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Through a copper-catalyzed oxidative three-component annulation reaction, a new method for direct and syn-selective construction of cyclic ether-fused tetrahydroquinolines from readily available feedstock was demonstrated.

PURPOSE To develop and evaluate an efficient precontrast T1 mapping technique suitable for quantitative high-resolution whole-brain dynamic contrast-enhanced-magnetic resonance imaging (DCE-MRI). METHODS Variable flip angle (VFA) T1 mapping was considered that provides 1 × 1 × 2 mm3 resolution to match a recent high-resolution whole-brain DCE-MRI protocol. Seven FAs were logarithmically spaced from 1.5° to 15°. T1 and M0 maps were estimated using model-based reconstruction. This approach was evaluated using an anatomically realistic brain tumor digital reference object (DRO) with noise-mimicking 3T neuroimaging and fully sampled data acquired from one healthy volunteer. Methods were also applied on fourfold prospectively undersampled VFA data from 13 patients with high-grade gliomas. RESULTS T1 -mapping precision decreased with undersampling factor R, althoughwhereas bias remained small before a critical R. In the noiseless DRO, T1 bias was

Power electronic devices and electrical equipment produce a large quantity of radiated electromagnetic interference (EMI) in the working process. Electromagnetic field analysis method of the near-field and far-field of the antenna can be employed to separate the radiated interference noises. However, the method has low separation accuracy and requires a lot of time. A novel method based on joint approximate diagonalization algorithm (JADA) to separate the radiated EMI noise is proposed in this paper. Compared with the traditional independent component algorithm method, the accuracy is improved by 14% and the efficiency is increased by 30%. The common-differential mode mixed noise source is taken as the experimental object to demonstrate the effectiveness and simplicity of the proposed method.

Aiming at the radiated electromagnetic interference (EMI) noise of electronic equipment, a novel method of radiated EMI noise analysis based on non-linear principal component analysis (NLPCA) algorithm is proposed in this paper. In order to obtain multiple independent common-mode / differential-mode radiated sources, and to find the sources that cause the radiated noises that exceed the limit of standard, NLPCA algorithm is used to process the near-field radiated signals superimposed by multiple radiated sources. The simulation results show that NLPCA can successfully screen out the radiated EMI noises which exceed the limit of standard. Moreover, the experiments are carried out with three models: double-common-mode hybrid sources, double-differential-mode hybrid sources and common-differential-mode hybrid sources. Compared with the traditional independent component algorithm (ICA), the method proposed in this paper can separate the radiated EMI noise sources more accurately and quickly. It can be concluded that the accuracy of NLPCA algorithm is 10% higher than ICA algorithm. This work will contribute to trace the radiated EMI noise sources, and to provide the theoretical basis for the future suppression.

Objective: To compare and analyze the clinical efficacy of piezosurgery osteotomy and high-speed drilling respectively in cervical laminoplasty, so as to explore the operability, safety and clinical application value of piezosurgery osteotomy in cervical laminoplasty. Methods: A total of 60 patients with multilevel cervical stenosis from the Inner Mongolia Autonomous Region People's Hospital were randomly divided into two groups (piezosurgery osteotomy group and high-speed drill group) with 30 patients in each group. Operation time, intraoperative blood loss, postoperative drainage, complication rate, Japanese orthopaedic association (JOA) score and JOA improvement rate were compared and analyzed. Results: The operation time in piezosurgery osteotomy group and high-speed drill group was (87 ± 22 min) and (96 ± 35 min), respectively, and the difference between two groups was not statistically significant (P > 0.05). The intraoperative blood loss in the piezosurgery osteotomy group was (121 ± 86 mL) lower than that (172 ± 93 mL) in the high-speed drill group (P 0.05). Dural injury developed 1 patient in high-speed drill group and cerebrospinal fluid leakage occurred after surgery. No serious complications such as spinal cord injury occurred in either group. Conclusion: Piezosurgery osteotomy in cervical laminoplasty is safe and effective, and is superior in operation time, intraoperative blood, and postoperative drainage. Its safety and efficacy are similar to high-speed drill in cervical laminoplasty. Keywords: Cervical laminoplasty; piezosurgery osteotomy; high-speed drill; cervical spondylotic myelopathy; safety; efficacy

