cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26180/5c874e555c96d
title: Figure8

description: Raw data and statistics files to accompany figure 8 of the manuscript <b>Metformin rescues muscle function in BAG3 myofibrillar myopathy</b><b> models</b>
DOI: 10.26180/5c874e6ce3f9b
title: SuppFigure5

description: Raw data and statistics files to accompany supplemntary figure 5 of the manuscript <b>Metformin rescues muscle function in BAG3 myofibrillar myopathy</b><b> models</b>
DOI: 10.26180/5c874e11da387
title: Figure6

description: Raw data and statistics files to accompany figure 6 of the manuscript <b>Metformin rescues muscle function in BAG3 myofibrillar myopathy</b><b> models</b>
DOI: 10.26180/5c874e2e969aa
title: Figure7

description: Raw data and statistics files to accompany figure 7 of the manuscript <b>Metformin rescues muscle function in BAG3 myofibrillar myopathy</b><b> models</b>
DOI: 10.26180/5c874df0106e8
title: Figure5

description: Raw data and statistics files to accompany figure 5 of the manuscript <b>Metformin rescues muscle function in BAG3 myofibrillar myopathy</b><b> models</b>
DOI: 10.23641/asha.7822592
title: Bayesian applications in auditory research (McMillan & Cannon, 2019)

description: <b>Purpose: </b>This article presents a basic exploration of Bayesian inference to inform researchers unfamiliar to this type of analysis of the many advantages this readily available approach provides.<b>Method: </b>First, we demonstrate the development of Bayes’ theorem, the cornerstone of Bayesian statistics, into an iterative process of updating priors. Working with a few assumptions, including normalcy and conjugacy of prior distribution, we express how one would calculate the posterior distribution using the prior distribution and the likelihood of the parameter. Next, we move to an example in auditory research by considering the effect of sound therapy for reducing the perceived loudness of tinnitus. In this case, as well as most real-world settings, we turn to Markov chain simulations because the assumptions allowing for easy calculations no longer hold. Using Markov chain Monte Carlo methods, we can illustrate several analysis solutions given by a straightforward Bayesian approach.<b>Conclusion: </b>Bayesian methods are widely applicable and can help scientists overcome analysis problems, including how to include existing information, run interim analysis, achieve consensus through measurement, and, most importantly, interpret results correctly.<br><b>Supplemental Material S1. </b>SAS code for running the analysis described in the article.<br><b>Supplemental Material S2.</b> MS Excel workbook, allowing the reader to experiment with the model described in the article. <br>McMillan, G. P., & Cannon, J. B. (2019). Bayesian applications in auditory research. <i>Journal of Speech, Language, and Hearing Research, 62,</i> 577–586. https://doi.org/10.1044/2018_JSLHR-L-ASTM-18-0250<br><b>Publisher Note: </b>This article is part of the Research Forum: Advancing Statistical Methods in Speech, Language, and Hearing Sciences.<br>
DOI: 10.23641/asha.7822592.v1
title: Bayesian applications in auditory research (McMillan & Cannon, 2019)

description: <b>Purpose: </b>This article presents a basic exploration of Bayesian inference to inform researchers unfamiliar to this type of analysis of the many advantages this readily available approach provides.<b>Method: </b>First, we demonstrate the development of Bayes’ theorem, the cornerstone of Bayesian statistics, into an iterative process of updating priors. Working with a few assumptions, including normalcy and conjugacy of prior distribution, we express how one would calculate the posterior distribution using the prior distribution and the likelihood of the parameter. Next, we move to an example in auditory research by considering the effect of sound therapy for reducing the perceived loudness of tinnitus. In this case, as well as most real-world settings, we turn to Markov chain simulations because the assumptions allowing for easy calculations no longer hold. Using Markov chain Monte Carlo methods, we can illustrate several analysis solutions given by a straightforward Bayesian approach.<b>Conclusion: </b>Bayesian methods are widely applicable and can help scientists overcome analysis problems, including how to include existing information, run interim analysis, achieve consensus through measurement, and, most importantly, interpret results correctly.<br><b>Supplemental Material S1. </b>SAS code for running the analysis described in the article.<br><b>Supplemental Material S2.</b> MS Excel workbook, allowing the reader to experiment with the model described in the article. <br>McMillan, G. P., & Cannon, J. B. (2019). Bayesian applications in auditory research. <i>Journal of Speech, Language, and Hearing Research, 62,</i> 577–586. https://doi.org/10.1044/2018_JSLHR-L-ASTM-18-0250<br><b>Publisher Note: </b>This article is part of the Research Forum: Advancing Statistical Methods in Speech, Language, and Hearing Sciences.<br>
DOI: 10.26180/5c874dbfe82a1
title: Figure3

