Background Peritoneal metastasis (PM) is an important pathological process in the progression of gastric cancer (GC). The metastatic potential of tumor and stromal cells is governed by hypoxia, which ...is a key molecular feature of the tumor microenvironment. Mesothelial cells also participate in this complex and dynamic process. However, the molecular mechanisms underlying the hypoxia-driven mesothelial-tumor interactions that promote peritoneal metastasis of GC remain unclear. Methods We determined the hypoxic microenvironment in PM of nude mice by immunohistochemical analysis and screened VEGFA by human growth factor array kit. The crosstalk mediated by VEGFA between peritoneal mesothelial cells (PMCs) and GC cells was determined in GC cells incubated with conditioned medium prepared from hypoxia-treated PMCs. The association between VEGFR1 and integrin alpha5 and fibronectin in GC cells was enriched using Gene Set Enrichment Analysis and KEGG pathway enrichment analysis. In vitro and xenograft mouse models were used to evaluate the impact of VEGFA/VEGFR1 on gastric cancer peritoneal metastasis. Confocal microscopy and immunoprecipitation were performed to determine the effect of hypoxia-induced autophagy. Results Here we report that in the PMCs of the hypoxic microenvironment, SIRT1 is degraded via the autophagic lysosomal pathway, leading to increased acetylation of HIF-1alpha and secretion of VEGFA. Under hypoxic conditions, VEGFA derived from PMCs acts on VEGFR1 of GC cells, resulting in p-ERK/p-JNK pathway activation, increased integrin alpha5 and fibronectin expression, and promotion of PM. Conclusions Our findings have elucidated the mechanisms by which PMCs promote PM in GC in hypoxic environments. This study also provides a theoretical basis for considering autophagic pathways or VEGFA as potential therapeutic targets to treat PM in GC. Keywords: Hypoxia, Autophagy, VEGFA, Migration, Adhesion
Porcine epidemic diarrhoea (PED) caused by the porcine epidemic diarrhoea virus (PEDV) is the most devastating disease in the global pig industry due to its high mortality rate in piglets. The host ...factors critical for PEDV replication are poorly understood. Here, we designed a pooled African green monkey genome-scale CRISPR/Cas9 knockout (VeroCKO) library containing 75,608 single guide RNAs targeting 18,993 protein-coding genes. Subsequently, we use the VeroCKO library to identify key host factors facilitating PEDV infection in Vero E6 cells. Several previously unreported genes associated with PEDV infection are highly enriched post-PEDV selection. We discovered that knocking out the tripartite motif 2 (TRIM2) and the solute carrier family 35 member A1 (SLC35A1) inhibited PEDV replication. Virtual screening and molecular docking approaches showed that chem-80,048,685 (M2) s ignificantly inhibited PEDV attachment and late replication by impeding SLC35A1. Furthermore, we found that knocking out SLC35A1 in Vero E6 cells upregulated a disintegrin and metalloprotease protein-17 (ADAM17) by splicing porcine aminopeptidase N (pAPN) and angiotensin-converting enzyme 2 (ACE2) ectodomains to reduce PEDV-infection in a CMP-Sialic Acid (CMP-SA) cell entry-independent manner. These findings provide a new perspective for a better understanding of host-pathogen interactions and new therapeutic targets for PEDV infection.
Due to the significant process variations, designers have to optimize the statistical performance distribution of nano-scale IC design in most cases. This problem has been investigated for decades ...under the formulation of stochastic optimization, which minimizes the expected value of a performance metric while assuming that the distribution of process variation is exactly given. This paper rethinks the variation-aware circuit design optimization from a new perspective. First, we discuss the variation shift problem, which means that the actual density function of process variations almost always differs from the given model and is often unknown. Consequently, we propose to formulate the variation-aware circuit design optimization as a distributionally robust optimization problem, which does not require the exact distribution of process variations. By selecting an appropriate uncertainty set for the probability density function of process variations, we solve the shift-aware circuit optimization problem using distributionally robust Bayesian optimization. This method is validated with both a photonic IC and an electronics IC. Our optimized circuits show excellent robustness against variation shifts: the optimized circuit has excellent performance under many possible distributions of process variations that differ from the given statistical model. This work has the potential to enable a new research direction and inspire subsequent research at different levels of the EDA flow under the setting of variation shift.
