dedup-isc-ft-v107-score
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0.404165 |
89a7b58790b549a8b085838cec4537a2
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GO and KEGG assays were performed to explore the possible function of the dysregulated genes in CC. (a) BP, CC, and MF in GO enrichment analyses. (b) The top 30 enriched KEGG pathways.
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PMC9045981
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JIR2022-4510462.006.jpg
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0.421321 |
2ef4af1f99bd42aead135a022ea340c4
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(a) The abundance of 22 infiltrating immunocyte subtypes in tumorous and normal biopsies for TCGA-CESE cohorts computed by the CIBERSORT approach. (b) Pearson correlation coefficient was utilized to study the matrix of 22 kinds of TIICs in pulmonary carcinoma.
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PMC9045981
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JIR2022-4510462.007.jpg
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0.514817 |
c0796c90a2bb44d3abe422a54dfd5d7c
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Correlation of TICs percentage with the expression of PMEPA1. Scatter plot displayed the correlation of 8 types of TICs percentage with the expression of PMEPA1 (P < 0.05), such as (a) T cells CD4 memory resting, (b) mast cells activated, (c) macrophages M0, (d) T cell CD4 memory stimulated, (e) dendritic cells resting, (f) T cell CD8, and (g) macrophagus M1. The correlation examination was completed via Pearson coefficient.
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PMC9045981
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JIR2022-4510462.008.jpg
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0.448603 |
efd790ead0804c179b1a7d16b8a48b63
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(a)–(f) K-M survival curves for PMEPA1 in pan-cancer (P < 0.05).
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PMC9045981
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JIR2022-4510462.009.jpg
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0.47862 |
61cbb14a90e9457881d06e95f9fc6e54
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Battery cycling data.Voltage and current profile in the first cycle of one CY25-0.5/1 NCA battery (a). A plot of relaxation voltage change (region III) while cycling for one NCA cell (b). NCA battery discharge capacity (until 71% of nominal capacity) versus cycle number of NCA battery (c), NCM battery (d), and NCM+NCA battery (e). The embedded plots in c, d, and e are the cycle distribution of cells at around 71% of nominal capacity, the points are offset randomly in the horizontal direction to avoid overlapping.
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PMC9046220
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41467_2022_29837_Fig1_HTML.jpg
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0.446426 |
5c731d66e40a495fa4887c25068a042c
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Extracted features from the voltage relaxation curves as a function of battery capacity for NCA cells.(a) Variance (Var), (b) skewness (Ske), (c) maxima (Max), (d) minima (Min), (e) mean (Mean), and (f) excess kurtosis (Kur). Feature changes between 3500 mAh and 2500 mAh (71% of nominal capacity) for NCA cells are shown to be consistent with the used datasets. The mathematical description of the six features is depicted in Supplementary Table 5.
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PMC9046220
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41467_2022_29837_Fig2_HTML.jpg
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0.453588 |
575e8b0bebb64be5a42a812df14601ab
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Cross-validation root-mean-square error (RMSE) of the XGBoost method using different feature combinations.(i, j) means different feature combinations referring the Supplementary Table 10. The (7, 1) = [Var, Ske, Max] obtains the best cross-validation RMSE = 1.0% within a three feature combination.
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PMC9046220
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41467_2022_29837_Fig3_HTML.jpg
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0.516722 |
4d38f8be26c5441383e2ee987d0d6928
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Results of battery capacity estimation with the input of three features [Var, Ske, Max] by different estimation methods.The capacity results are uniformized by the nominal capacity for comparison. root-mean-square error (RMSE) of battery capacity estimation (a), test results of estimated capacity versus real capacity by ElasticNet (b), XGBoost (c), and Support Vectors Regression (SVR) (d).
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PMC9046220
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41467_2022_29837_Fig4_HTML.jpg
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0.481936 |
0c4414331b60414d906002d6aaccb579
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AC electrochemical impedance variations of the lithium-ion cells during cycling.The resistance increment from the initial value (Rinit) is calculated for comparison. The ohmic resistance of NCA cells (a), NCM cells (b), and NCA+NCM cells (c). SEI resistance of NCA cells (d), NCM cells (e), and NCA+NCM cells (f). Charge transfer resistance of NCA cells (g), NCM cells (h), and NCA+NCM cells (i). Only resistances before the capacity reducing to 71% of nominal capacity are shown to be consistent with the datasets in the study. The coefficient of determination (R2) between the raw and fitted impedance data is summarized in Supplementary Table 12. The SEI resistances are not identified in some cycles (seen in Supplementary Table 12) for the NCA battery (d) and NCM battery (e). The shared information of raw impedance data and fitted data can be found in the data availability.
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PMC9046220
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41467_2022_29837_Fig5_HTML.jpg
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0.420032 |
b59510186d844130b823327731eb8406
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Test results of estimated capacity versus real capacity by transfer learning.The capacity results are uniformized by the nominal capacity for comparison. Results of TL2 embedding XGBoost method (a) and embedding SVR (b) on dataset 2. Results of TL2 embedding XGBoost method (c) and embedding SVR (d) on dataset 3. Additional results are disclosed in Supplementary Figs. 10–12.
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PMC9046220
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41467_2022_29837_Fig6_HTML.jpg
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0.437066 |
197a421dd2204e54871cef3257294de4
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Requallo Allocation leads to non-representative sets of completed tasks, and biased information about problem properties. (a) Easy tasks (with p far from 1/2) are over-represented in the set of completed tasks while hard tasks (with p near 1/2) are under-represented. (b) Estimates of p are biased, when looking at easy tasks there are pileups at \documentclass[12pt]{minimal}
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\begin{document}$$p=(0,1)$$\end{document}p=(0,1) and \documentclass[12pt]{minimal}
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\begin{document}$$p=(0.2, 0.8)$$\end{document}p=(0.2,0.8) for the “smoothed; distribution, looking at hard tasks, there are pileups at \documentclass[12pt]{minimal}
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\begin{document}$$p=0.5$$\end{document}p=0.5.
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PMC9046272
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41598_2022_10794_Fig1_HTML.jpg
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0.417595 |
98bc90c623f045a09617842948fcaed5
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Example decision priors for when a researcher is (a) less confident in the allocation method’s decisions, (b) more confident in the decisions.
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PMC9046272
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41598_2022_10794_Fig2_HTML.jpg
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0.447929 |
5d7a995b388e4175861ed4ae59db2cb7
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Inference of problem difficulty distributions using 25% of labels. Tasks are allocated using Requallo, then Wald estimation or DEPS estimation of problem difficulty is performed. These results are compared to the distribution of \documentclass[12pt]{minimal}
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\begin{document}$$\hat{p}$$\end{document}p^ estimated using the full dataset (100% of labels), show as histograms. The “smoothed” and “transformed” variants of Wald estimation tend to add too much probability near either the center (\documentclass[12pt]{minimal}
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\begin{document}$$p=1/2$$\end{document}p=1/2) or extremes (\documentclass[12pt]{minimal}
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\begin{document}$$p=0,1$$\end{document}p=0,1). DEPS achieves good agreement with the full data distribution.
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PMC9046272
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41598_2022_10794_Fig3_HTML.jpg
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0.528571 |
238f2888afc8498e9db70ce4c12bf968
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Convergence of estimated distributions as more crowd data are used. Visually, we see that DEPS and Wald (transformed) both converge relatively quickly, often after using only 10% of the available data.
