SVH-B interacts directly with p53 and suppresses the transcriptional activity of p53

AbstractWe previously reported that inhibition of SVH-B, a specific splicing variant of SVH, results in apoptotic cell death. In this study, we reveal that this apoptosis may be dependent on the presence of p53. Co-immunoprecipitation and GST pull-down assays have demonstrated that SVH-B directly interacts with p53. In both BEL-7404 cells and p53-null Saos-2 cells transfected with a temperature-sensitive mutant of p53, V143A, ectopically expressed SVH-B suppresses the transcriptional activity of p53, and suppression of SVH by RNA interference increases the transcriptional activity of p53. Our results suggested the function of SVH-B in accelerating growth and inhibition of apoptosis is related to its inhibitory binding to p53

Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving

A model used for velocity control during car following was proposed based on deep reinforcement learning (RL). To fulfil the multi-objectives of car following, a reward function reflecting driving safety, efficiency, and comfort was constructed. With the reward function, the RL agent learns to control vehicle speed in a fashion that maximizes cumulative rewards, through trials and errors in the simulation environment. A total of 1,341 car-following events extracted from the Next Generation Simulation (NGSIM) dataset were used to train the model. Car-following behavior produced by the model were compared with that observed in the empirical NGSIM data, to demonstrate the model's ability to follow a lead vehicle safely, efficiently, and comfortably. Results show that the model demonstrates the capability of safe, efficient, and comfortable velocity control in that it 1) has small percentages (8\%) of dangerous minimum time to collision values (\textless\ 5s) than human drivers in the NGSIM data (35\%); 2) can maintain efficient and safe headways in the range of 1s to 2s; and 3) can follow the lead vehicle comfortably with smooth acceleration. The results indicate that reinforcement learning methods could contribute to the development of autonomous driving systems.Comment: Submitted to IEEE transaction on IT

Attention Consistency Refined Masked Frequency Forgery Representation for Generalizing Face Forgery Detection

Due to the successful development of deep image generation technology, visual data forgery detection would play a more important role in social and economic security. Existing forgery detection methods suffer from unsatisfactory generalization ability to determine the authenticity in the unseen domain. In this paper, we propose a novel Attention Consistency Refined masked frequency forgery representation model toward generalizing face forgery detection algorithm (ACMF). Most forgery technologies always bring in high-frequency aware cues, which make it easy to distinguish source authenticity but difficult to generalize to unseen artifact types. The masked frequency forgery representation module is designed to explore robust forgery cues by randomly discarding high-frequency information. In addition, we find that the forgery attention map inconsistency through the detection network could affect the generalizability. Thus, the forgery attention consistency is introduced to force detectors to focus on similar attention regions for better generalization ability. Experiment results on several public face forgery datasets (FaceForensic++, DFD, Celeb-DF, and WDF datasets) demonstrate the superior performance of the proposed method compared with the state-of-the-art methods.Comment: The source code and models are publicly available at https://github.com/chenboluo/ACM

Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection

Unsupervised image Anomaly Detection (UAD) aims to learn robust and discriminative representations of normal samples. While separate solutions per class endow expensive computation and limited generalizability, this paper focuses on building a unified framework for multiple classes. Under such a challenging setting, popular reconstruction-based networks with continuous latent representation assumption always suffer from the "identical shortcut" issue, where both normal and abnormal samples can be well recovered and difficult to distinguish. To address this pivotal issue, we propose a hierarchical vector quantized prototype-oriented Transformer under a probabilistic framework. First, instead of learning the continuous representations, we preserve the typical normal patterns as discrete iconic prototypes, and confirm the importance of Vector Quantization in preventing the model from falling into the shortcut. The vector quantized iconic prototype is integrated into the Transformer for reconstruction, such that the abnormal data point is flipped to a normal data point.Second, we investigate an exquisite hierarchical framework to relieve the codebook collapse issue and replenish frail normal patterns. Third, a prototype-oriented optimal transport method is proposed to better regulate the prototypes and hierarchically evaluate the abnormal score. By evaluating on MVTec-AD and VisA datasets, our model surpasses the state-of-the-art alternatives and possesses good interpretability. The code is available at https://github.com/RuiyingLu/HVQ-Trans

Reflection-mode submicron-resolution in vivo photoacoustic microscopy

Submicron-resolution photoacoustic microscopy (PAM) currently exists only in transmission mode, due to the technical difficulties of combining high numerical-aperture (NA) optical illumination with high NA acoustic detection. The lateral resolution of reflection-mode PAM has not reached <2  μm in the visible light range. Here we develop the first reflection-mode submicron-resolution PAM system with a new compact design. By using a parabolic mirror to focus and reflect the photoacoustic waves, sufficient signals were collected for good sensitivity without distorting the optical focusing. By imaging nanospheres and a resolution test chart, the lateral resolution was measured to be ∼0.5  μm with an optical wavelength of 532 nm, an optical NA of 0.63. The axial resolution was measured at 15 μm. Here the axial resolution was measured by a different experiment with the lateral resolution measurement. But we didn’t describe the details of axial resolution measurement due to space limit. The maximum penetration was measured at ∼0.42  mm in optical-scattering soft tissue. As a comparison, both the submicron-resolution PAM and a 2.4 μm-resolution PAM were used to image a mouse ear in vivo with the same optical wavelength and similar pulse energy. Capillaries were resolved better by the submicron-resolution PAM. Therefore, the submicron-resolution PAM is suitable for in vivo high-resolution imaging, or even subcellular imaging, of optical absorption

Structural analysis of a novel rabbit monoclonal antibody R53 targeting an epitope in HIV-1 gp120 C4 region critical for receptor and co-receptor binding

The fourth conserved region (C4) in the HIV-1 envelope glycoprotein (Env) gp120 is a structural element that is important for its function, as it binds to both the receptor CD4 and the co-receptor CCR5/CXCR4. It has long been known that this region is highly immunogenic and that it harbors B-cell as well as T-cell epitopes. It is the target of a number of antibodies in animal studies, which are called CD4-blockers. However, the mechanism by which the virus shields itself from such antibody responses is not known. Here, we determined the crystal structure of R53 in complex with its epitope peptide using a novel anti-C4 rabbit monoclonal antibody R53. Our data show that although the epitope of R53 covers a highly conserved sequence (433)AMYAPPI(439), it is in the gp120 trimer and in the CD4-bound conformation. Our results suggest a masking mechanism to explain how HIV-1 protects this critical region from the human immune system