Facial Expression Recognition (FER) in the wild is an extremely challenging task. Recently, some Vision Transformers (ViT) have been explored for FER, but most of them perform inferiorly compared to ...Convolutional Neural Networks (CNN). This is mainly because the new proposed modules are difficult to converge well from scratch due to lacking inductive bias and easy to focus on the occlusion and noisy areas. TransFER, a representative transformer-based method for FER, alleviates this with multi-branch attention dropping but brings excessive computations. On the contrary, we present two attentive pooling (AP) modules to pool noisy features directly. The AP modules include Attentive Patch Pooling (APP) and Attentive Token Pooling (ATP). They aim to guide the model to emphasize the most discriminative features while reducing the impacts of less relevant features. The proposed APP is employed to select the most informative patches on CNN features, and ATP discards unimportant tokens in ViT. Being simple to implement and without learnable parameters, the APP and ATP intuitively reduce the computational cost while boosting the performance by ONLY pursuing the most discriminative features. Qualitative results demonstrate the motivations and effectiveness of our attentive poolings. Besides, quantitative results on six in-the-wild datasets outperform other state-of-the-art methods.
Skeletal motions have been heavily replied upon for human activity recognition (HAR). Recently, a universal vulnerability of skeleton-based HAR has been identified across a variety of classifiers and ...data, calling for mitigation. To this end, we propose the first black-box defense method for skeleton-based HAR to our best knowledge. Our method is featured by full Bayesian treatments of the clean data, the adversaries and the classifier, leading to (1) a new Bayesian Energy-based formulation of robust discriminative classifiers, (2) a new adversary sampling scheme based on natural motion manifolds, and (3) a new post-train Bayesian strategy for black-box defense. We name our framework Bayesian Energy-based Adversarial Training or BEAT. BEAT is straightforward but elegant, which turns vulnerable black-box classifiers into robust ones without sacrificing accuracy. It demonstrates surprising and universal effectiveness across a wide range of skeletal HAR classifiers and datasets, under various attacks. Code is available at https://github.com/realcrane/RobustActionRecogniser.
Long-tailed visual recognition has received increasing attention in recent years. Due to the extremely imbalanced data distribution in long-tailed learning, the learning process shows great ...uncertainties. For example, the predictions of different experts on the same image vary remarkably despite the same training settings. To alleviate the uncertainty, we propose a Nested Collaborative Learning (NCL++) which tackles the long-tailed learning problem by a collaborative learning. To be specific, the collaborative learning consists of two folds, namely inter-expert collaborative learning (InterCL) and intra-expert collaborative learning (IntraCL). In-terCL learns multiple experts collaboratively and concurrently, aiming to transfer the knowledge among different experts. IntraCL is similar to InterCL, but it aims to conduct the collaborative learning on multiple augmented copies of the same image within the single expert. To achieve the collaborative learning in long-tailed learning, the balanced online distillation is proposed to force the consistent predictions among different experts and augmented copies, which reduces the learning uncertainties. Moreover, in order to improve the meticulous distinguishing ability on the confusing categories, we further propose a Hard Category Mining (HCM), which selects the negative categories with high predicted scores as the hard categories. Then, the collaborative learning is formulated in a nested way, in which the learning is conducted on not just all categories from a full perspective but some hard categories from a partial perspective. Extensive experiments manifest the superiority of our method with outperforming the state-of-the-art whether with using a single model or an ensemble. The code will be publicly released.
Facial expression recognition plays an important role in human-computer interaction. In this paper, we propose the Coarse-to-Fine Cascaded network with Smooth Predicting (CFC-SP) to improve the ...performance of facial expression recognition. CFC-SP contains two core components, namely Coarse-to-Fine Cascaded networks (CFC) and Smooth Predicting (SP). For CFC, it first groups several similar emotions to form a rough category, and then employs a network to conduct a coarse but accurate classification. Later, an additional network for these grouped emotions is further used to obtain fine-grained predictions. For SP, it improves the recognition capability of the model by capturing both universal and unique expression features. To be specific, the universal features denote the general characteristic of facial emotions within a period and the unique features denote the specific characteristic at this moment. Experiments on Aff-Wild2 show the effectiveness of the proposed CFSP. We achieved 3rd place in the Expression Classification Challenge of the 3rd Competition on Affective Behavior Analysis in-the-wild. The code will be released at https://github.com/BR-IDL/PaddleViT.
PDF
