Syngeneic implantation of mouse hepatic progenitor cell-derived three-dimensional liver tissue with dense collagen fibrils

BACKGROUND Liver transplantation is a therapy for irreversible liver failure;however,at present,donor organs are in short supply.Cell transplantation therapy for liver failure is still at the developmental stage and is critically limited by a shortage of human primary hepatocytes.AIM To investigate the possibility that hepatic progenitor cells(HPCs)prepared from the portal branch-ligated hepatic lobe may be used in regenerative medicine,we attempted to enable the implantation of extracellular matrices containing organoids consisting of HPC-derived hepatocytes and non-parenchymal cells.METHODS In vitro liver organoid tissue has been generated by accumulating collagen fibrils,fibroblasts,and HPCs on a mesh of polylactic acid fabric using a bioreactor;this was subsequently implanted into syngeneic wild-type mice.RESULTS The in vitro liver organoid tissues generated transplantable tissues in the condensed collagen fibril matrix and were obtained from the mouse through partial hepatectomy.CONCLUSION Liver organoid tissue was produced from expanded HPCs using an originally designed bioreactor system.This tissue was comparable to liver lobules,and with fibroblasts embedded in the network collagen fibrils of this artificial tissue,it is useful for reconstructing the hepatic interstitial structure.

Three-dimensional consolidation deformation analysis of porous layered soft soils considering asymmetric effects

Long-term settlements for underground structures, such as tunnels and pipelines, are generally observed after the completion of construction in soft clay. The soil consolidation characteristic has great influences on the long-term deformation for underground structures. A three-dimensional consolidation analysis method under the asymmetric loads is developed for porous layered soil based on Biot's classical theory. Time-displacement effects can be fully considered in this work and the analytical solutions are obtained by the state space approach in the Cartesian coordinate. The Laplace and double Fourier integral transform are applied to the state variables in order to reduce the partial differential equations into algebraic differential equations and easily obtain the state space solution. Starting from the governing equations of saturated porous soil, the basic relationship of state space variables is established between the ground surface and the arbitrary depth in the integral transform domain. Based on the continuity conditions and boundary conditions of the multi-layered pore soil model, the multi-layered pore half-space solutions are obtained by means of the transfer matrix method and the inverse integral transforms. The accuracy of proposed method is demonstrated with existing classical solutions. The results indicate that the porous homogenous soils as well as the porous non-homogenous layered soils can be considered in this proposed method. When the consolidation time factor is 0.01, the value of immediate consolidation settlement coefficient calculated by the weighted homogenous solution is 27.4% bigger than the one calculated by the non-homogeneity solution. When the consolidation time factor is 0.05, the value of excess pore water pressure for the weighted homogenous solution is 27.2% bigger than the one for the non-homogeneity solution. It is shown that the material non-homogeneity has a great influence on the long-term settlements and the dissipation process of excess pore water pressure.

A novel trilinear decomposition algorithm:Three-dimension non-negative matrix factorization

Non-negative matrix factorization （NMF） is a technique for dimensionality reduction by placing non-negativity constraints on the matrix. Based on the PARAFAC model, NMF was extended for three-dimension data decomposition. The three-dimension nonnegative matrix factorization （NMF3） algorithm, which was concise and easy to implement, was given in this paper. The NMF3 algorithm implementation was based on elements but not on vectors. It could decompose a data array directly without unfolding, which was not similar to that the traditional algorithms do, It has been applied to the simulated data array decomposition and obtained reasonable results. It showed that NMF3 could be introduced for curve resolution in chemometrics.

基于改进蚁群算法的移动机器人路径规划研究

针对传统蚁群算法迭代次数多,规划路径长且存在冗余拐点等问题,提出一种改进蚁群算法。首先在启发函数中引入角度因子,并以局部影响系数、全局影响系数和角度影响系数表征局部影响因子、全局影响因子和角度影响因子的重要程度。同时改进信息素挥发系数和信息启发因子,增强信息素的引导作用。其次为了使算法在初期搜索时更具指引性,根据地图已知信息和优选路径特征初始化信息素浓度矩阵。最后对改进算法规划路径进行二次优化,减少路径长度和转弯次数。为验证算法有效性,使用Matlab在栅格地图中对改进算法进行仿真,结果表明改进算法能够以更少的迭代次数获得长度更短、转弯次数更少的最优路径。

