cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23790717.v1
title: Supplementary Material for: Left Main Coronary Artery Revascularization in Patients with Impaired Renal Function; Percutaneous Coronary Intervention vs. Coronary Artery Bypass Grafting

description: Introduction: The evidence about the optimal revascularization strategy in patients with left-main coronary artery (LMCA) disease and impaired renal function is limited. Thus, we aimed to compare the outcomes of LMCA disease revascularization (PCI vs. CABG) in patients with and without impaired renal function. Methods: This retrospective cohort study included 2138 patients recruited from 14 centers between 2015 to 2019. We compared patients with impaired renal function who had PCI (n= 316) to those who had CABG (n= 121) and compared patients with normal renal function who had PCI (n= 906) to those who had CABG (n= 795). The study outcomes were in-hospital and follow-up major adverse cardiovascular and cerebrovascular events (MACCE). Results: Multivariable logistic regression analysis showed that the risk of in-hospital MACCE was significantly higher in CABG compared to PCI in patients with impaired renal function [OR: 8.13 (95% CI: 4.19- 15.76), P&lt;0.001] and normal renal function [OR: 2.59 (95% CI: 1.79- 3.73); P&lt;0.001]. There were no differences in follow-up MACCE between CABG and PCI in patients with impaired renal function [HR: 1.14 (95% CI: 0.71- 1.81), P= 0.585] and normal renal function [HR: 1.12 (0.90- 1.39), P= 0.312]. Conclusions: PCI could have an advantage over CABG in revascularization of LMCA disease in patients with impaired renal function regarding in-hospital MACCE. The follow-up MACCE was comparable between PCI and CABG in patients with impaired and normal renal function.
DOI: 10.5517/ccdc.csd.cc2br49k
title: CCDC 2167188: Experimental Crystal Structure Determination

description: Related Article: Campbell F. R. Mackenzie, Le Zhang, David B. Cordes, Alexandra M. Z. Slawin, Ifor D.W. Samuel, Eli Zysman‐Colman|2023|Adv. Opt. Mater.|11|2201495|doi:10.1002/adom.202201495
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.cc2br4bl
title: CCDC 2167189: Experimental Crystal Structure Determination

description: Related Article: Campbell F. R. Mackenzie, Le Zhang, David B. Cordes, Alexandra M. Z. Slawin, Ifor D.W. Samuel, Eli Zysman‐Colman|2023|Adv. Opt. Mater.|11|2201495|doi:10.1002/adom.202201495
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.cc2ckjt9
title: CCDC 2191601: Experimental Crystal Structure Determination

description: Related Article: David L. Cain, Niall A. Anderson, David B. Cordes, Alexandra M. Z. Slawin, Allan J. B. Watson|2022|J.Org.Chem.|87|15559|doi:10.1021/acs.joc.2c02099
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.cc2fwvt0
title: CCDC 2261103: Experimental Crystal Structure Determination

description: Related Article: Jieqiong Hou, Huixian Guo, Tao Long, Wentao Huang, Jiaran Chen, Yanmei Chen, Wenwen Dong|2023|J.Mol.Struct.|1292|136163|doi:10.1016/j.molstruc.2023.136163
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.cc2fwvv1
title: CCDC 2261104: Experimental Crystal Structure Determination

description: Related Article: Jieqiong Hou, Huixian Guo, Tao Long, Wentao Huang, Jiaran Chen, Yanmei Chen, Wenwen Dong|2023|J.Mol.Struct.|1292|136163|doi:10.1016/j.molstruc.2023.136163
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.cc2fwvw2
title: CCDC 2261105: Experimental Crystal Structure Determination

description: Related Article: Jieqiong Hou, Huixian Guo, Tao Long, Wentao Huang, Jiaran Chen, Yanmei Chen, Wenwen Dong|2023|J.Mol.Struct.|1292|136163|doi:10.1016/j.molstruc.2023.136163
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: 10.34740/kaggle/dsv/6203681
title: Egypt Gold Prices | Daily Updated
DOI: 10.18112/openneuro.ds004647.v1.0.2
title: The neurocognitive role of working memory load when Pavlovian motivational control affects instrumental learning
DOI: 10.5281/zenodo.8189080
title: Supplementary data to "What are the effects of temperature on plasticity, shape symmetry and seasonal variation in freshwater benthic green microalga Micrasterias thomasiana?"

