cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.22002/qpdve-eef26
title: Legionella_delta-lspHIJK

description: Legionella pneumophila Philadelphia-1_Lp02. Lacks all the T2SS minor pseudopilins.
description: <strong>Tilt Series Date:</strong> 2019-05-16</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Legionella pneumophila</p>
<strong>Strain:</strong> Philadelphia-1_Lp02</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-45.0°, 45.0°), step: 3.0°, constant angular increment, dosage: 100.0 eV/Å²,  defocus: -7.0 μm,  magnification: 42000x. </p>
<strong>Microscope:</strong> Janelia Titan</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain sent by Nicholas Cianciotto, Northwestern University. Data collected and processed by Debnath Ghosal.</p>
<strong>Sample Preparation:</strong> 6 grids were frozen for this strain (2x concentrated culture); Vitrobot (MK iv) settings: 100% humidity, 4µl sample, Blot force 6, blot time 4.5 sec. Wait time 0 sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-05-16-39/rawdata/</p>DG7_20190516_tomo0065.mrc,  Tilt Series (Pixel Size 0.26 nm), 882.8 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-05-16-39/rawdata/DG7_20190516_tomo0065.mrc"
        > <i class="download icon"></i> Download </a></p>

        DG7_20190516_tomo0065_part31_7.rec,  Reconstruction (Pixel Size 1.04 nm), 445.4 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-05-16-39/3dimage_102698/DG7_20190516_tomo0065_part31_7.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/he1b2-ch172
title: Pseudomonas aeruinosa

description: This is from the transposon mutant library of Pseudomonas aeruginosa PAO1. The aim is to check for the presence of PL sub complexes in the absence of FliF protein.
description: <strong>Tilt Series Date:</strong> 2018-10-01</p>
<strong>Data Taken By:</strong> Mohammed Kaplan</p>
<strong>Species / Specimen:</strong> Pseudomonas aeruginosa</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 2°, constant angular increment, dosage: 150.0 eV/Å²,  defocus: -4.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> SerialEM</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Serial EM</p>
<strong>Collaborators and Roles:</strong> I obtained these cells from Dianne Newmann lab (Shannon Park)</p>
<strong>Purification / Growth Conditions / Treatment:</strong> The cells were grown overnight at 37 C in LB with 100 ug/ml gentamicin</p>
<strong>Sample Preparation:</strong> Samples from the overnight culture were used to prepare the grids.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mka2018-10-01-5/rawdata/</p>Test_Pseudomonas_dfliF_13.mrc,  Tilt Series (Pixel Size 0.37 nm), 1.7 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mka2018-10-01-5/rawdata/Test_Pseudomonas_dfliF_13.mrc"
        > <i class="download icon"></i> Download </a></p>

        Test_Pseudomonas_dfliF_13_part61_3.rec,  Reconstruction (Pixel Size 1.48 nm), 444.5 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mka2018-10-01-5/3dimage_97283/Test_Pseudomonas_dfliF_13_part61_3.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/0qwhw-8qa26
title: Legionella_delta-lspGHIJK

description: Legionella pneumophila Philadelphia-1_Lp02. Lacks all the T2SS major and minor pseudopilins.
description: <strong>Tilt Series Date:</strong> 2019-05-18</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Legionella pneumophila</p>
<strong>Strain:</strong> Philadelphia-1_Lp02</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-45.0°, 45.0°), step: 3.0°, constant angular increment, dosage: 100.0 eV/Å²,  defocus: -7.0 μm,  magnification: 42000x. </p>
<strong>Microscope:</strong> Janelia Titan</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain sent by Nicholas Cianciotto, Northwestern University. Data collected and processed by Debnath Ghosal.</p>
<strong>Sample Preparation:</strong> 6 grids were frozen for this strain (2x concentrated culture, OD~4.8); Vitrobot (MK iv) settings: 100% humidity, 4µl sample, Blot force 6, blot time 4.5 sec. Wait time 0 sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-05-18-108/rawdata/</p>DG8_201905180108.mrc,  Tilt Series (Pixel Size 0.26 nm), 882.8 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-05-18-108/rawdata/DG8_201905180108.mrc"
        > <i class="download icon"></i> Download </a></p>

