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0.453662 | 7d2f61643c914b6c9ffdba24c83b33e1 | Characterization of the express saccade epoch. (A) Probability density estimate of participants’ visually guided saccades with reaction times >90 ms in the metronome and random task. Bimodality coefficient analysis of these reactive saccades supported a bimodal distribution of saccade reaction times (skewness = 6.50; kurtosis = 71.7), supportive of distinct populations of saccades, demonstrating express and regular latencies. The deflection points of the distribution of SRTs occurred at ~120 ms in both task paradigms. Express saccades were thereby categorized as those with SRTs within 90–120 ms, inclusive, and regular-latency saccades were defined as those with RTs > 120 ms. (B) Schematic of eye position relative to target by saccade type. | PMC10323365 | fnins-17-1179765-g002.jpg |
0.41619 | e212f5c6dbdc4cf8af967204aa102909 | Duration of blinks by participant group in the metronome and random tasks combined. Kernel density estimate for eye loss durations between 50–300 ms are plotted by participant group in the upper left panel. The remaining three panels show eye loss durations with a range of 10–600 ms and a bin width of 50 ms. Dotted vertical lines represent the eye loss duration cut-off (50–300 ms) for blink analysis inclusion. Duration of blinks significantly varied by participant group, with ADHD/BPD participants making significantly longer blinks than control participants (p < 0.01). | PMC10323365 | fnins-17-1179765-g003.jpg |
0.427996 | 960dd36bd85349a88c7e809b4065ad44 | Moving average of blink rate in the metronome and random task across trial duration. Dashed vertical lines represent the random offset between 1,000–1,500 ms when the central fixation point at the start of trial disappeared. A main effect of blink rate by participant group was observed in the metronome task, with t-tests revealing higher blink rates in ADHD/BPD than BPD (p = 0.002) and controls (p < 0.001). Mean blink rates displayed a sinusoidal structure after the first 1–3 target steps, with the subsequent number of peaks corresponding to the number of target steps. No main effect of blink rate x group was observed when the ISI of targets was randomized (top left panel). In the bottom panel of the figure, participant mean blink reaction times to target are plotted by target ISI. Blink reaction times roughly approximated the half-way point of the ISI, with a participant mean of 275.2 ms ± standard deviation = 101.0 for 500 ms ISI, 404.4 ms ± 120.2 [750 ms ISI], 510.7 ms ± 136.7 [1,000 ms ISI], 567.5 ms ± 142.4 [1,250 ms ISI], and 698.5 ms ± 198.7 [1,500 ms ISI]. | PMC10323365 | fnins-17-1179765-g004.jpg |
0.416759 | b37ea99326994c6bada7913beb409340 | Moving average of clinical participants’ blink rate according to their psychotropic medication class prescription. No main effect of medication classes were observed on blink rates in the metronome task (averaged across the 5 ISI conditions): stimulant (t[42] = −0.89, p = 0.81), SSRI (t[42] = 1.48, p = 0.07), and second-generation antipsychotic (t[42] = 0.99, p = 0.16). | PMC10323365 | fnins-17-1179765-g005.jpg |
0.433813 | d0ffc3e668e34230b69233bc4254768f | (A) Blink probability relative to target onset (vertical line at 0 ms) on metronome trials when ADHD/BPD participants (dark blue) and controls (gray) made a predict saccade (left panel), express saccade (middle panel), or regular saccade (right panel) to target. Shaded regions represent the 95% CIs of the individual mean blink probability averaged across subjects. (B) Effect sizes of the mean difference score of blink probabilities between ADHD/BPD and control groups relative to target onset. The absolute values of the Cohen’s d effect sizes are plotted with the shaded regions representing the 95% CI. Regions of statistically significant differences in blink probabilities between ADHD/BPD and control groups are highlighted as tick marks with the color corresponding to which group had a higher blink probability at that timepoint. As seen by the blue tick marks, ADHD/BPD participants had periods of significantly higher blink probabilities versus controls in predict, express and regular saccade trials. | PMC10323365 | fnins-17-1179765-g006.jpg |
0.451801 | 16586976722e4a9f8d9ab7791d08d83f | Mean pupil size from the time of saccade completion to 200-ms post-saccade in both experimental paradigms. Each + represents the mean pupil size of a subject. Horizontal bars display the group mean in the metronome and random tasks. There was a main effect of pupil size x participant group in both experimental tasks, *p < 0.05. Only ADHD/BPD participants showed a task-based pupil size effect, with an increased pupil size in the metronome versus random task (Z = −2.06, p = 0.039). | PMC10323365 | fnins-17-1179765-g007.jpg |
0.377178 | b064f281098e45eeb9c20b158fa53703 | Clinical participants’ mean pupil size by psychotropic medication class prescription. Pupil size did not vary by psychotropic medication: stimulants (p = 0.122), SSRIs (p = 0.542), and second-generation antipsychotics (p = 0.339). | PMC10323365 | fnins-17-1179765-g008.jpg |
0.434213 | 1e213fd57a91414aac59307d4383f322 |
PRISMA flow chart of the study selection process applied in this systematic review. iPSC: Induced pluripotent stem cell. | PMC10324508 | WJSC-15-632-g001.jpg |
0.388998 | fd4fb50f41434bdeb11d1e1ffe33fa72 |
Induced pluripotent stem cell differentiation process into induced pluripotent stem cell-derived brain microvascular endothelial-like cells for reconstruction of the blood-brain barrier-on-a-chip model. A: Percent analysis of cells and culture media used in induced pluripotent stem cell expansion and differentiation before blood-brain barrier (BBB)-on-a-chip reconstruction; B: Schematic summary of the main findings in this systematic review on the cells and culture conditions applied in the BBB-on-a-chip reconstruction process. Studies were grouped by similar device designs. AAc: Ascorbic acid; AC: Astrocyte; ACS-1024: Bone-induced pluripotent stem cell line; AGF: Astrocyte growth factor; AGM: Astrocyte growth medium; BC1: Lymphoma cell line; BDNF: Brain-derived neurotrophic factor; bFGF: Bovine fibroblast growth factor; BMM: Basement membrane matrix; bPPP: Basic platelet-poor plasma; D1: Day 1; D2: Day 2; D3: Day 3; D38: Day 38; D4: Day 4; D5: Day 5; D6: Day 6; D8: Day 8; D9: Day 9; db-cAMP1: 2’-O-Dibutyryladenosine-3’,5’-cyclic monophosphate; DMEM/F12: Dulbecco’s modified Eagle medium with F12; DPBS: Dulbecco’s phosphate-buffered saline; DX: Doxycycline; E8: Essential 8 medium; EC-/-: Human endothelial serum-free medium without retinoic acid + basic fibroblast growth factor; EC: Endothelial cell; EC+/+: Human endothelial serum-free medium with retinoic acid + basic fibroblast growth factor; ECM: Extracellular matrix; EGM-2MV: Microvascular endothelial cell growth medium-2; ESFM: Endothelial serum-free medium; FBS: Fetal bovine serum; FCS: Fetal calf serum; FGF2: Fibroblast growth factor 2; GDNF: Glial cell line-derived neurotrophic factor; GFP: Green fluorescent protein; GFR: Growth factor reduced; GM25256: Cell line of induced pluripotent stem cell derived from adult skin fibroblasts; GM6001: Broad spectrum MMP inhibitor; HBVP: Human brain vascular pericytes; HEPES: 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid; HESFM: Human endothelial serum-free medium; hFGF: Human fibroblast growth factor; hiPSC-EC: Human-induced pluripotent stem cell-derived endothelial cell; hPDS: Human serum from platelet-poor human plasma; hPDS: Platelet-poor plasma-derived human serum; HUVEC: Human umbilical vein endothelial cell; iBMECs: Induced pluripotent stem cell-derived brain microvascular endothelial cells; IMR90-C4: Induced pluripotent stem cell line; iPSCs: Induced pluripotent stem cells; LN: Laminin; ms1: Brain-derived neurotrophic factor + glial cell line-derived neurotrophic factor + ascorbic acid + 2’-O-Dibutyryladenosine-3’,5’-cyclic monophosphate; ms2: Primocin + glutamax + doxycycline + neurotrophin-3 + brain-derived neurotrophic factor + fetal calf serum; mTeSR1: Basal medium type for induced pluripotent stem cells; N2B27: Culture medium; NA: Not applied; NR: Not reported; NT3: Neurotrophin-3; P/S: Penicillin/streptomycin; PCs: Pluripotent cells; PM: Pericyte medium; RA: Retinoic acid; RA: Retinoic acid; RT: Room temperature; SFB: Serum-free medium; UM: Unconditioned medium; VEGF: Vascular endothelial growth factor; Y27632: Dihydrochloride inhibitor. | PMC10324508 | WJSC-15-632-g002.jpg |
0.462436 | 86155301c09f4a26a364ab1da9708674 | Directed acyclic graph showing the assumptions of MR analyses. | PMC10324563 | fpsyt-14-1196055-g0001.jpg |
