cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4731850.v1
title: The evolution of venom by co-option of single copy genes

description: The classic model for the evolution of novel gene function is through gene duplication followed by evolution of new function by one copy (neofunctionalization). However, other modes have also been found, such as novel genes arising from non-coding DNA, chimeric fusions, and lateral gene transfers from other organisms. Here we use the rapid turnover of venom genes in parasitoid wasps to study how new gene functions evolve. In contrast to the classic gene duplication model, we find that a common mode of acquisition of new venom genes in parasitoid wasps is co-option of single copy genes from non-venom progenitors. Transcriptome and proteome sequencing reveal that recruitment and loss of venom genes occur primarily by rapid cis-regulatory expression evolution in the venom gland. Loss of venom genes is primarily due to down-regulation of expression in the gland rather than gene death through coding sequence degradation. While the majority of venoms have specialized expression in the venom gland, recent losses of venom function occur primarily among genes that show broader expression in development, suggesting that they can more readily switch functional roles. We propose that co-option of single copy genes may be a common but relatively understudied mechanism of evolution of new gene functions, particularly under conditions of rapid evolutionary change.
DOI: 10.6084/m9.figshare.4730302.v1
title: Bayesian Synthetic Likelihood

description: Having the ability to work with complex models can be highly beneficial. However, complex models often have intractable likelihoods, so methods that involve evaluation of the likelihood function are infeasible. In these situations, the benefits of working with likelihood-free methods become apparent. Likelihood-free methods, such as parametric Bayesian indirect likelihood that uses the likelihood of an alternative parametric auxiliary model, have been explored throughout the literature as a viable alternative when the model of interest is complex. One of these methods is called the synthetic likelihood (SL), which uses a multivariate normal approximation of the distribution of a set of summary statistics. This article explores the accuracy and computational efficiency of the Bayesian version of the synthetic likelihood (BSL) approach in comparison to a competitor known as approximate Bayesian computation (ABC) and its sensitivity to its tuning parameters and assumptions. We relate BSL to pseudo-marginal methods and propose to use an alternative SL that uses an unbiased estimator of the SL, when the summary statistics have a multivariate normal distribution. Several applications of varying complexity are considered to illustrate the findings of this article. Supplemental materials are available online. Computer code for implementing the methods on all examples is available at <i>https://github.com/cdrovandi/Bayesian-Synthetic-Likelihood</i>.
DOI: 10.6084/m9.figshare.4730296
title: Minimax and Minimax Projection Designs Using Clustering

description: Minimax designs provide a uniform coverage of a design space X⊆Rp by minimizing the maximum distance from any point in this space to its nearest design point. Although minimax designs have many useful applications, for example, for optimal sensor allocation or as space-filling designs for computer experiments, there has been little work in developing algorithms for generating these designs, due to its computational complexity. In this article, a new hybrid algorithm combining particle swarm optimization and clustering is proposed for generating minimax designs on any convex and bounded design space. The computation time of this algorithm scales linearly in dimension <i>p</i>, meaning our method can generate minimax designs efficiently for high-dimensional regions. Simulation studies and a real-world example show that the proposed algorithm provides improved minimax performance over existing methods on a variety of design spaces. Finally, we introduce a new type of experimental design called a minimax projection design, and show that this proposed design provides better minimax performance on projected subspaces of X compared to existing designs. An efficient implementation of these algorithms can be found in the R package minimaxdesign. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.4730278
title: MicroRNA-125b-5p mediates post-transcriptional regulation of hepatitis B virus replication via the LIN28B/<i>let-7</i> axis

