cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21192133
title: Additional file 5 of Comparative profiling of single-cell transcriptome reveals heterogeneity of tumor microenvironment between solid and acinar lung adenocarcinoma

description: Additional file 5: Table S5. Data of myeloid cells analysis. (1) Gene list of signatures for macrophages; (2) Differentially expressed genes between macrophages from solid and acinar samples; (3) Gene list of signatures for DCs; (4) Differentially expressed genes between mregDCs from solid and acinar samples.
DOI: 10.6084/m9.figshare.21192124
title: Additional file 3 of Comparative profiling of single-cell transcriptome reveals heterogeneity of tumor microenvironment between solid and acinar lung adenocarcinoma

description: Additional file 3: Table S3. Markers used for differing between primary immune cell types.
DOI: 10.6084/m9.figshare.21192136
title: Additional file 6 of Comparative profiling of single-cell transcriptome reveals heterogeneity of tumor microenvironment between solid and acinar lung adenocarcinoma

description: Additional file 6: Table S6. Gene list of signatures for fibroblasts.
DOI: 10.6084/m9.figshare.11769261
title: Additional file 4 of Dynamic transcriptome profiling exploring cold tolerance in forensically important blow fly, Aldrichina grahami (Diptera: Calliphoridae)

description: Additional file 4: Table S2. Expression level of the 9 DEGs in Aldrichina grahami.
DOI: 10.6084/m9.figshare.11769261.v1
title: Additional file 4 of Dynamic transcriptome profiling exploring cold tolerance in forensically important blow fly, Aldrichina grahami (Diptera: Calliphoridae)

description: Additional file 4: Table S2. Expression level of the 9 DEGs in Aldrichina grahami.
DOI: 10.6084/m9.figshare.21192135.v1
title: Additional file 6 of Genome-wide identification and expression analysis of the calmodulin-binding transcription activator (CAMTA) family genes in tea plant

description: Additional file 6: Table S4. The synteny analysis results of CsCAMTAs within ‘ShuChaZao’ genome.
DOI: 10.6084/m9.figshare.21192129
title: Additional file 5 of Genome-wide identification and expression analysis of the calmodulin-binding transcription activator (CAMTA) family genes in tea plant

description: Additional file 5:Table S3. The chromosomal distribution positions in chromosomes of three tea plant cultivars.
DOI: 10.6084/m9.figshare.21192298.v1
title: Additional file 2 of PACS-2 deficiency in tubular cells aggravates lipid-related kidney injury in diabetic kidney disease

description: Additional file 2: Table S1. The sequences of primer pairs.
DOI: 10.6084/m9.figshare.13333852
title: Forward Stepwise Deep Autoencoder-Based Monotone Nonlinear Dimensionality Reduction Methods

description: Dimensionality reduction is an unsupervised learning task aimed at creating a low-dimensional summary and/or extracting the most salient features of a dataset. Principal component analysis is a linear dimensionality reduction method in the sense that each principal component is a linear combination of the input variables. To allow features that are nonlinear functions of the input variables, many nonlinear dimensionality reduction (NLDR) methods have been proposed. In this article, we propose novel NLDR methods based on bottleneck deep autoencoders. Our contributions are 2-fold: (1) We introduce a monotonicity constraint into bottleneck deep autoencoders for estimating a single nonlinear component and propose two methods for fitting the model. (2) We propose a new, forward stepwise deep learning architecture for estimating multiple nonlinear components. The former helps extract interpretable, monotone components when the assumption of monotonicity holds, and the latter helps evaluate reconstruction errors in the original data space for a range of components. We conduct numerical studies to compare different model fitting methods and use two real data examples from the studies of human immune responses to HIV to illustrate the proposed methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13333852.v3
title: Forward Stepwise Deep Autoencoder-Based Monotone Nonlinear Dimensionality Reduction Methods

description: Dimensionality reduction is an unsupervised learning task aimed at creating a low-dimensional summary and/or extracting the most salient features of a dataset. Principal component analysis is a linear dimensionality reduction method in the sense that each principal component is a linear combination of the input variables. To allow features that are nonlinear functions of the input variables, many nonlinear dimensionality reduction (NLDR) methods have been proposed. In this article, we propose novel NLDR methods based on bottleneck deep autoencoders. Our contributions are 2-fold: (1) We introduce a monotonicity constraint into bottleneck deep autoencoders for estimating a single nonlinear component and propose two methods for fitting the model. (2) We propose a new, forward stepwise deep learning architecture for estimating multiple nonlinear components. The former helps extract interpretable, monotone components when the assumption of monotonicity holds, and the latter helps evaluate reconstruction errors in the original data space for a range of components. We conduct numerical studies to compare different model fitting methods and use two real data examples from the studies of human immune responses to HIV to illustrate the proposed methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8099318.v1
title: Additional file 3: of Initial function of microRNAs as a defence mechanism against invading organisms

