Antidiabetic Effect of an Active Components Group from Ilex kudingcha and Its Chemical Composition

The leaves of Ilex kudingcha are used as an ethnomedicine in the treatment of symptoms related with diabetes mellitus and obesity throughout the centuries in China. The present study investigated the antidiabetic activities of an active components group (ACG) obtained from Ilex kudingcha in alloxan-induced type 2 diabetic mice. ACG significantly reduced the elevated levels of serum glycaemic and lipids in type 2 diabetic mice. 3-Hydroxy-3-methylglutaryl coenzyme A reductase and glucokinase were upregulated significantly, while fatty acid synthetase, glucose-6-phosphatase catalytic enzyme was downregulated in diabetic mice after treatment of ACG. These findings clearly provided evidences regarding the antidiabetic potentials of ACG from Ilex kudingcha. Using LC-DAD/HR-ESI-TOF-MS, six major components were identified in ACG. They are three dicaffeoylquinic acids that have been reported previously, and three new triterpenoid saponins, which were the first time to be identified in Ilex kudingcha. It is reasonable to assume that antidiabetic activity of Ilex kudingcha against hyperglycemia resulted from these six major components. Also, synergistic effects among their compounds may exist in the antidiabetic activity of Ilex kudingcha

Triolein-based polycation lipid nanocarrier for efficient gene delivery: characteristics and mechanism

We proposed to develop a polycation lipid nanocarrier (PLN) with higher transfection efficiency than our previously described polycation nanostrucutred lipid nanocarrier (PNLC). PLN was composed of triolein, cetylated low-molecular-weight polyethylenimine, and dioleoyl phosphatidylethanolamine. The physicochemical properties of PLN and the PLN/DNA complexes (PDC) were characterized. The in vitro transfection was performed in human lung adenocarcinoma (SPC-A1) cells, and the intracellular mechanism was investigated as well. The measurements indicated that PLN and PDC are homogenous nanometer-sized particles with a positive charge. The transfection efficiency of PDC significantly increased with the content of triolein and was higher than that of PNLC and commercial Lipofectamine™ 2000. In particular, the transfection of PLN in the presence of 10% serum was more effective than that in its absence. With the help of specific inhibitors of chlorpromazine and filipin, the clathrin-dependent endocytosis pathway was determined to be the main contributor to the successful transfection mediated by PLN in SPC-A1 cells. The captured images verified that the fluorescent PDC was localized in the lysosomes and nuclei after endocytosis. Thus, PLN represents a novel efficient nonviral gene delivery vector

HODN: Disentangling Human-Object Feature for HOI Detection

The task of Human-Object Interaction (HOI) detection is to detect humans and their interactions with surrounding objects, where transformer-based methods show dominant advances currently. However, these methods ignore the relationship among humans, objects, and interactions: 1) human features are more contributive than object ones to interaction prediction; 2) interactive information disturbs the detection of objects but helps human detection. In this paper, we propose a Human and Object Disentangling Network (HODN) to model the HOI relationships explicitly, where humans and objects are first detected by two disentangling decoders independently and then processed by an interaction decoder. Considering that human features are more contributive to interaction, we propose a Human-Guide Linking method to make sure the interaction decoder focuses on the human-centric regions with human features as the positional embeddings. To handle the opposite influences of interactions on humans and objects, we propose a Stop-Gradient Mechanism to stop interaction gradients from optimizing the object detection but to allow them to optimize the human detection. Our proposed method achieves competitive performance on both the V-COCO and the HICO-Det datasets. It can be combined with existing methods easily for state-of-the-art results.Comment: Accepted by TMM 202

Identifying veraison process of colored wine grapes in field conditions combining deep learning and image analysis

Acknowledgments This work was supported by the National Key R&D Program Project of China (Grant No. 2019YFD1002500) and Guangxi Key R&D Program Project (Grant No. Gui Ke AB21076001) The authors would like to thank the anonymous reviewers for their helpful comments and suggestions.Peer reviewedPostprin

Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video

Real-time eyeblink detection in the wild can widely serve for fatigue detection, face anti-spoofing, emotion analysis, etc. The existing research efforts generally focus on single-person cases towards trimmed video. However, multi-person scenario within untrimmed videos is also important for practical applications, which has not been well concerned yet. To address this, we shed light on this research field for the first time with essential contributions on dataset, theory, and practices. In particular, a large-scale dataset termed MPEblink that involves 686 untrimmed videos with 8748 eyeblink events is proposed under multi-person conditions. The samples are captured from unconstrained films to reveal "in the wild" characteristics. Meanwhile, a real-time multi-person eyeblink detection method is also proposed. Being different from the existing counterparts, our proposition runs in a one-stage spatio-temporal way with end-to-end learning capacity. Specifically, it simultaneously addresses the sub-tasks of face detection, face tracking, and human instance-level eyeblink detection. This paradigm holds 2 main advantages: (1) eyeblink features can be facilitated via the face's global context (e.g., head pose and illumination condition) with joint optimization and interaction, and (2) addressing these sub-tasks in parallel instead of sequential manner can save time remarkably to meet the real-time running requirement. Experiments on MPEblink verify the essential challenges of real-time multi-person eyeblink detection in the wild for untrimmed video. Our method also outperforms existing approaches by large margins and with a high inference speed.Comment: Accepted by CVPR 202