0.394733 | 44cc64cbf7364c3e8ec71b1d1127616a | Scatter plots of MR analyses. | PMC10324563 | fpsyt-14-1196055-g0002.jpg |
0.407754 | 8f0fc3ec89464a6781f6cfacc9a2e977 | Leave-one-out plots of MR analyses. | PMC10324563 | fpsyt-14-1196055-g0003.jpg |
0.498874 | 9c2f0f00714343f1a94fdfb566e3cb93 | Summary of DEEP MOVEMENT. Our models are trained and evaluated on DSAs of patients before thrombectomy in task 1 (a). The stacked-Xception model is shown as an example. The pre-thrombectomy model is next evaluated to predict treatment outcome of post thrombectomy to assess the quality of reperfusion in task 2 (b) | PMC10326097 | 330_2023_9478_Fig1_HTML.jpg |
0.42987 | 5fe3df7a995c4895845a8ac88feb8619 | (a–d Clockwise from top left) Confusion matrix showing results of various models on the Stanford test set: (a) stacked-Xception, (b) 3D model (inception-3D), (c) stacked-Xception + 3D, (d) stacked-Xception + ViT | PMC10326097 | 330_2023_9478_Fig2_HTML.jpg |
0.460954 | 7ba936a38636422ebb215b71ae416ee9 | a F1 score of different architectures across the Stanford test set. b F1 score comparing 2D-only and stacked 2D | PMC10326097 | 330_2023_9478_Fig3_HTML.jpg |
0.460763 | c33a1fa5430f4012ad7e8b864edbcabe | Confusion matrix of the stacked-Xception model predicting videos after intervention in ICA, M1, and M2 | PMC10326097 | 330_2023_9478_Fig4_HTML.jpg |
0.422832 | f58ab122d4d14e4a90fa10ed6c68d351 | a Grad-cam attention maps from the stacked-Xception model on the test set. b Grad-cam visualisation of 3 patients with M1 occlusion pre and post treatments | PMC10326097 | 330_2023_9478_Fig5_HTML.jpg |
0.394524 | 96c932d7c01f43d5a746657e92c3f6d7 | a, b Illustrative example comparing the difference in the videos between internal and external validation (a left: Stanford M1/Pre, b right: IU M1/Pre) | PMC10326097 | 330_2023_9478_Fig6_HTML.jpg |
0.517085 | 793f2366aa8f42e793e5f5f31bbab2f7 | Illustration of \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ construction and sample applications. A viewing trajectories (green, top panel and red, bottom panel) as metric-measure spaces, we construct the distance \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau (X,Y)$$\end{document}GWτ(X,Y) between them. Pushing the measures \documentclass[12pt]{minimal}
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\begin{document}$$\mu _Y$$\end{document}μY forward to \documentclass[12pt]{minimal}
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\begin{document}$$d_X(r_X,\cdot )$$\end{document}dX(rX,·) and \documentclass[12pt]{minimal}
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\begin{document}$$d_Y(r_Y,\cdot )$$\end{document}dY(rY,·) results in the equivalence of \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ with the easily computable Wasserstein distances between the pushforwards (Proposition 1a). B illustration of Example 1: sample dataset of four trajectories lying in different dimensions, defined on different time scales, and having a different number of unequally spaced discrete time points. A distance matrix graphically summarizes \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ distances between pairs of trajectories. Trajectories with similar shapes are found to be similar (cyan), even though they lie in different dimensions. C proposed application workflow for \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ, applied in Sect. 3 (Color figure online) | PMC10326159 | 11538_2023_1175_Fig1_HTML.jpg |
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\begin{document}$$GW_\tau $$\end{document}GWτ performance in embedding and subsequent separation of classes in comparison to dynamic time warping (DTW) and Euclidean distances: synthetic and real world data (synthetic data is constructed to resemble real data characteristics in higher dimensions). The \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ distance matrix is used to embed time series from synthetic (A) and real (B) data (Left panel) into the plane, allowing for accurate separation of classes (right panel). It is more difficult to separate classes when embedding is performed with DTW or Euclidean distances in comparison to \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ distance (Color figure online) | PMC10326159 | 11538_2023_1175_Fig2_HTML.jpg |
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\begin{document}$$GW_\tau $$\end{document}GWτ performance in hierarchical clustering in comparison to Euclidean and dynamic time warping (DTW) distances: model simulation data. A top: simulated data from the three dimensional Lotka–Volterra system from Xiao and Li (2000). Three classes correspond to solution trajectories when starting in proximity to a stable focus (1, 1, 1) (Class 1), an unstable focus (1, 1, 1) (Class 2) or an unstable node (0, 0, 0) (Class 3), with 20 trajectories in each class corresponding to random initial conditions (one trajectory from each class is shown). Bottom: randomly rotated data (one trajectory from each is shown). B hierarchical (single linkage) clustering dendrograms constructed using Euclidean, DTW, and \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ distances as dissimilarity measures between trajectories for original data (top) and “rotation-corrupted” data (bottom). Note poor performance of Euclidean distance in both cases, and rapid decrease in performance of DTW distance when rotational noise is introduced. The performance of \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ is high in both cases (Color figure online) | PMC10326159 | 11538_2023_1175_Fig3_HTML.jpg |
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\begin{document}$$GW_\tau $$\end{document}GWτ performance on embedding and clustering: real world data. A electroencephalogram (EEG) data on selected three EEG channels (out of total 64 available in the dataset; Dua and Graff (2017)): 10 trajectories in each class represent EEG response to a stimulus for an alcoholic (magenta, Class 1) vs. non-alcoholic (blue, Class 2) patient. B Embeddings of the data into the plane using \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ distance matrices and results of k-means clustering in the embedded space. C hierarchical cluster (complete linkage) dendrograms using \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ distance matrices. D k-means clustering results on embedded data (such as in panel B): reporting number of channels (out of 64 total) with small (\documentclass[12pt]{minimal}
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\begin{document}$$\le 2$$\end{document}≤2) and large (\documentclass[12pt]{minimal}
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\begin{document}$$>6$$\end{document}>6) number of incorrectly clustered trajectories (“clustering mistakes”) when using Euclidean, DTW, and \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ distances. Note superior performance of \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ in this comprehensive evaluation (Color figure online) | PMC10326159 | 11538_2023_1175_Fig4_HTML.jpg |
0.415293 | 72f585c24aa14c0aaaf2fcfd7f8aa745 | Scalability of \documentclass[12pt]{minimal}
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\begin{document}$$GW\tau $$\end{document}GWτ with respect to dataset size and dimension. A left: runtimes (log scale) when calculating distances for 100 circle/line pairs (synthetic data used in Fig. 1B) in both 2D and 3D using \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ versus DTW. As expected, increase in dimension from 2D to 3D does not affect the runtimes; increase in dataset size (as number of points along each trajectory) results in steep increase in runtimes for DTW, while has almost negligible effect on \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ. The same trend is observed for the other two synthetic/simulated datasets used in Fig. 2A and 3 (right). B. runtimes (log scale) when calculating matrices of all pairwise distances between trajectories (left) and performing 1-NN classification (right) using \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ versus DTW for the real data UCRbio used in Table 1, listed in increasing data complexity appropriate for each task (t.s.length*(train size + test size) (left) and t.s.length*train size* test size (right)). Observe shorter runtimes when using \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ compared to DTW (Color figure online) | PMC10326159 | 11538_2023_1175_Fig5_HTML.jpg |
