Search

Additional file 3. Fig. S1. Original gel image for long PCR.

Objective To investigate the effect and safety of iron chelation therapy on hematopoiesis in hematological malignancy patients with iron overload after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Methods The clinical data of 38 hematological malignancy patients who suffered from iron overload after allo-HSCT and were treated in our hospital from May 2019 to June 2021 were retrospectively reviewed and analyzed. According to receiving iron chelation or not, they were divided into treatment group (n=21, oral administration of deferasirox) and control group (n=17, no iron chelation). The changes of hemoglobin (Hb) level, platelet (PLT) count, white blood cell (WBC) count and serum ferritin (SF) level were compared between the 2 groups, and the adverse reactions during administration were observed as well. Results At 2, 4 and 8 weeks after treatment, Hb level and PLT count in the treatment group were significantly improved from baseline as compared with the control group (P < 0.05), but the WBC count showed no significant difference between the 2 groups (P > 0.05). SF levels in the treatment group was decreased from baseline at 4 and 8 weeks, significantly lower than those of the control group [-867 (-1 918~ 477) vs 436 (-408~467) μg/L, P < 0.001;-1 243 (-2 784~364) vs 541 (-674~578) μg/L, P < 0.001]. None of the patients in the treatment group developed serious gastrointestinal symptoms or severe damage to liver and kidney functions. Conclusion Iron chelation therapy can effectively improve the hematopoiesis, with satisfactory safety, in the patients suffering from iron overload after allo-HSCT.

Ergodic theorem shows that ergodic averages of the posterior draws converge in probability to the posterior mean under the stationarity assumption. The literature also shows that the posterior distribution is asymptotically normal when the sample size of the original data considered goes to infinity. To the best of our knowledge, there is little discussion on the large sample behaviour of the posterior mean. In this paper, we aim to fill this gap. In particular, we extend the posterior mean idea to the conditional mean case, which is conditioning on a given summary statistics of the original data. We stablish a new asymptotic theory for the conditional mean estimator for the case when both the sample size of the original data concerned and the number of Markov chain Monte Carlo iterations go to infinity. Simulation studies show that this conditional mean estimator has very good finite sample performance. In addition, we employ the conditional mean estimator to estimate a GARCH(1,1) model for S&P 500 stock returns and find that the conditional mean estimator performs better than quasi-maximum likelihood estimation in terms of out-of-sample forecasting.

Ergodic theorem shows that ergodic averages of the posterior draws converge in probability to the posterior mean under the stationarity assumption. The literature

We propose two new nonparametric predictive models: the multi-step nonparametric predictive regression model and the multi-step additive predictive regression model, in which the predictive variables are locally stationary time series. We define estimation methods and establish the large sample properties of these methods in the short horizon and the long horizon case. We apply our methods to stock return prediction using a number of standard predictors such as dividend yield. The empirical results show that all of these models can substantially outperform the traditional linear predictive regression model in terms of both in-sample and out-of-sample performance. In addition, we _nd that these models can always beat the historical mean model in terms of in-sample fitting, and also for some cases in terms of the out-of-sample forecasting. We also compare our methods with the linear regression and historical mean methods according to an economic metric. In particular, we show how our methods can be used to deliver a trading strategy that beats the buy and hold strategy (and linear regression based alternatives) over our sample period.

In this paper, we study a class of high dimensional moment restriction panel data models with interactive effects, where factors are unobserved and factor loadings are nonparametrically unknown smooth functions of individual characteristics variables. We allow the dimension of the parameter vector and the number of moment conditions to diverge with sample size. This is a very general framework and includes many existing linear and nonlinear panel data models as special cases. In order to estimate the unknown parameters, factors and factor loadings, we propose a sieve-based generalized method of moments estimation method and we show that under a set of simple identification conditions, all those unknown quantities can be consistently estimated. Further we establish asymptotic distributions of the proposed estimators. In addition, we propose tests for over-identification, specification of factor loading functions, and establish their large sample properties. Moreover, a number of simulation studies are conducted to examine the performance of the proposed estimators and test statistics in finite samples. An empirical example on stock return prediction is studied to demonstrate the usefulness of the proposed framework and corresponding estimation methods and testing procedures.

