cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28046582
title: NMM_118567_CTTGTA_S2_L001_R1_001_Reads_per_uniqueLocation.bw

description: G-quadruplex (G4) structures can form in guanine-rich DNA or RNA and have been found to modulate cellular processes, including replication, transcription, and translation. Many studies on the cellular roles of G4s have focused on eukaryotic systems, with far fewer probing bacterial G4s. Using a chemical-genetic approach, we identified genes in <em>Escherichia coli</em> that are important for growth in G4-stabilizing conditions. Reducing levels of translation elongation factor Tu or slowing translation initiation or elongation with kasugamycin, chloramphenicol, or spectinomycin suppress the effects of G4-stabilizing compounds. In contrast, reducing the expression of specific translation termination or ribosome recycling proteins is detrimental to growth in G4- stabilizing conditions. Proteomic and transcriptomic analyses demonstrate that ribosome assembly factors and other proteins involved in translation are less abundant in G4-stabilizing conditions. Our results support a model in which reducing the rate of translation by altering translation initiation/elongation, or ribosome assembly can compensate for G4-related stress in E. coli.
DOI: 10.6084/m9.figshare.28046582.v1
title: NMM_118567_CTTGTA_S2_L001_R1_001_Reads_per_uniqueLocation.bw

description: G-quadruplex (G4) structures can form in guanine-rich DNA or RNA and have been found to modulate cellular processes, including replication, transcription, and translation. Many studies on the cellular roles of G4s have focused on eukaryotic systems, with far fewer probing bacterial G4s. Using a chemical-genetic approach, we identified genes in <em>Escherichia coli</em> that are important for growth in G4-stabilizing conditions. Reducing levels of translation elongation factor Tu or slowing translation initiation or elongation with kasugamycin, chloramphenicol, or spectinomycin suppress the effects of G4-stabilizing compounds. In contrast, reducing the expression of specific translation termination or ribosome recycling proteins is detrimental to growth in G4- stabilizing conditions. Proteomic and transcriptomic analyses demonstrate that ribosome assembly factors and other proteins involved in translation are less abundant in G4-stabilizing conditions. Our results support a model in which reducing the rate of translation by altering translation initiation/elongation, or ribosome assembly can compensate for G4-related stress in E. coli.
DOI: 10.6084/m9.figshare.28046585
title: 129924_GGCTAC_3_1x_NMM_R1_001_edited_Reads_per_uniqueLocation.bw

description: G-quadruplex (G4) structures can form in guanine-rich DNA or RNA and have been found to modulate cellular processes, including replication, transcription, and translation. Many studies on the cellular roles of G4s have focused on eukaryotic systems, with far fewer probing bacterial G4s. Using a chemical-genetic approach, we identified genes in <em>Escherichia coli</em> that are important for growth in G4-stabilizing conditions. Reducing levels of translation elongation factor Tu or slowing translation initiation or elongation with kasugamycin, chloramphenicol, or spectinomycin suppress the effects of G4-stabilizing compounds. In contrast, reducing the expression of specific translation termination or ribosome recycling proteins is detrimental to growth in G4- stabilizing conditions. Proteomic and transcriptomic analyses demonstrate that ribosome assembly factors and other proteins involved in translation are less abundant in G4-stabilizing conditions. Our results support a model in which reducing the rate of translation by altering translation initiation/elongation, or ribosome assembly can compensate for G4-related stress in E. coli.
DOI: 10.6084/m9.figshare.28046588
title: 129925_CTTGTA_3_2x_NMM_R1_001_edited_Reads_per_uniqueLocation.bw

description: G-quadruplex (G4) structures can form in guanine-rich DNA or RNA and have been found to modulate cellular processes, including replication, transcription, and translation. Many studies on the cellular roles of G4s have focused on eukaryotic systems, with far fewer probing bacterial G4s. Using a chemical-genetic approach, we identified genes in <em>Escherichia coli</em> that are important for growth in G4-stabilizing conditions. Reducing levels of translation elongation factor Tu or slowing translation initiation or elongation with kasugamycin, chloramphenicol, or spectinomycin suppress the effects of G4-stabilizing compounds. In contrast, reducing the expression of specific translation termination or ribosome recycling proteins is detrimental to growth in G4- stabilizing conditions. Proteomic and transcriptomic analyses demonstrate that ribosome assembly factors and other proteins involved in translation are less abundant in G4-stabilizing conditions. Our results support a model in which reducing the rate of translation by altering translation initiation/elongation, or ribosome assembly can compensate for G4-related stress in E. coli.
DOI: 10.6084/m9.figshare.28046540
title: Ctrl_118566_GCCAAT_S1_L001_R1_001_Reads_per_uniqueLocation.bw

