cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12981707
title: Effect of a natural supplement containing glucosinolates, phytosterols and citrus flavonoids on body weight and metabolic parameters in a menopausal murine model induced by bilateral ovariectomy

description: To evaluate the effect of a herbal preparation containing glucosinolates, phytosterols and citrus flavonoids (supplement) on body weight and metabolic parameters usually impaired by menopause. A pre-clinical experimental study carried out in twenty-five Swiss strain mice (<i>Mus musculus</i>) randomly distributed (1:1:1:1:1 ratio) to five groups to receive for ten weeks: (1) oral gelatinized maca extract 0.5625 mg/kg/day + bilateral ovariectomy (Maca + OVX); (2) oral supplement 0.5625 mg/kg/day + bilateral ovariectomy (S1 + OVX); (3) oral supplement 1.6875 mg/kg/day + bilateral ovariectomy (S2 + OVX); (4) oral saline 100 µl/kg/day + bilateral ovariectomy (OVX); and (5) oral saline 100 µl/kg/day + sham surgery (sham). The primary endpoint was change in body weight gain from baseline to final. Secondary endpoints were uterine weight and cholesterol, triglyceride, glucose, and glucose/triglycerides index values at the end of the study. A modified intention-to-treat analysis was performed through linear regression models and using the Bonferroni method to penalized <i>p</i>-values by multiple comparisons. Twenty-three animals completed the study. There was a significant average difference in weight gain, with a greater reduction in the S2 + OVX group compared to the OVX group (difference= −3.5; 95% CI (–5.27; −1.74); <i>p</i> &lt; .001). S2 + OVX group also displayed a significant average reduction of total blood cholesterol (difference: −16.94; 95% CI (–33.73; −0.15); <i>p</i> = .037). No significant effects of the supplement were found on other secondary endpoints. In this murine menopausal model, triple oral supplement dose resulted in an average reduction of weight gain and total cholesterol levels, suggesting that the compound could have a potential effect at regulating menopausal altered metabolism.
DOI: 10.6084/m9.figshare.13237006.v1
title: Shrinkage estimation for dose-response modeling in phase II trials with multiple schedules

description: Recently, phase II trials with multiple schedules (frequency of administrations) have become more popular, for instance in the development of treatments for atopic dermatitis. If the relationship of the dose and response is described by a parametric model, a simplistic approach is to scale doses from different schedules to a common unit and pool all re-scaled doses. However, this approach ignores the potential heterogeneity in dose-response curves between schedules. A more reasonable approach is the partial pooling, i.e. certain parameters of the dose-response curves are shared, while others are allowed to vary. Rather than using schedule-specific fixed-effects, we propose a Bayesian hierarchical model with random-effects to model the between-schedule heterogeneity with regard to certain parameters. Schedule-specific dose-response relationships can then be estimated using shrinkage estimation. Considering Emax models, the proposed method displayed desirable performance in terms of the mean absolute error and the coverage probabilities for the dose-response curve compared to the complete pooling. Furthermore, it outperformed the partial pooling with schedule-specific fixed-effects by producing lower mean absolute error and shorter credible intervals. The methods are illustrated using simulations and a phase II trial example in atopic dermatitis. A publicly available R package, ModStan, is developed to automate the implementation of the proposed method.
DOI: 10.15128/r3p8418n215
title: MPHOI rgb. Part 1 of 4 [dataset]

description: Code published on GitHub: https://github.com/tanqiu98/2G-GCN
description: Human-Object Interaction (HOI) recognition in videos is important for analyzing human activity. Most existing work focusing on visual features usually suffer from occlusion in the real-world scenarios. Such a problem will be further complicated when multiple people and objects are involved in HOIs. Consider that geometric features such as human pose and object position provide meaningful information to understand HOIs, we argue to combine the benefits of both visual and geometric features in HOI recognition, and propose a novel Two-level Geometric feature-informed Graph Convolutional Network (2G-GCN). The geometric-level graph models the interdependency between geometric features of humans and objects, while the fusion-level graph further fuses them with visual features of humans and objects. To demonstrate the novelty and effectiveness of our method in challenging scenarios, we propose a new multi-person HOI dataset (MPHOI-72). Extensive experiments on MPHOI-72 (multi-person HOI), CAD-120 (single-human HOI) and Bimanual Actions (two-hand HOI) datasets demonstrate our superior performance compared to state-of-the-arts.
DOI: 10.6084/m9.figshare.12851961.v1
title: Selection of established tumour cells through narrow diameter micropores enriches for elevated Ras/Raf/MEK/ERK MAPK signalling and enhanced tumour growth

