Effects of Recombinant Human Endostatin and Docetaxel on MMP and its Following Anti-neoplastic Effect under Different Administration Sequences

Background and objective The aim of this study is to observe the changes of MMP-2 and its regulators, and to investigate the mechanism of the two administration sequences of recombinant human endostatin (rh-endostatin) and docetaxel. Methods The experiment was performed as 2 stages. Firstly, nude mice with xenograft tumor were randomized into 2 groups as rh-endostatin-treated group with rh-endostatin 400 μg•d-1, d1-d14 and docetaxel-traeted group with docetaxel 10 mg•kg-1•3d-1, d1-d14. Secondly, nude mice with xenograft tumor were randomized into 3 groups as concurrent administration group (rh-endostatin 400 μg•d-1, d1-d35, docetaxel 10 mg•kg-1•3d-1, d1-d19), endo-first group (rh-endostatin 400 μg•d-1, d1-d35, docetaxel 10 mg•kg-1•3d-1, d16-d34) and model group (positive control, mice burdened tumor without treatment). The volume of tumor was measured during treatment. Detection of the expressions of MMP-2, TIMP-2, EMMPRIN and the count of microvessel density (MVD) by immunohistochemistry stain examination were carried out at the end of experiment. Results Compared with the docetaxel-treated group, more obvious down-regulation of expression of MMP-2, EMMPRIN (P=0.024, P=0.081) were observed in rh-endostatin-treated group. No significant difference was found in TIMP-2 expression between the 2 groups. In combined treatment groups, at the endpoint tumor volumes of concurrent administration group and the endo-first group were remarkably smaller than that in model group (P<0.001, P=0.003). According to the administration procedure, concurrent administration inhibited tumor growth stronger than endo-first treatment did. Both of the combined groups down-regulated the expression of MMP-2 and decreased microvessel density (P<0.05). Compared with model group, the expression of TIMP-2 was upregulated (P=0.001) as well as EMMPRIN down-regulated (P=0.018) in concurrent adminis-tration group. Oppositely, the same results were not observed in the endo-first group. Conclusion The schedule of the concurrent administration group could inhibit the tumor growth better, and it down-regulated MMP-2 expression through TIMP-2 and EMMPRIN, and thus slow down the tumor growth superiorly to another schedule of treatment

Continuous-time Mean-Variance Portfolio Selection with Stochastic Parameters

This paper studies a continuous-time market {under stochastic environment} where an agent, having specified an investment horizon and a target terminal mean return, seeks to minimize the variance of the return with multiple stocks and a bond. In the considered model firstly proposed by [3], the mean returns of individual assets are explicitly affected by underlying Gaussian economic factors. Using past and present information of the asset prices, a partial-information stochastic optimal control problem with random coefficients is formulated. Here, the partial information is due to the fact that the economic factors can not be directly observed. Via dynamic programming theory, the optimal portfolio strategy can be constructed by solving a deterministic forward Riccati-type ordinary differential equation and two linear deterministic backward ordinary differential equations

Neural Machine Translation with Dynamic Graph Convolutional Decoder

Existing wisdom demonstrates the significance of syntactic knowledge for the improvement of neural machine translation models. However, most previous works merely focus on leveraging the source syntax in the well-known encoder-decoder framework. In sharp contrast, this paper proposes an end-to-end translation architecture from the (graph \& sequence) structural inputs to the (graph \& sequence) outputs, where the target translation and its corresponding syntactic graph are jointly modeled and generated. We propose a customized Dynamic Spatial-Temporal Graph Convolutional Decoder (Dyn-STGCD), which is designed for consuming source feature representations and their syntactic graph, and auto-regressively generating the target syntactic graph and tokens simultaneously. We conduct extensive experiments on five widely acknowledged translation benchmarks, verifying that our proposal achieves consistent improvements over baselines and other syntax-aware variants