cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3160447
title: Oculomotor apraxia and dilated cardiomyopathy with ataxia syndrome: A case report

description: Dilated cardiomyopathy with ataxia syndrome (DCMA) is a rare mitochondrial condition associated with early onset cardiomyopathy and non-progressive ataxia. The cardiac manifestations may be progressive and often severe, resulting in significant morbidity and mortality. While optic nerve atrophy has been described in patients with DCMA, to our knowledge, there have been no reports of additional ocular phenotypes. We present two related Dariusleut Hutterite patients with documented DCMA syndrome and disorders of ocular motility: poor smooth pursuit and difficulty initiating saccadic eye movements and maintaining target fixation. We thus report the first cases of oculomotor apraxia in DCMA syndrome. By identifying these associated findings early in life, we hope to improve both the clinical diagnostic accuracy and timeliness of intervention in cases of DCMA.
DOI: 10.6084/m9.figshare.3160447.v1
title: Oculomotor apraxia and dilated cardiomyopathy with ataxia syndrome: A case report

description: Dilated cardiomyopathy with ataxia syndrome (DCMA) is a rare mitochondrial condition associated with early onset cardiomyopathy and non-progressive ataxia. The cardiac manifestations may be progressive and often severe, resulting in significant morbidity and mortality. While optic nerve atrophy has been described in patients with DCMA, to our knowledge, there have been no reports of additional ocular phenotypes. We present two related Dariusleut Hutterite patients with documented DCMA syndrome and disorders of ocular motility: poor smooth pursuit and difficulty initiating saccadic eye movements and maintaining target fixation. We thus report the first cases of oculomotor apraxia in DCMA syndrome. By identifying these associated findings early in life, we hope to improve both the clinical diagnostic accuracy and timeliness of intervention in cases of DCMA.
DOI: 10.6084/m9.figshare.3160387
title: Sensing abscisic acid

description: Publicity graphic to accompany PLOS ONE "Kinetic Characterisation of a Single Chain Antibody against the Hormone Abscisic Acid: Comparison with Its Parental Monoclonal" by George O. Badescu, Andrew Marsh, Timothy R. Smith, Andrew J. Thompson, Richard M. Napier
DOI: 10.6084/m9.figshare.3160387.v1
title: Sensing abscisic acid

description: Publicity graphic to accompany PLOS ONE "Kinetic Characterisation of a Single Chain Antibody against the Hormone Abscisic Acid: Comparison with Its Parental Monoclonal" by George O. Badescu, Andrew Marsh, Timothy R. Smith, Andrew J. Thompson, Richard M. Napier
DOI: 10.6084/m9.figshare.3160162
title: Geostatistical space–time mapping of house prices using Bayesian maximum entropy

description: Mapping spatial processes at a small scale is a challenge when observed data are not abundant. The article examines the residential housing market in Fort Worth, Texas, and builds price indices at the inter- and intra-neighborhood levels. To accomplish our objectives, we initially model price variability in the joint space<b>–</b>time continuum. We then use geostatistics to predict and map monthly housing prices across the area of interest over a period of 4 years. For this analysis, we introduce the Bayesian maximum entropy (BME) method into real estate research. We use BME because it rigorously integrates uncertain or secondary soft data, which are needed to build the price indices. The soft data in our analysis are property tax values, which are plentiful, publicly available, and highly correlated with transaction prices. The results demonstrate how the use of the soft data provides the ability to map house prices within a small areal unit such as a subdivision or neighborhood.
DOI: 10.6084/m9.figshare.3160156
title: Adiabatic preparation of Floquet condensates