description: As normal cells become cancer cells, and progress towards malignancy, they become progressively softer. Advantages of this change are that tumour cells become more deformable, and better able to move through narrow constraints. We designed a positive selection strategy that enriched for cells which could move through narrow diameter micropores to identify cell phenotypes that enabled constrained migration. Using human MDA MB 231 breast cancer and MDA MB 435 melanoma cancer cells, we found that micropore selection favoured cells with relatively higher Ras/Raf/MEK/ERK mitogen-activated protein kinase (MAPK) signalling, which affected actin cytoskeleton organization, focal adhesion density and cell elasticity. In this follow-up study, we provide further evidence that selection through micropores enriched for cells with altered cell morphology and adhesion. Additional analysis of RNA sequencing data revealed a set of transcripts associated with small cell size that was independent of constrained migration. Gene set enrichment analysis identified the ‘matrisome’ as the most significantly altered gene set linked with small size. When grown as orthotopic xenograft tumours in immunocompromised mice, micropore selected cells grew significantly faster than Parent or Flow-Sorted cells. Using mathematical modelling, we determined that there is an interaction between 1) the cell to gap size ratio; 2) the bending rigidity of the cell, which enable movement through narrow gaps. These results extend our previous conclusion that Ras/Raf/MEK/ERK MAPK signalling has a significant role in regulating cell biomechanics by showing that the selective pressure of movement through narrow gaps also enriches for increased tumour growth <i>in vivo</i>.
DOI: 10.6084/m9.figshare.12851961
title: Selection of established tumour cells through narrow diameter micropores enriches for elevated Ras/Raf/MEK/ERK MAPK signalling and enhanced tumour growth

description: As normal cells become cancer cells, and progress towards malignancy, they become progressively softer. Advantages of this change are that tumour cells become more deformable, and better able to move through narrow constraints. We designed a positive selection strategy that enriched for cells which could move through narrow diameter micropores to identify cell phenotypes that enabled constrained migration. Using human MDA MB 231 breast cancer and MDA MB 435 melanoma cancer cells, we found that micropore selection favoured cells with relatively higher Ras/Raf/MEK/ERK mitogen-activated protein kinase (MAPK) signalling, which affected actin cytoskeleton organization, focal adhesion density and cell elasticity. In this follow-up study, we provide further evidence that selection through micropores enriched for cells with altered cell morphology and adhesion. Additional analysis of RNA sequencing data revealed a set of transcripts associated with small cell size that was independent of constrained migration. Gene set enrichment analysis identified the ‘matrisome’ as the most significantly altered gene set linked with small size. When grown as orthotopic xenograft tumours in immunocompromised mice, micropore selected cells grew significantly faster than Parent or Flow-Sorted cells. Using mathematical modelling, we determined that there is an interaction between 1) the cell to gap size ratio; 2) the bending rigidity of the cell, which enable movement through narrow gaps. These results extend our previous conclusion that Ras/Raf/MEK/ERK MAPK signalling has a significant role in regulating cell biomechanics by showing that the selective pressure of movement through narrow gaps also enriches for increased tumour growth <i>in vivo</i>.
DOI: 10.6084/m9.figshare.13724487.v2
title: Aligning Calculus with Life Sciences Disciplines: The Argument for Integrating Statistical Reasoning

