Perilla Leaf Extract Attenuates Asthma Airway Inflammation by Blocking the Syk Pathway.

Perilla frutescens (L.) Britton is a classic herbal plant used widely against asthma in China. But its mechanism of beneficial effect remains undermined. In the study, the antiallergic asthma effects of Perilla leaf extract (PLE) were investigated, and the underlying mechanism was also explored. Results showed that PLE treatment significantly attenuated airway inflammation in OVA-induced asthma mice, by ameliorating lung pathological changes, inhibiting recruitment of inflammatory cells in lung tissues and bronchoalveolar lavage fluid (BALF), decreasing the production of inflammatory cytokines in the BALF, and reducing the level of immunoglobulin in serum. PLE treatment suppressed inflammatory response in antigen-induced rat basophilic leukemia 2H3 (RBL-2H3) cells as well as in OVA-induced human peripheral blood mononuclear cells (PBMCs). Furthermore, PLE markedly inhibited the expression and phosphorylation of Syk, NF-κB, PKC, and cPLA2 both in vivo and in vitro. By cotreating with inhibitors (BAY61-3606, Rottlerin, BAY11-7082, and arachidonyl trifluoromethyl ketone) in vitro, results revealed that PLE's antiallergic inflammatory effects were associated with the inhibition of Syk and its downstream signals NF-κB, PKC, and cPLA2. Collectively, the present results suggested that PLE could attenuate allergic inflammation, and its mechanism might be partly mediated through inhibiting the Syk pathway.

Hypseudohenrins I - K: three new polycyclic polyprenylated acylphloroglucinol derivatives from Hypericum pseudohenryi.

Three previously unidentified polycyclic polyprenylated acylphloroglucinols (PPAPs) derivatives, hypseudohenrins I-K (1-3), along with a known analogue hyphenrone X (4), were isolated from the aerial part of Hypericum pseudohenryi. The structures of the new compounds were elucidated by NMR spectroscopy and ECD calculation. The anti-inflammatory activity of the compounds was evaluated. Compounds 1-3 showed mild anti-inflammatory activity while hyphenrone X showed prominent anti-inflammatory activity.[Formula: see text].

Chondroprotective and anti-inflammatory effects of amurensin H by regulating TLR4/Syk/NF-κB signals.

The low-grade, chronic inflammation initiated by TLR4-triggered innate immune responses has a central role on early osteoarthritis. Amurensin H is a resveratrol dimer with anti-inflammatory and anti-apoptotic effects, while its effects on TLR-4 signals to inhibit osteoarthritis are still unclear. In the present study, treatment with amurensin H for 2 weeks in monosodium iodoacetate-induced mice significantly slows down cartilage degeneration and inflammation using macroscopic evaluation, haematoxylin and eosin (HE) staining and micro-magnetic resonance imaging. In IL-1ß-stimulated rat chondrocytes, amurensin H suppresses the production of inflammatory mediators including nitric oxide, IL-6, IL-17, PGE2 and TNF-α using Greiss and ELISA assay. Amurensin H inhibits matrix degradation via decreasing levels of MMP-9 and MMP-13 using Western blot assay, promotes synthesis of type II collagen and glycosaminoglycan using immunostaining and safranin O staining, respectively. Amurensin H inhibits intracellular and mitochondrial reactive oxygen species (ROS) generation, and mitochondrial membrane depolarization using DCFH-DA, MitoSOX Red and JC-1 assay as well. IL-1ß stimulates TLR4 activation and Syk phosphorylation in chondrocytes, while amurensin H inhibits TLR4/Syk signals and downstream p65 phosphorylation and translocation in a time and dose-dependent manner. Together, these results suggest that amurensin H exerts chondroprotective effects by attenuating oxidative stress, inflammation and matrix degradation via the TLR4/Syk/NF-κB pathway.

Hydroxylated Ethacrylic and Tiglic Acid Derivatives from the Stems and Branches of Enkianthus chinensis and Their Potential Anti-inflammatory Activities.

