cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20764681.v1
title: Supplementary Material for: Upregulation of miRNA-26a enhances the apoptosis of cerebral neurons by targeting EphA2 and inhibiting the MAPK pathway

description: Neural tube defects (NTDs) constitute the second most common congenital malformation of the central nervous system. The pathogenesis of NTDs is not entirely clear. In recent years, microRNAs have become a hot spot in genetic and developmental biology research. The present study aimed to explore the potential role of microRNA (miRNA)-26a in NTDs and the underlying pathogenesis thereof. First, we found significantly increased miRNA-26a expression in fetuses with NTDs (p&lt;0.0001), which significantly downregulated EphA2 and ERK1 mRNA and protein expression levels in fetuses with NTDs compared to normal controls (p&lt;0.01). In addition, dual-luciferase reporter assay showed that miR-26a negatively regulated EphA2 by directly binding with the 3'-untranslated region of EphA2. Second, upregulation of miRNA-26a expression increased caspase 3 and 9 protein expression levels (p&lt;0.01); and decreased EphA2 mRNA and protein expression levels (p&lt;0.01), as well as ERK1 and SRF protein expression levels (p&lt;0.01) in mouse neural stem cells (NE-4C) and human astroblastoma cells (U87MG). Furthermore, the upregulation of miRNA-26a inhibited cell proliferation and enhanced apoptosis of NE-4C and U87MG cells (p&lt;0.05). Similar results were observed with the MAPK inhibitor PD98059 (p&lt;0.01). These results suggest that miR-26a downregulates EphA2 to regulate SRF through the MAPK pathway, which affects nervous system development miR-26a targets EphA2, modulates phosphorylation of MAPK/ERK(MEK) pathway, regulates SRF, and participates in regulating nervous cell proliferation and apoptosis. Dysregulation of the aforementioned mechanism may be involved in the pathogenesis of NTDs.
DOI: 10.6084/m9.figshare.21443211.v1
title: Supplementary Material for: Multiparametric Neuroimaging and Its Association with Non-Contrast Computed Tomography in Late-Window Large Vessel Occlusion Acute Stroke

description: <b><i>Introduction:</i></b> Endovascular treatment (EVT) for acute ischemic stroke (AIS) between 6 and 24 h is established as a standard of care among patients selected by multiparametric neuroimaging. We aimed to explore neuroimaging parameters in late-window large vessel occlusion (LVO) patients and its association with non-contrast computed tomography (NCCT) findings. <b><i>Methods:</i></b> We included consecutive AIS patients within 6–24 h from the symptoms onset with LVO. We described multiparametric imaging findings, the rate of patients who fulfilled imaging perfusion criteria according to the DAWN and DEFUSE-3 trials that define the computed tomography perfusion mismatch (CTP-MM) group and its association with NCCT focused on Alberta Stroke Program Early CT Score (ASPECTS). We also analyzed the association between neuroimaging parameters and the clinical outcome determined by the 90-day modified Rankin scale (mRS). <b><i>Results:</i></b> We included 206 patients, of them, 176 (85.4%) presented CTP-MM and 184 (89.3%) presented an ASPECTS ≥6 on admission. The rate of CTP-MM was 90.8% in patients with ASPECTS ≥6, compared with 40.9% in those with low ASPECTS. ASPECTS was moderately correlated with ischemic core determined by cerebral blood flow &lt;30% volume (rS = −0.557, <i>p</i> &lt; 0.001). In EVT-treated patients (185, 89.8%), after adjusting for identifiable confounders, the presence of CTP-MM was a predictor of 90-day functional independence (OR: 3.38; 95% CI: 1.01–11.29; <i>p</i> = 0.048). We did not find an association between CTP-MM and 90-day functional disability (ordinal mRS shift, aOR: 1.39; 95% CI: 0.58–3.34; <i>p</i> = 0.459). <b><i>Conclusions:</i></b> A great majority of patients who presented a LVO in the late window fulfilled guidelines imaging criteria to undergo EVT, especially those with high ASPECTS (≥6). Our data suggest that NCCT with CT angiography could be a reasonable approach for AIS treatment selection also in the late window.
DOI: 10.6084/m9.figshare.21081034
title: Supplementary Material for: Does Sickle Cell Disease Protect against HIV Infection: A Systematic Review

