cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12844320.v1
title: General Path Models for Degradation Data with Multiple Characteristics and Covariates

description: Degradation data have been broadly used for assessing product and system reliability. Most existing work focuses on modeling and analysis of degradation data with a single characteristic. In some degradation tests, interest lies in measuring multiple characteristics of the product degradation process to understand different aspects of the reliability performance, resulting in degradation data with multiple characteristics. The literature on modeling such data is scarce. Motivated by the photodegradation process of polymeric material, we propose a multivariate general path model for analyzing degradation data with multiple degradation characteristics (DCs). The model incorporates covariates for modeling the nonlinear degradation path, and it also includes random effects that are correlated among the multiple DCs to capture the unit-to-unit variation in the individual degradation paths and to model the interdependence among the multivariate measurements. An expectation-maximization algorithm combined with the Markov Chain Monte Carlo simulation is developed for estimating the model parameters and predicting system reliability with quantified uncertainty. The performance of the developed method is evaluated and compared with existing methods through a simulation study. The implementation of the method is illustrated through two examples with different forms of reliability functions. The main motivating example analyzes the coating degradation data with a closed-form reliability function, while the second example on analyzing the Device-B data demonstrates a more general simulation approach for dealing with analytically intractable reliability functions. This paper has supplementary material online.
DOI: 10.6084/m9.figshare.12844320.v3
title: General Path Models for Degradation Data With Multiple Characteristics and Covariates

description: Degradation data have been broadly used for assessing product and system reliability. Most existing work focuses on modeling and analysis of degradation data with a single characteristic. In some degradation tests, interest lies in measuring multiple characteristics of the product degradation process to understand different aspects of the reliability performance, resulting in degradation data with multiple characteristics. The literature on modeling such data is scarce. Motivated by the photodegradation process of polymeric material, we propose a multivariate general path model for analyzing degradation data with multiple degradation characteristics (DCs). The model incorporates covariates for modeling the nonlinear degradation path. It also includes random effects that are correlated among the multiple DCs to capture the unit-to-unit variation in the individual degradation paths and to model the interdependence among the multivariate measurements. An expectation-maximization algorithm combined with the Markov chain Monte Carlo simulation is developed for estimating the model parameters and predicting system reliability with quantified uncertainty. The performance of the developed method is evaluated and compared with existing methods through a simulation study. The implementation of the method is illustrated through two examples with different forms of reliability functions. The main motivating example analyzes the coating degradation data with a closed-form reliability function, while the second example on analyzing the Device-B data demonstrates a more general simulation approach for dealing with analytically intractable reliability functions.
DOI: 10.6084/m9.figshare.12844320.v2
title: General Path Models for Degradation Data With Multiple Characteristics and Covariates

description: Degradation data have been broadly used for assessing product and system reliability. Most existing work focuses on modeling and analysis of degradation data with a single characteristic. In some degradation tests, interest lies in measuring multiple characteristics of the product degradation process to understand different aspects of the reliability performance, resulting in degradation data with multiple characteristics. The literature on modeling such data is scarce. Motivated by the photodegradation process of polymeric material, we propose a multivariate general path model for analyzing degradation data with multiple degradation characteristics (DCs). The model incorporates covariates for modeling the nonlinear degradation path. It also includes random effects that are correlated among the multiple DCs to capture the unit-to-unit variation in the individual degradation paths and to model the interdependence among the multivariate measurements. An expectation-maximization algorithm combined with the Markov chain Monte Carlo simulation is developed for estimating the model parameters and predicting system reliability with quantified uncertainty. The performance of the developed method is evaluated and compared with existing methods through a simulation study. The implementation of the method is illustrated through two examples with different forms of reliability functions. The main motivating example analyzes the coating degradation data with a closed-form reliability function, while the second example on analyzing the Device-B data demonstrates a more general simulation approach for dealing with analytically intractable reliability functions.
DOI: 10.6084/m9.figshare.12320054
title: TMT-based quantitative proteomics analysis reveals the attenuated replication mechanism of Newcastle disease virus caused by nuclear localization signal mutation in viral matrix protein

