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Caffeic acid O-methyltransferase from Ligusticum chuanxiong (LcCOMT) showed strict regiospecificity despite a relative degree of preference. Compared with caffeic acid, methyl caffeate was the preferential substrate by its low Km and high Kcat. In this study, we obtained the SAM binary (1.80 A) and SAH binary (1.95 A) complex LcCOMT crystal structures, and established the ternary complex structure with methyl caffeate by molecular docking. The active site of LcCOMT included phenolic substrate pocket, SAM/SAH ligand pocket and conserved catalytic residues as well. The regiospecificity of LcCOMT that permitted only 3-hydroxyl group to be methylated arise from the interactions between the active site and the phenyl ring. However, the propanoid tail governed the relative preference of LcCOMT. The ester group in methyl caffeate stabilized the anionic intermediate caused by His268-Asp269 pair, whereas caffeic acid was unable to stabilize the anionic intermediate due to the adjacent carboxylate anion in the propanoid tail. Ser183 residue formed an additional hydrogen bond with SAH and its role was identified by S183A mutation. Ile318 residue might be a potential site for determination of substrate preference, and its mutation led to the change of tertiary conformation. The results supported the selective mechanism of LcCOMT.

The energy management (EM) of active distribution network (ADN) under uncertainties is a stochastic, nonconvex and nonlinear problem, which cannot be solved by traditional algorithms in acceptable time. In this paper, we decouple this computationally intractable problem into a series of subproblems which are easier to handle, and then solve them successively according to an improved approximate dynamic programming (IADP) algorithm. Different from the existing approximate dynamic programming (ADP) algorithms, which need to update value functions iteratively, IADP obtains approximate value functions directly using Galerkin method. Such that the time of updating approximate value functions can be omitted. Furthermore, the influence of each basis function on the approximate value function is evaluated according to the absolute value inequality principle. Then the unimportant basis functions are removed from the basis function set to speed up the algorithm. The historical data can also be embedded into IADP to facilitate the online decision-making and reduce the dependency of real-time forecast information. Numerical simulations on two modified IEEE test systems and a real 682-bus ADN are given to illustrate the effectiveness of the proposed approach.

With the energy transition, the cyber-physical system (CPS) is expanded to the cyber-physical-social system (CPSS), and connected by the communication network, which is introduced in the form of a highly reliable cellular star network in this paper. Due to the uncertainties of wind turbine/photovoltaic power outputs, an improved rolling ultra-short-term forecast method with high reliability is proposed, which is based on long short-term memory networks, and the superiority is proved by comparing with multi-layer perception neural networks, linear regression, support vector regression and random forest. Then, two stochastic optimization models are constructed, one is based on CPS, the other is based on CPSS. As for social factors, electric vehicle aggregator is stimulated by cost to actively participate in the dispatch operation. Moreover, in order to ensure the stable operation of the smart energy system, the frequency deviation of automatic generation control system is studied as one security constraint. The optimal results display that social factors can not only make the system more economic and flexible, but also further increase the consumption of RE. Moreover, accurate forecast results can improve the performance of optimization dispatch operations.

This paper studies the real-time dynamic energy management (DEM) of multi microgrids (MMGs) considering active and reactive power flow constraints, voltage constraints, battery operational characters, and uncertainties in the renewable generation and load. A stochastic spatiotemporal decomposition decision-making framework is proposed based on approximate dynamic programming (ADP) to make decentralized decisions in both spatial and temporal dimensions. The tie-line power and the state of charge (SOC) of the battery are coordinated in real time to deal with the uncertainties in the upcoming future, preserving the decision independence of MGs and periods. And the shift factors are derived to consider active and reactive power flow constraints of MMGs in the stochastic spatiotemporal decomposition framework. Moreover, the historical information is utilized offline to avoid the dependency on forecast information and iterative calculation, while near-optimal solutions can be obtained in real time. Case studies on several MMG test systems and a real MMG system demonstrate the effectiveness of the proposed approach.