description: G-quadruplex (G4) structures can form in guanine-rich DNA or RNA and have been found to modulate cellular processes, including replication, transcription, and translation. Many studies on the cellular roles of G4s have focused on eukaryotic systems, with far fewer probing bacterial G4s. Using a chemical-genetic approach, we identified genes in <em>Escherichia coli</em> that are important for growth in G4-stabilizing conditions. Reducing levels of translation elongation factor Tu or slowing translation initiation or elongation with kasugamycin, chloramphenicol, or spectinomycin suppress the effects of G4-stabilizing compounds. In contrast, reducing the expression of specific translation termination or ribosome recycling proteins is detrimental to growth in G4- stabilizing conditions. Proteomic and transcriptomic analyses demonstrate that ribosome assembly factors and other proteins involved in translation are less abundant in G4-stabilizing conditions. Our results support a model in which reducing the rate of translation by altering translation initiation/elongation, or ribosome assembly can compensate for G4-related stress in E. coli.
DOI: 10.6084/m9.figshare.28046540.v1
title: Ctrl_118566_GCCAAT_S1_L001_R1_001_Reads_per_uniqueLocation.bw

description: G-quadruplex (G4) structures can form in guanine-rich DNA or RNA and have been found to modulate cellular processes, including replication, transcription, and translation. Many studies on the cellular roles of G4s have focused on eukaryotic systems, with far fewer probing bacterial G4s. Using a chemical-genetic approach, we identified genes in <em>Escherichia coli</em> that are important for growth in G4-stabilizing conditions. Reducing levels of translation elongation factor Tu or slowing translation initiation or elongation with kasugamycin, chloramphenicol, or spectinomycin suppress the effects of G4-stabilizing compounds. In contrast, reducing the expression of specific translation termination or ribosome recycling proteins is detrimental to growth in G4- stabilizing conditions. Proteomic and transcriptomic analyses demonstrate that ribosome assembly factors and other proteins involved in translation are less abundant in G4-stabilizing conditions. Our results support a model in which reducing the rate of translation by altering translation initiation/elongation, or ribosome assembly can compensate for G4-related stress in E. coli.
DOI: 10.6084/m9.figshare.28046585.v1
title: 129924_GGCTAC_3_1x_NMM_R1_001_edited_Reads_per_uniqueLocation.bw

description: G-quadruplex (G4) structures can form in guanine-rich DNA or RNA and have been found to modulate cellular processes, including replication, transcription, and translation. Many studies on the cellular roles of G4s have focused on eukaryotic systems, with far fewer probing bacterial G4s. Using a chemical-genetic approach, we identified genes in <em>Escherichia coli</em> that are important for growth in G4-stabilizing conditions. Reducing levels of translation elongation factor Tu or slowing translation initiation or elongation with kasugamycin, chloramphenicol, or spectinomycin suppress the effects of G4-stabilizing compounds. In contrast, reducing the expression of specific translation termination or ribosome recycling proteins is detrimental to growth in G4- stabilizing conditions. Proteomic and transcriptomic analyses demonstrate that ribosome assembly factors and other proteins involved in translation are less abundant in G4-stabilizing conditions. Our results support a model in which reducing the rate of translation by altering translation initiation/elongation, or ribosome assembly can compensate for G4-related stress in E. coli.
DOI: 10.6084/m9.figshare.28046588.v1
title: 129925_CTTGTA_3_2x_NMM_R1_001_edited_Reads_per_uniqueLocation.bw

description: G-quadruplex (G4) structures can form in guanine-rich DNA or RNA and have been found to modulate cellular processes, including replication, transcription, and translation. Many studies on the cellular roles of G4s have focused on eukaryotic systems, with far fewer probing bacterial G4s. Using a chemical-genetic approach, we identified genes in <em>Escherichia coli</em> that are important for growth in G4-stabilizing conditions. Reducing levels of translation elongation factor Tu or slowing translation initiation or elongation with kasugamycin, chloramphenicol, or spectinomycin suppress the effects of G4-stabilizing compounds. In contrast, reducing the expression of specific translation termination or ribosome recycling proteins is detrimental to growth in G4- stabilizing conditions. Proteomic and transcriptomic analyses demonstrate that ribosome assembly factors and other proteins involved in translation are less abundant in G4-stabilizing conditions. Our results support a model in which reducing the rate of translation by altering translation initiation/elongation, or ribosome assembly can compensate for G4-related stress in E. coli.
DOI: 10.15468/dl.cwwk5d
title: Occurrence Download

description: A dataset containing 65038 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Passerina caerulea (Linnaeus, 1758)"
 ]
}

