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Acute kidney injury (AKI) is a common, multicause clinical condition that, if ignored, often progresses to chronic kidney disease (CKD) and end-stage kidney disease, with a mortality rate of 40–50%. However, there is a lack of universal treatment for AKI. Inflammation is the basic pathological change of early kidney injury, and inflammation can exacerbate AKI. Macrophages are the primary immune cells involved in the inflammatory microenvironment of kidney disease. Therefore, regulating the function of macrophages is a crucial breakthrough for the AKI intervention. Our team chemically modified pyxinol, an ocotillol-type ginsenoside, to prepare PJ16 with higher solubility and bioavailability. In vitro, using a model of macrophages stimulated by LPS, it was found that PJ16 could regulate macrophage function, including inhibiting the secretion of inflammatory factors, promoting phagocytosis, inhibiting M1 macrophages, and promoting M1 transition to the M2c macrophage. Further investigation revealed that PJ16 may shield renal tubular epithelial cells (HK-2) damaged by LPS in vitro. Based on this, PJ16 was validated in the animal model of unilateral ureteral obstruction, which showed that it improves renal function and inhibits renal tissue fibrosis by decreasing inflammatory responses, reducing macrophage inflammatory infiltration, and preferentially upregulating M2c macrophages. In conclusion, our study is the first to show that PJ16 resists AKI and fibrosis by mechanistically regulating macrophage function by modulating the phenotypic transition from M1 to M2 macrophages, mainly M2c macrophages.

Selective harmonic elimination pulse-width modulation (SHE-PWM) method has been widely used in various industrial multilevel converters befitting from low switching frequencies and precise elimination of low-order harmonics. Most of existing studies focus on accelerating calculation of switching angles or optimizing predictive neural networks. However, these offline methods cannot dynamically deal with the errors introduced by nonuniform dc sources of multilevel converters or biased placements of switching angles. This paper proposes an online adaptive SHE (OA-SHE) algorithm to compensate these errors. Errors caused by nonideal dc (NID) sources and leading-lagging placements (LLPs) are analyzed in detail. The error of output waveform is comprehensive result of NID sources and LLPs. By introducing feedback, the output waveform accuracy of multilevel converters can be increased greatly. Realization of the particle swarm optimization (PSO) with an embedded control system is presented to acquire good real-time performance. With the OA-SHE method, the value of particles has been higher than the initial value. High order harmonics, including 1st, 5th, 7th, 11th, and 13th components, are suppressed by the low-pass filter. Comparing to existing studies, this online algorithm does not need detailed model parameters and can be directly applied to practical applications achieving multi-objective optimization. Finally, both simulations and experiments are carried out to verify the effectiveness of this algorithm.

The auto-recloser (AR) contributes to the service restoration of the transmission lines tripped by overload and relay protection mal-operation, which may complicate cascading outage processes. This brief develops a novel model to investigate the impact of ARs on cascading failure (CF) propagation in power systems with the representation of temporal evolution. The cascading outage processes with and without ARs are first compared in a timeline manner to highlight the function of ARs. Then, combined with the inverse time and hidden failure characteristics of overcurrent protections, the CF model considering ARs is established to elaborate the cascading propagation and reveal the reclosing effect. Finally, the time-constrained optimal dispatching strategy is proposed to block CFs in time before the next line excision. Case studies on IEEE 118-bus and IEEE 300-bus systems demonstrate the feasibility and effectiveness of the proposed method.

Special planar structure endows two-dimensional (2D) materials with unique physical and chemical properties and the application of 2D materials to cement-based materials ensures the mechanical properties of buildings and infrastructures. Herein, a novel 2D nanoplate is synthesized through hydrothermal method with controlled sizes and morphologies. TEM and SEM measurements are used to characterize the nanostructures, confirming the morphologies, the distribution of elements among the structures and the crystal structure. The as-prepared nanoplates are able to enhance the mechanical strength of the cement mortar by its specific morphology and pozzolanic activity. The flexural strength was improved by 45.73 % after 1 d curing due to the acceleration of cement hydration. From the analysis of XRD and TG-DTG patterns, the nanoplates have strong interaction with the cement paste and the amount of the hydration products varied by the addition of the nanoplates. This approach helps promote the development of cement-based materials with high performance.