        DG8_201905180108_full.rec,  Reconstruction (Pixel Size 1.04 nm), 445.4 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-05-18-108/3dimage_103107/DG8_201905180108_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.6084/m9.figshare.29473157
title: Tree-Enhanced Latent Space Models for Two-Mode Networks

description: Latent space models have garnered significant attention in the analysis of two-mode networks. In numerous applications, auxiliary information in the form of a hierarchical tree structure, which elucidates the interrelationships between nodes and provides extensive insights into connectivity patterns, can be easily obtained. To harness the potential of such tree-structured information, we introduce an innovative tree-enhanced latent space model (TLSM) for two-mode networks. In this framework, each node is characterized by a latent embedding vector, reparameterized as the aggregate of intermediate vectors corresponding to nodes within the tree structure. By optimizing the log-likelihood function augmented with a tree-based regularization term, the proposed model facilitates the simultaneous estimation of embedding vectors and the derivation of interpretable community structures. We have developed an efficient Alternating Direction Method of Multipliers (ADMM) algorithm to solve the resulting optimization problem. Theoretical analysis establishes the consistency of the proposed estimator under some mild conditions. Furthermore, comprehensive simulation studies and empirical applications on the Amazon review dataset substantiate the efficacy and practical relevance of the proposed model. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.29473157.v1
title: Tree-Enhanced Latent Space Models for Two-Mode Networks

description: Latent space models have garnered significant attention in the analysis of two-mode networks. In numerous applications, auxiliary information in the form of a hierarchical tree structure, which elucidates the interrelationships between nodes and provides extensive insights into connectivity patterns, can be easily obtained. To harness the potential of such tree-structured information, we introduce an innovative tree-enhanced latent space model (TLSM) for two-mode networks. In this framework, each node is characterized by a latent embedding vector, reparameterized as the aggregate of intermediate vectors corresponding to nodes within the tree structure. By optimizing the log-likelihood function augmented with a tree-based regularization term, the proposed model facilitates the simultaneous estimation of embedding vectors and the derivation of interpretable community structures. We have developed an efficient Alternating Direction Method of Multipliers (ADMM) algorithm to solve the resulting optimization problem. Theoretical analysis establishes the consistency of the proposed estimator under some mild conditions. Furthermore, comprehensive simulation studies and empirical applications on the Amazon review dataset substantiate the efficacy and practical relevance of the proposed model. Supplementary materials for this article are available online.
DOI: 10.15468/dl.jteqer
title: Occurrence Download

description: A dataset containing 263548 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Glyceria maxima"
 ]
}

 The dataset includes 263548 records from 774 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007766-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22002/h7skb-8z267
title: Legionella_delta-lspGHIJK

description: Legionella pneumophila Philadelphia-1_Lp02. Lacks all the T2SS major and minor pseudopilins.
description: <strong>Tilt Series Date:</strong> 2019-05-18</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Legionella pneumophila</p>
<strong>Strain:</strong> Philadelphia-1_Lp02</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-45.0°, 45.0°), step: 3.0°, constant angular increment, dosage: 100.0 eV/Å²,  defocus: -7.0 μm,  magnification: 42000x. </p>
<strong>Microscope:</strong> Janelia Titan</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain sent by Nicholas Cianciotto, Northwestern University. Data collected and processed by Debnath Ghosal.</p>
<strong>Sample Preparation:</strong> 6 grids were frozen for this strain (2x concentrated culture, OD~4.8); Vitrobot (MK iv) settings: 100% humidity, 4µl sample, Blot force 6, blot time 4.5 sec. Wait time 0 sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-05-18-41/rawdata/</p>DG8_201905180020.mrc,  Tilt Series (Pixel Size 0.26 nm), 882.8 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-05-18-41/rawdata/DG8_201905180020.mrc"
        > <i class="download icon"></i> Download </a></p>

        DG8_201905180020_full.rec,  Reconstruction (Pixel Size 1.04 nm), 445.4 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-05-18-41/3dimage_102973/DG8_201905180020_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/p4mqg-d9550
title: JV9082

description: DotF-GFP, defocus range -3 to 6
description: <strong>Tilt Series Date:</strong> 2019-12-05</p>
<strong>Data Taken By:</strong> Yuxi Liu</p>
<strong>Species / Specimen:</strong> Legionella pneumophila</p>
<strong>Strain:</strong> Philadelphia-1_Lp02</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-54.0°, 54.0°), step: 3°, constant angular increment, dosage: 100.0 eV/Å²,  defocus: -5.0 μm,  magnification: 33000x. </p>
<strong>Microscope:</strong> Janelia Titan</p>
<strong>Acquisition Software:</strong> Serial EM</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain from Joe Vogel lab</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Late exponential phase, early stationary phase</p>
<strong>Sample Preparation:</strong> 10nm gold, Quantifoil R2/2 copper mesh 200 Finder grid, Vitrobot 22 deg C, 100% humidity, blot . time 3 sec, blot force 6, drain 1 sec, wait 0 sec.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ylx2019-12-05-270/rawdata/</p>9082_box2_No3_75.mrc,  Tilt Series (Pixel Size 0.2612 nm), 1.7 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ylx2019-12-05-270/rawdata/9082_box2_No3_75.mrc"
        > <i class="download icon"></i> Download </a></p>