0.402332 | 939141246a6a4227beee6423694ed931 | Applying \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ for the analysis of time series arising in cell biology: grouping trajectories from cells under different experimental conditions. A schematic of the “wobbling” movement quantification and corresponding Wobble dataset from Ignacio et al. (2022) for change in angle of a pronuclear complex (yellow, with two centrosomes marked by red and blue) during centration and rotation in early C. elegans embryos (10 trajectories of empty vector (EV) control (Left), 12 trajectories of cells subjected to RNA interference against the protein GPB-1 (gpb-1(RNAi); Center), and 7 trajectories of cells subjected to RNA interference against the protein LET-99 (let-99(RNAi); Right)). B using \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ to construct the distance matrix between trajectories (left) to be used for k-medoids clustering and embedding of trajectories into the plane followed by k-means clustering on embedded coordinates. Both clustering methods using \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ distinguish EV from the RNAi knockdowns, with two RNAi knockdown trajectories found closer to EV than to other RNAi knockdown trajectories. C DTW and Euclidean distances have larger error in distinguishing EV from the RNAi knockdowns (Color figure online) | PMC10326159 | 11538_2023_1175_Fig6_HTML.jpg |
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\begin{document}$$GW_\tau $$\end{document}GWτ to analyse time series arising in cell biology: comparing different trajectory averaging methods. A
Wobble dataset from Ignacio et al. (2022) with mean trajectories and FGW barycenter trajectories based on FGW barycenter method of Vayer et al. (2020). Note that traditionally used mean trajectories appear to damp the oscillations found in the RNAi treatment data. B embedding with \documentclass[12pt]{minimal}
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\begin{document}$$GW_\tau $$\end{document}GWτ places mean trajectories of the RNAi-treated embryos (blue) inside the EV group, while the FGW barycenter trajectories (black) stay close to their respective trajectories (Color figure online) | PMC10326159 | 11538_2023_1175_Fig7_HTML.jpg |
0.438498 | ee8f4435bf464cbd9640907c31ead9a9 | Soft-templating route
for the synthesis of uniform hollow ZIF-8
nanospheres. | PMC10326808 | am3c06502_0002.jpg |
0.439988 | ee20feb291324b328e5ace4731265999 | The SEM images showing
the effect of variation in n-hexane’s volume
fraction on ZIF-8 growth: (a) 0 v/v % (0
mL), (b) 1 v/v % (0.5 mL), (c) 3 v/v % (1 mL), (d) 5 v/v % (2 mL),
and (e) 9 v/v % (4 mL) of n-hexane. (f) XRD pattern
for 0, 1, 3, 5, and 9 v/v % of n-hexane in comparison
to the XRD pattern for reference ZIF-8Lit (all the scale
bars are 2 μm). | PMC10326808 | am3c06502_0003.jpg |
0.468653 | 996e0c14119e4fb095c56ee60b528c3d | SEM images
for (a) 0, (b) 3, (c) 5, (d) 9 (ZIF-8-Out), and (e)
9 v/v % (ZIF-8-Ins) mL concentration of n-hexane
equivalent to the surfactant/oil (S/O) ratio of 0, 0.3, 0.15, 0.075,
and 0.075 g mL–1, respectively, keeping the amount
(0.3 g) of surfactant constant. The insets with figures (b–e)
show the respective TEM images. (f) XRD pattern for the abovementioned
samples (the scale bars are 5 μm unless specified in the image). | PMC10326808 | am3c06502_0004.jpg |
0.393948 | 08f3b9fd77cb4eebb9068905d16513b3 | (a)Variation in the average diameter with
a change in the surfactant/oil
(S/O) ratio. (b) The BET adsorption isotherm for the samples ZIF-8-Out,
ZIF-8-Ins, and the samples prepared with n-hexane
and without surfactant and n-hexane. (c) TGA analysis
for ZIF-8-Out and ZIF-8-Ins of reference. (d) FTIR spectra for the
samples ZIF-8Lit, ZIF-8-Out, and ZIF-8-Ins; inset: enlarged
region of the FTIR spectra between 1500 and 4000 cm–1 (inset: a photo of reference ZIF-8 sample and ZIF-8-Ins mixed with
water). | PMC10326808 | am3c06502_0005.jpg |
0.408179 | 7d291a5e802b4dfea8cf75dc25a0d9b3 | SEM images for (a) 0.10,
(b) 0.15, (c) 0.25, (d) 0.40, and (e)
0.60 g of HFS equivalent to the surfactant/oil (S/O) ratio of 0.025,
0.037, 0.063, 0.10, and 0.15 g mL–1, respectively,
keeping the volume of oil (n-hexane) constant at
4 mL (equivalent to 9 v/v %). The inset with figure (d,e) shows the
respective TEM images. (f) Average diameter corresponding to each
S/O ratio. (The scale bars are 5 μm unless specified in the
image). | PMC10326808 | am3c06502_0006.jpg |
0.456507 | 464e2e2988514474abdc03d5a504936f | (a) SEM-based estimation of the average spherical diameter
of ZIF-8
spheres for studied synthesis times, (b) Ostwald ripening, and (c)
a summarized trend to explain the PSD based on synthesis time. | PMC10326808 | am3c06502_0007.jpg |
0.430309 | 13dbdff3e8004fd3b3d47b772a67419e | (a) Synthesis time-based XRD analysis,
(b) XRD-based relative crystallinity
analysis for 0, 0.5, 1, 1.5, 2, 6, 14, and 24 h, and (c) a BET adsorption/desorption
isotherm for ZIF-8-Ins synthesized for 2 and 6 h. | PMC10326808 | am3c06502_0008.jpg |
0.436977 | f5962ee0f4fe402f928487052dbeedbe | (a) N2, (b) CO2, and (c) CO2/N2 adsorption selectivity evaluated using the
IAST model, respectively,
for ZIF-8-Ins, ZIF-8-Out, and ZIF-8Lit at 0 °C (circles)
and 25 °C (triangles); (d) the CO2 recyclability data
obtained at 25 °C, showing both CO2 adsorption capacity
as well as cyclic adsorption/desorption performance up to four cycles. | PMC10326808 | am3c06502_0009.jpg |
0.411538 | ef3b942334d047858db65c00ab159837 | Cell positioning is intrinsically heterogeneous in vivo and in vitro.A. A representative section of normal human mammary gland stained for keratin 19 and 14 (LEP and MEP markers, respectively). The cells are arranged in a bilaminar structure with MEP surrounding LEP (order), however the tissue also exhibits large variance in local geometry, cell proportions and cell positioning (disorder). A custom analysis workflow was used for pixel segmentation and image quantification (Supplemental Information). The density histograms show the distributions for effective tissue diameter (d), LEP proportion (Φ) and LEP positioning at the tissue boundary (ϕb). Analysis of n=128 tissue objects from 14 donors is shown. Scale bar = 50 μm.B. Reconstituted organoids provide an in vitro model to study intrinsic sources of positional heterogeneity in tissues with defined composition and geometry. Finite-passage human mammary epithelial cells (HMEC) were isolated from breast reduction mammoplasty, expanded in vitro, sorted as single cells and reaggregated in defined numbers and proportions. Organoids were cultured in Matrigel for 2 days.C. Mammary organoids contained similar number of GFP+ LEP (gold) and mCh+ MEP (purple). MEP spheroids contained similar number of GFP+ MEP (blue) and mCh+ MEP. Confocal images were processed to quantify the total LEP/GFP fraction (Φ) and the boundary LEP/GFP fraction (ϕb) (Supplemental Information). For each organoid, three central sections spaced 5 μm apart were analyzed. Scale bar = 50 μm.D. Processed mammary organoid and MEP spheroid images following segmentation illustrate population-level structural heterogeneity. Scale bar = 50 μm.E. Probability density histograms showing the population distribution of mammary organoids (gold) and MEP spheroids (blue) two days post-assembly. The dashed line represents a Gaussian fit to the MEP spheroid distribution. The number of observations is noted at the top right of the graph. | PMC10327153 | nihpp-2023.07.01.546933v1-f0001.jpg |
0.483918 | 46abc65bae384420a5f3aa2b253553fc | Tissues dynamically sample from the ensemble steady state distribution.A. Snapshots from time lapse microscopy after segmentation for a representative mammary organoid and MEP spheroid illustrate temporal structural heterogeneity at the steady state. Scale bar = 50 μm. Quantification of fluctuations in ϕb over time for the examples shown. Dashed line is the average of ϕb over time for the corresponding tissue.B. Probability density histograms showing the temporal distribution of a small number of mammary organoids (n=18) and MEP spheroids (n=24) at the steady state.C. Organoids at different timepoints were binned into 10 structural states according to their ϕb. The probability of transitioning between any two structural states over a 20 min window is represented by the size of the circles. Any transitions not observed during this window are marked by ‘+’. For organoids at the steady state, the diagonal symmetry of the transition probability matrix suggests there is no net flux across states. The same tissues were used for this analysis as Fig. 2B.D. Organoids at each time point were classified into 5 groups based on the difference between the instantaneous and average ϕb. For each bin, the average structure at different time intervals from the initial classification is plotted. The colors in the graph on the right represent the bins on the example trace. The same tissues were used for this analysis as panel Fig. 2B. | PMC10327153 | nihpp-2023.07.01.546933v1-f0002.jpg |