Perfect dispatch refers to finding the after-the-fact optimal allocation of output power among generating units based on actual loads. This paper proposes a new idea to solve the economic dispatch problem that regards it as a learning problem, not an optimal problem. The adaptive Gate Recurrent Unit (GRU) neural network is applied to construct the perfect dispatch learning model, which makes the perfect dispatch from an offline manual analysis method to an automatic online learning method. The hierarchical clustering analysis is performed to determine historical similar days for forecast day. Then the historical days that have the same load characteristic with the forecast day form the training set for the GRU learning model. The matrix correlation analysis is performed to select critical historical instants for each dispatch instant. According to matrix correlation analysis results, the number of the memory blocks in each GRU learning model is determined. The structure of GRU learning models is adaptive for different dispatch instants. The IEEE 39-bus test system is used to verify the feasibility and accuracy of the proposed perfect dispatch learning model. Study results indicate that the generation dispatch schedule obtained by the proposed GRU learning model is closer to the perfect dispatch schedule than the generation dispatch schedule obtained by solving the non-linear optimization problem.

Most system restoration strategies following blackouts are obtained by offline methods, which are efficient in theory. But this may not be the case. A new method is proposed to decide the generator startup sequence during the system restoration. The Monte Carlo tree search algorithm is used to search for the generator to be recovered according to the real-time situation of the power system. All paths from the restored system to the chosen generator are obtained by Depth-First Search. And the K-shortest paths are obtained by Yen’s Algorithm to decide the best restoration path. Then, key constraints are verified. Especially, the constraint on the hot start of generators is considered. Finally, the feasible scheme with the maximum power generation is selected for implementation. In addition, the cranking process of G33 to G32 in the IEEE39-bus test system is taken as an example to illustrate the efficiency of this method. Depth-First Search detects 42 restoration paths, and Yen’s Algorithm selects three most reasonable paths from them, which greatly improves the efficiency of online decision making.

Early pathogenesis of ischemia-reperfusion (I/R)-induced acute kidney injury (AKI) is dominated by intracellular calcium overload, which induces oxidative stress, intracellular energy metabolism disorder, inflammatory activation, and a series of pathologic cascaded reactions that are closely intertwined with self-amplifying and interactive feedback loops, ultimately resulting in cell damage and kidney failure. Currently, most nanomedicines originate from the perspective of antioxidant stress, which can only quench existing reactive oxide species (ROS) but cannot prevent the continuous production of ROS, resulting in insufficient efficacy. As a safe and promising drug, BAPTA-AM is hydrolyzed into BAPTA by intracellular esterase upon entering cells, which can rapidly chelate with overloaded Ca2+, restoring intracellular calcium homeostasis, thus inhibiting ROS regeneration at the source. Here, we designed a KTP-targeting peptide-modified yolk-shell structure of liposome–poly(ethylene glycol)methyl ether-block-poly (l-lactide-co-glycolic) (mPLGA) hybrid nanoparticles (<100 nm), with the characteristics of high encapsulation rate, high colloid stability, facile modification, and prolonged blood circulation time. Once the BA/mPLGA@Lipo-KTP was targeted to the site of kidney injury, the cholesteryl hemisuccinate (CHEMS) in the phospholipid bilayer, as an acidic cholesterol ester, was protonated in the simulated inflammatory slightly acidic environment (pH 6.5), causing the liposomes to rupture and release the BA/mPLGA nanoparticles, which were then depolymerized by intracellular esterase. The BAPTA-AM was diffused and hydrolyzed to produce BAPTA, which can rapidly cut off the malignant loop of calcium overload/ROS generation at its source, blocking the endoplasmic reticulum (ER) apoptosis pathway (ATF4–CHOP–Bax/Bcl-2, Casp-12–Casp-3) and the inflammatory pathway (TNF-α–NF-κB–IL-6 axes), thus alleviating pathological changes in kidney tissue, thereby inhibiting the expression of renal tubular marker kidney injury molecule 1 (Kim-1) (reduced by 82.9%) and also exhibiting prominent anti-apoptotic capability (TUNEL-positive ratio decreased from 40.2% to 8.3%), significantly restoring renal function. Overall, this research holds huge potential in the treatment of I/R injury-related diseases.