In this paper, we introduce a regime switching panel data model with interactive fixed effects. We propose a maximum likelihood estimation method and develop an expectation and conditional maximization algorithm to estimate the unknown parameters. Simulation results show that the algorithm works well in finite samples. The biases of the maximum likelihood estimators are negligible and the root mean squared errors of the maximum likelihood estimators decrease with the increase of either cross-sectional units N or time periods T.

Chang Shu,1 Shumei Han,2 Peng Xu,3 Ying Wang,1 Tingting Cheng,1 Cong Hu4 1Department of Obstetrics and Gynecology, The First Hospital of Jilin University, Jilin University, Changchun, Jilin, 130061, People’s Republic of China; 2Department of Medical Administration, The First Hospital of Jilin University, Jilin University, Changchun, Jilin, 130021, People’s Republic of China; 3Department of Sports Medicine, The First Hospital of Jilin University, Jilin University, Changchun, Jilin, 130021, People’s Republic of China; 4Reproductive Center, The First Hospital of Jilin University, Jilin University, Changchun, Jilin, 130021, People’s Republic of ChinaCorrespondence: Cong HuReproductive Center, The First Hospital of Jilin University, Jilin University, No. 71 Xinmin Street, Changchun, Jilin, 130061, People’s Republic of ChinaEmail conghu@jlu.edu.cnAbstract: There is a significant decline in the estrogen levels in preeclampsia, and exogenous administration of estradiol normalizes blood pressure and other associated symptoms of preeclampsia. The decrease in estrogen levels may be due to changes in enzyme activities of hydroxysteroid (17-β) dehydrogenase 1, aromatase, and COMT. There is also a decrease in the novel, estrogenic G-protein-coupled receptor 30 (GPR30) in the placental trophoblast cells in preeclampsia. The activation of GPR30 protects the placenta from hypoxia-reoxygenation injury, decreases apoptosis and increases proliferation through eNOS and PI3K-Akt signaling pathways. Estrogens may also increase Ca2+-activated K+ channel function, decrease the release of inflammatory cytokines, and oxidative stress to improve placental perfusion. Both preclinical and clinical studies show the decrease in the 2-methoxyestradiol levels in preeclampsia, which may be due to a decrease in estradiol itself along with a decrease in the enzymatic actions of the COMT enzyme. 2-Methoxyestradiol activates HIF1α and vascular endothelial growth factor receptors (VEGFR-2) to maintain placental perfusion by increasing angiogenesis. The present review discusses the preclinical and clinical studies describing the role of estrogen in preeclampsia along with possible mechanisms.Keywords: estradiol, oxidative stress, hypertension, inflammation, perfusion

An ultrasensitive fluorescent probe for monitoring NAD(P)H and revealing reductive stress induced by natural antioxidants in HepG2 cells under hypoxia., Reductive stress, the opposite of oxidative stress, represents a disorder in the redox balance state which is harmful to biological systems. For decades, the role of oxidative stress in tumor therapy has been the focus of attention, while the effects of reductive stress have been rarely studied. Here, we report the anti-cancer effects of reductive stress induced by three natural antioxidants (resveratrol, curcumin and celastrol). Considering the fact that the solid tumor microenvironment suffers from hypoxia, we performed cell experiments under hypoxic conditions. In order to observe the reductive stress, we first developed an ultrasensitive fluorescent probe (TCF-MQ) for specifically imaging NAD(P)H which is a marker of reductive stress. TCF-MQ responded to NAD(P)H rapidly and exhibited high sensitivity with a detection limit of 6 nM. With the help of TCF-MQ, we found that upon the treatment of HepG2 cells with pharmacological doses of three natural antioxidants under hypoxic conditions, high levels of NAD(P)H were produced before cell death. The excess NAD(P)H resulted in reductive stress instead of oxidative stress. In contrast, under normoxic conditions, there was no reductive stress involved in the process of cell death induced by three natural antioxidants. Therefore, we hypothesize that the mechanism of cancer cell death induced by natural antioxidants under hypoxia should be attributed to the reductive stress.

