cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5504149
title: A label-free technique for accurate detection of nucleic acid–based self-avoiding molecular recognition systems supplemented multiple cross-displacement amplification and nanoparticles based biosensor

description: Here, we devised a novel isothermal technique on the basis of standard multiple cross-displacement amplification (MCDA), which is assisted with self-avoiding molecular recognition system (SAMRS) components and antarctic thermal-sensitive uracil-DNA-glycosylase enzyme (AUDG), termed AUDG–SAMRS–MCDA. To enable product detection on the dipsticks, we firstly developed an analysis strategy, which did not require the labelled primers or probes, and thus, the analysis system avoids the false-positive results arising from undesired hybridization (between two labelled primers, or the labelled probe and primer). The SAMRS components are incorporated into MCDA primers for improve the assay’s specificity, which can prevent the false-positive results yielding from off-target hybrids, undesired interactions between (hetero-dimer) or within (self-dimerization) primers. Two additional components (AUDG enzyme and dUTP) were added into the reaction mixtures, which were used for removing the false-positive results generating from carryover contamination, and thus, the genuine positives results were produced from the amplification of target templates. For the demonstration, the label-free AUDG–SAMRS–MCDA technique was successfully applied to detect <i>Pseudomonas aeruginosa</i> from pure culture and blood samples. As a proof-of-concept technique, the label-free AUDG–SAMRS–MCDA method can be reconfigured to detect different target sequences by redesigning the specific primers.
DOI: 10.6084/m9.figshare.5504149.v1
title: A label-free technique for accurate detection of nucleic acid–based self-avoiding molecular recognition systems supplemented multiple cross-displacement amplification and nanoparticles based biosensor

description: Here, we devised a novel isothermal technique on the basis of standard multiple cross-displacement amplification (MCDA), which is assisted with self-avoiding molecular recognition system (SAMRS) components and antarctic thermal-sensitive uracil-DNA-glycosylase enzyme (AUDG), termed AUDG–SAMRS–MCDA. To enable product detection on the dipsticks, we firstly developed an analysis strategy, which did not require the labelled primers or probes, and thus, the analysis system avoids the false-positive results arising from undesired hybridization (between two labelled primers, or the labelled probe and primer). The SAMRS components are incorporated into MCDA primers for improve the assay’s specificity, which can prevent the false-positive results yielding from off-target hybrids, undesired interactions between (hetero-dimer) or within (self-dimerization) primers. Two additional components (AUDG enzyme and dUTP) were added into the reaction mixtures, which were used for removing the false-positive results generating from carryover contamination, and thus, the genuine positives results were produced from the amplification of target templates. For the demonstration, the label-free AUDG–SAMRS–MCDA technique was successfully applied to detect <i>Pseudomonas aeruginosa</i> from pure culture and blood samples. As a proof-of-concept technique, the label-free AUDG–SAMRS–MCDA method can be reconfigured to detect different target sequences by redesigning the specific primers.
DOI: 10.25582/data.2024-02.3029595/2305368
title: Data for EMSL Project 60141 from February 2024
DOI: 10.6084/m9.figshare.9698501
title: Effects of the <i>ABCB1</i> and <i>ABCG2</i> polymorphisms on the pharmacokinetics of afatinib in healthy Chinese volunteers

description: 1. Afatinib is an oral, selective tyrosine kinase inhibitor (TKI) primarily transported by P-glycoprotein (MDR1, gene code <i>ABCB1</i>) and breast cancer resistance protein (BCRP, gene code <i>ABCG2</i>). In the present study, the effects of <i>ABCB1</i> and <i>ABCG2</i> genetic polymorphisms on the pharmacokinetics of afatinib in healthy Chinese were investigated. 2. Blood samples from 24 healthy participants who received afatinib were used for genotyping <i>ABCB1</i> (1236C&gt;T, 2677G &gt; T/A, 3435C&gt;T) and <i>ABCG2</i> (34G&gt;A, 421C&gt;A) polymorphisms. Subsequently, the association between afatinib plasma concentrations and target single-nucleotide polymorphisms (SNPs) was analyzed. 3. Among the five polymorphisms, plasma concentrations of afatinib in healthy subjects with <i>ABCB1</i> 1236CC–3435CC were remarkably higher than in other genotype subjects. No significant differences of afatinib exposure were found between the <i>ABCG2</i> wild-type and heterozygous groups. 4. The <i>ABCB1</i> genetic polymorphism influenced the plasma exposure of afatinib, and gene testing before drug administration may be useful for clinically individualized use of afatinib. Our data suggest the usefulness of afatinib pharmacogenetics in treatment optimization.
DOI: 10.6084/m9.figshare.9698501.v1
title: Effects of the <i>ABCB1</i> and <i>ABCG2</i> polymorphisms on the pharmacokinetics of afatinib in healthy Chinese volunteers