Background CITED4 belongs to the CBP/p300‐interacting transactivator with glutamic acid and aspartic acid‐rich tail (CITED) family which is induced by various cytokines and participates in cytokine‐induced proliferation and differentiation. CITED4 is induced by HB‐EGF in lung cancer cells. However, it is unclear whether and how CITED4 contributes to the invasion and metastasis of lung adenocarcinoma (ADC). Methods CITED4 expression in lung adenocarcinoma and its association with disease‐free survival (DFS) and overall survival were analyzed based on a cohort of 261 patients. The roles of CITED4 were validated via loss‐of‐function and gain‐of‐function experiments. The relationship between CITED4 and CLDN3 was validated by immunohistochemistry, Western blotting, and luciferase reporter assays. The function of the CITED4‐CTNNB1‐CLDN3 complex was fully validated and described. Results CITED4 expression was significantly upregulated in ADC tissues and cells and a predictor for DFS. Downregulation of CITED4 attenuated the proliferation and invasion, whereas CITED4 overexpression enhanced these effects. Overexpression and knockdown of CITED4 resulted in the upregulation and downregulation of CLDN3, respectively. Moreover, CITED4 downregulation suppressed CLDN3‐mediated ADC cell metastasis in vivo. CITED4 was highly expressed and positively correlated with CLDN3. Mechanistically, CITED4 interacted with CTNNB1 and functioned synergistically to enhance CLDN3 transcription. Importantly, CITED4 induced ADC invasion via a CLDN3‐dependent pathway. CITED4 determined the level of CLDN3, which in turn affected the sensitivity of tumors to Clostridium perfringens enterotoxin treatment. Conclusions The CITED4‐CTNNB1‐CLDN3 axis plays a key role in the invasion and metastasis of ADC and provides a novel therapeutic target for lung cancer treatment., CITED4 expression is significantly upregulated in ADC tissues and cells. CITED4 interacted with CTNNB1 and functioned synergistically to enhance CLDN3 transcription. The CITED4‐CTNNB1‐CLDN3 axis plays a key role in the invasion and metastasis of ADC and provides a novel therapeutic target for lung cancer treatment.

An asymmetric total synthesis of the sarpagine alkaloid (-)-normacusine B is presented. Salient features of this synthesis include a photocatalytic nitrogen-centered radical cascade reaction to assemble the tetrahydrocarbolinone skeleton, a titanium-mediated intramolecular amide-alkene coupling to construct the bridged azabicyclo[3.3.1]nonane moiety, and a nickel-catalyzed reductive Heck coupling to assemble the azabicyclo[2.2.2]octane ring system.

Background Adjuvant epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are increasing considered for the tailored management of resectable non-small cell lung cancer (NSCLC). This study aimed to analyze the survival and toxicity profile of patients with EGFR mutation-positive NSCLC treated with adjuvant icotinib. Methods This was a single-center retrospective study of patients with EGFR mutation-positive NSCLC who underwent R0 (microscopically margin-negative) resection and received adjuvant icotinib between November 2011 and December 2017. The outcomes included 2-year disease-free survival (DFS) rate, 3-year overall survival (OS) rates, DFS, OS, and adverse events (AEs). Results A total of 86 patients receiving adjuvant icotinib were included. Their mean age was 59.7±10.0 years, and 26 (30.2%) patients were male. The 2-year DFS rate was 86.7%, and the 3-year OS rate was 95.3% with adjuvant icotinib. DFS (P=0.044) and OS (P=0.003) are better in stage I/II disease than in stage III disease. There seems no differences in DFS and OS between patients with low or high preoperative CEA levels (cutoff of 5 ng/mL), patients with exon 19 or 21 EGFR mutation or patients with or without smoking history. The most common AEs with adjuvant icotinib were rash (83.7%) and diarrhea (19.8%). One (1.2%) patient-reported grade ≥3 AEs. No treatment-related death occurred. Conclusions For patients with EGFR mutation-positive NSCLC, adjuvant icotinib might be associated with a promising survival benefit, with an acceptable toxicity profile.

This study presents a fully decentralised robust optimisation (RO) approach for multi-area economic dispatch (MA-ED) in the presence of wind power uncertainty. Unlike traditional algorithms, the authors formulate this MA-ED problem as dynamic programming problem, and decompose the centralised robust MA-ED problem into a series of sub-problems based on approximate dynamic programming algorithm. The value functions are proposed for each area to iteratively estimate the impacts of its dispatches on the dispatches of other areas which make decisions subsequently. The proposed algorithm does not require a central operator but only needs to exchange a small amount of information among neighbouring areas to achieve fully decentralised decision-making. It is practical in cases where the centralised operator cannot be implemented considering the dispatch independence and the detailed data of one area is unavailable considering the privacy. Additionally, the accuracy, adaptability and computational efficiency of the proposed algorithm are illustrated using numerical simulations on two test systems and an actual power system.