description: Governmental and educational organizations have pointed out that students coming into the biological sciences require stronger skills in statistics and data modeling that are not usually addressed in typical engineering-based calculus courses. Our work here documents how faculty in biology and mathematics addressed this issue at a large urban university in the southwestern USA. The Calculus for Life Sciences course was redesigned to integrate data analysis and engage students in activities connecting the foundational and practical aspects of statistics-based calculus for professionals in life sciences. The goal was to obtain a better-prepared cohort of students with a positive perception of the use of mathematics and statistics in their careers. Over the course of four semesters, the course doubled the average enrollment of students, the failure rate reduced by more than 50%, and students reported positive attitudinal responses regarding the application of math to biological studies. The perception of mastery reported by students throughout the semester, however, did not correlate with the mastery of skills demonstrated on graded activities. This article provides a case study of the successes and pitfalls that we encountered as we attempted to shift all aspects of the course, including pedagogy, grading, and curriculum.
DOI: 10.6084/m9.figshare.13724487
title: Aligning Calculus with Life Sciences Disciplines: The Argument for Integrating Statistical Reasoning

description: Governmental and educational organizations have pointed out that students coming into the biological sciences require stronger skills in statistics and data modeling that are not usually addressed in typical engineering-based calculus courses. Our work here documents how faculty in biology and mathematics addressed this issue at a large urban university in the southwestern USA. The Calculus for Life Sciences course was redesigned to integrate data analysis and engage students in activities connecting the foundational and practical aspects of statistics-based calculus for professionals in life sciences. The goal was to obtain a better-prepared cohort of students with a positive perception of the use of mathematics and statistics in their careers. Over the course of four semesters, the course doubled the average enrollment of students, the failure rate reduced by more than 50%, and students reported positive attitudinal responses regarding the application of math to biological studies. The perception of mastery reported by students throughout the semester, however, did not correlate with the mastery of skills demonstrated on graded activities. This article provides a case study of the successes and pitfalls that we encountered as we attempted to shift all aspects of the course, including pedagogy, grading, and curriculum.
DOI: 10.6084/m9.figshare.14377040
title: Generalized Bayes Quantification Learning under Dataset Shift

description: Quantification learning is the task of prevalence estimation for a test population using predictions from a classifier trained on a different population. Quantification methods assume that the sensitivities and specificities of the classifier are either perfect or transportable from the training to the test population. These assumptions are inappropriate in the presence of dataset shift, when the misclassification rates in the training population are not representative of those for the test population. Quantification under dataset shift has been addressed only for single-class (categorical) predictions and assuming perfect knowledge of the true labels on a small subset of the test population. We propose generalized Bayes quantification learning (GBQL) that uses the entire compositional predictions from probabilistic classifiers and allows for uncertainty in true class labels for the limited labeled test data. Instead of positing a full model, we use a model-free Bayesian estimating equation approach to compositional data using Kullback-Leibler loss-functions based only on a first-moment assumption. The idea will be useful in Bayesian compositional data analysis in general as it is robust to different generating mechanisms for compositional data and allows 0’s and 1’s in the compositional outputs thereby including categorical outputs as a special case. We show how our method yields existing quantification approaches as special cases. Extension to an ensemble GBQL that uses predictions from multiple classifiers yielding inference robust to inclusion of a poor classifier is discussed. We outline a fast and efficient Gibbs sampler using a rounding and coarsening approximation to the loss functions. We establish posterior consistency, asymptotic normality and valid coverage of interval estimates from GBQL, which to our knowledge are the first theoretical results for a quantification approach in presence of local labeled data. We also establish finite sample posterior concentration rate. Empirical performance of GBQL is demonstrated through simulations and analysis of real data with evident dataset shift.
DOI: 10.6084/m9.figshare.14377040.v1
title: Generalized Bayes Quantification Learning under Dataset Shift

