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Selective separation of trivalent actinide ions, An(III), from trivalent actinide ions, Ln(III), has been of great importance and considerable challenge in terms of the overall nuclear fuel cycle strategy. The construction of the hard-soft synergistic extraction system, commonly S containing-O containing ligands, aims to achieve the effective separation of the two metal ions. Nevertheless, the precise mechanism by which this system extracts metal ions remains the subject of controversy. Herein, the complexation of Nd(III) with bis(2,4,4-trimethylpentyl)dithiophosphinic acid (HL) and tributyl phosphate (TBP) has been investigated with spectroscopy and extraction experiments under different conditions to clarify the mechanisms in synergistic extraction. Five complex species, NdL3(HL)(TBP), NdL2(NO3)(TBP)3, NdL3(H2O)2(TBP), NdL3(H2O)(TBP)2, and NdL3(TBP)3, are identified in the synergistic extraction system, their molar absorption spectra are deconvoluted, and the corresponding extraction equilibrium constants are determined. In addition, a complex species of Nd(III) fully coordinated by TBP, NdL3(TBP)n (n ≥ 6), is found in titrations of organic solution containing NdL3(TBP)3 with more TBP, but it is not found in synergistic extraction. Furthermore, Density Functional Theory (DFT) is employed to confirm the stability of the complexes. Investigation of the mechanism of this system will facilitate the acquisition of further insights into the selection of O-containing ligands and the development of synergistic extraction process for the separation of An(III) and Ln(III). [ABSTRACT FROM AUTHOR]

A lot of infrastructure upgrade and algorithms have been developed for the information technology driven smart grids over the past twenty years, especially with increasing interest in their system design and real-world implementation. Meanwhile, the study of detecting and preventing intruders in ubiquitous smart grids environment is spurred significantly by the possibility of access points on various communication equipment. As a result, there are no comprehensive security protocols in place preventing from a malicious attacker's accessing to smart grids components, which would enable the interaction of attackers and system operators through the power grid control system. Recently, dynamics of time-extended interactions are believed to be predicted and solved by reinforcement learning technology. As a descriptive advantage of the approach compared with other methods, it provides the opportunities of simultaneously modeling several human continuous interactions features for decision-making process, rather than specifying an individual agent's decision dynamics and requiring others to follow specific kinematic and dynamic limitations. In this way, a machine-mediated human-human interaction's result is determined by how control and physical systems are designed. Technically, it is possible to design dedicated human-in-the-loop societal control systems that are attack-resistant by using simulations that predict such results with preventive assessment and acceptable accuracy. It is important to have a reliable model of both the control and physical systems, as well as of human decision-making, to make reliable assumptions. This study presents such a method to develop these tools, which includes a model that simulates the attacks of a cyber-physical intruder on the system and the operator's defense, demonstrating the overall performance benefit of such framework designs. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is the second most common malignancy in the world, and lung adenocarcinoma (LUAD) in particular is the leading cause of cancer death worldwide. Endothelin converting enzyme 1 (ECE1) is a membrane‐bound metalloprotease involved in endothelin‐1 (ET‐1) processing and regulates vasoconstriction. However, very few studies have reported the involvement of ECE1 in regulating tumor cell proliferation, and the mechanism remains poorly understood. Therefore, we aimed to determine the role of ECE1 in lung cancer development. Methods: The Cancer Genome Atlas database and Kaplan–Meier plotter were used to assess the association between ECE1 and lung cancer. The expression of ECE1 was detected using immunohistochemistry staining and western blotting. A variety of in vitro assays were performed to evaluate the effects of ECE1 on the colony formation, proliferation, migration and invasion using ECE1 knockdown lung cancer cells. The gene expression profiles regulated by ECE1 were investigated by RNA sequencing. An immunoprecipitation assay and immunofluorescence assay were used to evaluate the mechanism underlying the regulatory effect of ECE1 on protein kinase B (AKT). The effect of ECE1 on tumor development was assessed by xenografted lung cancer cells in either C57BL/6 mice or nude mice. Results: ECE1 was upregulated in LUAD and correlated with the poor prognosis of patients with LUAD. Functional studies showed that knockdown of ECE1 retarded the progression of tumors formed by lung cancer cells at least partly by inhibiting tumor cell proliferation. Moreover, ECE1 accelerated tumor cell proliferation through promoting AKT activation dispensable of its canonical target ET‐1. Mechanically, ECE1 interacted with the pleckstrin homology (PH) domain of AKT and facilitated its translocation to the plasma membrane for activation. Furthermore, the inhibition of AKT activity counteracted the lung cancer cell growth inhibition observed both in vitro and in xenografts caused by ECE1 suppression. Conclusions: The present study reveals a non‐canonical function of ECE1 in regulating AKT activation and cell proliferation, which provides the basis for the development of a novel strategy for the intervention of cancer including LUAD by abrogating ECE1‐AKT signaling. [ABSTRACT FROM AUTHOR]

With the increase of the number of decision variables, cooperative coevolution algorithm is easy to fall into local optimization in the process of searching the global optimal solution in large-scale high-dimensional optimization problems. Based on this, a cooperative coevolution algorithm with covariance analysis for differential evolution was proposed. After the optimization problems were grouped according to the correlation between the decision variables, the correlation between the internal variables of the subcomponents would affect the population evolution process. In the process of subcomponent optimization, covariance was used to calculate the characteristic vector of population distribution, and the correlation between variables was eliminated through coordinate rotation, which effectively avoided falling into local optimization in the process of population search and speeded up the optimization speed of the algorithm. Comparative experiments were carried out on the CEC 2014 test suite. The experimental results show that the proposed algorithm is feasible. [ABSTRACT FROM AUTHOR]

Pattern recognition receptors are critical for the sensing of pathogen-associated molecular patterns or danger-associated molecular patterns and subsequent mounting of innate immunity and shaping of adaptive immunity. The identification of 2′3′-cyclic guanosine monophosphate–adenosine monophosphate (cGAMP) synthase (cGAS) as a major cytosolic DNA receptor is a milestone in the field of DNA sensing. The engagement of cGAS by double-stranded DNA from different origins, including invading pathogens, damaged mitochondria, ruptured micronuclei, and genomic DNA results in the generation of cGAMP and activation of stimulator of interferon genes, which thereby activates innate immunity mainly characterized by the activation of type I interferon response. In recent years, great progress has been made in understanding the subcellular localization and novel functions of cGAS. In this review, we particularly focus on summarizing the multifaceted roles of cGAS in regulating senescence, autophagy, cell stemness, apoptosis, angiogenesis, cell proliferation, antitumor effect, DNA replication, DNA damage repair, micronucleophagy, as well as cell metabolism. [ABSTRACT FROM AUTHOR]