0.462522 | cfe57b587c1c453eaa440e2bde85dba7 | A statistical mechanical framework provides a quantitative description of organoid structural distributions.A. Schematic illustrating tensions at different cell-cell and cell-ECM interfaces. The total tissue mechanical energy is the sum of interfacial energy at each interface (product of the tension, γint, and the area, Aint, of the interface).B. Cortical tensions of single LEP and MEP in suspension as measured by micropipette aspiration.C. Cell-ECM contact angles for cells on Matrigel-coated glass were measured after 4 h.D. Cell-cell contact angles for cell pairs were measured after 3 h.E. Estimated cell-cell and cell-ECM tensions for LEP and MEP based on Young’s equation. For cell-ECM tensions, the γMEP–ECM was used as the reference and was assigned the value of 0. Confidence intervals were calculated using error propagation for standard error on cortical tension and contact angle measurements (Supplemental Information).F. 2D hexagonal or 3D body-centered cubic (BCC) lattice models were used to estimate the average mechanical energy and the degeneracy of structural macrostates (ϕb) (Supplemental Information). Only the 2D model is shown here for simplicity. Macrostates with ϕb≈0.5 comprise the greatest number of microstates (highest degeneracy).G. The average mechanical energy of mammary organoids for different values of ϕb estimated from the BCC model. Ten thousand tissue configurations were sampled for each ϕb. The dots and error bars represent the mean and standard deviation. The gold line represents a linear fit for average macrostate energy against ϕb. The slope (ΔE) is roughly proportional to the product of the difference in cell-ECM tensions and the total ECM surface area.H. Macrostate degeneracy (Ω) was calculated analytically (inset) (Supplemental Information). The corresponding probability density assuming random sampling of all microstates is shown with the dotted line. Additional variance due to uncertainty in measurements and degeneracy along other structural metrics was built into the model (Supplemental Information), and its prediction is shown using the solid line. The superimposed histogram for comparison is the measured ensemble distribution of MEP spheroids (from Fig. 1E).I. The structural distribution of organoid ensembles is modeled as a maximum entropy distribution, a function of the macrostate degeneracy (calculated analytically or from the distribution for MEP spheroids), mechanical energy (calculated from interfacial tensions), and tissue activity.J. The maximum entropy model (gold line) was fit to the measured ensemble distribution of mammary organoids (histogram, from Fig. 1E) to estimate the tissue activity. The predictions for distributions arising from only the scaled energy or macrostate degeneracy are also shown for comparison (gray and blue lines respectively).K. The diagram illustrates how the relative weights of the mechanical energy and macrostate degeneracy determine the extent of structural order. In the absence of a mechanical potential, the degeneracy dominates, and the system is maximally disordered. A large absolute mechanical potential drives the ensemble to an ordered state.The lines and hinges for boxplots in panels B-D show the median and the 1st and 3rd quartiles. The number of observations for panels B-D are noted at the bottom of the graphs. Asterisks represent the significance of difference from the reference group (MEP for B and C, MEP-MEP for D), as follows ns: p > 0.05; *: p < 0.05, **: p < 0.005; ***: p < 0.0005 based on Wilcoxon test. | PMC10327153 | nihpp-2023.07.01.546933v1-f0003.jpg |
0.442937 | 58591a8350144707991f7e10bcb42044 | Tissue activity sets the balance between the mechanical potential and macrostate degeneracy.A. Tissue activity is a measure of the kinetic component of the internal energy of tissues and is associated with cell motility. Cell speeds were measured by tracking cell nuclei in LEP- or MEP-only spheroids using time lapse microscopy (n=11 and 14 respectively). Speeds for MEP(purple) and LEP (gold) as a function of distance from the tissue boundary are shown. The Pearson’s correlation coefficients for linear regression are shown. Average speeds and their 95% confidence intervals are represented by the points and error bars respectively.B. The effective diffusion coefficients for cells in spheroids were calculated from the trends for the relative distance between cell pairs. This approach eliminates confounding dynamics from whole organoid movements. The left graph shows example traces of relative distance between cell pairs over time for a representative MEP spheroid. The change in relative distance (relative displacement) was calculated for different time intervals (Δt) and averaged across all times and cell pairs to get the mean squared relative displacement (MSRD). The MSRD vs Δt curves were used to estimate the effective cellular diffusion coefficients Deff for each organoid (Supplemental Information).C. Effective diffusion coefficients for LEP (gold) and MEP (purple) in the presence and absence of ECM interactions (in Matrigel and agarose microwells, respectively). The lines and hinges for boxplot show the median and the 1st and 3rd quartiles. The number of spheroids analyzed is noted at the bottom of the graph. Asterisks represent the significance of difference between conditions, as follows ns: p > 0.05; *: p < 0.05, **: p < 0.005; ***: p < 0.0005 based on Wilcoxon test.D. Equal proportions of GFP+ LEP and mCh+ MEP were aggregated and cultured in Matrigel (high activity) or agarose microwells (low activity).E. The macrostate energy calculations for organoids in Matrigel (gold) and agarose (navy) using the BCC lattice model.F. The histogram shows probability density for organoids cultured in agarose. The gold line is the fit for organoids in Matrigel (+ECM), and the navy dotted line is the theoretical prediction based on ΔE for agarose with no change in activity. The solid navy line is the theoretical fit to the measured distribution, predicting 5-fold lower activity in agarose compared to Matrigel.G. Structural fluctuations in ϕb over time for representative mammary organoids in Matrigel and agarose (gold and navy respectively). Dashed line is the average of ϕb for the corresponding condition. | PMC10327153 | nihpp-2023.07.01.546933v1-f0004.jpg |
0.501017 | af2f3a5dce4a45d7baae91f9676f3559 | Engineering the structural ensemble by programming the mechanical potential and activity.A. Experimental workflow: The MEP-ECM or MEP-MEP interfacial tensions were perturbed using shRNA against TLN1 (green) and CTNND1 (red). A non-targeting shRNA was used as control (blue). Equal proportion of mCh+ normal and GFP+ shRNA-transduced MEP were aggregated into spheroids (KD-MEP spheroids) and cultured either in Matrigel or agarose.B. The macrostate energy calculations for KD-MEP spheroids in Matrigel (top) and agarose (bottom) using the BCC lattice model.C. The predicted ensemble distributions for KD-MEP spheroids cultured in Matrigel (top) and agarose (bottom).D. The measured probability densities for KD-MEP spheroids cultured in Matrigel (top) and agarose (bottom). Histograms show the distribution of experimental data, dashed vertical lines are the average ϕb, and the solid curves are the theoretical predictions for each condition. The number of observations is noted at the top of the graphs. | PMC10327153 | nihpp-2023.07.01.546933v1-f0005.jpg |