description: Quantification learning is the task of prevalence estimation for a test population using predictions from a classifier trained on a different population. Quantification methods assume that the sensitivities and specificities of the classifier are either perfect or transportable from the training to the test population. These assumptions are inappropriate in the presence of dataset shift, when the misclassification rates in the training population are not representative of those for the test population. Quantification under dataset shift has been addressed only for single-class (categorical) predictions and assuming perfect knowledge of the true labels on a small subset of the test population. We propose generalized Bayes quantification learning (GBQL) that uses the entire compositional predictions from probabilistic classifiers and allows for uncertainty in true class labels for the limited labeled test data. Instead of positing a full model, we use a model-free Bayesian estimating equation approach to compositional data using Kullback-Leibler loss-functions based only on a first-moment assumption. The idea will be useful in Bayesian compositional data analysis in general as it is robust to different generating mechanisms for compositional data and allows 0’s and 1’s in the compositional outputs thereby including categorical outputs as a special case. We show how our method yields existing quantification approaches as special cases. Extension to an ensemble GBQL that uses predictions from multiple classifiers yielding inference robust to inclusion of a poor classifier is discussed. We outline a fast and efficient Gibbs sampler using a rounding and coarsening approximation to the loss functions. We establish posterior consistency, asymptotic normality and valid coverage of interval estimates from GBQL, which to our knowledge are the first theoretical results for a quantification approach in presence of local labeled data. We also establish finite sample posterior concentration rate. Empirical performance of GBQL is demonstrated through simulations and analysis of real data with evident dataset shift.
DOI: 10.5281/zenodo.5910334
title: Data for: Comparison of Friction Extrusion Processing from Bulk and Chips of Aluminum-Copper Alloys

description: This dataset contains measurement data, machine logs as well as microstructure and overview images for the publication "Comparison of Friction Extrusion Processing from Bulk and Chips of Aluminum-Copper Alloys".
DOI: 10.5281/zenodo.5910333
title: Data for: Comparison of Friction Extrusion Processing from Bulk and Chips of Aluminum-Copper Alloys

description: This dataset contains measurement data, machine logs as well as microstructure and overview images for the publication "Comparison of Friction Extrusion Processing from Bulk and Chips of Aluminum-Copper Alloys".
DOI: 10.5281/zenodo.6955908
title: Actionable and Interpretable Fault Localization for Recurring Failures in Online Service Systems

description: These are the datasets for our ESEC/FSE'22 paper "Actionable and Interpretable Fault Localization for Recurring Failures in Online Service Systems." In each dataset, <code>graph.yml </code>or <code>graphs/*.yml</code> are FDGs, <code>metrics.csv</code> is metrics, and <code>faults.csv</code> is failures (including ground truths).<code>FDG.pkl</code> is a pickle of the FDG object, which contains all the above data. Note that the pickle files are not compatible in different Python and Pandas versions. So if you cannot load the pickles, just ignore and delete them. They are only used to speed up data load. See more at https://github.com/NetManAIOps/DejaVu
DOI: 10.5281/zenodo.6956608
title: Synthetic river flow videos dataset

description: ###### ######### ########## ###### ######### ##########<br> <strong> Synthetic river flow videos for evaluating image-based velocimetry methods</strong><br> ###### ######### ########## ###### ######### ##########<br> # Year : 2022<br> # Authors : G.Bodart (guillaume.bodart@inrae.fr), J.Le Coz (jerome.lecoz@inrae.fr), M.Jodeau (magali.jodeau@edf.fr), A.Hauet (alexandre.hauet@edf.fr) ###<br> <em>This file describes the data attached to the article</em> ### -&gt;<strong> 00_article_cases</strong><br> ########################<br> <em>This folder contains the data used in the case studies: synthetic videos + reference files.</em> - 00_reference_velocities<br> -&gt; Reference velocities interpolated on a regular grid. Data are given in conventionnal units, i.e. m/s and m. - 01_XX<br> -&gt; Data of the first case study - 02_XX<br> -&gt; Data of the second case study ### -&gt; <strong>01_dev</strong><br> #############<br> <em>This folder contains the Python libraries and Mantaflow modified source code used in the paper. The libraries are provided as is. Feel free to contact us for support or guidelines.</em> - lspiv<br> -&gt; Python library used to extract, process and display results of LSPIV analysis carried out with Fudaa-LSPIV - mantaflow-modified<br> -&gt; Modified version of Mantaflow described in the article. Installation instructions can be found at http://mantaflow.com - syri<br> -&gt; Python library used to extract, process and display fluid simulations carried out on Mantaflow and Blender. (Require the lspiv library) ### -&gt; <strong>02_dataset</strong><br> #################<br> <em>This folder contains synthetic videos generated with the method described in the article. The fluid simulation parameters, and thus the reference velocities, are the same as those presented in the article. </em> - The videos can be used freely. Please consider citing the corresponding paper.
DOI: 10.5281/zenodo.6257391
title: Synthetic river flow videos dataset

