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