Effects of Ginsenosides on Cardiomyocytes and NF in Type 2 Diabetes Rats B Expression

Objective: To explore the clinical medicinal value of ginsenosides. Methods: 24 male type 2 diabetes rats aged 7 weeks were taken as the research object, and the myocardial cell morphology, infammatory factor content and NF in each group were observed by grouping them with diferent doses- κ B expression. Result: The swelling degree of cells in the CP Rg50 group was alleviated most signifcantly, with a signifcant reduction in deep staining of the nucleus, a signifcant reduction in cell shrinkage, and a basic trend towards normal cell morphology. Meanwhile, compared to the control group, the CP Rg50 and CP Rg25 groups showed signifcant diferences in IL-1 levels β/ IL-6, TNF- α It also signifcantly decreased horizontally (P0.05). Conclusion: Ginsenoside Rg1 has signifcant efects in the treatment of cardiomyopathy and is worth promoting in clinical practice

Scalable Label Distribution Learning for Multi-Label Classification

Multi-label classification (MLC) refers to the problem of tagging a given instance with a set of relevant labels. Most existing MLC methods are based on the assumption that the correlation of two labels in each label pair is symmetric, which is violated in many real-world scenarios. Moreover, most existing methods design learning processes associated with the number of labels, which makes their computational complexity a bottleneck when scaling up to large-scale output space. To tackle these issues, we propose a novel MLC learning method named Scalable Label Distribution Learning (SLDL) for multi-label classification which can describe different labels as distributions in a latent space, where the label correlation is asymmetric and the dimension is independent of the number of labels. Specifically, SLDL first converts labels into continuous distributions within a low-dimensional latent space and leverages the asymmetric metric to establish the correlation between different labels. Then, it learns the mapping from the feature space to the latent space, resulting in the computational complexity is no longer related to the number of labels. Finally, SLDL leverages a nearest-neighbor-based strategy to decode the latent representations and obtain the final predictions. Our extensive experiments illustrate that SLDL can achieve very competitive classification performances with little computational consumption

NormAUG: Normalization-guided Augmentation for Domain Generalization

Deep learning has made significant advancements in supervised learning. However, models trained in this setting often face challenges due to domain shift between training and test sets, resulting in a significant drop in performance during testing. To address this issue, several domain generalization methods have been developed to learn robust and domain-invariant features from multiple training domains that can generalize well to unseen test domains. Data augmentation plays a crucial role in achieving this goal by enhancing the diversity of the training data. In this paper, inspired by the observation that normalizing an image with different statistics generated by different batches with various domains can perturb its feature, we propose a simple yet effective method called NormAUG (Normalization-guided Augmentation). Our method includes two paths: the main path and the auxiliary (augmented) path. During training, the auxiliary path includes multiple sub-paths, each corresponding to batch normalization for a single domain or a random combination of multiple domains. This introduces diverse information at the feature level and improves the generalization of the main path. Moreover, our NormAUG method effectively reduces the existing upper boundary for generalization based on theoretical perspectives. During the test stage, we leverage an ensemble strategy to combine the predictions from the auxiliary path of our model, further boosting performance. Extensive experiments are conducted on multiple benchmark datasets to validate the effectiveness of our proposed method.Comment: Accepted by IEEE Transactions on Image Processing (TIP

Sustained release of VEGF from PLGA nanoparticles embedded thermo-sensitive hydrogel in full-thickness porcine bladder acellular matrix

We fabricated a novel vascular endothelial growth factor (VEGF)-loaded poly(lactic-co-glycolic acid) (PLGA)-nanoparticles (NPs)-embedded thermo-sensitive hydrogel in porcine bladder acellular matrix allograft (BAMA) system, which is designed for achieving a sustained release of VEGF protein, and embedding the protein carrier into the BAMA. We identified and optimized various formulations and process parameters to get the preferred particle size, entrapment, and polydispersibility of the VEGF-NPs, and incorporated the VEGF-NPs into the (poly(ethylene oxide)-poly(propylene oxide)-poly(ethylene oxide) (Pluronic®) F127 to achieve the preferred VEGF-NPs thermo-sensitive gel system. Then the thermal behavior of the system was proven by in vitro and in vivo study, and the kinetic-sustained release profile of the system embedded in porcine bladder acellular matrix was investigated. Results indicated that the bioactivity of the encapsulated VEGF released from the NPs was reserved, and the VEGF-NPs thermo-sensitive gel system can achieve sol-gel transmission successfully at appropriate temperature. Furthermore, the system can create a satisfactory tissue-compatible environment and an effective VEGF-sustained release approach. In conclusion, a novel VEGF-loaded PLGA NPs-embedded thermo-sensitive hydrogel in porcine BAMA system is successfully prepared, to provide a promising way for deficient bladder reconstruction therapy

Unified description of the productions of Dˉ∗D\bar{D}^*D and Dˉ∗D∗\bar{D}^*D^* molecules in BB decays

The exotic states $X(3872)$ and $Z_c(3900)$ have long been conjectured as isoscalar and isovector $\bar{D}^*D$ molecules, respectively. In this letter, we propose a unified framework to understand the productions of $\bar{D}^*D$ molecules as well as their heavy quark spin symmetry partners, $\bar{D}^*D^*$ molecules, in $B$ decays. We show that the large isospin breaking of the ratio $\mathcal{B}[B^+ \to X(3872) K^+]/\mathcal{B}[B^0 \to X(3872) K^0]$ can be attributed to the isospin breaking of the $\bar{D}^*D$ neutral and charged components. Because of this, the branching fractions of $Z_c(3900)$ in $B$ decays are smaller than the corresponding ones of $X(3872)$ by at least one order of magnitude, which naturally explains the non-observation of $Z_{c}(3900)$ in $B$ decays. Furthermore, we predict a hierarchy for the productions fractions of all the $\bar{D}^*D$ and $\bar{D}^*D^*$ molecules in $B$ decays, which are consistent with all the existing data and can help elucidate the internal structure of the $XZ$ states around the $\bar{D}^*D$ and $\bar{D}^*D^*$ mass thresholds, if confirmed by future experiments

Generation of spatially-separated spin entanglement in a triple quantum dot system

We propose a novel method for the creation of spatially-separated spin entanglement by means of adiabatic passage of an external gate voltage in a triple quantum dot system.Comment: 10 pages, 6 figure

DoubleAUG: Single-domain Generalized Object Detector in Urban via Color Perturbation and Dual-style Memory

Object detection in urban scenarios is crucial for autonomous driving in intelligent traffic systems. However, unlike conventional object detection tasks, urban-scene images vary greatly in style. For example, images taken on sunny days differ significantly from those taken on rainy days. Therefore, models trained on sunny day images may not generalize well to rainy day images. In this paper, we aim to solve the single-domain generalizable object detection task in urban scenarios, meaning that a model trained on images from one weather condition should be able to perform well on images from any other weather conditions. To address this challenge, we propose a novel Double AUGmentation (DoubleAUG) method that includes image- and feature-level augmentation schemes. In the image-level augmentation, we consider the variation in color information across different weather conditions and propose a Color Perturbation (CP) method that randomly exchanges the RGB channels to generate various images. In the feature-level augmentation, we propose to utilize a Dual-Style Memory (DSM) to explore the diverse style information on the entire dataset, further enhancing the model's generalization capability. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art methods. Furthermore, ablation studies confirm the effectiveness of each module in our proposed method. Moreover, our method is plug-and-play and can be integrated into existing methods to further improve model performance.Comment: Accepted by ACM Transactions on Multimedia Computing, Communications, and Application