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PMC9046272
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41598_2022_10794_Fig4_HTML.jpg
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0.442682 |
007d84394e3d4e07b0d83e71038b0fab
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DEPS generally provides more information (lower KL-divergence) about the distribution estimated using the full, unbiased dataset than Wald, whose performance degrades as more (biased) data are received, with the exception of Bluebirds. Shaded areas denote 95% CI computed over Requallo realizations.
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PMC9046272
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41598_2022_10794_Fig5_HTML.jpg
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0.47747 |
3ef8519eb0d84af7828fb1fd171f3324
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The KL-divergence (or relative entropy) \documentclass[12pt]{minimal}
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\begin{document}$$D_{\mathrm{KL}}$$\end{document}DKL between the inferred and true distributions of p over a range of parameters for the true distribution. Here we see that DEPS outperforms the Wald baselines for most parameter values, achieving a lower divergence in its estimates of the true distribution. Panel d shows the mean KL-divergence averaged over the values of \documentclass[12pt]{minimal}
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\begin{document}$$\alpha$$\end{document}α and \documentclass[12pt]{minimal}
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\begin{document}$$\beta$$\end{document}β in the matrices shown in panels a–c.
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PMC9046272
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41598_2022_10794_Fig6_HTML.jpg
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0.406129 |
5d30481895c1481fb7f67e8439d4775d
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A flow diagram showing the PRISMA study selection of publications. ACLR-R, anterior cruciate ligament tibial remnant-preserving reconstruction; ACLR-S, anterior cruciate ligament standard reconstruction
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PMC9046482
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10195_2022_641_Fig1_HTML.jpg
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0.429227 |
4b80af29b82e45548a2ea1215fec571e
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Change in RBD-binding immunoglobulin G response (BAU/mL) in people living with human immunodeficiency virus from time of priming dose, to time of second dose, and at 1 month after the second dose. Abbreviations: BAU, binding antibody unit; HCDR, high CD4 recovery; HCWs, healthcare workers; ICDR, intermediate CD4 recovery; IQR, interquartile range; PCDR, poor CD4 recovery; RBD, receptor binding domain.
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PMC9047161
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ciac238_fig1.jpg
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0.430237 |
c34c8535d2b14a17b9248e315736b2d0
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Humoral response in people living with human immunodeficiency virus and HCWs after the priming dose and the second dose of BNT162b2 or mRNA-1273 vaccine. Abbreviations: BAU, binding antibody unit; HCDR, high CD4 recovery; HCWs, healthcare workers; ICDR, intermediate CD4 recovery; IQR, interquartile range; MNA, microneutralization assay; PCDR, poor CD4 recovery; RBD, receptor binding domain; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.
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PMC9047161
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ciac238_fig2.jpg
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0.42326 |
e2b285843b0649c79da44d69e871eb67
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Increase in cell-mediated immunogenicity in people living with human immunodeficiency virus from T0 to T2, expressed as picograms per milliliter of IFN-γ or IL-2 release at the time of priming dose, at time of the second dose, and at 1 month after the second dose. Abbreviations: HCDR, high CD4 recovery; ICDR, intermediate CD4 recovery; IFN-γ, interferon-gamma; IL-2, interleukin-2; IQR, interquartile range; PCDR, poor CD4 recovery.
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PMC9047161
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ciac238_fig3.jpg
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0.411446 |
ea0ba43e0b864adaa048eeb79fc1511e
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Cell-mediated immunogenicity in people living with human immunodeficiency virus and HCWs at 1 month after the second dose. Immune response is expressed as median (IQR) release of IFN- γ and IL-2 (pg/mL) after severe acute respiratory syndrome coronavirus 2 spike peptide stimulation. Abbreviations: HCDR, high CD4 recovery; HCWs, healthcare workers; ICDR, intermediate CD4 recovery; IFN-γ, interferon-gamma; IL-2, interleukin-2; IQR, interquartile range; PCDR, poor CD4 recovery.
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PMC9047161
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ciac238_fig4.jpg
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0.438437 |
bfd5305761e640dcbfc0a93bbf0a89ca
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Scatter plots of the association between IFN-γ (pg/mL) and IL-2 (pg/mL) production in blood samples of PLWH collected 1 month after the second dose of severe acute respiratory syndrome coronavirus 2 mRNA vaccine. IFN-γ and IL-2 production in overall PLWH population (Pearson, r = 0.427 P < .001). A, IFN- γ and IL-2 production in PLWH with SID (Pearson, r = 0.80; P < .001). B, IFN-γ and IL-2 production in PLWH with MID (Pearson, r = 0.71; P < .001). C, IFN- γ and IL-2 production in PLWH with NID (Pearson, r = 0.48; P < .001). D, All P values were calculated using linear regression (r, Pearson correlation coefficient). Abbreviations: IFN-γ, interferon-gamma; IL-2, HCDR, high CD4 recovery; ICDR, intermediate CD4 recovery; PCDR, poor CD4 recovery.
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PMC9047161
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ciac238_fig5.jpg
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0.40029 |
6f4657615d834c80adab719e739869ce
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Scatter plots of the association between CD4 T-cell count (per mm3) at the time of priming dose of mRNA vaccine and RBD-binding immunoglobulin G (IgG) response, neutralizing antibody response, and IFN- γ production at T2 in people living with human immunodeficiency virus. CD4 T-cell count was performed at T0, and blood samples were collected for immunologic response 1 month after the dose of SARS-CoV-2 mRNA vaccine. RBD-binding IgG response (BAU/mL) at T2 and current CD4 T-cell count at T0 (rho = 0.44; P < .001). A, Neutralizing antibody MNA reciprocal dilution at T2 and current CD4 T-cell count at T0 (rho = 0.37; P < .001). B, Interferon gamma release after S-peptide stimulation (pg/mL) at T2 and current CD4 T-cell count at T0 (rho = 0.38; P < .001). C, rho, Spearman rank correlation coefficient. Abbreviations: BAU/mL, binding antibody units per milliliter; IFN-γ, interferon-gamma; MNA, microneutralization assay; RBD, receptor-binding domain; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.
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PMC9047161
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ciac238_fig6.jpg
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0.440877 |
a45bf82f3960415a9f8cab6b878374d7
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(A) Chemical structure of DSF (B) schematic illustration of the anti-cancer mechanism of DSF (C) schematic illustration of the preparation of DSF-SPI-Ns.
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PMC9047253
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c9ra09468g-f1.jpg
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0.508466 |
f5af434f0c8b4fc99fa134827ab0e1d8
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(A) Formulation optimization of DSF-SPI Ns, the influence of drug concentration in methanol, and (B) effect of the time length of ultrasonication on particle size and PDI (n = 3). (C) Size distribution and (D) zeta potential graph of optimized DSF-SPI-Ns.
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PMC9047253
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c9ra09468g-f2.jpg
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0.435106 |
9ecf46efe64d43df9771e1319fe0279e
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(A) Optical images of (a) freeze-dried DSF-SPI Ns, and (b) reconstituted DSF-SPI-Ns. (B) Short term stability study for 28 days at 4 °C. (n = 3). (C) TEM images of fresh prepared optimized DSF-SPI-Ns and (D) freeze-dried optimized DSF-SPI-Ns at scale bar 100 nm.