 The dataset includes 65038 records from 96 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037954-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.c.3624590_d12.v1
title: Additional file 16: of Genome analysis and avirulence gene cloning using a high-density RADseq linkage map of the flax rust fungus, Melampsora lini

description: Genotyping scores used to make the CH5 genetic map. Table showing the number of markers, number of genotyping scores of each type and number of missing/corrected genotyping scores for each linkage group in the CH5 genetic map. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.c.3624590_d14.v1
title: Additional file 6: of Genome analysis and avirulence gene cloning using a high-density RADseq linkage map of the flax rust fungus, Melampsora lini

description: Anchored scaffolds in the M. lini genome assembly. Table showing the numbers of markers, assignment to genetic linkage group and orientation information for each anchored scaffold in the M. lini genome assembly. (XLSX 255 kb)
DOI: 10.25378/janelia.24247297
title: Manual organelle segmentations (crop171) in near-isotropic, reconstructed volume electron microscopy (FIB-SEM) of mouse liver (jrc_mus-liver)

description: <b>This acquisition is part of the CellMap 2024 Segmentation Challenge</b><br><br>- <b>Challenge DOI:</b> https://doi.org/10.25378/janelia.c.7456966<br><br>- <b>Challenge Website:</b> https://cellmapchallenge.janelia.org/<br><br><b>Annotation description</b>: Dense segmentations of extracellular space, plasma membrane, vesicle membrane, endosome membrane, vesicle lumen, endoplasmic reticulum lumen, endoplasmic reticulum membrane, lysosome lumen, lysosome membrane, endosome lumen, cytosol, lysosome, endosome, vesicle, endoplasmic reticulum, cell, endoplasmic reticulum membrane collective in jrc_mus-liver using Amira 3D 2021.1, Classic Segmentation Workroom and the 'Using Amira to manually segment organelles in vEM for machine learning V.3' annotation protocol.<br><br><b>Annotation ID</b>: crop171<br><br><b>Primary Annotator</b>: Woohyun Park<br><br><b>Annotation protocol</b>: Using Amira to manually segment organelles in vEM for machine learning V.3 (http://dx.doi.org/10.17504/protocols.io.bp2l61rb5vqe/v3)<br><br><b>Software</b>: Amira 3D 2021.1, Classic Segmentation Workroom<br><br><b>Annotated voxel size (nm)</b>: 4 x 4 x 4 (x, y, z)<br><br><b>Annotated data dimensions (µm)</b>: 1.6 x 0.5 x 1.6 (x, y, z)<br><br><b>Annotated data offset (nm)</b>: 54246 x 52774 x 35750 (x, y, z)<br><br><b>Classes annotated</b>: extracellular space, plasma membrane, vesicle membrane, endosome membrane, vesicle lumen, endoplasmic reticulum lumen, endoplasmic reticulum membrane, lysosome lumen, lysosome membrane, endosome lumen, cytosol, lysosome, endosome, vesicle, endoplasmic reticulum, cell, endoplasmic reticulum membrane collective<br><br><b>Dataset URL</b>: s3://janelia-cosem-datasets/jrc_mus-liver/jrc_mus-liver.zarr/recon-1/labels/groundtruth/crop171<br><br><b>Source (EM) dataset ID</b>: jrc_mus-liver<br><br><b>Source (EM) voxel size (nm)</b>: 8 x 8 x 8 (x, y, z)<br><br><b>Source (EM) data dimensions (µm)</b>: 101.98 x 101.82 x 71.46 (x, y, z)<br><br><b>Source (EM) DOI</b>: https://doi.org/10.25378/janelia.16913047<br><br><b>Visualization website</b>: https://openorganelle.janelia.org/datasets/jrc_mus-liver<br><br><b>Publication:</b> <i>CellMap Segmentation Challenge,</i> 2024.<br><br><b>The CellMap Project Team during this time consisted of:</b> David Ackerman, Davis Bennett, Marley Bryant, Hannah Nguyen, Grace Park, Alyson Petruncio, Alannah Post, Jacquelyn Price, Diana Ramirez, Jeff Rhoades, Rebecca Vorimo, Aubrey Weigel, Marwan Zouinkhi, Yurii Zubov.<br><br><b>The CellMap Project Team Steering Committee during this time consisted of:</b> Misha Ahrens, Christopher Beck, Teng-Leong Chew, Daniel Feliciano, Jan Funke, Harald Hess, Wyatt Korff, Jennifer Lippincott-Schwartz, Zhe J. Liu, Kayvon Pedram, Stephan Preibisch, Stephan Saalfeld, Ronald Vale, and Aubrey Weigel.
DOI: 10.25378/janelia.24247297.v1
title: Manual organelle segmentations (crop171) in near-isotropic, reconstructed volume electron microscopy (FIB-SEM) of mouse liver (jrc_mus-liver)