description: Nuclear localization of cytoplasmic RNA virus proteins mediated by intrinsic nuclear localization signal (NLS) plays essential roles in successful virus replication. We previously reported that NLS mutation in the matrix (M) protein obviously attenuates the replication and pathogenicity of Newcastle disease virus (NDV), but the attenuated replication mechanism remains unclear. In this study, we showed that M/NLS mutation not only disrupted M’s nucleocytoplasmic trafficking characteristic but also impaired viral RNA synthesis and transcription. Using TMT-based quantitative proteomics analysis of BSR-T7/5 cells infected with the parental NDV rSS1GFP and the mutant NDV rSS1GFP-M/NLSm harboring M/NLS mutation, we found that rSS1GFP infection stimulated much greater quantities and more expression changes of differentially expressed proteins involved in host cell transcription, ribosomal structure, posttranslational modification, and intracellular trafficking than rSS1GFP-M/NLSm infection. Further in-depth analysis revealed that the dominant nuclear accumulation of M protein inhibited host cell transcription, RNA processing and modification, protein synthesis, posttranscriptional modification and transport; and this kind of inhibition could be weakened when most of M protein was confined outside the nucleus. More importantly, we found that the function of M protein in the cytoplasm effected the inhibition of TIFA expression in a dose-dependent manner, and promoted NDV replication by down-regulating TIFA/TRAF6/NF-κB-mediated production of cytokines. It was the first report about the involvement of M protein in NDV immune evasion. Taken together, our findings demonstrate that NDV replication is closely related to the nucleocytoplasmic trafficking of M protein, which accelerates our understanding of the molecular functions of NDV M protein.
DOI: 10.6084/m9.figshare.12320054.v1
title: TMT-based quantitative proteomics analysis reveals the attenuated replication mechanism of Newcastle disease virus caused by nuclear localization signal mutation in viral matrix protein

description: Nuclear localization of cytoplasmic RNA virus proteins mediated by intrinsic nuclear localization signal (NLS) plays essential roles in successful virus replication. We previously reported that NLS mutation in the matrix (M) protein obviously attenuates the replication and pathogenicity of Newcastle disease virus (NDV), but the attenuated replication mechanism remains unclear. In this study, we showed that M/NLS mutation not only disrupted M’s nucleocytoplasmic trafficking characteristic but also impaired viral RNA synthesis and transcription. Using TMT-based quantitative proteomics analysis of BSR-T7/5 cells infected with the parental NDV rSS1GFP and the mutant NDV rSS1GFP-M/NLSm harboring M/NLS mutation, we found that rSS1GFP infection stimulated much greater quantities and more expression changes of differentially expressed proteins involved in host cell transcription, ribosomal structure, posttranslational modification, and intracellular trafficking than rSS1GFP-M/NLSm infection. Further in-depth analysis revealed that the dominant nuclear accumulation of M protein inhibited host cell transcription, RNA processing and modification, protein synthesis, posttranscriptional modification and transport; and this kind of inhibition could be weakened when most of M protein was confined outside the nucleus. More importantly, we found that the function of M protein in the cytoplasm effected the inhibition of TIFA expression in a dose-dependent manner, and promoted NDV replication by down-regulating TIFA/TRAF6/NF-κB-mediated production of cytokines. It was the first report about the involvement of M protein in NDV immune evasion. Taken together, our findings demonstrate that NDV replication is closely related to the nucleocytoplasmic trafficking of M protein, which accelerates our understanding of the molecular functions of NDV M protein.
DOI: 10.6084/m9.figshare.12320054.v3
title: TMT-based quantitative proteomics analysis reveals the attenuated replication mechanism of Newcastle disease virus caused by nuclear localization signal mutation in viral matrix protein

