307 research outputs found
A priori error estimates of two fully discrete coupled schemes for Biot's consolidation model
This paper concentrates on a priori error estimates of two fully discrete
coupled schemes for Biot's consolidation model based on the three-field
formulation introduced by Oyarzua et al. (SIAM Journal on Numerical Analysis,
2016). The spatial discretizations are based on the Taylor-Hood finite elements
combined with Lagrange elements for the three primary variables. For time
discretization, we consider two methods. One uses the backward Euler method,
and the other applies a combination of the backward Euler and Crank-Nicolson
methods. A priori error estimates show that the two schemes are unconditionally
convergent with optimal error orders. Detailed numerical experiments are
presented to validate the theoretical analysis
Circ_0007385 promotes the proliferation and inhibits the apoptosis of non-small cell lung cancer cells via miR-337-3p-dependent regulation of LMO3
Background. This study intended to analyze
the expression characteristic, functions and underlying
mechanism of circular RNA_0007385 (circ_0007385) in
non-small cell lung cancer (NSCLC).
Methods and Results. RNA and protein expression
was examined by real-time quantitative polymerase
chain reaction (RT-qPCR) and Western blot assay. Cell
counting kit 8 (CCK8) assay, colony formation assay, 5-
Ethynyl-2’-deoxyuridine (EdU) assay and flow
cytometry were applied to analyze cell proliferation
ability. Flow cytometry was also conducted to assess cell
apoptosis. Dual-luciferase reporter assay and RNA
immunoprecipitation (RIP) assay were performed to
verify the predicted target relationships. Xenograft tumor
model was utilized to analyze the function of
circ_0007385 in vivo, and immunohistochemistry (IHC)
assay was used to analyze protein expression in
xenograft tumor tissues. Circ_0007385 expression was
notably enhanced in NSCLC tissues and cell lines.
Circ_0007385 facilitated the proliferation but suppressed
the apoptosis of NSCLC cells. Circ_0007385 acted as a
sponge for microRNA-337-3p (miR-337-3p), and
circ_0007385 overexpression-mediated effects were
largely overturned by the overexpression of miR-337-3p
in NSCLC cells. MiR-337-3p interacted with the 3’
untranslated region (3’UTR) of LIM-only protein 3
(LMO3). Circ_0007385 up-regulated LMO3 level by
absorbing miR-337-3p in NSCLC cells. LMO3
overexpression largely reversed miR-337-3p
overexpression-induced influences in NSCLC cells.
Circ_0007385 knockdown significantly restrained the
growth of xenograft tumors in vivo.
Conclusion. Circ_0007385 promoted the
proliferation ability and inhibited the apoptosis of
NSCLC cells by binding to miR-337-3p to induce
LMO3 expression
Learn Single-horizon Disease Evolution for Predictive Generation of Post-therapeutic Neovascular Age-related Macular Degeneration
Most of the existing disease prediction methods in the field of medical image
processing fall into two classes, namely image-to-category predictions and
image-to-parameter predictions. Few works have focused on image-to-image
predictions. Different from multi-horizon predictions in other fields,
ophthalmologists prefer to show more confidence in single-horizon predictions
due to the low tolerance of predictive risk. We propose a single-horizon
disease evolution network (SHENet) to predictively generate post-therapeutic
SD-OCT images by inputting pre-therapeutic SD-OCT images with neovascular
age-related macular degeneration (nAMD). In SHENet, a feature encoder converts
the input SD-OCT images to deep features, then a graph evolution module
predicts the process of disease evolution in high-dimensional latent space and
outputs the predicted deep features, and lastly, feature decoder recovers the
predicted deep features to SD-OCT images. We further propose an evolution
reinforcement module to ensure the effectiveness of disease evolution learning
and obtain realistic SD-OCT images by adversarial training. SHENet is validated
on 383 SD-OCT cubes of 22 nAMD patients based on three well-designed schemes
based on the quantitative and qualitative evaluations. Compared with other
generative methods, the generative SD-OCT images of SHENet have the highest
image quality. Besides, SHENet achieves the best structure protection and
content prediction. Qualitative evaluations also demonstrate that SHENet has a
better visual effect than other methods. SHENet can generate post-therapeutic
SD-OCT images with both high prediction performance and good image quality,
which has great potential to help ophthalmologists forecast the therapeutic
effect of nAMD
Label Adversarial Learning for Skeleton-level to Pixel-level Adjustable Vessel Segmentation
You can have your cake and eat it too. Microvessel segmentation in optical
coherence tomography angiography (OCTA) images remains challenging.
Skeleton-level segmentation shows clear topology but without diameter
information, while pixel-level segmentation shows a clear caliber but low
topology. To close this gap, we propose a novel label adversarial learning
(LAL) for skeleton-level to pixel-level adjustable vessel segmentation. LAL
mainly consists of two designs: a label adversarial loss and an embeddable
adjustment layer. The label adversarial loss establishes an adversarial
relationship between the two label supervisions, while the adjustment layer
adjusts the network parameters to match the different adversarial weights. Such
a design can efficiently capture the variation between the two supervisions,
making the segmentation continuous and tunable. This continuous process allows
us to recommend high-quality vessel segmentation with clear caliber and
topology. Experimental results show that our results outperform manual
annotations of current public datasets and conventional filtering effects.
Furthermore, such a continuous process can also be used to generate an
uncertainty map representing weak vessel boundaries and noise
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