description: Objective: The aim of this systematic review was to investigate if SCD protects against HIV infection by determining the association between SCD and the incidence and virulence of HIV infection. Methods: This is a systematic review that used MEDLINE, PubMed, CINAHL, and Academic Search Complete as data sources. Articles describing the relationship of SCD with HIV infection were included in this review. Hence, the effect measures were converted to correlation coefficients and synthesized accordingly to examine the putative protective role of SCD over HIV infection. Independent full-text screening and data extraction were conducted on all eligible studies. The risk of bias was assessed using the mixed methods appraisal tool. We employed a random-effects model of meta-analysis to estimate the pooled prevalence. We computed Cochrane's Q statistics, I2, and prediction interval to quantify effect size heterogeneity. Results: SCD reduces the risk of HIV infection by 75% (OR = 0.25; r = -0.36, p &lt; 0.001; I2 = 71.65). There was no publication bias (Egger’s t-value = 0.411; p = 0.721) (Figure 2). Similarly, risk of HIV virulence was reduced by 77% (OR = 0.23; r= -0.38; p &lt; 0.001; I2 = 63.07).The mechanisms implicated in the protective influence of SCD include autosplenectomy, reduced CCR5 expression, and increased expression of heme and iron-regulated genes. Conclusions: Sickle cell disease appears protective of HIV infection and slows HIV progression. PROSPERO registration Number: CRD42022304490
DOI: 10.6084/m9.figshare.21081034.v1
title: Supplementary Material for: Does Sickle Cell Disease Protect against HIV Infection: A Systematic Review

description: Objective: The aim of this systematic review was to investigate if SCD protects against HIV infection by determining the association between SCD and the incidence and virulence of HIV infection. Methods: This is a systematic review that used MEDLINE, PubMed, CINAHL, and Academic Search Complete as data sources. Articles describing the relationship of SCD with HIV infection were included in this review. Hence, the effect measures were converted to correlation coefficients and synthesized accordingly to examine the putative protective role of SCD over HIV infection. Independent full-text screening and data extraction were conducted on all eligible studies. The risk of bias was assessed using the mixed methods appraisal tool. We employed a random-effects model of meta-analysis to estimate the pooled prevalence. We computed Cochrane's Q statistics, I2, and prediction interval to quantify effect size heterogeneity. Results: SCD reduces the risk of HIV infection by 75% (OR = 0.25; r = -0.36, p &lt; 0.001; I2 = 71.65). There was no publication bias (Egger’s t-value = 0.411; p = 0.721) (Figure 2). Similarly, risk of HIV virulence was reduced by 77% (OR = 0.23; r= -0.38; p &lt; 0.001; I2 = 63.07).The mechanisms implicated in the protective influence of SCD include autosplenectomy, reduced CCR5 expression, and increased expression of heme and iron-regulated genes. Conclusions: Sickle cell disease appears protective of HIV infection and slows HIV progression. PROSPERO registration Number: CRD42022304490
DOI: 10.6084/m9.figshare.20975770.v2
title: Quantifying and Modeling Curved Thrust Fault-propagation Folds at Different Scales

description: This supporting information provides the all possible model parameters combinations generated by the three structural inversions in the text ( Supplementary Table S1 ), and the uninterpreted outcrop cross section and seismic reflection profiles (Figures S1 to S4). <br> Supplementary Table S1. Model parameters for inverse trishear modelling of the three contractional structures at different scales (fold simulation, field fold outcrop, and New Zealand deepwater fold). <br> Figure S1. An uninterpreted field fold outcrop. See the interpreted section in Figure 4a. <br> Figure S2. Uninterpreted seismic reflection profile A-A`. See the interpreted section in Figure 4a. <br> Figure S3. Uninterpreted seismic reflection profile of PEG-09. See the interpreted section in Figure 6a. <br> Figure S4. Uninterpreted seismic reflection profile of the New Zealand deepwater fold. See the interpreted section in Figure 7b. <br> <br> <br>
DOI: 10.6084/m9.figshare.21379197
title: Additional file 2. Protein-to-mRNA ratios among tissues

description: <strong>Protein-to-mRNA ratios among tissues.</strong> (A) Protein-to-mRNA ratios of the Human Protein Atlas. (B) Protein-to-mRNA ratios of GTEx. (C) GTEx samples. (D) Translational efficiency of brain, liver and testis. (E) High-PTR and low-PTR sets of proteins for each tissue. (F) Concordance of protein sets between tissues in both HPA and GTEx. (G) Protein half-lives of five different human cell lines. (H) mRNA half-lives of three different human cell lines. (I) Proteins secreted to blood.
DOI: 10.6084/m9.figshare.21379194
title: Additional file 3. Random Forest models of codon usage

