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In the healing process of myocardial infarction, cardiac fibroblasts are activated to produce collagen, leading to adverse remodeling and heart failure. Our previous study showed that ASPP1 promotes cardiomyocyte apoptosis by enhancing the nuclear trafficking of p53. We thus explored the influence of ASPP1 on myocardial fibrosis and the underlying mechanisms. Here, we observed that ASPP1 was increased after 4 weeks of MI. Both global and myofibroblast knockout of ASPP1 in mice mitigated cardiac dysfunction and fibrosis after MI. Strikingly, ASPP1 produced the opposite influence on p53 level and cell fate in cardiac fibroblasts and cardiomyocytes. Knockdown of ASPP1 increased p53 levels and inhibited the activity of cardiac fibroblasts. ASPP1 accumulated in the cytoplasm of fibroblasts while the level of p53 was reduced following TGF-β1 stimulation; however, inhibition of ASPP1 increased the p53 level and promoted p53 nuclear translocation. Mechanistically, ASPP1 is directly bound to deubiquitinase OTUB1, thereby promoting the ubiquitination and degradation of p53, attenuating myofibroblast activity and cardiac fibrosis, and improving heart function after MI. ASPP1 promotes apoptosis of cardiomyocytes by increasing the trafficking of p53 to the nucleus. Here the authors show that, in fibroblasts, ASPP1 promotes the ubiquitination and degradation of p53, decreasing myofibroblast activity and improving cardiac fibrosis and function after myocardial infarction. [ABSTRACT FROM AUTHOR]

Learning from large-scale contrastive language-image pre-training like CLIP has shown remarkable success in a wide range of downstream tasks recently, but it is still under-explored on the challenging few-shot action recognition (FSAR) task. In this work, we aim to transfer the powerful multimodal knowledge of CLIP to alleviate the inaccurate prototype estimation issue due to data scarcity, which is a critical problem in low-shot regimes. To this end, we present a CLIP-guided prototype modulating framework called CLIP-FSAR, which consists of two key components: a video-text contrastive objective and a prototype modulation. Specifically, the former bridges the task discrepancy between CLIP and the few-shot video task by contrasting videos and corresponding class text descriptions. The latter leverages the transferable textual concepts from CLIP to adaptively refine visual prototypes with a temporal Transformer. By this means, CLIP-FSAR can take full advantage of the rich semantic priors in CLIP to obtain reliable prototypes and achieve accurate few-shot classification. Extensive experiments on five commonly used benchmarks demonstrate the effectiveness of our proposed method, and CLIP-FSAR significantly outperforms existing state-of-the-art methods under various settings. The source code and models are publicly available at https://github.com/alibaba-mmai-research/CLIP-FSAR. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks (GANs) can synthesize more and more realistic images. However, one fundamental issue hinders their practical applications: the incapability of encoding real samples in the latent space. Many semantic image editing applications rely on inverting the given image into the latent space and then manipulating inverted code. One possible solution is to learn an encoder for GAN via Variational Auto-Encoder. However, the entanglement of the latent space poses a major challenge for learning the encoder. To tackle the challenge and enable inference in GANs, we propose a novel method named Latently Invertible Autoencoder (LIA). In LIA, an invertible network and its inverse mapping are symmetrically embedded in the latent space of an autoencoder. The decoder of LIA is first trained as a standard GAN with the invertible network, and then the encoder is learned from a disentangled autoencoder by detaching the invertible network from LIA. It thus avoids the entanglement problem caused by the latent space. Extensive experiments on the FFHQ face dataset and three LSUN datasets validate the effectiveness of LIA for the image inversion and its applications. Code and models are available at https://github.com/genforce/lia. [ABSTRACT FROM AUTHOR]

In this study, the relationship between the brain parenchymal density, the cerebral vessel density, the mean corpuscular hemoglobin (MCH) content, the mean corpuscular hemoglobin concentration (MCHC), and the morbidity associated with lacunar infarction of residents living in either the plains or the plateau regions were analyzed and compared for their potential clinical implications. Clinical data from the brain CT scans of individuals living in either the plain or plateau regions (129 each) were collected. Specifically, the CT values for basal ganglia, the middle cerebral artery, and the superior sagittal sinus, along with the number of patients with lacunar infarction, were collected. In addition, the MCH and MCHC values were measured in blood samples collected within 48 h following the CT scans. For statistical analysis, an independent sample t-test, Pearson's correlation test (permutation test), and Chi-squared test were employed. The inhabitants of the plateau had a significantly higher CT value of basal ganglia, the middle cerebral artery, and superior sagittal sinus and also higher levels of MCH and MCHC in the blood (ps < 0.001) than the inhabitants of the plains region. Further, there was a significant positive correlation between the three aforementioned CT values and the MCH and MCHC findings. However, no significant differences were found in the morbidity of lacunar infarction between these two regions (p > 0.05). The inhabitants in the plateau have a significantly higher brain parenchymal density, higher CT value for cerebral vessels density, and higher blood MCH and MCHC levels in comparison with individuals occupying the plains. Concurrently, the parenchymal density and the CT values are shown to be positively correlated with the MCH and MCHC content in the blood. [ABSTRACT FROM AUTHOR]

Existing action recognition algorithms require a set of positive exemplars to train a classifier for each action. However, the amount of action classes is very large and the users' queries vary dramatically. It is impractical to pre-define all possible action classes beforehand. To address this issue, we propose to perform action recognition with no positive exemplars, which is often known as the zero-shot learning. Current zero-shot learning paradigms usually train a series of attribute classifiers and then recognize the target actions based on the attribute representation. To ensure the maximum coverage of ad-hoc action classes, the attribute-based approaches require large numbers of reliable and accurate attribute classifiers, which are often unavailable in the real world. In this paper, we propose an approach that merely takes an action name as the input to recognize the action of interest without any pre-trained attribute classifiers and positive exemplars. Given an action name, we first build an analogy pool according to an external ontology, and each action in the analogy pool is related to the target action at different levels. The correlation information inferred from the external ontology may be noisy. We then propose an algorithm, namely adaptive multi-model rank-preserving mapping (AMRM), to train a classifier for action recognition, which is able to evaluate the relatedness of each video in the analogy pool adaptively. As multiple mapping models are employed, our algorithm has better capability to bridge the gap between visual features and the semantic information inferred from the ontology. Extensive experiments demonstrate that our method achieves the promising performance for action recognition only using action names, while no attributes and positive exemplars are available. [ABSTRACT FROM AUTHOR]