555 research outputs found
Curcumin inhibits epithelial-mesenchymal transition in colorectal cancer cells by regulating miR-206/SNAI2 pathway
Purpose: To examine the effects of curcumin on epithelial-mesenchymal transition (EMT) via regulation of miR-206 and SNAI2 in colorectal cancer (CRC) cells. Relationship between SNAI2 and miR-206 and the effects of curcumin on related mechanisms were also identified.
Methods: Transwell assays were used to analyze cellular migration and invasion. Genes associated with changes in protein and mRNA expression were evaluated by western blotting and quantitative reverse transcription PCR analyses, respectively. The relationship between SNAI2 and miR-206 was determined using a dual luciferase assay.
Results: Curcumin inhibited cell metastasis, upregulated miR-206 expression, and decreased SNAI2 levels. Furthermore, miR-206 directly targeted SNAI2 and inhibited EMT via downregulation of SNAI2 expression. Curcumin inhibited EMT in CRC cells by upregulating miR-206.
Conclusion: This study, for the first time, discovered the role of curcumin on epithelial-mesenchymal transition process in colorectal cancer cells by modulating miR-206/SNAI2 axis. These findings suggest that curcumin may be useful as a novel therapeutic agent to inhibit the metastasis of CRC
CTP: Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation
Vision-Language Pretraining (VLP) has shown impressive results on diverse
downstream tasks by offline training on large-scale datasets. Regarding the
growing nature of real-world data, such an offline training paradigm on
ever-expanding data is unsustainable, because models lack the continual
learning ability to accumulate knowledge constantly. However, most continual
learning studies are limited to uni-modal classification and existing
multi-modal datasets cannot simulate continual non-stationary data stream
scenarios. To support the study of Vision-Language Continual Pretraining
(VLCP), we first contribute a comprehensive and unified benchmark dataset P9D
which contains over one million product image-text pairs from 9 industries. The
data from each industry as an independent task supports continual learning and
conforms to the real-world long-tail nature to simulate pretraining on web
data. We comprehensively study the characteristics and challenges of VLCP, and
propose a new algorithm: Compatible momentum contrast with Topology
Preservation, dubbed CTP. The compatible momentum model absorbs the knowledge
of the current and previous-task models to flexibly update the modal feature.
Moreover, Topology Preservation transfers the knowledge of embedding across
tasks while preserving the flexibility of feature adjustment. The experimental
results demonstrate our method not only achieves superior performance compared
with other baselines but also does not bring an expensive training burden.
Dataset and codes are available at https://github.com/KevinLight831/CTP.Comment: Accepted by ICCV 2023. Code: https://github.com/KevinLight831/CT
DLCA-Recon: Dynamic Loose Clothing Avatar Reconstruction from Monocular Videos
Reconstructing a dynamic human with loose clothing is an important but
difficult task. To address this challenge, we propose a method named DLCA-Recon
to create human avatars from monocular videos. The distance from loose clothing
to the underlying body rapidly changes in every frame when the human freely
moves and acts. Previous methods lack effective geometric initialization and
constraints for guiding the optimization of deformation to explain this
dramatic change, resulting in the discontinuous and incomplete reconstruction
surface. To model the deformation more accurately, we propose to initialize an
estimated 3D clothed human in the canonical space, as it is easier for
deformation fields to learn from the clothed human than from SMPL. With both
representations of explicit mesh and implicit SDF, we utilize the physical
connection information between consecutive frames and propose a dynamic
deformation field (DDF) to optimize deformation fields. DDF accounts for
contributive forces on loose clothing to enhance the interpretability of
deformations and effectively capture the free movement of loose clothing.