description: The supplementary data consist of the files including the landmark coordinates of Micrasterias thomasiana semicells used for the analyses described in the manuscript submitted to Aquatic Ecology. The coordinates are presented in the TPS format.
DOI: 10.5281/zenodo.8189079
title: Supplementary data to "What are the effects of temperature on plasticity, shape symmetry and seasonal variation in freshwater benthic green microalga Micrasterias thomasiana?"

description: The supplementary data consist of the files including the landmark coordinates of Micrasterias thomasiana semicells used for the analyses described in the manuscript submitted to Aquatic Ecology. The coordinates are presented in the TPS format.
DOI: 10.6084/m9.figshare.21711776
title: Processed VSM measurement data

description: This folder contains the hysteresis parameters and the data of hysteresis loops after correction. The corrections made are indicated in table "HysteresisParameter.csv" in the folder "Sample lists, measurements and results" of this data collection.
DOI: 10.5281/zenodo.8047411
title: Axonal mRNA localization in response to neurotrophin treatment

description: This file contains the required processed data required to implement the code located at https://github.com/RLuisier/AxonLoc that related to the manuscript Axonal mRNA localization in response to neurotrophin treatment by Luisier et al. 2023.
DOI: 10.5281/zenodo.8047412
title: Axonal mRNA localization in response to neurotrophin treatment

description: This file contains the required processed data required to implement the code located at https://github.com/RLuisier/AxonLoc that related to the manuscript Axonal mRNA localization in response to neurotrophin treatment by Luisier et al. 2023.
DOI: 10.5281/zenodo.8189110
title: Axonal mRNA localization in response to neurotrophin treatment

description: This file contains the required processed data required to implement the code located at https://github.com/RLuisier/AxonLoc that related to the manuscript Axonal mRNA localization in response to neurotrophin treatment by Luisier et al. 2023.
DOI: 10.21227/cq6v-jm29
title: Climatic, population and daily water consumption dataset of sub tropical humid region of Pakistan

description: Seven years of water consumption data, along with population data, were manually collected in collaboration with the local municipality office. This data was then combined with climatic data to model the proposed machine learning algorithm. The weather data was recorded for a period of 7 years using precise meteorological instruments installed in Islamabad at coordinates 33.64° N and 72.98° E, with an elevation of 500 meters above sea level. The novel training dataset contains seven variables, providing daily information on population count, water consumption in cubic meters, ambient temperature, dewpoint temperature, percentage humidity, wind speed, and precipitation in inches, spanning seven years. This dataset offers valuable insights into the local population of selected sites and their water consumption, information that was not previously curated. The population data is categorized into two sub-groups: permanent residents and seasonal residents. Permanent residents reside in residential areas throughout the year, while seasonal residents live in the area for 8-10 months each year. The urban site selected for water consumption analysis in this dataset comprises three overhead tanks (OHTs) filled by nine different water pumps installed at distant locations. The data collection was carried out using the following instruments: 1. Campbell CS 215l: Used to record ambient temperature and relative humidity.2. NRG 40H Anemometer: Utilized for recording wind speed.3. Campbell CS 100: Employed for measuring barometric pressure. Overall, this dataset and the information gathered from the instruments will be instrumental in developing and improving the proposed machine learning algorithm for water consumption prediction and analysis.
DOI: 10.5281/zenodo.8032578
title: Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images - dataset

