cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1293027.v1
title: Instrumental Variables Estimation With Some Invalid Instruments and its Application to Mendelian Randomization

description: Instrumental variables have been widely used for estimating the causal effect between exposure and outcome. Conventional estimation methods require complete knowledge about all the instruments’ validity; a valid instrument must not have a direct effect on the outcome and not be related to unmeasured confounders. Often, this is impractical as highlighted by Mendelian randomization studies where genetic markers are used as instruments and complete knowledge about instruments’ validity is equivalent to complete knowledge about the involved genes’ functions. In this article, we propose a method for estimation of causal effects when this complete knowledge is absent. It is shown that causal effects are identified and can be estimated as long as less than 50% of instruments are invalid, without knowing which of the instruments are invalid. We also introduce conditions for identification when the 50% threshold is violated. A fast penalized ℓ<sub>1</sub> estimation method, called sisVIVE, is introduced for estimating the causal effect without knowing which instruments are valid, with theoretical guarantees on its performance. The proposed method is demonstrated on simulated data and a real Mendelian randomization study concerning the effect of body mass index(BMI) on health-related quality of life (HRQL) index. An R package <i>sisVIVE</i> is available on CRAN. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1293020
title: Correction to "Arabidopsis RNA-Dependent RNA Polymerases and Dicer-Like Proteins in Antiviral Defense and Small Interfering RNA Biogenesis during Turnip Mosaic Virus Infection" by Garcia-Ruiz et al., 2010

description: The figure depicted in the file Garcia-Ruiz_et_al_2010_PlantCell_Fig3-Corrected.jpg is a corrected version of Figure 3 originally published in Garcia-Ruiz et al., 2010 http://dx.doi.org/10.1105/tpc.109.073056 at The Plant Cell (http://www.plantcell.org), copyright American Society of Plant Biologists. In the original manuscript we determined that the immunoblot panel in Figure 3A does not correspond with the actual experiment that was run. Rather, the panel used corresponds to a blot from a different sample set that was used for Figure 3B. In tracing down the cause of the error, we determined that it occurred during final figure construction in which we used an incorrect blot panel from the same experiment. The file Garcia-Ruiz_et_al_2010_PlantCell_Fig3-Corrected.jpg now shows the correct blog image. For full transparency we are providing scans of the full coat protein blot films for Figure 3A and B. In the file Garcia-Ruiz_et_al_2010_PlantCell_Exp30-G1F-10Sec.tif, the left side of the blot has the samples for the inoculated leaf 7 days after infection (DAI) experiment (Figure 3A). The right side of the blot has unrelated samples that were not used in the experiment. In the file Garcia-Ruiz_et_al_2010_PlantCell_Exp30-G2F-15Sec.tif, the left side of the blot has the samples for the inflorescence 7 DAI experiment (Figure 3B). The right side of the blot has the samples for the inflorescence 15 DAI experiment (Figure 3B). We have contacted The Plant Cell regarding the error and submitted the corrected figure. We apologize for the mistake and regret that it was not detected prior to publication.
DOI: 10.6084/m9.figshare.1293020.v1
title: Garcia-Ruiz et al., 2010 correction

description: The figure depicted in the file Garcia-Ruiz_et_al_2010_PlantCell_Fig3-Corrected.jpg is a corrected version of Figure 3 originally published in Garcia-Ruiz et al., 2010 http://dx.doi.org/10.1105/tpc.109.073056 at The Plant Cell (http://www.plantcell.org), copyright American Society of Plant Biologists. In the original manuscript we determined that the immunoblot panel in Figure 3A does not correspond with the actual experiment that was run. Rather, the panel used corresponds to a blot from a different sample set that was used for Figure 3B. In tracing down the cause of the error, we determined that it occurred during final figure construction in which we used an incorrect blot panel from the same experiment. The file Garcia-Ruiz_et_al_2010_PlantCell_Fig3-Corrected.jpg now shows the correct blog image. For full transparency we are providing scans of the full coat protein blot films for Figure 3A and B. In the file Garcia-Ruiz_et_al_2010_PlantCell_Exp30-G1F-10Sec.tif, the left side of the blot has the samples for the inoculated leaf 7 days after infection (DAI) experiment (Figure 3A). The right side of the blot has unrelated samples that were not used in the experiment. In the file Garcia-Ruiz_et_al_2010_PlantCell_Exp30-G2F-15Sec.tif, the left side of the blot has the samples for the inflorescence 7 DAI experiment (Figure 3B). The right side of the blot has the samples for the inflorescence 15 DAI experiment (Figure 3B). We have contacted The Plant Cell regarding the error and submitted the corrected figure. We apologize for the mistake and regret that it was not detected prior to publication.
DOI: 10.6084/m9.figshare.1293008
title: Abstract ClinicalTrials