Two new hydroxylated ethacrylic acid derivatives (compounds 1 and 2) and 11 new hydroxylated tiglic acid derivatives (compounds 3-13), together with one known compound (compound 14), were isolated from the stems and branches of Enkianthus chinensis. Their structures were established by extensive spectroscopic analyses, while their absolute configurations were determined by X-ray crystallographic methods (compounds 1 and 2), Mo2(OAc)4-induced electronic circular dichroism experiments (compounds 3 and 4), and chemical methods (compounds 5-11). This study is the first investigation on the secondary metabolites of this species. The anti-inflammatory activities of all isolated compounds were evaluated in an LPS-induced mouse peritoneal macrophage model. Notably, compounds 3 and 12 both exerted potent inhibitory effects on NO production with IC50 values of 2.9 and 1.2 µM, respectively.

Anti-inflammatory effect of a resveratrol derivative 3,4,5-trimethoxy-4',5'-dihydroxy-trans-stilbene (WL-09-5) via ROS-mediated NF-κB pathway.

Inflammation derived from macrophages activation leads to various diseases. Synthetic modifications of resveratrol have been shown to have better anti-inflammatory activities. In this study, croton oil-induced mouse ear edema and lipopolysaccharides (LPS)-stimulated RAW264.7 macrophages were used to evaluate the anti-inflammatory effects of WL-09-5, a derivative of resveratrol. Furthermore, the activation of NF-κB was determined. Results showed that WL-09-5 significantly reduced the croton oil-induced ear edema, scavenged NO and ROS production, and reduced the levels of TNF-α, IL-6, and IL-1ß. Furthermore, WL-09-5 may significantly inhibit the translocation of NF-κB in macrophage cells stimulated by LPS in a dose-dependent manner, which is a potent mechanism of its anti-inflammatory effects. In conclusion, WL-09-5 is an underlying candidate for inflammatory diseases that need further investigations.

Anti-inflammatory Hydrolyzable Tannins from Myricaria bracteata.

Twelve hydrolyzable tannins were obtained from the twigs of Myricaria bracteata, including two new hellinoyl-type dimers, bracteatinins D1 (1) and D2 (2); a new hellinoyl-type trimer, bracteatinin T1 (3); two known monomers, nilotinin M4 (4) and 1,3-di-O-galloyl-4,6-O-(aS)-hexahydroxydiphenoyl-ß-d-glucose (5); six known dimers, tamarixinin A (6), nilotinin D8 (7), hirtellins A (10), B (9), and E (8), and isohirtellin C (11); and a known trimer, hirtellin T3 (12). The structures of the tannins were elucidated by spectroscopic data analysis and comparisons to known tannins. All compounds were evaluated as free radical scavengers using 1,1-diphenyl-2-picrylhydrazyl and hydroxy radicals and compared to the activity of BHT and Trolox. Compound 6 showed a significant anti-inflammatory effect on croton oil-induced ear edema in mice (200 mg/kg, inhibition rate 69.8%) and on collagen-induced arthritis in DBA/1 mice (20 mg/kg, inhibition rate 46.0% at day 57).

[Recent advances in the study of Nrf2 and inflammatory respiratory diseases].

Nuclear factor-erythroid 2 related factor 2 (Nrf2) is an ubiquitous and important transcription factor. It regulates antioxidant response elements (AREs)-mediated expression of antioxidant enzyme and cytoprotective proteins. A large body of research showed that Nrf2-Keap1 (Kelch-like ECH-associated protein 1, Keap 1)-ARE signaling pathway is involved in the endogenous antioxidant defense mechanisms. Nrf2 increases the expression of a number of cytoprotective genes, protects cells and tissues from the injury of a variety of toxicants and carcinogens. As a result, Nrf2 enhances the expression of glutathione and antioxidants such as superoxide dismutase and glutathione S-transferase, and subsequently scavenging free radicals. Air pollution especially from PM2.5 particles, is associated with an increasing morbidity of inflammatory pulmonary diseases and their deterioration. More and more studies demonstrated that Nrf2 was a novel signaling molecule in the modulation of inflammatory responses in these inflammatory respiratory diseases, such as asthma, acute lung injury (ALI) and COPD. Therefore, Nrf2 targeting might be a therapeutic target, which will provide clinical benefit by reducing both oxidative stress and inflammation in asthma, acute lung injury (ALI) and COPD. This review focused on the relationship between Nrf2 and inflammatory respiratory diseases and oxidative stress.