Clinically, the metastasis of tumor cells is the key factor of death in patients with cancer. In this study, we used a model of metastatic nasopharyngeal carcinoma (NPC) to explore the effects of a new chemical, cinobufagin (CB), combined with cisplatin (DDP). We observed that chemically synthesized CB strongly decreased the metastasis of NPC. Furthermore, a better therapeutic effect was shown when CB was combined with DDP. Molecular analysis revealed that CB induced ENKUR expression by deregulating the PI3K/AKT pathway and suppressing c-Jun, an oncogenic transcriptional factor that binds to the ENKUR promoter and negatively modulated its expression in NPC. ENKUR as a tumor suppressor binds to MYH9 and decreases its expression by recruiting β-catenin via its enkurin domain to prevent its nuclear accumulation, which therefore suppresses c-Jun-induced MYH9 expression. Subsequently, downregulated MYH9 reduces the enlistment of E3 ligase UBE3A and thus decreases the UBE3A-mediated ubiquitination degradation of p53, a key tumor suppressor that decreases epithelial-mesenchymal transition (EMT). Clinical sample analysis demonstrated that the ENKUR expression level was significantly reduced in NPC tissues. Its decreased expression substantially promoted clinical progression and reflected poor prognosis for patients with NPC. This study demonstrated that CB induced ENKUR to repress the β-catenin/c-Jun/MYH9 signal and thus decreased UBE3A-mediated p53 ubiquitination degradation. As a result, the EMT signal was inactivated to suppress NPC metastasis.

Purpose To develop and evaluate an efficient precontrast T1 mapping technique suitable for quantitative high‐resolution whole‐brain dynamic contrast‐enhanced–magnetic resonance imaging (DCE‐MRI). Methods Variable flip angle (VFA) T1 mapping was considered that provides 1 × 1 × 2 mm3 resolution to match a recent high‐resolution whole‐brain DCE‐MRI protocol. Seven FAs were logarithmically spaced from 1.5° to 15°. T1 and M0 maps were estimated using model‐based reconstruction. This approach was evaluated using an anatomically realistic brain tumor digital reference object (DRO) with noise‐mimicking 3T neuroimaging and fully sampled data acquired from one healthy volunteer. Methods were also applied on fourfold prospectively undersampled VFA data from 13 patients with high‐grade gliomas. Results T1‐mapping precision decreased with undersampling factor R, althoughwhereas bias remained small before a critical R. In the noiseless DRO, T1 bias was

A new method based on back propagation (BP) neural network for extracting RLCG parameter matrix of multi-core twisted cable is presented. With the properly selected parameter matrix sample, the variation characteristics of the parameter matrix of the multi-core twisted cable can be learned by the Levenberg-Marquardt (L-M) algorithm based on BP neural network. The proposed method is combined with the finite-difference time-domain (FDTD) method to calculate the near end crosstalk (NEXT) and the far end crosstalk (FEXT) of the multi-core twisted cable. To verify the new method, a three-core twisted cable is measured and analyzed in the frequency band of 100 kHz - 1 GHz. The results show that the verification error of the extraction network of the RLCG parameter matrix has good accuracy, which does not exceed 0.8%. Compared with the full wave method, the maximum deviations of FEXT and NEXT solved by the proposed methods are -2.71 dB and 10.56 dB, respectively, which are better than 29.32 dB and 32.45 dB solved by the conventional method.

The aim of this study was to investigate the diagnostic value of contrast-enhanced ultrasound (CEUS) based on the K-means clustering (KMC) algorithm for the vulnerability of carotid atherosclerotic plaque (CAA). In this study, 90 patients with CAA were enrolled into a control group (group A) and an experimental group (group B). The angiography method and KMC-based ultrasound detection were applied to diagnose the CAA patients from the two groups, respectively. The results showed that the sensitivity, specificity, and positive predictive value of patients from group B (92.3%, 90.1%, and 94.8%) for diagnosing CAA were obviously higher than those of patients from group A (81.4%, 88.6%, and 75.3%) ( P < 0.05 ). The detection rate of patients from group B (83%, 85%) was dramatically higher than that of patients from group A (65%, 71%) in terms of artery bifurcation and CAA ( P < 0.05 ). Besides, patients from group B were more satisfied with their diagnostic method than group A ( P < 0.05 ). In conclusion, the ultrasound detection method based on KMC had high sensitivity, specificity, and accuracy in the detection of CAA. In addition, ultrasound detection was better than angiography in the diagnosis of plaque in different parts, and it was worthwhile to apply the ultrasound detection method based on KMC in clinical practice.

