cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.25111542.v1
title: Carbapenem-resistant <i>Klebsiella pneumoniae</i>: the role of plasmids in emergence, dissemination, and evolution of a major clinical challenge

description: <i>Klebsiella pneumoniae</i> is a major agent of healthcare-associated infections and a cause of some community-acquired infections, including severe bacteremic infections associated with metastatic abscesses in liver and other organs. Clinical relevance is compounded by its outstanding propensity to evolve antibiotic resistance. In particular, the emergence and dissemination of carbapenem resistance in <i>K. pneumoniae</i> has posed a major challenge due to the few residual treatment options, which have only recently been expanded by some new agents. The epidemiological success of carbapenem-resistant <i>K. pneumoniae</i> (CR-Kp) is mainly linked with clonal lineages that produce carbapenem-hydrolyzing enzymes (carbapenemases) encoded by plasmids. Here, we provide an updated overview on the mechanisms underlying the emergence and dissemination of CR-Kp, focusing on the role that plasmids have played in this phenomenon and in the co-evolution of resistance and virulence in <i>K. pneumoniae</i>. CR-Kp have disseminated on a global scale, representing one of the most important contemporary public health issues. These strains are almost invariably associated with complex multi-drug resistance (MDR) phenotypes, which can also include recently approved antibiotics. The heterogeneity of the molecular bases responsible for these phenotypes poses significant hurdles for therapeutic and diagnostic purposes.
DOI: 10.6084/m9.figshare.24756097
title: Safety, tolerability, and pharmacokinetics of JX11502MA in Chinese healthy subjects: a first-in-human, randomized, double-blind, placebo-controlled study following single-dose administration

description: JX11502MA is a potent partial agonist of dopamine D2 and D3 receptors, with a preferential binding profile for D3 receptors in vitro, potentially for treating schizophrenia. A first-in-human, randomized, double-blind, placebo-controlled, single ascending dose clinical trial was designed. The subjects were randomly assigned to receive JX11502MA and placebo capsules with seven ascending dose groups: 0.25 mg, 0.5 mg, 1 mg, 2 mg, 3 mg, 6 mg, and 8 mg. The PK profiles of JX11502MA and its metabolites were evaluated, along with a safety and tolerability assessment. Considering the safety of participants, the dose escalation was halted at 3 mg. Following single-dose administration, JX11502MA exhibited rapid absorption with a median T<sub>max</sub> ranging from 1 to 1.75 h. The terminal half-life of JX11502MA ranged from 73.62 to 276.85 h. The most common treatment-emergent adverse events (TEAEs) for subjects receiving JX11502MA were somnolence (56.3%), dizziness (18.8%), nausea (21.9%), vomiting (18.8%), and hiccups (18.8%). JX11502MA was generally well tolerated at a single dose of 0.25 to 3 mg. The PK profiles and safety characteristics in this study indicated that JX11502MA has the potential to be a favorable treatment option for patients with schizophrenia. https://clinicaltrials.gov (identifier: NCT05233657).
DOI: 10.6084/m9.figshare.24756097.v1
title: Safety, tolerability, and pharmacokinetics of JX11502MA in Chinese healthy subjects: a first-in-human, randomized, double-blind, placebo-controlled study following single-dose administration

description: JX11502MA is a potent partial agonist of dopamine D2 and D3 receptors, with a preferential binding profile for D3 receptors in vitro, potentially for treating schizophrenia. A first-in-human, randomized, double-blind, placebo-controlled, single ascending dose clinical trial was designed. The subjects were randomly assigned to receive JX11502MA and placebo capsules with seven ascending dose groups: 0.25 mg, 0.5 mg, 1 mg, 2 mg, 3 mg, 6 mg, and 8 mg. The PK profiles of JX11502MA and its metabolites were evaluated, along with a safety and tolerability assessment. Considering the safety of participants, the dose escalation was halted at 3 mg. Following single-dose administration, JX11502MA exhibited rapid absorption with a median T<sub>max</sub> ranging from 1 to 1.75 h. The terminal half-life of JX11502MA ranged from 73.62 to 276.85 h. The most common treatment-emergent adverse events (TEAEs) for subjects receiving JX11502MA were somnolence (56.3%), dizziness (18.8%), nausea (21.9%), vomiting (18.8%), and hiccups (18.8%). JX11502MA was generally well tolerated at a single dose of 0.25 to 3 mg. The PK profiles and safety characteristics in this study indicated that JX11502MA has the potential to be a favorable treatment option for patients with schizophrenia. https://clinicaltrials.gov (identifier: NCT05233657).
DOI: 10.6084/m9.figshare.21836211
title: Supplemental Tables

