cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.17696696.v1
title: Phytochemical rich Himalayan <i>Rhododendron arboreum</i> petals inhibit SARS-CoV-2 infection <i>in vitro</i>

description: Phytochemicals with potential to competitively bind to the host receptors or inhibit SARS-CoV-2 replication, may prove to be useful as adjunct therapeutics for COVID-19. We profiled and investigated the phytochemicals of <i>Rhododendron arboreum</i> petals sourced from Himalayan flora, undertook <i>in vitro</i> studies and found it as a promising candidate against SARS-CoV-2. The phytochemicals were reported in various scientific investigations to act against a range of virus <i>in vitro</i> and <i>in vivo,</i> which prompted us to test against SARS-CoV-2. <i>In vitro</i> assays of <i>R. arboreum</i> petals hot aqueous extract confirmed dose dependent reduction in SARS-CoV-2 viral load in infected Vero E6 cells (80% inhibition at 1 mg/ml; IC50 = 173 µg/ml) and phytochemicals profiled were subjected to molecular docking studies against SARS CoV-2 target proteins. The molecules 5-O-Feruloyl-quinic acid, 3-Caffeoyl-quinic acid, 5-O-Coumaroyl-D-quinic acid, Epicatechin and Catechin showed promising binding affinity with SARS-CoV-2 Main protease (M<sup>Pro</sup>; PDB ID: 6LU7; responsible for viral replication) and Human Angiotensin Converting Enzyme-2 (ACE2; PDB ID: 1R4L; mediate viral entry in the host). Molecular dynamics (MD) simulation of 5-O-Feruloyl-quinic acid, an abundant molecule in the extract complexed with the target proteins showed stable interactions. Taken together, the phytochemical profiling, <i>in silico</i> analysis and <i>in vitro</i> anti-viral assay revealed that the petals extract act upon M<sup>Pro</sup> and may be inhibiting SARS-CoV-2 replication. This is the first report highlighting <i>R. arboreum</i> petals as a reservoir of antiviral phytochemicals with potential anti-SARS-CoV-2 activity using an <i>in vitro</i> system. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.17705133
title: Serine 106 preserves the tertiary structure, function, and stability of a cyclophilin from <i>Staphylococcus aureus</i>

description: SaCyp, a staphylococcal cyclophilin involved in both protein folding and pathogenesis, has a Ser residue at position 106 and a Trp residue at position 136. While Ser 106 of SaCyp aligned with a cyclosporin A (CsA) binding Ala residue, its Trp 136 aligned with a Trp or a Phe residue of most other cyclophilins. To demonstrate the exact roles of Ser 106 and Trp 136 in SaCyp, we have elaborately studied rCyp[S106A] and rCyp[W136A], two-point mutants of a recombinant SaCyp (rCyp) harboring an Ala substitution at positions 106 and 136, respectively. Of the mutants, rCyp[W136A] showed the rCyp-like CsA binding affinity and peptidyl-prolyl <i>cis</i>-<i>trans</i> isomerase (PPIase) activity. Conversely, the PPIase activity, CsA binding affinity, stability, tertiary structure, surface hydrophobicity, and Trp accessibility of rCyp[S106A] notably differed from those of rCyp. The computational experiments also reveal that the structure, dimension, and fluctuation of SaCyp are not identical to those of SaCyp[S106A]. Furthermore, Ser at position 106 of SaCyp, compared to Ala at the same position, formed a higher number of non-covalent bonds with CsA. Collectively, Ser 106 is an indispensable residue for SaCyp that keeps its tertiary structure, function, and stability intact. Communicated by Ramaswamy H. Sarma
DOI: 10.17028/rd.lboro.21904113.v1
title: Supplementary information files for Applying the COM-B model to understand wearable activity tracker use in children and adolescents