description: <b>This acquisition is part of the CellMap 2024 Segmentation Challenge</b><br><br>- <b>Challenge DOI:</b> https://doi.org/10.25378/janelia.c.7456966<br><br>- <b>Challenge Website:</b> https://cellmapchallenge.janelia.org/<br><br><b>Annotation description</b>: Dense segmentations of extracellular space, plasma membrane, vesicle membrane, endosome membrane, vesicle lumen, endoplasmic reticulum lumen, endoplasmic reticulum membrane, lysosome lumen, lysosome membrane, endosome lumen, cytosol, lysosome, endosome, vesicle, endoplasmic reticulum, cell, endoplasmic reticulum membrane collective in jrc_mus-liver using Amira 3D 2021.1, Classic Segmentation Workroom and the 'Using Amira to manually segment organelles in vEM for machine learning V.3' annotation protocol.<br><br><b>Annotation ID</b>: crop171<br><br><b>Primary Annotator</b>: Woohyun Park<br><br><b>Annotation protocol</b>: Using Amira to manually segment organelles in vEM for machine learning V.3 (http://dx.doi.org/10.17504/protocols.io.bp2l61rb5vqe/v3)<br><br><b>Software</b>: Amira 3D 2021.1, Classic Segmentation Workroom<br><br><b>Annotated voxel size (nm)</b>: 4 x 4 x 4 (x, y, z)<br><br><b>Annotated data dimensions (µm)</b>: 1.6 x 0.5 x 1.6 (x, y, z)<br><br><b>Annotated data offset (nm)</b>: 54246 x 52774 x 35750 (x, y, z)<br><br><b>Classes annotated</b>: extracellular space, plasma membrane, vesicle membrane, endosome membrane, vesicle lumen, endoplasmic reticulum lumen, endoplasmic reticulum membrane, lysosome lumen, lysosome membrane, endosome lumen, cytosol, lysosome, endosome, vesicle, endoplasmic reticulum, cell, endoplasmic reticulum membrane collective<br><br><b>Dataset URL</b>: s3://janelia-cosem-datasets/jrc_mus-liver/jrc_mus-liver.zarr/recon-1/labels/groundtruth/crop171<br><br><b>Source (EM) dataset ID</b>: jrc_mus-liver<br><br><b>Source (EM) voxel size (nm)</b>: 8 x 8 x 8 (x, y, z)<br><br><b>Source (EM) data dimensions (µm)</b>: 101.98 x 101.82 x 71.46 (x, y, z)<br><br><b>Source (EM) DOI</b>: https://doi.org/10.25378/janelia.16913047<br><br><b>Visualization website</b>: https://openorganelle.janelia.org/datasets/jrc_mus-liver<br><br><b>Publication:</b> <i>CellMap Segmentation Challenge,</i> 2024.<br><br><b>The CellMap Project Team during this time consisted of:</b> David Ackerman, Davis Bennett, Marley Bryant, Hannah Nguyen, Grace Park, Alyson Petruncio, Alannah Post, Jacquelyn Price, Diana Ramirez, Jeff Rhoades, Rebecca Vorimo, Aubrey Weigel, Marwan Zouinkhi, Yurii Zubov.<br><br><b>The CellMap Project Team Steering Committee during this time consisted of:</b> Misha Ahrens, Christopher Beck, Teng-Leong Chew, Daniel Feliciano, Jan Funke, Harald Hess, Wyatt Korff, Jennifer Lippincott-Schwartz, Zhe J. Liu, Kayvon Pedram, Stephan Preibisch, Stephan Saalfeld, Ronald Vale, and Aubrey Weigel.
DOI: 10.6084/m9.figshare.c.3624932_d1.v1
title: Additional file 1 of A semi-automated, KNIME-based workflow for biofilm assays