Objectives Recent genomic studies suggest the biological significance of the cylindromatosis (CYLD) gene in thymic epithelial tumors (TETs). CYLD is a crucial regulator of immune response, and we previously reported that CYLD mutation is associated with high PD-L1 expression in thymic carcinoma. Therefore, we wanted to explore the role and mechanism of CYLD in regulating PD-L1 expression in TETs. Materials and methods The role of CYLD in PD-L1 expression was assessed by knockdown of CYLD in TET cells upon stimulation with interferon gamma (IFN-γ), tumor necrosis factor-α (TNF-α) or polyinosinic-polycytidylic acid (poly I:C). The molecular mechanism was investigated through analysis of downstream molecules in the STAT1/IRF1 pathway. Moreover, the clinical correlation between low CYLD and high PD-L1 expression, and the clinical impact of CYLD expression were evaluated in tissue microarrays of 105 TET cases. Results CYLD knockdown significantly enhanced the expression of PD-L1 in presence of IFN-γ stimulation in most TET cell lines. However, this phenomenon was not observed in presence of TNF-α stimulation. CYLD knockdown upregulated IFN-γ mediated activation of the STAT1/IRF1 axis, which in turn induced PD-L1 expression. Interestingly, we found a significant association between low CYLD expression and ≥ 50 % PD-L1 expression (p = 0.001). In addition, the average proportion of tumor cells exhibiting PD-L1 staining was significantly higher in the low CYLD expression group (24.7 %) than in the high CYLD expression group (5.2 %) (p = 0.005). There was no correlation between CYLD expression and the frequency of pre-existing paraneoplastic auto-immune diseases. In advanced stages (III/IV), the low CYLD expressing group had numerically worse survival than the high CYLD group (log-rank p = 0.089). Conclusions Our findings provide insight into the mechanism of regulation of PD-L1 expression by CYLD in TET cells. Tumors with low CYLD expression could be potential targets for PD-1/PD-L1 inhibitors.

Background This study aimed to identify an efficient, simple, and specific method of detecting mutations in the epidermal growth factor receptor (EGFR) gene in isolated lung cancer circulating tumor cells (CTCs) and to improve the ability to obtain tumor tissue clinically. Methods EGFR peptide lipid magnetic spheres (EG‐P‐LMB) were prepared by reverse evaporation, and characterization and cell capture efficiency assessed. The peripheral blood samples of 30 lung cancer patients were isolated and identified with the EG‐P‐LMB using 20 healthy volunteers as controls. Finally, the isolated CTCs were tested for EGFR gene mutations, and the tissue samples selected for comparison. Results The prepared magnetic spheres had a smaller particle size and higher stability according to the particle size potential test. Their morphology was homogeneous by atomic force observation, and the UV test showed that there were peptides on the surface. The separation efficiency of EG‐P‐LMB was greater than 90% in PBS and greater than 80% in the blood simulation system. Compared with the tissue sample results, the positive rate of EGFR gene mutations was 94%. The CTC test results of 27 patients were consistent with the tissue test results of the corresponding patients, and the consistency with the tissue comparison test results was 90% (27/30). Conclusions EG‐P‐LMB can effectively capture CTCs in the peripheral blood of patients with lung cancer. CTC detection can accurately identify mutations in the EGFR gene and improve the ability to obtain tumor tissue in clinical practice. Key points Significant findings of the study EG‐P‐LMB can effectively capture CTCs in the peripheral blood of patients with lung cancer. CTC detection can accurately identify mutations in the EGFR gene and improve the ability to obtain tumor tissue in clinical practice. What this study adds This study added EGFR peptide lipid magnetic spheres to capture CTCs in the blood. Genetic testing was performed and compared with tissues. It solves the problem of clinically difficult tumor tissue sampling., Significant findings of the study: EG‐P‐LMB can effectively capture CTCs in the peripheral blood of patients with lung cancer. CTC detection can accurately identify mutations in the EGFR gene and improve the ability to obtain tumor tissue in clinical practice. What this study adds: This study added EGFR peptide lipid magnetic spheres to capture CTC in the blood. Genetic testing was performed and compared with tissues. It solves the problem of clinically difficult tumor tissue sampling.

Although digestive resistance of Kunitz protease inhibitors has been reported extensively, the molecular mechanism is not well established. In the present study, the first X-ray structure of Cassia obtusifolia trypsin inhibitor (COTI), a member of Kunitz protease inhibitors, was solved at a resolution of 1.9 Å. The structure adopted a classic β-trefoil fold with the inhibitory loop protruding from the hydrophobic core. The role of Phe139, a unique residue in Kunitz protease inhibitors, and Arg63 in the COTI structure was verified by F139A and R63E mutants. COTI was a specific inhibitor of bovine trypsin and the result was also verified by COTI-trypsin complex formation. Meanwhile, COTI showed equivalent inhibitory activity with that of soybean trypsin inhibitor against bovine trypsin and midgut trypsin from Pieris rapae. The F139 and R63E mutants further indicated that inhibitory specificity and efficiency of COTI were closely related to the global framework, the conformation and the amino acid composition of reactive loop. Finally, a midgut trypsin from P. rapae (PrSP40), which might be involve in the food digestion, was proposed to be a potential target of COTI and might be a promising target for future crop-protection strategy. The results supported the digestive resistance of COTI.

