Quadruple therapy for eradication of Helicobacter pylori

AIM:To investigate quadruple therapy with rabeprazole,amoxicillin,levofloxacin and furazolidone for the eradication of Helicobacter pylori (H.pylori) infection.METHODS:A total of 147 patients were divided into the experimental treatment group (n=78) and the standard triple treatment group (n=69).The experimental treatment group received rabeprazole 20 mg,amoxicillin 1.0 g,levofloxacin 0.2 g and furazolidone 0.1 g,twice daily.The standard triple treatment group received omeprazole 20 mg,amoxicillin 1.0 g and clarithromycin 0.5 g,twice daily.RESULTS:One month after treatment,the 13 C urea breath test was carried out to detect H.pylori.The eradication rate using per-protocol analysis was 94.3% in the experimental treatment group and 73% in the standard triple treatment group (P < 0.05),and using intention to test analysis,these figures were 86% and 67% in the two groups,respectively.Side effects were observed in 34 patients,and included mild dizziness,nausea,diarrhea and increased bowel movement.Eleven of the 34 patients needed no treatment for their side effects.CONCLUSION:Rabeprazole,amoxicillin,levofloxacinand furazolidone quadruple therapy is a safe method for the eradication of H.pylori with high efficacy and good tolerability.

Application of ESMD Method to Air-Sea Flux Investigation

The ESMD method can be seen as a new alternate of the well-known Hilbert-Huang transform (HHT) for non-steady data processing. It is good at finding the optimal adaptive global mean fitting curve, which is superior to the common least-square method and running-mean approach. Take the air-sea momentum flux investigation as an example, only when the non-turbulent wind components is well extracted, can the remainder signal be seen as actual oscillations caused by turbulence. With the aid of —5/3 power law for the turbulence, a mode-filtering approach based on ESMD decomposition is developed here. The test on observational data indicates that this approach is very feasible and it may greatly reduce the error caused by the non-turbulent components.

A novel hybrid algorithm for lucky imaging

Lucky imaging is a high-resolution astronomical image recovery technique with two classic implementation algorithms,i.e.image selecting,shifting and adding in image space,and data selecting and image synthesizing in Fourier space.This paper proposes a novel lucky imaging algorithm where with space-domain and frequency-domain selection rates as a link,the two classic algorithms are combined successfully,making each algorithm a proper subset of the novel hybrid algorithm.Experimental results show that with the same experiment dataset and platform,the high-resolution image obtained by the proposed algorithm is superior to that obtained by the two classic algorithms.This paper also proposes a new lucky image selection and storage scheme,which can greatly save computer memory and enable lucky imaging algorithm to be implemented in a common desktop or laptop with small memory and to process astronomical images with more frames and larger size.In addition,through simulation analysis,this paper discusses the binary star detection limits of the novel lucky imaging algorithm and traditional ones under different atmospheric conditions.

Melatonin inhibits ESCC tumor growth by mitigating the HDAC7/β-catenin/c-Myc positive feedback loop and suppressing the USP10-maintained HDAC7 protein stability

Background:Melatonin,a natural hormone secreted by the pineal gland,has been reported to exhibit antitumor properties through diverse mechanisms of action.However,the oncostatic function of melatonin on esophageal squamous cell carcinoma(ESCC) remains elusive.This study was conducted to investigate the potential effect and underlying molecular mechanism of melatonin as single anticancer agent against ESCC cells.Methods:ESCC cell lines treated with or without melatonin were used in this study.In vitro colony formation and 5-Ethynyl-2’-deoxyuridine(EdU) incorporation assays,and nude mice tumor xenograft model were used to confirm the proliferative capacities of ESCC cells.RNA-seq,qPCR,Western blotting,recombinant lentivirus-mediated target gene overexpression or knockdown,plasmids transfection and co-IP were applied to investigate the underlying molecular mechanism by which melatonin inhibited ESCC cell growth.IHC staining on ESCC tissue microarray and further survival analyses were performed to explore the relationship between target genes’ expression and prognosis of ESCC.Results:Melatonin treatment dose-dependently inhibited the proliferative ability and the expression of histone deacetylase 7(HDAC7),c-Myc and ubiquitin-specific peptidase 10(USP10) in ESCC cells(P<0.05).The expressions of HDAC7,c-Myc and USP10 in tumors were significantly higher than the paired normal tissues from 148 ESCC patients(P<0.001).Then,the Kaplan-Meier survival analysis suggested that ESCC patients with high HDAC7,c-Myc or USP10levels predicted worse overall survival(log-rank P<0.001).Co-IP and Western blotting further revealed that HDAC7physically deacetylated and activated β-catenin thus promoting downstream target c-Myc gene transcription.Notably,our mechanistic study validated that HDAC7/β-catenin/c-Myc could form the positive feedback loop to enhance ESCC cell growth,and USP10 could deubiquitinate and stabilize HDAC7 protein in the ESCC cells.Additionally,we verified that inhibition of the HDAC7/β-catenin/c-Myc axis and USP10/HDAC7 pathway mediated the anti-proliferative action of melatonin on ESCC cells.Conclusions:Our findings elucidate that melatonin mitigates the HDAC7/β-catenin/c-Myc positive feedback loop and inhibits the USP10-maintained HDAC7 protein stability thus suppressing ESCC cell growth,and provides the reference for identifying biomarkers and therapeutic targets for ESCC.