description: 1. Afatinib is an oral, selective tyrosine kinase inhibitor (TKI) primarily transported by P-glycoprotein (MDR1, gene code <i>ABCB1</i>) and breast cancer resistance protein (BCRP, gene code <i>ABCG2</i>). In the present study, the effects of <i>ABCB1</i> and <i>ABCG2</i> genetic polymorphisms on the pharmacokinetics of afatinib in healthy Chinese were investigated. 2. Blood samples from 24 healthy participants who received afatinib were used for genotyping <i>ABCB1</i> (1236C&gt;T, 2677G &gt; T/A, 3435C&gt;T) and <i>ABCG2</i> (34G&gt;A, 421C&gt;A) polymorphisms. Subsequently, the association between afatinib plasma concentrations and target single-nucleotide polymorphisms (SNPs) was analyzed. 3. Among the five polymorphisms, plasma concentrations of afatinib in healthy subjects with <i>ABCB1</i> 1236CC–3435CC were remarkably higher than in other genotype subjects. No significant differences of afatinib exposure were found between the <i>ABCG2</i> wild-type and heterozygous groups. 4. The <i>ABCB1</i> genetic polymorphism influenced the plasma exposure of afatinib, and gene testing before drug administration may be useful for clinically individualized use of afatinib. Our data suggest the usefulness of afatinib pharmacogenetics in treatment optimization.
DOI: 10.6084/m9.figshare.7077089
title: Statistical inference methods for cumulative incidence function curves at a fixed point in time

description: Competing risks data arise frequently in clinical trials. When the proportional subdistribution hazard assumption is violated or two cumulative incidence function (CIF) curves cross, researchers may be interested in focusing on a comparison of clinical utility at some fixed time points rather than comparing the overall treatment effects. This article extends a series of tests constructed based on a pseudo-value regression technique or different transformation functions for CIFs and their variances based on Gaynor’s or Aalen’s work, and the differences among CIFs at a given time point are compared.
DOI: 10.6084/m9.figshare.7077089.v1
title: Statistical inference methods for cumulative incidence function curves at a fixed point in time

description: Competing risks data arise frequently in clinical trials. When the proportional subdistribution hazard assumption is violated or two cumulative incidence function (CIF) curves cross, researchers may be interested in focusing on a comparison of clinical utility at some fixed time points rather than comparing the overall treatment effects. This article extends a series of tests constructed based on a pseudo-value regression technique or different transformation functions for CIFs and their variances based on Gaynor’s or Aalen’s work, and the differences among CIFs at a given time point are compared.
DOI: 10.15121/2305384
title: Utah FORGE: Composite 3D Seismic Velocity Model

description: This is a composite 3D seismic velocity that was constructed from compiled information from several local studies regarding seismic velocities and structural information. This seismic velocity model is provided in NonLinLoc format (slow_len), which is readily usable in NonLinLoc software. Other model formats and versions of the model can be produced using the Python script provided with this data set. Details on how the model was created and prior velocity and structural information was used is provided in the accompanying documentation.
DOI: 10.6084/m9.figshare.12844298
title: Elastic Depths for Detecting Shape Anomalies in Functional Data

description: We propose a new family of depth measures called the elastic depths that can be used to greatly improve shape anomaly detection in functional data. Shape anomalies are functions that have considerably different geometric forms or features from the rest of the data. Identifying them is generally more difficult than identifying magnitude anomalies because shape anomalies are often not distinguishable from the bulk of the data with visualization methods. The proposed elastic depths use the recently developed elastic distances to directly measure the centrality of functions in the amplitude and phase spaces. Measuring shape outlyingness in these spaces provides a rigorous quantification of shape, which gives the elastic depths a strong theoretical and practical advantage over other methods in detecting shape anomalies. A simple boxplot and thresholding method is introduced to identify shape anomalies using the elastic depths. We assess the elastic depth’s detection skill on simulated shape outlier scenarios and compare them against popular shape anomaly detectors. Finally, we use hurricane trajectories to demonstrate the elastic depth methodology on manifold valued functional data.
DOI: 10.15468/dp.qkaxcc
title: from address test

description: test 2 for Jan
DOI: 10.6084/m9.figshare.12844298.v1
title: Elastic Depths for Detecting Shape Anomalies in Functional Data

