cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7877477
title: gVCF_HG00266

description: 1000 Genomes gVCF mapped to hs37d5 for HG00266. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7877489.v1
title: gVCF_HG00267

description: 1000 Genomes gVCF mapped to hs37d5 for HG00267. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7877474
title: gVCF_HG00236

description: 1000 Genomes gVCF mapped to hs37d5 for HG00236. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7876442
title: gVCF_HG00265

description: 1000 Genomes gVCF mapped to hs37d5 for HG00265. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7877477.v1
title: gVCF_HG00266

description: 1000 Genomes gVCF mapped to hs37d5 for HG00266. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7876442.v1
title: gVCF_HG00265

description: 1000 Genomes gVCF mapped to hs37d5 for HG00265. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7874951
title: gVCF_HG00264

description: 1000 Genomes gVCF mapped to hs37d5 for HG00264. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7874954
title: gVCF_HG00309

description: 1000 Genomes gVCF mapped to hs37d5 for HG00309. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7875689
title: gVCF_HG00235

description: 1000 Genomes gVCF mapped to hs37d5 for HG00235. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7877474.v1
title: gVCF_HG00236

description: 1000 Genomes gVCF mapped to hs37d5 for HG00236. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7874840
title: gVCF_HG00234

description: 1000 Genomes gVCF mapped to hs37d5 for HG00234. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7875689.v1
title: gVCF_HG00235

description: 1000 Genomes gVCF mapped to hs37d5 for HG00235. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7874951.v1
title: gVCF_HG00264

description: 1000 Genomes gVCF mapped to hs37d5 for HG00264. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7874954.v1
title: gVCF_HG00309

description: 1000 Genomes gVCF mapped to hs37d5 for HG00309. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.6084/m9.figshare.7874717
title: Coleta de dados em Mosqueiro

description: As imagens são referentes a coleta de dados para subsidiar a elaboração de um relatorio para uma disciplina do curso de Engenharia de Pesca da turma de 2012.Nas imagens podemos ver matapis, redes de pesca, coleta de dados e até mesmo confecções e reparos em redes de pescas, bem como confecções de matapis.
DOI: 10.6084/m9.figshare.7874717.v1
title: Coleta de dados em Mosqueiro

description: As imagens são referentes a coleta de dados para subsidiar a elaboração de um relatorio para uma disciplina do curso de Engenharia de Pesca da turma de 2012.Nas imagens podemos ver matapis, redes de pesca, coleta de dados e até mesmo confecções e reparos em redes de pescas, bem como confecções de matapis.
DOI: 10.6084/m9.figshare.7874639
title: Product line selection of fast-moving consumer goods (instances)

description: Templates for testing the product line selection mathematical model. Files 0-19 correspond to the templates used in the sensitivity analysis. The remainder of the files was used to analyze the performance of the formulation. <br>The files should be used in conjunction with the paper. The parameters by line are:<br>TJLKMOphthetajtaujafrdcsk_ta0taumthetamx0dynk_ty0nk_t<br>Mnk_t<br>NmCNk
DOI: 10.6084/m9.figshare.7874426
title: Using Approximation Algorithms to Build Evidence Factors and Related Designs for Observational Studies

description: Observational or nonrandomized studies of treatment effects are often constructed with the aid of polynomial-time algorithms that optimally form matched treatment-control pairs or matched sets. Because each observational comparison may potentially be affected by bias, investigators often reinforce a single comparison with an additional comparison that is unlikely to be affected by the same biases, for instance using multiple control groups or evidence factors or control + instrument designs. Use of two comparisons affected by different biases may detect bias if the two comparisons disagree, or may show that two comparisons with different weakness concur in their conclusions. Even this simplest addition—a second comparison—creates design problems without polynomial-time solutions. Faced with a problem that no polynomial-time algorithm can solve, a so-called approximation algorithm is a type of compromise: it provides a solution in polynomial time that is provably not much worse than the unattainable optimal solution. Building upon existing techniques for related problems in operations research, we develop an approximation algorithm for minimum distance matching with near-fine balance for three comparison groups. This algorithm is a practical approach to most observational designs that add a second comparison. The method is applied to an observational study of the effects of side airbags on injury severity in the U.S. Fatality Analysis Reporting System. For many car makes and models, side airbags were initially unavailable, then later available as optional equipment for an additional fee, then still later provided as standard equipment. Within sets matched for make and model of car, for safety belt use, for direction of impact, and other covariates, we compare crashes in these three periods, where each comparison has different limitations. The method is implemented in the R package approxmatch, whose example reproduces some of the calculations. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7874426.v1
title: Using Approximation Algorithms to Build Evidence Factors and Related Designs for Observational Studies