description: <strong>Random Forest models of codon usage.</strong> (A) Results of RF models for each of the 36 tissues. (B) Codon ratios between high-PTR vs low-PTR genes for each tissue. (C) Codon ratios between high-PTR vs low-PTR genes for each tissue, after excluding secretome proteins. (D) Random Forest models based on codon usage of misframed CDSs and dinucleotide usage. (E) Codon ratios between high-PTR vs low-PTR genes for each tissue of HPA. (F) Codon ratios between high-PTR vs low-PTR genes for each tissue of GTEx. (G) Codon ratios between high-mRNA vs low-mRNA genes for each tissue. (H) Codon ratios between high-protein vs low-protein genes for each tissue.
DOI: 10.6084/m9.figshare.21379200
title: Additional file 4. Models of codon pair usage

description: <strong>Models of codon pair usage. </strong>(A) Codon pair ratios between high-PTR vs low-PTR proteins for each tissue. (B) Linear regression between observed vs expected codon pair ratios.
DOI: 10.6084/m9.figshare.21379200.v1
title: Additional file 4. Models of codon pair usage

description: <strong>Models of codon pair usage. </strong>(A) Codon pair ratios between high-PTR vs low-PTR proteins for each tissue. (B) Linear regression between observed vs expected codon pair ratios.
DOI: 10.5281/zenodo.7268450
title: 26,000+ machine-identified images of Modern planktonic foraminifera from 23 Atlantic coretops

description: This dataset contains 26,663 images of Modern planktonic foraminifera taken as part of the Endless Forams (endlessforams.org) initiative that did not receive species labels assigned by human classifiers in Hsiang et al. (2019). These images were processed using AutoMorph (Hsiang et al. 2018) and assigned species labels using a deep learning classifier with a validation accuracy of 87.4% (Hsiang et al. 2019). The machine-assigned species labels for these images are available as part of the supplementary data associated with Hsiang & Hull (in prep., under screening at bioRxiv). The code associated with the generation of this dataset is available at https://github.com/palaeomachinist/foram-comm-ecol. <strong>References</strong> Hsiang AY, Brombacher A, Rilo MC, Mleneck-Vautravers MJ, Conn S, Lordsmith S, Jentzen A, Henehan MJ, Metcalfe B, Fenton I, Wade B, Fox L, Meilland J, Davis CV, Baranowski U, Groeneveld J, Edgar KM, Movellan A, Aze T, Dowsett H, Miller G, Rios N & Hull PM (2019) Endless Forams: &gt;34,000 modern planktonic foraminiferal images for taxonomic training and automated species recognition using convolutional neural networks. <em>Paleoceanography and Paleoclimatology</em>. 34(7):1157-1177. (https://doi.org/10.1029/2019PA003612) Hsiang AY, Nelson K, Elder LE, Sibert EC, Kahanamoku SS, Burke JE, Kelly A, Liu Y, and Hull PM (2018) <em>AutoMorph</em>: Accelerating morphometrics with automated 2D and 3D image processing and shape extraction. <em>Methods in Ecology and Evolution. </em>9(3):605-612. (https://doi.org/10.1111/2041-210X.12915) Hsiang AY & Hull PM. Automated community ecology using deep learning: a case study of planktonic foraminifera.
description: AYH was supported by Vetenskapsrådet (Swedish Research Council) grant 2020-03515.
DOI: 10.5281/zenodo.7268451
title: 26,000+ machine-identified images of Modern planktonic foraminifera from 23 Atlantic coretops

description: This dataset contains 26,663 images of Modern planktonic foraminifera taken as part of the Endless Forams (endlessforams.org) initiative that did not receive species labels assigned by human classifiers in Hsiang et al. (2019). These images were processed using AutoMorph (Hsiang et al. 2018) and assigned species labels using a deep learning classifier with a validation accuracy of 87.4% (Hsiang et al. 2019). The machine-assigned species labels for these images are available as part of the supplementary data associated with Hsiang & Hull (in prep., under screening at bioRxiv). The code associated with the generation of this dataset is available at https://github.com/palaeomachinist/foram-comm-ecol. <strong>References</strong> Hsiang AY, Brombacher A, Rilo MC, Mleneck-Vautravers MJ, Conn S, Lordsmith S, Jentzen A, Henehan MJ, Metcalfe B, Fenton I, Wade B, Fox L, Meilland J, Davis CV, Baranowski U, Groeneveld J, Edgar KM, Movellan A, Aze T, Dowsett H, Miller G, Rios N & Hull PM (2019) Endless Forams: &gt;34,000 modern planktonic foraminiferal images for taxonomic training and automated species recognition using convolutional neural networks. <em>Paleoceanography and Paleoclimatology</em>. 34(7):1157-1177. (https://doi.org/10.1029/2019PA003612) Hsiang AY, Nelson K, Elder LE, Sibert EC, Kahanamoku SS, Burke JE, Kelly A, Liu Y, and Hull PM (2018) <em>AutoMorph</em>: Accelerating morphometrics with automated 2D and 3D image processing and shape extraction. <em>Methods in Ecology and Evolution. </em>9(3):605-612. (https://doi.org/10.1111/2041-210X.12915) Hsiang AY & Hull PM. Automated community ecology using deep learning: a case study of planktonic foraminifera.
description: AYH was supported by Vetenskapsrådet (Swedish Research Council) grant 2020-03515.
DOI: 10.5281/zenodo.7270253
title: 26,000+ machine-identified images of Modern planktonic foraminifera from 23 Atlantic coretops