Foodborne Staphylococcus aureus, including methicillin-resistant S. aureus(MRSA), is increasingly threatening human health. Pooled prevalence rates of S. aureuscontamination have been extensively studied in retail food products in mainland China, but data regarding antibiotic resistance rates of S. aureusremain scattered. This study was designed to collect researches published between 2007 and 2017 in mainland China and to evaluate the antibiotic resistance of S. aureusfrom retail foods using a meta-analytic approach. We systematically searched the China National Knowledge Infrastructure (CNKI) and Web of Science databases to identify peer-reviewed literature. A number of multilevel random-effects models were fitted to estimate mean occurrence rates of antibiotic-resistant S. aureus, and subgroup analyses were performed to compare antibiotic resistance rates of S. aureusthroughout the years and among the methods to determine the antimicrobial susceptibility. Among the considered antibiotics, S. aureusshowed the highest resistance rate to penicillin G (87%, 95% confidence interval [CI] 83–90%), followed by ampicillin (72%, 95% CI 62–81%) and erythromycin (41%, 95% CI 36–46%). MRSA showed the highest resistance rate to ampicillin (98%, 95% CI 89–100%), followed by oxacillin (97%, 95% CI 80–100%) and penicillin G (96%, 95% CI 89–99%). Multidrug resistance (MDR) of S. aureuswas most frequently observed to three antibiotics (17%, 95% CI 12–22%), and MRSA showed the highest resistance rate to four antibiotics (24%, 95% CI 5–67%). Subgroup analyses results proved that sources of heterogeneity among studies were neither publication year nor detection method. In conclusion, the meta-analysis showed that β-lactam antibiotics resistance of S. aureusand MRSA strains isolated from retail foods remained the most serious, and MDR of S. aureusand MRSA were also observed. Therefore, it is important to monitor the antibiotic resistance of S. aureusand MRSA in food chain, and food safety measures should be taken to reduce the transmission of this bacterium from foods to human beings.

This paper proposes a new backup protection scheme, named coordinated backup protection (CBP) scheme, for distribution networks containing distributed generation. The proposed protection scheme takes into account the issues faced by traditional backup protection, such as difficulty in setting parameters and complex cooperation, and considers the features of distribution networks, such as changeable power flow because of high penetration of distributed generation sources and insufficient measuring quantities. The CBP scheme includes two aspects: coordinated substation protection and regional master substation protection, who also work as nearby backup protection and remote backup protection respectively. The two protections support each other by local information coordination and regional information sharing, in order to improve the reliability of fault identification. The configuration principles and performances of the proposed backup protection scheme are addressed in the paper. Different fault conditions in the IEEE 14-node system have been used to illustrate and verify the feasibility of the CBP scheme.

This paper proposes regarding economic dispatch problem as learning problem not optimization problem that develops adaptive Long Short-term Memory (LSTM) neural network to construct perfect dispatch learning model for each dispatch instant. First, matrix correlation analysis is performed to select critical historical instants for each dispatch instant. Then, according to matrix correlation analysis results, the structure of LSTM neural network at each instant is determined. Fianlly, the structures of LSTM learning models are adaptive for different dispatch instants. The differentiated training sets are constructed for adaptive LSTM learning models. The WSCC 9-bus system and the IEEE 118-bus system are used to verify the effectiveness of the proposed method. Study results indicate that the generation dispatch schedule obtained by the proposed LSTM learning model is closer to the perfect dispatch schedule than the generation dispatch schedule obtained by solving non-linear optimization problem.

Anticorrosion epoxy coatings from Al and Zn based pigments were synthesized by adjusting their volume ratios, aiming at their increasing anticorrosion performances. The anticorrosion properties were examined viaelectrochemical impedance spectroscopy, Tafel polarization curve analysis and salt spray test. The coating morphologies before and after the salt spray tests were studied viascanning electron microscopy(SEM). The elemental and chemical compositions of the corroded surfaces of the coatings were analyzed by means of X-ray photoelectron spectroscopy(XPS). The results indicate that the coating composed of Al/Zn at 10:1(volume ratio) displays the maximum anticorrosion performances, which are superior to those of pristine Al or Zn based pigment.

Objective. ARID1A has been discovered as a potential cancer biomarker. But its role in hepatocellular carcinoma (HCC) is subject to considerable dispute. Methods. The relationship between ARID1A and clinical factors was investigated. Clinicopathological variables related to overall survival in HCC subjects were identified using Cox and Kaplan–Meier studies. The connection between immune infiltrating cells and ARID1A expression was investigated using the tumor Genome Atlas (TCGA) dataset for gene set enrichment analysis (GSEA). Finally, a cell experiment was used to confirm it. Results. The gender and cancer topography (T) categorization of HCC were linked to increased ARID1A expression. Participants with advanced levels of ARID1A expression had a worse prognosis than someone with lower levels. ARID1A was shown to be a risk indicator of overall survival on its own. ARID1A expression is inversely proportional to immune cell infiltration. In vitro, decreasing ARID1A expression substantially slowed the cell cycle and decreased HCC cell proliferation, migration, and invasion. Conclusion. The expression of ARID1A could be used to predict the outcome of HCC. It is closely related to tumor immune cell infiltration.

HtrA-family proteases are commonly employed to sense the protein folding stress and activate the regulated intramembrane proteolysis (RIP) cascade in Gram-negative bacteria. Here, we reveal the unique dual-signal activation and dynamic regulation properties of AlgW, an HtrA-type protease triggering the AlgU stress-response pathway, which controls alginate production and mucoid conversion in Pseudomonas aeruginosa.