14 research outputs found

    Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning

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    Controlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time. However, existing systems and methodologies for controlling traffic signals are insufficient for addressing the problem. To this end, we build a truly adaptive traffic signal control model in a traffic microsimulator, i.e., “Simulation of Urban Mobility” (SUMO), using the technology of modern deep reinforcement learning. The model is proposed based on a deep Q-network algorithm that precisely represents the elements associated with the problem: agents, environments, and actions. The real-time state of traffic, including the number of vehicles and the average speed, at one or more intersections is used as an input to the model. To reduce the average waiting time, the agents provide an optimal traffic signal phase and duration that should be implemented in both single-intersection cases and multi-intersection cases. The co-operation between agents enables the model to achieve an improvement in overall performance in a large road network. By testing with data sets pertaining to three different traffic conditions, we prove that the proposed model is better than other methods (e.g., Q-learning method, longest queue first method, and Webster fixed timing control method) for all cases. The proposed model reduces both the average waiting time and travel time, and it becomes more advantageous as the traffic environment becomes more complex

    A novel melittin nano-liposome exerted excellent anti-hepatocellular carcinoma efficacy with better biological safety

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    Abstract Melittin is the main effective component of bee venom and has extensive biological functions; however, serious side effects have restricted its clinical application. Preclinical and clinical studies showed that the main adverse events were allergic reaction and pain at the administration site. To decrease the toxicity, we prepared melittin nano-liposomes by encapsulating melittin with poloxamer 188 and explored the inhibitory activities on liver cancer together with biological safety. Here, we showed that melittin nano-liposomes significantly inhibited the survival of hepatocellular carcinoma (HCC) cells in vitro and prominently suppressed the growth of subcutaneous and orthotopic HCC transplantation tumors in vivo. It was important that it induced less inflammation and allergy in mice compared with melittin. Overall, melittin nano-liposomes would have a better application in HCC therapy due to its significant anti-tumor activity and better biological safety

    Additional file 4: Figure S3. of A novel melittin nano-liposome exerted excellent anti-hepatocellular carcinoma efficacy with better biological safety

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    Flow cytometry analysis of splenic immune cells including splenic T lymphocytes, neutrophil and B lymphocytes. Spleens were stripped and grinded into cell suspension after mice were treated with vehicle, blank liposomes (8 mg/kg), melittin (2 mg/kg) and melittin nano-liposomes (2 mg/kg) for two weeks. (PDF 730 kb

    Additional file 3: Figure S2. of A novel melittin nano-liposome exerted excellent anti-hepatocellular carcinoma efficacy with better biological safety

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    Photo of HepG2 tumors of vehicle, blank liposomes (8 mg/kg), melittin (2 mg/kg), melittin nano-liposomes (2, 4, and 8 mg/kg), and sorafenib (30 mg/kg) treated groups in the HepG2 subcutaneous transplanted tumor model. The data are presented as the mean ± SEM. Statistical significance was calculated using Student’s t test (**p ≤ 0.01; ***p ≤ 0.001). (PDF 1070 kb

    Additional file 2: Figure S1. of A novel melittin nano-liposome exerted excellent anti-hepatocellular carcinoma efficacy with better biological safety

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    The effects of melittin nano-liposomes on the proliferation and apoptosis of tumor cells. (a) Proliferation inhibiting rates of melittin and melittin nano-liposomes on various HCC cells lines including LM-3, Bel-7402, SMMC-7721, HepG2, and L02 cells. (b) Cell nucleus staining by DAPI to observe the apoptosis of Bel-7402 and SMMC-7721 cells after treated with vehicle, blank liposomes (1 μM), melittin (1 μM), or melittin nano-liposomes (1 μM) for 24 h. (c) Fluorescence staining of Annexin V-FITC and PI to detect the apoptosis of HepG2 cells after treatment with melittin and Melittin nano-liposomes for 24 h and observed by fluorescence microscope. HepG2 cells were pretreated with Z-VAD-FMK for 6 hours, and melittin and Melittin nano-liposomes were subsequently administered at a concentration of 2 μM. (PDF 848 kb

    Galphai1 and Galphai3 regulate macrophage polarization by forming a complex containing CD14 and Gab1

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    Heterotrimeric G proteins have been implicated in Toll-like receptor 4 (TLR4) signaling in macrophages and endothelial cells. However, whether guanine nucleotide-binding protein G(i) subunit alpha-1 and alpha-3 (Gαi1/3) are required for LPS responses remains unclear, and if so, the underlying mechanisms need to be studied. In this study, we demonstrated that, in response to LPS, Gαi1/3 form complexes containing the pattern recognition receptor (PRR) CD14 and growth factor receptor binding 2 (Grb2)-associated binding protein (Gab1), which are required for activation of PI3K-Akt signaling. Gαi1/3 deficiency decreased LPS-induced TLR4 endocytosis, which was associated with decreased phosphorylation of IFN regulatory factor 3 (IRF3). Gαi1/3 knockdown in bone marrow-derived macrophage cells (Gαi1/3 KD BMDMs) exhibited an M2-like phenotype with significantly suppressed production of TNF-α, IL-6, IL-12, and NO in response to LPS. The altered polarization coincidedwith decreased Akt activation. Further, Gαi1/3 deficiency caused LPS tolerance in mice. In vitro studies revealed that, in LPS-tolerant macrophages, Gαi1/3 were down-regulated partially by the proteasome pathway. Collectively, the present findings demonstrated that Gαi1/3 can interact with CD14/Gab1, which modulates macrophage polarization in vitro and in vivo.Fil: Li, Xiaolin. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Wang, Duowei. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Chen, Zen. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Lu, Ermei. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Wang, Zhuo. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Duan, Jingjing. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Tian, Wei. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Wang, Yun. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: You, Linjun. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Zou, Yulian. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Cheng, Yan. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Zhu, Qingyi. Jiangsu Province Hospital of Traditional Chinese Medicine. Departament of Urology; ChinaFil: Wan, Xiaojian. Second Military Medical University. Department of Anesthesiology and Intensive Care Medicine, Changhai Hospita; ChinaFil: Xia, Tao. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; ChinaFil: Birnbaumer, Lutz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. National Institute of Environmental Health Sciences. Laboratory of Neurobiology, ; Estados UnidosFil: Yang, Yong. China Pharmaceutical University. Center for New Drug Safety Evaluation and Research; Chin
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