Biomimetic synthesis of active isorhapontigenin dimers.

Synthetic isorhapontigenin was treated with several kinds of inorganic reagents and peroxidase so as to prepare active stilbene dimers. Among them, silver acetate in methanol gave two new isorhapontigenin dimers 4 and 5, together with four known natural stilbene dimers 2, 3, 6, and 7. Their structures and relative configurations were determined on the basis of spectral analysis, and their possible formation mechanisms were discussed, respectively. Compounds 2, 6, and 7 were artificially synthesized for the first time. All the products were evaluated for anti-inflammatory activities.

New phenols and one aminobenzoic acid derivative isolated from the roots of Rhus chinensis Mill.

Four new phenols and one new aminobenzoic acid derivative, with five known phenols were isolated from the roots of Rhus chinensis Mill. Their structures were elucidated by UV, IR, HRESIMS, 1D and 2D NMR spectra, as well as optical rotations. Compound 4 significantly inhibited mouse ear inflammation (inhibitory rate of 44.03%), and significantly extended the time of pain response (extension rate of 48.55%), showing significant anti-inflammatory and analgesic effects in vivo.

Training Compact CNNs for Image Classification Using Dynamic-Coded Filter Fusion.

The mainstream approach for filter pruning is usually either to force a hard-coded importance estimation upon a computation-heavy pretrained model to select "important" filters, or to impose a hyperparameter-sensitive sparse constraint on the loss objective to regularize the network training. In this paper, we present a novel filter pruning method, dubbed dynamic-coded filter fusion (DCFF), to derive compact CNNs in a computation-economical and regularization-free manner for efficient image classification. Each filter in our DCFF is first given an inter-similarity distribution with a temperature parameter as a filter proxy, on top of which, a fresh Kullback-Leibler divergence based dynamic-coded criterion is proposed to evaluate the filter importance. In contrast to simply keeping high-score filters in other methods, we propose the concept of filter fusion, i.e., the weighted averages using the assigned proxies, as our preserved filters. We obtain a one-hot inter-similarity distribution as the temperature parameter approaches infinity. Thus, the relative importance of each filter can vary along with the training of the compact CNN, leading to dynamically changeable fused filters without both the dependency on the pretrained model and the introduction of sparse constraints. Extensive experiments on classification benchmarks demonstrate the superiority of our DCFF over the compared counterparts. For example, our DCFF derives a compact VGGNet-16 with only 72.77M FLOPs and 1.06M parameters while reaching top-1 accuracy of 93.47% on CIFAR-10. A compact ResNet-50 is obtained with 63.8% FLOPs and 58.6% parameter reductions, retaining 75.60% top-1 accuracy on ILSVRC-2012. Our code, narrower models and training logs are available at https://github.com/lmbxmu/DCFF.

Prioritized Subnet Sampling for Resource-Adaptive Supernet Training.

A resource-adaptive supernet adjusts its subnets for inference to fit the dynamically available resources. In this paper, we propose prioritized subnet sampling to train a resource-adaptive supernet, termed PSS-Net. We maintain multiple subnet pools, each of which stores the information of substantial subnets with similar resource consumption. Considering a resource constraint, subnets conditioned on this resource constraint are sampled from a pre-defined subnet structure space and high-quality ones will be inserted into the corresponding subnet pool. Then, the sampling will gradually be prone to sampling subnets from the subnet pools. Moreover, the one with a better performance metric is assigned with higher priority to train our PSS-Net, if sampling is from a subnet pool. At the end of training, our PSS-Net retains the best subnet in each pool to entitle a fast switch of high-quality subnets for inference when the available resources vary. Experiments on ImageNet using MobileNet-V1/V2 and ResNet-50 show that our PSS-Net can well outperform state-of-the-art resource-adaptive supernets. Our project is publicly available at https://github.com/chenbong/PSS-Net.

