血管内皮生长因子结合可降解壳聚糖治疗大鼠心肌梗塞的研究

摘要:目的 研 究 血 管 内 皮 生 长 因 子 ( vascular endothelial growth factor, VEGF ) 结 合 可 降 解 壳 聚 糖 ( degradablechitosan,DC)对心肌梗塞大鼠的治疗作用。 方法 将 32 只 SD 大鼠随机分为假手术组、模型组、VEGF 组和 VEGF+ DC 组,每组 8 只。 4 组大鼠均采用结扎冠状动脉左前降支的方法进行造模,其中假手术组只穿线不结扎。 造模后, 向模型组大鼠心肌注射 100 μL 生理盐水,VEGF 组大鼠于同一位置注射同体积 VEGF 溶液,VEGF+DC 组大鼠注射 VEGF+DC 溶液。 造模 4 周 后,对 所 有 实 验 大 鼠 进 行 超 声 心 功 能 检 测,考 察 左 心 室 收 缩 期 及 舒 张 期 前 壁 厚 度( LVAW;s 和 LVAW;d) 、左心室收缩期及舒张期内径( LVID;s 和 LVID;d) 、左心室收缩期及舒张期容积( LV Vol;s 和 LV Vol;d) 、左室射血分数( EF% )及短轴缩短率( FS% ) ,血流动力学考察左室舒张末压( EDP ) 和等容收缩期及 舒张期左心室内压力上升最大速率( + dp / dt 和 - dp / dt) ,计算左心室指数( LVM / BW) 并对心肌进行苏木素-伊红 ( HE)染色观察各组实验大鼠心肌病理变化。 结果 VEGF 组和 VEGF+DC 组大鼠的心肌梗塞均得到有效改善,其 中 VEGF+DC 组的改善作用更加显著。 结论 VEGF 结合 DC 对心肌梗塞大鼠的心功能具有改善作用,具有一定的治疗作用。     

基于最佳断点的圆形件T形下料算法

【目的】圆形件下料问题广泛存在于工业生产中，如汽车、船舶零部件的制造等。通过提高材料利用率，减少切割成本可有效降低企业生产成本。基于此，提出了一种基于最佳断点的圆形件T形下料算法。【方法】首先根据需求的圆形件种类生成多规格的标准条带，然后根据规范长度求得点长度集合，再用全容量动态规划算法生成排样方式，遍历断点长度集合，得出最佳断点长度和排样方式，确定排样方式的使用次数并加入排样方案，直到满足所有圆形件的需求；最后根据价值修正公式不断调整圆形件价值，通过迭代生成多种排样方案，以防算法陷入局部最优。【结果】与文献数据相比，该算法能够提高材料利用率，降低切割成本。【结论】实验结果表明，该算法在降低企业生产总成本上具有有效性。     

磺酸化CNTs在钙钛矿太阳能电池电极中的应用

为解决钙钛矿太阳能电池(perovskite solar cells,PSCs)中采用贵金属作为对电极(counter electrode,CE)而导致的成本高昂问题,对碳纳米管(CNTs)进行磺酸化处理,并将磺酸化CNTs(s-CNTs)作为对电极材料应用到PSCs中.利用稳态荧光(PL)、电化学阻抗(EI)等方法对PSCs的电荷传输过程进行研究,结果如下:与CNTs对电极相比,s-CNTs对电极具有更好的导电性、更优异的空穴提取和传输能力,在s-CNTs基PSC上获得6.18％的能量转换效率.当喷涂量为1 mL/块,退火温度为100℃,退火时间为30 min时,s-CNTs基PSC的各项性能最优,并具有较好的重复性.研究结果为改善碳基PSCs的光电性能提供了新思路.     

非酒精性脂肪肝与心血管疾病相关性的研究

非酒精性脂肪肝是慢性肝功能障碍最常见的原因，是一个严重的全球性健康问题。非酒精性脂肪肝已被证明与胰岛素抵抗、肥胖和代谢综合征相关，而这些因素又与心血管疾病密不可分。大量研究表明，非酒精性脂肪肝患者发生心血管疾病的风险增加，心血管疾病是其主要的死亡原因。越来越多的证据提示非酒精性脂肪肝不仅是心血管疾病的一个易感因素，而且可能参与了心血管疾病的发病机制。     

基于改进Lagrange乘子法的交通信号配时优化研究

为提高交叉口的机动车通行效率及环境效益,采用改进罚参数来构造一种新的Lagrange乘子法对交叉路口的交通信号进行优化配时。通过权重系数建立车辆延误与尾气排放的数学模型,利用改进Lagrange乘子法进行优化,将其结果与两种典型智能算法的优化结果进行对比,并利用VISSIM(Verkehr in Stadten Simulation)微观交通可视化仿真软件进行验证。实验结果表明,该方法优化的信号配时使车辆延误降低19.89%,尾气排放量降低2.379%,可见大比例优化了交叉口的车辆延误,同时可以降低尾气排放量。     