description: This dataset includes 3 files with segmentation results for 3 different ODT reconstructions of SH-SY5Y neuroblastoma cell. The segmentation results contain: 3D binary masks of biological cells obtained through Cellpose [1] and ODT-SAS; 3D binary masks of organelles: nucleoli and lipid structures (LS) obtained through slice-by-slice manual segmentation and ODT-SAS. All files are .*mat files and consist of 7 variables: RECON – tomographic reconstruction of SH-SY5Y neuroblastoma cell;<br> n_imm – refractive index of object immersion medium;<br> dx – object space sample size in XY [\(\mu m\)];<br> rayXY – xy-coordinates of illumination vectors; maskManual – table with manually determined 3D binary masks of organelles;<br> maskCellpose – 3D binary mask of biological cell obtained through Cellpose;<br> maskODTSAS – table with 3D binary masks of biological cell and their organelles obtained through ODT-SAS.<br> <br> Access a particular 3D binary mask from 'maskManual' and 'maskODTSAS' tables, using the following names: 'Cell', 'Nucleoli', 'LS'.<br> For example: <pre><code>cellMask = maskODTSAS.Cell{1};</code></pre> <br> [1] Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. Nature methods, 18(1), 100-106.
DOI: 10.5281/zenodo.8032577
title: Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images - dataset

description: This dataset includes 4 files with segmentation results for 4 different ODT reconstructions of SH-SY5Y neuroblastoma cell. The segmentation results contain: 3D binary masks of biological cells obtained through Cellpose [1] and ODT-SAS; 3D binary masks of organelles: nucleoli and lipid structures (LS) obtained through slice-by-slice manual segmentation and ODT-SAS. All files are .*mat files. The files <em>REC_SH-SY5Y_1.mat, REC_SH-SY5Y_2.mat</em> and<em> REC_SH-SY5Y_3.mat</em> consist of 7 variables: RECON – tomographic reconstruction of SH-SY5Y neuroblastoma cell;<br> n_imm – refractive index of object immersion medium;<br> dx – object space sample size in XY [\(\mu m\)];<br> rayXY – xy-coordinates of illumination vectors; maskManual – table with manually determined 3D binary masks of organelles;<br> maskCellpose – 3D binary mask of biological cell obtained through Cellpose;<br> maskODTSAS – table with 3D binary masks of biological cell and their organelles obtained through ODT-SAS. File <em>REC_SH-SY5Y_4.mat</em> includes masks for the ODT-SAS and Cellpose segmentation of three closely packed cells and consists of 5 variables: RECON, n_imm, dx, maskCellpose and maskODTSAS.<br> <br> Access a particular 3D binary mask from 'maskManual' and 'maskODTSAS' tables, using the following names: 'Cell', 'Nucleoli', 'LS'.<br> For example: <pre><code>cellMask = maskODTSAS.Cell{1};</code></pre> <br> [1] Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. Nature methods, 18(1), 100-106.
DOI: 10.6084/m9.figshare.23104817
title: Nocturnal Temperature Physiology Data - 2020-2022

description: All raw environmental and physiology data published in the article; <br> "Night-time warming in the field reduces nocturnal stomatal conductance and grain yield but does not alter daytime physiological responses" New Phytologist (2023) <br> The data includes; <br> - Field environmental data - T-FACE setup environmental data - Gas exchange data - Porometer data - Leaf water potential dta - Water-solube carbohydrate, carbon and nitrogen data - Harvest data <br>
DOI: 10.6084/m9.figshare.23104817.v1
title: Nocturnal Temperature Physiology Data - 2020-2022

description: All raw environmental and physiology data published in the article; <br> "Night-time warming in the field reduces nocturnal stomatal conductance and grain yield but does not alter daytime physiological responses" New Phytologist (2023) <br> The data includes; <br> - Field environmental data - T-FACE setup environmental data - Gas exchange data - Porometer data - Leaf water potential dta - Water-solube carbohydrate, carbon and nitrogen data - Harvest data <br>
DOI: 10.6084/m9.figshare.23197373
title: Supplementary Material for: Attempt to establish prognostic predictive system for hepatocellular carcinoma using artificial intelligence for assistance with selection of treatment modality