Lottery Jackpots Exist in Pre-Trained Models.

Network pruning is an effective approach to reduce network complexity with acceptable performance compromise. Existing studies achieve the sparsity of neural networks via time-consuming weight training or complex searching on networks with expanded width, which greatly limits the applications of network pruning. In this paper, we show that high-performing and sparse sub-networks without the involvement of weight training, termed "lottery jackpots", exist in pre-trained models with unexpanded width. Our presented lottery jackpots are traceable through empirical and theoretical outcomes. For example, we obtain a lottery jackpot that has only 10% parameters and still reaches the performance of the original dense VGGNet-19 without any modifications on the pre-trained weights on CIFAR-10. Furthermore, we improve the efficiency for searching lottery jackpots from two perspectives. First, we observe that the sparse masks derived from many existing pruning criteria have a high overlap with the searched mask of our lottery jackpot, among which, the magnitude-based pruning results in the most similar mask with ours. In compliance with this insight, we initialize our sparse mask using the magnitude-based pruning, resulting in at least 3× cost reduction on the lottery jackpot searching while achieving comparable or even better performance. Second, we conduct an in-depth analysis of the searching process for lottery jackpots. Our theoretical result suggests that the decrease in training loss during weight searching can be disturbed by the dependency between weights in modern networks. To mitigate this, we propose a novel short restriction method to restrict change of masks that may have potential negative impacts on the training loss, which leads to a faster convergence and reduced oscillation for searching lottery jackpots. Consequently, our searched lottery jackpot removes 90% weights in ResNet-50, while it easily obtains more than 70% top-1 accuracy using only 5 searching epochs on ImageNet.

Distilling a Powerful Student Model via Online Knowledge Distillation.

Existing online knowledge distillation approaches either adopt the student with the best performance or construct an ensemble model for better holistic performance. However, the former strategy ignores other students' information, while the latter increases the computational complexity during deployment. In this article, we propose a novel method for online knowledge distillation, termed feature fusion and self-distillation (FFSD), which comprises two key components: FFSD, toward solving the above problems in a unified framework. Different from previous works, where all students are treated equally, the proposed FFSD splits them into a leader student set and a common student set. Then, the feature fusion module converts the concatenation of feature maps from all common students into a fused feature map. The fused representation is used to assist the learning of the leader student. To enable the leader student to absorb more diverse information, we design an enhancement strategy to increase the diversity among students. Besides, a self-distillation module is adopted to convert the feature map of deeper layers into a shallower one. Then, the shallower layers are encouraged to mimic the transformed feature maps of the deeper layers, which helps the students to generalize better. After training, we simply adopt the leader student, which achieves superior performance, over the common students, without increasing the storage or inference cost. Extensive experiments on CIFAR-100 and ImageNet demonstrate the superiority of our FFSD over existing works. The code is available at https://github.com/SJLeo/FFSD.

Pruning Networks With Cross-Layer Ranking & k-Reciprocal Nearest Filters.

This article focuses on filter-level network pruning. A novel pruning method, termed CLR-RNF, is proposed. We first reveal a "long-tail" pruning problem in magnitude-based weight pruning methods and then propose a computation-aware measurement for individual weight importance, followed by a cross-layer ranking (CLR) of weights to identify and remove the bottom-ranked weights. Consequently, the per-layer sparsity makes up the pruned network structure in our filter pruning. Then, we introduce a recommendation-based filter selection scheme where each filter recommends a group of its closest filters. To pick the preserved filters from these recommended groups, we further devise a k -reciprocal nearest filter (RNF) selection scheme where the selected filters fall into the intersection of these recommended groups. Both our pruned network structure and the filter selection are nonlearning processes, which, thus, significantly reduces the pruning complexity and differentiates our method from existing works. We conduct image classification on CIFAR-10 and ImageNet to demonstrate the superiority of our CLR-RNF over the state-of-the-arts. For example, on CIFAR-10, CLR-RNF removes 74.1% FLOPs and 95.0% parameters from VGGNet-16 with even 0.3% accuracy improvements. On ImageNet, it removes 70.2% FLOPs and 64.8% parameters from ResNet-50 with only 1.7% top-five accuracy drops. Our project is available at https://github.com/lmbxmu/CLR-RNF.