description: Supplemental tables describing the results in our study.07/27/2023: Tables have been updated in response to peer-reviews.10/02/2023: Tables have been updated to improve formatting and readability for final submission. Results did not change since the last revision. Table S10 have also been added as a supplement to Figure 1F.02/02/2023: Tables have been re-named and re-ordered to match publication. The previous datasets 8 and 9 are now "Extra_Datasets".Dataset 1: Subject InfoDataset 2: Sample InfoDataset 3: scRNA metadataDataset 4: scRNA DE genesDataset 5: PPC DeconvolutionDataset 6: PCA and pseudotime analysis of bulk RNA-seqDataset 7: Lead eQTLsDataset 8: Correlation between eQTL effect size and TF binding affinityDataset 9: Colocalization between PPC and adultDataset 10: eQTL annotationsDataset 11: eQTL modulesDataset 12: Chromatin state enrichmentsDataset 13: GWAS colocalizationDataset 14: 99% Credible Sets for iPSC-PPC eQTLs associated with GWASDataset 15: LD analysis with non-pancreatic GTEx tissuesExtra Dataset 16: Fine-mapped PPC eQTLs (PP &gt; 1%)Extra Dataset 17: deltaSVM TF Binding Prediction
DOI: 10.6084/m9.figshare.21836211.v5
title: Supplemental Tables

description: Supplemental tables describing the results in our study.07/27/2023: Tables have been updated in response to peer-reviews.10/02/2023: Tables have been updated to improve formatting and readability for final submission. Results did not change since the last revision. Table S10 have also been added as a supplement to Figure 1F.Dataset 1: Subject InfoDataset 2: Sample InfoDataset 3: scRNA metadataDataset 4: scRNA DE genesDataset 5: PPC DeconvolutionDataset 6: PCA and pseudotime analysis of bulk RNA-seq Dataset 7: Lead eQTLs Dataset 8: Finemapped eQTLs (SNPs with PP &gt; 1%)Dataset 9: deltaSVM TF bindingDataset 10: Correlation b/t eQTL effect size and TF binding affinityDataset 11: Colocalization b/t PPC and adultDataset 12: eQTL annotationsDataset 13: eQTL modulesDataset 14: Chromatin state enrichmentsDataset 15: GWAS colocalization
DOI: 10.15131/shef.data.22956002.v1
title: MTCue: Model Checkpoints

description: The model checkpoints contained here are associated with an ACL 2023 paper entitled "MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation" (citation is to be added when the Proceedings are published).
<br>
Each .zip file here contains a checkpoint to a baseline translation model and MTCue for the language pair in the name (e.g. en.de is the English-to-German language pair). How to use them is described in detail in the associated GitHub repository.
<br>
The models (checkpoints.zip) were trained in PyTorch and via the Fairseq toolkit:
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G.,  Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf,  A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S.,  Steiner, B., Fang, L., … Chintala, S. (2019). PyTorch: An imperative  style, high-performance deep learning library. Advances in Neural  Information Processing Systems, 32(NeurIPS).
<br>
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. fairseq: A Fast, Extensible Toolkit for Sequence Modeling. In <em>Proceedings  of the 2019 Conference of the North American Chapter of the Association  for Computational Linguistics (Demonstrations)</em>, pages 48–53, Minneapolis, Minnesota. Association for Computational Linguistics.
<br>
Full documentation to how to use the resources is included in the [GitHub repository](https://github.com/st-vincent1/MTCue) which contains a link to this ORDA page.
DOI: 10.34740/kaggle/dsv/7613988
title: Daily Public Opinion on Israel-Palestine War
DOI: 10.15131/shef.data.22956002
title: MTCue: Model Checkpoints