This study proposes a decentralised methodology to deal with the optimal energy flow (OEF) problem of the electricity–natural gas system (EGS). The OEF problem of EGS is formulated as a multi-stage dynamic programming (DP) process, and the approximate DP algorithm is used to decompose it into subproblems. The independent decision of each subproblem can be made by solving Bellman's equation, requiring just a moderate interchange of information among different subproblems. Such that the information privacy and dispatch independency of subsystems (including electricity and natural gas (NG) subsystems) can be ensured. Different from most existing decentralised algorithms, the impact of one subproblem's decision on other subproblems can be estimated in the proposed algorithm, and the parameters of the proposed algorithm are not required for tuning, which makes more sense in real applications. Furthermore, an improved linear cut technique is proposed to handle the non-convexity of the NG network model, so that the tightness of all gas flow constraints can be ensured. Case studies containing small, large and complicated EGSs validate the effectiveness of the proposed approach.

This study focuses on the real-time operation of a microgrid (MG). A novel approximate dynamic programming based spatiotemporal decomposition approach is developed to incorporate efficient management of distributed energy storage systems into MG real-time operation while considering uncertainties in renewable generation. The original dynamic energy management problem is decomposed into single-period and single-unit sub-problems, and the value functions are used to describe the interaction among the sub-problems. A two-stage procedure is further designed for the real-time decisions of those sub-problems. In the first stage, empirical data is utilised offline to approximate the value functions. Then in the second stage, each sub-problem can make immediate and independent decision in both temporal and spatial dimensions to mitigate adverse effects of intermittent renewable generation in a MG. No central operator intervention is required, and the near optimal decisions can be obtained at a very fast speed. Case studies based on a six-bus MG and an actual island MG are conducted to demonstrate the effectiveness of the proposed algorithm.

In recent years, the use of interconnected multi-area systems has increased significantly. To operate these systems efficiently, a proper coordination approach among different areas is required. With this context, a novel decentralised optimal dispatch algorithm is proposed in this study. The centralised economic dispatch model is decoupled into a series of sub-problems for different areas, allowing the independent decision of each area without a central operator. The detailed information of each area is not required for information exchange. To better estimate the impact of each area's decision on other areas, the approximate value functions of the tie-line powers are added into the local objective function of the sub-problem. This technique makes the decentralised solution of the proposed algorithm more explicit and rational compared with most existing distributed algorithms. Besides, the proposed technique does not require parameter tuning and has good convergence performance. Numerical simulations on several test systems and a real power system demonstrate that the proposed algorithm is favourable in terms of accuracy, adaptability, and computational efficiency.

Background To assess the potential prognostic value of the albumin to alkaline phosphatase ratio (AAPR) in patients with non‐small cell lung cancer (NSCLC) after surgery. Methods The log‐rank and Kaplan‐Meier analyses were performed to detect differences in survival levels between different groups. A model of Cox proportional hazards was used to perform univariate and multivariate survival analyses. Comparisons of receiver operating characteristic (ROC) curves and the likelihood ratio test (LRT) were also utilized to compare the prognostic abilities of different systems for overall survival (OS) prediction. Results The optimal cut‐off value of the preoperative AAPR was 0.64. A decreased AAPR was associated with several clinicopathological and clinicolaboratory variables related to cancer progression. The preoperative AAPR of patients was positively correlated with the poor prognosis of NSCLC. In multivariate analyses, the preoperative AAPR was identified as an independent prognostic factor for disease‐free survival (DFS; P = 0.001) and overall survival (OS; P = 0.003). The LRT showed that the AAPR tumor‐node‐metastasis (TNM) system presented a significantly larger χ2 value (112.4 vs. 89.2, respectively, P

Electric loads are essential for power system dynamic simulation. However, load modeling is one of the most challenging topics due to the diversity and time-varying behavior of the load. When considering the intervention of rapidly developing distributed generation (DG), load modeling becomes more difficult. In this paper, a new solution for determining the unknown generalized load model is proposed. The radial basis function (RBF) neural network-based sub-models of generalized load are stored in the form of a sub-model bank. A recursive Bayesian approach is used to identify the sub-models and then merge them into one generalized load model according to their probabilities. The proposed method can be implemented online and adapt to describing the diversity and time-varying behavior of the generalized load. Numerical studies are carried out using both simulation data and actual measurements. The comparisons with other load modeling methods verify the advantages of the proposed method.