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PMC9047253
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c9ra09468g-f3.jpg
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0.386675 |
075ede44d09b4f92b0b1e1fcddb2305f
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Fluorescence emission spectra of (A) native and denatured SPI and (B) different drug concentrations of DSF. Amount of SPI (1 mg mL−1). (C) The overall structure, and binding chains (A–D).
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PMC9047253
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c9ra09468g-f4.jpg
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0.469869 |
e4b9fccce2604bfb97d5a9eef3e992e5
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(A) FTIR spectra of (a) mannitol, (b) SPI, (c) DSF (d) physical mixture and (e) optimized formulation. (B) DSC thermograms of (a) DSF, (b) optimized formulation, (c) physical mixture, (d) SPI and (e) mannitol. (C) X-ray diffractograms of (a) DSF, (b) optimized formulation, (c) physical mixture (d) SPI and (e) mannitol. (D) TGA thermograms of (a) DSF, (b) physical mixture, and (c) optimized formulation.
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PMC9047253
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c9ra09468g-f5.jpg
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0.45053 |
6fd4ee08f0bc490999aaa60d62fc8376
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In vitro drug release profile of DSF from DSF-SPI-NS in PBS solutions (A) pH 5.5 (B) pH 7.4 at 37 °C for 24 h. (n = 3). Cell viability after incubation with (C) SPI (D) free DSF and DSF-SPI-Ns in MDA-MB-231 cells for 24 h (n = 3). ***P < 0.001 compared with DSF.
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PMC9047253
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c9ra09468g-f6.jpg
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0.371349 |
ed99520d3ab8453a8a9e61e9bc45fed5
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Cellular uptake of DSF-SPI-Ns. Confocal microscopy images of MDA-MB-231 cells treated with FITC-DSF-SPINs (green) for different durations at fixed FITC concentration of 20 μg mL−1 FITC. The nuclei (blue) were stained with DAPI. Free FITC was used as control. Scale bar: 20 μm.
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PMC9047253
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c9ra09468g-f7.jpg
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0.440365 |
a01c2fd80a5f4725b8c14c6f23fc76f1
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Flow cytometry images of (A and B) MDA-MB-231 cells treated with FITC-DSF-SPI-Ns for different durations at fixed FITC concentration of 20 μg mL−1 FITC.
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PMC9047253
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c9ra09468g-f8.jpg
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0.5224 |
9f4aa054074349319a6c2fd661fac3bb
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Selection of individuals
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PMC9047280
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12933_2022_1499_Fig1_HTML.jpg
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0.443822 |
2e196eb5b31e40359631b64622b70054
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Plasma phosphate and all-cause mortality in the total population and in subgroups of individuals with type 2 diabetes versus without diabetes at baseline
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PMC9047280
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12933_2022_1499_Fig2_HTML.jpg
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0.514691 |
8669738b18d94d68b0c14b8b84f99669
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Central complex and type of neurons innervating the ellipsoid body. (A) On the top, cartoon of the fly brain with a window framing the central complex (CX). Four different neuropils composed the CX: protocerebral bridge (PB); fan-shaped body (FB); ellipsoid body (EB) and noduli (NO). Other brain regions represent important hubs for the information transmitted to/from the CX: medulla (ME); anterior optic tubercle (AOTU); bulb (BU); gall (GA) and lateral accessory lobe (LAL). On the bottom, example of two different types of neurons innervating the CX: tangential and columnar neurons. (B) Circuit diagram of the columnar neurons involved in encoding the visual inputs and in translating them to information for navigation and goal-oriented behaviors. E-PG neurons are columnar neurons receiving inputs in the EB and sending outputs to PB and GA. P-EG and P-EN are columnar neurons receiving inputs in the PB and sending outputs to the EB. Δ7 neurons receive inputs from seven glomeruli in the PB and send output to three glomeruli spaced out by seven glomeruli. R2 and R5 are tangential neurons innervating similar regions of the EB and BU. MeTu neurons, relaying visual information to the AOTU, are also depicted. (C) EB domains innervated by the neurons object of our study: E-PG, R2 and R5. ExR2 (also known as PPM-EB) dopaminergic neurons are also depicted (image adapted from Omoto et al., 2018).
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PMC9048027
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fphys-13-849142-g001.jpg
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0.499299 |
c4eb54fffac9440bb1c0c5acdac83648
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Genetic techniques and Ca2+ imaging setup. (A) GFP-aequorin responders activated by the driver GAL4 used in this study. On the top, schematic of GFP and aequorin fusion gene with upstream activation sequence (UAS). Models of blue light emission by aequorin (grey dot represents coelenterazine), and of green light emission by GFP-aequorin in response to high levels of Ca2+ (orange dots) (image adapted from Xiong et al., 2014). On the bottom, schematic of the RNAi technique in which a double-stranded RNA (i.e., hairpin RNA, hpRNA) is expressed under the control of UAS, as a complementary sequence to the gene of interest. The dsRNA is then processed by Dicer-2 into siRNA which leads to sequence-specific degradation of the mRNA related to the gene of interest (image adapted from https://stockcenter.vdrc.at/control/library_rnai). (B) Image of the setup used for in vivo Ca2+ brain imaging based on the bioluminescence technique.
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PMC9048027
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fphys-13-849142-g002.jpg
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0.453921 |
2128a7e6ddb54a51b18a86fe6f5c061b
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Driver lines selected and neurons targeted. (A) Images of the pattern expressed by the GAL4 lines selected from the Janelia FlyLight Project database (https://flweb.janelia.org/cgi-bin/flew.cgi) (Jenett et al., 2012). Starting from the top: the R20D01, with the promoter sequence corresponding to the putative enhancer sequence of the nAChRα3 gene, targets the R2 neurons; in the middle the R72D06, with the promoter sequence of the D2R gene, targets the R5 neurons; and at the bottom the R70G12, with the promoter associated to Dop1R2 gene, targets the E-PG neurons. (B) Expression pattern of R72B07-GAL4 (image from https://flweb.janelia.org/cgi-bin/flew.cgi) which has the promoter sequence associated to the Dop1R1 gene. As can clearly be appreciated, its pattern is widely superimposable with that of R20D01, apart from the TuBu neurons. (C) Expression patterns of three different driver lines targeting the same subclass of neurons (i.e., R5). Starting from the top: image of the R58H05-GAL4 line expressing GFP (10xUAS-IVS-mCD8:GFP) which was considered by Omoto et al. (2018) to target the same neurons considered as R2 by Liu et al. (2016) and recently defined as R5 neurons (image from https://flweb.janelia.org/cgi-bin/flew.cgi); in the middle, pattern of the nv45-LexA:VP16 driver line expressing GFP (LexAop-mCD8:GFP) which was considered to target R3 neurons (image taken with permission from Kottler et al., 2017); at the bottom, image of the R69F08-GAL4 line (used by Liu et al., 2016) expressing GFP (10xUAS-IVS-mCD8:GFP) that was considered to target R2 neurons (image from https://flweb.janelia.org/cgi-bin/flew.cgi).
