cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1476246.v6
title: The novel function of JADE1S in cytokinesis of epithelial cells

description: JADE1 belongs to a small family of PHD zinc finger proteins that interacts with histone acetyl transferase (HAT) HBO1 and is associated with chromatin. We recently reported JADE1 chromatin shuttling and phosphorylation during G2/M to G1 transition, which was sensitive to Aurora A inhibition. In the current study we examined mechanisms of the cell cycle regulation by the small isoform of JADE1 protein, JADE1S, and report data showing that JADE1S has a novel function in the regulation of cytokinesis. Using FACS assays, we show that, JADE1S depletion facilitated rates of G1-cells accumulation in synchronously dividing HeLa cell cultures. Depletion of JADE1S protein in asynchronously dividing cells decreased the proportion of cytokinetic cells, and increased the proportion of multi-nuclear cells, indicative of premature and failed cytokinesis. In contrast, moderate overexpression of JADE1S increased the number of cytokinetic cells in time- and dose- dependent manner, indicating cytokinetic delay. Pharmacological inhibition of Aurora B kinase resulted in the release of JADE1S-mediated cytokinetic delay and allowed progression of abscission in cells over-expressing JADE1S. Finally, we show that JADE1S protein localized to centrosomes in interphase and mitotic cells, while during cytokinesis JADE1S localized to the midbody. Neither JADE1L nor partner of JADE1, HAT HBO1 was localized to the centrosomes or midbodies. Our study identifies the novel role for JADE1S in regulation of cytokinesis and suggests function in Aurora B kinase-mediated cytokinesis checkpoint.
DOI: 10.6084/m9.figshare.1476207
title: Average reaction time: Different trial number, different experimental conditions

description: Average reaction time at different trial number and different experimental conditions for 15 subjects.
DOI: 10.6084/m9.figshare.1476246.v5
title: The novel function of JADE1S in cytokinesis of epithelial cells

description: JADE1 belongs to a small family of PHD zinc finger proteins that interacts with histone acetyl transferase (HAT) HBO1 and is associated with chromatin. We recently reported JADE1 chromatin shuttling and phosphorylation during G2/M to G1 transition, which was sensitive to Aurora A inhibition. In the current study we examined mechanisms of the cell cycle regulation by the small isoform of JADE1 protein, JADE1S, and report data showing that JADE1S has a novel function in the regulation of cytokinesis. Using FACS assays, we show that, JADE1S depletion facilitated rates of G1-cells accumulation in synchronously dividing HeLa cell cultures. Depletion of JADE1S protein in asynchronously dividing cells decreased the proportion of cytokinetic cells, and increased the proportion of multi-nuclear cells, indicative of premature and failed cytokinesis. In contrast, moderate overexpression of JADE1S increased the number of cytokinetic cells in time- and dose- dependent manner, indicating cytokinetic delay. Pharmacological inhibition of Aurora B kinase resulted in the release of JADE1S-mediated cytokinetic delay and allowed progression of abscission in cells over-expressing JADE1S. Finally, we show that JADE1S protein localized to centrosomes in interphase and mitotic cells, while during cytokinesis JADE1S localized to the midbody. Neither JADE1L nor partner of JADE1, HAT HBO1 was localized to the centrosomes or midbodies. Our study identifies the novel role for JADE1S in regulation of cytokinesis and suggests function in Aurora B kinase-mediated cytokinesis checkpoint.
DOI: 10.6084/m9.figshare.1476205
title: Trial-to-trial movement ajustment

description: Trial-to-trial movement ajustment during sensorimotor adaptation, at differen trial number and different experiment condition, for 15 subjects.
DOI: 10.6084/m9.figshare.1476206
title: AveMvT_DifTrialNumber_Dif Conditions

description: Average movement duration for different trial number and different experimental condition for 15 subjects.
DOI: 10.6084/m9.figshare.1476204
title: Average Movement Error

