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Multi-station integration of various substations to participate in grid dispatching can fully mobilize load-side resources to participate in the power market, improve grid flexibility, and reduce grid peaking pressure, but the uncertainty of user behavior limits the development of demand response services. In response to this problem, this article first builds an incentive-based demand response implementation framework, expounds how load service entities gather demand-side resources to participate in the power market business, and convert the behavior of users responding to incentive policies to demand elasticity. Then, considering that the prediction model will ultimately need to be applied online, this paper proposes a data-driven incentive demand response user behavior prediction method that integrates LSTM based on the long short memory (LSTM) algorithm. At the same time, in order to improve the performance of the prediction model, The original data is smoothed and scaled, and the weight coefficient of the loss function is added. The results of the calculation examples show that, compared with the traditional LSTM algorithm and the k-neighbor prediction method, the prediction method proposed in this paper can significantly improve the prediction accuracy, and the preprocessing of the original data has positive significance for improving the prediction accuracy.

This paper proposes a practical method for formulating energy storage-assisted conventional unit peak shaving schemes based on multi-station integration of comprehensive technical evaluation indicators and economic evaluation indicators for switching stations, and adopts a planning system for regional power grids and different new energy generation penetration scenarios Two calculation examples were used for simulation and analysis, and energy storage auxiliary peak shaving schemes for conventional units were formulated for different scenarios. Finally, the feasibility of the method is verified through experiments.

HMGB1 has been identified as a pro-inflammatory mediator which leads to sepsis lethality. Previous studies suggested that CRISPLD2 had anti-inflammatory property and might severe as a therapeutic agent in sepsis. In the present study, we first conducted bioinformatic analysis to explore the expression profile of HMGB1 in septic survivors and non-survivors. We found that the serum HMGB1 level of septic non-survivors was significantly higher than that of septic survivors, and there was a positive correlation between CRISPLD2 and HMGB1 in mRNA expression in most of the cancer and normal tissue types, revealing a co-expression or dependency relationship between the two genes. In vitro, using cultured THP-1 cells, we confirmed that HMGB1 can induce the expression of CRISPLD2 in a time dependent manner through TLR4-dependent pathway. Given that CRISPLD2 and HMGB1 shared a wide range of time scales in gene expression and the anti-inflammatory property of CRISPLD2, we further verified that HMGB1 induced cytokines production might be partially reversed by CRISPLD2. In vivo, intravenously treatment of CRISPLD2 failed to rescue septic mice, although the serum levels of inflammatory cytokines were decreased. In conclusion, our study demonstrated that HMGB1 can act as stimuli to up-regulate the expression of CRISPLD2 in THP-1 cells, and in turn, increased CRISPLD2 can curtail HMGB1 induced pro-inflammatory cytokines production. Unfortunately, the anti-inflammatory effects of CRISPLD2 did not translate into survival benefit in mice with sepsis.

In this paper, based on the in-situ generation technology and the advantage of non-condensate gas (N2 and CO2)-assisted steam huff-n-puff, a new technology of heat self-generated mixed-gas (HSGMG)-...

Background To investigate the expression and significance of long noncoding RNA NORAD (lncRNA-NORAD) in breast cancer. Methods Q-PCR was adopted to detect the mRNA expression of lncRNA-NORAD in breast cancer and adjacent tissues, survival analysis to compare the low-expression groups with the Kaplan–Meier method. Knockout of lncRNA-NORAD was adopted to observe the effects on the cell proliferation, migration and invasion of breast cancer in vitro and in vivo. The TGF-β/RUNX2 signaling pathway was observed by Western blot after the knockout of lncRNA-NORAD. Results Increased expression of lncRNA-NORAD in breast cancer tissues promotes proliferation, invasion and migration of breast cancer cells and correlated with worse prognosis. LncRNA-NORAD activated TGF-β/RUNX2 signaling pathway in breast cancer cells. Conclusions These results strongly suggested that lncRNA-NORAD might play an important role in breast cancer progression and potentially be a new therapeutic target.

Additional file 2: Fig. S2. The ultrastructure of HSPCs changed. The photomicrographs of TEM showed that nuclear membrane, perinuclear cisternae, chromatin edge, inclusion body in cytoplasm of HSPC changed. Old mouse HSPC showed similar but less morphologic changes than model HSPC.

Additional file 1: Fig. S1. The percentage of SA-β-gal stain-positive cells. The percentage of SA-β-gal stain-positive cells increased gradually at day2, 4, 6, 8.

Additional file 3: Fig. S3. Proteomics KEGG pathway analysis of differentially expressed proteins. (a,b), Pyruvate kinase were significantly increased and pyruvate dehydrogenase E1were reduced in the model cells. (c,d), Dkc1, GAR1, Nop10, Nog1 and ribosomal proteins in the ribosome-sized subunits also decreased significantly in the model cells. (e), U2AF1 (U2AF) 1 were significantly decreased in the model cells. (f), Lysosomal related proteins such as Cathepsin, TPP1, lysosomal membrane protein LIMP, lysosomal-associated membrane protein (LAMP), and ATPeV were significantly increased in the model cells.