Passivity Analysis of Impulsive Complex Networks

In this paper, we first investigate input passivity and output passivity for a class of impulsive complex networks with time-varying delays. By constructing suitable Lyapunov functionals, some input passivity and output passivity conditions are derived for the impulsive complex networks. Finally, an example is given to show the effectiveness of the proposed criteria.

Ocean Wave Model and Wave Drift Caused by the Asymmetry of Crest and Trough

It follows from the review on classical wave models that the asymmetry of crest and trough is the direct cause for wave drift. Based on this, a new model of Lagrangian form is constructed. Relative to the Gerstner model, its improvement is reflected in the horizontal motion which includes an explicit drift term. On the one hand, the depth-decay factor for the new drift accords well with that of the particle’s horizontal velocity. It is more rational than that of Stokes drift. On the other hand, the new formula needs no Taylor expansion as for Stokes drift and is applicable for the waves with big slopes. In addition, the new formula can also yield a more rational magnitude for the surface drift than that of Stokes.

Group training on the improvement of college students'career decision-making self-efficacy

A group training was conducted on 17 college students to improve their career decision-making self-efficacy (CDMSE). The result showed that there was significant difference between the pre-test and the post-test for the experimental group (n = 17), whereas no significant difference was found between the pre-test and the post- test for the control group (n = 17). In the pre-test, there was no significant difference between the experimental group and the control group, and obvious difference between the two groups was found in the post-test. This indicated that the group training was effective on improving the CDMSE of the college students whose scores of CDMSE were below 27% point of the total students.

Machine Learning for Organic Photovoltaic Polymers:A Minireview

Machine learning is a powerful tool that can provide a way to revolutionize the material science.Its use for the designing and screening of materials for polymer solar cells is also increasing.Search of efficient polymeric materials for solar cells is really difficult task.Researchers have synthesized and fabricated so many materials.Sorting the results and get feedback for further research requires an innovative approach.In this minireview,we provides brief introduction of machine learning.The importance of machine learning is also mentioned,and the application of machine learning for polymeric material design is discussed.The key challenges that are hindering the wide spread use of machine are discussed.Suggestions are also given to improve the use of data science.The predictions using machine learning maybe not highly accurate but it definitely better than no prediction at all.

Asymptotic Weighted-Periodicity of the Impulsive Parabolic Equation with Time Delay

The main aim of this paper is to investigate the effects of the impulse and time delay on a type of parabolic equations. In view of the characteristics of the equation, a particular iteration scheme is adopted. The results show that Under certain conditions on the coefficients of the equation and the impulse, the solution oscillates in a particular manner--called ＂asymptotic weighted-periodicity＂.

Exponential Synchronization of Impulsive Complex Networks with Output Coupling

This paper proposes a new impulsive complex delayed dynamical network model with output coupling, which is totally different from some existing network models. Then, by employing impulsive delay differential inequalities, some sufficient conditions are obtained to guarantee the global exponential state synchronization and output synchronization of the impulsive complex delayed dynamical network. Finally, two numerical examples are given to demonstrate the effectiveness of the obtained results.