description: These data present extracted design information from randomized controlled trials (RCTs) reported in abstracts presented at the Association for Resarch in Vision and Ophthalmology from 2007 through 2009. These data also present extracted design information from the ClinicalTrials.gov registration record for the same RCT. Finally, the data include comparisons between corresponding design aspects of the two sources of RCT informtion.
DOI: 10.6084/m9.figshare.1293010
title: Distribution and frequencies of post-transcriptional modifications in tRNAs

description: Functional tRNA molecules always contain a wide variety of post-transcriptionally modified nucleosides. These modifications stabilize tRNA structure, allow for proper interaction with other macromolecules and fine-tune the decoding of mRNAs during translation. Their presence in functionally important regions of tRNA is conserved in all domains of life. However, the identities of many of these modified residues depend much on the phylogeny of organisms the tRNAs are found in, attesting for domain-specific strategies of tRNA maturation. In this work we present a new tool, tRNAmodviz web server (http://genesilico.pl/trnamodviz) for easy comparative analysis and visualization of modification patterns in individual tRNAs, as well as in groups of selected tRNA sequences. We also present results of comparative analysis of tRNA sequences derived from 7 phylogenetically distinct groups of organisms: Gram-negative bacteria, Gram-positive bacteria, cytosol of eukaryotic single cell organisms, Fungi and Metazoa, cytosol of Viridiplantae, mitochondria, plastids and Euryarchaeota. These data update the study conducted 20 y ago with the tRNA sequences available at that time.
DOI: 10.6084/m9.figshare.1293008.v1
title: Abstract ClinicalTrials

description: These data present extracted design information from randomized controlled trials (RCTs) reported in abstracts presented at the Association for Resarch in Vision and Ophthalmology from 2007 through 2009. These data also present extracted design information from the ClinicalTrials.gov registration record for the same RCT. Finally, the data include comparisons between corresponding design aspects of the two sources of RCT informtion.
DOI: 10.6084/m9.figshare.1293007
title: Registration information

description: This data contains information about all conference abstracts from the 2007 to 2009 annual meetings of the Association for Research in Vision and Ophthalmology that describes the results of a randomized controlled trial and included information about trial registration.
DOI: 10.6084/m9.figshare.1292989
title: Spatial organization of the tenascin-C microenvironment in experimental and human cancer

description: The extracellular matrix (ECM) molecule tenascin-C (TNC) promotes tumor progression. This has recently been demonstrated in the stochastic murine RIP1-Tag2 insulinoma model, engineered to either express TNC abundantly or to be devoid of TNC. However, our knowledge about organization of the TNC microenvironment is scant. Here we determined the spatial distribution of TNC together with other ECM molecules in murine RIP1-Tag2 insulinoma and human cancer tissue (insulinoma and colorectal carcinoma). We found that TNC is organized in matrix tracks together with other ECM molecules of the AngioMatrix signature, a previously described gene expression profile that characterizes the angiogenic switch. Moreover, stromal cells including endothelial cells, fibroblasts and leukocytes were enriched in the TNC tracks. Thus, TNC tracks may provide niches for stromal cells and regulate their behavior. Given similarities of TNC rich niches for stromal cells in human insulinoma and colon cancer, we propose that the RIP1-Tag2 model may be useful for providing insights into the contribution of the tumor stroma specific ECM as promoter of cancer progression.
DOI: 10.6084/m9.figshare.1293007.v1
title: Registration information