In this paper, we propose a bi-level, real-time economic dispatch method of a virtual power plant (VPP), including various distributed energy resources (DERs). Considering the different interests of VPPs and a system operator, the real-time economic dispatch of VPPs is described as a bi-level programming problem, where a system operator dispatches VPPs based on the price incentive mechanism on the upper level, and the VPPs provide response according to the optimal control of their DERs on the lower level. Considering the uncertainties of DERs and loads, the decision risks of a system operator on the upper level and VPPs on the lower level are further dealt with by the fuzzy chance constrained programming, such that they can make reasonable decisions according to their own preferred risks. The mapping method and the bi-level optimization method are also presented as the solutions for the proposed model. In this way, the fuzzy chance constraints and objective functions of both levels are transformed into deterministic forms and, then, are calculated dispersedly. As a result, the calculation burden of a system operator and the information privacies of VPPs all can be treated availably. The case studies verify the effectiveness of the proposed method in the end.

This paper proposes a deep reinforcement learning (DRL) based algorithm for the stochastic dynamic microgrid energy management. First, we consider AC power flow constraints which makes the problem non-convex and uncertainties in load, renewable generation and real-time electricity price which makes the problem stochastic. Then, the original multi-period optimization problem, which was difficult to deal with, is decomposed into several single-period optimization problems by the proposed deep reinforcement learning based method. Our method can obtain an approximate optimal policy for the original multi-period problem without long-term forecast and optimization or distribution knowledge of the uncertainty by learning from the results of single-period problems. Last, the superiority and effectiveness of the proposed method are demonstrated through several case studies.

BACKGROUND: Adjuvant epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are increasing considered for the tailored management of resectable non-small cell lung cancer (NSCLC). This study aimed to analyze the survival and toxicity profile of patients with EGFR mutation-positive NSCLC treated with adjuvant icotinib. METHODS: This was a single-center retrospective study of patients with EGFR mutation-positive NSCLC who underwent R0 (microscopically margin-negative) resection and received adjuvant icotinib between November 2011 and December 2017. The outcomes included 2-year disease-free survival (DFS) rate, 3-year overall survival (OS) rates, DFS, OS, and adverse events (AEs). RESULTS: A total of 86 patients receiving adjuvant icotinib were included. Their mean age was 59.7±10.0 years, and 26 (30.2%) patients were male. The 2-year DFS rate was 86.7%, and the 3-year OS rate was 95.3% with adjuvant icotinib. DFS (P=0.044) and OS (P=0.003) are better in stage I/II disease than in stage III disease. There seems no differences in DFS and OS between patients with low or high preoperative CEA levels (cutoff of 5 ng/mL), patients with exon 19 or 21 EGFR mutation or patients with or without smoking history. The most common AEs with adjuvant icotinib were rash (83.7%) and diarrhea (19.8%). One (1.2%) patient-reported grade ≥3 AEs. No treatment-related death occurred. CONCLUSIONS: For patients with EGFR mutation-positive NSCLC, adjuvant icotinib might be associated with a promising survival benefit, with an acceptable toxicity profile.

Dynamic energy management (DEM) is being recognized as an important tool for the economical and efficient operation of microgrid. This paper proposes an expected-cost realization-probability (ECRP) optimization approach for the DEM of microgrid. In the model, the expected cost is taken as an economic goal to find the schedule with the maximum ECRP (i.e., the maximum probability of obtaining an actual operation cost which is lower than this economic goal). It is a stochastic mixed-integer nonlinear programming (MINLP) problem composed of the AC power flow, battery operation characters and logical variables. To solve this NP-hard problem, an ECRP-based approximate dynamic programming is proposed. The original MINLP problem is decomposed into small nonlinear programming subproblems to facilitate the solution. And the ECRP-based value function is formed to describe the relationship among the decisions of subproblems, which helps to obtain the near-optimal decision in a decomposed manner. Simulation examples presented on a real microgrid indicate that the ECRP of the proposed method is higher than those of the other two comparison algorithms.