0.478197 | 76ce697307124a99981c83ff16fe37df | Engineering the structural ensemble by programming macrostate degeneracy.A. Engineering structure by varying LEP proportion: the proportion of GFP+ LEP in mammary organoids or GFP+MEP in MEP spheroids was varied. Tissues with Φ=0.25,0.5 or 0.75 (light pink, magenta, and dark purple respectively) were generated.B. Theoretical predictions for MEP spheroids (top row) and mammary organoids (bottom row) with varying Φ.C. The measured probability densities for MEP spheroids (top row) and mammary organoids (bottom row) with varying Φ. Histograms show the distribution of experimental data, dashed vertical lines are the average ϕb, and the solid curves are the theoretical predictions for each condition. The number of observations is noted at the top of the graphs.D. Engineering structure by varying tissue size: the total number of cells per organoid was varied by changing the tissue diameter. Tissues with average diameter of 70 μm, 90 μm, and 110 μm were generated (light orange, orange, and brown respectively). The cell proportions were held constant (Φ=0.5).E. Theoretical predictions for MEP spheroids (top row) and mammary organoids (bottom row) with varying size.F. The measured probability densities for MEP spheroids (top row) and mammary organoids (bottom row) with varying size. Histograms show the distribution of experimental data, dashed vertical lines are the average ϕb, and the solid curves are the theoretical predictions for each condition. The number of observations is noted at the top of the graphs.G. The equilibrium constant Keq for the partitioning of LEP between the tissue core to the boundary was calculated from the average occupancy of LEP and MEP in the tissue boundary and core. The free energy change (ΔG) associated with cell translocation is proportional to -logKeq and determines the favorability of cell translocation.H. Calculations of ΔG for different mechanical potentials and activities in tissues with a diameter = 80 μm containing equal number of LEP and MEP. The contour lines are predictions from the model and are colored by the value of ΔE. Estimated values of ΔG for different experimental conditions are also shown, where points and error bars are the average and standard deviations. The symbols represent different conditions ( ○: mammary organoids, △: MEP spheroids, ◇: TLN1-KD spheroids, □: CTNND1-KD spheroids), and the points are colored by their calculated ΔE. | PMC10327153 | nihpp-2023.07.01.546933v1-f0006.jpg |
0.470947 | 415780d8134d48108a1081621f64fcfa | A. The cathodal stimulation group had lower connectivity (relative to sham) to motor regions, with some modulation in cognitively oriented regions in the frontal lobe. B. The anodal stimulation group showed higher connectivity (relative to cathodal) to regions in the occipital lobe. The color bars denote the percentile range of z-scores. The maps are thresholded such that only significant (p-FDR < 0.05) results are presented. | PMC10327157 | nihpp-2023.06.26.546626v1-f0001.jpg |
0.431858 | 0f972cb195fe4448bd3c3b530f8f5e37 | Left. The temporal dynamics of connectivity of Lobules I-IV was differentially impacted in the cathodal and anodal stimulation groups relative to the sham group. A) During the early window, variability in connectivity was lower in the cathodal group relative to sham. B) In the late window both higher and lower variability were observed. Anodal stimulation reduced variability in the late window. Right. Connectivity dynamics of both Crus I and II was impacted by anodal stimulation (relative to sham). Variability in both C) early and D) late windows wa higher in regions of the lateral prefrontal cortex and medial visual regions. The color bars denote the percentile range of z-scores. The maps are thresholded such that only significant (p-FDR < 0.05) results are presented. | PMC10327157 | nihpp-2023.06.26.546626v1-f0002.jpg |
0.477534 | 5b64988e0dc94f648a259697c392bfe3 | Targeted activation of the HOXDeRNA leads to astrocyte transformation with phenotypic and transcriptomic switch to glioma.A. Timeline for transduction of astrocytes with the CRISPR activation system leading to transformation (top). Representative images of astrocytes transduced with non-targeting sgRNA (control astrocytes) and HOXDeRNA-activating sgRNA (HOXDeRNA activated astrocytes) are shown.B. Volcano plot (middle) showing differentially expressed genes (DEG) between the non-transformed and transformed astrocytes. The genes upregulated in transformed astrocytes (red), downregulated in transformed astrocytes (blue) (fold change > 2 and adjusted p-value 0.01), and 10% of non-significantly changed genes (grey) are shown. The 44 glioma specific TFs that are upregulated after astrocyte transformation are indicated. The heatmaps exhibit z-scores for cell junction and cell adhesion genes downregulated in transformed astrocytes (left, n=3) and major glioma-associated genes upregulated in transformed astrocytes (right, n=3).C. The top 5 categories of GO gene sets downregulated (left) or upregulated (right) in transformed astrocytes shown for DEG (FC>2, p<0.01).D. Control and transformed astrocytes are associated with “normal brain” and “glioma” expression signatures, respectively. Machine learning model trained on forebrain (n=857 samples), cerebellum (n=214), midbrain (n=57), low grade glioma (LGG, n=522), as well as mesenchymal (n=54), classical (n=41), proneural (n=39), neural (n=28), and unknown subtype (n=4) GBM samples from TCGA and GTEX datasets classifies control and transformed astrocytes as “normal brain” and “glioma”, correspondingly, based on the RNA-Seq data. PCA visualization is shown, and every dot represents an individual sample (see Methods for details). | PMC10327164 | nihpp-2023.06.30.547275v1-f0001.jpg |
0.463451 | beec16f9e44041abac81ad3b68957ffc | Genome-wide binding of HOXDeRNA is associated with exclusive PRC2 removal from transformation-induced genes.A. ChIRP-seq analysis demonstrates that transformed astrocytes and three GSC lines (GBM4, GBM6 and GBM8) exhibit similar distribution of HOXDeRNA peaks across the genome, with most peaks mapped to gene promoters.B. HOXDeRNA binds to the same gene promoters in transformed astrocytes and GSCs. HOXDeRNA ChIRP peaks were annotated to the nearest genes, and the gene lists produced for the four cell types were intersected and visualized as a Venn diagram (see Methods for details).C, D. ChIRP-seq raw read coverage signal, representing the HOXDeRNA binding at the TSS (+/− 5Kb) of the forward strand of genes downregulated or upregulated after astrocyte transformation, is visualized as an average value for each group (C) or for individual genes (D). The heatmap is accompanied by a colour scheme representing the value of the raw read counts. A similar coverage for reverse strand genes is shown in Supplemental Figure 2B, C.E, F. Epigenetic status of genes upregulated or downregulated after astrocyte transformation. H3K27Ac, H3K27Me3, and EZH2 ChIP-seq raw signals covering gene bodies of the positive strand (+/− 5Kb) were normalized to gene length and counted, followed by visualization of the average signal for 3 groups: genes upregulated after astrocytes transformation and bound by HOXDeRNA, downregulated after astrocyte transformation and HOXDeRNA-free, and unchanged genes (E). Individual gene body coverage values were visualized as heatmaps (F) (see reverse strand gene coverage in Supplemental Figure 2D, E).G. HOXDeRNA ChIRP-seq tracks in GSCs and transformed astrocytes, aligned with PRC2 (H3K27Me3, EZH2) ChIP-seq coverage, before and after astrocyte transformation, visualized for selected glioma master TF genes. | PMC10327164 | nihpp-2023.06.30.547275v1-f0002.jpg |
0.462799 | d56af92ff376457fadaffb523ff878d3 | Induction of HOXDeRNA activates GSC-specific super-enhancers.A. Raw H3K27Ac coverage was monitored in control and HOXDeRNA-transformed astrocytes at 174 glioma-specific SEs. For length normalization, the SEs were split into the same number of bins. The average read coverage value per bin across all SEs or the individual value per bin were plotted as line graphs (top) or heatmaps (bottom), correspondingly. The “Start” and “End” marks define the ends of the enhancer.B. H3K27Ac ChIP-seq signals are shown for representative GSC SEs in control and transformed astrocytes.C. The list of the top 5 TF binding motifs enriched in the SE-associated H3K27Ac peaks. TF enrichment analysis was performed with Homer software.D. ChIPseq demonstrates that SOX2, OLIG2, or both bind to 82% of GBM SEs. The lists of SEs bound by SOX2 or OLIG2 were intersected and visualized as Venn diagram. | PMC10327164 | nihpp-2023.06.30.547275v1-f0003.jpg |
0.447693 | 9bf3bb00b7d24d30921c5022a3d7077c | HOXDeRNA genome-wide binding depends on EZH2.A. ChIRP with probes for HOXDeRNA and LacZ (negative control) followed by the Western blots with antibodies recognizing EZH2 and ACTA1 was performed on transformed astrocytes and visualized, with 1% input. Two representative biological replicates per group are shown.B. CLIP with EZH2 and IgG antibodies followed by qRT-PCR detection of HOXDeRNA and GAPDH mRNA was performed in glioma LN229 cells and transformed astrocytes. EZH2/IgG ratios are demonstrated (n=3, mean+ SD).C. Western Blot validating EZH2 inhibition in transformed astrocytes at 48 hours post-transfection with EZH2 siRNAs (n=3).D. ChIRP HOXDeRNA signals were measured in transformed astrocytes transfected with either control or EZH2 siRNAs and visualized as average (line graph, top) or individual values (heatmap, bottom) at HOXDeRNA peaks (center +/− 3 kb). HOXDeRNA binding was analysed separately for the peaks covered or not by EZH2 in control astrocytes (blue and green lines, correspondingly).E. HOXDeRNA binding at the promoters of key glioma master TF genes is shown for GSCs (top three tracks) and transformed astrocytes transfected with either control siRNAs or EZH2 siRNAs (two bottom tracks). | PMC10327164 | nihpp-2023.06.30.547275v1-f0004.jpg |