description: Observational or nonrandomized studies of treatment effects are often constructed with the aid of polynomial-time algorithms that optimally form matched treatment-control pairs or matched sets. Because each observational comparison may potentially be affected by bias, investigators often reinforce a single comparison with an additional comparison that is unlikely to be affected by the same biases, for instance using multiple control groups or evidence factors or control + instrument designs. Use of two comparisons affected by different biases may detect bias if the two comparisons disagree, or may show that two comparisons with different weakness concur in their conclusions. Even this simplest addition—a second comparison—creates design problems without polynomial-time solutions. Faced with a problem that no polynomial-time algorithm can solve, a so-called approximation algorithm is a type of compromise: it provides a solution in polynomial time that is provably not much worse than the unattainable optimal solution. Building upon existing techniques for related problems in operations research, we develop an approximation algorithm for minimum distance matching with near-fine balance for three comparison groups. This algorithm is a practical approach to most observational designs that add a second comparison. The method is applied to an observational study of the effects of side airbags on injury severity in the U.S. Fatality Analysis Reporting System. For many car makes and models, side airbags were initially unavailable, then later available as optional equipment for an additional fee, then still later provided as standard equipment. Within sets matched for make and model of car, for safety belt use, for direction of impact, and other covariates, we compare crashes in these three periods, where each comparison has different limitations. The method is implemented in the R package approxmatch, whose example reproduces some of the calculations. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7874384
title: LDCmgm90 monthly geopotential model set with separate GIA model

description: This is an updated version of LDCmgm90, which has assimilated CSR RL06 mascon and GSFC SLR degree-2 Stokes coefficients besides all the data sets used in the previous version (see https://www.nature.com/articles/s41597-019-0239-7 for more details).In addition, The ICE-6G_C used for RL05 mascons, and ICE-6G_D used for RL06 mascons are added back because there are still somewhat large uncertainties in GIA modeling. Instead, we provide a corrected GIA model ICE6Gavg(GPS) as a separate data set (see the attached pdf for more details about this GIA model).While the GRACE (Gravity Recovery and Climate Experiment) satellite mission is of great significance in understanding various branches of Earth sciences, the quality of GRACE monthly products can be unsatisfactory due to strong longitudinal stripe-pattern errors and other flaws. Based on corrected GRACE Mascon (mass concentration) gridded mass transport time series and updated LDCgam (Least Differential Combination global angular momenta) data, we present a new set of monthly gravity models called LDCmgm90, in the form of Stokes coefficients with order and degree both up to 90. The LDCgam inputs are developed by assimilating degree-2 Stokes coefficients from various versions of GRACE (including Mascon products) and SLR (Satellite Laser Ranging) monthly gravity data into combinations of outputs from various global atmospheric, oceanic, and hydrological circulation models, under the constraints of accurately measured Earth orientation parameters in the Least Differential Combination (LDC) scheme. Taking advantages of the relative strengths of the various input solutions, the LDCmgm90 is free of stripes and some other flaws of classical GRACE products.
DOI: 10.6084/m9.figshare.7874384.v1
title: LDCmgm90 monthly geopotential model set

description: While the GRACE (Gravity Recovery and Climate Experiment) satellite mission is of great significance in understanding various branches of Earth sciences, the quality of GRACE monthly products can be unsatisfactory due to strong longitudinal stripe-pattern errors and other flaws. Based on corrected GRACE Mascon (mass concentration) gridded mass transport time series and updated LDCgam (Least Differential Combination global angular momenta) data, we present a new set of monthly gravity models called LDCmgm90, in the form of Stokes coefficients with order and degree both up to 90. The LDCgam inputs are developed by assimilating degree-2 Stokes coefficients from various versions of GRACE (including Mascon products) and SLR (Satellite Laser Ranging) monthly gravity data into combinations of outputs from various global atmospheric, oceanic, and hydrological circulation models, under the constraints of accurately measured Earth orientation parameters in the Least Differential Combination (LDC) scheme. Taking advantages of the relative strengths of the various input solutions, the LDCmgm90 is free of stripes and some other flaws of classical GRACE products.
DOI: 10.6084/m9.figshare.7874384.v2
title: LDCmgm90 monthly geopotential model set