description: How average movement error changed with trial number and experimental conditions in sensorimotor adaptation, from 15 subjects.
DOI: 10.6084/m9.figshare.1476207.v1
title: Average reaction time: Different trial number, different experimental conditions

description: Average reaction time at different trial number and different experimental conditions for 15 subjects.
DOI: 10.6084/m9.figshare.1476204.v1
title: Average Movement Error

description: How average movement error changed with trial number and experimental conditions in sensorimotor adaptation, from 15 subjects.
DOI: 10.6084/m9.figshare.1476205.v1
title: Trial-to-trial movement ajustment

description: Trial-to-trial movement ajustment during sensorimotor adaptation, at differen trial number and different experiment condition, for 15 subjects.
DOI: 10.6084/m9.figshare.1476206.v1
title: AveMvT_DifTrialNumber_Dif Conditions

description: Average movement duration for different trial number and different experimental condition for 15 subjects.
DOI: 10.6084/m9.figshare.1476182
title: Analysis of 2006 and 2010 TWMFE Survey

description: Stata .so files for analysis of 2006 and 2010 TWMFE Survey. Used in PLOS ONE paper submission
DOI: 10.6084/m9.figshare.1476182.v1
title: Analysis of 2006 and 2010 TWMFE Survey

description: Stata .so files for analysis of 2006 and 2010 TWMFE Survey. Used in PLOS ONE paper submission
DOI: 10.6084/m9.figshare.1476176
title: SM3_ExampleScript_Data_From_Defining an additivity framework for mixture research in inducible whole-cell biosensors

description: Tutorial, R-script and Dataset: Fitting non-monotonic dose-response with differential maximal effects and analysis of departures from additivity based on multivariate Loewe additivity using R. From: Defining an additivity framework for mixture research in inducible whole-cell biosensors Martin-Betancor, K1; Ritz, C2; Fernández-Piñas, F1; Leganés, F1; & Rodea-Palomares I1* 1 Departament of Biology, Facultad de Ciencias, Universidad Autónoma de Madrid. 28049 Madrid, Spain. 2 Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Rolighedsvej 30, DK-1958 Frederiksberg C, Denmark. *Corresponding author: Ismael Rodea-Palomares Mailing address: Departamento de Biología, Facultad de Ciencias, Universidad Autónoma de Madrid. C/ Darwin, 2. 28049, Madrid. Spain. Tel.: +34 914978180<br>Fax: +34 914978344 E-mail: ismael.rodea@uam.es
DOI: 10.6084/m9.figshare.1476178
title: Disposable E Cig

description: http://www.iloveecigs.com/<br>As former smokers, we know how difficult it is to quit smoking. We know that according to research and public information, smoking regular tobacco cigarettes is just unhealthy. As part of our commitment to supplying smokers with superior smoking products, we came up with a simple and easy-to-follow program that may help smokers transform their smoking habits.
DOI: 10.6084/m9.figshare.1476178.v1
title: Disposable E Cig

description: http://www.iloveecigs.com/<br>As former smokers, we know how difficult it is to quit smoking. We know that according to research and public information, smoking regular tobacco cigarettes is just unhealthy. As part of our commitment to supplying smokers with superior smoking products, we came up with a simple and easy-to-follow program that may help smokers transform their smoking habits.
DOI: 10.6084/m9.figshare.1476164
title: Waveform Similarity Analysis: in vivo data and simulation - PLoS

description: This zip file contains MATLAB code, compound nerve and muscle data from rat sciatic/sural nerves and a simulation experiment to accompany our manuscript: "Waveform Similarity Analysis: A simple template comparing approach for detecting and quantifying noisy evoked compound action potentials"
DOI: 10.6084/m9.figshare.1474035
title: ClinicalTrials.gov - Search Query (July 2, 2015) = Closed Studies | Spain - 5374 studies

