cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20462413.v1
title: Additional file 1 of Predictive value of proteomic markers for advanced rectal cancer with neoadjuvant chemoradiotherapy

description: Additional file 1: Supplementary Table 1. Clinical features of serum samples for iTRAQ experiment. Supplementary Table 2. Clinical features of tested serum samples. Supplementary Table 3. List of proteins identified and quantified across all the individual sera samples. Supplementary Table 4. List of significant differential proteins between different groups identified by iTRAQ. Supplementary Table 5. The pepetides used for quantification: characteristics and performance. Supplementary Table 6. The importance of each feature in predition model.
DOI: 10.6084/m9.figshare.20988010
title: Plasma Wave and Particle Dynamics During Interchange Events in the Jovian Magnetosphere Using Juno Observations

description: Dataset used to produce figures for the paper and supporting information submitted to Geophysical Research Letters with the title "Plasma Wave and Particle Dynamics During Interchange Events in the Jovian Magnetosphere Using Juno Observations"
DOI: 10.6084/m9.figshare.13647086.v1
title: Additional file 2 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

description: Additional file 2 The metadata of the full list of 114 GWASs
DOI: 10.17615/8bde-hp79
title: Labeled numerical phantom of abdominal wall for wave-physics based ultrasound imaging: applications to image reconstruction and parameter estimation (Part E)

description: The abdominal wall consists of an acoustically complex organization of tissue layers that generate significant degradation in diagnostic ultrasound imaging of internal organs. As with any non-invasive diagnostic medical imaging modality, the underlying ground truth is a fundamentally unknown quantity in a living patient. A realistic labeled 3D numerical phantom of the human abdominal wall is used to establish the acoustical properties of human tissue. Simulations based on the first principles of wave propagation, including the effects of propagation, aberration, reflection, refraction, attenuation, scattering, reverberation, and nonlinearity are used to generate a large data set of raw ultrasound data for 2D imaging. This data set is then used to train a physics-informed neural network to generate local sound speed estimates. Finally the translation of this approach is demonstrated in vivo on clinical data of liver ultrasound images. It is shown that together, the neural network and this training data set generate high quality estimates of the sound speed as measured by improvements in clinical image quality. The labeled abdominal data set, the simulation tools that model wave propagation, and the neural network approach are made publicly available. Other training and optimization approaches can be applied to this data.
DOI: 10.17615/gmdc-7733
title: Labeled numerical phantom of abdominal wall for wave-physics based ultrasound imaging: applications to image reconstruction and parameter estimation (Part F)

description: The abdominal wall consists of an acoustically complex organization of tissue layers that generate significant degradation in diagnostic ultrasound imaging of internal organs. As with any non-invasive diagnostic medical imaging modality, the underlying ground truth is a fundamentally unknown quantity in a living patient. A realistic labeled 3D numerical phantom of the human abdominal wall is used to establish the acoustical properties of human tissue. Simulations based on the first principles of wave propagation, including the effects of propagation, aberration, reflection, refraction, attenuation, scattering, reverberation, and nonlinearity are used to generate a large data set of raw ultrasound data for 2D imaging. This data set is then used to train a physics-informed neural network to generate local sound speed estimates. Finally the translation of this approach is demonstrated in vivo on clinical data of liver ultrasound images. It is shown that together, the neural network and this training data set generate high quality estimates of the sound speed as measured by improvements in clinical image quality. The labeled abdominal data set, the simulation tools that model wave propagation, and the neural network approach are made publicly available. Other training and optimization approaches can be applied to this data.
DOI: 10.17615/gzdd-s294
title: Labeled numerical phantom of abdominal wall for wave-physics based ultrasound imaging: applications to image reconstruction and parameter estimation (Part G)

