cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.16786615.v2
title: Genomic and pathogenic investigations of <i>Streptococcus suis</i> serotype 7 population derived from a human patient and pigs

description: <i>Streptococcus suis</i> is one of the important emerging zoonotic pathogens. Serotype 2 is most prevalent in patients worldwide. In the present study, we first isolated one <i>S. suis</i> serotype 7 strain GX69 from the blood culture of a patient with septicemia complicated with pneumonia in China. In order to deepen the understanding of <i>S. suis</i> serotype 7 population characteristics, we investigated the phylogenetic structure, genomic features, and virulence of <i>S. suis</i> serotype 7 population, including 35 strains and 79 genomes. Significant diversities were revealed in <i>S. suis</i> serotype 7 population, which were clustered into 22 sequence types (STs), five minimum core genome (MCG) groups, and six lineages. Lineages 1, 3a, and 6 were mainly constituted by genomes from Asia. Genomes of Lineages 2, 3b, and 5a were mainly from Northern America. Most of genomes from Europe (41/48) were clustered into Lineage 5b. In addition to strain GX69, 13 of 21 <i>S. suis</i> serotype 7 representative strains were classified as virulent strains using the C57BL/6 mouse model. Virulence-associated genes preferentially present in highly pathogenic <i>S. suis</i> serotype 2 strains were not suitable as virulence indicators for <i>S. suis</i> serotype 7 strains. Integrative mobilizable elements were widespread and may play a critical role in disseminating antibiotic resistance genes of <i>S. suis</i> serotype 7 strains. Our study confirmed <i>S. suis</i> serotype 7 is a non-negligible pathotype and deepened the understanding of the population structure of <i>S. suis</i> serotype 7, which provided valuable information for the improved surveillance of this serotype.
DOI: 10.6084/m9.figshare.21989391.v2
title: On Bivariate Time-Varying Price Staleness

description: Price staleness refers to the extent of zero returns in price dynamics. Bandi, Pirino, and Reno introduce two types of staleness: systematic and idiosyncratic staleness. In this study, we allow price staleness to be time-varying and study the statistical inference for idiosyncratic and common price staleness between two assets. We propose consistent estimators for both time-varying idiosyncratic and systematic price staleness and derive their asymptotic theory. Moreover, we develop a feasible nonparametric test for the simultaneous constancy of idiosyncratic and common price staleness. Our inference is based on infill asymptotics. Finally, we conduct simulation studies under various scenarios to assess the finite sample performance of the proposed approaches and provide an empirical application of the proposed theory.
DOI: 10.6084/m9.figshare.21989391.v1
title: On bivariate time-varying price staleness

description: Price staleness refers to the extent of zero returns in price dynamics. Bandi et al. (2020c) introduce two types of staleness: systematic and idiosyncratic staleness. In this study, we allow price staleness to be time-varying and study the statistical inference for idiosyncratic and common price staleness between two assets. We propose consistent estimators for both time-varying idiosyncratic and systematic price staleness and derive their asymptotic theory. Moreover, we develop a feasible nonparametric test for the simultaneous constancy of idiosyncratic and common price staleness. Our inference is based on infill asymptotics. Finally, we conduct simulation studies under various scenarios to assess the finite sample performance of the proposed approaches and provide an empirical application of the proposed theory.
DOI: 10.6084/m9.figshare.19408193
title: Additional file 1 of Integration of transcriptomic and proteomic analyses reveals several levels of metabolic regulation in the excess starch and early senescent leaf mutant lses1 in rice

description: Additional file 1: Table S1. Detection data of various physiological characteristics in leaves of the wild type (WT) and the lses1 mutant. Table S2. Summary of the transcriptome sequencing quality. Table S3. Identification of differentially expressed genes in leaves of the wild type (WT) and the lses1 mutant. Table S4. Identification of differentially expressed proteins in leaves of the wild type (WT) and the lses1 mutant. Table S5. Protein-protein interactions of the four functional clusters. Table S6. List of partial differentially expressed genes/proteins related to carbohydrate metabolism. Table S7. List of partial differentially expressed genes/proteins involved in apoptosis-related metabolism. Table S8. List of differentially expressed genes/proteins involved in multiple metabolic regulation in schematic.
DOI: 10.6084/m9.figshare.19408193.v1
title: Additional file 1 of Integration of transcriptomic and proteomic analyses reveals several levels of metabolic regulation in the excess starch and early senescent leaf mutant lses1 in rice