description: Database of clinical trials and observational studies: - pharmacological and non-pharmacological interventions. - Location: Spain. - ClinicalTrials.gov database available in this figshare fileset - version 1.0 (July 6, 2015). - Database is also available via Google Spreadsheets (open data - URL below).
DOI: 10.6084/m9.figshare.1476164.v2
title: Waveform Similarity Analysis: in vivo data and simulation - PLoS

description: This zip file contains MATLAB code, compound nerve and muscle data from rat sciatic/sural nerves and a simulation experiment to accompany our manuscript: "Waveform Similarity Analysis: A simple template comparing approach for detecting and quantifying noisy evoked compound action potentials"
DOI: 10.6084/m9.figshare.1476164.v1
title: Waveform Similarity Analysis: in vivo data and simulation

description: This zip file contains MATLAB code, compound nerve and muscle data from rat sciatic/sural nerves and a simulation experiment to accompany our manuscript: "Waveform Similarity Analysis: A simple template comparing approach for detecting and quantifying noisy evoked compound action potentials"
DOI: 10.6084/m9.figshare.1473752
title: Bayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity

description: This article proposes a phase I/II clinical trial design for adaptively and dynamically optimizing each patient’s dose in each of two cycles of therapy based on the joint binary efficacy and toxicity outcomes in each cycle. A dose-outcome model is assumed that includes a Bayesian hierarchical latent variable structure to induce association among the outcomes and also facilitate posterior computation. Doses are chosen in each cycle based on posteriors of a model-based objective function, similar to a reinforcement learning or Q-learning function, defined in terms of numerical utilities of the joint outcomes in each cycle. For each patient, the procedure outputs a sequence of two actions, one for each cycle, with each action being the decision to either treat the patient at a chosen dose or not to treat. The cycle 2 action depends on the individual patient’s cycle 1 dose and outcomes. In addition, decisions are based on posterior inference using other patients’ data, and therefore, the proposed method is adaptive both within and between patients. A simulation study of the method is presented, including comparison to two-cycle extensions of the conventional 3 + 3 algorithm, continual reassessment method, and a Bayesian model-based design, and evaluation of robustness. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1473749
title: Unifying Amplitude and Phase Analysis: A Compositional Data Approach to Functional Multivariate Mixed-Effects Modeling of Mandarin Chinese

description: Mandarin Chinese is characterized by being a tonal language; the pitch (or <i>F</i><sub>0</sub>) of its utterances carries considerable linguistic information. However, speech samples from different individuals are subject to changes in amplitude and phase, which must be accounted for in any analysis that attempts to provide a linguistically meaningful description of the language. A joint model for amplitude, phase, and duration is presented, which combines elements from functional data analysis, compositional data analysis, and linear mixed effects models. By decomposing functions via a functional principal component analysis, and connecting registration functions to compositional data analysis, a joint multivariate mixed effect model can be formulated, which gives insights into the relationship between the different modes of variation as well as their dependence on linguistic and nonlinguistic covariates. The model is applied to the COSPRO-1 dataset, a comprehensive database of spoken Taiwanese Mandarin, containing approximately 50,000 phonetically diverse sample <i>F</i><sub>0</sub> contours (syllables), and reveals that phonetic information is jointly carried by both amplitude and phase variation. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1473752.v1
title: Bayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity

description: This article proposes a phase I/II clinical trial design for adaptively and dynamically optimizing each patient’s dose in each of two cycles of therapy based on the joint binary efficacy and toxicity outcomes in each cycle. A dose-outcome model is assumed that includes a Bayesian hierarchical latent variable structure to induce association among the outcomes and also facilitate posterior computation. Doses are chosen in each cycle based on posteriors of a model-based objective function, similar to a reinforcement learning or Q-learning function, defined in terms of numerical utilities of the joint outcomes in each cycle. For each patient, the procedure outputs a sequence of two actions, one for each cycle, with each action being the decision to either treat the patient at a chosen dose or not to treat. The cycle 2 action depends on the individual patient’s cycle 1 dose and outcomes. In addition, decisions are based on posterior inference using other patients’ data, and therefore, the proposed method is adaptive both within and between patients. A simulation study of the method is presented, including comparison to two-cycle extensions of the conventional 3 + 3 algorithm, continual reassessment method, and a Bayesian model-based design, and evaluation of robustness. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1473749.v1
title: Unifying Amplitude and Phase Analysis: A Compositional Data Approach to Functional Multivariate Mixed-Effects Modeling of Mandarin Chinese