Quantitative Structure-Activity Relationship Studies on Indenoisoquinoline Topoisomerase I Inhibitors as Anticancer Agents in Human Renal Cell Carcinoma Cell Line SN12C

Topoisomerase I is important for DNA replication and cell division, making it an attractive drug target for anticancer therapy. A series of indenoisoquinolines displaying potent Top1 inhibitory activity in human renal cell carcinoma cell line SN12C were selected to establish 3D-QSAR models using CoMFA and CoMSIA methods. Internal and external cross-validation techniques were investigated, as well as some measures taken, including region focusing, bootstrapping and the “leave-group-out” cross-validation method. The satisfactory CoMFA model predicted a q2 value of 0.659 and an r2 value of 0.949, indicating that electrostatic and steric properties play a significant role in potency. The best CoMSIA model, based on a combination of steric, electrostatic and H-bond acceptor descriptors, predicted a q2 value of 0.523 and an r2 value of 0.902. The models were graphically interpreted by contour plots which provided insight into the structural requirements for increasing the activity of a compound, providing a solid basis for future rational design of more active anticancer agents

Segmentation of field grape bunches via an improved pyramid scene parsing network

With the continuous expansion of wine grape planting areas, the mechanization and intelligence of grape harvesting have gradually become the future development trend. In order to guide the picking robot to pick grapes more efficiently in the vineyard, this study proposed a grape bunches segmentation method based on Pyramid Scene Parsing Network (PSPNet) deep semantic segmentation network for different varieties of grapes in the natural field environments. To this end, the Convolutional Block Attention Module (CBAM) attention mechanism and the atrous convolution were first embedded in the backbone feature extraction network of the PSPNet model to improve the feature extraction capability. Meanwhile, the proposed model also improved the PSPNet semantic segmentation model by fusing multiple feature layers (with more contextual information) extracted by the backbone network. The improved PSPNet was compared against the original PSPNet on a newly collected grape image dataset, and it was shown that the improved PSPNet model had an Intersection-over-Union (IoU) and Pixel Accuracy (PA) of 87.42% and 95.73%, respectively, implying an improvement of 4.36% and 9.95% over the original PSPNet model. The improved PSPNet was also compared against the state-of-the-art DeepLab-V3+ and U-Net in terms of IoU, PA, computation efficiency and robustness, and showed promising performance. It is concluded that the improved PSPNet can quickly and accurately segment grape bunches of different varieties in the natural field environments, which provides a certain technical basis for intelligent harvesting by grape picking robots

Multi-threshold second-order phase transition

We present a theory of the multi-threshold second-order phase transition, and experimentally demonstrate the multi-threshold second-order phase transition phenomenon. With carefully selected parameters, in an external cavity diode laser system, we observe second-order phase transition with multiple (three or four) thresholds in the measured power-current-temperature three dimensional phase diagram. Such controlled death and revival of second-order phase transition sheds new insight into the nature of ubiquitous second-order phase transition. Our theory and experiment show that the single threshold second-order phase transition is only a special case of the more general multi-threshold second-order phase transition, which is an even richer phenomenon.Comment: 5 pages, 3 figure

Aggregation Pheromone for an Invasive Mussel Consists of a Precise Combination of Three Common Purines.

Most marine benthic invertebrates have a pelagic larval phase, after which they settle preferentially on or near conspecific adults, forming aggregations. Although settlement pheromones from conspecific adults have been implicated as critical drivers of aggregation for more than 30 years, surprisingly few have been unambiguously identified. Here we show that in the invasive dreissenid mussel Mytilopsis sallei (an ecological and economic pest), three common purines (adenosine, inosine, and hypoxanthine) released from adults in a synergistic and precise ratio (1:1.125:3.25) serve as an aggregation pheromone by inducing conspecific larval settlement and metamorphosis. Our results demonstrate that simple common metabolites can function as species-specific pheromones when present in precise combinations. This study provides important insights into our understanding of the ecology and communication processes of invasive organisms and indicates that the combination and ratio of purines might be critical for purine-based signaling systems that are fundamental and widespread in nature