产学研协同发展、知识积累与技术创新效率——基于动态面板门限机理实证分析

基于产学研耦合协调度测度模型、数据包络分析模型以及动态面板门限回归模型，运用2013~2017年中国30个省市的面板数据，研究以知识积累为门限变量时，产学研协同发展水平对区域技术创新效率产生的影响。实证结果表明：当以知识积累水平为门限变量时，产学研协同发展水平与技术创新效率间存在明显的双门限效应；随着知识积累水平的提升，产学研协同发展水平对于技术创新效率的影响由负向影响转变为正向影响，且影响程度不断提升，进而可以划分低知识积累区间(KS≤0.324 0)、中知识积累区间(0.324 0＜KS≤2.021 2)及高知识积累区间(KS＞2.021 2)3种类型；中国各地区的知识积累水平存在明显的时间与空间异质性，处于低知识积累区间的地区多为西部偏远地区。因此应加强区域间知识与技术的扩散、吸收以及传承积累，加强中西部与东部地区的联动以促进区域技术创新效率的提升。     

俄英医学同义术语的类型划分及英俄、汉俄医学词典编纂启示

同义现象是术语学界长期关注的热点问题,也是当代极具现实意义和有争议的问题。术语的同义现象是建立在表达同一个专业概念的不同术语单位基础上的一种语义关系。通过对医学各领域同义术语(主要是俄语和英语)的分析和描写,划分同义术语的类型,确定现代医学术语的特点,进而发现现代医学双语(主要是英俄、汉俄)词典中暂时还缺乏关于在术语系统功能化条件下使用某一同义术语的相关信息。因此,英俄、汉俄医学词典应对同义术语做相关的信息标注,以帮助使用者选取某一具体医学领域最适合的同义术语。     

Cystic fibrosis transmembrane conductance regulator—emerging regulator of cancer

Mutations of cystic fibrosis transmembrane conductance regulator (CFTR) cause cystic fibrosis, the most common life-limiting recessive genetic disease among Caucasians. CFTR mutations have also been linked to increased risk of various cancers but remained controversial for a long time. Recent studies have begun to reveal that CFTR is not merely an ion channel but also an important regulator of cancer development and progression with multiple signaling pathways identified. In this review, we will first present clinical findings showing the correlation of genetic mutations or aberrant expression of CFTR with cancer incidence in multiple cancers. We will then focus on the roles of CFTR in fundamental cellular processes including transformation, survival, proliferation, migration, invasion and epithelial–mesenchymal transition in cancer cells, highlighting the signaling pathways involved. Finally, the association of CFTR expression levels with patient prognosis, and the potential of CFTR as a cancer prognosis indicator in human malignancies will be discussed.     

Bidirectional Feedback Dynamic Particle Filter with Big Data for the Particle Degeneracy Problem

Particle Filter(PF) is a data assimilation method to solve recursive state estimation problem which does not depend on the assumption of Gaussian noise, and is able to be applied for various systems even with non-linear and non-Gaussian noise. However, while applying PF in dynamic systems, PF undergoes particle degeneracy,sample impoverishment, and problems of high computational complexity. Rapidly developing sensing technologies are providing highly convenient availability of real-time big traffic data from the system under study like never before. Moreover, some sensors can even receive control commands to adjust their monitoring parameters. To address these problems, a bidirectional dynamic data-driven improvement framework for PF(B3 DPF) is proposed.The B3 DPF enhances feedback between the simulation model and the big traffic data collected by the sensors,which means the execution strategies(sensor data management, parameters used in the weight computation,resampling) of B3 DPF can be optimized based on the simulation results and the types and dimensions of traffic data injected into B3 DPF can be adjusted dynamically. The first experiment indicates that the B3 DPF overcomes particle degeneracy and sample impoverishment problems and accurately estimates the state at a faster speed than the normal PF. More importantly, the new method has higher accuracy for multidimensional random systems.In the rest of experiments, the proposed framework is applied to estimate the traffic state on a real road network and obtains satisfactory results. More experiments can be designed to validate the universal properties of B3 DPF.     

Real-Time Pricing of Smart Grid Based on Piece-Wise Linear Functions

In a power grid system, utility is a measure of the satisfaction of users' electricity consumption; cost is a monetary value of electricity generated by the supplier. The utility and cost functions represent the satisfaction of different users and the supplier. Quadratic utility, logarithmic utility,and quadratic cost functions are widely used in social welfare maximization models of real-time pricing. These functions are not universal; they have to be discussed in detail for individual models. To overcome this problem, a piece-wise linear utility function and a piece-wise linear cost function with general properties are proposed in this paper. By smoothing the piece-wise linear utility and cost functions, a social welfare maximization model can be transformed into a differentiable convex optimization problem. A dual optimization method is used to solve the smoothed model. Through mathematical deduction and numerical simulations, the rationality of the model and the validity of the algorithm are verified as long as the elastic and cost coefficients take appropriate values. Thus, different user types and the supplier can be determined by selecting different elastic and cost coefficients.