Neural differentiation from pluripotent stem cells:The role of natural and synthetic extracellular matrix

Neural cells differentiated from pluripotent stem cells(PSCs), including both embryonic stem cells and induced pluripotent stem cells, provide a powerful tool for drug screening, disease modeling and regenerative medicine. High-purity oligodendrocyte progenitor cells(OPCs) and neural progenitor cells(NPCs) have been derived from PSCs recently due to the advancements in understanding the developmental signaling pathways. Extracellular matrices(ECM) have been shown to play important roles in regulating the survival, proliferation, and differentiation of neural cells. To improve the function and maturation of the derived neural cells from PSCs, understanding the effects of ECM over the course of neural differentiation of PSCs is critical. During neural differentiation of PSCs, the cells are sensitive to the properties of natural or synthetic ECMs, including biochemical composition, biomechanical properties, and structural/topographical features. This review summarizes recent advances in neural differentiation of humanPSCs into OPCs and NPCs, focusing on the role of ECM in modulating the composition and function of the differentiated cells. Especially, the importance of using three-dimensional ECM scaffolds to simulate the in vivo microenvironment for neural differentiation of PSCs is highlighted. Future perspectives including the immediate applications of PSC-derived neural cells in drug screening and disease modeling are also discussed.

Simulated photo-degradation of dissolved organic matter in lakes revealed by three-dimensional excitation-emission matrix with regional integration and parallel factor analysis

Simulated photo-degradation of fluorescent dissolved organic matter(FDOM) in Lake Baihua(BH) and Lake Hongfeng(HF) was investigated with three-dimensional excitationemission matrix(3 DEEM) fluorescence combined with the fluorescence regional integration(FRI),parallel factor(PARAFAC) analysis,and multi-order kinetic models.In the FRI analysis,fulvic-like and humic-like materials were the main constituents for both BH-FDOM and HF-FDOM.Four individual components were identified by use of PARAFAC analysis as humic-like components(C1),fulvic-like components(C2),protein-like components(C3) and unidentified components(C4).The maximum 3 DEEM fluorescence intensity of PARAFAC components C1-C3 decreased by about 60%,70% and 90%,respectively after photo-degradation.The multi-order kinetic model was acceptable to represent the photo-degradation of FDOM with correlation coefficient(Radj2)(0.963-0.998).The photo-degradation rate constants(kn) showed differences of three orders of magnitude,from 1.09 × 10-6 to 4.02 × 10-4 min-1,and half-life of multi-order model(T1/2n)ranged from 5.26 to 64.01 min.The decreased values of fluorescence index(FI) and biogenic index(BI),the fact that of percent fluorescence response parameter of Region I(PⅠ,n) showed the greatest change ratio,followed by percent fluorescence response parameter of Region II(PⅡ,n,while the largest decrease ratio was found for C3 components,and the lowest T1/2n was observed for C3,indicated preferential degradation of protein-like materials/components derived from biological sources during photodegradation.This research on the degradation of FDOM by 3 DEEM/FRI-PARAFAC would be beneficial to understanding the photo-degradation of FD OM in natural environments and accurately predicting the environmental behaviors of contaminants in the presence of FDOM.

Enhancing cartilage regeneration and repair through bioactive and biomechanical modification of 3D acellular dermal matrix

Acellular dermal matrix(ADM)shows promise for cartilage regeneration and repair.However,an effective decellularization technique that removes cellular components while preserving the extracellular matrix,the transformation of 2D-ADM into a suitable 3D scaffold with porosity and the enhancement of bioactive and biomechanical properties in the 3D-ADM scaffold are yet to be fully addressed.In this study,we present an innovative decellularization method involving 0.125%trypsin and 0.5%SDS and a 1%Triton X-100 solution for preparing ADM and converting 2D-ADM into 3D-ADM scaffolds.These scaffolds exhibit favorable physicochemical properties,exceptional biocompatibility and significant potential for driving cartilage regeneration in vitro and in vivo.To further enhance the cartilage regeneration potential of 3D-ADM scaffolds.we incorporated porcine-derived small intestinal submucosa(SIS)for bioactivity and calcium sulfate hemihydrate(CSH)for biomechanical reinforcement.The resulting 3D-ADM+SIS scaffolds displayed heightened biological activity,while the 3D-ADM+CSH scaffolds notably bolstered biomechanical strength.Both scaffold types showed promise for cartilage regeneration and repair in vitro and in vivo,with considerable improvements observed in repairing cartilage defects within a rabbit articular cartilage model.In summary,this research introduces a versatile 3D-ADM scaffold with customizable bioactive and biomechanical properties,poised to revolutionize the field of cartilageregeneration.

