To achieve efficient systemic gene delivery to the lung with minimal toxicity, a vector was developed by chemically conjugating a cationic polymer, polyethylenimine (PEI), with anti-platelet ...endothelial cell adhesion molecule (PECAM) antibody (Ab). Transfection of mouse lung endothelial cells with a plasmid expression vector with cDNA to luciferase (pCMVL) complexed with anti-PECAM Ab-PEI conjugate was more efficient than that with PEI-pCMVL complexes. Furthermore, the anti-PECAM Ab-PEI conjugate mediated efficient transfection at lower charge plus-to-minus ratios. Conjugation of PEI with a control IgG (hamster IgG) did not enhance transfection of mouse lung endothelial cells, suggesting that the cellular uptake of anti-PECAM Ab-PEI-DNA complexes and subsequent gene expression were governed by a receptor-mediated process rather than by a nonspecific charge interaction. Conjugation of PEI with anti-PECAM Ab also led to significant improvement in lung gene transfer to intact mice after intravenous administration. The increase in lung transfection was associated with a decrease compared with PEI-pCMVL with respect to circulating proinflammatory cytokine (tumor necrosis factor-alpha) levels. These results indicate that targeted gene delivery to the lung endothelium is an effective strategy to enhance gene delivery to the pulmonary circulation while simultaneously reducing toxicity.
A major hurdle to lipoplex-based systemic gene delivery is acute inflammatory toxicity. In this study, a safe, simple, and effective alternative to lipoplex administration, specifically, sequential ...injection of cationic liposome and plasmid DNA, was evaluated. When plasmid DNA was injected into the tail vein of mice 2-5 min after the injection of cationic liposomes, 50-80% lower levels of proinflammatory cytokines, including TNF-alpha, IL-12, and IFN-gamma, were observed compared to lipoplex injection. The sequential injection technique yielded a two- to fivefold higher level of transgene expression in the lung and was more effective in repeated dosing than lipoplex. Other types of lipoplex-associated toxicities, such as neutropenia, lymphopenia, thrombocytopenia, and complement depletion, were also significantly reduced with sequential injection. The reduction in cytokine release was observed with several different liposome formulations and appeared to be a general phenomenon.
This article proposes a charging and discharging method for electric vehicle aggregate that takes into account the peak valley correction coefficient of the grid bus load. Based on the load ...prediction results of the power grid bus, this article aims to optimize the overall economic benefits of electric vehicle aggregates for electric vehicle users in the future, with the goal of minimizing the total charging and discharging costs. It also takes into account the charging during peak and low load periods, which is the charging and discharging point method that is opposite to the peak and valley of the load, and increases the correction coefficient of load peaks and valleys in the optimization goal, That is, considering the impact of load peaks and valleys, in order to reduce the charging amount during peak loads, and reserve as much dischargeable electricity as possible, increase the charging amount during low periods, and achieve peak shaving and valley filling while improving the economic benefits of electric vehicle users, ensuring the safe operation of the power grid. Through numerical simulation, it can be seen that the strategy proposed in this article achieves peak shaving and valley filling, effectively reducing the peak valley difference. Moreover, the effect is very significant in reducing the charging cost of car owners and improving the revenue of charging stations, achieving a win-win situation between car owners and charging stations in terms of economic benefits. Therefore, the method proposed in this article is effective and has practical application value.
Due to the complexity and uncertainty of the power system, the scheduling and planning of the power grid has brought corresponding difficulties. In this paper, a long-term electricity consumption ...prediction model is developed for use under abnormal operating conditions. The fractional-order grey prediction model captures the shortfall of electricity consumption after the occurrence of abnormal working conditions, the restriction vector autoregressive model is used to achieve the prediction of electricity consumption, and the particle swarm optimization algorithm is used to optimize the parameters of the model to improve the prediction accuracy of the model. The results of the three prediction models are compared to verify the effectiveness of the model. The experimental results show that the model shows good performance in electricity consumption prediction and makes better use of all data information.