description: Nuclear localization of cytoplasmic RNA virus proteins mediated by intrinsic nuclear localization signal (NLS) plays essential roles in successful virus replication. We previously reported that NLS mutation in the matrix (M) protein obviously attenuates the replication and pathogenicity of Newcastle disease virus (NDV), but the attenuated replication mechanism remains unclear. In this study, we showed that M/NLS mutation not only disrupted M’s nucleocytoplasmic trafficking characteristic but also impaired viral RNA synthesis and transcription. Using TMT-based quantitative proteomics analysis of BSR-T7/5 cells infected with the parental NDV rSS1GFP and the mutant NDV rSS1GFP-M/NLSm harboring M/NLS mutation, we found that rSS1GFP infection stimulated much greater quantities and more expression changes of differentially expressed proteins involved in host cell transcription, ribosomal structure, posttranslational modification, and intracellular trafficking than rSS1GFP-M/NLSm infection. Further in-depth analysis revealed that the dominant nuclear accumulation of M protein inhibited host cell transcription, RNA processing and modification, protein synthesis, posttranscriptional modification and transport; and this kind of inhibition could be weakened when most of M protein was confined outside the nucleus. More importantly, we found that the function of M protein in the cytoplasm effected the inhibition of TIFA expression in a dose-dependent manner, and promoted NDV replication by down-regulating TIFA/TRAF6/NF-κB-mediated production of cytokines. It was the first report about the involvement of M protein in NDV immune evasion. Taken together, our findings demonstrate that NDV replication is closely related to the nucleocytoplasmic trafficking of M protein, which accelerates our understanding of the molecular functions of NDV M protein.
DOI: 10.6084/m9.figshare.12320054.v2
title: TMT-based quantitative proteomics analysis reveals the attenuated replication mechanism of Newcastle disease virus caused by nuclear localization signal mutation in viral matrix protein

description: Nuclear localization of cytoplasmic RNA virus proteins mediated by intrinsic nuclear localization signal (NLS) plays essential roles in successful virus replication. We previously reported that NLS mutation in the matrix (M) protein obviously attenuates the replication and pathogenicity of Newcastle disease virus (NDV), but the attenuated replication mechanism remains unclear. In this study, we showed that M/NLS mutation not only disrupted M’s nucleocytoplasmic trafficking characteristic but also impaired viral RNA synthesis and transcription. Using TMT-based quantitative proteomics analysis of BSR-T7/5 cells infected with the parental NDV rSS1GFP and the mutant NDV rSS1GFP-M/NLSm harboring M/NLS mutation, we found that rSS1GFP infection stimulated much greater quantities and more expression changes of differentially expressed proteins involved in host cell transcription, ribosomal structure, posttranslational modification, and intracellular trafficking than rSS1GFP-M/NLSm infection. Further in-depth analysis revealed that the dominant nuclear accumulation of M protein inhibited host cell transcription, RNA processing and modification, protein synthesis, posttranscriptional modification and transport; and this kind of inhibition could be weakened when most of M protein was confined outside the nucleus. More importantly, we found that the function of M protein in the cytoplasm effected the inhibition of TIFA expression in a dose-dependent manner, and promoted NDV replication by down-regulating TIFA/TRAF6/NF-κB-mediated production of cytokines. It was the first report about the involvement of M protein in NDV immune evasion. Taken together, our findings demonstrate that NDV replication is closely related to the nucleocytoplasmic trafficking of M protein, which accelerates our understanding of the molecular functions of NDV M protein.
DOI: 10.6084/m9.figshare.6552902
title: Original Data.xlsx

description: It's the original data required by reviewer and for the figures included in the manuscript
DOI: 10.6084/m9.figshare.12844368
title: Assessing via Simulation the Operating Characteristics of the WHO Scale for COVID-19 Endpoints