Dynamic contact resistance measurement (DRM) is an effective technique to evaluate the contact condition in substations without dismantling circuit breakers. However, performing DRM tests with unsuitable test parameters will cause low repeatability of the test results, and even lead to the misdiagnosis of contact condition. Performing the DRM tests at high DC injected current can improve the repeatability. This paper studies the mechanism of the DC injected current on the repeatability of the DRM test results, and it also promotes a new determination method of the DC injected current amplitude. The impact of the DC injected current on DRM test results is reviewed. The mechanism of the DC injected current on DRM test results is investigated by analysing the chemical composition of the contact surface, the force exerted on the contacts, and the temperatures of a-spots on the contacts’ surface. Results indicate that not the Lorentz force, but the temperature of a-spots is the primary influencing factor. The amplitude of the DC injected current can be determined by calculating the temperatures of the a-spot on the contacts’ surface. And this method can also be used for other SF6 circuit breakers.

The no-load curve of the dielectric recovery strength of the ultra-fast mechanical vacuum switch (MVS) changing with time is the theoretical foundation to optimize the turn-off strategy of the hybrid direct current circuit breaker with load commutation switch. To avoid the shortcomings of the classic synthetic circuit, a novel test circuit is proposed to measure the no-load dielectric recovery curve of the MVS in this paper. In the test circuit, a succession of pulse voltages is automatically applied on the MVS, and multiple breakdowns of the MVS occur in one test. Therefore, the dielectric strength of the MVS at multiple time points is obtained, and the envelope of the applied voltage on the MVS during one test can describe the dielectric recovery curve of the MVS; as a result, the test workload could be reduced dramatically and the measurement is more authentic. By changing test conditions, including the rate of rise of the test voltage and the operating speed of the MVS, no-load dielectric recovery characteristics are studied. The research has shown that the dielectric recovery speed of the MVS is irrelative with the rate of rise of the test voltage and it is directly proportional to the opening speed of the MVS.

Dynamic contact resistance measurement (DRM) is an effective technique to evaluate the contact conditions of high voltage SF 6 circuit breaker. However, the relations between DRM characteristics and contact performance remain ambiguous, the influence of the electrical stress on the DRM curves is equivocal as well, and there is no contact erosion evaluation method based on DRM. To investigate the relations between DRM characteristics and contact erosion, the DRM results of high voltage SF 6 circuit breaker at various degrees of erosion obtained from the designed experiments are presented. The experiments demonstrate that with the degradation of the contacts, the average arcing contact resistance ( R a ) increases, and the arcing contact wipe ( D a ) decreases. Then, the simulation of the erosion test is performed to analyse the relations between R a and D a and the contact performance. The results show that with the decrease of R a , the threshold of D a to ensure the success of the current commuting from the main contacts to the arcing contacts becomes shorter. Moreover, the influence of the electrical stress on R a and D a are investigated. Results demonstrate that R a reflects the influence of arc by-products and contact geometry caused by electrical stress, and that D a reflects the influence of the interrupted current, the arcing time, the contact geometry, and the contact material. Finally, an evaluation method of the contact erosion for SF 6 circuit breaker based on DRM is proposed.

The oncogenic roles of ETV4 have been revealed in multiple cancers. However, its expression and functions in lung cancer are rarely explored. Here, we firstly detected the expression of ETV4 in lung adenocarcinoma (LUAD) via online data and local experiment validation. Furthermore, we explored the functions and corresponding mechanisms of ETV4 in LUAD. Upregulation of ETV4 in LUAD is indicated by online data and our results of qPCR, Western blot and immunohistochemistry in collective tissue samples. ETV4 knockdown significantly inhibits proliferation and invasion in LUAD indicated by the outcomes of CCK8, plate clone formation, and Transwell invasion assays. Mechanistically, chromatin immunoprecipitation and luciferase reporter system assays indicated that ETV4 could directly bind at the promoter of MSI2 and promote its transcription. Furthermore, ectopic expression MSI2 can rescue the inhibitory effects caused by ETV4 knockdown in LUAD. Therefore, we proved that upregulation of ETV4 could promote proliferation and invasion of LUAD by transcriptionally upregulating MSI2 offering a potential therapy treatment target of LUAD.