description: We propose a new family of depth measures called the elastic depths that can be used to greatly improve shape anomaly detection in functional data. Shape anomalies are functions that have considerably different geometric forms or features from the rest of the data. Identifying them is generally more difficult than identifying magnitude anomalies because shape anomalies are often not distinguishable from the bulk of the data with visualization methods. The proposed elastic depths use the recently developed elastic distances to directly measure the centrality of functions in the amplitude and phase spaces. Measuring shape outlyingness in these spaces provides a rigorous quantification of shape, which gives the elastic depths a strong theoretical and practical advantage over other methods in detecting shape anomalies. A simple boxplot and thresholding method is introduced to identify shape anomalies using the elastic depths. We assess the elastic depth’s detection skill on simulated shape outlier scenarios and compare them against popular shape anomaly detectors. Finally, we use hurricane trajectories to demonstrate the elastic depth methodology on manifold valued functional data.
DOI: 10.6084/m9.figshare.12844298.v3
title: Elastic Depths for Detecting Shape Anomalies in Functional Data

description: We propose a new family of depth measures called the elastic depths that can be used to greatly improve shape anomaly detection in functional data. Shape anomalies are functions that have considerably different geometric forms or features from the rest of the data. Identifying them is generally more difficult than identifying magnitude anomalies because shape anomalies are often not distinguishable from the bulk of the data with visualization methods. The proposed elastic depths use the recently developed elastic distances to directly measure the centrality of functions in the amplitude and phase spaces. Measuring shape outlyingness in these spaces provides a rigorous quantification of shape, which gives the elastic depths a strong theoretical and practical advantage over other methods in detecting shape anomalies. A simple boxplot and thresholding method is introduced to identify shape anomalies using the elastic depths. We assess the elastic depth’s detection skill on simulated shape outlier scenarios and compare them against popular shape anomaly detectors. Finally, we use hurricane trajectories to demonstrate the elastic depth methodology on manifold valued functional data.
DOI: 10.6084/m9.figshare.12844298.v2
title: Elastic depths for detecting shape anomalies in functional data

description: We propose a new family of depth measures called the elastic depths that can be used to greatly improve shape anomaly detection in functional data. Shape anomalies are functions that have considerably different geometric forms or features from the rest of the data. Identifying them is generally more difficult than identifying magnitude anomalies because shape anomalies are often not distinguishable from the bulk of the data with visualization methods. The proposed elastic depths use the recently developed elastic distances to directly measure the centrality of functions in the amplitude and phase spaces. Measuring shape outlyingness in these spaces provides a rigorous quantification of shape, which gives the elastic depths a strong theoretical and practical advantage over other methods in detecting shape anomalies. A simple boxplot and thresholding method is introduced to identify shape anomalies using the elastic depths. We assess the elastic depth’s detection skill on simulated shape outlier scenarios and compare them against popular shape anomaly detectors. Finally, we use hurricane trajectories to demonstrate the elastic depth methodology on manifold valued functional data. Supplementary materials, including additional simulations, data examples, and an R-package are available online.
DOI: 10.6084/m9.figshare.12844302
title: Statistical Modeling and Analysis of <i>k</i>-Layer Coverage of Two-Dimensional Materials in Inkjet Printing Processes

description: Two-dimensional layered materials/flakes, also known as crystalline atom-thick layer nanosheets, have recently been receiving great attention in electronics fabrication due to their unique and intriguing properties. The <i>k</i>-layer coverage area (i.e., the area covered by <i>k</i> number of overlapping layers) of the printed flake pattern significantly impacts on the properties of the printed electronics. In this work, we constructed a statistical model to describe the <i>k</i>-layer coverage of randomly distributed two-dimensional materials. A series of results are obtained to provide not only the expectation but also the variance of the coverage area. The boundary effects on the random flakes coverage are also studied. In addition, an approximated statistical testing approach is also developed in this work to detect abnormal coverage patterns. The case studies based on simulated data and real flakes images obtained from the inkjet printing process demonstrate the accuracy and effectiveness of the proposed model and analysis methods.
DOI: 10.6084/m9.figshare.12844302.v2
title: Statistical Modeling and Analysis of <i>k</i>-Layer Coverage of Two-Dimensional Materials in Inkjet Printing Processes