description: Additional file 1: Table S1. Detection data of various physiological characteristics in leaves of the wild type (WT) and the lses1 mutant. Table S2. Summary of the transcriptome sequencing quality. Table S3. Identification of differentially expressed genes in leaves of the wild type (WT) and the lses1 mutant. Table S4. Identification of differentially expressed proteins in leaves of the wild type (WT) and the lses1 mutant. Table S5. Protein-protein interactions of the four functional clusters. Table S6. List of partial differentially expressed genes/proteins related to carbohydrate metabolism. Table S7. List of partial differentially expressed genes/proteins involved in apoptosis-related metabolism. Table S8. List of differentially expressed genes/proteins involved in multiple metabolic regulation in schematic.
DOI: 10.6084/m9.figshare.13641101
title: High Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Non-Covalent Inhibitor

description: Publication forthcoming<br>For Scientific and Technical Information Only For All InformationUnless otherwise indicated, this information has been authored by an employee or employees of UChicago Argonne, LLC., operator of Argonne National Laboratory, with the U.S. Department of Energy. The U.S. Government has rights to use, reproduce, and distribute this information. The public may copy and use this information without charge, provided that this Notice and any statement of authorship are reproduced on all copies.While every effort has been made to produce valid data, by using this data, User acknowledges that neither the Government nor operating contractors of the above national laboratories makes any warranty, express or implied, of either the accuracy or completeness of this information or assumes any liability or responsibility for the use of this information. Additionally, this information is provided solely for research purposes and is not provided for purposes of offering medical advice. Accordingly, the U.S. Government and operating contractors of the above national laboratories are not to be liable to any user for any loss or damage, whether in contract, tort (including negligence), breach of statutory duty, or otherwise, even if foreseeable, arising under or in connection with use of or reliance on the content displayed in this report.<br><br><b> </b>
DOI: 10.6084/m9.figshare.24126881
title: Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs

description: Shapley value is originally a concept in econometrics to fairly distribute both gains and costs to players in a coalition game. In the recent decades, its application has been extended to other areas such as marketing, engineering and machine learning. For example, it produces reasonable solutions for problems in sensitivity analysis, local model explanation toward the interpretable machine learning, node importance in social network, attribution models, etc. However, it could be very expensive to compute the Shapley value. Specifically, in a <i>d</i>-player coalition game, calculating a Shapley value requires the evaluation of d! or 2d marginal contribution values, depending on whether we are taking the permutation or combination formulation of the Shapley value. Hence, it becomes infeasible to calculate the Shapley value when <i>d</i> is reasonably large. A common remedy is to take a random sample of the permutations to surrogate for the complete list of permutations. We find an advanced sampling scheme can be designed to yield much more accurate estimation of the Shapley value than the simple random sampling (SRS). Our sampling scheme is based on combinatorial structures in the field of design of experiments (DOE), particularly the order-of-addition experimental designs for the study of how the orderings of components would affect the output. We show that the obtained estimates are unbiased, and can sometimes deterministically recover the original Shapley value. Both theoretical and simulations results show that our DOE-based sampling scheme outperforms SRS in terms of estimation accuracy. Surprisingly, it is also slightly faster than SRS. Lastly, real data analysis is conducted for the <i>C. elegans</i> nervous system and the 9/11 terrorist network. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24126881.v1
title: Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs

description: Shapley value is originally a concept in econometrics to fairly distribute both gains and costs to players in a coalition game. In the recent decades, its application has been extended to other areas such as marketing, engineering and machine learning. For example, it produces reasonable solutions for problems in sensitivity analysis, local model explanation toward the interpretable machine learning, node importance in social network, attribution models, etc. However, it could be very expensive to compute the Shapley value. Specifically, in a <i>d</i>-player coalition game, calculating a Shapley value requires the evaluation of d! or 2d marginal contribution values, depending on whether we are taking the permutation or combination formulation of the Shapley value. Hence, it becomes infeasible to calculate the Shapley value when <i>d</i> is reasonably large. A common remedy is to take a random sample of the permutations to surrogate for the complete list of permutations. We find an advanced sampling scheme can be designed to yield much more accurate estimation of the Shapley value than the simple random sampling (SRS). Our sampling scheme is based on combinatorial structures in the field of design of experiments (DOE), particularly the order-of-addition experimental designs for the study of how the orderings of components would affect the output. We show that the obtained estimates are unbiased, and can sometimes deterministically recover the original Shapley value. Both theoretical and simulations results show that our DOE-based sampling scheme outperforms SRS in terms of estimation accuracy. Surprisingly, it is also slightly faster than SRS. Lastly, real data analysis is conducted for the <i>C. elegans</i> nervous system and the 9/11 terrorist network. Supplementary materials for this article are available online.
DOI: 10.34965/i10866
title: 20225475
DOI: 10.6084/m9.figshare.25145303.v1
title: Dataset correlation*certainty matrix of final CGM networks

description: Dataset of partial correlations and certainties of the main CGM network for the MPEDS and item-level dataset, for variables associated with PhenoAgeAccel. Certainties were retrieved after 100 times resampling of the datasets. An association found in ≥95% of the resampled datasets (i.e., certainty of ≥0.95) was considered as “certain”.
DOI: 10.6084/m9.figshare.25145303
title: Dataset correlation*certainty matrix of final CGM networks

description: Dataset of partial correlations and certainties of the main CGM network for the MPEDS and item-level dataset, for variables associated with PhenoAgeAccel. Certainties were retrieved after 100 times resampling of the datasets. An association found in ≥95% of the resampled datasets (i.e., certainty of ≥0.95) was considered as “certain”.
DOI: 10.6084/m9.figshare.24127004
title: A Randomized Pairwise Likelihood Method for Complex Statistical Inferences

description: Pairwise likelihood methods are commonly used for inference in parametric statistical models in cases where the full likelihood is too complex to be used, such as multivariate count data. Although pairwise likelihood methods represent a useful solution to perform inference for intractable likelihoods, several computational challenges remain. The pairwise likelihood function still requires the computation of a sum over all pairs of variables and all observations, which may be prohibitive in high dimensions. Moreover, it may be difficult to calculate confidence intervals of the resulting estimators, as they involve summing all pairs of pairs and all of the four-dimensional marginals. To alleviate these issues, we consider a randomized pairwise likelihood approach, where only summands randomly sampled across observations and pairs are used for the estimation. In addition to the usual tradeoff between statistical and computational efficiency, it is shown that, under a condition on the sampling parameter, this two-way random sampling mechanism makes the individual bivariate likelihood scores become asymptotically independent, allowing more computationally efficient confidence intervals to be constructed. The proposed approach is illustrated in tandem with copula-based models for multivariate count data in simulations, and in real data from a transcriptome study. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24127004.v2
title: A Randomized Pairwise Likelihood Method for Complex Statistical Inferences

description: Pairwise likelihood methods are commonly used for inference in parametric statistical models in cases where the full likelihood is too complex to be used, such as multivariate count data. Although pairwise likelihood methods represent a useful solution to perform inference for intractable likelihoods, several computational challenges remain. The pairwise likelihood function still requires the computation of a sum over all pairs of variables and all observations, which may be prohibitive in high dimensions. Moreover, it may be difficult to calculate confidence intervals of the resulting estimators, as they involve summing all pairs of pairs and all of the four-dimensional marginals. To alleviate these issues, we consider a randomized pairwise likelihood approach, where only summands randomly sampled across observations and pairs are used for the estimation. In addition to the usual tradeoff between statistical and computational efficiency, it is shown that, under a condition on the sampling parameter, this two-way random sampling mechanism makes the individual bivariate likelihood scores become asymptotically independent, allowing more computationally efficient confidence intervals to be constructed. The proposed approach is illustrated in tandem with copula-based models for multivariate count data in simulations, and in real data from a transcriptome study. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8398376
title: A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors

description: Computer simulations often involve both qualitative and numerical inputs. Existing Gaussian process (GP) methods for handling this mainly assume a different response surface for each combination of levels of the qualitative factors and relate them via a multiresponse cross-covariance matrix. We introduce a substantially different approach that maps each qualitative factor to underlying numerical latent variables (LVs), with the mapped values estimated similarly to the other correlation parameters, and then uses any standard GP covariance function for numerical variables. This provides a parsimonious GP parameterization that treats qualitative factors the same as numerical variables and views them as affecting the response via similar physical mechanisms. This has strong physical justification, as the effects of a qualitative factor in any physics-based simulation model must <i>always</i> be due to some underlying numerical variables. Even when the underlying variables are many, sufficient dimension reduction arguments imply that their effects can be represented by a low-dimensional LV. This conjecture is supported by the superior predictive performance observed across a variety of examples. Moreover, the mapped LVs provide substantial insight into the nature and effects of the qualitative factors. Supplementary materials for the article are available online.
DOI: 10.6084/m9.figshare.8398376.v2
title: A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors

description: Computer simulations often involve both qualitative and numerical inputs. Existing Gaussian process (GP) methods for handling this mainly assume a different response surface for each combination of levels of the qualitative factors and relate them via a multiresponse cross-covariance matrix. We introduce a substantially different approach that maps each qualitative factor to underlying numerical latent variables (LVs), with the mapped values estimated similarly to the other correlation parameters, and then uses any standard GP covariance function for numerical variables. This provides a parsimonious GP parameterization that treats qualitative factors the same as numerical variables and views them as affecting the response via similar physical mechanisms. This has strong physical justification, as the effects of a qualitative factor in any physics-based simulation model must <i>always</i> be due to some underlying numerical variables. Even when the underlying variables are many, sufficient dimension reduction arguments imply that their effects can be represented by a low-dimensional LV. This conjecture is supported by the superior predictive performance observed across a variety of examples. Moreover, the mapped LVs provide substantial insight into the nature and effects of the qualitative factors. Supplementary materials for the article are available online.
DOI: 10.6084/m9.figshare.16787010
title: Novel circular RNA circATRNL1 accelerates the osteosarcoma aerobic glycolysis through targeting miR-409-3p/LDHA

description: In recent researches, circular RNAs (circRNAs) have been shown to exert critical functions in osteosarcoma biology. Nevertheless, the contribution of circRNAs to osteosarcoma remains largely unclear. Results indicated that expression of circATRNL1 was higher in osteosarcoma tissues and cells. The high-expression of circATRNL1 was significantly correlated with aggressive features and acted as an independent risk factor for osteosarcoma patients’ overall survival. Functionally, our findings demonstrate that circATRNL1 promotes the osteosarcoma aerobic glycolysis in vitro. Mechanistically, circATRNL1 up-regulated the expression level of LDHA, which was also targeted by miR-409-3p. Therefore, circATRNL1 exerted the accelerative roles of osteosarcoma aerobic glycolysis through miR-409-3p/LDHA axis. In conclusion, circATRNL1 promoted osteosarcoma progression by enhancing glycolysis via circATRNL1/miR-409-3p/LDHA axis, which may inspire a novel therapeutic target for osteosarcoma.
DOI: 10.6084/m9.figshare.16787011
title: LINC00839/miR-144-3p/WTAP (WT1 Associated protein) axis is involved in regulating hepatocellular carcinoma progression