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PMC9048027
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fphys-13-849142-g003.jpg
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0.502123 |
8aa5fb3139c74b859678389e7023272f
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Experimental procedure and Ca2+-response to drugs application. (A) Cartoon of the fly preparation depicting the main components which are not drawn to scale. (B) Schematic drawing of the protocol used to stimulate the neurons. (C) Ca2+-response profiles to drug application in the selected ROIs (cbL-R: left and right cell bodies; BUL-R: left and right bulb; EB: ellipsoid body; GAL-R: gall region). Starting from the top: nicotine-evoked (NIC) activity of the R2 neurons (R20D01 driver) in the five ROIs drawn around cb, BU and EB (see also Supplementary Movie S1 for a representative brain response); in the middle picrotoxin-evoked (PCT) activity of the R5 neurons (R72D06 driver) in the five ROIs drawn as in the previous neurons (see also Supplementary Movie S2 for a representative brain response); at the bottom, nicotine-evoked (NIC) activity of the E-PG neurons (R70G12 driver) in the three ROIs drawn around the 2 GA regions and EB (see also Supplementary Movie S3 for a representative brain response).
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PMC9048027
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fphys-13-849142-g004.jpg
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0.475744 |
52ca5dbcb7e14a9eb60dd6f38327bda9
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Dopamine modulation in R2 > G5A flies. (A) Image of the protocol used to modulate the neurons before the drug application (nicotine or picrotoxin). (B) Ca2+-response profiles of the R2 neurons in the EB ROI. In black is depicted the condition with the nicotine alone while in blue the condition with dopamine application before nicotine. (C) Estimated parameters of the Ca2+-response referred to the interaction between condition and ROIs (i.e., fixed effect). The dots represent the estimated values while the error bars correspond to the 97.5% confidence intervals (CI) computed with parametric bootstrap of 10,000 simulations. On the left is represented the Ca2+-response to nicotine (NIC) alone in the five ROIs of the R2 neurons (n = 13), while on the right is represented their Ca2+-response to nicotine after dopamine (DA) application (n = 12) (see also Supplementary Movie S4 for a representative brain response). (D) Estimated parameters of the response latency referred to the interaction between condition and ROIs (i.e., fixed effect) with corresponding 97.5% CI (computed as in Figure 5C). On the left is represented the latency response to nicotine alone in the five ROIs of the R2 neurons while on the right is represented their latency response to nicotine after dopamine application. (E) Plot of random effect referred to Ca2+-response (i.e., random fly intercept). Dots represent each fly (known as BLUPs, Best Linear Unbiased Predictions) while the horizontal lines crossing dots (i.e., error bars) correspond to the standard deviation (SD).
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PMC9048027
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fphys-13-849142-g005.jpg
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0.470024 |
93c1ca88520e47aaae0ac80ed294005b
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Dopamine modulation of R5 > G5A (A–C) and E-PG > G5A flies (D–F). (A) Estimated parameters of the Ca2+-response referred to the interaction between condition and ROIs (i.e., fixed effect) with corresponding CI (computed as in Figure 5C). On the left is represented the Ca2+-response to picrotoxin (PCT) alone in the five ROIs of the R5 neurons (back, n = 15), while on the right is represented their Ca2+-response to picrotoxin after dopamine (DA) application (blue, n = 21) (see also Supplementary Movie S5 for a representative brain response). (B) Estimated parameters of the response latency referred to the interaction between condition and ROIs (computed as in Figure 5D). On the left is represented the latency response to picrotoxin alone in the five ROIs of the R5 neurons (black) while on the right is represented their latency response to picrotoxin after dopamine application (blue). (C) Plot of random effect referred to Ca2+-response of R5 neurons (as in Figure 5E). (D) Estimated parameters of the Ca2+-response referred to the interaction between condition and ROIs (as in Figure 5C). On the left is represented the Ca2+-response to nicotine (NIC) alone in the three ROIs of the E-PG neurons (black, n = 21), while on the right is represented their Ca2+-response to nicotine after dopamine (DA) application (blue, n = 15) (see also Supplementary Movie S6 for a representative brain response). (E) Estimated parameters of the response latency referred to the interaction between condition and ROIs (computed as in Figure 5D). On the left is represented the latency response to nicotine alone in the three ROIs of the E-PG neurons (black) while on the right is represented their latency response to picrotoxin after dopamine application (blue). (F) Plot of random effect referred to Ca2+-response of E-PG neurons (as in Figure 5E).
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PMC9048027
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fphys-13-849142-g006.jpg
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0.503373 |
ada16f3287864abfbf00222de1a0acc5
|
Knockdown of Dop1R1 in R2>G5A flies. (A) Estimated parameters of the Ca2+-response referred to the interaction between condition and ROIs with corresponding CI (computed as in Figure 5C). The inset represents the comparison of the estimated interaction parameters (i.e., between condition and ROIs) referred to EB in the R2 normal (ctrl) and R2 >Dop1R1-RNAi (D1R1RNAi) flies in response to nicotine. On the left is represented the Ca2+-response to nicotine (NIC) alone in the five ROIs of the R2 neurons with knockdown of Dop1R1 (R2 > Dop1R1-RNAi; grey, n = 7), while on the right is represented their Ca2+-response to nicotine after dopamine (DA) application (red, n = 8). (B) Estimated parameters of the response latency referred to the interaction between condition and ROIs (computed as in Figure 5D). On the left is represented the latency response to nicotine alone in the five ROIs of the R2 neurons with knockdown of Dop1R1 (grey) while on the right is represented their latency response to nicotine after dopamine application (red). (C) Plot of random effect referred to Ca2+-response (as in Figure 5E).
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PMC9048027
|
fphys-13-849142-g007.jpg
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0.441381 |
e4c5f96a11924e4b931606f33bdbe490
|
Knockdown of Dop1R2 (A–D) and Dop1R1 (E–H) in E-PG > G5A flies. (A) Estimated parameters of the Ca2+-response referred to the interaction between condition and ROIs (as in Figure 5C). The inset at the center represents the comparisons of the estimated interaction parameters (i.e., between condition and ROIs) referred to EB in the E-PG normal (ctrl), E-PG > Dop1R2-RNAi (D1R2RNAi) and E-PG > Dop1R1-RNAi (D1R1RNAi) flies in response to nicotine (post hoc comparisons adjusted with Bonferroni correction). On the left of the inset is represented the Ca2+-response to nicotine (NIC) alone in the three ROIs of the E-PG neurons with knockdown of Dop1R2 (E-PG > Dop1R2-RNAi; black, n = 9), while on the right is represented their Ca2+-response to nicotine after dopamine (DA) application (magenta, n = 8). (B) Estimated parameters of the response latency referred to the interaction between condition and ROIs in E-PG > Dop1R2-RNAi flies (computed as in Figure 5D). On the left is represented the latency response to nicotine alone in the three ROIs of the E-PG neurons with knockdown of Dop1R2 (black) while on the right is represented their latency response to nicotine after dopamine application (magenta). (C) Plot of random effect referred to Ca2+-response of E-PG neurons with Dop1R2 knockdown (as in Figure 5E). (D) Ca2+-response profiles to nicotine of the E-PG neurons with Dop1R2 knockdown in the three ROIs with (magenta) or without (black) dopamine application before. Starting from the left: left GA region, EB and right GA region. (E) Estimated parameters of the Ca2+-response in E-PG neurons with knockdown of Dop1R1 referred to the interaction between condition and ROIs. On the left the condition with nicotine (NIC) alone (E-PG > Dop1R1-RNAi; grey, n = 7) while on the right the condition with dopamine (DA) application before nicotine (red, n = 7). (F) Estimated parameters of the response latency in E-PG neurons with knockdown of Dop1R1 referred to the interaction between condition and ROIs (computed as in Figure 5D). On the left the condition with nicotine alone (grey) while on the right the condition with dopamine application before nicotine (red). (G) Plot of random effect referred to Ca2+-response of E-PG neurons with Dop1R1 knockdown. (H) Ca2+-response profiles to nicotine of the E-PG neurons with Dop1R1 knockdown in the three ROIs defined as in G with (red) or without dopamine application before (grey).