共享信息素矩阵:一种新的并行ACO方法

提出并实现了一种新的蚁群优化(ACO)并行化策略SHOP(Sharing one pheromone matrix)．主要思想是基于多蚁群在解的构造过程和信息素更新过程中共享同一个信息素矩阵．以ACS和MMAS的SHOP并行实现为例,简要描述了SHOP设计思想和实现过程,尝试了ACS和MMAS并行混合．以对称TSP测试集为对象,将SHOP的实现与相应串行算法在相同计算环境下的实验结果比较,以及与现有的并行实现进行比较,结果表明SHOP并行策略相对于串行ACO及现有的并行策略具有一定的优势．

量子谐振子蚁群算法

通过分析目前蚁群算法存在的问题和改进算法的优点,发现量子谐振子系统物理特性能够保证算法最终的收敛性。通过量子谐振子高能态到低能态的转变过程和信息素的增加过程相对应,从而更新信息素,在物理上给算法提供了理论依据。通过量子旋转门改变城市转移规则,通过泡利矩阵变异使蚂蚁有更广阔的空间。综合量子谐振子以上的优点,提出了量子谐振子蚁群算法,并在旅行商问题(TSP)上取得了较好的寻优路径。

基于Hama并行蚁群算法模型及TSP应用研究

Hama是建立在Hadoop上的分布式并行计算模型,基于BSP(Bulk Synchronous Parallel,BSP)计算技术的开放式并行计算平台,它的主要功能是支持并行及大数据的科学计算.目前改进传统启发式算法,移植到Hama平台提高算法效率是研究热点之一.蚁群算法是适应性极强的启发式算法,应用广泛,但由于蚁群中个体的随机性,解的收敛速度与解的多样性、稳定性之间存在矛盾.而该矛盾可通过将蚁群算法并行化得到缓解,算法求解性能因此得到提升.本文在Hama平台上,选择以信息素矩阵进行交互的策略,建立了并行蚁群算法模型,并通过该模型求解多种规模下的旅行商问题.实验表明,本文提出的并行蚁群算法模型可行,并能有效地提高算法性能.

双向ACO算法应用于静态机器人全局路径规划研究

提出双向蚁群算法并应用于静态环境下的机器人全局路径规划问题.对栅格法环境建模进行改进,将传统的栅格法改进为膨胀栅格法;使用双向蚁群算法在出发点和目标点设置带有不同标记的两族蚂蚁相向爬行完成搜索,启发信息主要通过目标点、出发点和蚂蚁的当前位置二维坐标值计算得出;信息素存储采用方向信息素矩阵.仿真实验证明:即使在障碍物非常复杂的地理环境,用本算法也能迅速规划出最优路径.

基于CMP的多种并行蚁群算法及比较

基于片上多核处理器(Chip Multi-processor,CMP)的多种并行蚁群算法,包括并行最大最小蚂蚁系统、并行蚁群系统及两者的混合等5个并行算法,提出一种在CMP的每个处理器核心上模拟一个子蚁群,整体蚁群共享同一信息素矩阵,实现信息素隐式交流的方法.用多线程实时优先级实现该算法,并用若干旅行商问题实例进行了测试,分析了不同并行策略的影响.测试结果表明,基于CMP的并行蚁群具有相对于核心数目的线性加速比,异种蚁群混合策略在解的稳定性上更具优势。

基于GPU的共享信息素矩阵多蚁群算法

在研究并行蚁群信息素交流方法的基础上,提出了一种适于GPU统一计算架构模型的多蚁群算法。采用多个同构和异构蚁群共享同一信息素矩阵的交流策略,解决信息素多样性和算法性能之间的矛盾。在路径探索阶段,多只获得迭代最优解且差异较大的蚂蚁共同释放信息素,以利群体多样性;在路径开发阶段,获得唯一全局最优解的蚂蚁释放信息素,以利迅速收敛。多蚁群映射到GPU的线程块而群内蚂蚁对应块内多线程。以MMAS和ACS混合为例给出了该策略下信息素初始化和动态界限的新方法,证明了算法是值收敛和解收敛的。在标准TSP问题实例上的实验评测表明,该算法不仅提升了性能,在充分收敛条件下获得了更高质量的解。

