Comparison of Repeated Doses of C-kit-Positive Cardiac Cells versus a Single Equivalent Combined Dose in a Murine Model of Chronic Ischemic Cardiomyopathy

Using a murine model of chronic ischemic cardiomyopathy caused by an old myocardial infarction (MI), we have previously found that three doses of 1 × 10⁶ c-kit positive cardiac cells (CPCs) are more effective than a single dose of 1 × 10⁶ cells. The goal of this study was to determine whether the beneficial effects of three doses of CPCs (1 × 10⁶ cells each) can be fully replicated by a single combined dose of 3 × 10⁶ CPCs. Mice underwent a 60-min coronary occlusion; after 90 days of reperfusion, they received three echo-guided intraventricular infusions at 5-week intervals: (1) vehicle × 3; (2) one combined dose of CPCs (3 × 10⁶) and vehicle × 2; or (3) three doses of CPCs (1 × 10⁶ each). In the combined-dose group, left ventricular ejection fraction (LVEF) improved after the 1st CPC infusion, but not after the 2nd and 3rd (vehicle) infusions. In contrast, in the multiple-dose group, LVEF increased after each CPC infusion; at the final echo, LVEF averaged 35.2 ± 0.6% (p < 0.001 vs. the vehicle group, 27.3 ± 0.2%). At the end of the study, the total cumulative change in EF from pretreatment values was numerically greater in the multiple-dose group (6.6 ± 0.6%) than in the combined-dose group (4.8 ± 0.8%), although the difference was not statistically significant (p = 0.08). Hemodynamic studies showed that several parameters of LV function in the multiple-dose group were numerically greater than in the combined-dose group (p = 0.08 for the difference in LVEF). Compared with vehicle, cardiomyocyte cross-sectional area was reduced only in the multiple-dose group (−32.7%, 182.6 ± 15.1 µm² vs. 271.5 ± 27.2 µm², p < 0.05, in the risk region and −28.5%, 148.5 ± 12.1 µm² vs. 207.6 ± 20.5 µm², p < 0.05, in the noninfarcted region). LV weight/body weight ratio and LV weight/tibia length ratios were significantly reduced in both cell treated groups vs. the vehicle group, indicating the attenuation of LV hypertrophy; however, the lung weight/body weight ratio was significantly reduced only in the multiple-dose group, suggesting decreased pulmonary congestion. Taken together, these results indicate that in mice with chronic ischemic cardiomyopathy, the beneficial effects of three doses of CPCs on LV function and hypertrophy cannot be fully replicated with a single dose, notwithstanding the fact that the total number of cells delivered with one or three doses is the same. Thus, it is the multiplicity of doses, and not the total number of cells, that accounts for the superiority of the repeated-dose paradigm. This study supports the idea that the efficacy of cell therapy in heart failure can be augmented by repeated administrations.     

基于多学习单元卷积神经网络的雷达辐射源信号识别

现有基于人工提取特征的复杂体制雷达辐射源信号识别方法时效性低,识别准确率不佳. 为此,提出了一种基于多学习单元卷积神经网络的识别方法. 首先对辐射源信号的模糊函数进行高斯平滑,以校正噪声带来的毛刺与畸变;然后提取其正交切片作为进一步的特征提取对象;最后构建多学习单元卷积神经网络,学习和提取正交切片深层、泛在的特征,并通过softmax分类器进行分类识别. 仿真实验结果表明,所提方法在信噪比为-2 dB时对6类典型雷达信号的整体平均识别率均保持在99.86%以上,即便是在-6 dB环境中,雷达信号的识别率也可达到88.50%,在极低信噪比条件下具有良好的性能和可行性.     

基于动态模式分解的移动用户信道容量预测算法

在多输入多输出环境下,为了能够连续预测出移动用户的信道容量并以此合理地分配用户资源,提出了一种基于动态模式分解（DMD）的信道容量预测方法及其优化方法：基于经验模态分解的选择性归一化动态模式分解（ESN-DMD）.仿真结果表明,DMD算法只适用于预测低移速低复杂度的用户信号,ESN-DMD算法可以预测不同移速的用户信道容量.     

考虑限制性卖空的多期模糊投资组合优化模型

在实际证券交易中, 卖空操作是一种重要的投资手段, 因此本文研究考虑限制性卖空的多期模糊投资组合优化问题。将风险资产的收益视为梯形模糊数。在允许卖空的情况下, 建立了带单期最低期望收益约束、破产控制约束和投资比例边界约束的多期可信性均值−下半方差−偏度投资组合优化模型。设计了一个改进的多种群粒子群算法对模型进行求解。最后, 采用真实股票数据进行数值算例分析, 说明了所提出的优化模型和算法的有效性。     