// Xiaoliang Zhao 1 , Xiaohua Wen 2 , Wei Wei 1 , Yulong Chen 1 , Jianquan Zhu 1 and Changli Wang 1 1 Department of Lung Cancer Tianjin Medical University Cancer Institute and Hospital, Tianjin Lung Cancer Center, Tianjin Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer Tianjin, Tianjin, P.R. China 2 Tianjin University of Traditional Chinese Medicine, Tianjin, P.R. China Correspondence to: Changli Wang, email: // Keywords : metastatic lung tumor, surgical treatment, prognostic factor Received : December 09, 2016 Accepted : January 17, 2017 Published : January 26, 2017 Abstract The clinical characteristics of metastatic lung tumors are not well understood. To explore the surgical indications, surgical modes, and factors that influence postoperative outcomes, we analyzed clinical data from 42 patients with metastatic lung tumors who received surgical treatment at Tianjin Medical University Cancer Institute and Hospital between January 2000 and January 2014. Gender, age, nature of resections, surgical mode, smoking index, disease-free intervals (DFIs), number of metastatic lesions, and lymph node metastases were analyzed. Patients were followed for 6 to 98 months. We found that surgical treatment is feasible for resectable metastatic lung tumors, though postoperative radiochemotherapy had no significant effect on postoperative survival rates among patients with metastatic lung tumors. No patients died perioperatively. The 1-year, 3-year, and 5-year survival rates after surgical resection of metastatic lung tumors were 88.1%, 45.7%, and 34.6%, respectively. Univariate analysis indicated that DFIs and lymph node metastasis correlated with patient prognoses, while multivariate analysis indicated these two variables were independent prognostic factors. Thus surgical treatment may be indicated, depending on patients’ specific condition, to lengthen DFIs in patients with metastatic lung tumors with or without evident lymph node metastasis.

The ligand/receptor pair C‑C motif chemokine ligand 20 (CCL20)/C‑C motif chemokine receptor 6 (CCR6) is considered to be highly activated in lung cancer and significantly accelerates lung cancer progression through activation of ERK signaling. In addition, it has been shown that long non‑coding RNA‑u50535 (lncRNA‑u50535) upregulates CCL20 expression and facilitates cancer progression in colorectal cancer (CRC). However, the effects of lncRNA‑u50535 in lung cancer progression and whether lncRNA‑u50535 regulates CCL20/CCR6/ERK signaling in lung cancer remain ill‑defined. Therefore, the aim of the present study was to investigate the effects of lncRNA‑u50535 on CCL20/CCR6/ERK signaling in lung cancer progression. The results demonstrated that lncRNA‑u50535 expression was upregulated in lung cancer tissues and cell lines compared with normal tissues and cells. Knockdown of lncRNA‑u50535 decreased lung cancer cell proliferation and migration, induced G0/G1 phase arrest and promoted cell apoptosis. Western blot and luciferase reporter gene assays demonstrated that lncRNA‑u50535 overexpression increased the translation and transcription of CCL20. In addition, knockdown of lncRNA‑u50535 decreased CCL20, CCR6 and p‑ERK levels. The effects of lncRNA‑u50535 on cell proliferation and cell apoptosis were weakened when CCL20 was silenced. Overall, the present study demonstrated that lncRNA‑u50535 may function as an oncogene in lung cancer progression by regulating CCL20/ERK signaling.

Introduction Platinum-based chemotherapy remains the standard treatment for patients with SCLC, but the benefit of the treatment is often hampered by rapid development of drug resistance. Thus far, there is no targeted therapy available for SCLC. More than 90% of SCLC tumors harbor mutations in the tumor suppressor gene tumor protein p53 (p53), an important DNA damage checkpoint regulator, and these tumor cells rely predominantly on the checkpoint kinases to control DNA damage response. Methods We examined whether and how inhibition of checkpoint kinase 1 (Chk1) affects cisplatin cytotoxicity in SCLC cells with and without p53 mutations, and evaluated the effect of Chk1 inhibitor and cisplatin combination in cisplatin-sensitive and -resistant preclinical models. Results Inhibition of Chk1 synergized with cisplatin to induce mitotic cell death in the p53-deficeint SCLC cells. The effect was regulated in part through activation of caspase 2 and downregulation of E2F transcription factor 1 (E2F1). Furthermore, Chk1 inhibitors prexasertib and AZD7762 enhanced cisplatin antitumor activity and overcame cisplatin resistance in SCLC preclinical models in vitro an in vivo. We also observed that higher expression of Chk1 was associated with poorer overall survival of patients with SCLC. Conclusions Our data account Chk1 as a potential therapeutic target in SCLC, and rationalize clinical development of Chk1 inhibitor and cisplatin combinational strategy for the treatment of SCLC.