        9082_box2_No3_75_full.rec,  Reconstruction (Pixel Size 1.0448 nm), 516.1 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ylx2019-12-05-270/3dimage_110611/9082_box2_No3_75_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.6084/m9.figshare.29473281
title: Non-Conjugate Variational Bayes for Pseudo-Likelihood Mixed Effect Models

description: We propose a unified, yet simple to code, non-conjugate variational Bayes algorithm for posterior approximation of generic Bayesian generalized mixed effect models. Specifically, we consider regression models identified by a linear predictor, eventually transformed using a bijective link, where the prediction misfit is measured using, possibly non-differentiable, loss functions. Examples include generalized linear models, quasi-likelihood models, and robust regression. To address the limitations of non-conjugate settings, we employ an efficient message passing optimization strategy under a Gaussian variational approximation of the posterior. The resulting algorithms automatically account for non-conjugate priors and non-smooth losses, without requiring model-specific data augmented representations. Besides the general formulation, we provide closed-form updates for popular model specifications, including quantile regression and support vector machines. Overall, theoretical and empirical results highlight the effectiveness of the proposed method, demonstrating its computational efficiency and approximation accuracy as an alternative to existing Bayesian techniques. Supplementary materials for this article are available online.
DOI: 10.22002/69mjb-kf321
title: bio12-2_lam4_ts1

description: .  [added 2019-10-11 16:53:00] IT 0.8s
description: <strong>Tilt Series Date:</strong> 2019-09-08</p>
<strong>Data Taken By:</strong> William Nicolas</p>
<strong>Species / Specimen:</strong> Gluconacetobacter hansenii</p>
<strong>Strain:</strong> ATCC 23769</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-50.0°, 50.0°), step: °, constant angular increment, dosage: 88.0 eV/Å²,  defocus: -8.0 μm,  magnification: 19500x. </p>
<strong>Microscope:</strong> Caltech Titan Krios</p>
<strong>Acquisition Software:</strong> Undefined</p>
<strong>Upload Method:</strong> webload</p>
<strong>Processing Software Used:</strong> IMOD</p>
<strong>Purification / Growth Conditions / Treatment:</strong> I inoculated 10 petri dishes filled with 1mL of SH with a biofilm from 12.02.19. 5 grids will be incubated 3 hours at 30C, static conditions</p>
<strong>Sample Preparation:</strong> Quantifoil Au R2/2 NH2 200 glowdischarged 1min - 15mA. Coated with Fibronectin BT5s BF15 DT2s (frontblot only) - Nice biofilm that peeled off nicely. FIB milled grid</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/wnj2019-09-08-9/rawdata/</p>bio12-2_lam4_ts1.st,  Tilt Series (Pixel Size 0.48 nm), 1.6 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/wnj2019-09-08-9/rawdata/bio12-2_lam4_ts1.st"
        > <i class="download icon"></i> Download </a></p>

        bio12-2_lam4_ts1.rec,  Reconstruction (Pixel Size 1.8 nm), 202.0 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/wnj2019-09-08-9/3dimage_106820/bio12-2_lam4_ts1.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.6084/m9.figshare.29473281.v2
title: Non-Conjugate Variational Bayes for Pseudo-Likelihood Mixed Effect Models

description: We propose a unified, yet simple to code, non-conjugate variational Bayes algorithm for posterior approximation of generic Bayesian generalized mixed effect models. Specifically, we consider regression models identified by a linear predictor, eventually transformed using a bijective link, where the prediction misfit is measured using, possibly non-differentiable, loss functions. Examples include generalized linear models, quasi-likelihood models, and robust regression. To address the limitations of non-conjugate settings, we employ an efficient message passing optimization strategy under a Gaussian variational approximation of the posterior. The resulting algorithms automatically account for non-conjugate priors and non-smooth losses, without requiring model-specific data augmented representations. Besides the general formulation, we provide closed-form updates for popular model specifications, including quantile regression and support vector machines. Overall, theoretical and empirical results highlight the effectiveness of the proposed method, demonstrating its computational efficiency and approximation accuracy as an alternative to existing Bayesian techniques. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.29473281.v1
title: Non-conjugate variational Bayes for pseudo-likelihood mixed effect models