description: MicroRNAs (miRNAs) are able to modulate hepatitis B virus (HBV) replication and play an important role in the pathogenesis of HBV infection. Recently, we have identified that serum miR-125b-5p level correlated with HBV DNA levels and liver necroinflammation. In the present study, we addressed how miR-125b-5p regulated HBV replication at the different steps, inclduing viral transcription, assembly, and virion production and investigated the underlying mechanisms. We found that miR-125b-5p overexpression increased HBV replication without altering HBV transcription. This is the first demonstration of post-transcriptional miRNA regulation of HBV replication. In contrast, transfection of miR-125b-5p inhibitor resulted in downregulation of HBV replication in hepatoma cells. Further, miR-125b-5p inhibited the phosphorylation of retinoblastoma protein and blocked cell cycle progression at the G1/S phase in hepatoma cell lines. Our results indicate that certain miRNAs are able to arrest the cell cycle at G1 phase and may increase HBV replication. RNA sequencing revealed a number of liver-specific metabolic pathways regulated by miR-125b-5p, which was also found to suppress LIN28B and induce <i>let-7</i> family members. Additional data demonstrated that miR-125b-5p targeted the LIN28B/<i>let-7</i> axis to stimulate HBV replication at a post-transcriptional step.
DOI: 10.6084/m9.figshare.4730278.v1
title: MicroRNA-125b-5p mediates post-transcriptional regulation of hepatitis B virus replication via the LIN28B/<i>let-7</i> axis

description: MicroRNAs (miRNAs) are able to modulate hepatitis B virus (HBV) replication and play an important role in the pathogenesis of HBV infection. Recently, we have identified that serum miR-125b-5p level correlated with HBV DNA levels and liver necroinflammation. In the present study, we addressed how miR-125b-5p regulated HBV replication at the different steps, inclduing viral transcription, assembly, and virion production and investigated the underlying mechanisms. We found that miR-125b-5p overexpression increased HBV replication without altering HBV transcription. This is the first demonstration of post-transcriptional miRNA regulation of HBV replication. In contrast, transfection of miR-125b-5p inhibitor resulted in downregulation of HBV replication in hepatoma cells. Further, miR-125b-5p inhibited the phosphorylation of retinoblastoma protein and blocked cell cycle progression at the G1/S phase in hepatoma cell lines. Our results indicate that certain miRNAs are able to arrest the cell cycle at G1 phase and may increase HBV replication. RNA sequencing revealed a number of liver-specific metabolic pathways regulated by miR-125b-5p, which was also found to suppress LIN28B and induce <i>let-7</i> family members. Additional data demonstrated that miR-125b-5p targeted the LIN28B/<i>let-7</i> axis to stimulate HBV replication at a post-transcriptional step.
DOI: 10.6084/m9.figshare.4730281
title: A Skewed and Heavy-Tailed Latent Random Field Model for Spatial Extremes

description: This article develops Bayesian inference of spatial models with a flexible skew latent structure. Using the multivariate skew-normal distribution of Sahu et al., a valid random field model with stochastic skewing structure is proposed to take into account non-Gaussian features. The skewed spatial model is further improved via scale mixing to accommodate more extreme observations. Finally, the skewed and heavy-tailed random field model is used to describe the parameters of extreme value distributions. Bayesian prediction is done with a well-known Gibbs sampling algorithm, including slice sampling and adaptive simulation techniques. The model performance—as far as the identifiability of the parameters is concerned—is assessed by a simulation study and an analysis of extreme wind speeds across Iran. We conclude that our model provides more satisfactory results according to Bayesian model selection and predictive-based criteria. R code to implement the methods used is available as online supplementary material.
DOI: 10.6084/m9.figshare.4730281.v1
title: A Skewed and Heavy-Tailed Latent Random Field Model for Spatial Extremes

description: This article develops Bayesian inference of spatial models with a flexible skew latent structure. Using the multivariate skew-normal distribution of Sahu et al., a valid random field model with stochastic skewing structure is proposed to take into account non-Gaussian features. The skewed spatial model is further improved via scale mixing to accommodate more extreme observations. Finally, the skewed and heavy-tailed random field model is used to describe the parameters of extreme value distributions. Bayesian prediction is done with a well-known Gibbs sampling algorithm, including slice sampling and adaptive simulation techniques. The model performance—as far as the identifiability of the parameters is concerned—is assessed by a simulation study and an analysis of extreme wind speeds across Iran. We conclude that our model provides more satisfactory results according to Bayesian model selection and predictive-based criteria. R code to implement the methods used is available as online supplementary material.
DOI: 10.6084/m9.figshare.4730296.v2
title: Minimax and Minimax Projection Designs Using Clustering