Plug-in electric buses have great potential to enhance the profits of operators in multiple-microgrid systems via the use of vehicle-to-grid technology. In contrast to stationary energy storage units, a plug-in electric bus can move among different microgrids to provide not only passenger transportation service but also energy transportation service. In this paper, a single-leader and multiple-follower model based on the Stackelberg game method is proposed for the look-ahead dispatch of bus routes and power allocation in microgrids. First, a time-space trip model for a fleet of plug-in electric buses was established, and the total costs of the plug-in electric bus operator were optimized as a leader-level problem. Then a rolling energy management strategy was formulated as a follower-level problem to optimize the total costs of each microgrid operator and to deal with the prediction error of the distributed energy resources and loads. Next, the proposed bi-level optimization problem was transformed into a single-level mixed-integer linear programming problem. Finally, case studies were carried out on three real microgrids in China and on BYD-K9 plug-in electric buses. The simulation results indicated that the costs for a plug-in electric bus operator would decrease by 33.35% in the proposed bi-level model compared with the fixed charging model, and the costs for an isolated MG would decrease by 24.16% compared with the model regardless of the plug-in electric buses.

This study discusses the implementation of a fully decentralised optimisation for the multi-area dynamic economic dispatch of large-scale power systems based on the cutting plane consensus (CPC) algorithm. Each area constructs a local master problem to approximate the original problem by generating its own cutting planes and receiving cutting planes from other areas, hence an upper-level coordinator is not required. Two modifications of the standard CPC algorithm have been developed. First, only the newest cutting planes are transmitted among different areas to reduce the amount of information transferred. Second, to reduce the required number of iterations, a cutting plane is deleted only if it is inactive over consecutive iterations. Theoretically, the convergence and correctness of this algorithm can be guaranteed for power systems with a wide range of scales without tuning the optimisation parameters, and a high-quality suboptimal solution can still be obtained if the algorithm is prematurely terminated. The proposed method is applied to simulations of a three-area IEEE Reliability Test System and an actual four-area 2298-bus provincial power system to demonstrate its effectiveness.

In this paper, we present a spatiotemporal decomposition solution approach to the fully decentralized dynamic economic dispatch (DED) problem in a microgrid. Our approach divides the centralized DED problem into a series of sub-problems in the spatiotemporal dimensions and relies on multiple agents to solve those sub-problems. The proposed method requires no central operator intervention, preserving the decision independence and information privacy of each unit. Approximate value functions are used to describe the interaction among those sub-problems. With the approximate value functions, one agent not only knows the impact of its decision on the decisions of other agents in the same period, but also knows the impact of this decision on the decisions of its subsequent periods. Unlike the existing value function update strategy, which updates the state variables and value functions in one direction, we update the state variables and value functions in two directions based on a forward-push-back strategy. In this manner, the time-delayed problem can be solved, and the iteration speed of the algorithm is greatly improved. Moreover, the proposed algorithm does not require parameter tuning and has good accuracy and adaptability. Numerical simulations for multiple cases demonstrate the effectiveness of the proposed algorithm.

Responding to the uncertainty challenges of high proportion wind power and the privacy of multi-area power system, a decentralized robust optimization method is proposed. Firstly, the uncertainty of wind power is considered by wind power prediction interval. The uncertain information of wind power is converted into boundary information of prediction interval by robust optimization. Secondly, the automatic generation control (AGC) unit is used to deal with the fluctuation of wind power. By optimizing the participation factors of AGC units, the robust dispatch model is formulated. Then, a decentralized robust optimization method is formulated based on approximate dynamic programming (ADP). The proposed method decouples centralized problem by approximate value function. Finally, a real 4-region 2298-node power system was tested to demonstrate the effectiveness of the proposed method.

This paper proposes a new generalized power load modeling method, which contains multiple RBF neural network model structures. Firstly, based on the RBF neural network, a sub-model is built to describe typical load characteristics. It breaks through the limitations of load components, and adapts to the diverse development of modern power system. Then, based on Bayesian estimation theory, multiple typical load models are merged into one integrated load model. It solves the problem of insufficient generalization ability of traditional load model, and adapts to the time variation of power load. Finally, the proposed method was applied in IEEE 14-bus test system. The results obtained proved its validity.

Pieris rapae, a serious Lepidoptera pest of cultivated crucifers, utilizes midgut enzymes to digest food and detoxify secondary metabolites from host plants. A recombinant trypsin inhibitor (COTI) from nonhost plant, Cassia obtusifolia, significantly decreased activities of trypsin-like proteases in the larval midgut on Pieris rapae and could suppress the growth of larvae. In order to know how COTI took effect, transcriptional profiles of P. rapae midgut in response to COTI was studied. A total of 51,544 unigenes were generated and 45.86% of which had homologs in public databases. Most of the regulated genes associated with digestion, detoxification, homeostasis, and resistance were downregulated after ingestion of COTI. Meanwhile, several unigenes in the integrin signaling pathway might be involved in response to COTI. Furthermore, using comparative transcriptome analysis, we detected differently expressing genes and identified a new reference gene, UPF3, by qRT-polymerase chain reaction (PCR). Therefore, it was suggested that not only proteolysis inhibition, but also suppression of expression of genes involved in metabolism, development, signaling, and defense might account for the anti-insect resistance of COTI.

