On the output function in a Ginsburg’s machine

We give the form of the output function in Ginsburg’s machine in which the input and output dictionaries are abelian groups and the transition function is of a special form.     

Comparative absorption kinetics of seven active ingredients of Eucommia ulmoides extracts by intestinal in situ circulatory perfusion in normal and spontaneous hypertensive rats

Eucommia ulmoides Oliv. (E. ulmoides) is a valuable and nourishing medicinal herb in China that has been used in the treatment of hypertension. Given the fact that most traditional Chinese medicine is mainly used to treat disease, investigating the pharmacokinetics of traditional Chinese medicines in the pathological state is more useful than that in the normal state. However, the differences in the absorption kinetics of active ingredients of E. ulmoides extract between pathological and physiological conditions have not been reported. Therefore, in this study, the rat intestinal in situ circulatory perfusion model was used to investigate the differences in absorption kinetics of seven active ingredients of E. ulmoides extract in normal and spontaneously hypertensive rats, namely, genipinic acid, protocatechuic acid, neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, (+)-pinoresinol di-O-β-D -glucopyranoside and (+)-pinoresinol 4′-O-β-D -glucopyranoside. Our results indicate that the pathological state of spontaneous hypertension may change the absorption of active components of E. ulmoides extracts, and these findings may provide a reference for improving the rational use of E. ulmoides in the clinic.     

The Berry–Esseen bounds of the weighted estimator in a nonparametric regression model

Consider the following nonparametric model: \(Y_{ni}=g(x_{ni})+ \varepsilon _{ni},1\le i\le n,\) where \(x_{ni}\in {\mathbb {A}}\) are the nonrandom design points and \({\mathbb {A}}\) is a compact set of \({\mathbb {R}}^{m}\) for some \(m\ge 1\), \(g(\cdot )\) is a real valued function defined on \({\mathbb {A}}\), and \(\varepsilon _{n1},\ldots ,\varepsilon _{nn}\) are \(\rho ^{-}\)-mixing random errors with zero mean and finite variance. We obtain the Berry–Esseen bounds of the weighted estimator of \(g(\cdot )\). The rate can achieve nearly \(O(n^{-1/4})\) when the moment condition is appropriate. Moreover, we carry out some simulations to verify the validity of our results.