description: The abdominal wall consists of an acoustically complex organization of tissue layers that generate significant degradation in diagnostic ultrasound imaging of internal organs. As with any non-invasive diagnostic medical imaging modality, the underlying ground truth is a fundamentally unknown quantity in a living patient. A realistic labeled 3D numerical phantom of the human abdominal wall is used to establish the acoustical properties of human tissue. Simulations based on the first principles of wave propagation, including the effects of propagation, aberration, reflection, refraction, attenuation, scattering, reverberation, and nonlinearity are used to generate a large data set of raw ultrasound data for 2D imaging. This data set is then used to train a physics-informed neural network to generate local sound speed estimates. Finally the translation of this approach is demonstrated in vivo on clinical data of liver ultrasound images. It is shown that together, the neural network and this training data set generate high quality estimates of the sound speed as measured by improvements in clinical image quality. The labeled abdominal data set, the simulation tools that model wave propagation, and the neural network approach are made publicly available. Other training and optimization approaches can be applied to this data.
DOI: 10.17615/c45w-tt48
title: Labeled numerical phantom of abdominal wall for wave-physics based ultrasound imaging: applications to image reconstruction and parameter estimation (Part I)

description: The abdominal wall consists of an acoustically complex organization of tissue layers that generate significant degradation in diagnostic ultrasound imaging of internal organs. As with any non-invasive diagnostic medical imaging modality, the underlying ground truth is a fundamentally unknown quantity in a living patient. A realistic labeled 3D numerical phantom of the human abdominal wall is used to establish the acoustical properties of human tissue. Simulations based on the first principles of wave propagation, including the effects of propagation, aberration, reflection, refraction, attenuation, scattering, reverberation, and nonlinearity are used to generate a large data set of raw ultrasound data for 2D imaging. This data set is then used to train a physics-informed neural network to generate local sound speed estimates. Finally the translation of this approach is demonstrated in vivo on clinical data of liver ultrasound images. It is shown that together, the neural network and this training data set generate high quality estimates of the sound speed as measured by improvements in clinical image quality. The labeled abdominal data set, the simulation tools that model wave propagation, and the neural network approach are made publicly available. Other training and optimization approaches can be applied to this data.
DOI: 10.17615/ar0n-5a82
title: Labeled numerical phantom of abdominal wall for wave-physics based ultrasound imaging: applications to image reconstruction and parameter estimation (Part H)

description: The abdominal wall consists of an acoustically complex organization of tissue layers that generate significant degradation in diagnostic ultrasound imaging of internal organs. As with any non-invasive diagnostic medical imaging modality, the underlying ground truth is a fundamentally unknown quantity in a living patient. A realistic labeled 3D numerical phantom of the human abdominal wall is used to establish the acoustical properties of human tissue. Simulations based on the first principles of wave propagation, including the effects of propagation, aberration, reflection, refraction, attenuation, scattering, reverberation, and nonlinearity are used to generate a large data set of raw ultrasound data for 2D imaging. This data set is then used to train a physics-informed neural network to generate local sound speed estimates. Finally the translation of this approach is demonstrated in vivo on clinical data of liver ultrasound images. It is shown that together, the neural network and this training data set generate high quality estimates of the sound speed as measured by improvements in clinical image quality. The labeled abdominal data set, the simulation tools that model wave propagation, and the neural network approach are made publicly available. Other training and optimization approaches can be applied to this data.
DOI: 10.17615/3vd7-gt09
title: Labeled numerical phantom of abdominal wall for wave-physics based ultrasound imaging: applications to image reconstruction and parameter estimation (Part K)

description: The abdominal wall consists of an acoustically complex organization of tissue layers that generate significant degradation in diagnostic ultrasound imaging of internal organs. As with any non-invasive diagnostic medical imaging modality, the underlying ground truth is a fundamentally unknown quantity in a living patient. A realistic labeled 3D numerical phantom of the human abdominal wall is used to establish the acoustical properties of human tissue. Simulations based on the first principles of wave propagation, including the effects of propagation, aberration, reflection, refraction, attenuation, scattering, reverberation, and nonlinearity are used to generate a large data set of raw ultrasound data for 2D imaging. This data set is then used to train a physics-informed neural network to generate local sound speed estimates. Finally the translation of this approach is demonstrated in vivo on clinical data of liver ultrasound images. It is shown that together, the neural network and this training data set generate high quality estimates of the sound speed as measured by improvements in clinical image quality. The labeled abdominal data set, the simulation tools that model wave propagation, and the neural network approach are made publicly available. Other training and optimization approaches can be applied to this data.
DOI: 10.17615/0q30-3z47
title: Labeled numerical phantom of abdominal wall for wave-physics based ultrasound imaging: applications to image reconstruction and parameter estimation (Part J)