Moreover, we propagate SMPL skinning weights to each individual and refine pose
and skinning weights during the optimization to improve skinning
transformation. Based on more reasonable initialization and DDF, we can
simulate real-world physics more accurately. Extensive experiments on public
and our own datasets validate that our method can produce superior results for
humans with loose clothing compared to the SOTA methods
Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) identifies unseen categories by
knowledge transferred from the seen domain, relying on the intrinsic
interactions between visual and semantic information. Prior works mainly
localize regions corresponding to the sharing attributes. When various visual
appearances correspond to the same attribute, the sharing attributes inevitably
introduce semantic ambiguity, hampering the exploration of accurate
semantic-visual interactions. In this paper, we deploy the dual semantic-visual
transformer module (DSVTM) to progressively model the correspondences between
attribute prototypes and visual features, constituting a progressive
semantic-visual mutual adaption (PSVMA) network for semantic disambiguation and
knowledge transferability improvement. Specifically, DSVTM devises an
instance-motivated semantic encoder that learns instance-centric prototypes to
adapt to different images, enabling the recast of the unmatched semantic-visual
pair into the matched one. Then, a semantic-motivated instance decoder
strengthens accurate cross-domain interactions between the matched pair for
semantic-related instance adaption, encouraging the generation of unambiguous
visual representations. Moreover, to mitigate the bias towards seen classes in
GZSL, a debiasing loss is proposed to pursue response consistency between seen
and unseen predictions. The PSVMA consistently yields superior performances
against other state-of-the-art methods. Code will be available at:
https://github.com/ManLiuCoder/PSVMA.Comment: Accepted by CVPR202
Structural dynamic model updating based on Kriging model using frequency response data
Metamodel technique is attracting more and more attention in structural dynamic model updating. In this paper, an attempt is made to explore the effectiveness of Kriging method for acceleration frequency response function based model updating. A Kriging model is constructed based on the input variables selected by F-test method specially, which is applied to the results of design of experiment. The response of design of experiment is obtained based on the errors between acceleration response curves of analytical model and experimental model. Two examples of representative structure are discussed, the comparison of updated results of different metamodel shows that a less error of updated results can be obtained based on Kriging model, and the updated analytical model has a good prediction capability. It can be concluded that the Kriging model is suitable for the frequency response function based model updating
Bright solitons in a spin-orbit-coupled dipolar Bose-Einstein condensate trapped within a double-lattice
By effectively controlling the dipole-dipole interaction, we investigate the
characteristics of the ground state of bright solitons in a spin-orbit coupled
dipolar Bose-Einstein condensate. The dipolar atoms are trapped within a
double-lattice which consists of a linear and a nonlinear lattice. We derive
the motion equations of the different spin components, taking the controlling
mechanisms of the diolpe-dipole interaction into account. An analytical
expression of dipole-dipole interaction is derived. By adjusting the dipole
polarization angle, the dipole interaction can be adjusted from attraction to
repulsion. On this basis, we study the generation and manipulation of the
bright solitons using both the analytical variational method and numerical
imaginary time evolution. The stability of the bright solitons is also analyzed
and we map out the stability phase diagram. By adjusting the long-range
dipole-dipole interaction, one can achieve manipulation of bright solitons in
all aspects, including the existence, width, nodes, and stability. Considering
the complexity of our system, our results will have enormous potential
applications in quantum simulation of complex systems
You Can Mask More For Extremely Low-Bitrate Image Compression
Learned image compression (LIC) methods have experienced significant progress
during recent years. However, these methods are primarily dedicated to
optimizing the rate-distortion (R-D) performance at medium and high bitrates (>
0.1 bits per pixel (bpp)), while research on extremely low bitrates is limited.
Besides, existing methods fail to explicitly explore the image structure and
texture components crucial for image compression, treating them equally
alongside uninformative components in networks. This can cause severe
perceptual quality degradation, especially under low-bitrate scenarios. In this
work, inspired by the success of pre-trained masked autoencoders (MAE) in many
downstream tasks, we propose to rethink its mask sampling strategy from
structure and texture perspectives for high redundancy reduction and
discriminative feature representation, further unleashing the potential of LIC
methods. Therefore, we present a dual-adaptive masking approach (DA-Mask) that
samples visible patches based on the structure and texture distributions of
original images. We combine DA-Mask and pre-trained MAE in masked image
modeling (MIM) as an initial compressor that abstracts informative semantic
context and texture representations. Such a pipeline can well cooperate with
LIC networks to achieve further secondary compression while preserving
promising reconstruction quality. Consequently, we propose a simple yet
effective masked compression model (MCM), the first framework that unifies MIM
and LIC end-to-end for extremely low-bitrate image compression. Extensive
experiments have demonstrated that our approach outperforms recent
state-of-the-art methods in R-D performance, visual quality, and downstream
applications, at very low bitrates. Our code is available at
https://github.com/lianqi1008/MCM.git.Comment: Under revie
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