Effect of inhibiting ACAT-1 expression on the growth and metastasis of Lewis lung carcinoma.

Accumulating evidence suggests that acetyl-CoA acetryltransferase 1 (ACAT-1) may mediate tumor development and metastasis. However, the specific function served by ACAT-1 in lung cancer is not well understood. Therefore, the present study initially verified that ACAT-1 was overexpressed in Lewis lung carcinoma (LLC) tissues compared with non-LLC mice and that this overexpression promoted the proliferation, invasion and metastasis of these LLC samples. Western blotting, immunofluorescence microscopy and flow cytometry allowed the present study to determine that the ACAT-1 inhibitor avasimibe significantly reduced the expression of ACAT-1 in LLC compared with LLC cells that are not treated with avasimibe (P<0.05). A combination of Cell Counting Kit-8 and wound healing assays demonstrated that downregulating ACAT-1 expression sufficiently inhibited the proliferation of LLC cells. Avasimibe promoted LLC cell apoptosis as assessed by a Annexin V/propidium iodide double staining assay. Furthermore, avasimibe inhibited tumor growth in vivo and improved immune responses, with tissue biopsies from LLC model mice exhibiting higher levels of ACAT-1 compared with in healthy controls. Altogether, the results of the present study reveal that avasimibe may inhibit the progression of LLC by downregulating the expression of ACAT-1, which may thus be a potential novel therapeutic target for lung cancer treatment.

Comparison of case-based learning and traditional method in teaching postgraduate students of medical oncology.

Purpose: Case-based learning (CBL) is now used as a teaching strategy to promote clinical problem-solving ability. The purpose of this study was to determine whether CBL is superior to the traditional teaching method in teaching lung cancer curriculum to oncology students. Methods: This study was a randomized controlled trial, enrolled 80 first-year oncology postgraduates from Bengbu medical college in the past 3 years. They were randomized to divide into 2 groups, had courses with the same lung cancer contents and timing. The experimental group (n = 40) utilized the CBL method while the control group (n = 40) used the traditional lecture-based teaching method. A questionnaire was used to attain the students' learning satisfaction and self-efficacy of the course, and a post-study examination was used to assess end-of-course performance. Results: Complete data were obtained from participating students (n = 40 in CBL; n = 40 in traditional teaching). The CBL group performed significantly better in questionnaire and examination compared to traditional teaching groups. Students showed high levels of satisfaction and problem-solving ability in the CBL group. Conclusion: Compared with the traditional teaching method. The case teaching method is a more effective teaching method to improve the ability of problem-solving for graduate students in medical oncology.

Study on dynamic adsorption of p-nitrophenol by multi-walled carbon nanotubes dispersed cyclodextrin.

In this paper, the modified multi-walled carbon nanotubes were prepared by ß-cyclodextrin denoted as ß-CD-MWNTs. The structure and morphology of ß-CD-MWNTs was characterized by TEM and the dynamic adsorption of p-nitrophenol on ß-CD-MWNTs was studied by the Thomas model. Some affecting factors of dynamic adsorption and the adsorbent regeneration process such as the sewage concentration, the amount of absorbent in column, including the type of reagent, solid-liquid ratio, regeneration time, and regeneration times were investigated and optimized. The results indicated that the p-nitrophenol removal rate could reach 84% under stuffing 2 g ß-CD-MWNTs. The curves of p-nitrophenol's dynamic adsorption conformed to the Thomas model. Moreover, the adsorption capacity of regenerated ß-CD-MWNTs was similar to the fresh ß-CD-MWNT column. The optimal conditions of regenerations of ß-CD-MWNTs were shown as follows: the type of reagent is anhydrous ethanol, the solid-liquid ratio is 200:40 (mg/mL) and the regeneration time is 120 min.

SLAE-CPS: Smart Lean Automation Engine Enabled by Cyber-Physical Systems Technologies.