Breast cancer (BC) had been one of the deadliest types of cancers in women worldwide. More than 65% of advanced-stage BC patients were identified to have bone metastasis. However, the molecular mechanisms involved in the BC spinal metastases remained largely unclear. This study screened dysregulated genes in the progression of BC spinal metastases by analyzing GSE22358. Moreover, we constructed PPI networks to identify key regulators in this progression. Bioinformatics analysis showed that these key regulators were involved in regulating the metabolic process, cell proliferation, Toll-like receptor and RIG-I-like receptor signaling, and mRNA surveillance. Furthermore, our analysis revealed that key regulators, including C1QB, CEP55, HIST1H2BO, IFI6, KIAA0101, PBK, SPAG5, SPP1, DCN, FZD7, KRT5, and TGFBR3, were correlated to the OS time in BC patients. In addition, we analyzed TCGA database to further confirm the expression levels of these hub genes in breast cancer. Our results showed that these regulators were significantly differentially expressed in breast cancer, which were consistent with GSE22358 dataset analysis. Furthermore, our analysis demonstrated that CEP55 was remarkably upregulated in the advanced stage of breast cancer compared to the stage I breast cancer sample and was significantly upregulated in triple-negative breast cancers (TNBC) compared to other types of breast cancers, including luminal and HER2-positive cancers, demonstrating CEP55 may have a regulatory role in TNBC. Finally, our results showed that CEP55 was the most highly expressed in Basal-like 1 TNBC and Basal-like 2 TNBC samples but the most lowly expressed in mesenchymal stem-like TNBC samples. Although more studies are still needed to understand the functions of key regulators in BC, this study provides useful information to understand the mechanisms underlying BC spinal metastases.

BackgroundHyperhomocysteinemia (HHcy) is an independent risk factor for atherosclerosis. However, the mechanisms of HHcy-induced arteriosclerosis are largely unknown. Objective:To clarify the effect of Hcy on adventitial fibroblasts (AFs) and its relation with angiotensin II type 1 receptor (AT1R).Method:The apolipoprotein E gene-deficient mice (Apoe−/−) were used as the murine model for atherosclerosis. HHcy was induced and treated by feeding them 1.5% methionine and telmisartan (gavage 10 mg/kg/d) for 12 weeks, respectively. The AFs and HEK293A cells transfected with the AT1R plasmid were used to investigate the interaction between Hcy and AT1R. All data were expressed as mean ± SEM. The data were analyzed by one-way ANOVA or repeated measurement ANOVA.Results:HHcy aggravated the plaque area of the aortic root and increased the expression of IL-6, MCP-1, and the macrophage marker Mac3 in the plaque and adventitia of the aorta, whereas telmisartan improved this effect. Hyperhomocysteinemia induced the occurrence of the AFs marker protein ER-TR7 in the plaque and the entire layer of the aorta, whereas telmisartan also improved this effect, indicating that hyperhomocysteinemia induced AFs migration and that AT1R mediated this process. Hcy increased the production of AFs H2O2, ROS, and IL-6 of AFs, indicating that Hcy activated the oxidative stress and inflammatory reactions, which may induce cell migration. The subsequent scratch and transwell experiments confirmed that Hcy induced the AFs migration and that telmisartan inhibited this effect. Hcy increased the expression of AFs AT1R and the phosphorylation levels of PKC and ERK1/2 in the AF and HEK293A cells transfected with the AT1R plasmid, whereas telmisartan inhibited this effect, indicating that Hcy activated AT1R intracellular signaling pathway.Conclusion:Hcy promoted adventitial inflammation, induced AFs migration, and aggravated atherosclerosis by activating AT1R.

Text emotion mining has attracted many researchers’ attention in recent years. Most existing studies focus on identifying emotion of the text’s author. In this paper, we are concerned with the problem of predicting reader’s emotion response from the text content of news article, aka social emotion prediction. Social emotion prediction can not only be used for analyzing people’s reaction to news events, but also help news producers to forecast the underlying influence of their unpublished news articles. We propose a hierarchical LSTM network with attention mechanism to identify the relation between news article and evoked emotion of reader. Compared with existing word-level and topic-level methods which don’t consider the order of words, our method with hierarchical LSTM structure is more efficient to construct the news article representation. With attention mechanism, our method can also find out readers’ emotion reaction to specific word or phrase and generate social emotion lexicon. Through experiments on real-world datasets, we demonstrate that our approach outperforms word-level and topic-level baselines, and a state-of-the-art word2vec-based method.