description: We propose a unified, yet simple to code, non-conjugate variational Bayes algorithm for posterior approximation of generic Bayesian generalized mixed effect models. Specifically, we consider regression models identified by a linear predictor, eventually transformed using a bijective link, where the prediction misfit is measured using, possibly non-differentiable, loss functions. Examples include generalized linear models, quasi-likelihood models, and robust regression. To address the limitations of non-conjugate settings, we employ an efficient message passing optimization strategy under a Gaussian variational approximation of the posterior. The resulting algorithms automatically account for non-conjugate priors and non-smooth losses, without requiring model-specific data-augmented representations. Besides the general formulation, we provide closed-form updates for popular model specifications, including quantile regression and support vector machines. Overall, theoretical and empirical results highlight the effectiveness of the proposed method, demonstrating its computational efficiency and approximation accuracy as an alternative to existing Bayesian techniques.
DOI: 10.22002/x7btw-qw102
title: JV9298

description: Legionella pneumophila JV9298 DotF-GFP DotB E191K
description: <strong>Tilt Series Date:</strong> 2020-04-23</p>
<strong>Data Taken By:</strong> Yuxi Liu</p>
<strong>Species / Specimen:</strong> Legionella pneumophila</p>
<strong>Strain:</strong> Philadelphia-1_Lp02</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-54.0°, 54.0°), step: 3°, constant angular increment, dosage: 100.0 eV/Å²,  defocus: -4.0 μm,  magnification: 33000x. </p>
<strong>Acquisition Software:</strong> Serial EM</p>
<strong>Upload Method:</strong> rundir</p>
<strong>Processing Software Used:</strong> imod, tomo3d</p>
<strong>Collaborators and Roles:</strong> Sample preparation & data collection: Yuxi Liu Data processing: Phyllis Wang & Yuxi Liu</p>
<strong>Purification / Growth Conditions / Treatment:</strong> O.D. 2.5-3.0 in AYE</p>
<strong>Sample Preparation:</strong> Quantifoil R2/2 copper grid, 10nm gold, 100% humidity, 25deg C, 4uL of 2X concentrated cells Blotting: force 6, blot 3sec, wait 1sec</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ylx2020-04-23-170/3dimage_124018/</p>JV9298_PD-1-3_012_SIRT_1k.mrc,  Reconstruction (Pixel Size 1.04 nm), 2.1 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ylx2020-04-23-170/3dimage_124018/JV9298_PD-1-3_012_SIRT_1k.mrc"
        > <i class="download icon"></i> Download </a></p>

        JV9298_PD-1-3_012.mrc,  Tilt Series (Pixel Size 0.26 nm), 1.7 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ylx2020-04-23-170/rawdata/JV9298_PD-1-3_012.mrc"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/v4wam-bdr14
title: Legionella IcmQ71::TevS 42C_On

description: Legionella IcmQ71::TevS 42C_On (HS004 E)
description: <strong>Tilt Series Date:</strong> 2019-05-31</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Legionella pneumophila</p>
<strong>Strain:</strong> Philadelphia-1_Lp02</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-51.0°, 51.0°), step: 3.0°, constant angular increment, dosage: 100.0 eV/Å²,  defocus: -6.0 μm,  magnification: 42000x. </p>
<strong>Microscope:</strong> Janelia Titan</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain sent to us by Howard Shuman (University of Chicago). Data collected by Debnath Ghosal and Yuxi Liu.</p>
<strong>Sample Preparation:</strong> Vitrobot (MK iv) settings: 100% humidity, 4µl sample, Blot force 6, blot time 3 sec. Wait time 0 sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200. Add gold to cells prior to freezing: 1ul of gold to 4 ul of cells, freeze.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-05-31-98/rawdata/</p>HS004E-area9-tomo0021.mrc,  Tilt Series (Pixel Size 0.26 nm), 996.7 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-05-31-98/rawdata/HS004E-area9-tomo0021.mrc"
        > <i class="download icon"></i> Download </a></p>

        HS004E-area9-tomo0021_full.rec,  Reconstruction (Pixel Size 1.04 nm), 445.4 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-05-31-98/3dimage_103803/HS004E-area9-tomo0021_full.rec"
        > <i class="download icon"></i> Download </a></p>