In the context of Industry 4.0, the demand for the mass production of highly customized products will lead to complex products and an increasing demand for production system flexibility. Simply implementing lean production-based human-centered production or high automation to improve system flexibility is insufficient. Currently, lean automation (Jidoka) that utilizes cyber-physical systems (CPS) is considered a cost-efficient and effective approach for improving system flexibility under shrinking global economic conditions. Therefore, a smart lean automation engine enabled by CPS technologies (SLAE-CPS), which is based on an analysis of Jidoka functions and the smart capacity of CPS technologies, is proposed in this study to provide an integrated and standardized approach to design and implement a CPS-based smart Jidoka system. A set of comprehensive architecture and standardized key technologies should be presented to achieve the above-mentioned goal. Therefore, a distributed architecture that joins service-oriented architecture, agent, function block (FB), cloud, and Internet of things is proposed to support the flexible configuration, deployment, and performance of SLAE-CPS. Then, several standardized key techniques are proposed under this architecture. The first one is for converting heterogeneous physical data into uniform services for subsequent abnormality analysis and detection. The second one is a set of Jidoka scene rules, which is abstracted based on the analysis of the operator, machine, material, quality, and other factors in different time dimensions. These Jidoka rules can support executive FBs in performing different Jidoka functions. Finally, supported by the integrated and standardized approach of our proposed engine, a case study is conducted to verify the current research results. The proposed SLAE-CPS can serve as an important reference value for combining the benefits of innovative technology and proper methodology.

Inference for local autocorrelations in locally stationary models.

For non-stationary processes, the time-varying correlation structure provides useful insights into the underlying model dynamics. We study estimation and inferences for local autocorrelation process in locally stationary time series. Our constructed simultaneous confidence band can be used to address important hypothesis testing problems, such as whether the local autocorrelation process is indeed time-varying and whether the local autocorrelation is zero. In particular, our result provides an important generalization of the R function acf() to locally stationary Gaussian processes. Simulation studies and two empirical applications are developed. For the global temperature series, we find that the local autocorrelations are time-varying and have a "V" shape during 1910-1960. For the S&P 500 index, we conclude that the returns satisfy the efficient-market hypothesis whereas the magnitudes of returns show significant local autocorrelations.

Nonparametric Functional Central Limit Theorem for Time Series Regression with Application to Self-normalized Confidence Interval.

This paper is concerned with the inference of nonparametric mean function in a time series context. The commonly used kernel smoothing estimate is asymptotically normal and the traditional inference procedure then consistently estimates the asymptotic variance function and relies upon normal approximation. Consistent estimation of the asymptotic variance function involves another level of nonparametric smoothing. In practice, the choice of the extra bandwidth parameter can be difficult, the inference results can be sensitive to bandwidth selection and the normal approximation can be quite unsatisfactory in small samples leading to poor coverage. To alleviate the problem, we propose to extend the recently developed self-normalized approach, which is a bandwidth free inference procedure developed for parametric inference, to construct point-wise confidence interval for nonparametric mean function. To justify asymptotic validity of the self-normalized approach, we establish a functional central limit theorem for recursive nonparametric mean regression function estimates under primitive conditions and show that the limiting process is a Gaussian process with non-stationary and dependent increments. The superior finite sample performance of the new approach is demonstrated through simulation studies.

Efficient Regressions via Optimally Combining Quantile Information.

We develop a generally applicable framework for constructing efficient estimators of regression models via quantile regressions. The proposed method is based on optimally combining information over multiple quantiles and can be applied to a broad range of parametric and nonparametric settings. When combining information over a fixed number of quantiles, we derive an upper bound on the distance between the efficiency of the proposed estimator and the Fisher information. As the number of quantiles increases, this upper bound decreases and the asymptotic variance of the proposed estimator approaches the Cramér-Rao lower bound under appropriate conditions. In the case of non-regular statistical estimation, the proposed estimator leads to super-efficient estimation. We illustrate the proposed method for several widely used regression models. Both asymptotic theory and Monte Carlo experiments show the superior performance over existing methods.

Specification test for Markov models with measurement errors.

Most existing works on specification testing assume that we have direct observations from the model of interest. We study specification testing for Markov models based on contaminated observations. The evolving model dynamics of the unobservable Markov chain is implicitly coded into the conditional distribution of the observed process. To test whether the underlying Markov chain follows a parametric model, we propose measuring the deviation between nonparametric and parametric estimates of conditional regression functions of the observed process. Specifically, we construct a nonparametric simultaneous confidence band for conditional regression functions and check whether the parametric estimate is contained within the band.