PinX1 has been identified as a suppressor of telomerase enzymatic activity. However, the tumour-suppressive roles of PinX1 in different types of human cancers are unclear. PinX1 expression status and its correlation with clinicopathological features in non-small-cell lung cancer (NSCLC) have not been investigated. Accordingly, in this study, we aimed to evaluate the roles of PinX1 in NSCLC. PinX1 expression status was examined by immunohistochemistry using tissue microarray from a total of 158 patients. Correlations among PinX1 expression, clinicopathological variables, and patient survival were analysed. Furthermore, we overexpressed PinX1 in NSCLC cells and tested telomerase activity using real-time quantitative telomeric repeat amplification protocol (qTRAP) assays. Proliferation and migration of NSCLC cells were examined using the MTS method, wound healing assays, and transwell assays, respectively. Our results showed that negative PinX1 expression was associated with a poor prognosis in NSCLC. Sex, smoking status, lymph gland status, subcarinal lymph node status, pathological stage, and PinX1 expression were related to survival. PinX1 was not an independent prognostic factor in NSCLC. PinX1 overexpression inhibited proliferation and migration in NSCLC cells by suppressing telomerase activity. Our findings suggested that PinX1 could be a potential tumour suppressor in NSCLC and that loss of PinX1 promoted NSCLC progression.

The clinical characteristics of metastatic lung tumors are not well understood. To explore the surgical indications, surgical modes, and factors that influence postoperative outcomes, we analyzed clinical data from 42 patients with metastatic lung tumors who received surgical treatment at Tianjin Medical University Cancer Institute and Hospital between January 2000 and January 2014. Gender, age, nature of resections, surgical mode, smoking index, disease-free intervals (DFIs), number of metastatic lesions, and lymph node metastases were analyzed. Patients were followed for 6 to 98 months. We found that surgical treatment is feasible for resectable metastatic lung tumors, though postoperative radiochemotherapy had no significant effect on postoperative survival rates among patients with metastatic lung tumors. No patients died perioperatively. The 1-year, 3-year, and 5-year survival rates after surgical resection of metastatic lung tumors were 88.1%, 45.7%, and 34.6%, respectively. Univariate analysis indicated that DFIs and lymph node metastasis correlated with patient prognoses, while multivariate analysis indicated these two variables were independent prognostic factors. Thus surgical treatment may be indicated, depending on patients' specific condition, to lengthen DFIs in patients with metastatic lung tumors with or without evident lymph node metastasis.

To clone and characterize a novel bi-functional α-amylase/subtilisin inhibitor (LASI) from the rhizome of Ligusticum chuanxiong, a traditional Chinese medicine. The LASI showed strong homology with members of the Kunitz trypsin inhibitor family. Its putative amino acid sequence has a 40 % identity with that of the α-amylase/subtilisin inhibitor from rice. LASI gene without signal peptide was expressed in E. coli Rosetta. After purification, the recombinant LASI protein was inhibitory against not only α-amylase from porcine pancreas, Helicoverpa armigera, Spodoptera litura and Plutella xylostella, but also subtilisin A, but not against trypsin or chymotrypsin. In addition, the expression level of LASI in rhizome was higher than that in leaf and LASI expression was enhanced by salt, chilling and drought treatment. This is the first member of the Kunitz-protease inhibitor family identified in traditional Chinese medicine and it might be involved in the plant defense responses against lepidopterous pests, microorganisms and abiotic stresses.

This paper proposes an assessment framework for branch parameter estimation of power systems, based on which the estimation accuracy can be improved and the estimation reliability can be ensured. The proposed framework comprises three parts: 1) Parameter credibility assessment to evaluate the credibility of original parameters in the case that their true values are unknown; 2) estimation accuracy assessment to provide quantitative information about the validity of the estimation; 3) parameter dominance assessment to find out the key parameters for the estimation. This framework can be incorporated into the conventional parameter estimation process easily such that the accuracy and reliability of the estimation are improved. The implementation problems are addressed in details. Tests are carried out on the IEEE 14-bus system, IEEE 30-bus system, and IEEE 300-bus system to show the correctness and effectiveness of the proposed assessment framework.