description: Supplementary files for article Applying the COM-B model to understand wearable activity tracker use in children and adolescents <br> Aim: Wearable activity trackers (wearables) are increasingly popular intervention tools for increasing child and adolescent physical activity (PA) levels. However, the large-scale habitual use of wearables in children and adolescents is unknown. This study investigated the prevalence of wearable use in children and adolescents, and what factors impact their use. <br> Subjects and methods: This study utilised a cross-sectional survey and the ‘Capability, Opportunity, Motivation and Behaviour’ (COM-B) model was applied to explore what child/adolescent and parental characteristics impact wearable use. Parents/guardians of 5- to 17-year-olds were invited to complete the survey. The survey was open internationally, and consisted of between 19 and 23 questions, depending on child/adolescent wearable use. Multinomial logistic regression analyses were conducted to explore variables impacting wearable use, in children (5 to 9 years) and adolescents (10 to 17 years). <br> Results: The survey was completed by 652 parents, representing 831 children/adolescents. Most children/adolescents had never used a wearable (n = 429; 51.6%), and 252 (30.3%) and 150 (18.1%) currently or had previously used a wearable, respectively. Child age and sex, capability, opportunity and motivation for PA were associated with wearable use, and differences were present between child (5 to 9 years) and adolescent (10 to 17 years) wearable use. <br> Conclusions: This study offers a novel contribution to the understanding of child and adolescent habitual wearable use, and what impacts wearable use in these age groups.
DOI: 10.5281/zenodo.7540321
title: Model data archive for a model-data intercomparison of the Eocene-Oligocene transition

description: This data package contains data used for an model-data intercomparison originally<br> published in: D. K. Hutchinson, H. K. Coxall, D. J. Lunt, M. Steinthorsdottir, A. M. de Boer, M. Baatsen, A. von der Heydt, M. Huber, A. T. Kennedy-Asser, L. Kunzmann, J.-B. Ladant, C. H. Lear, K. Moraweck, P. N. Pearson, E. Piga, M. J. Pound, U. Salzmann, H. D. Scher, W. P. Sijp, K. K. Śliwińska, P. A. Wilson, and Z. Zhang, 2021: <strong>The Eocene-Oligocene transition: a review of marine and terrestrial proxy data, models and model-data comparisons</strong>, Climate of the Past, 17, 269-315.<br> https://doi.org/10.5194/cp-17-269-2021 These data are also used in a further model-data intercomparison of Antarctic temperatures: Emily Tibbett, Natalie J Burls, David K. Hutchinson, Sarah J Feakins, (2023), <strong>Proxy-Model Comparison for the Eocene-Oligocene Transition in Southern High Latitudes, Paleoceanography and Paleocliamtology</strong>, In Review. Pre-print avaiable from:<br> https://www.authorea.com/doi/full/10.1002/essoar.10511735.2 The package contains surface air temperature and sea surface temperature from an ensemble of model simulations of the Eocene-Oligocene transition. These data are provided at annual and monthly frequency. They are also provided on the original model grid, and an interpolated common grid used for the intercomparison. (The common grid is based on the HadCM3BL model grid.) All data are provided in NETCDF format with self-describing variable names. The name and explanation of the interpolated data files are contained in:<br> <strong>table_of_experiments.xlsx</strong> Please read that spreadsheet to interpret the filenames, and see <strong>Table 2 (p291)</strong> of Hutchinson et al (2021) for experiment descriptions. Please also be mindful to cite the original authors of the simulations when using these data, whose work made this dataset possible. The appropriate citations are listed below: Reference DOI link <br> <strong>Baatsen et al (2020)</strong> https://doi.org/10.5194/cp-16-2573-2020 <br> <strong>Goldner et al (2014)</strong> https://doi.org/10.1038/nature13597 <br> <strong>Ladant et al (2014a,b)</strong> https://doi.org/10.5194/cp-10-1957-2014 https://doi.org/10.1002/2013PA002593 <br> <strong>Hutchinson et al (2018, 2019) </strong>https://doi.org/10.5194/cp-14-789-2018 https://doi.org/10.1038/s41467-019-11828-z <br> <strong>Kennedy et al (2015)</strong> https://doi.org/10.1098/rsta.2014.0419 <br> <strong>Zhang et al (2012, 2014)</strong> https://doi.org/10.5194/gmd-5-523-2012 https://doi.org/10.1038/nature13705 <br> <strong>Sijp et al (2009)</strong> https://doi.org/10.1175/2009JCLI3003.1
DOI: 10.5519/qd.xvdc5ikx
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (13 records)
DOI: 10.5281/zenodo.4652801
title: CCG Starter Data Kit: Rwanda

description: A starter data kit for Rwanda
DOI: 10.5281/zenodo.4756687
title: CCG Starter Data Kit: Rwanda

description: A starter data kit for Rwanda
DOI: 10.5281/zenodo.5733328
title: CCG Starter Data Kit: Rwanda

description: A starter data kit for Rwanda
DOI: 10.5281/zenodo.7540320
title: CCG Starter Data Kit: Rwanda

description: A starter data kit for Rwanda
DOI: 10.5281/zenodo.4652800
title: CCG Starter Data Kit: Rwanda

description: A starter data kit for Rwanda
DOI: 10.5281/zenodo.7540388
title: Attenuation of Methylglyoxal-Induced Cell Toxicity by Quercetin

description: The data of this study are available from the authors upon reasonable request.
DOI: 10.5281/zenodo.7540387
title: Attenuation of Methylglyoxal-Induced Cell Toxicity by Quercetin

description: The data of this study are available from the authors upon reasonable request.
DOI: 10.5281/zenodo.7540285
title: Dataset related to article "Imaging the kidney with an unconventional scanning electron microscopy technique: analysis of the subpodocyte space in diabetic mice".