description: We argue that a Bose–Einstein condensate can be transformed into a Floquet condensate, that is, into a periodically time-dependent many-particle state possessing the coherence properties of a mesoscopically occupied single-particle Floquet state. Our reasoning is based on the observation that the denseness of the many-body system’s quasienergy spectrum does not necessarily obstruct effectively adiabatic transport. Employing the idealized model of a driven bosonic Josephson junction, we demonstrate that only a small amount of Floquet entropy is generated when a driving force with judiciously chosen frequency and maximum amplitude is turned on smoothly.
DOI: 10.6084/m9.figshare.3160162.v1
title: Geostatistical space–time mapping of house prices using Bayesian maximum entropy

description: Mapping spatial processes at a small scale is a challenge when observed data are not abundant. The article examines the residential housing market in Fort Worth, Texas, and builds price indices at the inter- and intra-neighborhood levels. To accomplish our objectives, we initially model price variability in the joint space<b>–</b>time continuum. We then use geostatistics to predict and map monthly housing prices across the area of interest over a period of 4 years. For this analysis, we introduce the Bayesian maximum entropy (BME) method into real estate research. We use BME because it rigorously integrates uncertain or secondary soft data, which are needed to build the price indices. The soft data in our analysis are property tax values, which are plentiful, publicly available, and highly correlated with transaction prices. The results demonstrate how the use of the soft data provides the ability to map house prices within a small areal unit such as a subdivision or neighborhood.
DOI: 10.6084/m9.figshare.3160156.v1
title: Adiabatic preparation of Floquet condensates

description: We argue that a Bose–Einstein condensate can be transformed into a Floquet condensate, that is, into a periodically time-dependent many-particle state possessing the coherence properties of a mesoscopically occupied single-particle Floquet state. Our reasoning is based on the observation that the denseness of the many-body system’s quasienergy spectrum does not necessarily obstruct effectively adiabatic transport. Employing the idealized model of a driven bosonic Josephson junction, we demonstrate that only a small amount of Floquet entropy is generated when a driving force with judiciously chosen frequency and maximum amplitude is turned on smoothly.
DOI: 10.6084/m9.figshare.3160021
title: List of macromolecular structures visualization tools

description: Extracted from these review for my presentation.<br>Visualization of macromolecular structures.Nat Methods. 2010 Mar;7(3 Suppl):S42-55. doi: 10.1038/nmeth.1427. Epub 2010 Mar 1.O'Donoghue SI1, Goodsell DS, Frangakis AS, Jossinet F, Laskowski RA, Nilges M, Saibil HR, Schafferhans A, Wade RC, Westhof E, Olson AJ.http://www.nature.com/nmeth/journal/v7/n3s/full/nmeth.1427.html
DOI: 10.6084/m9.figshare.3160021.v1
title: List of macromolecular structures visualization tools

description: Extracted from these review for my presentation.<br>Visualization of macromolecular structures.Nat Methods. 2010 Mar;7(3 Suppl):S42-55. doi: 10.1038/nmeth.1427. Epub 2010 Mar 1.O'Donoghue SI1, Goodsell DS, Frangakis AS, Jossinet F, Laskowski RA, Nilges M, Saibil HR, Schafferhans A, Wade RC, Westhof E, Olson AJ.http://www.nature.com/nmeth/journal/v7/n3s/full/nmeth.1427.html
DOI: 10.6084/m9.figshare.3159991
title: A Digital Elevation Model for Cyprus based on the ALOS 2 W3D30 Digital Surface Model

description: For a description of the DEM and the steps for its compilation see or download the accompanying pdf document .<br><br>For the DEM and Hillshade data, download the zip file.<br>Files and information are also available here: https://www.academia.edu/23922627/A_Digital_Elevation_Model_for_Cyprus_based_on_the_ALOS_2_W3D30_Digital_Surface_Model<br><br>All data for the production of this DEM are © Japan Aerospace Exploration Agency (JAXA).<br>Data used for the production of the 1:5000 coastline used to clip the DEM are © Department of Lands and Surveys Cyprus (DLS).<br><br>The dataset is available to use with no charge and is provided under the same conditions set by JAXA, as follows:<br>- When the user provides or publishes the products and services to a third party using this dataset, it is necessary to display that the original data is provided by JAXA.<br>- You are kindly requested to show the copyright (© JAXA) and the source of data, when you publish the fruits using this dataset.<br>- JAXA does not guarantee the quality and reliability of this dataset and JAXA assume no responsibility whatsoever for any direct or indirect damage and loss caused by use of this dataset. Also, JAXA will not be responsible for any damages of users due to changing, deleting or terminating the provision of this dataset.
DOI: 10.6084/m9.figshare.3159967
title: Java computer programs and its rank