description: Mandarin Chinese is characterized by being a tonal language; the pitch (or <i>F</i><sub>0</sub>) of its utterances carries considerable linguistic information. However, speech samples from different individuals are subject to changes in amplitude and phase, which must be accounted for in any analysis that attempts to provide a linguistically meaningful description of the language. A joint model for amplitude, phase, and duration is presented, which combines elements from functional data analysis, compositional data analysis, and linear mixed effects models. By decomposing functions via a functional principal component analysis, and connecting registration functions to compositional data analysis, a joint multivariate mixed effect model can be formulated, which gives insights into the relationship between the different modes of variation as well as their dependence on linguistic and nonlinguistic covariates. The model is applied to the COSPRO-1 dataset, a comprehensive database of spoken Taiwanese Mandarin, containing approximately 50,000 phonetically diverse sample <i>F</i><sub>0</sub> contours (syllables), and reveals that phonetic information is jointly carried by both amplitude and phase variation. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1473749.v3
title: Unifying Amplitude and Phase Analysis: A Compositional Data Approach to Functional Multivariate Mixed-Effects Modeling of Mandarin Chinese

description: Mandarin Chinese is characterized by being a tonal language; the pitch (or <i>F</i><sub>0</sub>) of its utterances carries considerable linguistic information. However, speech samples from different individuals are subject to changes in amplitude and phase, which must be accounted for in any analysis that attempts to provide a linguistically meaningful description of the language. A joint model for amplitude, phase, and duration is presented, which combines elements from functional data analysis, compositional data analysis, and linear mixed effects models. By decomposing functions via a functional principal component analysis, and connecting registration functions to compositional data analysis, a joint multivariate mixed effect model can be formulated, which gives insights into the relationship between the different modes of variation as well as their dependence on linguistic and nonlinguistic covariates. The model is applied to the COSPRO-1 dataset, a comprehensive database of spoken Taiwanese Mandarin, containing approximately 50,000 phonetically diverse sample <i>F</i><sub>0</sub> contours (syllables), and reveals that phonetic information is jointly carried by both amplitude and phase variation. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1473748
title: Index Models for Sparsely Sampled Functional Data

description: The regression problem involving functional predictors has many important applications and a number of functional regression methods have been developed. However, a common complication in functional data analysis is one of sparsely observed curves, that is predictors that are observed, with error, on a small subset of the possible time points. Such sparsely observed data induce an <i>errors-in-variables</i> model, where one must account for measurement error in the functional predictors. Faced with sparsely observed data, most current functional regression methods simply estimate the unobserved predictors and treat them as fully observed; thus failing to account for the extra uncertainty from the measurement error. We propose a new functional errors-in-variables approach, sparse index model functional estimation (SIMFE), which uses a functional index model formulation to deal with sparsely observed predictors. SIMFE has several advantages over more traditional methods. First, the index model implements a nonlinear regression and uses an accurate supervised method to estimate the lower dimensional space into which the predictors should be projected. Second, SIMFE can be applied to both scalar and functional responses and multiple predictors. Finally, SIMFE uses a mixed effects model to effectively deal with very sparsely observed functional predictors and to correctly model the measurement error. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1473749.v2
title: Unifying Amplitude and Phase Analysis: A Compositional Data Approach to Functional Multivariate Mixed-Effects Modeling of Mandarin Chinese

