cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2bbf6b
title: CCDC 2154971: Experimental Crystal Structure Determination

description: Related Article: Qiuju Jiang, Pengwei Han, Haijun Ning, Jiaquan Jiang, Hui Chen, Yonghong Xiao, Chun-Rong Ye, Jinming Chen, Man Lin, Feng He, Xiao-Chun Huang, Qinghe Wu|2022|Nano Energy|101|107611|doi:10.1016/j.nanoen.2022.107611
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.cc2bc7nm
title: CCDC 2155760: Experimental Crystal Structure Determination

description: Related Article: Raymundo Cea-Olivares, Analuisa Ruiz-Hernández, Rodrigo Said Razo-Hernández, Hugo Tlahuext, Marcela López-Cardoso, Perla Román-Bravo, Gabriela Vargas-Pineda, Vojtech Jancik, Joaquín Barroso-Flores, Kayim Pineda-Urbina, Juan Pablo Mojica-Sánchez|2022|Polyhedron|225|116027|doi:10.1016/j.poly.2022.116027
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.cc2bc7pn
title: CCDC 2155761: Experimental Crystal Structure Determination

description: Related Article: Raymundo Cea-Olivares, Analuisa Ruiz-Hernández, Rodrigo Said Razo-Hernández, Hugo Tlahuext, Marcela López-Cardoso, Perla Román-Bravo, Gabriela Vargas-Pineda, Vojtech Jancik, Joaquín Barroso-Flores, Kayim Pineda-Urbina, Juan Pablo Mojica-Sánchez|2022|Polyhedron|225|116027|doi:10.1016/j.poly.2022.116027
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.cc2bb988
title: CCDC 2154849: Experimental Crystal Structure Determination

description: Related Article: Liridona Useini, Marija Mojić, Markus Laube, Peter Lönnecke, Jonas Dahme, Menyhárt B. Sárosi, Sanja Mijatović, Danijela Maksimović-Ivanić, Jens Pietzsch, Evamarie Hey-Hawkins|2022|ACS Omega|7|24282|doi:10.1021/acsomega.2c01523
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.cc2bc7qp
title: CCDC 2155762: Experimental Crystal Structure Determination

description: Related Article: Raymundo Cea-Olivares, Analuisa Ruiz-Hernández, Rodrigo Said Razo-Hernández, Hugo Tlahuext, Marcela López-Cardoso, Perla Román-Bravo, Gabriela Vargas-Pineda, Vojtech Jancik, Joaquín Barroso-Flores, Kayim Pineda-Urbina, Juan Pablo Mojica-Sánchez|2022|Polyhedron|225|116027|doi:10.1016/j.poly.2022.116027
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.cc2bb977
title: CCDC 2154848: Experimental Crystal Structure Determination

description: Related Article: Liridona Useini, Marija Mojić, Markus Laube, Peter Lönnecke, Jonas Dahme, Menyhárt B. Sárosi, Sanja Mijatović, Danijela Maksimović-Ivanić, Jens Pietzsch, Evamarie Hey-Hawkins|2022|ACS Omega|7|24282|doi:10.1021/acsomega.2c01523
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.cc2bb955
title: CCDC 2154846: Experimental Crystal Structure Determination

description: Related Article: Liridona Useini, Marija Mojić, Markus Laube, Peter Lönnecke, Jonas Dahme, Menyhárt B. Sárosi, Sanja Mijatović, Danijela Maksimović-Ivanić, Jens Pietzsch, Evamarie Hey-Hawkins|2022|ACS Omega|7|24282|doi:10.1021/acsomega.2c01523
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.cc2bb966
title: CCDC 2154847: Experimental Crystal Structure Determination

description: Related Article: Liridona Useini, Marija Mojić, Markus Laube, Peter Lönnecke, Jonas Dahme, Menyhárt B. Sárosi, Sanja Mijatović, Danijela Maksimović-Ivanić, Jens Pietzsch, Evamarie Hey-Hawkins|2022|ACS Omega|7|24282|doi:10.1021/acsomega.2c01523
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.cc2bb944
title: CCDC 2154845: Experimental Crystal Structure Determination

description: Related Article: Liridona Useini, Marija Mojić, Markus Laube, Peter Lönnecke, Jonas Dahme, Menyhárt B. Sárosi, Sanja Mijatović, Danijela Maksimović-Ivanić, Jens Pietzsch, Evamarie Hey-Hawkins|2022|ACS Omega|7|24282|doi:10.1021/acsomega.2c01523
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.cc2bkgw8
title: CCDC 2161750: Experimental Crystal Structure Determination