description: The predicted domains of all ago and ago-like protein of the 17 species investigated. The domain IDs along with the p values of the predicted domains were listed in this file. (XLSX 23 kb)
DOI: 10.6084/m9.figshare.12285347
title: Additional file 4 of The seasonal development dynamics of the yak hair cycle transcriptome

description: Additional file 4 Table S2. GO terms enriched by the DEGs in the nine expression patterns.
DOI: 10.6084/m9.figshare.13333852.v1
title: Forward Stepwise Deep Autoencoder-based Monotone Nonlinear Dimensionality Reduction Methods

description: Dimensionality reduction is an unsupervised learning task aimed at creating a low-dimensional summary and/or extracting the most salient features of a dataset. Principal components analysis (PCA) is a linear dimensionality reduction method in the sense that each principal component is a linear combination of the input variables. To allow features that are nonlinear functions of the input variables, many nonlinear dimensionality reduction methods have been proposed. In this paper we propose novel nonlinear dimensionality reduction methods based on bottleneck deep autoencoders (Kramer, 1991). Our contributions are two-fold: (1) We introduce a monotonicity constraint into bottleneck deep autoencoders for estimating a single nonlinear component and propose two methods for fitting the model. (2) We propose a new, forward stepwise (FS) deep learning architecture for estimating multiple nonlinear components. The former helps extract interpretable, monotone components when the assumption of monotonicity holds, and the latter helps evaluate reconstruction errors in the original data space for a range of components. We conduct numerical studies to compare different model fitting methods and use two real data examples from the studies of human immune responses to HIV to illustrate the proposed methods.
DOI: 10.6084/m9.figshare.13333852.v4
title: Forward Stepwise Deep Autoencoder-Based Monotone Nonlinear Dimensionality Reduction Methods

description: Dimensionality reduction is an unsupervised learning task aimed at creating a low-dimensional summary and/or extracting the most salient features of a dataset. Principal component analysis is a linear dimensionality reduction method in the sense that each principal component is a linear combination of the input variables. To allow features that are nonlinear functions of the input variables, many nonlinear dimensionality reduction (NLDR) methods have been proposed. In this article, we propose novel NLDR methods based on bottleneck deep autoencoders. Our contributions are 2-fold: (1) We introduce a monotonicity constraint into bottleneck deep autoencoders for estimating a single nonlinear component and propose two methods for fitting the model. (2) We propose a new, forward stepwise deep learning architecture for estimating multiple nonlinear components. The former helps extract interpretable, monotone components when the assumption of monotonicity holds, and the latter helps evaluate reconstruction errors in the original data space for a range of components. We conduct numerical studies to compare different model fitting methods and use two real data examples from the studies of human immune responses to HIV to illustrate the proposed methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12285344.v1
title: Additional file 2 of Gut microbiota in early pregnancy among women with Hyperglycaemia vs. Normal blood glucose

description: Additional file 2 Table S3. Differential microbial abundance between HIPs and controls
DOI: 10.6084/m9.figshare.8099378.v1
title: Additional file 2: of Transcript analyses reveal a comprehensive role of abscisic acid in modulating fruit ripening in Chinese jujube

description: Statistic information of mapping data. (XLSX 11 kb)
DOI: 10.6084/m9.figshare.12285290.v1
title: Additional file 1 of MEPHAS: an interactive graphical user interface for medical and pharmaceutical statistical analysis with R and Shiny

description: Additional file 1: AF_table1.docx Graphic user interfaces and statistical methods in MEPHAS; AF_table2.docx Comparison of methods in MEPHAS with EZR, FSFS, and Radiant; AF_list.docx R packages used in MEPHAS; AF_result.docx The results in Example 1 and Example 2.
DOI: 10.6084/m9.figshare.8099414
title: Additional file 7: of Transcript analyses reveal a comprehensive role of abscisic acid in modulating fruit ripening in Chinese jujube

description: DEGs related to transcription factors involved in ripening regulation. (XLSX 27 kb)
DOI: 10.6084/m9.figshare.13333852.v2
title: Forward Stepwise Deep Autoencoder-Based Monotone Nonlinear Dimensionality Reduction Methods

