脑胶质瘤磁共振弥散张量成像定量参数与VEGF、MMP-9表达的相关性

本研究旨在分析脑胶质瘤患者的磁共振弥散张量成像(DTI)参数与病变组织血管内皮生长因子(VEGF)和基质金属蛋白酶-9(MMP-9)表达的相关性,以及DTI参数对脑胶质瘤进行分级诊断的价值.根据病理分级将102例脑胶质瘤患者分为低级别组(47例)和高级别组(55例),均行MRI和DTI检查,定量测定表观弥散系数(ADC...     

Piceatannol Protects Brain Endothelial Cell Line (bEnd.3) against Lipopolysaccharide-Induced Inflammation and Oxidative Stress

Dysfunction of the blood–brain barrier (BBB) is involved in the pathogenesis of many cerebral diseases. Oxidative stress and inflammation are contributing factors for BBB injury. Piceatannol, a natural ingredient found in various plants, such as grapes, white tea, and passion fruit, plays an important role in antioxidant and anti-inflammatory responses. In this study, we examined the protective effects of piceatannol on lipopolysaccharide (LPS) insult in mouse brain endothelial cell line (bEnd.3) cells and the underlying mechanisms. The results showed that piceatannol mitigated the upregulated expression of adhesion molecules (ICAM-1 and VCAM-1) and iNOS in LPS-treated bEnd.3 cells. Moreover, piceatannol prevented the generation of reactive oxygen species in bEnd.3 cells stimulated with LPS. Mechanism investigations suggested that piceatannol inhibited NF-κB and MAPK activation. Taken together, these observations suggest that piceatannol reduces inflammation and oxidative stress through inactivating the NF-κB and MAPK signaling pathways on cerebral endothelial cells in vitro.     

Machine Learning Approaches for Metalloproteins

Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.     

基于深度学习的MIMO系统联合信号检测与信道译码

为进一步提高多输入多输出(multiple-input multiple-output,MIMO)系统性能,研究了深度学习方法来联合解决MIMO系统信号检测与信道译码问题.通过将深度神经网络、自动编码器神经网络与传统MIMO通信系统的物理层架构进行有机融合,构建了基于神经网络的MIMO系统模型,可获取系统发射端的信息比...     

一种无先验地图的移动机器人导航方法

为了提高移动机器人在复杂环境下的无先验地图导航算法模型训练速度及导航成功率,提出一种基于深度确定性策略梯度(deep deterministic policy gradient, DDPG)的移动机器人导航方法。利用2D激光雷达的均匀分布测距信息,降低环境噪声的干扰及高维度环境信息的计算量;采用人工势场法构建移动机器人从初始位置到目标点过程的奖励函数;通过Actor-Critic网络结构提高模型训练的稳定性和泛化能力。实验结果表明,提出的方法具有模型训练速度快、导航成功率高及泛化能力强等优点。     

广西丹霞地区文化旅游资源深度开发研究

随着旅游业的迅猛发展,传统观光旅游方式已不能满足广大游客对精神体验的需求,发展文化旅游迫在眉睫.广西丹霞地区文化旅游资源丰富,应加快发掘物质性和非物质性历史遗存,在充分研究其文化生态的基础上,通过旅游线路合理设计及媒介宣传等措施,实现丹霞地区及其周边地区文化旅游资源深度开发,把广西旅游业推上一个新的水平.     

深空光通信中的图像信标捕获技术

为实现深空光通信链路建立过程中精确的对准,提出了一种深空光通信系统扩展信标的捕获方案。该方案以可视地球图像作为信标,在航天器上存储一幅信标图像作为参考图像,采用天线扫描的方式在各点对所瞄准的区域成像,利用像素扫描的方式,使参考图像和实际探测图像进行相关,在天线扫描完成以后,找出相关性最大的位置,即可认为在该点捕获到地球图像。在计算两图像相关系数的过程中,由于傅里叶梅林变换幅度谱具有伸缩及旋转不变性,因此利用傅里叶梅林变换即可消除两图像相关系数因为旋转和伸缩的影响。利用蒙特卡罗方法随机产生1000个视场,仿真结果表明,3σ内正确捕获到信标图像的概率为99.6%,表明这是一种可行的方法。     

电液比例控制技术在快速深拉伸液压机中的应用

目前快速深拉伸液压机大多采用液压开关式控制系统,它存在着响应速度低、冲击振动大及拉伸件废品率高等问题。文章在比较了3种液压控制技术性能及特点的基础上,设计了一种快速深拉伸液压机的电液比例控制系统,使压边力的变化符合了拉伸工艺要求,滑块速度转变平稳,较好地解决了上述问题。     

页岩结构完整性评价的热-流体-变形耦合理论与方法

该文应用连续介质力学理论建立了与蒸汽注入过程相关的热-流-变形耦合问题的基本数学模型;应用全隐式顺序Galerkin有限元数值解方案,对注蒸汽井的热-流体-变形耦合过程进行了数值模拟。结果表明,由于低渗透页岩地层流体高温膨胀导致的超高孔隙压力,有可能产生拉张应力,严重时会导致地层破裂。     

Network Bending: Expressive Manipulation of Generative Models in Multiple Domains

This paper presents the network bending framework, a new approach for manipulating and interacting with deep generative models. We present a comprehensive set of deterministic transformations that can be inserted as distinct layers into the computational graph of a trained generative neural network and applied during inference. In addition, we present a novel algorithm for analysing the deep generative model and clustering features based on their spatial activation maps. This allows features to be grouped together based on spatial similarity in an unsupervised fashion. This results in the meaningful manipulation of sets of features that correspond to the generation of a broad array of semantically significant features of the generated results. We outline this framework, demonstrating our results on deep generative models for both image and audio domains. We show how it allows for the direct manipulation of semantically meaningful aspects of the generative process as well as allowing for a broad range of expressive outcomes.