description: Two-dimensional layered materials/flakes, also known as crystalline atom-thick layer nanosheets, have recently been receiving great attention in electronics fabrication due to their unique and intriguing properties. The <i>k</i>-layer coverage area (i.e., the area covered by <i>k</i> number of overlapping layers) of the printed flake pattern significantly impacts on the properties of the printed electronics. In this work, we constructed a statistical model to describe the <i>k</i>-layer coverage of randomly distributed two-dimensional materials. A series of results are obtained to provide not only the expectation but also the variance of the coverage area. The boundary effects on the random flakes coverage are also studied. In addition, an approximated statistical testing approach is also developed in this work to detect abnormal coverage patterns. The case studies based on simulated data and real flakes images obtained from the inkjet printing process demonstrate the accuracy and effectiveness of the proposed model and analysis methods.
DOI: 10.6084/m9.figshare.12844302.v1
title: Statistical Modeling and Analysis of <i>k</i>-Layer Coverage of Two-Dimensional Materials in Inkjet Printing Processes

description: Two-dimensional layered materials/flakes, also known as crystalline atom-thick layer nanosheets, have recently been receiving great attention in electronics fabrication due to their unique and intriguing properties. The <i>k</i>-layer coverage area (i.e., the area covered by <i>k</i> number of overlapping layers) of the printed flake pattern significantly impacts on the properties of the printed electronics. In this work, we constructed a statistical model to describe the <i>k</i>-layer coverage of randomly distributed two-dimensional materials. A series of results are obtained to provide not only the expectation but also the variance of the coverage area. The boundary effects on the random flakes coverage are also studied. In addition, an approximated statistical testing approach is also developed in this work to detect abnormal coverage patterns. The case studies based on simulated data and real flakes images obtained from the inkjet printing process demonstrate the accuracy and effectiveness of the proposed model and analysis methods.
DOI: 10.6084/m9.figshare.12844311
title: Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning

description: Multivariate functional data from a complex system are naturally high-dimensional and have a complex cross-correlation structure. The complexity of data structure can be observed as that (1) some functions are strongly correlated with similar features, while some others may have almost no cross-correlations with quite diverse features; and (2) the cross-correlation structure may also change over time due to the system evolution. With this regard, this article presents a dynamic subspace learning method for multivariate functional data modeling. In particular, we consider that different functions come from different subspaces, and only functions of the same subspace have cross-correlations with each other. The subspaces can be automatically formulated and learned by reformatting the problem as a sparse regression. By allowing but regularizing the regression change over time, we can describe the cross-correlation dynamics. The model can be efficiently estimated by the fast iterative shrinkage-thresholding algorithm, and the features of each subspace can be extracted using the smooth multi-channel functional principal component analysis. Some theoretical properties of the model are presented. Numerical studies, together with case studies, demonstrate the efficiency and applicability of the proposed methodology.
DOI: 10.6084/m9.figshare.12844311.v1
title: Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning

description: Multivariate functional data from a complex system are naturally high-dimensional and have a complex cross-correlation structure. The complexity of data structure can be observed as that (1) some functions are strongly correlated with similar features, while some others may have almost no cross-correlations with quite diverse features; and (2) the cross-correlation structure may also change over time due to the system evolution. With this regard, this article presents a dynamic subspace learning method for multivariate functional data modeling. In particular, we consider that different functions come from different subspaces, and only functions of the same subspace have cross-correlations with each other. The subspaces can be automatically formulated and learned by reformatting the problem as a sparse regression. By allowing but regularizing the regression change over time, we can describe the cross-correlation dynamics. The model can be efficiently estimated by the fast iterative shrinkage-thresholding algorithm, and the features of each subspace can be extracted using the smooth multi-channel functional principal component analysis. Some theoretical properties of the model are presented. Numerical studies, together with case studies, demonstrate the efficiency and applicability of the proposed methodology.
DOI: 10.6084/m9.figshare.12844311.v2
title: Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning

description: Multivariate functional data from a complex system are naturally high-dimensional and have a complex cross-correlation structure. The complexity of data structure can be observed as that (1) some functions are strongly correlated with similar features, while some others may have almost no cross-correlations with quite diverse features; and (2) the cross-correlation structure may also change over time due to the system evolution. With this regard, this article presents a dynamic subspace learning method for multivariate functional data modeling. In particular, we consider that different functions come from different subspaces, and only functions of the same subspace have cross-correlations with each other. The subspaces can be automatically formulated and learned by reformatting the problem as a sparse regression. By allowing but regularizing the regression change over time, we can describe the cross-correlation dynamics. The model can be efficiently estimated by the fast iterative shrinkage-thresholding algorithm, and the features of each subspace can be extracted using the smooth multi-channel functional principal component analysis. Some theoretical properties of the model are presented. Numerical studies, together with case studies, demonstrate the efficiency and applicability of the proposed methodology.
DOI: 10.6084/m9.figshare.12844320
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.