description: Mandarin Chinese is characterized by being a tonal language; the pitch (or <i>F</i><sub>0</sub>) of its utterances carries considerable linguistic information. However, speech samples from different individuals are subject to changes in amplitude and phase, which must be accounted for in any analysis that attempts to provide a linguistically meaningful description of the language. A joint model for amplitude, phase, and duration is presented, which combines elements from functional data analysis, compositional data analysis, and linear mixed effects models. By decomposing functions via a functional principal component analysis, and connecting registration functions to compositional data analysis, a joint multivariate mixed effect model can be formulated, which gives insights into the relationship between the different modes of variation as well as their dependence on linguistic and nonlinguistic covariates. The model is applied to the COSPRO-1 dataset, a comprehensive database of spoken Taiwanese Mandarin, containing approximately 50,000 phonetically diverse sample <i>F</i><sub>0</sub> contours (syllables), and reveals that phonetic information is jointly carried by both amplitude and phase variation. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1473752.v2
title: Bayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity

description: This article proposes a phase I/II clinical trial design for adaptively and dynamically optimizing each patient’s dose in each of two cycles of therapy based on the joint binary efficacy and toxicity outcomes in each cycle. A dose-outcome model is assumed that includes a Bayesian hierarchical latent variable structure to induce association among the outcomes and also facilitate posterior computation. Doses are chosen in each cycle based on posteriors of a model-based objective function, similar to a reinforcement learning or Q-learning function, defined in terms of numerical utilities of the joint outcomes in each cycle. For each patient, the procedure outputs a sequence of two actions, one for each cycle, with each action being the decision to either treat the patient at a chosen dose or not to treat. The cycle 2 action depends on the individual patient’s cycle 1 dose and outcomes. In addition, decisions are based on posterior inference using other patients’ data, and therefore, the proposed method is adaptive both within and between patients. A simulation study of the method is presented, including comparison to two-cycle extensions of the conventional 3 + 3 algorithm, continual reassessment method, and a Bayesian model-based design, and evaluation of robustness. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1473752.v4
title: Bayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity

description: This article proposes a phase I/II clinical trial design for adaptively and dynamically optimizing each patient’s dose in each of two cycles of therapy based on the joint binary efficacy and toxicity outcomes in each cycle. A dose-outcome model is assumed that includes a Bayesian hierarchical latent variable structure to induce association among the outcomes and also facilitate posterior computation. Doses are chosen in each cycle based on posteriors of a model-based objective function, similar to a reinforcement learning or Q-learning function, defined in terms of numerical utilities of the joint outcomes in each cycle. For each patient, the procedure outputs a sequence of two actions, one for each cycle, with each action being the decision to either treat the patient at a chosen dose or not to treat. The cycle 2 action depends on the individual patient’s cycle 1 dose and outcomes. In addition, decisions are based on posterior inference using other patients’ data, and therefore, the proposed method is adaptive both within and between patients. A simulation study of the method is presented, including comparison to two-cycle extensions of the conventional 3 + 3 algorithm, continual reassessment method, and a Bayesian model-based design, and evaluation of robustness. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1473752.v3
title: Bayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity

description: This article proposes a phase I/II clinical trial design for adaptively and dynamically optimizing each patient’s dose in each of two cycles of therapy based on the joint binary efficacy and toxicity outcomes in each cycle. A dose-outcome model is assumed that includes a Bayesian hierarchical latent variable structure to induce association among the outcomes and also facilitate posterior computation. Doses are chosen in each cycle based on posteriors of a model-based objective function, similar to a reinforcement learning or Q-learning function, defined in terms of numerical utilities of the joint outcomes in each cycle. For each patient, the procedure outputs a sequence of two actions, one for each cycle, with each action being the decision to either treat the patient at a chosen dose or not to treat. The cycle 2 action depends on the individual patient’s cycle 1 dose and outcomes. In addition, decisions are based on posterior inference using other patients’ data, and therefore, the proposed method is adaptive both within and between patients. A simulation study of the method is presented, including comparison to two-cycle extensions of the conventional 3 + 3 algorithm, continual reassessment method, and a Bayesian model-based design, and evaluation of robustness. Supplementary materials for this article are available online.