description: Minimax designs provide a uniform coverage of a design space X⊆Rp by minimizing the maximum distance from any point in this space to its nearest design point. Although minimax designs have many useful applications, for example, for optimal sensor allocation or as space-filling designs for computer experiments, there has been little work in developing algorithms for generating these designs, due to its computational complexity. In this article, a new hybrid algorithm combining particle swarm optimization and clustering is proposed for generating minimax designs on any convex and bounded design space. The computation time of this algorithm scales linearly in dimension <i>p</i>, meaning our method can generate minimax designs efficiently for high-dimensional regions. Simulation studies and a real-world example show that the proposed algorithm provides improved minimax performance over existing methods on a variety of design spaces. Finally, we introduce a new type of experimental design called a minimax projection design, and show that this proposed design provides better minimax performance on projected subspaces of X compared to existing designs. An efficient implementation of these algorithms can be found in the R package minimaxdesign. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.4730299.v1
title: Regularized Estimation of Piecewise Constant Gaussian Graphical Models: The Group-Fused Graphical Lasso

description: The time-evolving precision matrix of a piecewise-constant Gaussian graphical model encodes the dynamic conditional dependency structure of a multivariate time-series. Traditionally, graphical models are estimated under the assumption that data is drawn identically from a generating distribution. Introducing sparsity and sparse-difference inducing priors we relax these assumptions and propose a novel regularized M-estimator to jointly estimate both the graph and changepoint structure. The resulting estimator possesses the ability to therefore favor sparse dependency structures and/or smoothly evolving graph structures, as required. Moreover, our approach extends current methods to allow estimation of changepoints that are grouped across multiple dependencies in a system. An efficient algorithm for estimating structure is proposed. We study the empirical recovery properties in a synthetic setting. The qualitative effect of grouped changepoint estimation is then demonstrated by applying the method on a genetic time-course data-set.
DOI: 10.6084/m9.figshare.4730299.v3
title: Regularized Estimation of Piecewise Constant Gaussian Graphical Models: The Group-Fused Graphical Lasso

description: The time-evolving precision matrix of a piecewise-constant Gaussian graphical model encodes the dynamic conditional dependency structure of a multivariate time-series. Traditionally, graphical models are estimated under the assumption that data are drawn identically from a generating distribution. Introducing sparsity and sparse-difference inducing priors, we relax these assumptions and propose a novel regularized M-estimator to jointly estimate both the graph and changepoint structure. The resulting estimator possesses the ability to therefore favor sparse dependency structures and/or smoothly evolving graph structures, as required. Moreover, our approach extends current methods to allow estimation of changepoints that are grouped across multiple dependencies in a system. An efficient algorithm for estimating structure is proposed. We study the empirical recovery properties in a synthetic setting. The qualitative effect of grouped changepoint estimation is then demonstrated by applying the method on a genetic time-course dataset. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.4730299.v2
title: Regularized Estimation of Piecewise Constant Gaussian Graphical Models: The Group-Fused Graphical Lasso

description: The time-evolving precision matrix of a piecewise-constant Gaussian graphical model encodes the dynamic conditional dependency structure of a multivariate time-series. Traditionally, graphical models are estimated under the assumption that data are drawn identically from a generating distribution. Introducing sparsity and sparse-difference inducing priors, we relax these assumptions and propose a novel regularized M-estimator to jointly estimate both the graph and changepoint structure. The resulting estimator possesses the ability to therefore favor sparse dependency structures and/or smoothly evolving graph structures, as required. Moreover, our approach extends current methods to allow estimation of changepoints that are grouped across multiple dependencies in a system. An efficient algorithm for estimating structure is proposed. We study the empirical recovery properties in a synthetic setting. The qualitative effect of grouped changepoint estimation is then demonstrated by applying the method on a genetic time-course dataset. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.4730281.v2
title: A Skewed and Heavy-Tailed Latent Random Field Model for Spatial Extremes