description: This is an updated version of LDCmgm90, which has assimilated CSR RL06 mascon and GSFC SLR degree-2 Stokes coefficients besides all the data sets used in the previous version (see https://www.nature.com/articles/s41597-019-0239-7 for more details).In addition, The ICE-6G_C used for RL05 mascons, and ICE-6G_D used for RL06 mascons are added back because there are still somewhat large uncertainties in GIA modeling. Instead, we provide a corrected GIA model ICE6Gavg(GPS) as a separate data set (see the attached pdf for more details about this GIA model).While the GRACE (Gravity Recovery and Climate Experiment) satellite mission is of great significance in understanding various branches of Earth sciences, the quality of GRACE monthly products can be unsatisfactory due to strong longitudinal stripe-pattern errors and other flaws. Based on corrected GRACE Mascon (mass concentration) gridded mass transport time series and updated LDCgam (Least Differential Combination global angular momenta) data, we present a new set of monthly gravity models called LDCmgm90, in the form of Stokes coefficients with order and degree both up to 90. The LDCgam inputs are developed by assimilating degree-2 Stokes coefficients from various versions of GRACE (including Mascon products) and SLR (Satellite Laser Ranging) monthly gravity data into combinations of outputs from various global atmospheric, oceanic, and hydrological circulation models, under the constraints of accurately measured Earth orientation parameters in the Least Differential Combination (LDC) scheme. Taking advantages of the relative strengths of the various input solutions, the LDCmgm90 is free of stripes and some other flaws of classical GRACE products.
DOI: 10.6084/m9.figshare.7874384.v3
title: LDCmgm90 monthly geopotential model set with separate GIA model

description: This is an updated version of LDCmgm90, which has assimilated CSR RL06 mascon and GSFC SLR degree-2 Stokes coefficients besides all the data sets used in the previous version (see https://www.nature.com/articles/s41597-019-0239-7 for more details).In addition, The ICE-6G_C used for RL05 mascons, and ICE-6G_D used for RL06 mascons are added back because there are still somewhat large uncertainties in GIA modeling. Instead, we provide a corrected GIA model ICE6Gavg(GPS) as a separate data set (see the attached pdf for more details about this GIA model).While the GRACE (Gravity Recovery and Climate Experiment) satellite mission is of great significance in understanding various branches of Earth sciences, the quality of GRACE monthly products can be unsatisfactory due to strong longitudinal stripe-pattern errors and other flaws. Based on corrected GRACE Mascon (mass concentration) gridded mass transport time series and updated LDCgam (Least Differential Combination global angular momenta) data, we present a new set of monthly gravity models called LDCmgm90, in the form of Stokes coefficients with order and degree both up to 90. The LDCgam inputs are developed by assimilating degree-2 Stokes coefficients from various versions of GRACE (including Mascon products) and SLR (Satellite Laser Ranging) monthly gravity data into combinations of outputs from various global atmospheric, oceanic, and hydrological circulation models, under the constraints of accurately measured Earth orientation parameters in the Least Differential Combination (LDC) scheme. Taking advantages of the relative strengths of the various input solutions, the LDCmgm90 is free of stripes and some other flaws of classical GRACE products.
DOI: 10.6084/m9.figshare.7874177.v1
title: Smoothing with Couplings of Conditional Particle Filters

description: In state space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and confidence intervals can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique for Markov chains, with a Markov chain Monte Carlo algorithm for smoothing. The resulting procedure is widely applicable and we show in numerical experiments that the removal of the bias comes at a manageable increase in variance. We establish the validity of the proposed estimators under mild assumptions. Numerical experiments are provided on toy models, including a setting of highly-informative observations, and for a realistic Lotka-Volterra model with an intractable transition density.
DOI: 10.6084/m9.figshare.7874162.v1
title: A Bayesian Approach to Multi-State Hidden Markov Models: Application to Dementia Progression

description: People are living longer than ever before, and with this arises new complications and challenges for humanity. Among the most pressing of these challenges is of understanding the role of aging in the development of dementia. This paper is motivated by the Mayo Clinic Study of Aging data for 4742 subjects since 2004, and how it can be used to draw inference on the role of aging in the development of dementia. We construct a hidden Markov model (HMM) to represent progression of dementia from states associated with the buildup of amyloid plaque in the brain, and the loss of cortical thickness. A hierarchical Bayesian approach is taken to estimate the parameters of the HMM with a truly time-inhomogeneous infinitesimal generator matrix, and response functions of the continuous-valued biomarker measurements are cut-point agnostic. A Bayesian approach with these features could be useful in many disease progression models. Additionally, an approach is illustrated for correcting a common bias in delayed enrollment studies, in which some or all subjects are not observed at baseline. Standard software is incapable of accounting for this critical feature, so code to perform the estimation of the model described below is made available online.
DOI: 10.6084/m9.figshare.7874162.v2
title: A Bayesian Approach to Multistate Hidden Markov Models: Application to Dementia Progression