description: <strong>Datasets:</strong> <strong>Table 1_Systemic parameters.xlsx </strong>- dataset related to the systemic parameters. These data are presented in Table 1. <strong>Figure 6_Morphometric analysis.xlsx </strong>- dataset related to the morphometric characterization of the subpodocyte space and podocytes. These data are presented in Figure 6. <strong>Abstract of the manuscript</strong> Transmission electron microscopy (TEM) remains the gold standard for renal histopathological diagnoses, given its higher resolving power compared to light microscopy. However, it imposes several limitations on pathologists, including longer sample preparation time and a small observation area. To overcome these, we introduced a scanning electron microscopy (SEM) technique for imaging resin-embedded semi-thin sections of renal tissue. We developed a rapid tissue preparation protocol for experimental models and human biopsies which, alongside SEM digital imaging acquisition of secondary electrons (SE-SEM), enables fast electron microscopy examination, with a resolution similar to that achieved by TEM. We used this unconventional SEM imaging approach to investigate the subpodocyte space (SPS) in BTBR <em>ob/ob</em> mice with type 2 diabetes. Analysis of semi-thin sections with secondary electrons revealed that the SPS had expanded in volume and covered large areas of the glomerular basement membrane, forming wide spaces between the podocyte body and underlying filtering membrane. Our results show that SE-SEM is a valuable tool for imaging the kidney at the ultrastructural level, filling the magnification gap between light microscopy and TEM, and reveal that in diabetic mice the SPS is larger than in normal controls, which is associated with podocyte damage and impaired kidney function.
description: {"references": ["Conti S, Remuzzi G, Benigni A, Tomasoni S. Imaging the kidney with an unconventional scanning electron microscopy technique: analysis of the subpodocyte space in diabetic mice. Int J Mol Sci. 2022 Feb 1;23(3):1699. doi: 10.3390/ijms23031699. PMID: 35163622; PMCID: PMC8836024."]}
DOI: 10.5281/zenodo.7540284
title: Dataset related to article "Imaging the kidney with an unconventional scanning electron microscopy technique: analysis of the subpodocyte space in diabetic mice".

description: <strong>Datasets:</strong> <strong>Table 1_Systemic parameters.xlsx </strong>- dataset related to the systemic parameters. These data are presented in Table 1. <strong>Figure 6_Morphometric analysis.xlsx </strong>- dataset related to the morphometric characterization of the subpodocyte space and podocytes. These data are presented in Figure 6. <strong>Abstract of the manuscript</strong> Transmission electron microscopy (TEM) remains the gold standard for renal histopathological diagnoses, given its higher resolving power compared to light microscopy. However, it imposes several limitations on pathologists, including longer sample preparation time and a small observation area. To overcome these, we introduced a scanning electron microscopy (SEM) technique for imaging resin-embedded semi-thin sections of renal tissue. We developed a rapid tissue preparation protocol for experimental models and human biopsies which, alongside SEM digital imaging acquisition of secondary electrons (SE-SEM), enables fast electron microscopy examination, with a resolution similar to that achieved by TEM. We used this unconventional SEM imaging approach to investigate the subpodocyte space (SPS) in BTBR <em>ob/ob</em> mice with type 2 diabetes. Analysis of semi-thin sections with secondary electrons revealed that the SPS had expanded in volume and covered large areas of the glomerular basement membrane, forming wide spaces between the podocyte body and underlying filtering membrane. Our results show that SE-SEM is a valuable tool for imaging the kidney at the ultrastructural level, filling the magnification gap between light microscopy and TEM, and reveal that in diabetic mice the SPS is larger than in normal controls, which is associated with podocyte damage and impaired kidney function.
description: {"references": ["Conti S, Remuzzi G, Benigni A, Tomasoni S. Imaging the kidney with an unconventional scanning electron microscopy technique: analysis of the subpodocyte space in diabetic mice. Int J Mol Sci. 2022 Feb 1;23(3):1699. doi: 10.3390/ijms23031699. PMID: 35163622; PMCID: PMC8836024."]}
DOI: 10.5519/qd.jgm3spo4
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (6 records)
DOI: 10.5281/zenodo.7534922
title: Raw data files for the manuscript entitled "Hybridization of Synthetic Humins with a Metal–Organic Framework for Precious Metal Recovery and Reuse"