description: This data contains information about all conference abstracts from the 2007 to 2009 annual meetings of the Association for Research in Vision and Ophthalmology that describes the results of a randomized controlled trial and included information about trial registration.
DOI: 10.6084/m9.figshare.1293010.v1
title: Distribution and frequencies of post-transcriptional modifications in transfer RNAs

description: Functional tRNA molecules always contain a wide variety of post-transcriptionally modified nucleosides. These modifications stabilize tRNA structure, allow for proper interaction with other macromolecules and fine-tune the decoding of mRNAs during translation. Their presence in functionally important regions of tRNA is conserved in all domains of life. However, the identities of many of these modified residues depend much on the phylogeny of organisms the tRNAs are found in, attesting for domain-specific strategies of tRNA maturation. In this work we present a new tool, tRNAmodviz web server (http://genesilico.pl/trnamodviz) for easy comparative analysis and visualization of modification patterns in individual tRNAs, as well as in groups of selected tRNA sequences. We also present results of comparative analysis of tRNA sequences derived from seven phylogenetically distinct groups of organisms: Gram-negative bacteria, Gram-positive bacteria, cytosol of eukaryotic single cell organisms, Fungi and Metazoa, cytosol of Viridiplantae, mitochondria, plastids and Euryarchaeota. These data update the study conducted 20 years ago with the tRNA sequences available at that time.
DOI: 10.6084/m9.figshare.1292987
title: Exploiting structure of maximum likelihood estimators for extreme value threshold selection

description: In order to model the tail of a distribution, one has to define the threshold above or below which an extreme value model produces a suitable fit. Parameter stability plots, whereby one plots maximum likelihood estimates of supposedly threshold-independent parameters against threshold, form one of the main tools for threshold selection by practitioners, principally due to their simplicity. However, one repeated criticism of these plots is their lack of interpretability, with pointwise confidence intervals being strongly dependent across the range of thresholds. In this article, we exploit the independent-increments structure of maximum likelihood estimators in order to produce complementary plots with greater interpretability, and a suggest simple likelihood-based procedure which allows for automated threshold selection.
DOI: 10.6084/m9.figshare.1292988
title: Tail Estimation for Window-Censored Processes

description: This article develops methods to estimate the tail and full distribution of the lengths of the 0-intervals in a continuous time stationary ergodic stochastic process that takes the values 0 and 1 in alternating intervals. The setting is that each of many such 0–1 processes has been observed during a short time window. Thus, the observed 0-intervals could be noncensored, right-censored, left-censored, or doubly-censored, and the lengths of 0-intervals that are ongoing at the beginning of the observation window have a length-biased distribution. We exhibit parametric conditional maximum likelihood estimators for the full distribution, develop maximum likelihood tail estimation methods based on a semiparametric generalized Pareto model, and propose goodness-of-fit plots. Finite sample properties are studied by simulation, and asymptotic normality is established for the most important case. The methods are applied to estimation of the length of off-road glances in the 100-car study, a big naturalistic driving experiment. Supplementary materials that include MatLab code for the estimation routines and a simulation study are available online.
DOI: 10.6084/m9.figshare.1292987.v1
title: Exploiting structure of maximum likelihood estimators for extreme value threshold selection

description: In order to model the tail of a distribution, one has to define the threshold above or below which an extreme value model produces a suitable fit. Parameter stability plots, whereby one plots maximum likelihood estimates of supposedly threshold-independent parameters against threshold, form one of the main tools for threshold selection by practitioners, principally due to their simplicity. However, one repeated criticism of these plots is their lack of interpretability, with pointwise confidence intervals being strongly dependent across the range of thresholds. In this article, we exploit the independent-increments structure of maximum likelihood estimators in order to produce complementary plots with greater interpretability, and a suggest simple likelihood-based procedure which allows for automated threshold selection.
DOI: 10.6084/m9.figshare.1292989.v1
title: Spatial organization of the tenascin-C microenvironment in experimental and human cancer