NON-PARAMETRIC ESTIMATION UNDER STRONG DEPENDENCE.

We study non-parametric regression function estimation for models with strong dependence. Compared with short-range dependent models, long-range dependent models often result in slower convergence rates. We propose a simple differencing-sequence based non-parametric estimator that achieves the same convergence rate as if the data were independent. Simulation studies show that the proposed method has good finite sample performance.

Asymptotics of nonparametric L-1 regression models with dependent data.

We investigate asymptotic properties of least-absolute-deviation or median quantile estimates of the location and scale functions in nonparametric regression models with dependent data from multiple subjects. Under a general dependence structure that allows for longitudinal data and some spatially correlated data, we establish uniform Bahadur representations for the proposed median quantile estimates. The obtained Bahadur representations provide deep insights into the asymptotic behavior of the estimates. Our main theoretical development is based on studying the modulus of continuity of kernel weighted empirical process through a coupling argument. Progesterone data is used for an illustration.

Inference for modulated stationary processes.

We study statistical inferences for a class of modulated stationary processes with time-dependent variances. Due to non-stationarity and the large number of unknown parameters, existing methods for stationary or locally stationary time series are not applicable. Based on a self-normalization technique, we address several inference problems, including self-normalized central limit theorem, self-normalized cumulative sum test for the change-point problem, long-run variance estimation through blockwise self-normalization, and self-normalization-based wild boot-strap. Monte Carlo simulation studies show that the proposed self-normalization-based methods outperform stationarity-based alternatives. We demonstrate the proposed methodology using two real data sets: annual mean precipitation rates in Seoul during 1771-2000, and quarterly U.S. Gross National Product growth rates during 1947-2002.

Testing for changes in autocovariances of nonparametric time series models.

In the literature on change-point analysis, much attention has been paid to detecting changes in certain marginal characteristics, such as mean, variance, and marginal distribution. For time series data with nonparametric time trend, we study the change-point problem for the autocovariance structure of the unobservable error process. To derive the asymptotic distribution of the cumulative sum test statistic, we develop substantial theory for uniform convergence of weighted partial sums and weighted quadratic forms. Our asymptotic results improve upon existing works in several important aspects. The performance of the test statistic is examined through simulations and an application to interest rates data.

Unified inference for sparse and dense longitudinal models.

In longitudinal data analysis, statistical inference for sparse data and dense data could be substantially different. For kernel smoothing estimate of the mean function, the convergence rates and limiting variance functions are different under the two scenarios. The latter phenomenon poses challenges for statistical inference as a subjective choice between the sparse and dense cases may lead to wrong conclusions. We develop self-normalization based methods that can adapt to the sparse and dense cases in a unified framework. Simulations show that the proposed methods outperform some existing methods.

PROFILE CONTROL CHARTS BASED ON NONPARAMETRIC L-1 REGRESSION METHODS.

Classical statistical process control often relies on univariate characteristics. In many contemporary applications, however, the quality of products must be characterized by some functional relation between a response variable and its explanatory variables. Monitoring such functional profiles has been a rapidly growing field due to increasing demands. This paper develops a novel nonparametric L-1 location-scale model to screen the shapes of profiles. The model is built on three basic elements: location shifts, local shape distortions, and overall shape deviations, which are quantified by three individual metrics. The proposed approach is applied to the previously analyzed vertical density profile data, leading to some interesting insights.

Nonparametric model validations for hidden Markov models with applications in financial econometrics.

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.

A self-normalized confidence interval for the mean of a class of nonstationary processes.

We construct an asymptotic confidence interval for the mean of a class of nonstationary processes with constant mean and time-varying variances. Due to the large number of unknown parameters, traditional approaches based on consistent estimation of the limiting variance of sample mean through moving block or non-overlapping block methods are not applicable. Under a block-wise asymptotically equal cumulative variance assumption, we propose a self-normalized confidence interval that is robust against the nonstationarity and dependence structure of the data. We also apply the same idea to construct an asymptotic confidence interval for the mean difference of nonstationary processes with piecewise constant means. The proposed methods are illustrated through simulations and an application to global temperature series.