description: This dataset contains 26,663 images of Modern planktonic foraminifera taken as part of the Endless Forams (endlessforams.org) initiative that did not receive species labels assigned by human classifiers in Hsiang et al. (2019). These images were processed using AutoMorph (Hsiang et al. 2018) and assigned species labels using a deep learning classifier with a validation accuracy of 87.4% (Hsiang et al. 2019). The machine-assigned species labels for these images are available as part of the supplementary data associated with Hsiang & Hull (in prep., under screening at bioRxiv). The code associated with the generation of this dataset is available at https://github.com/palaeomachinist/foram-comm-ecol. <strong>References</strong> Hsiang AY, Brombacher A, Rilo MC, Mleneck-Vautravers MJ, Conn S, Lordsmith S, Jentzen A, Henehan MJ, Metcalfe B, Fenton I, Wade B, Fox L, Meilland J, Davis CV, Baranowski U, Groeneveld J, Edgar KM, Movellan A, Aze T, Dowsett H, Miller G, Rios N & Hull PM (2019) Endless Forams: &gt;34,000 modern planktonic foraminiferal images for taxonomic training and automated species recognition using convolutional neural networks. <em>Paleoceanography and Paleoclimatology</em>. 34(7):1157-1177. (https://doi.org/10.1029/2019PA003612) Hsiang AY, Nelson K, Elder LE, Sibert EC, Kahanamoku SS, Burke JE, Kelly A, Liu Y, and Hull PM (2018) <em>AutoMorph</em>: Accelerating morphometrics with automated 2D and 3D image processing and shape extraction. <em>Methods in Ecology and Evolution. </em>9(3):605-612. (https://doi.org/10.1111/2041-210X.12915) Hsiang AY & Hull PM. Automated community ecology using deep learning: a case study of planktonic foraminifera.
description: AYH was supported by Vetenskapsrådet (Swedish Research Council) grant 2020-03515.
DOI: 10.6084/m9.figshare.20445141
title: The qualitative methodology exposure and experiences of journal reviewers

description: Reviewing is a necessary part of the scientific validity and quality of research. Quality outputs promote the advancement of a field such as psychology and growing methods like the qualitative method. This study sought to explore the qualitative knowledge and experiences of reviewers of psychology journals to gain insight into how this process could be improved and if reviewers feel equipped to review the broad qualitative research method. A mixed-method lite research design utilising an online survey (n=27) and purposive and snowball sampling was conducted. Data were analysed using frequency counts and thematic analysis. Overall, reviewers received their education as postgraduates and reading, which they reported, equipped them to review qualitative studies. Reviewers experienced authors as unskilled, which created difficulty in accepting and reviewing qualitative articles and made various recommendations on the aspects authors should focus on when preparing their manuscript for publication.
DOI: 10.6084/m9.figshare.20445141.v2
title: The qualitative methodology exposure and experiences of journal reviewers

description: Reviewing is a necessary part of the scientific validity and quality of research. Quality outputs promote the advancement of a field such as psychology and growing methods like the qualitative method. This study sought to explore the qualitative knowledge and experiences of reviewers of psychology journals to gain insight into how this process could be improved and if reviewers feel equipped to review the broad qualitative research method. A mixed-method lite research design utilising an online survey (n=27) and purposive and snowball sampling was conducted. Data were analysed using frequency counts and thematic analysis. Overall, reviewers received their education as postgraduates and reading, which they reported, equipped them to review qualitative studies. Reviewers experienced authors as unskilled, which created difficulty in accepting and reviewing qualitative articles and made various recommendations on the aspects authors should focus on when preparing their manuscript for publication.
DOI: 10.6084/m9.figshare.20445141.v1
title: Investigating the qualitative methodology exposure and experiences of journal reviewers: A mixed-method approach

