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New power system that takes strong smart grid as the platform is supported by source-grid-load-storage interaction and multi-energy complementarity, because the system is green, efficient, flexible and digital. This paper focused on the research of multi-dimensional evaluation method of the new power system. Firstly, the internal and external elements of the new power system were sorted out, and KPI indicators were obtained through the analysis of the logical relationship of indicators. Secondly, this paper innovatively proposed to use “Generation Side Grid and Load” (GSGL) as the internal element and “Reliable Efficient Green and Intelligent” (REGI) as the evaluation effect. Meanwhile, this paper innovatively put forward a new multi-dimensional evaluation system of power system, with 4 secondary indicators and 16 tertiary indicators. Finally, the AAHM-weighted grey relational TOPSIS comprehensive evaluation model was constructed, and the validity and applicability of the proposed evaluation index system and model method were verified by numerical examples. Through the example analysis, under the new multi-dimensional evaluation system of power system, the comprehensive evaluation results and ranking of different provincial power enterprises were obtained. This paper provides theoretical support to further promote the transformation and upgrading of energy and electric power industry and power grid enterprises to speed up the construction of safe, reliable, green, efficient and intelligent modern power grid.

This paper proposes a double-tier bidding(DTB) model on the generation side to introduce gas-fired units(GFU) into the market bidding by analyzing the electricity cost of the whole society and the generation cost of gas-fired units. With the introduction of a sub-process, the GFUs are incorporated into the main bidding process or the sub-process independently, and the bidding prices of each process are cleared separately. Based on the traditional market bidding algorithm, the DTB model can effectively solve the problem of increasing market price after units with higher generation costs join the market. Besides, transmission congestion and power shortages during peak hours caused by the drawback of market mechanism can be avoided. In addition, recessive electricity price subsidy from markets is visualized, realizing the non-price advantages of low-emission units. Thus, the promotion of the overall optimal social benefits and the implementation of the low-carbon development concept are realized. Moreover, the algorithm incorporates a transverse comparison method of generation costs to restrict the market speculation of power suppliers. Numerical example shows that this algorithm is effective and feasible, and can be adopted in other areas with similar market characteristics.

In most acute promyelocytic leukemia (APL) cells, promyelocytic leukemia (PML) fuses to retinoic acid receptor α (RARα) due to chromosomal translocation, thus generating PML/RARα oncoprotein, which is a relatively stable oncoprotein for degradation in APL. Elucidating the mechanism regulating the stability of PML/RARα may help to degrade PML/RARα and eradicate APL cells. Here, we describe a deubiquitinase (DUB)-involved regulatory mechanism for the maintenance of PML/RARα stability and develop a novel pharmacological approach to degrading PML/RARα by inhibiting DUB. We utilized a DUB siRNA library to identify the ovarian tumor protease (OTU) family member deubiquitinase YOD1 as a critical DUB of PML/RARα. Suppression of YOD1 promoted the degradation of PML/RARα, thus inhibiting APL cells and prolonging the survival time of APL cell-bearing mice. Subsequent phenotypic screening of small molecules allowed us to identify ubiquitin isopeptidase inhibitor I (G5) as the first YOD1 pharmacological inhibitor. As expected, G5 notably degraded PML/RARα protein and eradicated APL, particularly drug-resistant APL cells. Importantly, G5 also showed a strong killing effect on primary patient-derived APL blasts. Overall, our study not only reveals the DUB-involved regulatory mechanism on PML/RARα stability and validates YOD1 as a potential therapeutic target for APL, but also identifies G5 as a YOD1 inhibitor and a promising candidate for APL, particularly drug-resistant APL treatment.

Chronic obstructive pulmonary disease (COPD) is a widely prevalent disease with significant mortality and disability rates and has become the third leading cause of death globally. Patients with acute exacerbation of COPD (AECOPD) often substantially suffer deterioration and death. Therefore, COPD patients deserve special consideration regarding treatment in this fragile population for pre-clinical health management. Based on the above, this paper proposes an AECOPD prediction model based on the Auto-Metric Graph Neural Network (AMGNN) using inspiratory and expiratory chest low-dose CT images. This study was approved by the ethics committee in the First Affiliated Hospital of Guangzhou Medical University. Subsequently, 202 COPD patients with inspiratory and expiratory chest CT Images and their annual number of AECOPD were collected after the exclusion. First, the inspiratory and expiratory lung parenchyma images of the 202 COPD patients are extracted using a trained ResU-Net. Then, inspiratory and expiratory lung Radiomics and CNN features are extracted from the 202 inspiratory and expiratory lung parenchyma images by Pyradiomics and pre-trained Med3D (a heterogeneous 3D network), respectively. Last, Radiomics and CNN features are combined and then further selected by the Lasso algorithm and generalized linear model for determining node features and risk factors of AMGNN, and then the AECOPD prediction model is established. Compared to related models, the proposed model performs best, achieving an accuracy of 0.944, precision of 0.950, F1-score of 0.944, ad area under the curve of 0.965. Therefore, it is concluded that our model may become an effective tool for AECOPD prediction.