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PMC9048027
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fphys-13-849142-g008.jpg
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0.433565 |
385eec3462e84c30895bed9600c57678
|
Dopamine modulation of the EB and comparison with the vertebrate basal ganglia. On the left is depicted the dopamine circuit involved in the vertebrate basal ganglia. The dopamine release from the substantia nigra pars compacta (SNc) modulates the direct and the indirect pathway. The striatal neurons (STD) of the direct pathway express the D1R (excitatory) and they inhibit the GABAergic neurons of the substantia nigra pars reticulata (SNr) and globus pallidus interna (GPi). In the indirect pathway, the striatal neurons express the D2R (inhibitory) and they inhibit the GABAergic neurons of the globus pallidus externa (GPe) which, in turn, inhibit the GPi. Both these pathways converge back to the thalamus (with efference copies) and to motor command regions like the optic tectum (OT). On the right is proposed a simple model about the dopamine modulation of the CX. As for the vertebrate basal ganglia, dopamine might excite (via Dop1R1) and inhibit (via Dop2R) specific R-neurons, R2 and R5, respectively, which synapse directly and/or indirectly to the E-PG neurons. A combination of excitatory (Dop1R1) and inhibitory (Dop1R2) dopamine receptors would be expressed by the E-PG neurons. The overall computation could serve the selection of goal-directed behaviors that is subsequently passed to the LAL for motor control. T-shaped bars mean inhibitory connections while arrows mean excitatory connections.
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PMC9048027
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fphys-13-849142-g009.jpg
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0.477464 |
9f285f84521f420c802b4e1a6b787170
|
UHPLC and UV-Vis analysis of the purified yellow pigment fraction of fresh-cut yam. (A) UHPLC chromatogram of the yellow fraction detected at 410 nm and UV-Vis spectra of (B) peak 1, (C) peak 2, and (D) peak 3.
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PMC9048222
|
c9ra07641g-f1.jpg
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0.427365 |
e9a6a1001c8f4abd8e4b3def281ccc9a
|
Mass spectra of the purified yellow pigment fraction of fresh-cut yam. (A) MS spectrum of peak 1. (B) Molecular ion isotope analysis of peak 1. (C) MS spectrum of peak 1 at cone voltages of 10, 20, and 40 V. (D) Comparison of the MS spectra of the samples and spectral library information of bisdemethoxycurcumin at cone voltages of 10, 20, and 40 V (the red lines represent the ions of peak 3, while the black lines represent the spectral library ions of bisdemethoxycurcumin).
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PMC9048222
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c9ra07641g-f2.jpg
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0.469075 |
19bfe01e7b4140178d15652e79fe678b
|
Images and quality analysis results of yellow and white fresh-cut yam slices. Images of (A) fresh-cut yam stored for 0 h, (B) white fresh-cut yam, and (C) yellow fresh-cut yam. (D) Quality analysis results for the fresh-cut yam slices. Data presented are the means of three replicates. Different letters indicate significant differences among mean values (P < 0.05).
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PMC9048222
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c9ra07641g-f3.jpg
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0.396036 |
aa5dbf5068c84c9bb0fb79752955924f
|
Antioxidant activities of fresh-cut yam slices. (A) ORAC values of fresh-cut yam slices and (B) DPPH radical scavenging activities of fresh-cut yam slices. Data presented are the means of three replicates. Different letters indicate significant differences among mean values (P < 0.05).
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PMC9048222
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c9ra07641g-f4.jpg
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0.417094 |
e37560361cab4b1b98b1cd15921811ff
|
Serotonin and melatonin biosynthetic pathways. In animals (black arrows), L-tryptophan is hydroxylated by tryptophan hydroxylase (TrpH; EC 1.14.16.4) to form 5-hydroxytryptophan, which is then decarboxylated by aromatic amino acid decarboxylase (AAAD; EC 4.1.1.28) to produce serotonin. From serotonin, N-acetylserotonin is generated by serotonin-N-acetyltransferase (SNAT; EC 2.3.1.87), and finally into melatonin, by N-acetylserotonin O-methyltransferase (ASMT; EC 2.1.1.4; Yabut et al., 2019). In plants (red arrows), L-tryptophan is decarboxylated by tryptophan decarboxylase (TrpD; EC 4.1.1.105) to form tryptamine, which is then hydroxylated by tryptamine/tryptophan 5-hydroxylase (T5H, EC 1.14.16.4) to produce serotonin. Serotonin is then converted into N-acetylserotonin by serotonin-N-acetyltransferase (SNAT; EC 2.3.1.87), and finally into melatonin, by N-acetylserotonin O-methyltransferase (ASMT; EC 2.1.1.4) or by caffeic O-methyltransferase (COMT; EC 2.1.1.4). Alternatively, melatonin can also be synthetized via 5-methoxytryptamine in plants (Tan et al., 2016). Light blue dashed arrows represent putative serotonin and melatonin biosynthetic pathways for bacteria - tryptophan conversion into serotonin via 5-hydroxytryptophan, through phenylalanine hydroxylase (PheH; EC 1.14.16.1; Lin et al., 2014; Jiao et al., 2021) and bacterial AAAD activity (Koyanagi et al., 2012), and serotonin conversion into melatonin via 5-methoxytryptamine or N-acetylserotonin, through bacterial ASMT and SNAT enzymes (Tan et al., 2016; Ma et al., 2017; Jiao et al., 2021). Alternatively, tryptophan conversion into tryptamine by bacterial tryptophan decarboxylase (TrpD; EC 4.1.1.105) has also been detected before.