description: The present work aimed to explore LINC00839 expression level and its function in hepatocellular carcinoma (HCC), and identify the downstream molecular mechanisms. qRT-PCR (Real-Time Quantitative Reverse Transcription PCR) and western blot were employed to detect mRNA and protein levels. Functional investigations were performed by flow cytometric-based apoptosis assay, CCK8 (Cell Counting Kit-8) assay, clone formation assay, Transwell migration and invasion assay. Functional interactions between LINC00839 and miR-144-3p or miR-144-3p and WTAP were validated by dual luciferase reporter assay. siRNA (small interfering RNA) was used for LINC00839 silencing, and microRNA mimic or inhibitor were employed to modulate miR-144-3p activity. LINC00839 was upregulated in HCC cells and tissues. Silencing LINC00839 suppressed the proliferation, invasion, migration of HCC cells and induced apoptosis. Additionally, LINC00839 served as a sponge to negatively impact on miR-144-3p activity, which contributed to the high expression of WTAP (WT1 Associated Protein) and the malignant phenotype of HCC cells. Our study revealed an oncogenic role of LINC00839 in HCC, and identified miR-144-3p/WTAP axis as downstream effectors mediating the oncogenic function of LINC00839. LINC00839 might serve as a potential therapeutic target and prognostic marker for HCC.
DOI: 10.6084/m9.figshare.16786822
title: Additional file 3 of Source-tracking of the Chinese Chikungunya viruses suggests that Indian subcontinent and Southeast Asia act as major hubs for the recent global spread of Chikungunya virus

description: Additional file 3: Table S3. The distinct mutations associated with CHIKV genotype/lineages.
DOI: 10.6084/m9.figshare.16786819.v1
title: Additional file 2 of Source-tracking of the Chinese Chikungunya viruses suggests that Indian subcontinent and Southeast Asia act as major hubs for the recent global spread of Chikungunya virus

description: Additional file 2: Table S2. Summary of the Chinese CHIKV strains with genome sequences.
DOI: 10.6084/m9.figshare.16786816
title: Additional file 1 of Source-tracking of the Chinese Chikungunya viruses suggests that Indian subcontinent and Southeast Asia act as major hubs for the recent global spread of Chikungunya virus

description: Additional file 1: Table S1. The CHIKV genomes used in this study.
DOI: 10.6084/m9.figshare.16786819
title: Additional file 2 of Source-tracking of the Chinese Chikungunya viruses suggests that Indian subcontinent and Southeast Asia act as major hubs for the recent global spread of Chikungunya virus

description: Additional file 2: Table S2. Summary of the Chinese CHIKV strains with genome sequences.
DOI: 10.6084/m9.figshare.19408294
title: Additional file 1 of Mesenchymal stem/stromal cell therapy for COVID-19 pneumonia: potential mechanisms, current clinical evidence, and future perspectives

description: Additional file 1. Table S1. Clinical trials of cell-based therapies for COVID-19 patients registered in clinicaltrials.gov/ . Table S2. Current outcomes of clinical trials of MSCs in COVID-19 treatment.
DOI: 10.6084/m9.figshare.19408294.v1
title: Additional file 1 of Mesenchymal stem/stromal cell therapy for COVID-19 pneumonia: potential mechanisms, current clinical evidence, and future perspectives

description: Additional file 1. Table S1. Clinical trials of cell-based therapies for COVID-19 patients registered in clinicaltrials.gov/ . Table S2. Current outcomes of clinical trials of MSCs in COVID-19 treatment.
DOI: 10.6084/m9.figshare.25145339
title: Untitled Item

description: Data set
DOI: 10.6084/m9.figshare.16786816.v1
title: Additional file 1 of Source-tracking of the Chinese Chikungunya viruses suggests that Indian subcontinent and Southeast Asia act as major hubs for the recent global spread of Chikungunya virus

description: Additional file 1: Table S1. The CHIKV genomes used in this study.
DOI: 10.6084/m9.figshare.16786822.v1
title: Additional file 3 of Source-tracking of the Chinese Chikungunya viruses suggests that Indian subcontinent and Southeast Asia act as major hubs for the recent global spread of Chikungunya virus

description: Additional file 3: Table S3. The distinct mutations associated with CHIKV genotype/lineages.
DOI: 10.5281/zenodo.10619648
title: This is just a test

description: no mor to declare
DOI: 10.5281/zenodo.10619649
title: This is just a test

description: no mor to declare