description: Background/Aim: Because of recent developments in treatments for hepatocellular carcinoma (HCC), methods for determining suitable therapy for initial or recurrent HCC have become important. This study used artificial intelligence (AI) findings to establish a system for predicting prognosis of HCC patients at time of reoccurrence based on clinical data as a reference for selection of treatment modalities. Material/Methods: As a training cohort, 5701 observations obtained at the initial and each subsequent treatment for recurrence from 1985 HCC patients at a single center from 2000 to 2021 were used. The validation cohort was 5692 observations from patients at multiple centers obtained at the time of the initial treatment. An AI calculating system (PRAID) was constructed based on 25 clinical factors noted at each treatment from the training cohort, then predictive prognostic values for one- and three-year survival in both cohorts were evaluated. Results: After exclusion of patients lacking clinical data regarding albumin-bilirubin grade (ALBI) and/or tumor-node-metastasis stage (TNM), ALBI-TNM (ALBI-T) and modified ALBI-T scores confirmed that prognosis for patients in both cohorts was similar. The area under the curve (AUC) for prediction of both one- and three-year survival in the validation cohort were 0.841 [sensitivity 0.933 (95%CI 0.925-0.940), specificity 0.517 (95%CI 0.484-0.549)] and 0.796 [sensitivity 0.806 (95%CI 0.790-0.821), specificity 0.646 (95%CI 0.624-0.668)], respectively. Conclusion: The present PRAID system might provide useful prognostic information related to short and medium survival for decision making regarding best therapeutic modality for both initial and recurrent HCC cases.
DOI: 10.6084/m9.figshare.23197373.v1
title: Supplementary Material for: Attempt to establish prognostic predictive system for hepatocellular carcinoma using artificial intelligence for assistance with selection of treatment modality

description: Background/Aim: Because of recent developments in treatments for hepatocellular carcinoma (HCC), methods for determining suitable therapy for initial or recurrent HCC have become important. This study used artificial intelligence (AI) findings to establish a system for predicting prognosis of HCC patients at time of reoccurrence based on clinical data as a reference for selection of treatment modalities. Material/Methods: As a training cohort, 5701 observations obtained at the initial and each subsequent treatment for recurrence from 1985 HCC patients at a single center from 2000 to 2021 were used. The validation cohort was 5692 observations from patients at multiple centers obtained at the time of the initial treatment. An AI calculating system (PRAID) was constructed based on 25 clinical factors noted at each treatment from the training cohort, then predictive prognostic values for one- and three-year survival in both cohorts were evaluated. Results: After exclusion of patients lacking clinical data regarding albumin-bilirubin grade (ALBI) and/or tumor-node-metastasis stage (TNM), ALBI-TNM (ALBI-T) and modified ALBI-T scores confirmed that prognosis for patients in both cohorts was similar. The area under the curve (AUC) for prediction of both one- and three-year survival in the validation cohort were 0.841 [sensitivity 0.933 (95%CI 0.925-0.940), specificity 0.517 (95%CI 0.484-0.549)] and 0.796 [sensitivity 0.806 (95%CI 0.790-0.821), specificity 0.646 (95%CI 0.624-0.668)], respectively. Conclusion: The present PRAID system might provide useful prognostic information related to short and medium survival for decision making regarding best therapeutic modality for both initial and recurrent HCC cases.
DOI: 10.6084/m9.figshare.13360296
title: The effect of confounding variables in studies of lead exposure and IQ

