The differences in micro-environment between cancer cells and the normal ones offer the possibility to develop stimuli-responsive drug-delivery systems for overcoming the drawbacks in the clinical use of anticancer drugs, such as paclitaxel, doxorubicin, and etc. Hence, we developed a novel endosomal pH-sensitive paclitaxel (PTX) prodrug micelles based on functionalized poly(ethylene glycol)-poly(ε-caprolactone) (mPEG-PCL) diblock polymer with an acid-cleavable acetal (Ace) linkage (mPEG-PCL-Ace-PTX). The mPEG-PCL-Ace-PTX5 with a high drug content of 23.5 wt % was self-assembled in phosphate buffer (pH 7.4, 10 mM) into nanosized micelles with an average diameter of 68.5 nm. The in vitro release studies demonstrated that mPEG-PCL-Ace-PTX5 micelles was highly pH-sensitive, in which 16.8%, 32.8%, and 48.2% of parent free PTX was released from mPEG-PCL-Ace-PTX5 micelles in 48 h at pH 7.4, 6.0, and 5.0, respectively. Thiazolyl Blue Tetrazolium Bromide (MTT) assays suggested that the pH-sensitive PTX prodrug micelles displayed higher therapeutic efficacy against MCF-7 cells compared with free PTX. Therefore, the PTX prodrug micelles with acetal bond may offer a promising strategy for cancer therapy.

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A motion-compensation method that applies sparse reconstruction (SR) to reconstruct the Doppler spectrum of targets based on a random transmission scheme is proposed for time-division multiplexing (TDM) multiple-input multiple-output (MIMO) radar. Since the random transmission can eliminate the characteristic of periodic time-delay in conventional TDM scheme between transmit cycles, the angle information of a target is not affected by its motion. Therefore, the angle and velocity are no longer coupled with each other and can be estimated separately. This method not only overcome the space-frequency coupling problem but also enhances the unambiguous Doppler interval. Another advantage is that the method is valid even when the estimated target velocity is ambiguous. The results reported here offer the possibility of utilising SR to solve conventional TDM MIMO problems. The effectiveness of the proposed method is demonstrated by experimental results.

With the continuous development of synthetic aperture radar (SAR) systems, multi-polarization information has been increasingly applied to numerous fields, and automatic target recognition (ATR) in polarimetric SAR (POLSAR) has been recognized as vital problem. The SAR recognition methods can primarily fall into handcrafted feature-based algorithms and deep learning algorithms. The former exhibits excellent interpretability but insufficient generalization; the latter achieves stronger representational ability but relies on a considerable number of samples. To solve above problems, a feature fusion framework is proposed in this paper based on monogenic signal and complex-valued non-local network (CVNLNet) for POLSAR target recognition. The proposed feature fusion framework effectively uses the complementarity of handcrafted features and deep features, while making up for the disadvantages of single feature-based methods. First, a Mono-BOVW model is proposed based on monogenic signal and bag-of-visual-words (BOVW) model to extract handcrafted features, which can more fully mine the information covered in POL-SAR data in multi-scale space. Moreover, CVNLNet is built for deep feature extraction to use both the amplitude and phase covered in POLSAR data. Next, a kernel discrimination correlation analysis algorithm (KDCA) is proposed to jointly analyze and transform the two features, so as to remove redundant information while retaining effective and discriminative information. Experiments on the MSTAR dataset and the GOTCHA dataset show that the proposed framework has superior performance on single polarimetric and fully polarimetric datasets.