基于MM-EMD的改进HHT及应用

针对传统Hilbert-Huang变换假设"信号由IMF(Instinct Mode Functions)分量与普通趋势项构成"这一固有局限,根据油压信号的自身特点,提出了基于MM-EMD(Mathematical morphology and Empirical Mode Decomposition)的Hilbert-Huang变换改进方法,并将其应用于油压信号分析处理中。该方法的基本思想如下:针对存在非IMF、非普通趋势项分量的信号,先用数学形态滤波器分离出该分量,再对剩余信号进行EMD分解与Hilbert谱分析。仿真信号与实测信号表明,新方法能取得比传统Hilbert-Huang变换更好的分析效果。     

Multi‐modal brain tumor image segmentation based on SDAE

Accurate tumor segmentation has the ability to provide doctors with a basis for surgical planning. Moreover, brain tumor segmentation needs to extract different tumor tissues (Edema, tumor, tumor enhancement, and necrosis) from normal tissues which is a big challenge because tumor structures vary considerably across patients in terms of size, extension, and localization. In this article, we evaluate a fully automated method for segmenting brain tumor images from multi‐modal magnetic resonance imaging volumes based on stacked de‐noising auto‐encoders (SDAEs). Specially, we adopted multi‐modality information from T1, T1c, T2, and Flair images, respectively. We extracted gray level patches from different modalities as the input of the SDAE. After trained by the SDAE, the raw network parameters will be obtained, which are adopted as a parameter of the feed forward neural network for classification. A simple post‐processing is implemented by threshold segmentation method to generate a mask to get the final segmentation result. By evaluating the proposed method on the BRATS 2015, it can be proven that our method obtains the better performance than other state‐of‐the‐art counterpart methods. And a preliminary dice score of 0.86 for whole tumor segmentation has been achieved.     

Red‐Emitting Upconverting Nanoparticles for Photodynamic Therapy in Cancer Cells Under Near‐Infrared Excitation

Upconverting nanoparticles (UCNPs) have attracted considerable attention as potential photosensitizer carriers for photodynamic therapy (PDT) in deep tissues. In this work, a new and efficient NIR photosensitizing nanoplatform for PDT based on red‐emitting UCNPs is designed. The red emission band matches well with the efficient absorption bands of the widely used commercially available photosensitizers (Ps), benefiting the fluorescence resonance energy transfer (FRET) from UCNPs to the attached photosensitizers and thus efficiently activating them to generate cytotoxic singlet oxygen. Three commonly used photosensitizers, including chlorine e6 (Ce6), zinc phthalocyanine (ZnPc) and methylene blue (MB), are loaded onto the alpha‐cyclodextrin‐modified UCNPs to form Ps@UCNPs complexes that efficiently produce singlet oxygen to kill cancer cells under 980 nm near‐infrared excitation. Moreover, two different kinds of drugs are co‐loaded onto these nanoparticles: chemotherapy drug doxorubicin and PDT agent Ce6. The combinational therapy based on doxorubicin (DOX)‐induced chemotherapy and Ce6‐triggered PDT exhibits higher therapeutic efficacy relative to the individual means for cancer therapy in vitro.     

室内空气中VOC现场快速检测方法研究

对基于PID原理的VOC快速检测仪测定室内空气中总挥发性有机物进行了深入的研究,探讨了快检仪测定VOCs的特点和优势。分别采用已知组分的VOC标准气体和现场采样分析,对两种VOC测定方法的测定结果进行了比较研究,得出比值R的范围,提出VOCs表观浓度的概念,对进一步研究两种方法的可比性提供了可靠的依据。     

粒子尺寸分布的激光衍射测量研究

提出了用于喷雾雾化分析和固体粉末粒子的激光粒度分析仪的设计原则。给出了进行粒子测试的数学模型,编制了系统软件。用实际设计的仪器进行了喷雾雾化分析和流化床催化剂粒度测量,证明了所提出的设计原则的正确和该仪器研制的成功。     

半监督极限学习机用于Android手机活动识别的研究

基于对现有Android手机活动识别技术的分析,针对从不完全、不充分的移动传感器数据中推断人体活动的难题,将能根据无标签样本提高识别预测准确性和速度的半监督(SS)学习和体现模式分类回归的有效学习机制的极限学习机(ELM)相结合给出了解决Android手机平台的人体活动识别问题的半监督极限学习机(SS-ELM)方法,并进一步提出了主成分分析(PCA)和半监督极限学习机(SS-ELM)结合的PCA+SS-ELM新方法。实验结果表明,该方法对人体活动的识别正确率能达到95%,优于最近提出的混合专家半监督模型的正确率,从而验证了该新方法是可行性。     

锚网支护技术在综采工作面收作中的应用

主要介绍了锚网支护技术在综采工作面收作中的应用原理及效果。     

硒蛋白结构与营养功能研究进展

硒元素是人类必需的微量元素之一,主要依靠在硒蛋白中的表达,发挥抗氧化、提高免疫力、抗肿瘤等多种营养功能.该文参考近十年国内外关于硒蛋白的研究,对硒蛋白的结构,检测方法以及营养功能进行综述,为深入探讨硒蛋白的营养功能提供科学参考依据.     

Hierarchical Tumor Microenvironment‐Responsive Nanomedicine for Programmed Delivery of Chemotherapeutics

Nanomedicines have been demonstrated to have passive or active tumor targeting behaviors, which are promising for cancer chemotherapy. However, most nanomedicines still suffer from a suboptimal targeting effect and drug leakage, resulting in unsatisfactory treatment outcome. Herein, a hierarchical responsive nanomedicine (HRNM) is developed for programmed delivery of chemotherapeutics. The HRNMs are prepared via the self‐assembly of cyclic Arg‐Gly‐Asp (RGD) peptide conjugated triblock copolymer, poly(2‐(hexamethyleneimino)ethyl methacrylate)‐poly(oligo‐(ethylene glycol) monomethyl ether methacrylate)‐poly[reduction‐responsive camptothecin] (PC7A‐POEG‐PssCPT). In blood circulation, the RGD peptides are shielded by the POEG coating; therefore, the nanosized HRNMs can achieve effective tumor accumulation through passive targeting. Once the HRNMs reach a tumor site, due to the hydrophobic‐to‐hydrophilic conversion of PC7A chains induced by the acidic tumor microenvironment, the RGD peptides will be exposed for enhanced tumor retention and cellular internalization. Moreover, in response to the glutathione inside cells, active CPT drugs will be released rapidly for chemotherapy. The in vitro and in vivo results confirm effective tumor targeting, potent antitumor effect, and reduced systemic toxicity of the HRNMs. This HRNM is promising for enhanced chemotherapeutic delivery.