description: The extracellular matrix (ECM) molecule tenascin-C (TNC) promotes tumor progression. This has recently been demonstrated in the stochastic murine RIP1-Tag2 insulinoma model, engineered to either express TNC abundantly or to be devoid of TNC. However, our knowledge about organization of the TNC microenvironment is scant. Here we determined the spatial distribution of TNC together with other ECM molecules in murine RIP1-Tag2 insulinoma and human cancer tissue (insulinoma and colorectal carcinoma). We found that TNC is organized in matrix tracks together with other ECM molecules of the AngioMatrix signature, a previously described gene expression profile that characterizes the angiogenic switch. Moreover, stromal cells including endothelial cells, fibroblasts and leukocytes were enriched in the TNC tracks. Thus, TNC tracks may provide niches for stromal cells and regulate their behavior. Given similarities of TNC rich niches for stromal cells in human insulinoma and colon cancer, we propose that the RIP1-Tag2 model may be useful for providing insights into the contribution of the tumor stroma specific ECM as promoter of cancer progression.
DOI: 10.6084/m9.figshare.1292988.v1
title: Tail Estimation for Window Censored Processes

description: This paper develops methods to estimate the tail and full distribution of the lengths of the 0-intervals in a continuous time stationary ergodic stochastic process which takes the values 0 and 1 in alternating intervals. The setting is that each of many such 0-1 processes have been observed during a short time window. Thus the observed 0-intervals could be non-censored, right censored, left censored or doubly censored, and the lengths of 0-intervals which are ongoing at the beginning of the observation window have a length-biased distribution. We exhibit parametric conditional maximum likelihood estimators for the full distribution, develop maximum likelihood tail estimation methods based on a semi-parametric generalized Pareto model, and propose goodness of fit plots. Finite sample properties are studied by simulation, and asymptotic normality is established for the most important case. The methods are applied to estimation of the length of off-road glances in the 100-car study, a big naturalistic driving experiment. Supplementary materials that include MatLab code for the estimation routines and a simulation study are available online.
DOI: 10.6084/m9.figshare.1293010.v3
title: Distribution and frequencies of post-transcriptional modifications in tRNAs

description: Functional tRNA molecules always contain a wide variety of post-transcriptionally modified nucleosides. These modifications stabilize tRNA structure, allow for proper interaction with other macromolecules and fine-tune the decoding of mRNAs during translation. Their presence in functionally important regions of tRNA is conserved in all domains of life. However, the identities of many of these modified residues depend much on the phylogeny of organisms the tRNAs are found in, attesting for domain-specific strategies of tRNA maturation. In this work we present a new tool, tRNAmodviz web server (http://genesilico.pl/trnamodviz) for easy comparative analysis and visualization of modification patterns in individual tRNAs, as well as in groups of selected tRNA sequences. We also present results of comparative analysis of tRNA sequences derived from 7 phylogenetically distinct groups of organisms: Gram-negative bacteria, Gram-positive bacteria, cytosol of eukaryotic single cell organisms, Fungi and Metazoa, cytosol of Viridiplantae, mitochondria, plastids and Euryarchaeota. These data update the study conducted 20 y ago with the tRNA sequences available at that time.
DOI: 10.6084/m9.figshare.1293010.v2
title: Distribution and frequencies of post-transcriptional modifications in tRNAs

description: Functional tRNA molecules always contain a wide variety of post-transcriptionally modified nucleosides. These modifications stabilize tRNA structure, allow for proper interaction with other macromolecules and fine-tune the decoding of mRNAs during translation. Their presence in functionally important regions of tRNA is conserved in all domains of life. However, the identities of many of these modified residues depend much on the phylogeny of organisms the tRNAs are found in, attesting for domain-specific strategies of tRNA maturation. In this work we present a new tool, tRNAmodviz web server (http://genesilico.pl/trnamodviz) for easy comparative analysis and visualization of modification patterns in individual tRNAs, as well as in groups of selected tRNA sequences. We also present results of comparative analysis of tRNA sequences derived from 7 phylogenetically distinct groups of organisms: Gram-negative bacteria, Gram-positive bacteria, cytosol of eukaryotic single cell organisms, Fungi and Metazoa, cytosol of Viridiplantae, mitochondria, plastids and Euryarchaeota. These data update the study conducted 20 y ago with the tRNA sequences available at that time.
DOI: 10.6084/m9.figshare.1292984
title: A decomposition strategy for the variational inference of complex systems