description: Reviewing is a necessary part of the scientific validity and quality of research. Quality outputs promote the advancement of a field such as psychology and growing methods like the qualitative method. This study sought to explore the qualitative knowledge and experiences of reviewers of psychology journals to gain insight into how this process could be improved and if reviewers feel equipped to review the broad qualitative research method. A mixed-method lite research design utilising an online survey (n=27) and purposive and snowball sampling was conducted. Data were analysed using frequency counts and thematic analysis. Overall, reviewers received their education as postgraduates and reading, which they reported, equipped them to review qualitative studies. Reviewers experienced authors as unskilled, which created difficulty in accepting and reviewing qualitative articles and made various recommendations on the aspects authors should focus on when preparing their manuscript for publication.
DOI: 10.6084/m9.figshare.21379206
title: Additional file 6. Flow cytometry of cell lines

description: <strong>Flow cytometry of cell lines.</strong> The eGFP and mCherry fluorescence is detected by flow cytometry for each of the four constructs transfected into HEK293T and A549 cell lines.
DOI: 10.6084/m9.figshare.21379209
title: Additional file 7. Flow cytometry of primary cells

description: <strong>Flow cytometry of primary cells.</strong> The eGFP and mCherry fluorescence is detected by flow cytometry for each of the four constructs transfected into Small Airway Epithelial Cells and Renal Epithelial Cells.
DOI: 10.6084/m9.figshare.21407610
title: Additional file 5. CUSTOM-optimized fluorescent variants

description: <strong>CUSTOM-optimized fluorescent variants.</strong> (A) Relative Codon Usage of optimized protein variants. (B) Sequences of optimized protein variants. (C) Targeted proteomics of GFP and mCherry peptides. (D) RT-qPCR of GFP and mCherry transcripts.
DOI: 10.6084/m9.figshare.20975770
title: Quantifying and Modeling Curved Thrust Fault-propagation Folds at Different Scales

description: This supporting information provides the all possible model parameters combinations generated by the three structural inversions in the text ( Supplementary Table S1 ), and the uninterpreted outcrop cross section and seismic reflection profiles (Figures S1 to S4). <br> Supplementary Table S1. Model parameters for inverse trishear modelling of the three contractional structures at different scales (fold simulation, field fold outcrop, and New Zealand deepwater fold). <br> Figure S1. An uninterpreted field fold outcrop. See the interpreted section in Figure 4a. <br> Figure S2. Uninterpreted seismic reflection profile A-A`. See the interpreted section in Figure 4a. <br> Figure S3. Uninterpreted seismic reflection profile of PEG-09. See the interpreted section in Figure 6a. <br> Figure S4. Uninterpreted seismic reflection profile of the New Zealand deepwater fold. See the interpreted section in Figure 7b. <br> <br> <br>
DOI: 10.6084/m9.figshare.20975770.v4
title: Quantifying and Modeling Curved Thrust Fault-propagation Folds at Different Scales

description: This supporting information provides the all possible model parameters combinations generated by the three structural inversions in the text ( Supplementary Table S1 ), and the uninterpreted outcrop cross section and seismic reflection profiles (Figures S1 to S4). <br> Supplementary Table S1. Model parameters for inverse trishear modelling of the three contractional structures at different scales (fold simulation, field fold outcrop, and New Zealand deepwater fold). <br> Figure S1. An uninterpreted field fold outcrop. See the interpreted section in Figure 4a. <br> Figure S2. Uninterpreted seismic reflection profile A-A`. See the interpreted section in Figure 4a. <br> Figure S3. Uninterpreted seismic reflection profile of PEG-09. See the interpreted section in Figure 6a. <br> Figure S4. Uninterpreted seismic reflection profile of the New Zealand deepwater fold. See the interpreted section in Figure 7b. <br> <br> <br>
DOI: 10.6084/m9.figshare.20975770.v3
title: Quantifying and Modeling Curved Thrust Fault-propagation Folds at Different Scales

description: This supporting information provides the all possible model parameters combinations generated by the three structural inversions in the text ( Supplementary Table S1 ), and the uninterpreted outcrop cross section and seismic reflection profiles (Figures S1 to S4). <br> Supplementary Table S1. Model parameters for inverse trishear modelling of the three contractional structures at different scales (fold simulation, field fold outcrop, and New Zealand deepwater fold). <br> Figure S1. An uninterpreted field fold outcrop. See the interpreted section in Figure 4a. <br> Figure S2. Uninterpreted seismic reflection profile A-A`. See the interpreted section in Figure 4a. <br> Figure S3. Uninterpreted seismic reflection profile of PEG-09. See the interpreted section in Figure 6a. <br> Figure S4. Uninterpreted seismic reflection profile of the New Zealand deepwater fold. See the interpreted section in Figure 7b. <br> <br> <br>