description: The abdominal wall consists of an acoustically complex organization of tissue layers that generate significant degradation in diagnostic ultrasound imaging of internal organs. As with any non-invasive diagnostic medical imaging modality, the underlying ground truth is a fundamentally unknown quantity in a living patient. A realistic labeled 3D numerical phantom of the human abdominal wall is used to establish the acoustical properties of human tissue. Simulations based on the first principles of wave propagation, including the effects of propagation, aberration, reflection, refraction, attenuation, scattering, reverberation, and nonlinearity are used to generate a large data set of raw ultrasound data for 2D imaging. This data set is then used to train a physics-informed neural network to generate local sound speed estimates. Finally the translation of this approach is demonstrated in vivo on clinical data of liver ultrasound images. It is shown that together, the neural network and this training data set generate high quality estimates of the sound speed as measured by improvements in clinical image quality. The labeled abdominal data set, the simulation tools that model wave propagation, and the neural network approach are made publicly available. Other training and optimization approaches can be applied to this data.
DOI: 10.6084/m9.figshare.13646899
title: Additional file 3 of Putative ligand binding sites of two functionally characterized bark beetle odorant receptors

description: Additional file 3: Supplementary Data 1. Multiple sequence alignment used to generate the ItypOR models.
DOI: 10.6084/m9.figshare.13647095
title: Additional file 5 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

description: Additional file 5 BioVU table
DOI: 10.6084/m9.figshare.13647098
title: Additional file 6 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

description: Additional file 6 OMIM genes included in the analysis
DOI: 10.6084/m9.figshare.13647101
title: Additional file 7 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

description: Additional file 7 Rare variant silver standard genes included in the analysis
DOI: 10.6084/m9.figshare.13647101.v1
title: Additional file 7 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

description: Additional file 7 Rare variant silver standard genes included in the analysis
DOI: 10.6084/m9.figshare.13647089.v1
title: Additional file 3 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

description: Additional file 3 Presumed causal genes included in the OMIM database
DOI: 10.6084/m9.figshare.13647107.v1
title: Additional file 9 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

description: Additional file 9 PrediXcan and enloc results for presumed causal genes in the rare variant based silver standard
DOI: 10.6084/m9.figshare.13647092.v1
title: Additional file 4 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

description: Additional file 4 Genes suggested as causal by rare variant association studies
DOI: 10.6084/m9.figshare.6832484
title: Impact of 1,25(OH) 2 D 3 on TG content in liver of rats with type 2 diabetes

description: Abstract Purpose: To evaluate the effects of 1,25 dihydroxy vitamin D3 (1,25(OH)2D3) on the content of triglyceride (TG), as well as on the gene and protein expressions of adiponectin receptor 2 (AdipoR2), p38 mitogen-activated protein kinase (P38MAPK), and lipoprotein lipase (LPL) in the liver of rats with type 2 diabetes mellitus (T2DM) so as to provide theoretical basis for exploring the mechanism by which 1,25(OH)2D3 regulates TG. Methods: Wistar rats were divided into four groups (n=25), with different treatments and detected the gene and protein expressions of AdipoR2, p38MAPK, and LPL in the liver tissue by reverse transcription polymerase chain reaction (RT-PCR) and Western blotting. Meanwhile, the content of TG in the liver tissue was detected by the Enzyme-linked immunosorbent assay. Results: The expression of AdipoR2, p38MAPK, LPL gene and protein in the liver of VitD intervention group was significantly higher than that in T2DM group (P &lt;0.05), while the TG content was significantly lower than that in T2DM group (P &lt;0.05). Conclusion: 1,25(OH)2D3 can decrease the content of TG in the liver, and its mechanism may be achieved by upregulating the expressions of AdipoR2, p38MAPK, and LPL in the liver.
DOI: 10.25387/g3.7356776
title: Supplemental Material for Meng et al., 2019

description: The complex SV discovered by PacBio targeted sequencing data
DOI: 10.6084/m9.figshare.7881251
title: Maternal and Offspring Genetic Risk of Type 2 Diabetes and Offspring Birthweight Among African Ancestry Populations: Supplemental Information