0.426194 | 85ebc1bf40fd4e09ba8d2807a9f303c1 | An RNA quadruplex rG4–1 element in HOXDeRNA mediates its EZH2 binding and PRC2 removal, global regulatory activity, and transformation capacity.A. CLIP with EZH2 antibody followed by qRT-PCR detection of three putative HOXDeRNA rG4-containing regions, using GAPDH mRNA as a negative control (mean + SD, n=3).B. Schematic timeline for rG4–1 base editing experiments (top). DNA analysis confirming efficient C-to-T editing in the HOXDeRNA rG4–1 genomic region, corresponding to the G-to-A editing in the HOXDeRNA. Alleles with a substitution rate of > 0.1% are visualized. Substituted nucleotides are shown in bold.C. CLIP analysis of WT and rG4–1-edited cells with EZH2 antibody followed by qRT-PCR for HOXDeRNA and GAPDH mRNA (mean + SD, n=3).D. rG4–1 base editing abolishes the binding of HOXDeRNA to its targets in transformed astrocytes. ChIRP-qPCR analysis of HOXDeRNA/gene promoter binding was performed in control (n=3) and base edited transformed astrocytes (n=3). The data were normalized to the GAPDH gene and presented as bars (mean + SD).E. rG4–1 base editing disrupts the derepression of HOXDeRNA target genes after astrocyte transformation. qRT-PCR analysis of the corresponding set of HOXDeRNA-induced target mRNAs was performed in both control (n=3) and base-edited (n=3) transformed astrocytes and normalized to GAPDH mRNA levels. The data are shown as bars (mean+ SD).F. rG4–1 base editing prevents removal of PRC2 from HOXDeRNA targets after its activation. ChIP-qPCR reactions with EZH2 and H3K27Me3 antibodies on control (n=3) and edited clones (n=3) were normalized for input and shown as bars (mean+ SD).G. rG4–1 base editing inhibits astrocyte transformation. Number of spheroids were quantified in both control (n=3) and base-edited (n=3) transformed astrocytes (mean + SD).H. A model of genome-wide function of HOXDeRNA. rG4-dependent HOXDeRNA binding to EZH2 and recruitment to PRC2-silenced promoters in astrocytes (1) leads to reduced PRC2 repression in the corresponding chromatin regions, gene derepression, and active state of glioma master TFs (2), followed by SE activation (3) and further transcriptional reprograming (4). | PMC10327164 | nihpp-2023.06.30.547275v1-f0005.jpg |
0.454084 | 4638f28bdd8b4478bd65cd8407e42bf0 | Representative images of pretreatment endoscopy. (A) Ulcerative gastric cancer with a clot attached to the posterior wall of the midbody. (B) An ulcerative gastric cancer lesion of approximately 10 mm on the lesser curvature of the angulus. (C) An ulcerative gastric cancer lesion of approximately 15 mm on the anterior wall of the antrum. | PMC10328654 | ms9-85-3269-g001.jpg |
0.495 | 0e31441c711e4be8ba3f5460aebfb4e3 | PRISMA 2020 flow diagram | PMC10328878 | 40902_2023_392_Fig1_HTML.jpg |
0.501702 | 7eb8c6d24f9e402eb49d689e6010185e | Forest plot for pooled success rate of implants in free fibula flap graft | PMC10328878 | 40902_2023_392_Fig2_HTML.jpg |
0.458248 | 2c1db44ce01148b28b3ebf606c30c2fc | Forest plot for pooled success rate of free fibula flap grafts | PMC10328878 | 40902_2023_392_Fig3_HTML.jpg |
0.400649 | f3552a789d5b403a9668a7d448d6cb88 | Forest plot for risk ratio of implant failure between fibula graft and natural bone/between fibula graft and other graft types | PMC10328878 | 40902_2023_392_Fig4_HTML.jpg |
0.436653 | 10e3e86578384f858c6c7137e53e7be8 | Forest plot for risk ratio of implant failure between the smoking and control group/radiotherapy and control group | PMC10328878 | 40902_2023_392_Fig5_HTML.jpg |
0.413555 | 28d4ef4691204958910217f698b93f8b | Experimental design: collection, processing, extraction of peptides and metabolites, and analysis using nLC-MS/MS. | PMC10330998 | ab-22-0249f1.jpg |
0.457015 | eac5d6f8037a443c873fb7d3c3436b06 | Disc diffusion assay of urinary peptides containing aqueous extract from different animals belonging to physiological groups: (A) heifer (B) lactation and (C) Pregnant. Urinary peptide from 30 Sahiwal cow were coated onto discs and Disc diffusion assay was performed against S. aureus, E. coli and S. agalactiae. (D) Diameter of zone of inhibition observed against S. aureus, E. coli and S. agalactiae. The bar and error bar in the figure represent mean±standard error of mean. | PMC10330998 | ab-22-0249f2.jpg |
0.44715 | d110bbb6a1ea44c3995167a875a42bc0 | Characterization of antimicrobial activity of urinary peptide: (A) Antimicrobial activity of different phases obtained after ethyl acetate extraction; O+P: C18 eluate, P: urinary peptide-containing aqueous extract, O: metabolite containing organic phase. (B) Urinary peptide visualization from different physiological groups using Tricine-SDS-PAGE. Ladder shown in the image ranges from 8 to 240 kDa (C) Resazurin-based broth microdilution assay for MIC determination. The well with blue color indicates the dead bacterial population and pink color indicates the well with viable bacterial population. The ‘+’ sign indicates the well with bacterial growth (positive control) whereas ‘−’ sign are well without bacterial inoculation (negative control or sterility control) (D) bacterial survival rates in a dose-dependent manner. (E) Haemolysis assay at different concentrations of urinary aqueous extract. (F) Cytotoxicity assay against BuMEC cell line (Buffalo mammary epithelial cells) at different concentrations of urinary aqueous extract. Experiments were performed in triplicate and the points on the line graph represent mean±standard error of the mean. SDS-PAGE, sodium dodecyl sulfate-polyacrylamide gel electrophoresis. | PMC10330998 | ab-22-0249f3.jpg |
0.501333 | 7e3f07b2476c4dc2b80f2346e529ed69 | Kill kinetics study at three different concentrations viz. 0.5×, 1×, and 2× MIC against: (A) Escherichia coli, (B) Staphylococcus aureus, and (C) Streptococcus agalactiae. Experiments were performed in triplicate and the points on the line graph represent mean± standard error of the mean. | PMC10330998 | ab-22-0249f4.jpg |
0.46986 | 1d06be9c98994bdf8d172ddeb88dfb32 | Antimicrobial activity of urinary peptide fractions obtained from C18 and characterization: (A) HPLC-based fractionation of pooled aqueous extract from different animals. (B) Sequence logo generation of predicted antimicrobial sequences. (C) Antimicrobial activity determination of different fractions. (D) Tricine SDS-PAGE of peptides in fractions 3, 4, and 8 of urinary peptides. Frequency distribution: (E) molecular weight of peptide (F) amino acids present in fractions 3, 4, and 8. HPLC, high performance liquid chromatography; SDS-PAGE, sodium dodecyl sulfate-polyacrylamide gel electrophoresis. | PMC10330998 | ab-22-0249f5.jpg |
0.461069 | 1207559608834559a68e926d435dd7ed | Intraoperative findings in the two cases of sclerosing encapsulating peritonitis. (a) Case 1: intraoperative presentation of the small intestines. Thick, opaque fibrous tissue coated the descending duodenum, the jejunum and ileum, terminating at the ileocaecocolic junction. There were no signs of intestinal motility visible. The remaining abdominal organs were of normal appearance. (b) Case 2: intraoperative presentation immediately after incision of the abdominal wall, cranial to the left. All abdominal organs, including the liver and stomach, are coated in a layer of thick, fibrous material, with the small intestines being wrapped in a further outer layer of fibrous tissue, forming a dense, opalescent capsule. (c) Case 2: intraoperative presentation after incision of the outer fibrous membrane covering the intestines (cranial to the left). The layer of fibrous tissue coating the small bowel and stomach was carefully incised and could be easily removed with minimal irritation to the underlying intestines. In the background, the stomach is visible, being coated in the same, opaque layer of fibrous tissue. (d) Case 2: intraoperative presentation after partial removal of the fibrous tissue from the jejunum (cranial to the left). The intestinal loops expand and have an otherwise unremarkable appearance | PMC10331345 | 10.1177_20551169231178447-fig1.jpg |
