Non-alcoholic steatohepatitis (NASH), which is the most severe manifestation of non-alcoholic fatty liver disease (NAFLD), has been recognized as a major hepatocellular carcinoma (HCC) catalyst. However, the molecular mechanism of NAFLD-NASH-HCC sequence remains unclear and a specific and effective treatment for NASH has not yet been established. The progress in this field depends on the availability of reliable preclinical models which show the steady progress to NASH, liver cirrhosis, and HCC. However, most of NASH mouse models that have been described to date develop NASH generally for more than 24 weeks and there is an uncertainty of HCC development. To overcome such shortcomings of experimental NASH studies, we established a novel NASH-HCC mouse model with very high reproducibility, generality, and convenience. We treated male C57BL/6J mice with a newly developed choline-deficient and methionine-restricted high-fat diet (CDMRHFD) for 60 weeks. Treatment of CDMRHFD for 3 weeks revealed marked steatosis, lobular inflammation, and fibrosis, histologically diagnosed as NASH. Liver cirrhosis was observed in all mice with 36-week treatment. Liver nodule emerged at 12 weeks of the treatment, and diameter >2 mm liver tumors developed in all mice at 24 weeks of the treatment. In conclusion, our rapidly progressive and highly reproducible NASH-liver cirrhosis-HCC model is helpful for preclinical development and research on the pathogenesis of human NAFLD-NASH-HCC. Our mouse model would be useful for the development of novel chemistry for NASH-HCC-targeted therapies or HCC prevention strategies.

Three monoruthenium complexes 1(PF6)23(PF6)2 bearing a N(CH3)-bridged ligand have been synthesized and characterized. These complexes have a general formula of [Ru(bpy)2(L)](PF6)2, where L is a 2,5-di(N-methyl-N-(pyrid-2-yl)amino)pyrazine (dapz) derivative with various substituents and bpy is 2,2′-bipyridine. The photophyscial and electrochemical properties of these compounds have been examined. The solid-state structure of complex 3(PF6)2 is studied by single crystal X-ray analysis. These complexes show two well-separated emission bands centered at 451 and 646 nm (max = 195 nm) for 1(PF6)2, 465 and 627 nm (max = 162 nm) for 2(PF6)2, and 455 and 608 nm (max = 153 nm) for 3(PF6)2 in dilute acetonitrile solution, respectively. The emission maxima of the higher-energy emission bands of these complexes are similar, while the lower-energy emission bands are dependent on the electronic nature of substituents. These complexes display two consecutive redox couples owing to the stepwise oxidation of the N(CH3)-bridged ligand and ruthenium component. In addition, density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations are carried out to elucidate these experimental findings.

Unmanned agricultural tractor (UAT) represents the advanced stage of autonomous navigation and serves as a core technology in agricultural production. It reduces the operator’s workload, and improves operational accuracy and efficiency. This article reviews three aspects of key autonomous navigation technologies for UATs: perception, path planning and tracking, and motion control. The advantages, and shortages of these key technologies on UATs are clarified by analyzing technical principles and the current research status. We conducted summaries and analyses of existing unmanned navigation solutions for different application scenarios in order to identify current bottleneck issues. Based on the analysis of the applicability of key autonomous navigation technologies on UATs, it can be seen that fruitful research progresses have been achieved. The review also summarizes the common problems in current technologies. The application research on sharing and integrating multi-source data for autonomous navigation are relatively weak. There is an urgent need of high-precision, and high-stability sensing equipment. The universality of path planning methods, and the efficiency and precision of path tracking need to be improved. It is necessary to develop high reliability electrical control modules to enhance motion control performance. Overall, advanced sensors, high-performance intelligent algorithms, and reliable electrical control hardware are key factors in promoting the development of UAT technology.

Antisense oligonucleotides (ASONs) have proven potential for the treatment of various diseases. However, their limited bioavailability restricts their clinical application. New structures with improved enzyme resistance stability and efficient drug delivery are needed. In this work, we propose a novel category of ASONs bearing anisamide conjugation at phosphorothioate sites for oncotherapy. ASONs can be conjugated with the ligand anisamide very efficiently and flexibly in solution. The conjugation sites and ligand amount both influence anti-enzymatic stability and cellular uptake, resulting in changes in antitumor activity that are detectable by cytotoxicity assay. T6 (with double end AA-conjugation) was identified as the optimal conjugate, and its antitumor activity and the underlying mechanism were examined further in vitro and in vivo. This paper presents a new strategy for the design of nucleic acid–based therapeutics with improved drug delivery and biophysical and biological efficacy.

The integration of deep neural networks into sparse Synthetic Aperture Radar (SAR) imaging has been explored to enhance SAR imaging performance and reduce the system’s sampling rate. However, the scarcity of training samples and mismatches between the training data and the SAR system pose significant challenges to the method’s further development. In this paper, we propose a novel SAR imaging approach based on Deep Image Prior Powered by RED (DeepRED), enabling unsupervised SAR imaging without the need for additional training data. Initially, DeepRED is introduced as the regularization technique within the sparse SAR imaging model. Subsequently, variable splitting and the Alternating Direction Method of Multipliers (ADMM) are employed to solve the imaging model, alternately updating the magnitude and phase of the SAR image. Additionally, the SAR echo simulation operator is utilized as an observation model to enhance computational efficiency. Through simulations and real data experiments, we demonstrate that our method maintains imaging quality and system downsampling rate on par with deep neural network-based sparse SAR imaging, but without the requirement for training data.

In the modern power grid, characterized by the increased penetration of power electronics and extensive utilization of renewable energy, inverters-based power plants play a pivotal role as the principal interface of renewable energy sources (RESs) and the grid. Considering the stability characteristics of grid-following (GFL) inverters when the grid is relatively weak, the application of grid-forming (GFM) controls becomes imperative to enhance the stability of the entire power plant. Thus, there is an urgent need for suitable and effective models to study the interaction and stability of the paralleled inverters employing GFL and GFM controls. Thus, the small-signal modeling with full-order state space model and eigenvalues analysis is presented in this paper. First, the small-signal state-space model of the individual GFL and GFM inverters is obtained considering the control loop, interaction, reference frame, transmissions, and time delays. Then, the models of the individual inverter are extended to the hybrid inverters to study the effects of the GFM inverters on the small- signal stability of the entire system. And, the impacts of the inertia and damping are analyzed by the eigenvalues of the state-transition matrix. Case comprising three parallel GFL inverters and two GFL inverters with one GFM inverter, respectively, are studied to examine the effectiveness and accuracy of the model. Finally, the stability margin obtained from the eigenvalue analysis of the entire system is verified by time-domain simulations.