Carrying Out CNN Channel Pruning in a White Box.

Channel pruning has been long studied to compress convolutional neural networks (CNNs), which significantly reduces the overall computation. Prior works implement channel pruning in an unexplainable manner, which tends to reduce the final classification errors while failing to consider the internal influence of each channel. In this article, we conduct channel pruning in a white box. Through deep visualization of feature maps activated by different channels, we observe that different channels have a varying contribution to different categories in image classification. Inspired by this, we choose to preserve channels contributing to most categories. Specifically, to model the contribution of each channel to differentiating categories, we develop a class-wise mask for each channel, implemented in a dynamic training manner with respect to the input image's category. On the basis of the learned class-wise mask, we perform a global voting mechanism to remove channels with less category discrimination. Lastly, a fine-tuning process is conducted to recover the performance of the pruned model. To our best knowledge, it is the first time that CNN interpretability theory is considered to guide channel pruning. Extensive experiments on representative image classification tasks demonstrate the superiority of our White-Box over many state-of-the-arts (SOTAs). For instance, on CIFAR-10, it reduces 65.23% floating point operations per seconds (FLOPs) with even 0.62% accuracy improvement for ResNet-110. On ILSVRC-2012, White-Box achieves a 45.6% FLOP reduction with only a small loss of 0.83% in the top-1 accuracy for ResNet-50. Code is available at https://github.com/zyxxmu/White-Box.

Dynamic Support Network for Few-Shot Class Incremental Learning.

Few-shot class-incremental learning (FSCIL) is challenged by catastrophically forgetting old classes and over-fitting new classes. Revealed by our analyses, the problems are caused by feature distribution crumbling, which leads to class confusion when continuously embedding few samples to a fixed feature space. In this study, we propose a Dynamic Support Network (DSN), which refers to an adaptively updating network with compressive node expansion to "support" the feature space. In each training session, DSN tentatively expands network nodes to enlarge feature representation capacity for incremental classes. It then dynamically compresses the expanded network by node self-activation to pursue compact feature representation, which alleviates over-fitting. Simultaneously, DSN selectively recalls old class distributions during incremental learning to support feature distributions and avoid confusion between classes. DSN with compressive node expansion and class distribution recalling provides a systematic solution for the problems of catastrophic forgetting and overfitting. Experiments on CUB, CIFAR-100, and miniImage datasets show that DSN significantly improves upon the baseline approach, achieving new state-of-the-arts.

Phenolic glycosides and indole alkaloids isolated from the roots and rhizomes of Clematis chinensis and their anti-inflammatory activity.

Six undescribed compounds, including three phenolic glycosides (1-3) and three indole alkaloids (4-6), together with ten known alkaloids (7-16) and three known phenolic glycosides (17-19), were isolated from 70% EtOH aqueous extracts of the roots and rhizomes of Clematis chinensis Osbeck. The structures were elucidated by NMR, HRESIMS and X-ray diffraction spectroscopies. The anti-inflammatory activity of these compounds was evaluated, and twelve compounds showed significant inhibitory activity against TNF-α with an inhibition ratio from 47.87% to 94.70% at a dose of 10 µM. Compound 7 exhibited significant inhibitory activity against TNF-α and IL-6 with IC50 values of 3.99 µM and 2.24 µM, respectively. Compound 8 displayed potent anti-inflammatory activity against mouse ear edema induced by croton oil. A mechanistic study suggested that compounds 7 and 8 decreased the activation of the NF-κB signaling pathway to reduce the secretion of inflammatory factors in LPS-induced RAW 264.7 cells.

SiMaN: Sign-to-Magnitude Network Binarization.