The effect of spring snowmelt infiltration in a seasonal soil frost area on groundwater recharge was evaluated by systematically monitoring meteorological factors, soil temperature and humidity, groundwater table and temperature, electrical conductivity, and the value of δ18O in a small field site over a 2-year period. The variation of soil temperature and humidity, groundwater table during the freezing period, and the snowmelt period respectively, as well as their correspondence to the relevant environmental factors, and the influencing factors of the permeability of frozen layer were analyzed. The results showed that the evaluation of precipitation infiltration in seasonal soil frost areas should be divided into three stages: a non-freezing period, a freezing period, and a snowmelt period. Snow is the main form of precipitation during the freezing period, and groundwater cannot be recharged. During the snowmelt period of spring, the snow cover that accumulated during the freezing period infiltrates together with rainfall and has a significant effect on groundwater recharge. The general precipitation infiltration process occurs after the frozen soil thaws completely. These research results can improve the accuracy of groundwater recharge calculations for snowmelt infiltration in the seasonal soil frost area of Northeast China and provide a scientific basis for the evaluation and management of regional water resources.

Though deep learning has shown successful performance in classifying the label and severity stage of certain disease, most of them give few evidence on how to make prediction. Here, we propose to exploit the interpretability of deep learning application in medical diagnosis. Inspired by Koch's Postulates, a well-known strategy in medical research to identify the property of pathogen, we define a pathological descriptor that can be extracted from the activated neurons of a diabetic retinopathy detector. To visualize the symptom and feature encoded in this descriptor, we propose a GAN based method to synthesize pathological retinal image given the descriptor and a binary vessel segmentation. Besides, with this descriptor, we can arbitrarily manipulate the position and quantity of lesions. As verified by a panel of 5 licensed ophthalmologists, our synthesized images carry the symptoms that are directly related to diabetic retinopathy diagnosis. The panel survey also shows that our generated images is both qualitatively and quantitatively superior to existing methods., Comment: to appear in AAAI (2019). The first two authors contributed equally to the paper. Corresponding Author: Feng Lu

This thesis investigates three main topics, which are bandwidth selection for local linear estimation of time–varying coefficient time series models, nonparametric estimation of functional coefficient time series models with trending regressors and semiparametric localised bandwidth selection in kernel density estimation. First, we propose a Bayesian approach to bandwidth selection for local linear estimation of time–varying coefficient time series models, where the errors are assumed to follow the Gaussian kernel error density. A Markov chain Monte Carlo algorithm is presented to simultaneously estimate the bandwidths for local linear estimators in the regression function and the bandwidth for the Gaussian kernel error–density estimator. A Monte Carlo simulation study shows that: 1) our proposed Bayesian approach achieves better performance in estimating the bandwidths for local linear estimators than normal reference rule and cross–validation; and 2) compared with the parametric assumption of either the Gaussian or a mixture of two Gaussians, Gaussian kernel error–density assumption is a data–driven choice and helps gain robustness in terms of different specifications of the true error density. Moreover, we apply our proposed Bayesian sampling method to the estimation of bandwidth for the time–varying coefficient models that explain Okun’s law and the relationship between consumption growth and income growth in the U.S. For each model, we also provide calibrated parametric forms of its time–varying coefficients. Second, we develop a functional coefficient time series model with trending regressors. We propose a local linear estimation method to estimate the unknown coefficient functions. The asymptotic distributions of the proposed local linear estimator are established under mild conditions. We further propose a Bayesian approach to select bandwidths involved in the proposed local linear estimator. Several numerical examples are provided to illustrate the finite sample behavior of the proposed methods. The results show that the local linear estimator works very well and the proposed Bayesian bandwidth selection method is better than cross–validation method. Furthermore, we employ the functional coefficient model to study the relationship between consumption per capita and income per capita in the U.S. and the results show that this functional coefficient model with our proposed local linear estimator and Bayesian bandwidth selection method performs better than other competing models in terms of both in–sample fitting and out–of–sample forecasting. Third, we propose a semiparametric localised bandwidth estimator for kernel density estimation based on strictly stationary mixing processes. We prove that the semiparametric localised bandwidth estimator is asymptotically normally distributed with root–n rate of convergence. To carry out the computation of the semiparametric localised bandwidth estimator for a given sample of data, we propose a sampling–based likelihood approach to hyperparameter estimation. Monte Carlo simulation studies show that the proposed hyperparameter estimation approach works very well, and that the proposed semiparametric localised bandwidth estimator outperforms its competitors. Applications of the new bandwidth estimator to the kernel density estimation of Eurodollar deposit rate, as well as the S&P 500 daily return under conditional heteroscedasticity, demonstrate the effectiveness and competitiveness of the proposed semiparametric localised bandwidth. In addition, we present an easily computable expression for integrated squared error of normal density estimators, mixture of two normals density estimators and Gaussian kernel density estimators under different specifications of the true density. This provides a new way of evaluating the performance of the above three common–used density estimators. The numerical studies show that: 1) closed–form of integrated squared error is more accurate than grid–point approximation; 2) gird–point approximation is not robust, especially when the true density is asymmetric; 3) when the true density is neither normal nor mixture normal densities, Gaussian kernel density estimators can provide us with more accurate estimation.