基于蚁群算法的k条路径生成研究

通过分析物流企业车辆路径选择难的问题,对现有的蚁群算法进行分析和改进,提出了多径向蚁群算法。算法中设置多径向因子、路网拓扑结构、拓扑矩阵改进蚂蚁路径选择。实验结果表明算法具有合理性、可行性和有效性,可应用于求大规模路网中的多条路径问题。

基于蚁群算法的图像边缘提取改进方法

利用蚁群算法(ACA)对含有噪声的图像进行边缘提取时,会出现边缘部分丢失、边缘不够平滑、噪声较多等现象。为了有效提高边缘提取质量并且降低噪声,首先,利用图像局部纹理来确定蚁群算法启发因子;其次,将经过蚁群算法得到的不同信息素矩阵进行低阈值处理后进行相乘;然后,再通过相应的阈值选取进行边缘提取。实验结果证明,该改进方法能有效提取图像的边缘。

基于隐式协调机制的制造单元构建技术研究

根据生产任务构建制造单元是缩小生产设备调度范围、提高机床利用率的一个有效途径。针对制造单元构建问题,给出了一种基于信息素的制造单元构建算法。首先,构造了基于信息素的单元构建模型。其次,给出了基于信息素的设备关联矩阵模型和工艺路线关联矩阵模型,并给出了算法的具体实施步骤。该算法具有模型简单、设计变量少等优点。最后,通过具体实例验证了该算法的可行性。

基于改进蚁群算法的多批次协同三维航迹规划

针对基本蚁群算法容易陷入局部寻优、收敛速度慢的缺陷以及解决多批次协同航迹规划问题的需要,提出了基于改进蚁群算法的多批次三维航迹规划算法。该算法采用基于加权排序的信息素更新规则,扩大各优劣蚂蚁的差异,提高了算法收敛速度,并采用了一种信息素挥发系数的随机自适应调节方法,在确保收敛速度的同时使算法具有全局寻优,解决了基本蚁群算法容易过早陷入局部最优缺点;在此基础上,引入蚂蚁子群间多约束条件下的协同进化策略,解决了多批次协同三维航迹规划。仿真结果表明:改进的蚁群算法在运算效率和收敛性上明显优于基本蚁群算法,多批次协同航迹规划能有效提高无人机的作战效能。

基于蚁群聚类的路面识别分析方法

道路是城市的重要基础设施.路面状况的鉴定与评价直接关系到公路的养护决策,是一项至关重要的工作.本研究将蚁群聚类算法应用于路面识别领域,提出一种基于蚁群聚类优化的路面识别分析方法,将此方法运用于路面识别系统.该系统由数据采集、数据预处理和蚁群聚类优化3个模块组成,其中蚁群聚类主要提取了纵轴方向的3个特征属性,通过不断更新聚类中心产生的信息素矩阵,并通过分类器进行路面分类.实验结果表明:蚁群聚类优化具有全局收敛以及启发式学习等优点,运算效率高,路面识别准确率达到95.3%,验证了此方法的有效性.

基于新颖蚁群算法的加工中心组成问题研究

针对蚁群算法求解加工中心组成问题易陷入早熟收敛状态的缺点,提出了将听觉信号、记忆矩阵与蚁群算法相融合的一种新颖蚁群算法。在仿真实验中,分别采用蚁群算法、加入听觉信号的蚁群算法、加入记忆矩阵的蚁群算法和新颖蚁群算法对加工中心组成问题进行求解。实验结果表明,新颖蚁群算法能够有效提高蚁群算法的全局寻优能力,收敛速度快,且所求得的组功效优于以上三个策略及以往的混合遗传算法。

改进蚁群算法求解绿色周期性车辆路径问题

针对带时间窗的绿色周期性车辆路径问题(GPVRPTW),同时以最小化运输时间和总能耗为优化目标,提出一种改进蚁群算法(IACO)进行求解。首先,IACO采用三维概率矩阵记录不同配送日期的车辆路径子问题的优质解信息,并设计基于信息熵的信息素更新机制进行合理地学习和积累,从而增强算法全局搜索的引导性;其次,引入基于5种邻域操作的变邻域搜索以提高算法的局部搜索能力;最后,在不同规模问题上进行仿真实验与算法对比,结果验证了IACO的有效性。