description: List of Java computer programs and their ranking order using rubric's assessment and n-gram features.<br>
DOI: 10.6084/m9.figshare.3159961
title: Fujita_etal_2016_data.csv

description: Dataset used for a paper "Context dependent effect of landscape on the occurrence of an apex predator across different climate regions" published in PLOS ONE
DOI: 10.6084/m9.figshare.3159967.v1
title: Java computer programs and its rank

description: List of Java computer programs and their ranking order using rubric's assessment and n-gram features.<br>
DOI: 10.18462/iir.rankine.2020.1146
title: Performance prediction and optimisation of twin-screw expander.
DOI: 10.6084/m9.figshare.3159934
title: Effect of thermal treatments on the degradation of antibiotic residues in food

description: The thermal stability of antibiotics commonly detected in food is reviewed. To quantify degradation, 2 major techniques have been reported: liquid chromatography-based methods and microbiological tests. As the degradation products may also display some antimicrobial activity, microbiological tests may not be considered accurate analytical methods for quantifying antibiotic residues' degradation. Degradation percentages are summarized for different antibiotics and for various media (water, oil, milk, and animal tissues). Studies presented in the literature confirm that the thermal degradation of β-lactams, quinolones, sulfonamides, and tetracyclines can be described using a first-order kinetic model. Degradation rates, k, derived for this model for liquid matrix (water) at 100 °C, followed the general trend amongst antibiotic classes: β-lactams = tetracyclines (most heat-labile) &gt; lincomycin &gt; amphenicols &gt; sulfonamides &gt; oxfendazole &gt; levamisole (most heat-stable). Although thermal processing results in a decrease in the concentration of parent antibiotic residues, degradation by-products have not been properly characterized to date. As some of these products were shown to be hazardous, further investigation is needed to determine their impact on food safety and human health. It is therefore currently difficult to definitively conclude whether or not antibiotic degradation during food processing is necessarily beneficial in terms of food safety.
DOI: 10.6084/m9.figshare.3159961.v1
title: Fujita_etal_2016_data.csv

description: Dataset used for a paper "Context dependent effect of landscape on the occurrence of an apex predator across different climate regions" published in PLOS ONE
DOI: 10.6084/m9.figshare.3159931
title: Single Unit Recordings of MSN and FSI in Dorsal Striatum

description: Dataset of single unit neuronal recordings from the dorsal striatum of awake, behaving mice
DOI: 10.6084/m9.figshare.3159934.v1
title: Effect of thermal treatments on the degradation of antibiotic residues in food

description: The thermal stability of antibiotics commonly detected in food is reviewed. To quantify degradation, 2 major techniques have been reported: liquid chromatography-based methods and microbiological tests. As the degradation products may also display some antimicrobial activity, microbiological tests may not be considered accurate analytical methods for quantifying antibiotic residues' degradation. Degradation percentages are summarized for different antibiotics and for various media (water, oil, milk, and animal tissues). Studies presented in the literature confirm that the thermal degradation of β-lactams, quinolones, sulfonamides, and tetracyclines can be described using a first-order kinetic model. Degradation rates, k, derived for this model for liquid matrix (water) at 100 °C, followed the general trend amongst antibiotic classes: β-lactams = tetracyclines (most heat-labile) &gt; lincomycin &gt; amphenicols &gt; sulfonamides &gt; oxfendazole &gt; levamisole (most heat-stable). Although thermal processing results in a decrease in the concentration of parent antibiotic residues, degradation by-products have not been properly characterized to date. As some of these products were shown to be hazardous, further investigation is needed to determine their impact on food safety and human health. It is therefore currently difficult to definitively conclude whether or not antibiotic degradation during food processing is necessarily beneficial in terms of food safety.
DOI: 10.6084/m9.figshare.3159958.v1
title: Inspection, visualisation and analysis of quantitative proteomics data