description: Raw data and statistics files to accompany figure 3 of the manuscript <b>Metformin rescues muscle function in BAG3 myofibrillar myopathy</b><b> models</b>
DOI: 10.26180/5c874dc8b6057
title: Figure4

description: Raw data and statistics files to accompany figure 4 of the manuscript <b>Metformin rescues muscle function in BAG3 myofibrillar myopathy</b><b> models</b>
DOI: 10.23641/asha.7822568
title: Functional logistic mixed effects models (Paulon et al., 2019)

description: <b>Purpose: </b>We present functional logistic mixed-effects models (FLMEMs) for estimating population and individual-level learning curves in longitudinal experiments.<b>Method: </b>Using functional analysis tools in a Bayesian hierarchical framework, the FLMEM captures nonlinear, smoothly varying learning curves, appropriately accommodating uncertainty in various aspects of the analysis while also borrowing information across different model layers. An R package implementing our method is available as part of the Supplemental Materials.<b>Results: </b>Application to speech learning data from Reetzke, Xie, Llanos, and Chandrasekaran (2018) and a simulation study demonstrate the utility of FLMEM and its many advantages over linear and logistic mixed-effects models.<b>Conclusion: </b>The FLMEM is highly flexible and efficient in improving upon the practical limitations of linear models and logistic linear mixed-effects models. We expect the FLMEM to be a useful addition to the speech, language, and hearing scientist’s statistical toolkit.<br>Included is an <b>R package</b> implementing the authors' method, including an instruction manual and demos. <br>The paper here includes posterior computation, convergence diagnostics, and additional comments on implementation issues. <br> Included are the following supplemental figures:<br><b><br></b><b> Supplemental Material S1.</b> Traceplots of the sampled values of three individual curves <i>μ<sub>i</sub></i>(<i>t<sub>0</sub></i>), (denoted by the different colors) at the same time point <i>t</i><sub>0</sub> = 1.<b> </b><b><br></b><b> Supplemental Material S2. </b>Plot of 8 quadratic (<i>q</i> = 2) B-splines on [<i>a</i>, <i>b</i>] defined using 11 knot points that divide [<i>a</i>, <i>b</i>] into <i>K</i> = 6 equal subintervals.<br><b><br></b><b> Supplemental Material S3.</b> Flexibility of spline mixtures. The red curve is the function produced by the weighted sum of the spline bases of Figure S.2, weighted by coefficients <i>β</i>. The weighted splines are shown in other varied colors.<br><b><br></b><b> Supplemental Material S4. </b>Distribution of the mean integrated squared errors (MISEs) between the true and the estimated population function <i>π</i>(<i>t</i>) estimated by the three models under different simulation scenarios.<br><br>Paulon, G., Reetzke, R., Chandrasekaran, B., & Sarkar, A. (2019). Functional logistic mixed-effects models for learning curves from longitudinal binary data. <i>Journal of Speech, Language, and Hearing Research, 62,</i> 543–553. https://doi.org/10.1044/2018_JSLHR-S-ASTM-18-0283<br><br><b>Publisher Note: </b>This article is part of the Research Forum: Advancing Statistical Methods in Speech, Language, and Hearing Sciences.
DOI: 10.23641/asha.7822568.v1
title: Functional logistic mixed effects models (Paulon et al., 2019)