0.42466 | 3e81b9fa306f4781a00b735e8bd2558d | Histopathological findings of the examined biopsy samples. (a) Case 1: severely thickened visceral peritoneum (double arrow) mostly composed of sparsely cellular, fibrous tissue; haematoxylin and eosin (H&E), bar represents 400 µm. Figure 2 (Continued) (b) Case 1: photomicrography of ablated fibrous membranes showing local variation in distribution of interlacing bundles of collagen fibres (blue); Masson trichrome staining (MTS), bar represents 80 µm. (c) Case 1: enlarged, activated fibroblasts (black arrows) interspersed throughout the fibrous membrane containing large nuclei with 1–2 evident nucleoli; H&E, bar represents 30 µm. (d) Case 1: locally abundant fibrin deposits (red) with interspersed activated fibroblasts and tender collagen fibre production (blue) reflecting initiated organisation of the exudative mass; MTS, bar represents 40 µm. (e) Case 1: neovascularisation and perivascular bleeding (black arrows) accompanying the fibrous proliferation; H&E, bar represents 80 µm; and (f) mild mononuclear cell infiltration predominantly composed of lymphocytes and plasma cells; H&E, bar represents 40 µm. (g) Case 1: areas of iron deposition (blue) in encapsulating membrane; Prussian blue staining, bar represents 40 µm. (h) Case 2: regional low cellularity of fibrous membrane covering the intestine; H&E, bar represents 80 µm | PMC10331345 | 10.1177_20551169231178447-fig2.jpg |
0.393745 | ddbf9c39eb8d484182ea4cd1144dff2b | Ultrasound images of (a) the small intestine and (b) large intestine of case 2. Note the plication of the small intestine and the marked corrugation of the descending colon encased by a smooth capsule (arrow) and surrounded by peritoneal effusion (*) | PMC10331345 | 10.1177_20551169231178447-fig3.jpg |
0.445309 | 4bd611bbce5c42339ce147169fd8f320 | Transverse CT images of case 2 in a soft tissue window at the level of (a) the second, (b) third and (c) fourth lumbar vertebra. Abdominal organs together with mesenteric fat (**) are encapsulated in the centre (‘cocooning’) and surrounded by peritoneal effusion (*) in the periphery. (b, c) The plication of the small intestine can be appreciated. C = colon; L = liver; LK = left kidney; RK = right kidney; SI = small intestine; Sp = spleen; St = stomach | PMC10331345 | 10.1177_20551169231178447-fig4.jpg |
0.486637 | 655ed485ce4e48caacb100c6721f50e1 | The proportion of responses to each 10 items in Dermatology Life Quality Index (DILQ) among psoriasis patients in China. | PMC10332216 | IANN_A_2231847_F0001_C.jpg |
0.445005 | 559d61839dfa40ca99c8a2615c36b1d2 | The association between PASI score and DLQI score by gender and by age among patients in China. | PMC10332216 | IANN_A_2231847_F0002_C.jpg |
0.461591 | 2ba8cb155bfa4c0b9bba7160fc6e4cf1 | (a) Overall survival (OS) and (b) overall survival from time of stem cell transplantation (aHSCT-OS) (based on Kaplan-Meier estimates). Gain(1q): isolated gain(1q) three copies of 1q; Amp(1q): isolated amp(1q) > 3 copies of 1q; High risk: del(17p), t(4;14), t(14;16), t(14;20); Other cytogenetics: all cytogenetic changes without gain(1q) or amp(1q) or high risk. | PMC10332865 | jh-12-109-g001.jpg |
0.48769 | 1c74df32c9b145eb84e2f2cc6112cde9 | Multivariable subgroup analysis using Cox proportional-hazards model for overall survival (HR, 95% CI, and P value). Gain(1q): three copies of 1q; Amp(1q): > 3 copies of 1q; High risk: del(17p), t(4;14), t(14;16), t(14;20); Bor + IMID + DMT: bortezomib, immunomodulatory drug, dexamethasone; Bor + Cyc + DMT: bortezomib, cyclophosphamide, dexamethasone; HR: hazard ratio; CI: confidence interval. | PMC10332865 | jh-12-109-g002.jpg |
0.426552 | 3bd1b7567fd6417bbf7065d417a35dd8 | Schematic of the hypothetical model of microglial activation and cognitive impairment in schizophrenia, and potential therapeutic agents.Microglial activation in the brain is induced by various stressful events, mainly psychological stress and events such as viral infection or traumatic brain injury, which usually occur prior to the onset of psychotic symptoms in schizophrenia. In addition, genetic factors [i.e., complement component (C4) variation and those affecting microglial cells in schizophrenia] may increase the risk of excessive microglial activation. Activated microglia produce and release key pro-inflammatory cytokines and free radicals, which have been shown to cause brain structural abnormalities; higher levels of these substances are associated with lower gray matter volumes, especially in brain regions responsible for memory and other cognitive functions. Microglial activation may thus be an important contributor to the development and progression of cognitive impairment in schizophrenia. Its inhibition through the use of microglial inhibitors, such as minocycline and natural products, or the targeting of microglia-expressed receptors, such as with GLP-1R or α7nAChR agonists, holds promise as a potential therapeutic intervention to improve the cognitive function of patients with schizophrenia. GLP-1R, glucagon-like peptide-1 receptor; α7nAChR, α7 nicotinic acetylcholine receptor; IL, interleukin; TNF, tumor necrosis factor. | PMC10333203 | 41537_2023_370_Fig1_HTML.jpg |
0.421665 | 442bdc561e6148a381e406cfe43a9e81 | Penile glans necrosis | PMC10334505 | 12894_2023_1289_Fig1_HTML.jpg |
0.447411 | eb2f49f456874ac683582440cd5983c8 | Intraoperative findings revealed an extensive Corpus spongiosum necrosis and excision of approximately 14 cm up to healthy tissue | PMC10334505 | 12894_2023_1289_Fig2_HTML.jpg |
0.473054 | 2d3f9cbcd7144c838420efb060b364bd | Postoperative view of the preserved penis | PMC10334505 | 12894_2023_1289_Fig3_HTML.jpg |
0.393406 | c020282834d54c37b471dd1396d083da | Phylogenetic tree of fatty acid- and retinol-binding protein coding genes of Haemonchus contortus and their transcription among developmental stages. A A maximum-likelihood phylogenetic tree based on amino acid sequences deduced from manually curated HCON_00042410, HCON_00089630, HCON_00092780, HCON_00092790, HCON_00092800, HCON_00092810, HCON_00092770, HCON_00093170, HCON_00093190, HCON_00093410, HCON_00109090, and HCON_00120470 in H. contortus (WBPS15), by integrating previous work [26, 32], and FAR-1 (F02A9.2), FAR-2 (F02A9.3), FAR-3 (F15B9.1), FAR-4 (F15B9.2), FAR-5 (F15B9.3), FAR-6 (W02A2.2), FAR-7 (K01A2.2), FAR-8 (K02F3.3), FAR-9 (C07G3.10), and PERM-5 (C55C3.5) in Caenorhabditis elegans (WS287). B Transcription profiles of far homologues among the egg, first-(L1), second-(L2), third-(L3), and fourth-larval (L4; female and male) and adult (female and male) stages. L4f and L4m indicate sexes at the L4 stage, and Af and Am represent sexes at the adult stage. The colour scale indicates normalised fragments per kilobase per million (FPKM) of transcriptomic data [67]. C FPKM of Ce-far-1, Ce-far-2, and Ce-far-6 among egg, L1, L2, L3, L4, and adult stages of C. elegans, and relative mRNA levels of HCON_00092800 to beta-tubulin gene among egg, L1, L2, L3, L4, and adult stages of H. contortus. Error bars indicate the mean ± standard error of the mean (SEM) | PMC10334587 | 13071_2023_5836_Fig1_HTML.jpg |
0.401162 | b6f7af554dc64c119879ed0e5fa6dcd9 | In silico molecular docking and in vitro fatty acid binding analyses for HCON_00092800 protein. A Three-dimensional structure of HCON_00092800 protein modelled using AlphaFold2 [39]. B Molecular docking of HCON_00092800 protein and DAUDA, retinol, or oleic acid, with binding free energy (ΔG) measured at − 109.81 kcal/mol, − 103.79 kcal/mol, or − 97.16 kcal/mol, respectively. C, D Relative fluorescence intensity of recombinant HCON_00092800 (rHCON_00092800) mixed with DAUDA, retinol, and/or oleic acid to that of fatty acids dissolved in phosphate-buffered saline (PBS). And rTg-PME (a recombinant protein from Toxoplasma gondii) is employed as an irrelevant control. E Structural alignment and superposition of HCON_00092800 with Ce-FAR-2 (UniProt ID P34383) and Ce-FAR-6 (UniProt ID Q9XUB7) | PMC10334587 | 13071_2023_5836_Fig2_HTML.jpg |
0.533008 | 812a4f2a221d4aa8943cd428d74ca21d | Influence of Ce-far-6 deficiency and heterologous HCON_00092800 RNA interference (RNAi) on fatty acid content in Caenorhabditis elegans. A, B Fat content in C. elegans after Ce-far-6 and HCON_00092800 sequence-mediated RNA interference treatment, determined by Oil Red staining. cry1Ac is used as an irrelative control. Scale bar: 80 μm. C, D Fat content in wildtype (N2), far-6 mutant (RB1515), and HCON_00092800 rescuing C. elegans, determined by Oil Red staining. cry1Ac is used as an irrelevant control. Scale bar: 100 μm. Relative Oil Red signal is quantified using ImageJ software [47]. E, F Relative content of select (C14:0, C16:0, C16:1, C18:0, and C18:1) and total fatty acids in N2, RB1515, and HCON_00092800 rescuing worms. GLC 17A (PRIME) (fatty acid methyl esters containing chains from C8:0 to C24:0; Nu-Chek Prep, Inc.) served as inner controls. Error bars indicate mean ± standard deviation (SD); ns = not significant | PMC10334587 | 13071_2023_5836_Fig3_HTML.jpg |