description: The model checkpoints contained here are associated with an ACL 2023 paper entitled "MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation" (citation is to be added when the Proceedings are published).
<br>
Each .zip file here contains a checkpoint to a baseline translation model and MTCue for the language pair in the name (e.g. en.de is the English-to-German language pair). How to use them is described in detail in the associated GitHub repository.
<br>
The models (checkpoints.zip) were trained in PyTorch and via the Fairseq toolkit:
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G.,  Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf,  A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S.,  Steiner, B., Fang, L., … Chintala, S. (2019). PyTorch: An imperative  style, high-performance deep learning library. Advances in Neural  Information Processing Systems, 32(NeurIPS).
<br>
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. fairseq: A Fast, Extensible Toolkit for Sequence Modeling. In <em>Proceedings  of the 2019 Conference of the North American Chapter of the Association  for Computational Linguistics (Demonstrations)</em>, pages 48–53, Minneapolis, Minnesota. Association for Computational Linguistics.
<br>
Full documentation to how to use the resources is included in the [GitHub repository](https://github.com/st-vincent1/MTCue) which contains a link to this ORDA page.
DOI: 10.34740/kaggle/dsv/7613999
title: Public Opinion on Climate Change (Updated Daily)
DOI: 10.5281/zenodo.10652018
title: Contextualizing rename decisions using refactorings, commit messages, and data types

description: Dataset for the paper: "Contextualizing rename decisions using refactorings, commit messages, and data types"

https://doi.org/10.1016/j.jss.2020.110704
DOI: 10.5281/zenodo.10652019
title: Contextualizing rename decisions using refactorings, commit messages, and data types

description: Dataset for the paper: "Contextualizing rename decisions using refactorings, commit messages, and data types"

https://doi.org/10.1016/j.jss.2020.110704
DOI: 10.5281/zenodo.10651398
title: Enhanced Waste Segmentation

description: The rise in global waste production presents a significant threat to both people and the environment. To combat this issue, it is necessary to implement resource-friendly waste separation and efficient recycling of recyclable materials. The utilization of convolutional neural networks shows great potential, particularly for separating various types of waste. However, recent developments indicate that state-of-the-art object segmentation models are insufficient in dealing with the complexity and diversity of recycling waste. This thesis presents a low-cost electric actuator system (EAS) in the form of a vibration plate. Additionally, a waste segmentation dataset is created to train, validate, and test two Mask R-CNN and two YOLOv8 segmentation models with varying specifications. The dataset comprises 947 manually annotated images, with 22,630 annotated objects belonging to cardboard, hard plastic, metal, and soft plastic classes. It was augmented using various techniques and enriched with complexity, resulting in 5,716 images and 137,324 annotated instances. The vibration generated by the EAS was effective in separating waste objects of different materials to a significant extent. This resulted in an increase in the mean average precision for segmentation mask (mAP mask) of a Mask R-CNN model with R101-FPN-3x backbone from 26.5% to 60.9%. The other models also demonstrated a significant improvement in mAP mask: Mask R-CNN R50-FPN-1x (24.4% to 58.8%), pre-trained YOLOv8 nano (24.9% to 50.6%) and non-pre-trained YOLOv8 nano (20.2% to 51.1%). It has been confirmed that the use of EAS improves the results of convolutional neural networks. In the future, this system could be used as a sub-process in automated waste separation.

The GitHub repository for the project can be found here.
DOI: 10.5281/zenodo.10651399
title: Enhanced Waste Segmentation

description: The rise in global waste production presents a significant threat to both people and the environment. To combat this issue, it is necessary to implement resource-friendly waste separation and efficient recycling of recyclable materials. The utilization of convolutional neural networks shows great potential, particularly for separating various types of waste. However, recent developments indicate that state-of-the-art object segmentation models are insufficient in dealing with the complexity and diversity of recycling waste. This thesis presents a low-cost electric actuator system (EAS) in the form of a vibration plate. Additionally, a waste segmentation dataset is created to train, validate, and test two Mask R-CNN and two YOLOv8 segmentation models with varying specifications. The dataset comprises 947 manually annotated images, with 22,630 annotated objects belonging to cardboard, hard plastic, metal, and soft plastic classes. It was augmented using various techniques and enriched with complexity, resulting in 5,716 images and 137,324 annotated instances. The vibration generated by the EAS was effective in separating waste objects of different materials to a significant extent. This resulted in an increase in the mean average precision for segmentation mask (mAP mask) of a Mask R-CNN model with R101-FPN-3x backbone from 26.5% to 60.9%. The other models also demonstrated a significant improvement in mAP mask: Mask R-CNN R50-FPN-1x (24.4% to 58.8%), pre-trained YOLOv8 nano (24.9% to 50.6%) and non-pre-trained YOLOv8 nano (20.2% to 51.1%). It has been confirmed that the use of EAS improves the results of convolutional neural networks. In the future, this system could be used as a sub-process in automated waste separation.