description: Markov chain Monte Carlo approaches have been widely used for Bayesian inference. The drawback of these methods is that they can be computationally prohibitive especially when complex models are analyzed. In such cases, variational methods may provide an efficient and attractive alternative. However, the variational methods reported to date are applicable to relatively simple models and most are based on a factorized approximation to the posterior distribution. Here, we propose a variational approach that is capable of handling models that consist of a system of differential-algebraic equations and whose posterior approximation can be represented by a multivariate distribution. Under the proposed approach, the solution of the variational inference problem is decomposed into three steps: a Maximum A Posteriori optimization which is facilitated by using an orthogonal collocation approach, a Preprocessing step which is based on the estimation of the eigenvectors of the posterior covariance matrix, and an Expected Propagation optimization problem. To tackle multivariate integration, we employ quadratures derived from the Smolyak rule (sparse grids). Examples are reported to elucidate the advantages and limitations of the proposed methodology. The results are compared to the solutions obtained from a Markov chain Monte Carlo approach. It is demonstrated that significant computational savings can be gained using the proposed approach. This paper has supplementary material online.
DOI: 10.6084/m9.figshare.1292989.v2
title: Spatial organization of the tenascin-C microenvironment in experimental and human cancer

description: The extracellular matrix (ECM) molecule tenascin-C (TNC) promotes tumor progression. This has recently been demonstrated in the stochastic murine RIP1-Tag2 insulinoma model, engineered to either express TNC abundantly or to be devoid of TNC. However, our knowledge about organization of the TNC microenvironment is scant. Here we determined the spatial distribution of TNC together with other ECM molecules in murine RIP1-Tag2 insulinoma and human cancer tissue (insulinoma and colorectal carcinoma). We found that TNC is organized in matrix tracks together with other ECM molecules of the AngioMatrix signature, a previously described gene expression profile that characterizes the angiogenic switch. Moreover, stromal cells including endothelial cells, fibroblasts and leukocytes were enriched in the TNC tracks. Thus, TNC tracks may provide niches for stromal cells and regulate their behavior. Given similarities of TNC rich niches for stromal cells in human insulinoma and colon cancer, we propose that the RIP1-Tag2 model may be useful for providing insights into the contribution of the tumor stroma specific ECM as promoter of cancer progression.
DOI: 10.6084/m9.figshare.1292984.v1
title: A decomposition strategy for the variational inference of complex systems

description: Markov chain Monte Carlo approaches have been widely used for Bayesian inference. The drawback of these methods is that they can be computationally prohibitive especially when complex models are analyzed. In such cases, variational methods may provide an efficient and attractive alternative. However, the variational methods reported to date are applicable to relatively simple models and most are based on a factorized approximation to the posterior distribution. Here, we propose a variational approach that is capable of handling models that consist of a system of differential-algebraic equations and whose posterior approximation can be represented by a multivariate distribution. Under the proposed approach, the solution of the variational inference problem is decomposed into three steps: a Maximum A Posteriori optimization which is facilitated by using an orthogonal collocation approach, a Preprocessing step which is based on the estimation of the eigenvectors of the posterior covariance matrix, and an Expected Propagation optimization problem. To tackle multivariate integration, we employ quadratures derived from the Smolyak rule (sparse grids). Examples are reported to elucidate the advantages and limitations of the proposed methodology. The results are compared to the solutions obtained from a Markov chain Monte Carlo approach. It is demonstrated that significant computational savings can be gained using the proposed approach. This paper has supplementary material online.