0.419755 | 235c86496cff4ed39da663beae18db8d | Effect of heterologous RNA interference (RNAi) or overexpression of Ce-far-6 on the development, reproduction, and survival of Caenorhabditis elegans and heterologous expression of HCON_00092800. A–D Body length, number of eggs, and lifespan of C. elegans after HCON_00092800 sequence mediated RNAi. Ce-far-6 and cry1Ac are used as positive and irrelative controls, respectively. E–H Body length, number of eggs, and lifespan of wildtype (N2), far-6 mutant (RB1515), and HCON_00092800 rescuing C. elegans. Error bars are the mean ± standard deviation (SD). ***P < 0.001, *P < 0.05, ns = not significant. I Heterologous expression of HCON_00092800 driven by the promoter of Ce-far-6 in RB1515 strain of C. elegans. The overall, head, middle, and tail views of heterologous expression are indicated in subpanels a, b, c, and d, respectively. Numbers 1, 2, and 3 represent green fluorescence, differential interference contrast (DIC), and merge channels, respectively. Scale bars: 200 μm or 20 μm as indicated | PMC10334587 | 13071_2023_5836_Fig4_HTML.jpg |
0.414038 | 1338688222a24fb7b0755a3f639b1e5e | Tissue localisation of Hc-FAR-6 in Haemonchus contortus. A Indirect immunofluorescence of Hc-FAR-6 in the infective third larval (L3) stage of H. contortus. Mouse anti-rHc-FAR-6 is used as the primary antibody and Alexa Fluor™ Plus 647 (red) is employed as the second antibody. B Indirect immunofluorescence of Hc-FAR-6 in the fourth larval (L4) stage of H. contortus. Mouse anti-rHc-FAR-6 is used as the primary antibody and Alexa Fluor™ Plus 488 (green) is employed as the second antibody. The pre-immune serum is employed as a negative control. Lowercase letter 'a' indicates the intestine and 'b' indicates the gonad of H. contortus. DAPI, 4ʹ,6-diamidino-2-phenylindole. FITC, fluorescein isothiocyanate. Scale bars: 50 μm, 20 μm, or 10 μm as indicated. C Subcellular distribution of Hc-FAR-6 with/without signal peptide in human embryonic kidney 293 (HEK 293 T) cells. SP, signal peptide | PMC10334587 | 13071_2023_5836_Fig5_HTML.jpg |
0.4913 | 3e1d5b34896142998a1c08824e1267c2 | Lysozyme activity of Nile tilapia treated with various doses of extract of L. caspica. Each bar represents mean ± SD. There were significant differences among treatments (P ≤ 0.05). ns, not significant. | PMC10335874 | ANU2023-8882736.001.jpg |
0.465441 | 726aadd2495a4ecab5d7f8baaebeb5b7 | Total IgM activity of Nile tilapia treated with various doses of extract of L. caspica. Each bar represents mean ± SD. There were significant differences among treatments (P < 0.05). ns, not significant. | PMC10335874 | ANU2023-8882736.002.jpg |
0.429535 | fa51d971fa5e4dd2bf124e0b27e059ef | C3 activity of Nile tilapia treated with various doses of extract of L. caspica. Each bar represents mean ± SD. There were significant differences among the treatments (P < 0.05). ns, not significant. | PMC10335874 | ANU2023-8882736.003.jpg |
0.435607 | e8b4e4914e8a40968f5ae2dd93fed4b0 | Kaplan–Meier estimate of cumulative survival rate for 14 days after the treated Nile tilapia challenged with S. agalactiae. | PMC10335874 | ANU2023-8882736.004.jpg |
0.395245 | 8df416330f644b58a52b48ed903fa84b | Physical model diagram. | PMC10336453 | gr1.jpg |
0.498583 | 20210fd85378404bb15380d455211063 | (A): Impact of θr and λ on Skin Friction in x− direction. (B): Impact of θr and λ on Skin friction in y− direction .(C): Impact of θr and λ on Nusselt number in x− direction. (D): Impact of Ec and λ on Nusselt number in x− direction. | PMC10336453 | gr10.jpg |
0.445181 | 80f6855c4a604b00abe52f17e4fa0ba3 | Mathematical demonstration of problem. | PMC10336453 | gr2.jpg |
0.47932 | 6cc1d7c61dbf4014a4c898b4822699c3 | (A): Impact of φ on f′(η).(B): Impact of φ on g(η).(C): Impact of φ on θ(η).. | PMC10336453 | gr3.jpg |
0.508473 | 7e77c56a52404e65b0fa50ea073cf9e0 | (A): Impact of M on f′(η).(B): Impact of M on g(η).(C): Impact of M on θ(η).. | PMC10336453 | gr4.jpg |
0.505059 | 87c8a446347542cd80fdbeccbdca6b19 | (A): Impact of S on f′(η).(B): Impact of S on g(η).(C): Impact of S on θ(η).. | PMC10336453 | gr5.jpg |
0.524455 | 8507d83d0bfc4ba2af4be4c0f6051e03 | (A): Impact of θr on f′(η).(B): Impact of θr on g(η).(C): Impact of θr on θ(η). (D): Impact of Ec on θ(η). | PMC10336453 | gr6.jpg |
0.434114 | 8eb08cfd637c4cd1a7463ac659c50b7b | (A) Impact of M and λ on skin friction in x− direction. (B): Effect of M and λ on skin friction in y− direction .(C): Impact of M and λ on Nusselt number in x− direction. | PMC10336453 | gr7.jpg |
0.475198 | ff9e24a227e04504a55f0b1d34ef5e48 | (A): Impact of φ and λ on skin friction in x− direction. (B): Effect of φ and λ on skin friction in y− direction .(C): Impact of φ and λ on Nusselt number in x− direction. | PMC10336453 | gr8.jpg |
0.400578 | 5d615b61e81e464aa2894a3f194d1354 | (A): Impact of S and λ on Skin Friction in x− direction. (B): Impact of S and λ on Skin Friction in y− direction .(C): Impact of S and λ on Nusselt number in x− direction. | PMC10336453 | gr9.jpg |
0.561841 | 31c211d5e2fd46c283444f17a11ed858 | An illustration of the
three fundamentally different modes of connectivity
between 2π-heteroatoms and associated odd-conjugated fragments
in heterocyclic mesomeric betaines. | PMC10337032 | jo3c00225_0002.jpg |
0.5185 | 2e5c34a330d64da79da395f28fdd4eca | ACID π
+ σ and π electron current density maps
for rings 1, 5a, 5f, and 5hplanar. | PMC10337032 | jo3c00225_0003.jpg |
0.500443 | 55c93c4410754930a6368410c32366a2 | The DFT/B3LYP
calculated frontier orbitals of 1 and 5a, 5f and 5h. | PMC10337032 | jo3c00225_0004.jpg |
0.511732 | 315da8bdbf2a44eca6e7ee6534e857fc | Inter-Relationship
of Resonance Forms of Structure 1 | PMC10337032 | jo3c00225_0005.jpg |
0.443308 | 03e4c7bf0f8146baa3b8ac3a1b9ed34d | Optical images of seven print patterns and their structural integrity: (A) Different pore sizes of 600, 900 and 1200 µm; (B) Nozzle sizes of 400 and 500 µm; (C) Layer heights of 67 and 83% of the filament diameter (600 µm); (D) Structural integrity of different patterns (n=5). | PMC10338535 | 41598_2023_38323_Fig10_HTML.jpg |
0.412315 | e42ba5799c95452bac7eb9f1581fe5cb | Cyclic compression-tension behavior of different printed structures: (A) Stress-stretch curves showing the effects of layer height, pore size and nozzle diameter (from left to right); (B) Maximum nominal stresses in tension and compression for different print patterns; (C) Hysteresis area (n=5). | PMC10338535 | 41598_2023_38323_Fig11_HTML.jpg |
0.466893 | 00a5a84f45744ec0a87a3fb5bf892747 | The effect of layer height (A), pore size (B), and nozzle size (C) on the relaxation behavior of different printed structures (n=5) in compression (top row) and tension (bottom row). | PMC10338535 | 41598_2023_38323_Fig12_HTML.jpg |
0.442113 | a735b1fe72a34dcda61307eead05a5db | Schematic diagram of the essential steps used to optimize the printing process: Using a pre-cooling step in combination with rheological measurements and printability tests led to highly printable AG bioinks to fabricate different 3D constructs with variable mesostructures and viscoelastic properties. | PMC10338535 | 41598_2023_38323_Fig1_HTML.jpg |
0.473403 | 75bee53c705d4a83b82c80bc17d7acb3 | Effect of the cooling step on the rheological properties of AG bioinks: (A) Storage and loss moduli, viscosity, and tan (delta) of the 23 °C, 25 °C, and 27 °C; (B) Variations of rheological parameters at 20 mins of testing with and without the pre-cooling step at different temperatures. Significance value *p < 0.05, **p < 0.01, ***p < 0.001. | PMC10338535 | 41598_2023_38323_Fig2_HTML.jpg |
0.45429 | ca8d9520c3c3472c8afcc5355364dcb8 | Effect of the cooling time (5, 10 and 15 mins) on the rheological properties of the AG bioink. | PMC10338535 | 41598_2023_38323_Fig3_HTML.jpg |
0.397604 | 6a48377945a34daf9771e2b195d00610 | Printability tests of the AG bioink: (A) Extruded bioink at 23 °C, 24 °C, and 25 °C; (B–D) Two layers of bioink for the calculation of the Pr value based on the designed pattern; (E,F) Fusion of filaments during printing at different temperatures. | PMC10338535 | 41598_2023_38323_Fig4_HTML.jpg |
0.454745 | 18a5304f7a544ec086d240236c0a3922 | Bioink stability: (A) Collapse test to evaluate the resistance against gravity at 25 °C and 110, 120 and 130 kPa pressures; (B) Mass extrusion stability of the bioink at 25 °C during the printing time. | PMC10338535 | 41598_2023_38323_Fig5_HTML.jpg |
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