高温高湿盐环境下SBS改性沥青胶浆的高温性能

基于自行设计的室内盐蚀干湿循环试验,采用动态剪切流变仪,对SBS改性沥青胶浆进行温度扫描试验和多重应力重复蠕变恢复(MSCR)试验. 以3.2 kPa应力下的不可恢复蠕变柔量J_nr,3.2为胶浆高温流变性能评价指标,分析试验环境和干湿循环耦合作用对胶浆流变性能的影响. 采用灰色关联理论,探究J_nr,3.2与常规流变参数、干湿循环次数及试验环境之间的关联性. 结果表明,随着盐蚀干湿循环次数的增加,胶浆的复数切变模量、车辙因子及J_nr,3.2均呈增大趋势,相位角和蠕变恢复率呈减小趋势. 在同种试验条件下,硫酸盐环境对胶浆高温性能的影响最大. J_nr,3.2与改进型车辙因子、试验环境的灰色关联度最大,关联度系数均大于0.93. 建议采用日常清扫、定期洒水冲洗的方式来减小路面盐分的积累,提高高温高湿环境中沥青路面的抵抗变形的能力.     

一种引入通信的多移动机器人编队方法*

提出以视觉跟踪为基础并引入通信进行多机器人的编队控制方法,根据需要编写了一种新的通信协议,采用闭环l-Φ实现编队算法.这种多机器人编队控制避免了视觉系统的局限,能够更好地在复杂未知环境中协作完成任务,解决了编队控制的无反馈和实时性不高的问题,使得机器人能够准确迅速地进行跟踪和通信编队,一起顺利达到目标点.试验结果证明了该方法的有效性.     

视频处理器软硬件协同设计

为了提高视频图像处理速度与硬件资源利用,针对一种基于精简指令集处理器与数字信号处理器（RISC/DSP）混合体系结构的媒体处理器：浙大数芯（MD32）,给出了一种软硬件协同设计策略.所给策略结合视频处理核心算法,研究分析MPEG视频编码标准的处理过程,进行了视频处理指令扩展设计,提高了数据的并行处理能力,利用了指令内并行执行特性.为有效实现扩展指令,处理器执行级采用了可扩展流水级技术.实验结果表明,指令扩展硬件成本仅占MD32的2.7%,逆离散余弦变换实现性能比MMX/SSE指令集实现的性能分别提高31%和23%,运动补偿性能比MMX指令集实现的性能提高了40%.     

基于多模型随机组合的水文集合预报方法研究

准确、可靠的水文预报是水资源开发利用的基础。集合预报以概率或区间的形式表征预报的不确定性,是未来水文预报研究的重点发展方向。本文提出了一种基于多模型随机组合的水文集合预报方法。首先通过加权形式将多种预报模型进行组合;再采用多目标优化算法率定各成员模型权重的上、下限;最后在优化的上、下限内随机生成权重以构建集合预报。以汉江黄金峡水库中长期径流预报为研究对象,考虑月、旬两种预见期,构建了六种单一预报模型,采用非支配排序遗传算法对权重进行优化后得到集合预报样本。结果表明：所提方法能更好地反映预报的不确定性,且均值预报结果明显优于贝叶斯模型平均法和单一确定性预报模型,具有一定竞争力。     

The Contribution of Small Vessel Disease to Neurodegeneration: Focus on Alzheimer’s Disease,Parkinson’s Disease and Multiple Sclerosis

Brain small vessel disease (SVD) refers to a variety of structural and functional changes affecting small arteries and micro vessels, and manifesting as white matter changes, microbleeds and lacunar infarcts. Growing evidence indicates that SVD might play a significant role in the neurobiology of central nervous system (CNS) neurodegenerative disorders, namely Alzheimer’s disease (AD) and Parkinson’s disease (PD), and neuroinflammatory diseases, such as multiple sclerosis (MS). These disorders share different pathophysiological pathways and molecular mechanisms (i.e., protein misfolding, derangement of cellular clearance systems, mitochondrial impairment and immune system activation) having neurodegeneration as biological outcome. In these diseases, the actual contribution of SVD to the clinical picture, and its impact on response to pharmacological treatments, is not known yet. Due to the high frequency of SVD in adult-aged patients, it is important to address this issue. In this review, we report preclinical and clinical data on the impact of SVD in AD, PD and MS, with the main aim of clarifying the predictability of SVD on clinical manifestations and treatment response.     

基于改进DaNN的综合能源系统多能负荷预测

随着能源革命的推进及双碳目标的提出,综合能源系统越发受到广大研究者的重视,对综合能源系统进行高效的规划和控制离不开精准的多能负荷预测。基于上述需求,引入迁移学习理论,提出一种改进领域自适应神经网络（DaNN）负荷预测模型对综合能源系统中的冷、热、电负荷进行统一建模与预测。首先,通过历史数据分别构筑冷、热、电负荷特征图,随后输入改进DaNN的参数共享卷积层和全连接层;其次,基于冷、热、电负荷联合预测的特点改进传统神经网络的损失函数,加入最大均值差异指标,并优化训练模型;最后,通过3个各自独立的全连接层分别输出冷、热、电负荷的预测值。通过采用实际算例验证并与基准模型对比可知,所提改进DaNN模型能够有效提高综合能源多能负荷预测精度。