In this paper, we present a cantilever-type silicon microheater operating at high temperature with low power consumption for the catalytic combustion methane sensor. The cantilever supported silicon microheaters were designed and fabricated with thick device silicon of silicon-on-insulator (SOI) wafer. The temperature dependence and electrical characteristic of the thick-silicon microheater resistor, including a key temperature corresponding to the twist point of temperature coefficient of resistance (TCR) were tested. A series of test microheaters was also designed in order to further investigate the influence of the length of support cantilever on power consumption. The twist point of TCR of the microheater was used as the reference point for the power evaluation. Experimental results demonstrated that the solid state heat conduction dominates the power losses and the total power consumption can be reduced to a satisfied minimum about 60 mW with the length-extension of the support cantilever for the thick silicon heater. Results also showed that the thick silicon heater is suitable for gas sensor operating at the high temperature with low power consumption, which is a particularly attractive quality for the use of battery-operated handheld methane gas monitoring.

Since conventional cross–validation bandwidth selection methods don’t work for the case where the data considered are dependent time series, alternative bandwidth selection methods are needed. In recent years, Bayesian based global bandwidth selection methods have been proposed. Our experience shows that the use of a global bandwidth is however less suitable than using a localized bandwidth in kernel density estimation in the case where the data are dependent time series as discussed in an empirical application of this paper. Nonetheless, a difficult issue is how we can consistently estimate a localized bandwidth. In this paper, we propose a semiparametric estimation method, for which we establish a completely new asymptotic theory for the proposed semiparametric localized bandwidth estimator. Applications of the new bandwidth estimator to the kernel density estimation of Eurodollar deposit rate and the S&P 500 daily return demonstrate the effectiveness and competitiveness of the proposed semiparametric localized bandwidth.

Based on the three-factor model (Fama and French, 1993) and two-stage FM method (Fama and Macbeth, 1973), this paper employs quantile regression technique to analyse the relationship between cross-section returns of all A stocks in Shanghai and Shenzhen stock markets and risk factors which include company specific variables(trading volume, company size, book-at-market ratio) and market macro variables(risk-free rate, term structure of interest rates). Empirical results show that: (1) there exists significant differences between the results of Ordinary Least Squares(OLS) and quantile regression; (2) the values of Beta risk are different for overperform and underperform stocks, which contradicts with the traditional CAPM theory; (3) company specific factors can effectively explain cross-section returns, while there exists only weak correlation between cross-section stock returns and markets macro factors.

Based on the daily closed prices and volumes of 1087 stocks in Shanghaiing and Shenzhen stock markets from April 28th, 2005 to April 28th, 2008, this paper uses quantile regression method to study price-volume relation in Chinese stock markets. The empirical results show that price-volume relation in Chinese stock markets is significant: there exists a positive increasing relation between trading volumes and returns, and a positive hook relation between trading volume fluctuations and returns under all quantiles of stock returns.