Due to the growing penetration of renewable energies (REs) in integrated energy system (IES), it is imperative to assess and reduce the negative impacts caused by the uncertain REs. In this paper, an unscented transformation-based mean-standard (UT-MS) deviation model is proposed for the stochastic optimization of cost-risk for IES operation considering wind and solar power correlated. The unscented transformation (UT) sampling method is adopted to characterize the uncertainties of wind and solar power considering the correlated relationship between them. Based on the UT, a mean-standard (MS) deviation model is formulated to depict the trade-off between the cost and risk of stochastic optimization for the IES optimal operation problem. Then the UT-MS model is tackled by a multi-objective group search optimizer with adaptive covariance and Lévy flights embedded with a multiple constraints handling technique (MGSO-ACL-CHT) to ensure the feasibility of Peratooptimal solutions. Furthermore, a decision making method, improve entropy weight (IEW), is developed to select a final operation point from the set of Perato-optimal solutions. In order to verify the feasibility and efficiency of the proposed UT-MS model in dealing with the uncertainties of correlative wind and solar power, simulation studies are conducted on a test IES. Simulation results show that the UT-MS model is capable of handling the uncertainties of correlative wind and solar power within much less samples and less computational burden. Moreover, the MGSO-ACL-CHT and IEW are also demonstrated to be effective in solving the multi-objective UT-MS model of the IES optimal operation problem.

Long interspersed nuclear element 1 (LINE-1) is a dominant autonomous retrotransposon in human genomes which plays a role in affecting the structure and function of somatic genomes, resulting in human disorders including genetic disease and cancer. LINE-1 encoded ORF1p protein which possesses RNA-binding and nucleic acid chaperone activity, and interacts with LINE-1 RNA to form a ribonucleoprotein particle (RNP). ORF1p can be detected in many kinds of tumors and its overexpression has been regarded as a hallmark of histologically aggressive cancers. In this study, we developed an In-Cell Western (ICW) assay in T47D cells to screen the compounds which can decrease the expression of ORF1p. Using this assay, we screened 1,947 compounds from the natural products library of Target Mol and Selleckchem, among which three compounds, Hydroxyprogesterone, 2,2':5′,2″-Terthiophene and Ethynyl estradiol displayed potency in diminishing LINE-1 ORF1p expression level. Further mechanistic studies indicated the compounds act by affecting LINE-1 RNA transcription. Notably, we demonstrated that the compounds have an inhibitory effect on the proliferation of several lung and breast cancer cell lines. Taken together, we established a high throughput screening system for ORF1p expression inhibitors and the identified compounds provide some clues to the development of a novel anti-tumor therapeutic strategy by targeting ORF1p.