Enhancing cell membrane phase separation for inhibiting cancer metastasis with a stimuli-responsive DNA nanodevice

Phase separation in cell membranes promotes the assembly of transmembrane receptors to initiate signal transduction in response to environmental cues. Many cellular behaviors are manipulated by promoting membrane phase separation through binding to multivalent extracellular ligands. However, available extracellular molecule tools that enable manipulating the clustering of transmembrane receptors in a controllable manner are rare. In the present study, we report a DNA nanodevice that enhances membrane phase separation through the clustering of dynamic lipid rafts. This DNA nanodevice is anchored in the lipid raft region of the cell membrane and initiated by ATP. In a tumor microenvironment, this device could be activated to form a long DNA duplex on the cell membrane, which not only enhances membrane phase separation, but also blocks the interaction between the transmembrane surface adhesion receptor and extracellular matrix, leading to reduced migration. We demonstrate that the ATP-activated DNA nanodevice could inhibit cancer cell migration both in vitro and in vivo. The concept of using DNA to regulate membrane phase separation provides new possibilities for manipulating versatile cell functions through rational design of functional DNA structures.

A DNA nanodevice is developed to enhance the cell membrane phase separation in a tumor microenvironment to weaken the formation of focal adhesion. As a result, the migration of cancer cells is inhibited both in vitro and in vivo.     

蝙蝠喉部发声组织建模及发声机理*

建立蝙蝠发声组织模型对超声机理研究及在智能设备的应用具有重要意义。根据蝙蝠喉部发声组织结构特点,通过有限元方法构建了蝙蝠的3种不同发声组织模型,分析了尺寸、材料力学参数、组织结构和张力4个因素对发声组织特征频率的影响。结果表明,如果用人类声带,按比例缩小构建蝙蝠喉部组织模型,蝙蝠无法发出超声波。构建组织结构含甲状软骨和声带的半鼓状模型和只含声带的条状模型,两种模型的特征频率相近且在合理的参数域内均无法达到超声范围。而含膜条状模型的特征频率可以通过张力进行超声频率的调节,这与文献的实验结果一致。因此,可基于含膜条状模型对蝙蝠喉管发声组织进行建模及其发声机理研究。     

经典弹性力学与应用力学方法

通过对经典弹性力学半反演法的特点与应用力学方法的特点的对比,阐明应用力学方法是在经典弹性力学半反演法的基础上发展出来的,指出应用力学方法源自经典弹性力学．讨论了在弹性力学的教学过程中,突出半反演法和应用力学方法之间的关联,会产生很好的教学效果．     

偏序集上的滤子极大理想

在偏序集上引入并考察了滤子极大理想的概念,证明了相应的存在性定理。引入并考察了伪极大元和伪既约元的概念,利用图表的形式对连续格中各种类型的既约元和素元之间的关系进行了归纳总结,完善了文献《Continuous Lattices and Domains》(作者:G.Gierz,et al)中的一个图表的相关内容,填补了在分配的连续格情形该图表的一个未知内容,部分地回答了该文献中的一个问题。     

最大相关模糊查询及其应用研究

在文档类模糊查询方面进行了较深入的研究,首次提出并实现了间接相关全衰期和最大相关查询,其原理有别于传统字符串相等比率的模糊查询技术,采用相关性定义及最大相关传递模式,目的在于实现更广泛意义下的模糊查询。在适当定义相关度情形下,不但能够查找出传统模糊查询搜索到的内容,而且可以查找到字面上完全不同但意思相关的条文,最大相关传递模式能够使得词语之间的相关度按一定规则传递,因此更具一般性和实用性。     

稠油油藏高温盐析调剖的孔渗表征方法研究

深层高温油藏在开采过程中,由于近井地带压力变化剧烈,地层水蒸发致使结晶盐析出,进而导致储层孔隙度、渗透率降低.为研究稠油油藏高温盐析调剖的孔渗变化规律,根据盐类溶解/沉淀原理及储层孔隙度渗透率关系和填砂管模型高温盐析实验结果,建立了考虑地层水蒸发、水中氯化钠析出和储层孔渗变化的综合盐析模型.研究结果表明:随着堵剂注入量...     

MOCVD生长的InN薄膜中In分凝现象

利用金属有机物化学气相淀积(MOCVD)方法不同生长条件下在c面蓝宝石衬底上制备了InN薄膜,通过不同的物理表征手段研究了InN薄膜的物理性质,结果表明:合适的生长温度可以抑制InN薄膜表面分凝现象。研究认为较低的生长温度使作为N源的NH3分解率较低,In—N的成键可能性小,导致In在表面聚集出现分凝;而较高生长温度时,InN薄膜中In—N键能较小,易发生断裂,反应活性较强的N和H原子逸离薄膜表面,In滞留在薄膜表面也导致In分凝现象的出现。相对于表面分凝的样品,未出现表面分凝的样品的薄膜晶体质量和表面形貌也得到了提高。同时,通过Raman散射谱研究了晶格振动E2声子模的应力效应。     

柔性有机电致发光器件导电基板的工艺性能

在聚对苯二甲酸乙二醇酯(PET)柔性衬底上采用直流磁控溅射技术制备了氧化铟锡(ITO)透明导电薄膜,研究了衬底温度、溅射功率和溅射压强等工艺条件对薄膜光电性能的影响,并利用原子力显微镜(AFM)表征了衬底及ITO薄膜的表面形貌。结果表明,在PET衬底温度50℃、溅射功率100W和溅射压强2.66×10-1Pa的条件下,可以得到低方阻(50Ω/□)和高透过率(>90%)的透明导电薄膜。以此柔性ITO衬底为阳极,制备了结构为PET/ITO/NPB/Alq3/Mg∶Ag的柔性有机电致发光器件,在驱动电压为13V时,器件的发光亮度达到了2834cd/m2。     

不同复合角对平板气膜冷却特性影响的实验研究

本文对不同复合角的典型单排孔冷却结构的平板气膜冷却特性进行了实验研究,研究对象为α为30°,β分别为30°、45°、60°的射流孔板,对它们在不同吹风比条件下的绝热气膜冷却效率分布特性进行了分析.实验结果表明,所有复合角条件下的冷却性能都优于相同条件下的简单角; M=0.5、β=60°时的冷却性能明显优于其他两个复合角;M=1.0时.三者的下游横向平均冷却效率相差不大; M=1.5、β=30°时的冷却性能最优.     

Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory

Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertainty may lead to models with unreliable and suboptimal classification results for the target domain task. However, most previous works focus on reducing the gap in data distribution between the source and target domains. They do not consider the uncertainty of source domain data about the target domain task and cannot apply the uncertainty to learn an adaptive classifier. Aimed at this problem, we revisit the domain adaptation from source domain data uncertainty based on evidence theory and thereby devise an adaptive classifier with the uncertainty measure. Based on evidence theory, we first design an evidence net to estimate the uncertainty of source domain data about the target domain task. Second, we design a general loss function with the uncertainty measure for the adaptive classifier and extend the loss function to support vector machine. Finally, numerical experiments on simulation datasets and real-world applications are given to comprehensively demonstrate the effectiveness of the adaptive classifier with the uncertainty measure.