The lead-lag relationships of stock prices are of great importance for its effectiveness in the fields of risk control and the portfolio investments. In this paper, we propose a new method to analyze the inherent lead-lag relationships of stocks in financial market. Considering the “golden crosses” and the “death crosses”. we represent multivariable stock time series as dynamic competitivity graph series to capture the meaningful signals of the changing trends. Inspired by the corresponding relation of optimal path in the process of calculating Dynamic Time Warping, we propose a new method called LLR to detect the dynamic time evolution of the lead-lag structure between two dynamic competitivity graph series. Finally, we verify the obtained relationships in Chinese A-share market. The experimental results demonstrate that it is effective to use the relationships revealed by LLR to make prediction of stock trend and explain the financial law of stock market.

Thioridazine (THZ) has been identified as a potential regulator of tumour progression, and programmed cell death 4 (PDCD4) has been reported as a novel tumour suppressor. This study aimed to investigate the link between PDCD4 and THZ in the regulation of nasopharyngeal cancer (NPC) cell proliferation. The effect of THZ on NPC cells was determined using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assays. Then, the involvement of apoptosis and cell cycle in the THZ-mediated regulation of cell viability was assessed by flow cytometry. Related mRNAs and proteins were subsequently examined by real-time PCR and western blot, respectively. After transfection with the PDCD4-siRNA, pGC-FU-GFP-PDCD4 vector and phosphoinositide 3-kinase (PI3K) inhibitor Ly294002, we investigated the antagonistic effects of THZ and PDCD4 on NPC-related protein expression. MTT assays showed that THZ treatment suppressed cell viability. THZ-treated cells were arrested at the G1/G0 phase and showed a significantly increased apoptotic fraction. Furthermore, PDCD4-siRNA antagonized THZ treatment and promoted NPC cell proliferation. Western blot analysis showed that PDCD4 overexpression or PI3K inhibition by LY294002 significantly reduced the expression of phospho-PI3K, phospho-Akt, phospho-mammalian target of rapamycin and phospho-p70s6k, but not their total protein levels. In conclusion, our findings show that THZ and PDCD4 exert antagonistic effects on NPC cell proliferation, probably through the PI3K/Akt pathway. Moreover, these results provide an insight into the mechanism by which THZ targets PDCD4 in NPC cell lines and suggest that the ectopic expression of PDCD4 is a potential therapeutic strategy.

PageRank has been widely used for the problem of evaluating the importance of data in many applications, such as Web science, information systems, and social network analysis. As vast amounts of data generate, the development of efficient PageRank computation is a vibrant area of contemporary research. In this paper, we propose a fast PageRank computation method based on network decomposition and the structure of directed acyclic graph (DAG). A network decomposition technique is first introduced to decompose the original network into three parts, including a general sub-network and two sub-DAGs. Based on the acyclic characteristic, we demonstrate that these two sub-DAGs can be theoretically lumped into two single nodes by a similarity transformation. Then, the PageRank problem on the original network is transformed into a PageRank problem on a much smaller network, and the full PageRank vector can be easily recovered from the result of new problem. As the time taken by the estimation of PageRank is directly proportional to the network size, the proposed method achieves an improved time complexity and is more efficient as the size of two sub-DAGs increases. Experimental results on real data sets show that our method provides a significant speedup compared with existing alternatives.

Three novel polydiacetylenes (PDAs) are synthesized through the self-assembly followed by the topochemical polymerization via controllable electrophoretic deposition. All the samples could undergo a multi-step thermochromic process, turning purple and red successively over a wide range from room temperature to above 250 °C. Resulting PDAs are studied by UV–vis, IR, Raman spectroscopies, and chromoisomerism by naked eye visualization; their stabilities by thermogravimetric method, and emission behavior by fluorescence spectroscopy. To study the mechanism of the thermochromic response, temperature-dependent UV–vis spectra, the results of which successfully highlighted the close relationship between chromatic transitions and the conformational changes.

The line impedance stabilization network (LISN) is commonly used to measure the conducted electromagnetic interference (EMI) noise produced by electronic equipment. In order to improve the measurement accuracy of the conducted EMI noise, the characteristics of the LISN should be verified and calibrated, such as impedance and voltage division factor. However, the parasitic parameters of capacitance and inductance are critical to the performance of the LISN. In this paper, the high-frequency model of the LISN is proposed based on the topology and parasitic parameters. The impedance of the LISN is obtained from 150 ​kHz to 30 MHz based on scattering parameters method and vector network analyzer (VNA). The parasitic parameters of capacitance and inductance are extracted by employing the elite genetic algorithm (EGA), LISN topology and impedance. The voltage division factor and impedance of R&S LISN ENV216 is obtained and calculated by employing the proposed method, which verify the proposed method effectiveness. The work will contribute to the further design and calibration of the LISN, and improve the conducted EMI noise measurement accuracy.