description: <b>Purpose: </b>We present functional logistic mixed-effects models (FLMEMs) for estimating population and individual-level learning curves in longitudinal experiments.<b>Method: </b>Using functional analysis tools in a Bayesian hierarchical framework, the FLMEM captures nonlinear, smoothly varying learning curves, appropriately accommodating uncertainty in various aspects of the analysis while also borrowing information across different model layers. An R package implementing our method is available as part of the Supplemental Materials.<b>Results: </b>Application to speech learning data from Reetzke, Xie, Llanos, and Chandrasekaran (2018) and a simulation study demonstrate the utility of FLMEM and its many advantages over linear and logistic mixed-effects models.<b>Conclusion: </b>The FLMEM is highly flexible and efficient in improving upon the practical limitations of linear models and logistic linear mixed-effects models. We expect the FLMEM to be a useful addition to the speech, language, and hearing scientist’s statistical toolkit.<br>Included is an <b>R package</b> implementing the authors' method, including an instruction manual and demos. <br>The paper here includes posterior computation, convergence diagnostics, and additional comments on implementation issues. <br> Included are the following supplemental figures:<br><b><br></b><b> Supplemental Material S1.</b> Traceplots of the sampled values of three individual curves <i>μ<sub>i</sub></i>(<i>t<sub>0</sub></i>), (denoted by the different colors) at the same time point <i>t</i><sub>0</sub> = 1.<b> </b><b><br></b><b> Supplemental Material S2. </b>Plot of 8 quadratic (<i>q</i> = 2) B-splines on [<i>a</i>, <i>b</i>] defined using 11 knot points that divide [<i>a</i>, <i>b</i>] into <i>K</i> = 6 equal subintervals.<br><b><br></b><b> Supplemental Material S3.</b> Flexibility of spline mixtures. The red curve is the function produced by the weighted sum of the spline bases of Figure S.2, weighted by coefficients <i>β</i>. The weighted splines are shown in other varied colors.<br><b><br></b><b> Supplemental Material S4. </b>Distribution of the mean integrated squared errors (MISEs) between the true and the estimated population function <i>π</i>(<i>t</i>) estimated by the three models under different simulation scenarios.<br><br>Paulon, G., Reetzke, R., Chandrasekaran, B., & Sarkar, A. (2019). Functional logistic mixed-effects models for learning curves from longitudinal binary data. <i>Journal of Speech, Language, and Hearing Research, 62,</i> 543–553. https://doi.org/10.1044/2018_JSLHR-S-ASTM-18-0283<br><br><b>Publisher Note: </b>This article is part of the Research Forum: Advancing Statistical Methods in Speech, Language, and Hearing Sciences.
DOI: 10.6084/m9.figshare.7822553
title: Accuracy of presepsin in neonatal sepsis: systematic review and meta-analysis

description: <b>Introduction</b>: Neonatal sepsis represents a major cause of morbidity and mortality in neonates. No diagnostic test has been demonstrated to be sufficiently accurate to confirm or exclude neonatal sepsis. This study aimed to evaluate the diagnostic accuracy of presepsin (P-SEP) for neonatal sepsis. <b>Areas covered</b>: A systematic review of literature was performed on Medline and EMBASE. A meta-analysis was performed to calculate pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic of P-SEP for neonatal sepsis. Eight studies were included, involving 636 neonates. Pooled sensitivity and specificity were 0.90 and 0.90, respectively. The pooled DOR was 120.94, and the Area Under Curve (AUC) was 0.968, indicating a high level of diagnostic accuracy. Using cut-off values &lt;600 ng/L, sensitivity reached 0.93, with a specificity of 0.81 and AUC 0.8195, while using a threshold &gt;600 ng/L, sensitivity was 0.87 and specificity 0.97, with higher diagnostic accuracy (AUC 0.976). Significant heterogeneity was found between studies. <b>Expert commentary</b>: Diagnostic accuracy of P-SEP resulted high in detecting neonatal sepsis. Even though it cannot be recommended as a single diagnostic test, P-SEP could be a helpful and valuable biomarker in neonates with suspected sepsis.
DOI: 10.6084/m9.figshare.7822553.v1
title: Accuracy of presepsin in neonatal sepsis: systematic review and meta-analysis

description: <b>Introduction</b>: Neonatal sepsis represents a major cause of morbidity and mortality in neonates. No diagnostic test has been demonstrated to be sufficiently accurate to confirm or exclude neonatal sepsis. This study aimed to evaluate the diagnostic accuracy of presepsin (P-SEP) for neonatal sepsis. <b>Areas covered</b>: A systematic review of literature was performed on Medline and EMBASE. A meta-analysis was performed to calculate pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic of P-SEP for neonatal sepsis. Eight studies were included, involving 636 neonates. Pooled sensitivity and specificity were 0.90 and 0.90, respectively. The pooled DOR was 120.94, and the Area Under Curve (AUC) was 0.968, indicating a high level of diagnostic accuracy. Using cut-off values &lt;600 ng/L, sensitivity reached 0.93, with a specificity of 0.81 and AUC 0.8195, while using a threshold &gt;600 ng/L, sensitivity was 0.87 and specificity 0.97, with higher diagnostic accuracy (AUC 0.976). Significant heterogeneity was found between studies. <b>Expert commentary</b>: Diagnostic accuracy of P-SEP resulted high in detecting neonatal sepsis. Even though it cannot be recommended as a single diagnostic test, P-SEP could be a helpful and valuable biomarker in neonates with suspected sepsis.
DOI: 10.6084/m9.figshare.7822544
title: CCDB_1056.jpg