description: Material Quantitative Proteomics and Data Analysis Course. 4 - 5 April 2016, Queen Hotel, Chester, UK Table D - Inspection, visualisation and analysis of quantitative proteomics data, Laurent Gatto (University of Cambridge)
DOI: 10.6084/m9.figshare.3159928
title: A Bayesian Multivariate Functional Dynamic Linear Model

description: We present a Bayesian approach for modeling multivariate, dependent functional data. To account for the three dominant structural features in the data—<i>functional</i>, <i>time dependent</i>, and <i>multivariate</i> components—we extend hierarchical dynamic linear models for multivariate time series to the functional data setting. We also develop Bayesian spline theory in a more general constrained optimization framework. The proposed methods identify a time-invariant functional basis for the functional observations, which is smooth and interpretable, and can be made common across multivariate observations for additional information sharing. The Bayesian framework permits joint estimation of the model parameters, provides exact inference (up to MCMC error) on specific parameters, and allows generalized dependence structures. Sampling from the posterior distribution is accomplished with an efficient Gibbs sampling algorithm. We illustrate the proposed framework with two applications: (1) multi-economy yield curve data from the recent global recession, and (2) local field potential brain signals in rats, for which we develop a multivariate functional time series approach for multivariate time–frequency analysis. Supplementary materials, including R code and the multi-economy yield curve data, are available online.
DOI: 10.6084/m9.figshare.3159928.v2
title: A Bayesian Multivariate Functional Dynamic Linear Model

description: We present a Bayesian approach for modeling multivariate, dependent functional data. To account for the three dominant structural features in the data—<i>functional</i>, <i>time dependent</i>, and <i>multivariate</i> components—we extend hierarchical dynamic linear models for multivariate time series to the functional data setting. We also develop Bayesian spline theory in a more general constrained optimization framework. The proposed methods identify a time-invariant functional basis for the functional observations, which is smooth and interpretable, and can be made common across multivariate observations for additional information sharing. The Bayesian framework permits joint estimation of the model parameters, provides exact inference (up to MCMC error) on specific parameters, and allows generalized dependence structures. Sampling from the posterior distribution is accomplished with an efficient Gibbs sampling algorithm. We illustrate the proposed framework with two applications: (1) multi-economy yield curve data from the recent global recession, and (2) local field potential brain signals in rats, for which we develop a multivariate functional time series approach for multivariate time–frequency analysis. Supplementary materials, including R code and the multi-economy yield curve data, are available online.
DOI: 10.6084/m9.figshare.3159919
title: Modeling the Evolution of Dynamic Brain Processes During an Associative Learning Experiment

description: We develop a new time series model to investigate the dynamic interactions between the nucleus accumbens and the hippocampus during an associative learning experiment. Preliminary analyses indicated that the spectral properties of the local field potentials at these two regions changed over the trials of the experiment. While many models already take into account nonstationarity within a single trial, the evolution of the dynamics across trials is often ignored. Our proposed model, the slowly evolving locally stationary process (SEv-LSP), is designed to capture nonstationarity both within a trial and across trials. We rigorously define the evolving evolutionary spectral density matrix, which we estimate using a two-stage procedure. In the first stage, we compute the within-trial time-localized periodogram matrix. In the second stage, we develop a data-driven approach that combines information from trial-specific local periodogram matrices. Through simulation studies, we show the utility of our proposed method for analyzing time series data with different evolutionary structures. Finally, we use the SEv-LSP model to demonstrate the evolving dynamics between the hippocampus and the nucleus accumbens during an associative learning experiment. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3159919.v2
title: Modeling the Evolution of Dynamic Brain Processes During an Associative Learning Experiment