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PMC9048412
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fmicb-13-873555-g001.jpg
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0.497828 |
3223c54f4f894932a67bd0203e2bcdde
|
Learning GRNs with the Inferelator (A) The response to the sugar galactose in S.cerevisiae is mediated by the Gal4 and Gal80 TFs, a prototypical mechanism for altering cellular gene expression in response to stimuli. (B) Gal4 and Gal80 regulation represented as an unsigned directed graph connecting regulatory TFs to target genes. (C) Genome-wide GRNs are inferred from gene expression data and prior knowledge about network connections using the Inferelator, and the resulting networks are scored by comparison with a gold standard of known interactions. A subset of genes are held out of the prior knowledge and used for evaluating performance
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PMC9048651
|
btac117f1.jpg
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0.481364 |
daf859627b804df596a56786dc3a77f0
|
Network inference performance on multiple model organism datasets. (A) Schematic of Inferelator workflow and a brief summary of the differences between GRN model selection methods. (B) Results from 10 replicates of GRN inference for each modeling method on (i) B.subtilis GSE67023 (B1), GSE27219 (B2) and (ii) S.cerevisiae GSE142864 (S1), and Tchourine et al. (2018) (S2). Precision–recall curves are shown for replicates where 20% of genes are held out of the prior and used for evaluation, with a smoothed consensus curve. The black dashed line on the precision–recall curve is the expected random performance based on random sampling from the gold standard. AUPR is plotted for each cross-validation result in gray, with mean ± standard deviation in color. Experiments labeled with (S) are shuffled controls, where the labels on the prior adjacency matrix have been randomly shuffled. A total of 10 shuffled replicates are shown as gray dots, with mean ± standard deviation in black. The blue dashed line is the performance of the GRNBOOST2 network inference algorithm, which does not use prior network information, scored against the entire gold standard network. (C) Results from 10 replicates of GRN inference using two datasets as two network inference tasks on (i) B.subtilis and (ii) S.cerevisiae. AMuSR is a multi-task-learning method; BBSR and StARS-LASSO are run on each task separately and then combined into a unified GRN. AUPR is plotted as in (B)
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PMC9048651
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btac117f2.jpg
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0.406108 |
54aa841b7b364728bce6a2199c861225
|
Construction and performance of network connectivity priors using TF motif scanning. (A) Schematic of inferelator-prior workflow, scanning identified regulatory regions (e.g. by ATAC) for TF motifs to construct adjacency matrices. (B) Jaccard similarity index between S.cerevisiae prior adjacency matrices generated by the inferelator-prior package, by the CellOracle package, and obtained from the YEASTRACT database. Prior matrices were generated using TF motifs from the CIS-BP, JASPAR and TRANSFAC databases with each pipeline (n is the number of edges in each prior adjacency matrix). (C) The performance of Inferelator network inference using each motif-derived prior. Performance is evaluated by AUPR, scoring against genes held out of the prior adjacency matrix, based on inference using 2577 genome-wide microarray experiments. Experiments labeled with (S) are shuffled controls, where the labels on the prior adjacency matrix have been randomly shuffled. The black dashed line is the performance of the GRNBOOST2 algorithm, which does not incorporate prior knowledge, scored against the entire gold standard network
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PMC9048651
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btac117f3.jpg
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0.464002 |
906150c0514d4522ad913777f04fcbe0
|
Network inference performance using S.cerevisiae single-cell data. (A) Uniform Manifold Approximation and Projection plot of yeast scRNAseq data, colored by the experimental grouping of individual cells (tasks). (B) The effect of preprocessing methods on network inference using BBSR model selection on 14 task-specific expression datasets, as measured by AUPR. Colored dots represent mean ± standard deviation of all replicates. Data are either untransformed (raw counts), transformed by Freeman–Tukey Transform (FTT), or transformed by log2(x + 1) pseudocount. Non-normalized data are compared to data normalized so that all cells have identical count depth. Network inference performance is compared to two baseline controls; data, which have been replaced by Gaussian noise (N) and network inference using shuffled labels in the prior network (S). (C) Performance evaluated as in (B) on StARS-LASSO model selection. (D) Performance evaluated as in (B) on AMuSR model selection. (E) Precision–recall of a network constructed using FTT-transformed, non-normalized AMuSR model selection, as determined by the recovery of the prior network. Dashed red line is the retention threshold identified by MCC. (F) MCC of the same network as in (E). Dashed red line is the confidence score of the maximum MCC. (G) Performance evaluated as in (B) comparing the Inferelator (FTT-transformed, non-normalized AMuSR) against the SCENIC and CellOracle network inference pipelines. (H) Performance of the Inferelator (FTT-transformed, non-normalized AMuSR) compared to SCENIC and CellOracle without holding genes out of the prior knowledge network. Additional edges are added randomly to the prior knowledge network as a percentage of the true edges in the prior. Colored dashed lines represent controls for each method where the labels on the prior knowledge network are randomly shuffled. The black dashed line represents performance of the GRNBOOST2 algorithm, which identifies gene adjacencies as the first part of the SCENIC pipeline without using prior knowledge
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PMC9048651
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btac117f4.jpg
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0.414038 |
6efb5de97d3544dfb81e95937c045253
|
Processing large single-cell mouse brain data for network inference (A) Uniform Manifold Approximation and Projection plot of all mouse brain scRNAseq data with excitatory neurons, interneurons, glial cells and vascular cells colored. (B) Uniform Manifold Approximation and Projection plot of cells from each broad category colored by Louvain clusters and labeled by cell type. (C) Heatmap of normalized gene expression for marker genes that distinguish cluster cell types within broad categories. (D) Uniform Manifold Approximation and Projection plot of mouse brain scATAC data with excitatory neurons, interneurons and glial cells colored. (E) Heatmap of normalized mean gene accessibility for marker genes that distinguish broad categories of cells. (F) The number of scRNAseq and scATAC cells in each of the broad categories. (G) The number of scRNAseq cells in each cell-type-specific cluster
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PMC9048651
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btac117f5.jpg
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0.450814 |
ff34461bf2254564a09b75a21c6a02e2
|
Learned GRN for the mouse brain (A) MCC for the aggregate network based on Inferelator prediction confidence. The dashed line shows the confidence score which maximizes MCC. Network edges at and above this line are retained in the final network. (B) Aggregate GRN learned. (C) Network edges, which are present in every individual task. (D) Jaccard similarity index between each task network. (E) Network targets of the EGR1 TF in neurons. (F) Network targets of the EGR1 TF in both neurons and glial cells. (G) Network targets of the EGR1 TF in glial cells. (H) Network of the ATF4 TF where blue edges are neuron specific, orange edges are glial specific and black edges are present in both categories
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PMC9048651
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btac117f6.jpg
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0.442281 |
fd9cf582e57e4c0b94158b0ff2a67032
|
UMAP visualization for RNAmix data. Cells are colored according to labels from clustering algorithm in Seurat. Highlighted cells suggest the conflicting result between clustering and visualization methods
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PMC9048682
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btac131f1.jpg
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0.404171 |
3d667ee473a84be9a7d3b48ef49fbf35
|
Visualization of Synthetic data. (a) MDS on cluster means. The size of point is proportion to the variance of cluster. (b) PCA. (c) supervised PCA. (d) t-SNE. (e) UMAP with cosine distance. (f) supUMAP with cosine distance. (g) CPM. (h) supCPM with Euclidean distance and w = 0.6. (i) supCPM with Geodesic distance and w = 0.7
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PMC9048682
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btac131f2.jpg
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0.411951 |
ba07014f9ddf4189b4d54a60b7ebb377
|
Visualization of RNAmix data. (a) MDS on the cluster means. The size of point is proportional to the variance of cluster (b) PCA. (c) supPCA. (d) t-SNE. (e) UMAP with cosine distance. (f) supUMAP with cosine distance. (g) CPM. (h) supCPM with Euclidean distance and w = 0.8. (i) supCPM with geodesic distance and w = 0.7
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PMC9048682
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btac131f3.jpg
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0.423492 |
1b0a4d8bedd340b1b9e8b2c6e1608000
|
Visualization of 11 Cancer cell lines data. (a) MDS on cluster means. The size of point is proportional to the variance of cluster. (b) PCA. (c) supPCA. (d) t-SNE. (e) UMAP with cosine distance. (f) supUMAP with cosine distance. (g) CPM. (h) supCPM with Euclidean distance and w = 0.6. (i) supCPM with geodesic distance and w = 0.6
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PMC9048682
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btac131f4.jpg
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0.438236 |
2531e7f996a1489f9bf93179807623ef
|
Visualization of PBMC data. (a) MDS on cluster means. The size of point is proportional to the variance of cluster. (b) PCA. (c) supPCA. (d) t-SNE. (e) UMAP with cosine distance. (f) supUMAP with cosine distance. (g) CPM. (h) supCPM with Euclidean distance and w = 0.9. (i) supCPM with geodesic distance and w = 0.9. Platelet cells are highlighted in a black box in figures
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PMC9048682
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btac131f5.jpg
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0.443831 |
834da0b7cef94be7931c82ac63bb82ea
|
T cells in bronchial alveolar lavage fluid from COVID-19 patients were visualized using different methods. (a) MDS on cluster means. The size of point is proportional to the variance of cluster. (b) PCA. (c) supPCA. (d) t-SNE. (e) UMAP with cosine distance. (f) supUMAP with cosine distance. (g) CPM. (h) supCPM with Euclidean distance and w = 0.85. (i) supCPM with geodesic distance and w = 0.85
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PMC9048682
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btac131f6.jpg
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0.430881 |
d410045938f14dbb9087be5829c14f7a
|
Visualization methods comparison using five metrics
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PMC9048682
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btac131f7.jpg
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0.451063 |
62a1d1ec8ba74c6583917b041311a2a1
|
Sketch map of the synthetic of SBA/ZnO, SBA/CuO, and SBA/CuZnO.