description: Supplemental information of the manuscript -"Maternal and Offspring Genetic Risk of Type
2 Diabetes and Offspring Birthweight Among African Ancestry Populations"<b></b>Mohammad
L. Rahman<sup>1,2</sup>, Deepika Shrestha<sup>1</sup>, Tsegaselassie
Workalemahu<sup>1</sup>, Jing Wu<sup>3</sup>, Chunming Zhu<sup>3</sup>, Cuilin
Zhang<sup>1</sup>, Fasil Tekola-Ayele<sup>1*</sup>

<sup> </sup>

<sup>1
</sup>Epidemiology
Branch, Division of Intramural Population Health Research, <i>Eunice Kennedy Shriver</i> National Institute of Child Health and Human
Development, National Institutes of Health, Bethesda, MD, USA

<sup>2</sup>
Current affiliation: Harvard Medical School, Department of Population Medicine
and Harvard Pilgrim Health Care Institute

<sup>3
</sup>Division
of Intramural Population Health Research, <i>Eunice
Kennedy Shriver</i> National Institute of Child Health and Human Development,
National Institutes of Health, Bethesda, MD, USA
DOI: 10.6084/m9.figshare.6831356
title: MOESM3 of Transcriptional network analysis on brains reveals a potential regulatory role of PPP1R3F in autism spectrum disorders

description: Additional file 2. The constructed networks from the Parikshak et al. dataset [19].
DOI: 10.6084/m9.figshare.24972522
title: urchin BRUV data

description: Dataset of urchin BRUV data over 25 nights
DOI: 10.6084/m9.figshare.24972522.v1
title: urchin BRUV data

description: Dataset of urchin BRUV data over 25 nights
DOI: 10.6084/m9.figshare.20464338
title: Effects of Climate Change Knowledge on Adolescents’ Atti-tudes and Willingness to Participate in Carbon Neutrality Ed-ucation

description:  Achieving carbon neutrality has become increasingly important. Therefore, it is urgent to use education to increase public understanding of carbon neutrality and facilitate low-carbon behaviors. Climate change knowledge is an effective measure to promote people’s interest and enthusiasm for specific educational projects. The present study analyzed the effects of climate change knowledge on adolescents’ attitudes and their willingness to participate in carbon neutrality education, and validated the mediating effect of environmental responsibility. The findings showed that climate change knowledge improves adolescents’ attitudes toward carbon neutrality education and that environmental responsibility plays a mediating role in this. The findings provide insightful implications for carbon neutrality-related policy making and education promotion.
DOI: 10.6084/m9.figshare.6831356.v1
title: MOESM3 of Transcriptional network analysis on brains reveals a potential regulatory role of PPP1R3F in autism spectrum disorders

description: Additional file 2. The constructed networks from the Parikshak et al. dataset [19].
DOI: 10.6084/m9.figshare.6831389
title: MOESM2 of Identification of long non-coding RNA signatures in triple-negative breast cancer

description: Additional file 2: Table S2. Functional enrichment terms of DEGs.
DOI: 10.6084/m9.figshare.1590038
title: Neuroprotective properties of the PrP-like Shadoo glycoprotein assessed in the middle cerebral artery occlusion model of ischemia