description: We develop a new time series model to investigate the dynamic interactions between the nucleus accumbens and the hippocampus during an associative learning experiment. Preliminary analyses indicated that the spectral properties of the local field potentials at these two regions changed over the trials of the experiment. While many models already take into account nonstationarity within a single trial, the evolution of the dynamics across trials is often ignored. Our proposed model, the slowly evolving locally stationary process (SEv-LSP), is designed to capture nonstationarity both within a trial and across trials. We rigorously define the evolving evolutionary spectral density matrix, which we estimate using a two-stage procedure. In the first stage, we compute the within-trial time-localized periodogram matrix. In the second stage, we develop a data-driven approach that combines information from trial-specific local periodogram matrices. Through simulation studies, we show the utility of our proposed method for analyzing time series data with different evolutionary structures. Finally, we use the SEv-LSP model to demonstrate the evolving dynamics between the hippocampus and the nucleus accumbens during an associative learning experiment. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3159907
title: Shrinkage Estimation for Multivariate Hidden Markov Models

description: Motivated from a changing market environment over time, we consider high-dimensional data such as financial returns, generated by a hidden Markov model that allows for switching between different regimes or states. To get more stable estimates of the covariance matrices of the different states, potentially driven by a number of observations that are small compared to the dimension, we modify the expectation–maximization (EM) algorithm so that it yields the shrinkage estimators for the covariance matrices. The final algorithm turns out to reproduce better estimates not only for the covariance matrices but also for the transition matrix. It results into a more stable and reliable filter that allows for reconstructing the values of the hidden Markov chain. In addition to a simulation study performed in this article, we also present a series of theoretical results that include dimensionality asymptotics and provide the motivation for certain techniques used in the algorithm. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3159919.v1
title: Modeling the evolution of dynamic brain processes during an associative learning experiment

description: We develop a new time series model to investigate the dynamic interactions between the nucleus accumbens and the hippocampus during an associative learning experiment. Preliminary analyses indicated that the spectral properties of the local field potentials at these two regions changed over the trials of the experiment. While many models already take into account nonstationarity within a single trial, the evolution of the dynamics across trials is often ignored. Our proposed model, the slowly evolving locally stationary process (SEv-LSP), is designed to capture nonstationarity both within a trial and across trials. We rigorously define the evolving evolutionary spectral density matrix, which we estimate using a two-stage procedure. In the first stage, we compute the within-trial time-localized periodogram matrix. In the second stage, we develop a data-driven approach that combines information from trial-specific local periodogram matrices. Through simulation studies, we show the utility of our proposed method for analyzing time series data with different evolutionary structures. Finally, we use the SEv-LSP model to demonstrate the evolving dynamics between the hippocampus and the nucleus accumbens during an associative learning experiment.
DOI: 10.6084/m9.figshare.3159907.v1
title: Shrinkage Estimation for Multivariate Hidden Markov Models

description: Motivated from a changing market environment over time, we consider high-dimensional data such as financial returns, generated by a hidden Markov model that allows for switching between different regimes or states. To get more stable estimates of the covariance matrices of the different states, potentially driven by a number of observations that are small compared to the dimension, we modify the expectation–maximization (EM) algorithm so that it yields the shrinkage estimators for the covariance matrices. The final algorithm turns out to reproduce better estimates not only for the covariance matrices but also for the transition matrix. It results into a more stable and reliable filter that allows for reconstructing the values of the hidden Markov chain. In addition to a simulation study performed in this article, we also present a series of theoretical results that include dimensionality asymptotics and provide the motivation for certain techniques used in the algorithm. Supplementary materials for this article are available online.