description: Optical section series through a protoplasmic astrocyte in rat hippocampal area CA1 intracellulaly injected Lucifer Yellow and imaged with confocal microscopy
DOI: 10.6084/m9.figshare.7822574.v1
title: AlPO Structure Database

description: This is a database for aluminophosphate structures. All structures are stored in SHELX format. The corresponding structure information for each structure can be found in the attached EXCEL file. The original data were derived from literature and our own experimental results at the Molecular Engineering Group in Jilin University, China.<br>We are grateful to Dr. Lynne B. McCusker and Dr. Christian Bärlocher for their valuable discussions and advice. We also thank Prof. Michael O'Keeffe and Prof. Michael M. J. Treacy for their CSQ calculation programs. This database was setup by the efforts of many people at Jilin University, including Jiuxing Jiang, Zhuopeng Wang, Jianan Zhang, Jiyang Li, Bo Yang, and Xuchu Dong, et al. Thank all of them for their tireless work and unselfish contributions.<br>
DOI: 10.6084/m9.figshare.7822538
title: CCDB_1054.jpg

description: Optical section series through adjacent protoplasmic astrocytes in rat hippocampal area CA1 intracellulaly injected Lucifer Yellow (green) and Alexa 568 (red) respectively, imaged with confocal microscopy
DOI: 10.6084/m9.figshare.7822541
title: CCDB_1055.jpg

description: Optical section series through adjacent protoplasmic astrocytes in rat hippocampal area CA1 intracellulaly injected Lucifer Yellow (green) and Alexa 568 (red) respectively, imaged with confocal microscopy
DOI: 10.6084/m9.figshare.7822544.v1
title: CCDB_1056.jpg

description: Optical section series through a protoplasmic astrocyte in rat hippocampal area CA1 intracellulaly injected Lucifer Yellow and imaged with confocal microscopy
DOI: 10.6084/m9.figshare.7822538.v1
title: CCDB_1054.jpg

description: Optical section series through adjacent protoplasmic astrocytes in rat hippocampal area CA1 intracellulaly injected Lucifer Yellow (green) and Alexa 568 (red) respectively, imaged with confocal microscopy
DOI: 10.6084/m9.figshare.7822541.v1
title: CCDB_1055.jpg

description: Optical section series through adjacent protoplasmic astrocytes in rat hippocampal area CA1 intracellulaly injected Lucifer Yellow (green) and Alexa 568 (red) respectively, imaged with confocal microscopy
DOI: 10.6084/m9.figshare.7822574.v2
title: AlPO Structures

description: This is a database for aluminophosphate structures. All structures are stored in SHELX format. The corresponding structure information for each structure can be found in the attached EXCEL file. The original data were derived from literature and our own experimental results at the Molecular Engineering Group in Jilin University, China.<br>We are grateful to Dr. Lynne B. McCusker and Dr. Christian Bärlocher for their valuable discussions and advice. We also thank Prof. Michael O'Keeffe and Prof. Michael M. J. Treacy for their CSQ calculation programs. This database was setup by the efforts of many people at Jilin University, including Jiuxing Jiang, Zhuopeng Wang, Jianan Zhang, Jiyang Li, Bo Yang, and Xuchu Dong, et al. Thank all of them for their tireless work and unselfish contributions.<br>
DOI: 10.6084/m9.figshare.7822532
title: CCDB_1053.jpg

description: Optical section series through adjacent protoplasmic astrocytes in rat hippocampal area CA1 intracellulaly injected Lucifer Yellow (green) and Alexa 568 (red) respectively, imaged with confocal microscopy
DOI: 10.6084/m9.figshare.7822532.v1
title: CCDB_1053.jpg

description: Optical section series through adjacent protoplasmic astrocytes in rat hippocampal area CA1 intracellulaly injected Lucifer Yellow (green) and Alexa 568 (red) respectively, imaged with confocal microscopy
DOI: 10.6084/m9.figshare.7822508
title: DNA distance data & calculations for Boreophilia

description: DNA distance data for Boreophilia species calculated by PAUP 4.0a (build 164) in support of Klimaszewski et al. Species review of the genus Boreophilia Benick from North America (Coleoptera: Staphylinidae: Aleocharinae: Athetini): systematics, biology and distribution. Only sequences of specimens identified to species via morphology are included below (this excludes sequences not identified to species that are on BOLD and used in our analysis).
DOI: 10.6084/m9.figshare.7822574.v3
title: AlPO Structures