description: This article develops Bayesian inference of spatial models with a flexible skew latent structure. Using the multivariate skew-normal distribution of Sahu et al., a valid random field model with stochastic skewing structure is proposed to take into account non-Gaussian features. The skewed spatial model is further improved via scale mixing to accommodate more extreme observations. Finally, the skewed and heavy-tailed random field model is used to describe the parameters of extreme value distributions. Bayesian prediction is done with a well-known Gibbs sampling algorithm, including slice sampling and adaptive simulation techniques. The model performance—as far as the identifiability of the parameters is concerned—is assessed by a simulation study and an analysis of extreme wind speeds across Iran. We conclude that our model provides more satisfactory results according to Bayesian model selection and predictive-based criteria. R code to implement the methods used is available as online supplementary material.
DOI: 10.6084/m9.figshare.4730296.v1
title: Minimax and minimax projection designs using clustering

description: Minimax designs provide a uniform coverage of a design space X⊆Rp by minimizing the maximum distance from any point in this space to its nearest design point. Although minimax designs have many useful applications, e.g., for optimal sensor allocation or as space-filling designs for computer experiments, there has been little work in developing algorithms for generating these designs, due to its computational complexity. In this paper, a new hybrid algorithm combining particle swarm optimization and clustering is proposed for generating minimax designs on any convex and bounded design space. The computation time of this algorithm scales linearly in dimension <i>p</i>, meaning our method can generate minimax designs efficiently for high-dimensional regions. Simulation studies and a real-world example show that the proposed algorithm provides improved minimax performance over existing methods on a variety of design spaces. Finally, we introduce a new type of experimental design called a minimax projection design, and show that this proposed design provides better minimax performance on projected subspaces of X compared to existing designs. An efficient implementation of these algorithms can be found in the R package minimaxdesign.
DOI: 10.6084/m9.figshare.4730281.v4
title: A Skewed and Heavy-Tailed Latent Random Field Model for Spatial Extremes

description: This article develops Bayesian inference of spatial models with a flexible skew latent structure. Using the multivariate skew-normal distribution of Sahu et al., a valid random field model with stochastic skewing structure is proposed to take into account non-Gaussian features. The skewed spatial model is further improved via scale mixing to accommodate more extreme observations. Finally, the skewed and heavy-tailed random field model is used to describe the parameters of extreme value distributions. Bayesian prediction is done with a well-known Gibbs sampling algorithm, including slice sampling and adaptive simulation techniques. The model performance—as far as the identifiability of the parameters is concerned—is assessed by a simulation study and an analysis of extreme wind speeds across Iran. We conclude that our model provides more satisfactory results according to Bayesian model selection and predictive-based criteria. R code to implement the methods used is available as online supplementary material.
DOI: 10.6084/m9.figshare.4730281.v3
title: A Skewed and Heavy-Tailed Latent Random Field Model for Spatial Extremes

description: This article develops Bayesian inference of spatial models with a flexible skew latent structure. Using the multivariate skew-normal distribution of Sahu et al., a valid random field model with stochastic skewing structure is proposed to take into account non-Gaussian features. The skewed spatial model is further improved via scale mixing to accommodate more extreme observations. Finally, the skewed and heavy-tailed random field model is used to describe the parameters of extreme value distributions. Bayesian prediction is done with a well-known Gibbs sampling algorithm, including slice sampling and adaptive simulation techniques. The model performance—as far as the identifiability of the parameters is concerned—is assessed by a simulation study and an analysis of extreme wind speeds across Iran. We conclude that our model provides more satisfactory results according to Bayesian model selection and predictive-based criteria. R code to implement the methods used is available as online supplementary material.
DOI: 10.6084/m9.figshare.4730254
title: Articulated Lucy (A.L. 288-1) model

description: Articulated model of Lucy (A.L. 288-1) used for convex hulling and body mass estimation.
DOI: 10.6084/m9.figshare.4730254.v1
title: Articulated Lucy (A.L. 288-1) model

description: Articulated model of Lucy (A.L. 288-1) used for convex hulling and body mass estimation.
DOI: 10.6084/m9.figshare.4730164
title: Hippocampal primary cultures surface GluA1 expression