description: ###### ######### ########## ###### ######### ##########<br> <strong> Synthetic river flow videos for evaluating image-based velocimetry methods</strong><br> ###### ######### ########## ###### ######### ##########<br> # Year : 2022<br> # Authors : G.Bodart (guillaume.bodart@inrae.fr), J.Le Coz (jerome.lecoz@inrae.fr), M.Jodeau (magali.jodeau@edf.fr), A.Hauet (alexandre.hauet@edf.fr) ###<br> <em>This file describes the data attached to the article</em> ### -&gt;<strong> 00_article_cases</strong><br> ########################<br> <em>This folder contains the data used in the case studies: synthetic videos + reference files.</em> - 00_reference_velocities<br> -&gt; Reference velocities interpolated on a regular grid. Data are given in conventionnal units, i.e. m/s and m. - 01_XX<br> -&gt; Data of the first case study - 02_XX<br> -&gt; Data of the second case study ### -&gt; <strong>01_dev</strong><br> #############<br> <em>This folder contains the Python libraries and Mantaflow modified source code used in the paper. The libraries are provided as is. Feel free to contact us for support or guidelines.</em> - lspiv<br> -&gt; Python library used to extract, process and display results of LSPIV analysis carried out with Fudaa-LSPIV - mantaflow-modified<br> -&gt; Modified version of Mantaflow described in the article. Installation instructions can be found at http://mantaflow.com - syri<br> -&gt; Python library used to extract, process and display fluid simulations carried out on Mantaflow and Blender. (Require the lspiv library) ### -&gt; <strong>02_dataset</strong><br> #################<br> <em>This folder contains synthetic videos generated with the method described in the article. The fluid simulation parameters, and thus the reference velocities, are the same as those presented in the article. </em> - The videos can be used freely. Please consider citing the corresponding paper.
DOI: 10.5281/zenodo.6638737
title: Data for "Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis"

description: Supplementary Files for systematic review and meta-analysis: Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis
DOI: 10.5281/zenodo.5517419
title: Data for "Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis"

description: Supplementary Files for systematic review and meta-analysis: Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis
DOI: 10.34740/kaggle/dsv/4023506
title: Health Workforce Shortage Areas
DOI: 10.34740/kaggle/dsv/4023519
title: AutoXGB
DOI: 10.6084/m9.figshare.20424282
title: cds and protein files for ten Ganoderma genomes

description: cds and protein files for ten Ganoderma genomes
DOI: 10.6084/m9.figshare.20424282.v1
title: Ganoderma cds and peo

description: cds and protein files for ten Ganoderma genomes
DOI: 10.6084/m9.figshare.20424324
title: Climate and Energy editorials UK publications 2011 - 2021

description: Complete dataset of UK published editorials for selected newspapers between 2011 and end of 2021. Editorials contain one or more of the following keywords: <em>climate change or global warming or wind power or wind farm or wind energy or renewables or nuclear power or nuclear energy or fracking or shale gas or coal or oil or renewable energy or renewable power or solar power or solar energy or solar or Arctic or Antarctic or Paris Agreement or Paris climate deal or electric vehicles or electric cars or energy prices or steel </em>
DOI: 10.6084/m9.figshare.20424324.v1
title: Climate and Energy editorials UK publications 2011 - 2021

description: Complete dataset of UK published editorials for selected newspapers between 2011 and end of 2021. Editorials contain one or more of the following keywords: <em>climate change or global warming or wind power or wind farm or wind energy or renewables or nuclear power or nuclear energy or fracking or shale gas or coal or oil or renewable energy or renewable power or solar power or solar energy or solar or Arctic or Antarctic or Paris Agreement or Paris climate deal or electric vehicles or electric cars or energy prices or steel </em>