description: This is a database for open-framework aluminophosphate structures. All structures are stored in CIF format. The corresponding structure information for each structure can be found in the attached EXCEL file. The original data were derived from literature and our own experimental results at the Molecular Engineering Group in Jilin University, China.<br>We are grateful to Dr. Lynne B. McCusker and Dr. Christian Bärlocher for their valuable discussions and advice. We also thank Prof. Michael O'Keeffe and Prof. Michael M. J. Treacy for their CSQ calculation programs. This database was setup by the efforts of many people at Jilin University, including Chuting Zhang, Jiuxing Jiang, Zhuopeng Wang, Jianan Zhang, and Jiyang Li, et al. Thank all of them for their tireless work and unselfish contributions.<br>
DOI: 10.6084/m9.figshare.7822508.v1
title: DNA distance data & calculations for Boreophilia

description: DNA distance data for Boreophilia species calculated by PAUP 4.0a (build 164) in support of Klimaszewski et al. Species review of the genus Boreophilia Benick from North America (Coleoptera: Staphylinidae: Aleocharinae: Athetini): systematics, biology and distribution. Only sequences of specimens identified to species via morphology are included below (this excludes sequences not identified to species that are on BOLD and used in our analysis).
DOI: 10.6084/m9.figshare.7822574.v4
title: Database of Open-Framework Aluminophosphate Structures

description: This is a database for open-framework aluminophosphate structures. All structures are stored in CIF format. The corresponding structure information for each structure can be found in the attached EXCEL file. The original data were derived from literature and our own experimental results at the Molecular Engineering Group in Jilin University, China.<br>We are grateful to Dr. Lynne B. McCusker and Dr. Christian Bärlocher for their valuable discussions and advice. We also thank Prof. Michael O'Keeffe and Prof. Michael M. J. Treacy for their CSQ calculation programs. This database was setup by the efforts of many people at Jilin University, including Chuting Zhang, Jiuxing Jiang, Zhuopeng Wang, Jianan Zhang, and Jiyang Li, et al. Thank all of them for their tireless work and unselfish contributions.<br>
DOI: 10.6084/m9.figshare.7822496
title: Nexus file for Boreophilia

description: Nexus formatted, aligned dataset of DNA barcodes used for phylogenetic analysis, including resulting tree.<br>
DOI: 10.6084/m9.figshare.7822493
title: Dynamically Rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Models

description: Dynamically rescaled Hamiltonian Monte Carlo is introduced as a computationally fast and easily implemented method for performing full Bayesian analysis in hierarchical statistical models. The method relies on introducing a modified parameterization so that the reparameterized target distribution has close to constant scaling properties, and thus is easily sampled using standard (Euclidian metric) Hamiltonian Monte Carlo. Provided that the parameterizations of the conditional distributions specifying the hierarchical model are “constant information parameterizations” (CIPs), the relation between the modified- and original parameterization is bijective, explicitly computed, and admit exploitation of sparsity in the numerical linear algebra involved. CIPs for a large catalogue of statistical models are presented, and from the catalogue, it is clear that many CIPs are currently routinely used in statistical computing. A relation between the proposed methodology and a class of explicitly integrated Riemann manifold Hamiltonian Monte Carlo methods is discussed. The methodology is illustrated on several example models, including a model for inflation rates with multiple levels of nonlinearly dependent latent variables. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7822496.v1
title: Nexus file for Boreophilia

description: Nexus formatted, aligned dataset of DNA barcodes used for phylogenetic analysis, including resulting tree.<br>
DOI: 10.6084/m9.figshare.7822574.v5
title: Database of Open-Framework Aluminophosphate Structures

description: This is a database for open-framework aluminophosphate structures. All structures are stored in CIF format. The corresponding structure information for each structure can be found in the attached EXCEL file. The original data were derived from literature and our own experimental results at the Molecular Engineering Group in Jilin University, China.<br>We are grateful to Dr. Lynne B. McCusker and Dr. Christian Bärlocher for their valuable discussions and advice. We also thank Prof. Michael O'Keeffe and Prof. Michael M. J. Treacy for their CSQ calculation programs. This database was setup by the efforts of many people at Jilin University, including Chuting Zheng, Jiuxing Jiang, Zhuopeng Wang, Jianan Zhang, and Jiyang Li, et al. Thank all of them for their tireless work and unselfish contributions.<br>