Binary neural networks (BNNs) have attracted broad research interest due to their efficient storage and computational ability. Nevertheless, a significant challenge of BNNs lies in handling discrete constraints while ensuring bit entropy maximization, which typically makes their weight optimization very difficult. Existing methods relax the learning using the sign function, which simply encodes positive weights into +1s, and -1s otherwise. Alternatively, we formulate an angle alignment objective to constrain the weight binarization to {0,+1} to solve the challenge. In this article, we show that our weight binarization provides an analytical solution by encoding high-magnitude weights into +1s, and 0s otherwise. Therefore, a high-quality discrete solution is established in a computationally efficient manner without the sign function. We prove that the learned weights of binarized networks roughly follow a Laplacian distribution that does not allow entropy maximization, and further demonstrate that it can be effectively solved by simply removing the l2 regularization during network training. Our method, dubbed sign-to-magnitude network binarization (SiMaN), is evaluated on CIFAR-10 and ImageNet, demonstrating its superiority over the sign-based state-of-the-arts. Our source code, experimental settings, training logs and binary models are available at https://github.com/lmbxmu/SiMaN.

1xN Pattern for Pruning Convolutional Neural Networks.

Though network pruning receives popularity in reducing the complexity of convolutional neural networks (CNNs), it remains an open issue to concurrently maintain model accuracy as well as achieve significant speedups on general CPUs. In this paper, we propose a novel 1×N pruning pattern to break this limitation. In particular, consecutive N output kernels with the same input channel index are grouped into one block, which serves as a basic pruning granularity of our pruning pattern. Our 1×N pattern prunes these blocks considered unimportant. We also provide a workflow of filter rearrangement that first rearranges the weight matrix in the output channel dimension to derive more influential blocks for accuracy improvements and then applies similar rearrangement to the next-layer weights in the input channel dimension to ensure correct convolutional operations. Moreover, the output computation after our 1×N pruning can be realized via a parallelized block-wise vectorized operation, leading to significant speedups on general CPUs. The efficacy of our pruning pattern is proved with experiments on ILSVRC-2012. For example, given the pruning rate of 50% and N=4, our pattern obtains about 3.0% improvements over filter pruning in the top-1 accuracy of MobileNet-V2. Meanwhile, it obtains 56.04ms inference savings on Cortex-A7 CPU over weight pruning. Our project is made available at https://github.com/lmbxmu/1xN.

An intravenous exposure mouse model for prediction of potential drug-sensitization using reporter antigens popliteal lymph node assay.

Immune-mediated drug hypersensitivity is a particularly concerning health-safety issue among clinicians given its unpredictability and potentially life-threatening effects, especially with exposure to intravenous drugs. Therefore, the development of intravenous drug-exposure models for drug-hazard assessments has garnered increasing interest in recent years. In this study, we used reporter antigens popliteal lymph node assay to investigate the potential value of intravenous exposure to a selected variety of allergenic compounds, including ovalbumin (OVA), concanavalin A (ConA) and diclofenac. The trinitrophenyl (TNP)-specific antibody-forming cells were used to assess the systemic immune responses to a bystander antigen. Mice were subsequently sensitized by TNP-OVA, and then intravenous exposure to one of the selective compounds. As expected, all positive compounds induced significant popliteal lymph node (PLN) proliferation compared with the control. OVA significantly increased Cluster of Differentiation 4 receptors (CD4)⁺ interleukin-4 (IL-4)⁺ T-helper 2 (Th2) cells and, consequently, increased the ratios of IL-4/interferon-γ (IFN-γ) antibody-forming cells (AFCs) in PLNs, while bringing about a dose-dependent increase in immunoglobulin G1 (IgG1) AFCs; these findings indicate that a Th2 hypersensitivity response was induced. A Th2 response was also observed in diclofenac sodium-treated groups, and for ConA, a more mixed Th1/Th2 immune response appeared to be induced. In addition, there was no marked reaction with the negative compound. Together, it seems likely that the intravenous exposure model may be useful for drug-induced systemic hypersensitivity assessments.