description: People are living longer than ever before, and with this arises new complications and challenges for humanity. Among the most pressing of these challenges is of understanding the role of aging in the development of dementia. This article is motivated by the Mayo Clinic Study of Aging data for 4742 subjects since 2004, and how it can be used to draw inference on the role of aging in the development of dementia. We construct a hidden Markov model (HMM) to represent progression of dementia from states associated with the buildup of amyloid plaque in the brain, and the loss of cortical thickness. A hierarchical Bayesian approach is taken to estimate the parameters of the HMM with a truly time-inhomogeneous infinitesimal generator matrix, and response functions of the continuous-valued biomarker measurements are cut-point agnostic. A Bayesian approach with these features could be useful in many disease progression models. Additionally, an approach is illustrated for correcting a common bias in delayed enrollment studies, in which some or all subjects are not observed at baseline. Standard software is incapable of accounting for this critical feature, so code to perform the estimation of the model described below is made available online. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.
DOI: 10.6084/m9.figshare.7874048
title: Connecting the dots in the mechanism of action of Cucurbitacin E (CurE) – path analysis and steered molecular dynamics reveal the precise site of entry and the passage of CurE in filamentous actin

description: Cucurbitacin E (CurE) modulates actin cytoskeleton by forming an irreversible covalent bond with Cys257 of actin. The reported binding conformation of CurE is deeply buried in the subdomain 4 of actin and is closely situated to the ATP-binding site. The entry and the path taken by CurE to reach this buried site remain a mystery. In this study, steered molecular dynamics (SMD) simulations were conducted to delineate the diffusion of CurE to its binding site. SMD simulations reveal that the distinctive entry site of CurE found in subdomain 4 is by itself a closed and compact region of two loops lying beside each other like a closed door and CurE induces it to open. From this point, CurE moves toward its binding site through a path facilitated by Thr188, Leu261, Ile267, Ile309, Tyr306. This study is also an insight into how CurE distinctly differentiates its tunnel to the binding site from the ATP-binding site. The conformational changes of CurE along the path to the binding site are surprisingly very minimal and closely resemble the attack conformation at the end of simulation. The study reveals that the little energy spent by the molecule is compensated by the enthalpic contribution to binding-free energy barrier making it undoubtedly the most preferred path of CurE. This study is the first of its kind in which the SMD was used to derive the complete and continuous translocation of one of the most potent phytochemicals, CurE through the binding site gorge of actin. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.7874048.v3
title: Connecting the dots in the mechanism of action of Cucurbitacin E (CurE) – path analysis and steered molecular dynamics reveal the precise site of entry and the passage of CurE in filamentous actin

description: Cucurbitacin E (CurE) modulates actin cytoskeleton by forming an irreversible covalent bond with Cys257 of actin. The reported binding conformation of CurE is deeply buried in the subdomain 4 of actin and is closely situated to the ATP-binding site. The entry and the path taken by CurE to reach this buried site remain a mystery. In this study, steered molecular dynamics (SMD) simulations were conducted to delineate the diffusion of CurE to its binding site. SMD simulations reveal that the distinctive entry site of CurE found in subdomain 4 is by itself a closed and compact region of two loops lying beside each other like a closed door and CurE induces it to open. From this point, CurE moves toward its binding site through a path facilitated by Thr188, Leu261, Ile267, Ile309, Tyr306. This study is also an insight into how CurE distinctly differentiates its tunnel to the binding site from the ATP-binding site. The conformational changes of CurE along the path to the binding site are surprisingly very minimal and closely resemble the attack conformation at the end of simulation. The study reveals that the little energy spent by the molecule is compensated by the enthalpic contribution to binding-free energy barrier making it undoubtedly the most preferred path of CurE. This study is the first of its kind in which the SMD was used to derive the complete and continuous translocation of one of the most potent phytochemicals, CurE through the binding site gorge of actin. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.7874048.v2
title: Connecting the dots in the mechanism of action of Cucurbitacin E (CurE) – path analysis and steered molecular dynamics reveal the precise site of entry and the passage of CurE in filamentous actin

description: Cucurbitacin E (CurE) modulates actin cytoskeleton by forming an irreversible covalent bond with Cys257 of actin. The reported binding conformation of CurE is deeply buried in the subdomain 4 of actin and is closely situated to the ATP-binding site. The entry and the path taken by CurE to reach this buried site remain a mystery. In this study, steered molecular dynamics (SMD) simulations were conducted to delineate the diffusion of CurE to its binding site. SMD simulations reveal that the distinctive entry site of CurE found in subdomain 4 is by itself a closed and compact region of two loops lying beside each other like a closed door and CurE induces it to open. From this point, CurE moves toward its binding site through a path facilitated by Thr188, Leu261, Ile267, Ile309, Tyr306. This study is also an insight into how CurE distinctly differentiates its tunnel to the binding site from the ATP-binding site. The conformational changes of CurE along the path to the binding site are surprisingly very minimal and closely resemble the attack conformation at the end of simulation. The study reveals that the little energy spent by the molecule is compensated by the enthalpic contribution to binding-free energy barrier making it undoubtedly the most preferred path of CurE. This study is the first of its kind in which the SMD was used to derive the complete and continuous translocation of one of the most potent phytochemicals, CurE through the binding site gorge of actin. Communicated by Ramaswamy H. Sarma