description: Related Article: Bin Zhang|2022|CSD Communication|||
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.cc2bb999
title: CCDC 2154850: Experimental Crystal Structure Determination

description: Related Article: Liridona Useini, Marija Mojić, Markus Laube, Peter Lönnecke, Jonas Dahme, Menyhárt B. Sárosi, Sanja Mijatović, Danijela Maksimović-Ivanić, Jens Pietzsch, Evamarie Hey-Hawkins|2022|ACS Omega|7|24282|doi:10.1021/acsomega.2c01523
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.cc2bs1ck
title: CCDC 2168058: Experimental Crystal Structure Determination

description: Related Article: Xiao-Yan Xin, Na Qiao, Chun-Shuai Cao, Feng-Jiao Chen, Wen-Yu Li, Yan-Ni Ling, Xiao-Hong Shi, Ying Shi, Min Li, Zhi-Lei Wu, Wen-Min Wang|2022|Inorg.Chim.Acta|541|121092|doi:10.1016/j.ica.2022.121092
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.cc297tds
title: CCDC 2122675: Experimental Crystal Structure Determination

description: Related Article: Xiao-Yan Xin, Na Qiao, Chun-Shuai Cao, Feng-Jiao Chen, Wen-Yu Li, Yan-Ni Ling, Xiao-Hong Shi, Ying Shi, Min Li, Zhi-Lei Wu, Wen-Min Wang|2022|Inorg.Chim.Acta|541|121092|doi:10.1016/j.ica.2022.121092
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.cc2bs9s7
title: CCDC 2168319: Experimental Crystal Structure Determination

description: Related Article: Xiao-Yan Xin, Na Qiao, Chun-Shuai Cao, Feng-Jiao Chen, Wen-Yu Li, Yan-Ni Ling, Xiao-Hong Shi, Ying Shi, Min Li, Zhi-Lei Wu, Wen-Min Wang|2022|Inorg.Chim.Acta|541|121092|doi:10.1016/j.ica.2022.121092
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.cc2c2yhx
title: CCDC 2177579: Experimental Crystal Structure Determination

description: Related Article: Lorenzo Biancalana, Matteo Fiaschi, Stefano Zacchini, Fabio Marchetti|2022|Inorg.Chim.Acta|541|121093|doi:10.1016/j.ica.2022.121093
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.cc29xryy
title: CCDC 2142811: Experimental Crystal Structure Determination

description: Related Article: Tania Chowdhury, Tonmoy Chakraborty, Avik Ghosh, Abhijit K. Das, Debasis Das|2022|ACS Applied Materials and Interfaces|14|32457|doi:10.1021/acsami.2c04875
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: 10.5281/zenodo.6951989
title: Comparison between MAST-U conventional and Super-X configurations through SOLPS-ITER modelling

description: Input and output files for the simulations presented in the Nuclear Fusion paper "Comparison between MAST-U conventional and Super-X configurations through SOLPS-ITER modelling"
DOI: 10.5281/zenodo.6951988
title: Comparison between MAST-U conventional and Super-X configurations through SOLPS-ITER modelling

description: Input and output files for the simulations presented in the Nuclear Fusion paper "Comparison between MAST-U conventional and Super-X configurations through SOLPS-ITER modelling"
DOI: 10.6084/m9.figshare.19785268
title: Supplementary Material for: Predicted fat mass, lean body mass, and risk of hypertension: results from a Chinese male cohort study