The GitHub repository for the project can be found here.
DOI: 10.15131/shef.data.22795733.v2
title: Data for: Assessing ice sheet models against the landform record: The Likelihood of Accordant Lineations Analysis (LALA) tool

description: thk - ice thickness uvel - ice velocity in the x direction vvel - ice velocity in the y direction mask - a mask of ice cover 
NetCDF file of a model simulation of the former British Irish Ice Sheet.
<br>
<br>
The folder is provided as a .tar.gz file. This can be uncompressed using a command (linux) such as:  tar -xf archive.tar.gz or in Windows using 7zip.
<br>
Within the folder is output from a numerical model simulation of the former British Irish Ice Sheet, modelled using PISM: https://www.pism.io/
<br>
The netcdf file contains several variables, output every 100 years:
<br>
This can be viewed using the ncview software and manipulated in python using packages such as netCDF4 and xarray.
DOI: 10.15131/shef.data.22795733.v1
title: Data for: Comparing observations of subglacial linations to the output of ice sheet models: The Likelihood of Accordant Lineations Analysis (LALA) Tool

description: NetCDF file of a model simulation of the former British Irish Ice Sheet.
Supplement to:
 Archer, R.E., Ely, J.C., Heaton, T.J., Butcher, F.E.G., Hughes, A.L.C. and Clark, C.D. Comparing observations of subglacial linations to the output of ice sheet models: The Likelihood of Accordant Lineations Analysis (LALA) Tool. <em>Earth Surface Processes and Landforms</em> 
<br>
The folder is provided as a .tar.gz file. This can be uncompressed using a command (linux) such as:  tar -xf archive.tar.gz or in Windows using 7zip.
<br>
Within the folder is output from a numerical model simulation of the former British Irish Ice Sheet, modelled using PISM: https://www.pism.io/
<br>
The netcdf file contains several variables, output every 100 years:

 thk - ice thickness
 uvel - ice velocity in the x direction
 vvel - ice velocity in the y direction
 mask - a mask of ice cover

<br>
This can be viewed using the ncview software and manipulated in python using packages such as netCDF4 and xarray.
DOI: 10.15131/shef.data.22795733
title: Data for: Assessing ice sheet models against the landform record: The Likelihood of Accordant Lineations Analysis (LALA) tool

description: thk - ice thickness uvel - ice velocity in the x direction vvel - ice velocity in the y direction mask - a mask of ice cover 
NetCDF file of a model simulation of the former British Irish Ice Sheet.
<br>
<br>
The folder is provided as a .tar.gz file. This can be uncompressed using a command (linux) such as:  tar -xf archive.tar.gz or in Windows using 7zip.
<br>
Within the folder is output from a numerical model simulation of the former British Irish Ice Sheet, modelled using PISM: https://www.pism.io/
<br>
The netcdf file contains several variables, output every 100 years:
<br>
This can be viewed using the ncview software and manipulated in python using packages such as netCDF4 and xarray.
DOI: 10.6084/m9.figshare.24547155
title: Nutritional characterization on growth and ionome profiles in Japanese wasabi cultivars (<i>Eutrema japonicum</i>) under hydroponics