description: Plate layout template for recording sample placement in multi-well plates and merging metadata with measurements in KNIME. See Fig. 1 for illustration. (XLSX 24.4 kb)
DOI: 10.25378/janelia.24247228
title: Manual organelle segmentations (crop157) in near-isotropic, reconstructed volume electron microscopy (FIB-SEM) of mouse liver (jrc_mus-liver)

description: <b>This acquisition is part of the CellMap 2024 Segmentation Challenge</b><br><br>- <b>Challenge DOI:</b> https://doi.org/10.25378/janelia.c.7456966<br><br>- <b>Challenge Website:</b> https://cellmapchallenge.janelia.org/<br><br><b>Annotation description</b>: Dense segmentations of extracellular space, plasma membrane, mitochondrial membrane, mitochondrial lumen, vesicle membrane, vesicle lumen, lysosome membrane, lysosome lumen, lysosome, endoplasmic reticulum lumen, endoplasmic reticulum membrane, cytosol, vesicle, mitochondria, endoplasmic reticulum, cell, endoplasmic reticulum membrane collective in jrc_mus-liver using Amira 3D 2021.1, Classic Segmentation Workroom and the 'Using Amira to manually segment organelles in vEM for machine learning V.3' annotation protocol.<br><b>Annotation ID</b>: crop157<br><br><b>Primary Annotator</b>: Woohyun Park<br><br><b>Annotation protocol</b>: Using Amira to manually segment organelles in vEM for machine learning V.3 (http://dx.doi.org/10.17504/protocols.io.bp2l61rb5vqe/v3)<br><br><b>Software</b>: Amira 3D 2021.1, Classic Segmentation Workroom<br><br><b>Annotated voxel size (nm)</b>: 4 x 4 x 4 (x, y, z)<br><br><b>Annotated data dimensions (µm)</b>: 0.8 x 0.8 x 0.8 (x, y, z)<br><br><b>Annotated data offset (nm)</b>: 23998 x 38198 x 69238 (x, y, z)<br><br><b>Classes annotated</b>: extracellular space, plasma membrane, mitochondrial membrane, mitochondrial lumen, vesicle membrane, vesicle lumen, lysosome membrane, lysosome lumen, lysosome, endoplasmic reticulum lumen, endoplasmic reticulum membrane, cytosol, vesicle, mitochondria, endoplasmic reticulum, cell, endoplasmic reticulum membrane collective<br><br><b>Dataset URL</b>: s3://janelia-cosem-datasets/jrc_mus-liver/jrc_mus-liver.zarr/recon-1/labels/groundtruth/crop157<br><br><b>Source (EM) dataset ID</b>: jrc_mus-liver<br><br><b>Source (EM) voxel size (nm)</b>: 8 x 8 x 8 (x, y, z)<br><br><b>Source (EM) data dimensions (µm)</b>: 101.98 x 101.82 x 71.46 (x, y, z)<br><br><b>Source (EM) DOI</b>: https://doi.org/10.25378/janelia.16913047<br><br><b>Visualization website</b>: https://openorganelle.janelia.org/datasets/jrc_mus-liver<br><br><b>Publication:</b> <i>CellMap Segmentation Challenge,</i> 2024.<br><br><b>The CellMap Project Team during this time consisted of:</b> David Ackerman, Davis Bennett, Marley Bryant, Hannah Nguyen, Grace Park, Alyson Petruncio, Alannah Post, Jacquelyn Price, Diana Ramirez, Jeff Rhoades, Rebecca Vorimo, Aubrey Weigel, Marwan Zouinkhi, Yurii Zubov.<br><br><b>The CellMap Project Team Steering Committee during this time consisted of:</b> Misha Ahrens, Christopher Beck, Teng-Leong Chew, Daniel Feliciano, Jan Funke, Harald Hess, Wyatt Korff, Jennifer Lippincott-Schwartz, Zhe J. Liu, Kayvon Pedram, Stephan Preibisch, Stephan Saalfeld, Ronald Vale, and Aubrey Weigel.
DOI: 10.25378/janelia.24247228.v1
title: Manual organelle segmentations (crop157) in near-isotropic, reconstructed volume electron microscopy (FIB-SEM) of mouse liver (jrc_mus-liver)