We developed a direct vicinal difunctionalization of alkenes with iodine and TBHP at room temperature. This iodination and peroxidation in a one-pot synthesis produces 1-(tert-butylperoxy)-2-iodoethanes, which are inaccessible through conventional synthetic methods. This method generates multiple radical intermediates in situ and has excellent regioselectivity, a broad substrate scope and mild conditions. The iodine and peroxide groups of 1-(tert-butylperoxy)-2-iodoethanes have several potential applications and allow further chemical modifications, enabling the preparation of synthetically valuable molecules.

The effects of hydrothermal dewatering (HTD) on physicochemical properties and moisture re-adsorption capacity of Zhaotong lignite were analyzed. The results showed that HTD could improve the quality of lignite. During the process of HTD, oxygen-containing functional groups decomposed. Both high temperature and pressure favor the formation of large pore volume and small pore diameter. Treated temperature was the key factor governing the moisture re-adsorption capacity of lignite. The moisture re-adsorption degree decreased from 12.47% to 6.13% when the temperature varied from 200 to 300°C. While, the pressure had weaker effect on the degree of moisture re-adsorption.

Via an imine-protection strategy, we herein present an unprecedented copper-catalyzed oxidative multicomponent annulation reaction for direct synthesis of quinazolinones. The construction of various products is achieved via formation of three C-N and one C-C bonds in conjunction with the benzylic functionalization. The merits of easily available feedstocks, naturally abundant catalyst, good functional group and substrate compatibility, and release of H2O as the byproduct make the developed chemistry a practical way to access quinazolinones.

The next-generation sequencing technologies provide a great deal of transcripts for bioinformatics research. Specially, because of the regulation of long non-coding RNAs (lncRNAs) in various cellular processes, the research on IncRNAs is in full swing. And the solution of IncRNAs identification is the basis for the in-depth study of its functions. In this study, we present an approach to identify the IncRNAs from large scale transcripts, named IncDML which is completely different from previous identification methods. In our model, we extract signal to noise ratio (SNR) and k-mer from transcripts sequences as features. Firstly, we just use the SNR to cluster the original dataset to three parts. In this process, we achieve preliminary identification effect to some extent. Then abandoning traditional feature selection, we directly measure the relationship between each pair of samples by deep metric learning for each part of data. Finally, a novel classifier based on complex network is applied to achieve the final identification. The experiment results show that IncDML is a very effective method for identifying IncRNAs.

Time series analysis based on pattern discovery has received a lot of interests in the fields of economic physics and machine learning due to its simplicity and ability to reveal complex nonlinear behavior in stock market. Dynamic Time Warping (DTW) is a useful tool to extract morphological characteristics of time series for its capacity to cope with time shifts and warpings. In this paper, we propose a new time series representation method for stock time series based on dynamic time warping (DTW) called PR-DTW. A combinatorial optimization model with strict constraints is built to get the pattern representation of stock time series. To simplify the calculation, we construct another unconstrained global optimization problem whose optimal solution includes the optimal solution of the original combinatorial optimization problem based on a theorem proved in this paper. Particle Swarm Optimization algorithm is used to solve the global optimization problem, then the results can be converted into the optimal solution of the combinatorial optimization problem through a few simple formulas given in the theorem. The results of three classifiers (1NN, Decision Tree, Multi-layer Perceptron) implemented on 15 sectors in Chinese A-share market unanimously demonstrate that PR-DTW has the capability of extracting time series short-term patterns which is widely regarded as difficulty. And we conclude that PR-DTW has the capability of prevention of End Effect, anti-noise and segmentation. Moreover, by extracting the top ten patterns predicting stock’s rise and fall in short term (10 days) according to the ranking of stock’s rising probability in the next three days, we find out the short-term patterns obtained by PR-DTW have prospective directive to the stock trend analysis in short term.

With the popularization of the Internet, the impact of news events on the society is growing. Research on the evolution of news event popularity could help netizens to grasp the overall development of news events. At present, the timeliness of the popularity prediction method for news event is prevalently weak and the data set is insufficient. This paper captures the real-time data from Baidu news search engine, and builds the predictive model of news event popularity based on ELM (Extreme Learning Machine). According to the similarity between news events and the prediction error of news event popularity, we propose the ELM-based prediction method by introducing the transfer learning strategy which considers the normalization of peak values of the popularity series. Our method is beneficial for establishing the prediction model under the non-independent distribution with missing data in the modeling process. The experimental results show that our method can predict the popularity of news event timely and effectively.