description: Datasets for the data presented in the manuscript entitled "Hybridization of Synthetic Humins with a Metal–Organic Framework for Precious Metal Recovery and Reuse" published in ACS Applied Materials and Interfaces.
DOI: 10.5281/zenodo.7534921
title: Raw data files for the manuscript entitled "Hybridization of Synthetic Humins with a Metal–Organic Framework for Precious Metal Recovery and Reuse"

description: Datasets for the data presented in the manuscript entitled "Hybridization of Synthetic Humins with a Metal–Organic Framework for Precious Metal Recovery and Reuse" published in ACS Applied Materials and Interfaces.
DOI: 10.14768/c417e612-015d-4812-9b59-294a6570c7c3
title: STRATEOLE2 - C1: TSEN - Thermodynamics SENsor created

description: In-situ temperature, pressure and wind observations.
DOI: 10.5281/zenodo.7540406
title: Long-CoDe: A Longitudinal Twitter Dataset of Depression in the COVID Era

description: # Long-CoDe: A Longitudinal Twitter Dataset of Depression in the COVID Era ## Content of Long-CoDe Long-CoDe contains 15 million tweets from 597 depressed users and 804 control users. The temporal span of collected tweets is from Jan. 1st, 2019 to Dec. 31, 2022. We do not have the right to release text of the tweets due to privacy issues. We include user-ids, tweet-ids, date of posting, and label of the users in this dataset. We disguised user-ids with C00n and D00n to protect privacy and to represent their group labels. Tweets can be accessed with tweet-ids from the Twitter-APIs. The format of the data is like follows: | user-id | tweet-id | label |<br> | ------- | -------- | -------------- |<br> | C001| xxxxxxxx | 0 (Control) |<br> | D001| xxxxxxxx | 1 (Depression) | ## Quality All users in both depressed and control groups posted steadily from 2019 to 2022. Control users were selected from 43 trending topics in 2020 evenly from Feb. to Dec. with broad interests. ## Structure The Long-CoDe dataset is a single CSV file with tabular data. The format of the CSV file is as follows: | user_id | tweet_id | label<br> | ------- | -------- | -------------- |<br> | C001| xxxxxxxx | 0 (Control) |<br> | D001| xxxxxxxx | 1 (Depression) | ## Potential uses of the dataset General analysis of tweets from the labeled depressed users, compared with the control.<br> Benchmark binary user-classification with contents of tweets or other extracted features.<br> Analyze the impact of Covid-19 on both depression and control groups in multiple phases of the pandemic. ## Data Collection The data collection procedure contains four steps: (a) identifying depressed users, (b) selecting control users, (c) collecting tweets in 2020 and identifying regular users, and (d) collecting tweets from 2019 to 2022 for all selected users. ### Identifying Depressed Users To identify targeted depressed users, we crawled tweets containing depression self-claims, such as ”I am/was/have been diagnosed with depression”, using regular expressions from Feb 2020 to Dec 2020,when Covid-19 had a significant influence on our society. The data was manually annotated, and keep the users who did a valid self-claim on depression diagnosis. ### Identifying Control User In an attempt to create a diverse control user population, we gathered users that have shown interests in a wide array of trending topics, so that this would result in users with different backgrounds. From that, we randomly selected 6,200 unique users, and filtered out the users who ever made depression claims to give us a total of 5,929 control users. ### Identifying Regular Users For each user from both groups, we crawled the tweets (without re-tweets) from Feb. to Dec. in 2020 and analyzed their posting behaviors. We keep the users who have stable posting behaviors. (posting between 75 and 205 tweets per month) ### Collecting tweets from 2019 to 2022 We crawled all tweets for each of the identified regular users for four years from Jan., 2019 to Dec., 2022.<br>
DOI: 10.5281/zenodo.7540405
title: Long-CoDe: A Longitudinal Twitter Dataset of Depression in the COVID Era

