample size for the identified probes by 1.5- to 9-fold, with corresponding improvements in precision of the intra-subject PK variability estimates in this library. These improved variability estimates will allow better assessment of the sample sizes needed for DDI studies in future.
DOI: 10.6084/m9.figshare.5849430
title: Baozhen.dat

description: water level
DOI: 10.6084/m9.figshare.5849418
title: Revisiting DNA barcoding of true bugs of the infraorder Pentatomomorpha (Hemiptera: Heteroptera) from India

description: <i>Cytochrome c oxidase subunit I gene (COI)</i> sequences of roughly 509 bp length for various species of the Infraorder Pentatomomorpha were generated. K2P divergences within and between species and genera were calculated and compared using newly generated sequences and the ones available on online portals. Mean interspecific (within-genus) genetic divergence (14.23%) was ∼ eight times greater than mean intraspecific (within-species) divergence (1.79%). Distance-based as well as character-based approaches were used towards constructing <i>(COI)</i> trees. In total, 20 sequences were of the species that were previously not part of the Barcode Of Life Database (BOLD), hence representing additions to the barcode library of Indian Heteroptera. Some of the analyzed species are well-known agricultural pests. All the <i>COI</i> sequences and the associated specimen data have been deposited on BOLD.
DOI: 10.6084/m9.figshare.5849418.v1
title: Revisiting DNA barcoding of true bugs of the infraorder Pentatomomorpha (Hemiptera: Heteroptera) from India

description: <i>Cytochrome c oxidase subunit I gene (COI)</i> sequences of roughly 509 bp length for various species of the Infraorder Pentatomomorpha were generated. K2P divergences within and between species and genera were calculated and compared using newly generated sequences and the ones available on online portals. Mean interspecific (within-genus) genetic divergence (14.23%) was ∼ eight times greater than mean intraspecific (within-species) divergence (1.79%). Distance-based as well as character-based approaches were used towards constructing <i>(COI)</i> trees. In total, 20 sequences were of the species that were previously not part of the Barcode Of Life Database (BOLD), hence representing additions to the barcode library of Indian Heteroptera. Some of the analyzed species are well-known agricultural pests. All the <i>COI</i> sequences and the associated specimen data have been deposited on BOLD.
DOI: 10.6084/m9.figshare.5849349
title: Figure15.pdf

description: Figure15
DOI: 10.6084/m9.figshare.5849346
title: Figure16.pdf

description: Figure16
DOI: 10.6084/m9.figshare.5849343
title: Figure14.pdf

description: Figure14
DOI: 10.6084/m9.figshare.5849340
title: Figure8.pdf

description: Figure8
DOI: 10.6084/m9.figshare.5849337
title: Figure13.pdf

description: Figure13
DOI: 10.6084/m9.figshare.5849334
title: Figure12.pdf

description: Figure12
DOI: 10.6084/m9.figshare.5849331
title: Figure10.pdf

description: Figure10
DOI: 10.6084/m9.figshare.5849328
title: Figure11.pdf

description: Figure11
DOI: 10.6084/m9.figshare.5849325
title: Figure9.pdf

description: Figure9
DOI: 10.6084/m9.figshare.5849322
title: Figure3.pdf

description: Figure3
DOI: 10.6084/m9.figshare.5849319
title: Figure2.pdf

description: Figure2
DOI: 10.6084/m9.figshare.5849316
title: Figure4.pdf

description: Figure4
DOI: 10.6084/m9.figshare.5849313
title: Figure6.pdf

description: Figure6
DOI: 10.6084/m9.figshare.5849310
title: Figure5.pdf

description: Figure5
DOI: 10.6084/m9.figshare.5849307
title: Figure7.pdf

description: Figure7
DOI: 10.6084/m9.figshare.5849151
title: Additional file 9 of The complex simplicity of the brittle star nervous system

description: Mixed nerves. Interactive 3D model. See caption to Additional fileÂ 6 for instructions. (HTML 5079 kb)
DOI: 10.6084/m9.figshare.5849151.v1
title: Additional file 9 of The complex simplicity of the brittle star nervous system

description: Mixed nerves. Interactive 3D model. See caption to Additional fileÂ 6 for instructions. (HTML 5079 kb)
DOI: 10.6084/m9.figshare.5849145
title: Additional file 8 of The complex simplicity of the brittle star nervous system

description: Hyponeural system. Interactive 3D model. See caption to Additional fileÂ 6 for instructions. (HTML 4680 kb)
DOI: 10.6084/m9.figshare.5849145.v1
title: Additional file 8 of The complex simplicity of the brittle star nervous system

description: Hyponeural system. Interactive 3D model. See caption to Additional fileÂ 6 for instructions. (HTML 4680 kb)
DOI: 10.6084/m9.figshare.5849139
title: Additional file 7 of The complex simplicity of the brittle star nervous system

description: Ectoneural system. Interactive 3D model. See caption to Additional fileÂ 6 for instructions. (HTML 6502 kb)
DOI: 10.6084/m9.figshare.5849139.v1
title: Additional file 7 of The complex simplicity of the brittle star nervous system

description: Ectoneural system. Interactive 3D model. See caption to Additional fileÂ 6 for instructions. (HTML 6502 kb)
DOI: 10.6084/m9.figshare.5849130
title: Additional file 6 of The complex simplicity of the brittle star nervous system

description: All components of the arm nervous system (green â ectoneural system; magenta â hyponeural system; light blue â mixed peripheral nerves) plus the intervertebral muscles (brown) and hydrocoel (red). This is an interactive 3D model generated from the original .blend file (Additional file 2) using the Blend4Web tool ( https://www.blend4web.com ). The model can be opened in any modern web browser. Upon opening, zoom out using the scroll wheel. To rotate, press and hold the left mouse button. To pan the view, press and hold the right mouse button. (HTML 23,040 kb)