description: Introduction: Few studies have deciphered whether lean body mass (LBM) or fat mass (FM) is predominantly responsible for the body mass index (BMI)–hypertension association. This study aimed to assess the associations of predicted LBM and FM with hypertension risk among Chinese male adults. Methods: A prospective cohort study was conducted among 2,812 male participants free of hypertension in the Fangchenggang Area Males Health and Examination Survey in 2009. We performed multivariable Cox models and restricted cubic spline (RCS) to examine the associations of predicted LBM and FM, and BMI with hypertension, and to further explore the mediating roles of lipid and glycemic traits in the relationship between predicted FM and blood pressure. Results: Of 1,238 participants included in the cohort study, 306 (24.8%) hypertension cases were identified during a median follow-up of 3.8 years, with an incidence rate of 7.0 per 100 person-years. A positive linear-shaped association was consistently observed between BMI and hypertension (P for trend &lt; 0.001). Multivariable adjusted Cox models including predicted LBM and FM observed a positive association between predicted FM and hypertension. Compared with those in the lowest quartile of predicted FM, men in the highest quartile had a hazard ratio (HR) of 1.83 (95% confidence interval (CI): 1.13 to 2.97) for hypertension. The HR per standard deviation (SD) increase of BMI and predicted FM was 1.11 (95% CI: 1.04 to 1.19) above 23.1 kg/m2 and 1.05 (95% CI: 1.02 to 1.15) above 14.6 kg, respectively. However, predicted LBM was not associated with hypertension. In addition, high-density lipoprotein cholesterol (HDL-c) and fasting blood glucose (FBG) mediated the relationship of predicted FM with systolic blood pressure, with a mediation ratio of 37.1% and 8.2%, respectively. Furthermore, total cholesterol (TC) and triglyceride (TG) positively mediated the association of predicted FM with diastolic blood pressure, with a mediation ratio of 9.5% and 9.9%, respectively. Conclusion: Higher predicted FM might play a central role in the positive linear relationship of the BMI–hypertension association in Chinese male adults, and the link from predicted FM to blood pressure was partially mediated by TC, TG, HDL-c, and FBG.
DOI: 10.6084/m9.figshare.20418567.v1
title: Supplementary Material for: Screening of Serum miRNAs as Diagnostic Biomarkers for Lung Cancer Using the Minimal-Redundancy-Maximal-Relevance Algorithm and Random Forest Classifier Based on a Public Database

description: <b><i>Background:</i></b> Lung cancer is one of the deadliest cancers, early diagnosis of which can efficiently enhance patient’s survival. We aimed to screening out the serum miRNAs as diagnostic biomarkers for patients with lung cancer. <b><i>Methods:</i></b> A total of 416 remarkably differentially expressed miRNAs were acquired using the limma package, and next feature ranking was derived by the minimal-redundancy-maximal-relevance method. An incremental feature selection algorithm of a random forest (RF) classifier was utilized to choose the top 5 miRNA combination with the optimum predictive performance. The performance of the RF classifier of top 5 miRNAs was analyzed using the receiver operator characteristic (ROC) curve. Afterward, the classification effect of the 5-miRNA combination was validated through principal component analysis and hierarchical clustering analysis. Analysis of top 5 miRNA expressions between lung cancer patients and normal people was performed based on GSE137140 dataset, and their expression was validated by qPCR. The hierarchical clustering analysis was used to analyze the similarity of 5 miRNAs expression profiles. ROC analysis was undertaken on each miRNA. <b><i>Results:</i></b> We acquired top 5 miRNAs finally, with the Matthews correlation coefficient value as 0.988 and the area under the curve (AUC) value as 0.996. The 5 feature miRNAs were capable of distinguishing most cancer patients and normal people. Furthermore, except for the lowly expressed miR-6875-5p in lung cancer tissue, the other 4 miRNAs all expressed highly in cancer patients. Performance analysis revealed that their AUC values were 0.92, 0.96, 0.94, 0.95, and 0.93, respectively. <b><i>Conclusion:</i></b> By and large, the 5 feature miRNAs screened here were anticipated to be effective biomarkers for lung cancer.
DOI: 10.5281/zenodo.6583118
title: An inventory of school buildings in Central Sulawesi (Indonesia) developed through a mixed-mode data collection approach

description: Urban disaster risk management and reduction requires the development and periodic updating of regional building inventories. However, the development of such inventories can be very cost-intensive and time-consuming, making this a challenging task, particularly for low- and middle-income countries. We used a combination of side-walk and remote survey methods to develop an inventory of 2536 school buildings in Central Sulawesi, Indonesia. The Central Sulawesi region was affected by the 28th September 2018 Palu earthquake. The moment magnitude (Mw) 7.5 earthquake caused intense ground shaking in the epicentral area, triggering liquefaction, tsunami and major landslides, leading to over 4,000 fatalities and 165,000 displaced people. At least 1299 schools were reported to have been damaged or collapsed, affecting about 184,000 students. The developed inventory includes information on location, number of stories, occupancy characteristics (building use and population), age, type of lateral load resisting systems, building materials, structural vulnerabilities, and structural conditions following the 2018 earthquake. The developed database can be used in pre-event/long-term risk analysis and management, post-event/near-real-time loss estimation, and regional-level decision-making on school assets and related policies. The database has been made available for public use and can be readily harmonised with similar databases for other regions. This data repository also contains information on interviews with 30 school principals in the region. The interview details the damage states and functional recovery trajectory of school buildings in the 30 schools.
DOI: 10.6084/m9.figshare.17089025
title: Evaluating stability of human spine in static tasks: a combined <i>in vivo</i>-computational study