description: Methods proposed to address confounding variables frequently do not adequately distinguish confounding from covariation. A confounder is a variable that correlates both with the outcome and the major exposure variable. Accurate treatment of confounding is crucial to low dose extrapolation of the effects of chemical exposures based on epidemiology studies. This study explores the limitations of current regression models in extrapolation to the low dose region of the dose-response curve due to the existence of unrecognized and uncontrolled confounding, using epidemiological data for lead. Based on the reported data in analyses by Lanphear and colleagues and Crump and colleagues, and drawing on other studies, Wilson and Wilson considered maternal IQ, HOME score, SES, parental education, birthweight, smoking, and race as characteristic variables which may have interaction effects. This analysis identifies confounding variables based on the seven longitudinal cohorts in analyses conducted by Lanphear and colleagues and by Crump and colleagues and confirms maternal IQ, HOME score, maternal education and maternal marital status at birth are “Highly Likely” confounders, while race is a “Likely” confounder. The cohort data were reanalyzed using the methods presented by Crump and colleagues while also considering the interaction among the identified confounding variables. This analysis determined that confounders influence IQ estimates in a quantifiable way that may exceed or at least obscure previously-reported effects of blood lead on IQ with blood lead levels below 5 µg/dL; however, limitations in the datasets make predictions of the low dose dose-response analysis questionable.
DOI: 10.6084/m9.figshare.13286478
title: Towards a mechanism-based approach for the prediction of nongenotoxic carcinogenic potential of agrochemicals

description: Chemical substances are subjected to assessment of genotoxic and carcinogenic effects before being marketed to protect man and the environment from health risks. For agrochemicals, the long-term rodent carcinogenicity study is currently required from a regulatory perspective. Although it is the current mainstay for the detection of nongenotoxic carcinogens, carcinogenicity studies are shown to have prominent weaknesses and are subject to ethical and scientific debate. A transition toward a mechanism-based weight-of-evidence approach is considered a requirement to enhance the prediction of carcinogenic potential for environmental (agro)chemicals. The resulting approach should make optimal use of innovative (computational) tools and be less animal demanding. To identify the various mode of actions (MOAs) underlying the nongenotoxic carcinogenic potential of agrochemicals, we conducted an extensive analysis of 411 unique agrochemicals that have been evaluated for carcinogenicity by the United States Environmental Protection Agency (US EPA) and the European Chemicals Agency (ECHA). About one-third of these substances could be categorized as nongenotoxic carcinogens with an average of approximately two tumor types per substance, observed in a variety of organs. For two-third of the tumor cases, an underlying MOA (network) could be identified. This analysis demonstrates that a limited set of MOA (networks) is underlying nongenotoxic carcinogenicity of agrochemicals, illustrating that the transition toward a MOA-driven approach appears manageable. Ultimately the approach should cover relevant MOAs and its associated key events; this will also facilitate the evaluation of the human relevance. This manuscript describes the results of the analysis while identifying knowledge gaps and necessities to achieve a mechanism-based weight-of-evidence approach.
DOI: 10.6084/m9.figshare.13360296.v1
title: The effect of confounding variables in studies of lead exposure and IQ

description: Methods proposed to address confounding variables frequently do not adequately distinguish confounding from covariation. A confounder is a variable that correlates both with the outcome and the major exposure variable. Accurate treatment of confounding is crucial to low dose extrapolation of the effects of chemical exposures based on epidemiology studies. This study explores the limitations of current regression models in extrapolation to the low dose region of the dose-response curve due to the existence of unrecognized and uncontrolled confounding, using epidemiological data for lead. Based on the reported data in analyses by Lanphear and colleagues and Crump and colleagues, and drawing on other studies, Wilson and Wilson considered maternal IQ, HOME score, SES, parental education, birthweight, smoking, and race as characteristic variables which may have interaction effects. This analysis identifies confounding variables based on the seven longitudinal cohorts in analyses conducted by Lanphear and colleagues and by Crump and colleagues and confirms maternal IQ, HOME score, maternal education and maternal marital status at birth are “Highly Likely” confounders, while race is a “Likely” confounder. The cohort data were reanalyzed using the methods presented by Crump and colleagues while also considering the interaction among the identified confounding variables. This analysis determined that confounders influence IQ estimates in a quantifiable way that may exceed or at least obscure previously-reported effects of blood lead on IQ with blood lead levels below 5 µg/dL; however, limitations in the datasets make predictions of the low dose dose-response analysis questionable.