description: Many clinical trials of treatments for patients hospitalised for COVID-19 use an ordinal scale recommended by the World Heath Organisation. The scale represents intensity of medical intervention, with higher scores for interventions more burdensome for the patient, and highest score for death. There is uncertainty about use of this ordinal scale in testing hypotheses. With the objective of assessing the power and Type I error of potential endpoints and analyses based on the ordinal scale, trajectories of the score over 28 days were simulated for scenarios based closely on results of two trials recently published. The simulation used transition probabilities for the ordinal scale over time. No one endpoint was optimal across scenarios, but a ranked measure of trajectory fared moderately well in all scenarios. Type I error was controlled at close to the nominal level for all endpoints. Because not tied to a particular population with regard to baseline severity, the use of transition probabilities allows plausible assessment of endpoints in populations with configurations of baseline score for which data is not yet published, provided some data on the relevant transition probabilities are available. The results could support experts in the choice of endpoint based on the ordinal scale.
DOI: 10.6084/m9.figshare.12844368.v1
title: Assessing via Simulation the Operating Characteristics of the WHO Scale for COVID-19 Endpoints

description: Many clinical trials of treatments for patients hospitalised for COVID-19 use an ordinal scale recommended by the World Heath Organisation. The scale represents intensity of medical intervention, with higher scores for interventions more burdensome for the patient, and highest score for death. There is uncertainty about use of this ordinal scale in testing hypotheses. With the objective of assessing the power and Type I error of potential endpoints and analyses based on the ordinal scale, trajectories of the score over 28 days were simulated for scenarios based closely on results of two trials recently published. The simulation used transition probabilities for the ordinal scale over time. No one endpoint was optimal across scenarios, but a ranked measure of trajectory fared moderately well in all scenarios. Type I error was controlled at close to the nominal level for all endpoints. Because not tied to a particular population with regard to baseline severity, the use of transition probabilities allows plausible assessment of endpoints in populations with configurations of baseline score for which data is not yet published, provided some data on the relevant transition probabilities are available. The results could support experts in the choice of endpoint based on the ordinal scale.
DOI: 10.6084/m9.figshare.12844374
title: Use of Weighted Performance Goals in Prospective Single-Arm Clinical Studies Designed to Assess the Safety and Effectiveness of Medical Devices

description: In addition to extensive preclinical testing, commercialization of medical devices in the United States under some circumstances may be supported with clinical data derived from single arm studies where the clinical endpoint is compared with a performance goal (PG). Since the performance is expected to be different between two clinically defined mutually exclusive patient subpopulations, the use of weighted PG is often adopted. In this article, we discuss the limitations of the current practice where weights need to be prespecified and fixed, and we propose an alternative approach that relaxes the fixed weight requirements.
DOI: 10.5281/zenodo.10663426
title: Simulated rates and efficiencies for multi-nucleon transfer reaction
DOI: 10.5281/zenodo.10663427
title: Simulated rates and efficiencies for multi-nucleon transfer reaction
DOI: 10.6084/m9.figshare.12844374.v1
title: Use of Weighted Performance Goals in Prospective Single-Arm Clinical Studies Designed to Assess the Safety and Effectiveness of Medical Devices

description: In addition to extensive preclinical testing, commercialization of medical devices in the United States under some circumstances may be supported with clinical data derived from single arm studies where the clinical endpoint is compared with a performance goal (PG). Since the performance is expected to be different between two clinically defined mutually exclusive patient subpopulations, the use of weighted PG is often adopted. In this article, we discuss the limitations of the current practice where weights need to be prespecified and fixed, and we propose an alternative approach that relaxes the fixed weight requirements.
DOI: 10.6084/m9.figshare.12320096
title: Cryptic species inside the genus <i>Hariotina</i> (Scenedesmaceae, Sphaeropleales), with descriptions of four new species in this genus