description: Various interpretations and parameters have been proposed to assess spinal stability such as antagonist muscle coactivity, trunk stiffness and spinal buckling load; however, the correlation between these parameters remains unknown. We evaluated spinal stability during different tasks while changing the external moment and load height and investigated likely relationships between different EMG- and model-based parameters (e.g., EMG coactivity ratio, trunk stiffness, force coactivity ratio) and stability margins. EMG and kinematics of 40 young healthy subjects were recorded during various quasi-static tasks. Muscle forces, trunk stiffness and stability margins were calculated by a nonlinear subject-specific EMG-assisted-optimization musculoskeletal model of the trunk. The load elevation and external moment increased muscle activities and trunk stiffness while all stability margins (i.e., buckling loads) decreased. The force coactivity ratio was strongly correlated with the hand-load stability margin (i.e., additional weight in hands to initiate instability; <i>R</i><sup>2</sup> = 0.54) demonstrating the stabilizing role of abdominal muscles. The total trunk stiffness (Pearson’s <i>r</i> = 0.96) and the sum of EMGs of back muscles (Pearson’s <i>r</i> = 0.65) contributed the most to the T1 stability margin (i.e., additional required load at T1 for instability/buckling). Force coactivity ratio and trunk stiffness can be used as alternative spinal stability metrics.
DOI: 10.6084/m9.figshare.17089025.v2
title: Evaluating stability of human spine in static tasks: a combined <i>in vivo</i>-computational study

description: Various interpretations and parameters have been proposed to assess spinal stability such as antagonist muscle coactivity, trunk stiffness and spinal buckling load; however, the correlation between these parameters remains unknown. We evaluated spinal stability during different tasks while changing the external moment and load height and investigated likely relationships between different EMG- and model-based parameters (e.g., EMG coactivity ratio, trunk stiffness, force coactivity ratio) and stability margins. EMG and kinematics of 40 young healthy subjects were recorded during various quasi-static tasks. Muscle forces, trunk stiffness and stability margins were calculated by a nonlinear subject-specific EMG-assisted-optimization musculoskeletal model of the trunk. The load elevation and external moment increased muscle activities and trunk stiffness while all stability margins (i.e., buckling loads) decreased. The force coactivity ratio was strongly correlated with the hand-load stability margin (i.e., additional weight in hands to initiate instability; <i>R</i><sup>2</sup> = 0.54) demonstrating the stabilizing role of abdominal muscles. The total trunk stiffness (Pearson’s <i>r</i> = 0.96) and the sum of EMGs of back muscles (Pearson’s <i>r</i> = 0.65) contributed the most to the T1 stability margin (i.e., additional required load at T1 for instability/buckling). Force coactivity ratio and trunk stiffness can be used as alternative spinal stability metrics.
DOI: 10.6084/m9.figshare.17089025.v1
title: Evaluating stability of human spine in static tasks: a combined <i>in vivo</i>-computational study

description: Various interpretations and parameters have been proposed to assess spinal stability such as antagonist muscle coactivity, trunk stiffness and spinal buckling load; however, the correlation between these parameters remains unknown. We evaluated spinal stability during different tasks while changing the external moment and load height and investigated likely relationships between different EMG- and model-based parameters (e.g., EMG coactivity ratio, trunk stiffness, force coactivity ratio) and stability margins. EMG and kinematics of 40 young healthy subjects were recorded during various quasi-static tasks. Muscle forces, trunk stiffness and stability margins were calculated by a nonlinear subject-specific EMG-assisted-optimization musculoskeletal model of the trunk. The load elevation and external moment increased muscle activities and trunk stiffness while all stability margins (i.e., buckling loads) decreased. The force coactivity ratio was strongly correlated with the hand-load stability margin (i.e., additional weight in hands to initiate instability; <i>R</i><sup>2</sup> = 0.54) demonstrating the stabilizing role of abdominal muscles. The total trunk stiffness (Pearson’s <i>r</i> = 0.96) and the sum of EMGs of back muscles (Pearson’s <i>r</i> = 0.65) contributed the most to the T1 stability margin (i.e., additional required load at T1 for instability/buckling). Force coactivity ratio and trunk stiffness can be used as alternative spinal stability metrics.