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PMC9048980
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c9ra09829a-f1.jpg
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0.476608 |
414cc5a8b3704ef999dd7cb5063a98c6
|
X-ray photoelectron spectroscopy (XPS) of Zn 2p (a); Cu 2p (b); O 1s (c); Si 2p (d); N 1s (e); C 1s (f) the survey of SBA-3, G-SBA, SBA/CuO, SBA/ZnO, SBA/CuZnO; G-SBA/Cu2+, G-SBA/Zn2+ and G-SBA/Cu2+–Zn2+ (g).
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PMC9048980
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c9ra09829a-f10.jpg
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0.533358 |
ffa779d064af4186ab447309403b8f4b
|
UV-vis DRS of SBA-3, SBA/CuO, SBA/ZnO, and SBA/CuZnO.
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PMC9048980
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c9ra09829a-f11.jpg
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0.498127 |
488038d5c1784bf7bface689161dd2a2
|
Thermogravimetric (TGA) and DTG curves of SBA-3 (a), G-SBA (b), G-SBA/Cu2+ (c), G-SBA/Zn2+ (d) and G-SBA/Cu2+–Zn2+ (e).
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PMC9048980
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c9ra09829a-f12.jpg
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0.401909 |
7bedeacf05e54e8fb07a648dad9fa8c7
|
Images of the antibacterial assays of SBA, SBA/CuO, SBA/ZnO and SBA/CuZnO at different concentrations inhibit E. coli (a–d) and S. aureus (e–h) (the scale in the picture is 20 μm). Their antibacterial reduction rate with E. coli (i) and S. aureus (j).
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PMC9048980
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c9ra09829a-f13.jpg
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0.422392 |
17308bccc96a40e493ec2d09c0b323e6
|
Images of the antibacterial assays of SBA/CuO (a1 and c1), SBA + CuO (b1 and d1), SBA/ZnO (a2 and c2), SBA + ZnO (b2 and d2), SBA/CuZnO (a3 and c3) and SBA + CuZnO (b3 and d3) inhibit E. coli (a and b) and S. aureus (c and d) (the scale in the picture is 20 μm). Their antibacterial reduction rate with E. coli and S. aureus (e).
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PMC9048980
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c9ra09829a-f14.jpg
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0.480034 |
984d934753b9486aadfbb0fe05accd40
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Schematic representation of hypothetical antibacterial mechanism of mesoporous silica supported metal oxide composites.
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PMC9048980
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c9ra09829a-f15.jpg
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0.392857 |
b365f2c50d7943c68b1a9fdc00072b30
|
Absorbance variation of production of H2O2 in different suspension under ultraviolet light (a) and in dark (b).
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PMC9048980
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c9ra09829a-f16.jpg
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0.462939 |
10c7881cb29e4cd6befb55d36cfc9ef9
|
Images of the photocatalytic antibacterial assays of SBA/CuO, SBA/ZnO and SBA/CuZnO with 150 ppm against S. aureus (a and b) (the scale in the picture is 20 μm).
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PMC9048980
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c9ra09829a-f17.jpg
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0.520905 |
a3573a39e0a54bbc8b4fb356d0ff10ca
|
FTIR spectra of SBA-3, G-SBA, G-SBA/Zn2+, G-SBA/Cu2+, G-SBA/Cu2+–Zn2+, SBA/ZnO, SBA/CuO and SBA/CuZnO.
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PMC9048980
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c9ra09829a-f2.jpg
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0.505261 |
4da61654c02942bcaf226869492a07ee
|
N2 adsorption/desorption isotherms (a and c) and pore size distribution (b and d) of SBA-3, G-SBA, SBA/CuO, SBA/ZnO and SBA/CuZnO.
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PMC9048980
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c9ra09829a-f3.jpg
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0.401956 |
5ab608bcc6ad4ef0bd1f8ff6da7215a1
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SAXS profiles measured on reflexion on five representative samples: SBA-3, G-SBA, SBA/CuO, SBA/ZnO, SBA/CuZnO.
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PMC9048980
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c9ra09829a-f4.jpg
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0.395205 |
43790fd3a47b454d82d1692875ca7e2f
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WXRD of G-SBA, SBA/CuO, SBA/ZnO, and SBA/CuZnO (a) and SAED pattern of SBA/ZnO (b).
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PMC9048980
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c9ra09829a-f5.jpg
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0.446103 |
b02915abb5024db98da11c491d652e47
|
Raman spectra of the SBA-3, SBA/ZnO, SBA/CuO, and SBA/CuZnO.
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PMC9048980
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c9ra09829a-f6.jpg
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0.414176 |
59ec6193ec524ae8b0590ff598327ac3
|
Photograph of the selected area, Raman map and Raman spectra of SBA/CuO (a), SBA/ZnO (b) and SBA/CuZnO (c).
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PMC9048980
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c9ra09829a-f7.jpg
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0.426407 |
4c73eb139146429f8b109ca9fb5ca6db
|
Scanning Electron Microscope (SEM) of SBA-3 (a); G-SBA (b); SBA/CuO (c); SBA/ZnO (d) and SBA/CuZnO (e) (the scale in the small picture is 10 μm).
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PMC9048980
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c9ra09829a-f8.jpg
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0.45024 |
fa512f7f2bd74c11ac09663cba00790a
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Transmission electron microscopic (TEM) images of SBA-3 (a); G-SBA (b); SBA/CuO (c) SBA/ZnO (d) and SBA/CuZnO (e). HRTEM images of SBA/CuO (f); SBA/CuZnO (g) and the diameter distribution of nano-CuO (h) and nano-CuZnO (i).
