Adaptive contrast weighted learning for multi‐stage multi‐treatment decision‐making

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136487/1/biom12539.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136487/2/biom12539_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136487/3/biom12539-sup-0001-SuppData.pd

Geraniin inhibits bladder cancer cell growth via regulation of PI3K/AKT signaling pathways

Purpose: The effect of geraniin on human bladder transitional carcinoma was not clear, this study was thus intended to reveal it and reveal the mechanism. Methods: T24 cells were treated with 5, 10, and 20 μM of geraniin and the viability and apoptosis of T24 cells were determined using thiazolyl blue tetrazolium bromide (MTT) assay and flow cytometry. The protein expression levels of Cyclin D1, p21, BAL-2, BAX, cleaved caspase-3 and PI3K/AKT pathway were evaluated using western blot. Results: Geraniin decreased T24 cell viability and induced T24 cell cycle arrest. The proportion of T24 cells in S phase was decreased by geraniin. Besides, geraniin promoted T24 cell apoptosis and regulated PI3K/AKT pathway. Conclusion: Geraniin appears to regulate bladder cancer cell growth by decreasing the levels of PI3K and AKT phosphorylation. Thus, this agent may be useful in the management of bladder cancer Keywords: Geraniin, T24 cells, Apoptosis, PI3K/AKT signalin

FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset

We present FaceVerse, a fine-grained 3D Neural Face Model, which is built from hybrid East Asian face datasets containing 60K fused RGB-D images and 2K high-fidelity 3D head scan models. A novel coarse-to-fine structure is proposed to take better advantage of our hybrid dataset. In the coarse module, we generate a base parametric model from large-scale RGB-D images, which is able to predict accurate rough 3D face models in different genders, ages, etc. Then in the fine module, a conditional StyleGAN architecture trained with high-fidelity scan models is introduced to enrich elaborate facial geometric and texture details. Note that different from previous methods, our base and detailed modules are both changeable, which enables an innovative application of adjusting both the basic attributes and the facial details of 3D face models. Furthermore, we propose a single-image fitting framework based on differentiable rendering. Rich experiments show that our method outperforms the state-of-the-art methods.Comment: https://github.com/LizhenWangT/FaceVers