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19841
Data_Sheet_2_A Rapid, Whole Genome Sequencing Assay for Detection and Characterization of Novel Coronavirus (SARS-CoV-2) Clinical Specimens Using Nanopore Sequencing.PDF
Published 2022“…<p>A new human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged at the end of 2019 in Wuhan, China that caused a range of disease severities; including fever, shortness of breath, and coughing. …”
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19842
Data_Sheet_1_A Rapid, Whole Genome Sequencing Assay for Detection and Characterization of Novel Coronavirus (SARS-CoV-2) Clinical Specimens Using Nanopore Sequencing.FASTA
Published 2022“…<p>A new human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged at the end of 2019 in Wuhan, China that caused a range of disease severities; including fever, shortness of breath, and coughing. …”
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19843
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19844
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19845
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19846
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19847
ASO-miR-129-5p down-regulates miR-129-5p and reduces proliferation and migration of Hep-2 cells
Published 2013“…<p>. (A) miR-129-5p expression was significantly decreased after ASO-miR-129-5p transfection compared to GFP transfection (control) or untreated Hep-2 cells (Hep-2 cell) measured by real-time RT-PCR. (* <i>P</i><0.05). …”
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19848
Image_5_5-Methylcytosine Related LncRNAs Reveal Immune Characteristics, Predict Prognosis and Oncology Treatment Outcome in Lower-Grade Gliomas.tif
Published 2022“…Consequently, we identified four lncRNAs, including LINC00265, CIRBP-AS1, GDNF-AS1, and ZBTB20-AS4, and established a novel m5C-related lncRNAs signature (m5CrLS) that was effective in predicting prognosis. …”
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19849
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19850
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19851
Supplementary Material for: HIV-1 Nef Interacts with HCV Core, Recruits TRAF2, TRAF5 and TRAF6, and Stimulates HIV-1 Replication in Macrophages
Published 2013“…The knockdown of TRAF2, TRAF5 and TRAF6 resulted in decreased NF-κB activation and reduced HIV-1 replication in MDMs. …”
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19852
Representative photographs of exoprotein assessment in a 96-well master plate.
Published 2022“…Wells A1–3 (black box) contain MH96 (wild-type); A4–6 (red box) contain K18 (non-secretion control); A7–9, E5–8, and H10–12 (purple box) contain LB broth blanks. …”
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19853
Groups 5 and 6 Terminal Hydrazido(2−) Complexes: N<sub>β</sub> Substituent Effects on Ligand-to-Metal Charge-Transfer Energies and Oxidation States
Published 2012“…Perturbing the electronic environment of the β (NR<sub>2</sub>) nitrogen affects the energy of the lowest-energy charge-transfer (CT) transition in these complexes. For group 5 complexes, increasing the energy of the N<sub>β</sub> lone pair decreases the ligand-to-metal CT (LMCT) energy, except for electron-rich niobium dialkylhydrazides, which pyramidalize N<sub>β</sub> in order to reduce the overlap between the NbN<sub>α</sub> π bond and the N<sub>β</sub> lone pair. …”
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19854
Structure diagram of ensemble model.
Published 2024“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”
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19855
Fitting formula parameter table.
Published 2024“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”
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19856
Test plan.
Published 2024“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”
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19857
Fitting surface parameters.
Published 2024“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”
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19858
Model generalisation validation error analysis.
Published 2024“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”
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19859
Empirical model prediction error analysis.
Published 2024“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”
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19860
Fitting curve parameters.
Published 2024“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”