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PMC9048980
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c9ra09829a-f9.jpg
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0.463264 |
df594d729cf04429b25042ea1f1a03b5
|
(a) Absorption spectra of 2.5 μM PBS solution of PtL2 (solid line) and 10 μM PtL2 in DMSO (dashed line) normalized by absorbance at each maximum absorption wavelength. (b) The temporal change of absorption spectra of 5.0 μM PtL2 solubilized in PBS for a week. (c) Temperature change of PBS solution of PtL2 at different concentrations under the irradiation by 730 nm laser (2 W cm−2). [PtL2] = 0, 2.5, 5.0, 10, 20, and 40 μM in PBS. (d) Absorption spectra of PBS solution of 5.0 μM PtL2 before and after irradiation of 730 nm laser light (2 W cm−2) for 30 min.
|
PMC9049767
|
d0ra00652a-f1.jpg
|
0.396302 |
2b483c220bef461bbb45de49c4d44a5e
|
(a) Pseudo color image of MCF-7 cells incubated in the culture medium containing solubilized PtL2 (20 μM) for 2 h. (b) Absorption spectra of the black frame region in (a) (solid line, left axis) and PtL2 solubilized in PBS (dashed line, right axis). I and IBG are intensity of the transmitted light through the black flame region and background without the cells, respectively. (c) The distribution which shows the similar spectrum as the black frame region. Scale bars represent 10 μm.
|
PMC9049767
|
d0ra00652a-f2.jpg
|
0.448114 |
2755501f1f244f8c839683a80e7a69e5
|
Subcellular localization of solubilized PtL2 in MCF-7 cells analyzed by using spectral angle mapper algorithm. R: the solubilized complex, G: Rhodamine 123 (mitochondria) or ER-GFP (endoplasmic reticulum), B: Hoechst 33342 (nuclei). Scale bars represent 10 μm.
|
PMC9049767
|
d0ra00652a-f3.jpg
|
0.406705 |
a9b12ba6ddb14b55a1b8c39e03f0d674
|
Bright field and fluorescence images of live and dead MCF-7 cells with and without PtL2 (20 μM). The cells were irradiated by 730 nm laser light (0.28 W, spot size: 1 mm) for 15 min. The dashed circle shows the laser spot. Scale bars represent 200 μm.
|
PMC9049767
|
d0ra00652a-f4.jpg
|
0.540014 |
90282456e1ba41c49216240e6e0c9572
|
Comparison of SES scores between the two groups.
|
PMC9050290
|
ECAM2022-4330059.001.jpg
|
0.564031 |
3e49d663801b459aa7d35138f6fc6b17
|
Comparison of Morisky scores between the two groups.
|
PMC9050290
|
ECAM2022-4330059.002.jpg
|
0.437975 |
3a820c0d3a3b431d81facb7744057db9
|
Comparison of nursing satisfaction between the two groups.
|
PMC9050290
|
ECAM2022-4330059.003.jpg
|
0.403635 |
a639c38e83f24586a9b6a0fa65977197
|
Cumulative probability of dying by group status: SARS-CoV-2 cases (all (A) and by disease severity (B)) and reference group, one year post- SARS-CoV-2 infection period, Estonia 2020-2021.
|
PMC9051903
|
gr1.jpg
|
0.425765 |
d988e766183b40eab5d6cc0ad2ee09a4
|
Cumulative probability of dying by age group among SARS-CoV-2 cases and reference group by the period of observation (early, mid- and long-term post-acute SARS-CoV-2 infection), Estonia 2020-2021. (A) SARS-CoV-2 cases compared to reference group (among those 60 years and older); (B) SARS-CoV-2 cases compared to reference group (among those less than 60 years old).
|
PMC9051903
|
gr2.jpg
|
0.413813 |
a5264b7acf5d4180b1f4fc05d3ce12b1
|
Time-varying hazard ratios for death from any cause among SARS-CoV-2 cases compared to reference group (early, mid- and long-term post-acute SARS-CoV-2 infection), Estonia 2020-2021. (A) SARS-CoV-2 cases compared to reference group (among those 60 years and older); (B) SARS-CoV-2 cases compared to reference group (among those less than 60 years old).
|
PMC9051903
|
gr3.jpg
|
0.453209 |
1dcddc9813564eb98b23649f2eba4c30
|
Competing risk analysis for cause-specific mortality of SARS-CoV-2 cases and in the reference group, Estonia 2020-2021. (A) SARS-CoV-2 cases compared to reference group (among those 60 years and older); (B) SARS-CoV-2 cases compared to reference group (among those less than 60 years old).
|
PMC9051903
|
gr4.jpg
|
0.395579 |
939fb52cb4764c14bdca88d42dcbaf03
|
CONSORT diagram
|
PMC9052530
|
12913_2022_7910_Fig1_HTML.jpg
|
0.49379 |
6184e0ffc6584f8ca387c33f68e56c27
|
Schematic of a three-electrode system and an electrode sample made of steel grid.
|
PMC9053585
|
c9ra11012g-f1.jpg
|
0.494649 |
9794e3f47a2346e49ad49937e3894f7a
|
X-ray diffraction patterns of GO, RGO, EuNRs, and EuNR-RGO nanocomposites.
|
PMC9053585
|
c9ra11012g-f2.jpg
|
0.445619 |
792a5beafe4945c89d1163e315d2fa08
|
FE-SEM images of (a) GO, (b) EuNRs and (c and d) EuNR-RGO.
|
PMC9053585
|
c9ra11012g-f3.jpg
|
0.434767 |
d031225d814c458d812e12a84effc2c8
|
FT-IR spectra of EuNRs, EuNR-RGO nanocomposite, GO and RGO.
|
PMC9053585
|
c9ra11012g-f4.jpg
|
0.443172 |
e8533553597c412dbf1df6484e04e604
|
(a) Cyclic voltammograms of EuNR, RGO and EuNR-RGO electrodes in 3.0 M KCl at 50 mV s−1; (b) cyclic voltammograms of EuNR electrode in 3.0 M KCl at different scan rates from 5 to 100 mV s−1; (c) cyclic voltammograms of EuNR-RGO electrode in 3.0 M KCl at different scan rates from 5 to 100; and (d) capacitance versus sweep rate for EuNR and EuNR-RGO.
|
PMC9053585
|
c9ra11012g-f5.jpg
|
0.49105 |
128b8bf2b1694626981610ea4a3db71a
|
(a) RGO, EuNR and EuNR-RGO specific capacitance changes at 150 mV s−1. 3D-CCV curves at 150 mV s−1 for (b) RGO, (c) EuNR and (d) EuNR-RGO.
|
PMC9053585
|
c9ra11012g-f6.jpg
|
0.551334 |
83a363713e2641fa960827e961d19c5e
|
(a) RGO, EuNR and EuNR-RGO charge/discharge curves at 2.0 A g−1 current density; (b) EuNR charge/discharge curves at 1–16 A g−1 current densities; (c) EuNR-RGO charge/discharge curves at 1–16 A g−1 current densities and (d) EuNR and EuNR-RGO specific capacitance changes with changing current densities from 1 to 16 A g−1.
|
PMC9053585
|
c9ra11012g-f7.jpg
|
0.462444 |
3fc7fdb768bb4db6b2647c7a74a7cff4
|
Ragone plots of EuNR and EuNR-RGO.
|
PMC9053585
|
c9ra11012g-f8.jpg
|
0.450492 |
7f3278f28a6a4a9aaa431f9d821f0044
|
Impedance spectra of EuNR and EuNR-RGO. The frequency range is 0.1–105 Hz.
|
PMC9053585
|
c9ra11012g-f9.jpg
|
0.412525 |
cfd3e12f0aee45cf968430ea5e118d55
|
Schematic illustration of the synthetic process of the MoS2/GNS nanocomposite.
|
PMC9053862
|
d0ra03539d-f1.jpg
|
0.368392 |
f97489cac2ff46848ddbe0a2a4e41d6c
|
Wide-range XRD patterns of the as-prepared MoS2/GNS samples as anodes.
|
PMC9053862
|
d0ra03539d-f2.jpg
|
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