description: Biochemical similarities have been noted between the natively unstructured region of the cellular prion protein, PrP<sup>C</sup>, and a GPI-linked glycoprotein called Shadoo (Sho); these proteins are encoded by the <i>Prnp</i> and <i>Sprn</i> genes, respectively. Both proteins are expressed in the adult central nervous system and they share overlapping partners, including each other, in interactome studies. As prior studies have ascribed neuroprotective properties to the N-terminal region of PrP<sup>C</sup>, specifically the octarepeat region, we investigated Sho's neuroprotective properties. To this end we assessed Sho-null (<i>Sprn</i><sup>0/0</sup>) and hemizygous (<i>Sprn</i><sup><i><i>0</i>/+</i></sup>) mice in the middle cerebral artery occlusion (MCAO) model versus wild type mice and also vs. transgene-rescued <i>Sprn</i><sup>0/0</sup>-Tg<i>Sprn</i> mice. <i>Sprn</i><sup>0/0</sup> mice had a tendency to greater fragility in reaching endpoint and deficits in parameters including infarct volume and neurogenesis, with a reciprocal trend noted in transgene-rescued mice; however these effects did not reach significance. Loss of both PrP<sup>C</sup> and Sho immunostaining occurred in parallel to neuronal loss on the ipsilateral side of MCAO-lesioned animals; while focal elevations in immunostaining in the penumbra region were sometimes evident for PrP<sup>C</sup>, they were not noted for Sho. Our studies argue against discernible neuroprotective action of Sho in the genetic backgrounds used for this MCAO paradigm. Whether or not the positively charged N-terminal regions in Sho and PrP<sup>C</sup> fulfil different roles <i>in vivo</i> remains to be determined.
DOI: 10.6084/m9.figshare.1590038.v1
title: Neuroprotective properties of the PrP-like Shadoo glycoprotein assessed in the middle cerebral artery occlusion model of ischemia

description: Biochemical similarities have been noted between the natively unstructured region of the cellular prion protein, PrP<sup>C</sup>, and a GPI-linked glycoprotein called Shadoo (Sho); these proteins are encoded by the <i>Prnp</i> and <i>Sprn</i> genes, respectively. Both proteins are expressed in the adult central nervous system and they share overlapping partners, including each other, in interactome studies. As prior studies have ascribed neuroprotective properties to the N-terminal region of PrP<sup>C</sup>, specifically the octarepeat region, we investigated Sho's neuroprotective properties. To this end we assessed Sho-null (<i>Sprn</i><sup>0/0</sup>) and hemizygous (<i>Sprn</i><sup><i><i>0</i>/+</i></sup>) mice in the middle cerebral artery occlusion (MCAO) model versus wild type mice and also vs. transgene-rescued <i>Sprn</i><sup>0/0</sup>-Tg<i>Sprn</i> mice. <i>Sprn</i><sup>0/0</sup> mice had a tendency to greater fragility in reaching endpoint and deficits in parameters including infarct volume and neurogenesis, with a reciprocal trend noted in transgene-rescued mice; however these effects did not reach significance. Loss of both PrP<sup>C</sup> and Sho immunostaining occurred in parallel to neuronal loss on the ipsilateral side of MCAO-lesioned animals; while focal elevations in immunostaining in the penumbra region were sometimes evident for PrP<sup>C</sup>, they were not noted for Sho. Our studies argue against discernible neuroprotective action of Sho in the genetic backgrounds used for this MCAO paradigm. Whether or not the positively charged N-terminal regions in Sho and PrP<sup>C</sup> fulfil different roles <i>in vivo</i> remains to be determined.
DOI: 10.6084/m9.figshare.13647092
title: Additional file 4 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

description: Additional file 4 Genes suggested as causal by rare variant association studies
DOI: 10.6084/m9.figshare.6831368
title: MOESM1 of Identification of long non-coding RNA signatures in triple-negative breast cancer

description: Additional file 1: Table S1. Differentially expressedÂ lncRNAs in the two microarrays.
DOI: 10.6084/m9.figshare.13647089
title: Additional file 3 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

description: Additional file 3 Presumed causal genes included in the OMIM database
DOI: 10.6084/m9.figshare.13647095.v1
title: Additional file 5 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

description: Additional file 5 BioVU table
DOI: 10.6084/m9.figshare.13647104
title: Additional file 8 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

description: Additional file 8 PrediXcan and enloc results for predicted causal genes selected based on OMIM
DOI: 10.6084/m9.figshare.13647098.v1
title: Additional file 6 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

description: Additional file 6 OMIM genes included in the analysis
DOI: 10.6084/m9.figshare.13647104.v1
title: Additional file 8 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

description: Additional file 8 PrediXcan and enloc results for predicted causal genes selected based on OMIM
DOI: 10.6084/m9.figshare.6831368.v1
title: MOESM1 of Identification of long non-coding RNA signatures in triple-negative breast cancer

description: Additional file 1: Table S1. Differentially expressedÂ lncRNAs in the two microarrays.