description: Hippocampal primary cultures from Tspan6 KO and WT animals immnuostained with GluA1 (red) and VGlut1 (white). <br>Confocal images.<br><br>Excel file with the density, area and intensity quantification done with ImageJ. <br><br>Every column represent independent neurons from 1 to 3 different embryos (WT1.(1-3), WT2 (1-4), KO1 (1-3), etc
DOI: 10.6084/m9.figshare.4730149
title: Behavioral experiments

description: Excel files with the raw results from the behavioral studies<br>
DOI: 10.6084/m9.figshare.4730053
title: Golgi staining Tspan6 KO adult mice

description: Microscope images from Tspan6 WT and KO brain slices after Golgi staning. Images from basal dendrites Ca1 pyramidal neurons. <br>
DOI: 10.6084/m9.figshare.4730089
title: Tspan6 KO Hippocampal primary cultures spine density

description: Confocal images from 14 DIV hippocampal primary neurons from Tspan6 KO and WT mice transfected with GFP and used for spine density quantification. <br><br>Excel file with the quantification results per individual image. <br>
DOI: 10.6084/m9.figshare.4730146
title: Electrophysiological recordings

description: Excel files with the original data from <br>
DOI: 10.6084/m9.figshare.4730164.v2
title: Hippocampal primary cultures surface GluA1 expression

description: Hippocampal primary cultures from Tspan6 KO and WT animals immnuostained with GluA1 (red) and VGlut1 (white). <br>Confocal images.<br><br>Excel file with the density, area and intensity quantification done with ImageJ. <br><br>Every column represent independent neurons from 1 to 3 different embryos (WT1.(1-3), WT2 (1-4), KO1 (1-3), etc
DOI: 10.6084/m9.figshare.4730053.v2
title: Golgi staining Tspan6 KO adult mice

description: Microscope images from Tspan6 WT and KO brain slices after Golgi staning. Images from basal dendrites Ca1 pyramidal neurons. <br>
DOI: 10.6084/m9.figshare.4730149.v1
title: Behavioral experiments

description: Excel files with the raw results from the behavioral studies<br>
DOI: 10.6084/m9.figshare.4730164.v1
title: Hippocampal primary cultures surface GluA1 expression

description: Hippocampal primary cultures from Tspan6 KO and WT animals immnuostained with GluA1 (red) and VGlut1 (white). <br>Confocal images.<br><br>Excel file with the density, area and intensity quantification done with ImageJ. <br>
DOI: 10.6084/m9.figshare.4730038
title: RT-qPCR Tspan6 KO results

description: 1. Excel file with the qPCR results.<br>2. Western blot images from primary cortical lysates immunoblotted with Tspan6 and Actin antibodies. <br>
DOI: 10.6084/m9.figshare.4730089.v2
title: Tspan6 KO Hippocampal primary cultures spine density

description: Confocal images from 14 DIV hippocampal primary neurons from Tspan6 KO and WT mice transfected with GFP and used for spine density quantification. <br><br>Excel file with the quantification results per individual image. <br>
DOI: 10.6084/m9.figshare.4730026
title: Figure 1 RNA scope pictures

description: Original pictures from RNA scope done in brain slices from WT animals and incubated with probes for Tspan6 and synaptophysin<br>
DOI: 10.6084/m9.figshare.4730053.v1
title: Golgi staining Tspan6 KO adult mice

description: Microscope images from Tspan6 WT and KO brain slices after Golgi staning. Images from basal dendrites Ca1 pyramidal neurons. <br>
DOI: 10.6084/m9.figshare.4730089.v1
title: Tspan6 KO Hippocampal primary cultures spine density

description: Confocal images from 14 DIV hippocampal primary neurons from Tspan6 KO and WT mice transfected with GFP and used for spine density quantification. <br><br>Excel file with the quantification results per individual image. <br>
DOI: 10.6084/m9.figshare.4730026.v1
title: Figure 1 RNA scope pictures

description: Original pictures from RNA scope done in brain slices from WT animals and incubated with probes for Tspan6 and synaptophysin<br>
DOI: 10.6084/m9.figshare.4730038.v2
title: RT-qPCR Tspan6 KO results

description: 1. Excel file with the qPCR results.<br>2. Western blot images from primary cortical lysates immunoblotted with Tspan6 and Actin antibodies. <br>