description: Dimensionality reduction is an unsupervised learning task aimed at creating a low-dimensional summary and/or extracting the most salient features of a dataset. Principal component analysis is a linear dimensionality reduction method in the sense that each principal component is a linear combination of the input variables. To allow features that are nonlinear functions of the input variables, many nonlinear dimensionality reduction (NLDR) methods have been proposed. In this article, we propose novel NLDR methods based on bottleneck deep autoencoders. Our contributions are 2-fold: (1) We introduce a monotonicity constraint into bottleneck deep autoencoders for estimating a single nonlinear component and propose two methods for fitting the model. (2) We propose a new, forward stepwise deep learning architecture for estimating multiple nonlinear components. The former helps extract interpretable, monotone components when the assumption of monotonicity holds, and the latter helps evaluate reconstruction errors in the original data space for a range of components. We conduct numerical studies to compare different model fitting methods and use two real data examples from the studies of human immune responses to HIV to illustrate the proposed methods. Supplementary materials for this article are available online.
DOI: 10.5519/qd.htmyk922
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5773385 records)
DOI: 10.6084/m9.figshare.12285290
title: Additional file 1 of MEPHAS: an interactive graphical user interface for medical and pharmaceutical statistical analysis with R and Shiny

description: Additional file 1: AF_table1.docx Graphic user interfaces and statistical methods in MEPHAS; AF_table2.docx Comparison of methods in MEPHAS with EZR, FSFS, and Radiant; AF_list.docx R packages used in MEPHAS; AF_result.docx The results in Example 1 and Example 2.
DOI: 10.6084/m9.figshare.12285299.v1
title: Additional file 2 of Physiological effects of the combined stresses of freezing-thawing, acid precipitation and deicing salt on alfalfa seedlings

description: Additional file 2.
DOI: 10.6084/m9.figshare.12285314.v1
title: Additional file 4 of Physiological effects of the combined stresses of freezing-thawing, acid precipitation and deicing salt on alfalfa seedlings

description: Additional file 4.
DOI: 10.6084/m9.figshare.12285344
title: Additional file 2 of Gut microbiota in early pregnancy among women with Hyperglycaemia vs. Normal blood glucose

description: Additional file 2 Table S3. Differential microbial abundance between HIPs and controls
DOI: 10.6084/m9.figshare.12285293
title: Additional file 1 of Physiological effects of the combined stresses of freezing-thawing, acid precipitation and deicing salt on alfalfa seedlings

description: Additional file 1.
DOI: 10.6084/m9.figshare.12285299
title: Additional file 2 of Physiological effects of the combined stresses of freezing-thawing, acid precipitation and deicing salt on alfalfa seedlings

description: Additional file 2.
DOI: 10.6084/m9.figshare.12285314
title: Additional file 4 of Physiological effects of the combined stresses of freezing-thawing, acid precipitation and deicing salt on alfalfa seedlings

description: Additional file 4.
DOI: 10.6084/m9.figshare.12285317.v1
title: Additional file 5 of Physiological effects of the combined stresses of freezing-thawing, acid precipitation and deicing salt on alfalfa seedlings

description: Additional file 5.
DOI: 10.6084/m9.figshare.12285356.v1
title: Additional file 5 of The seasonal development dynamics of the yak hair cycle transcriptome

description: Additional file 5 Table S3. KEGG pathways enriched by the DEGs in the nine expression patterns.
DOI: 10.6084/m9.figshare.12285347.v1
title: Additional file 4 of The seasonal development dynamics of the yak hair cycle transcriptome

description: Additional file 4 Table S2. GO terms enriched by the DEGs in the nine expression patterns.
DOI: 10.6084/m9.figshare.12285356
title: Additional file 5 of The seasonal development dynamics of the yak hair cycle transcriptome

description: Additional file 5 Table S3. KEGG pathways enriched by the DEGs in the nine expression patterns.
DOI: 10.6084/m9.figshare.12285317
title: Additional file 5 of Physiological effects of the combined stresses of freezing-thawing, acid precipitation and deicing salt on alfalfa seedlings

description: Additional file 5.
DOI: 10.6084/m9.figshare.12285308.v1
title: Additional file 3 of Physiological effects of the combined stresses of freezing-thawing, acid precipitation and deicing salt on alfalfa seedlings

description: Additional file 3.
DOI: 10.6084/m9.figshare.12285308
title: Additional file 3 of Physiological effects of the combined stresses of freezing-thawing, acid precipitation and deicing salt on alfalfa seedlings

description: Additional file 3.
DOI: 10.6084/m9.figshare.12285293.v1
title: Additional file 1 of Physiological effects of the combined stresses of freezing-thawing, acid precipitation and deicing salt on alfalfa seedlings

description: Additional file 1.
DOI: 10.22002/89xcx-vxs67
title: BacillusSubtilis_293