description: Japanese wasabi is traditionally cultivated in terraced fields with running stream water conditions which typically produce a high-quality of yield. Previous studies have explored some of the effects of water nutrient status under such conditions; however, the relationship between water nutrient status in these cultivation conditions and the growth and quality of wasabi is still completely unknown. We evaluated the effects of nutrient strength, inorganic nitrogen sources, and pH of the medium on the growth of wasabi plants. Then, we analyzed the photosynthetic capacity and the ionomes of two major Japanese wasabi cultivars (‘Onimidori,’ an early-growing green-stem type; and ‘Mazuma No.1,’ a late-growing red-stem type) grown under a range of pH conditions. These experiments were conducted using a hydroponic system. Wasabi plants showed the best shoot and root growth in 1/10- and 1/2-strength Hoagland’s solution with a 50:50 ratio of NH<sub>4</sub>-N:NO<sub>3</sub>-N. On the basis of the accumulation patterns of NO<sub>3</sub><sup>−</sup>, amino acids, and chlorophylls, the 1/10-strength nutrient solution provided optimal conditions for wasabi growth. For both wasabi cultivars, the best shoot and root growth was in medium at pH 6.0. The photosynthetic capacity of ‘Onimidori’ was greater than that of ‘Mazuma No.1,’ suggesting that this may contribute to the faster growth of ‘Onimidori.’ Ionome analyses revealed tissue-specific mineral accumulation patterns and their differences among different pH conditions and between the two cultivars. Ionome-based multiple-regression analysis revealed a relationship between element concentration profiles in the wasabi plants, especially those in root, and the shoot growth. Wasabi plants are ammonium-sensitive and nitrate-preferring plants, in addition to relatively low nutrient requirements for optimal growth. The results of this study describe the basic nutritional characteristics of wasabi plants. These findings represent an important step toward optimizing fertilization to control wasabi quality and growth in the traditional flooded cultivation system.
DOI: 10.6084/m9.figshare.24547155.v1
title: Nutritional characterization on growth and ionome profiles in Japanese wasabi cultivars (<i>Eutrema japonicum</i>) under hydroponics

description: Japanese wasabi is traditionally cultivated in terraced fields with running stream water conditions which typically produce a high-quality of yield. Previous studies have explored some of the effects of water nutrient status under such conditions; however, the relationship between water nutrient status in these cultivation conditions and the growth and quality of wasabi is still completely unknown. We evaluated the effects of nutrient strength, inorganic nitrogen sources, and pH of the medium on the growth of wasabi plants. Then, we analyzed the photosynthetic capacity and the ionomes of two major Japanese wasabi cultivars (‘Onimidori,’ an early-growing green-stem type; and ‘Mazuma No.1,’ a late-growing red-stem type) grown under a range of pH conditions. These experiments were conducted using a hydroponic system. Wasabi plants showed the best shoot and root growth in 1/10- and 1/2-strength Hoagland’s solution with a 50:50 ratio of NH<sub>4</sub>-N:NO<sub>3</sub>-N. On the basis of the accumulation patterns of NO<sub>3</sub><sup>−</sup>, amino acids, and chlorophylls, the 1/10-strength nutrient solution provided optimal conditions for wasabi growth. For both wasabi cultivars, the best shoot and root growth was in medium at pH 6.0. The photosynthetic capacity of ‘Onimidori’ was greater than that of ‘Mazuma No.1,’ suggesting that this may contribute to the faster growth of ‘Onimidori.’ Ionome analyses revealed tissue-specific mineral accumulation patterns and their differences among different pH conditions and between the two cultivars. Ionome-based multiple-regression analysis revealed a relationship between element concentration profiles in the wasabi plants, especially those in root, and the shoot growth. Wasabi plants are ammonium-sensitive and nitrate-preferring plants, in addition to relatively low nutrient requirements for optimal growth. The results of this study describe the basic nutritional characteristics of wasabi plants. These findings represent an important step toward optimizing fertilization to control wasabi quality and growth in the traditional flooded cultivation system.
DOI: 10.5517/ccdc.csd.cc2gmvpn
title: CCDC 2283202: Experimental Crystal Structure Determination

description: Related Article: Fan Xia, Li-Sha Lin, Yan-Ling Li, Lian Yang, Yan-Song Ye, Xian Li, Jin-Hua Zhao, Jun Deng, Gang Xu|2024|J.Org.Chem.|89|1858|doi:10.1021/acs.joc.3c02584
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2gmvqp
title: CCDC 2283203: Experimental Crystal Structure Determination

description: Related Article: Fan Xia, Li-Sha Lin, Yan-Ling Li, Lian Yang, Yan-Song Ye, Xian Li, Jin-Hua Zhao, Jun Deng, Gang Xu|2024|J.Org.Chem.|89|1858|doi:10.1021/acs.joc.3c02584
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2gmvrq
title: CCDC 2283204: Experimental Crystal Structure Determination

description: Related Article: Fan Xia, Li-Sha Lin, Yan-Ling Li, Lian Yang, Yan-Song Ye, Xian Li, Jin-Hua Zhao, Jun Deng, Gang Xu|2024|J.Org.Chem.|89|1858|doi:10.1021/acs.joc.3c02584
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.