description: <b>This acquisition is part of the CellMap 2024 Segmentation Challenge</b><br><br>- <b>Challenge DOI:</b> https://doi.org/10.25378/janelia.c.7456966<br><br>- <b>Challenge Website:</b> https://cellmapchallenge.janelia.org/<br><br><b>Annotation description</b>: Dense segmentations of extracellular space, plasma membrane, mitochondrial membrane, mitochondrial lumen, vesicle membrane, vesicle lumen, lysosome membrane, lysosome lumen, lysosome, endoplasmic reticulum lumen, endoplasmic reticulum membrane, cytosol, vesicle, mitochondria, endoplasmic reticulum, cell, endoplasmic reticulum membrane collective in jrc_mus-liver using Amira 3D 2021.1, Classic Segmentation Workroom and the 'Using Amira to manually segment organelles in vEM for machine learning V.3' annotation protocol.<br><b>Annotation ID</b>: crop157<br><br><b>Primary Annotator</b>: Woohyun Park<br><br><b>Annotation protocol</b>: Using Amira to manually segment organelles in vEM for machine learning V.3 (http://dx.doi.org/10.17504/protocols.io.bp2l61rb5vqe/v3)<br><br><b>Software</b>: Amira 3D 2021.1, Classic Segmentation Workroom<br><br><b>Annotated voxel size (nm)</b>: 4 x 4 x 4 (x, y, z)<br><br><b>Annotated data dimensions (µm)</b>: 0.8 x 0.8 x 0.8 (x, y, z)<br><br><b>Annotated data offset (nm)</b>: 23998 x 38198 x 69238 (x, y, z)<br><br><b>Classes annotated</b>: extracellular space, plasma membrane, mitochondrial membrane, mitochondrial lumen, vesicle membrane, vesicle lumen, lysosome membrane, lysosome lumen, lysosome, endoplasmic reticulum lumen, endoplasmic reticulum membrane, cytosol, vesicle, mitochondria, endoplasmic reticulum, cell, endoplasmic reticulum membrane collective<br><br><b>Dataset URL</b>: s3://janelia-cosem-datasets/jrc_mus-liver/jrc_mus-liver.zarr/recon-1/labels/groundtruth/crop157<br><br><b>Source (EM) dataset ID</b>: jrc_mus-liver<br><br><b>Source (EM) voxel size (nm)</b>: 8 x 8 x 8 (x, y, z)<br><br><b>Source (EM) data dimensions (µm)</b>: 101.98 x 101.82 x 71.46 (x, y, z)<br><br><b>Source (EM) DOI</b>: https://doi.org/10.25378/janelia.16913047<br><br><b>Visualization website</b>: https://openorganelle.janelia.org/datasets/jrc_mus-liver<br><br><b>Publication:</b> <i>CellMap Segmentation Challenge,</i> 2024.<br><br><b>The CellMap Project Team during this time consisted of:</b> David Ackerman, Davis Bennett, Marley Bryant, Hannah Nguyen, Grace Park, Alyson Petruncio, Alannah Post, Jacquelyn Price, Diana Ramirez, Jeff Rhoades, Rebecca Vorimo, Aubrey Weigel, Marwan Zouinkhi, Yurii Zubov.<br><br><b>The CellMap Project Team Steering Committee during this time consisted of:</b> Misha Ahrens, Christopher Beck, Teng-Leong Chew, Daniel Feliciano, Jan Funke, Harald Hess, Wyatt Korff, Jennifer Lippincott-Schwartz, Zhe J. Liu, Kayvon Pedram, Stephan Preibisch, Stephan Saalfeld, Ronald Vale, and Aubrey Weigel.
DOI: 10.6084/m9.figshare.c.3624926_d2.v1
title: Additional file 2: of Biofeedback in the prophylactic treatment of medication overuse headache: a pilot randomized controlled trial

description: Statistical analysis on headache variables. (RTF 1850 kb)