description: The genus <i>Hariotina</i> was established in 1889 based on the type species <i>Hariotina reticulata</i>. This species was then transferred to <i>Coelastrum</i> by Senn (1899). The genus <i>Hariotina</i> was eventually restored by Hegewald <i>et al</i>. (2002) based on morphological characteristics. Species of <i>Hariotina</i> are ubiquitous in fresh water, especially in some tropical and subtropical areas. The taxonomic position of the genus is now considered to be within the family Scenedesmaceae. However, until now, there was an insufficient understanding of the molecular diversity within this genus, and the related reference sequences in the database were inadequate to resolve the species taxonomy. In this study, 20 strains of <i>Hariotina</i> were identified and successfully cultured in the laboratory. We used a combined approach, joining molecular and morphological analyses, to determine their taxonomic relationships and phylogenetic positions. The molecular analyses based on 18S rDNA+ITS sequences and the <i>tuf</i>A gene resolved the analysed strains into at least six species, including the known species <i>H. reticulata</i> and <i>H. polychorda</i> and four new species herein described, i.e. <i>H. hainanensis</i> sp. nov., <i>H. compacta</i> sp. nov., <i>H. guilinensis</i> sp. nov. and <i>H. laxa</i> sp. nov. The genus <i>Hariotina</i> was confirmed in the Scenedesmaceae as a monophyletic clade that was closely related to the genus <i>Coelastrum</i> in <i>tuf</i>A gene phylogeny. Strains with <i>H. reticulata</i> morphotype were found to be located on two different branches, and formed sister relationships with the other two morphologically distinct species. In addition, the topology was supported by all three DNA markers. The congruence between the phylogeny and the classical taxonomic system based on morphological criteria for this taxon is discussed.
DOI: 10.6084/m9.figshare.12844389
title: Improving Dose-Finding for New Agents as Monotherapy and Add-On Therapy

description: With the recent advancement in immuno-oncology, next-stage early oncology development is focusing on identifying the best combinations of established immunotherapies with new agents to either overcome drug resistance or achieve synergic effects. Although the combination is the focus, safety profile of the new agent alone must be explored. Therefore, many trials have both monotherapy and add-on combination therapy arms. Finding the maximum tolerable dose (MTD) for the new agent in both arms is critical. Traditional oncology dose-finding methods and MTD estimation algorithm do not handle the correlation and interplay between the two arms and the selected MTDs may contradict with each other. To overcome these issues, we applied a two-dimensional pool-adjacent-violators algorithm to MTD estimation and modified the standard Bayesian optimal interval design (BOIN) to allow for information flow between arms during dose-finding. We also showed that a naïve adaptation of standard BOIN that is much simpler to implement demonstrated empirically similar performance. These new approaches were assessed with simulations and demonstrated improvement for trials with both monotherapy and add-on combination therapy arms. Albeit proposed in the context with immunotherapy as the backbone drug, our approaches can be applied to any new agent in combination with a fixed dose of another drug. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12844389.v1
title: Improving Dose-finding for New Agents as Monotherapy and Add-on Therapy

description: With the recent advancement in immuno-oncology, next-stage early oncology development is focusing on identifying the best combinations of established immunotherapies with new agents to either overcome drug resistance or achieve synergic effects. Although the combination is the focus, safety profile of the new agent alone must be explored. Therefore, many trials have both monotherapy and add-on combination therapy arms. Finding the maximum tolerable dose (MTD) for the new agent in both arms is critical. Traditional oncology dose-finding methods and MTD estimation algorithm do not handle the correlation and interplay between the two arms and the selected MTDs may contradict with each other. To overcome these issues, we applied a two-dimensional pool-adjacent-violators algorithm (2D-PAVA) to MTD estimation and modified the standard BOIN to allow for information flow between arms during dose-finding. We also showed that a naïve adaptation of standard BOIN that is much simpler to implement demonstrated empirically similar performance. These new approaches were assessed with simulations and demonstrated improvement for trials with both monotherapy and add-on combination therapy arms. Albeit proposed in the context with immunotherapy as the backbone drug, our approaches can be applied to any new agent in combination with a fixed dose of another drug.