Source-Grid-Load-Storage (SGLS) is an important part of a power system, and each part affects the safe operation of the power system. In extreme weather conditions, the SGLS link in the power system ...will be affected. This article starts from the four aspects of "source-grid-load-storage", first analyzing the impact of normal weather and extreme weather on power system operation, then modeling the "source-grid-load-storage" based on several typical extreme weather scenarios, and then presenting several technical solutions to cope with extreme weather. Finally, the related issues of "source-grid-load-storage" and the future development direction are summarized.
In order to solve the problem that it is difficult to predict the interactive power of transmission and distribution boundary after a large number of distributed generators appear on the distribution ...network side, this paper proposes an interval prediction method of transmission and distribution interactive power based on quantile regression. Based on the initial cause of physical characteristics that include factors of temperature, light, wind speed and other weather information of distributed renewable power generation output are closely related, and the net load on the distribution network side is closely related to temperature, so a quantile regression model for the interactive power of the transmission and distribution boundary is established by taking temperature, light, wind speed as independent variables, taking advantage of the advantages that quantile regression is not sensitive to abnormal points, and taking the interactive active power of the transmission and distribution boundary as dependent variable, The historical real data is taken as the training set and test set of samples, and the quantile with the smallest error, prediction interval and confidence degree are determined according to the sample test set. The prediction interval can considers the volatility of various random factors on the distribution network side, thus reserving space for various randomized factors not considered. The simulation results of the example show that the algorithm proposed in this paper has higher accuracy compared to other algorithms when dealing with distribution networks containing multiple distributed power sources. It is more effective in dealing with the influence of various random factors when dealing with distribution networks containing multiple distributed power sources, and has obvious advantages, which verifies the effectiveness of the proposed method.
This letter presents a variable weight short-term photovoltaic (PV) power combination forecasting method based on similar numerical weather prediction (NWP)data. This method firstly identifies and ...reconstructs the abnormal data, and divides the photovoltaic power data after data processing into the training set, intermediate set, and test set together with NWP data. Secondly, the parameters of single prediction models are optimized by training set data. Thirdly, in the intermediate set, based on the prediction results of single models and the actual photovoltaic power data, the optimal weight coefficient at each time is obtained by constructing the objective function and constraint conditions. Lastly, by matching the NWP data of the intermediate set closest to the NWP data to be predicted, the corresponding weight coefficient set is extracted to determine the time to be predicted. Several experimental results are provided, demonstrating the superiority of the proposed variable weight combination model over both fixed weight combination models and single prediction models.
Facing the strategic goal of " carbon peak and carbon neutrality, " we should coordinate the relationship between various markets such as carbon market, electricity market and green certificate ...market under the unified framework system of energy, climate and environmental governance, and make effective connection at the level of market mechanism. This paper analyzes the mechanism of carbon emissions trading, summarizes the interaction between carbon market and electricity market, and analyzes the feasibility and suggestion of coupling development of electricity market and carbon market. Finally, based on the uncoordinated problems existing in China 's electricity market and carbon market, the implementation path of coordinated development of electricity market and carbon market is proposed, as well as relevant suggestions for coordinated development of electricity market and carbon market.
There have been more than 850,000 confirmed cases and over 48,000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by novel severe acute respiratory syndrome coronavirus ...(SARS-CoV-2), in the United States alone. However, there are currently no proven effective medications against COVID-19. Drug repurposing offers a promising way for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, genes, pathways, and expressions, from a large scientific corpus of 24 million PubMed publications. Using Amazon AWS computing resources, we identified 41 repurposable drugs (including indomethacin, toremifene and niclosamide) whose therapeutic association with COVID-19 were validated by transcriptomic and proteomic data in SARS-CoV-2 infected human cells and data from ongoing clinical trials. While this study, by no means recommends specific drugs, it demonstrates a powerful deep learning methodology to prioritize existing drugs for further investigation, which holds the potential of accelerating therapeutic development for COVID-19.
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