description: Bacterial nanotubes (like what is described in Sigal ben Yehuda and Ilan Rosenshine work).
description: <strong>Tilt Series Date:</strong> 2020-02-16</p>
<strong>Data Taken By:</strong> Mohammed Kaplan</p>
<strong>Species / Specimen:</strong> Bacillus subtilis</p>
<strong>Strain:</strong> AB293, ΔponA amyE::fliP-2XHA sacA::ymdB Δmbl</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 3°, constant angular increment, dosage: 150.0 eV/Å²,  defocus: -6.0 μm,  magnification: 19500x. </p>
<strong>Microscope:</strong> Caltech Titan Krios</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> The samples were prepared with Amit and Ritesh (from Sigal Ben Yehuda and Ilan Rosenshine . labs at the Hebrew University). These Samples were incubated for four hours on grids on plates</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Cells were streaked on an agar plate from a -80C stock and incubated at 37C. Next day, the . cells were inoculated in 3 ml of LB and grown at 37C to OD 0.8-1. Cells were then added to . grids on agar plates and incubated for 4 hours at 37 C. The grids were washed with PBS and . then blotted and plunge frozen.</p>
<strong>Sample Preparation:</strong> The sample was manually blotted to increase the chance of finding an intercellular nanotube.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mka2020-02-16-66/rawdata/</p>Bsubtilis_293_033_af.mrc,  Tilt Series (Pixel Size 0.449 nm), 1.9 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mka2020-02-16-66/rawdata/Bsubtilis_293_033_af.mrc"
        > <i class="download icon"></i> Download </a></p>

        Bsubtilis_293_033_af_full.rec,  Reconstruction (Pixel Size 1.796 nm), 737.3 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mka2020-02-16-66/3dimage_111503/Bsubtilis_293_033_af_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/qhtev-avr35
title: V. cholerae RT4236 (refreshed)

description: Classical strain with ctxA deletion.Cell harbors pMT5 plasmid (inducible toxT).
description: <strong>Tilt Series Date:</strong> 2015-12-19</p>
<strong>Data Taken By:</strong> Yi-Wei Chang</p>
<strong>Species / Specimen:</strong> Vibrio cholerae</p>
<strong>Strain:</strong> O395-N1</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1.0°, constant angular increment, dosage: 160.0 eV/Å²,  defocus: -6.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</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 from Ronald K. Taylor.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> V. cholerae 0395-N1 pMT5 grown in 5ml LB pH 6.5, 1 mM IPTG and 100 ug/mL ampicilin in glass tube shaking for overnight. After took the tube out from shaker, place the tube on bench for ~20 min, supernatant was removed and pellet was resuspended by gentle pipetting in 1ml fresh LB without IPTG nor ampicillin. Culture was incubated at room temperature for ~20 min before mixing with gold and freezing.</p>
<strong>Sample Preparation:</strong> Vitrobot (MK iv) settings: 100% humidity, 4µl sample, 10 mg/ml BSA treated gold. Blot force 6, blot time 2.5 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/ycw2015-12-19-33/3dimage_71617/</p>20151219_YWC206_10033_full.rec,  Reconstruction (Pixel Size 1.56 nm), 534.5 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2015-12-19-33/3dimage_71617/20151219_YWC206_10033_full.rec"
        > <i class="download icon"></i> Download </a></p>

        20151219_YWC206_10033.mrc,  Tilt Series (Pixel Size 0.39 nm), 3.4 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2015-12-19-33/rawdata/20151219_YWC206_10033.mrc"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.3535/jv4-wm2-wcn
title: Eucidaris tribuloides (Lamarck, 1816)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.22002/whtg2-fan33
title: Staphylococcus aureus_WT

description: S. aureus WT cells grown in TSB media.
description: <strong>Tilt Series Date:</strong> 2017-07-20</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Staphylococcus aureus</p>
<strong>Strain:</strong> subsp. aureus str. Newman</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-50.0°, 50.0°), step: 2°, constant angular increment, dosage: 240.0 eV/Å²,  defocus: -8.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</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 Tracy Palmer, University of Dundee. Data collected by Debnath Ghosal.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Cells were grown in TSA and in TSB  (Oxoid TSA and TSB from Thermo Fisher 10525943 & 10137562). Dilute an overnight culture 1/100 and grow for ~4 hr (to OD 2) then freeze.</p>
<strong>Sample Preparation:</strong> Vitrobot (MK iv) settings: 100% humidity, 4µl sample, Blot force 6, blot time 6 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/dga2017-07-20-67/rawdata/</p>Saureus_WT_201707200067.mrc,  Tilt Series (Pixel Size 0.39 nm), 1.5 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-07-20-67/rawdata/Saureus_WT_201707200067.mrc"
        > <i class="download icon"></i> Download </a></p>

        Saureus_WT_201707200067.rec,  Reconstruction (Pixel Size 1.56 nm), 889.0 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-07-20-67/3dimage_87399/Saureus_WT_201707200067.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.6084/m9.figshare.29750453
title: PRISMA_DM.docx

description: PRISMA diagram