description: # Long-CoDe: A Longitudinal Twitter Dataset of Depression in the COVID Era ## Content of Long-CoDe Long-CoDe contains 15 million tweets from 597 depressed users and 804 control users. The temporal span of collected tweets is from Jan. 1st, 2019 to Dec. 31, 2022. We do not have the right to release text of the tweets due to privacy issues. We include user-ids, tweet-ids, date of posting, and label of the users in this dataset. We disguised user-ids with C00n and D00n to protect privacy and to represent their group labels. Tweets can be accessed with tweet-ids from the Twitter-APIs. The format of the data is like follows: | user-id | tweet-id | label |<br> | ------- | -------- | -------------- |<br> | C001| xxxxxxxx | 0 (Control) |<br> | D001| xxxxxxxx | 1 (Depression) | ## Quality All users in both depressed and control groups posted steadily from 2019 to 2022. Control users were selected from 43 trending topics in 2020 evenly from Feb. to Dec. with broad interests. ## Structure The Long-CoDe dataset is a single CSV file with tabular data. The format of the CSV file is as follows: | user_id | tweet_id | label<br> | ------- | -------- | -------------- |<br> | C001| xxxxxxxx | 0 (Control) |<br> | D001| xxxxxxxx | 1 (Depression) | ## Potential uses of the dataset General analysis of tweets from the labeled depressed users, compared with the control.<br> Benchmark binary user-classification with contents of tweets or other extracted features.<br> Analyze the impact of Covid-19 on both depression and control groups in multiple phases of the pandemic. ## Data Collection The data collection procedure contains four steps: (a) identifying depressed users, (b) selecting control users, (c) collecting tweets in 2020 and identifying regular users, and (d) collecting tweets from 2019 to 2022 for all selected users. ### Identifying Depressed Users To identify targeted depressed users, we crawled tweets containing depression self-claims, such as ”I am/was/have been diagnosed with depression”, using regular expressions from Feb 2020 to Dec 2020,when Covid-19 had a significant influence on our society. The data was manually annotated, and keep the users who did a valid self-claim on depression diagnosis. ### Identifying Control User In an attempt to create a diverse control user population, we gathered users that have shown interests in a wide array of trending topics, so that this would result in users with different backgrounds. From that, we randomly selected 6,200 unique users, and filtered out the users who ever made depression claims to give us a total of 5,929 control users. ### Identifying Regular Users For each user from both groups, we crawled the tweets (without re-tweets) from Feb. to Dec. in 2020 and analyzed their posting behaviors. We keep the users who have stable posting behaviors. (posting between 75 and 205 tweets per month) ### Collecting tweets from 2019 to 2022 We crawled all tweets for each of the identified regular users for four years from Jan., 2019 to Dec., 2022.<br>
DOI: 10.5281/zenodo.7540396
title: Haptic simulation for emergency procedures in nursing training - Data collected

description: Data collected during the evaluation presented in "Haptic simulation for emergency procedures in nursing training" paper.
DOI: 10.6084/m9.figshare.21904176
title: Supplementary Material for: Effects of Cognitive Training in Flanker Task on Mobility of Older Adults: A Randomized Controlled Trail

description: Introduction: Cognitive function is considered to be correlated with the older adults’ mobility. Flanker task, reflecting the conflict resolution, is barely explored as a simple approach to train the elderly’s cognitive function. Hence, the first aim of this study is to explore the effect of cognitive intervention adopting flanker task on mobility. The second aim of this study is to assess the effect of cognitive training by comparing it with physical training to disclose how much dosage of cognitive training can have the equal improvement of mobility with physical training. Methods: 232 older adults (age, mean ± SD: 66.16 ± 7.08, female: 74.3%) were retrained and assigned into three groups (cognitive intervention group, physical exercise intervention group, and control group). The intervention lasted for 22 weeks (cognitive intervention: flanker task, 30 min per session, 3 sessions a week; physical exercise intervention: 30 min muscle and balance training session, twice a week, and 30 min daily walking and housework, 2-4 times a week). Grip strength, 30-s chair-stand test, single leg balance with eye closed, and the timed “Up & Go” (TUG) were used as indicators of mobility. A mixed model analysis of variance (ANOVA), interaction effect, and simple effect were performed in statistical analysis. Results: Both intervention groups improved mobility, and the results were similar behaviorally. However, the exercise intervention group performed better in 30-s chair-stand test (25.14±0.67 vs. 23.71±0.76 with p=0.01) and single leg balance with eye closed (8.15±0.69 vs. 6.14±0.43 with p&lt;0.001). Discussion/ Conclusion: 22-week cognitive intervention (for total 1980 min) could improve the elderly’s mobility to the similar extent of mild to moderate physical exercises (1320 min of functional muscle and balance training and 1980 daily physical activities), which ascertained the far transfer effects from cognition to untrained mobility domain and found a comparable dosage of cognitive and physical exercise training.