Protein‐Affinitive Polydopamine Nanoparticles as an Efficient Surface Modification Strategy for Versatile Porous Scaffolds Enhancing Tissue Regeneration

Porous scaffolds for tissue regeneration are often functionalized with extracellular matrix proteins to enhance surface/cell interactions and tissue regeneration. However, continuous coatings produced by commonly used surface modification strategies may preclude cells from contacting and sensing the chemical and physical cues of the scaffold. Here, it is shown that polydopamine nanoparticles (PDA‐NPs) tightly adhere on various scaffolds to form nanostructures, and the coverage can be finely tuned. Furthermore, the PDA‐NPs have good affinity to a variety of proteins and peptides. Thus, the PDA‐NPs act as an anchor to immobilize signal biomolecules on scaffolds, and consequently promote cell activity and tissue regeneration. β‐Tricalcium phosphate (TCP) scaffolds decorated with PDA‐NPs demonstrate excellent osteoinductivity and bone‐regeneration performance due to the protein affinity of PDA‐NPs and the intrinsic bioactive characteristics of TCP scaffolds. In summary, PDA‐NPs with excellent affinity for protein adhesion represent a versatile platform to modify porous scaffolds while not compromising the biological functions of the scaffolds, and might have potential applications in tissue regeneration.     

Electrical Properties of HeLa Cells Based on Scalable 3D Interdigital Electrode Array

This study analyzed the electrical properties of a multiple‐cells using the electric cell‐substrate impedance sensing, a scalable three‐dimensional electrode array and an equivalent circuit model (ECM). The experimental results validated the accuracy and validity of the extraction method for the ECM. The ECM simulation results using the electrical properties extracted by measuring 52 HeLa cells successfully forecasted the impedance magnitudes and phases for 15, 29, 78, and 98 HeLa cells. Comparing the ECM simulations and measurements, the maximum average errors in magnitude and phase were 3.06 % and 4.67 %, suggesting the number of HeLa cells can be classified by their electrical properties.     

Toward a Constitutive Law of Cartilage: A Polymer Physics Perspective

To describe load bearing and lubrication of cartilage requires treating its collagen network and proteoglycan (PG) phases separately in a constitutive law of the tissue. We propose a framework for developing such an empirical constitutive law that treats the cartilage extracellular matrix (ECM) as a composite medium, with a PG phase that exerts a swelling pressure, and a collagen network phase that restrains it. We compare and contrast this model to a biomechanical constitutive law that aggregates the collagen and PG phases into a single “solid-like” elastic tissue matrix, and show that aggregation obscures essential differences in the physical-chemical properties of the collagen and PG constituents as well as their distinct biological roles within cartilage's ECM. We also relate moduli in the aggregate constitutive model to quantities measured in an osmotic stress titration experiment.     

用能量自洽法研究部分卤化钙分子CaX (X=Br, Cl, F)的解析势能函数

采用研究解析势能函数的能量自洽法Energy Consistent Method (ECM), 研究了部分卤化钙分子CaX (X=Br, Cl, F) 的C2S+、A2和B2S+态等基态和低激发态的解析势能曲线。并与曾经常使用的Morse 势、Huxley-Murrell-Sorbie (HMS)势和基于实验的Rydberg- Klein-Rees (RKR) 数据进行了比较。结果表明,这些 ECM 势能不仅能与RKR 数据符合得很好,而且还能得到实验方法难以得到、常用的Morse 势和HMS 势函数可能会产生较大误差、但在许多研究中又非常重要的渐近区和离解区的势能数据,得到了物理性质优秀的全程解析势能。     

向量自回归模型与向量误差修正模型预测功能的比较——基于我国国内生产总值和居民消费支出变量的实证研究

建立国内生产总值与居民消费支出有关形式变量的向量自回归模型和误差修正模型,依据国内生产总值和居民消费支出的历史数据分别对模型进行估计,并利用估计好的2个模型对近2年的相应变量进行预测,将预测结果与实际数据相比较,从而得出2个模型的预测效果.通过比较显示：误差修正模型预测效果优于向量自回归模型.     

Factorization of the tenth Fermat number

We describe the complete factorization of the tenth Fermat number by the elliptic curve method (ECM). is a product of four prime factors with 8, 10, 40 and 252 decimal digits. The 40-digit factor was found after about 140 Mflop-years of computation. We also discuss the complete factorization of other Fermat numbers by ECM, and summarize the factorizations of .

     

基于ECM方法的人力资本与中国经济增长关系研究

人力资本是经济长期稳定增长的关键因素,人力资本通过内部作用和外溢作用推动经济增长,经济增长又进一步支持人力资本的发展.本文从数量分析角度,利用协整、误差修正模型(ECM)和G ranger检验考察各类教育程度人力资本与中国经济增长的关系,并建立方差分解模型刻画它们的动态调整关系.实证分析表明,在长期中国经济增长是研究生教育人力资本和高中教育人力资本增长的原因.     

Monotonically Overrelaxed EM Algorithms

We explore the idea of overrelaxation for accelerating the expectation-maximization (EM) algorithm, focusing on preserving its simplicity and monotonic convergence properties. It is shown that in many cases, a trivial modification in the M-step results in an algorithm that maintains monotonic increase in the log-likelihood, but can have an appreciably faster convergence rate, especially when EM is very slow. The method is applicable to more general fixed point algorithms. Its simplicity and effectiveness are illustrated with several statistical problems, including probit regression, least absolute deviations regression, Poisson inverse problems, and finite mixtures. This article has supplemental materials available online.     

Analysis of Data with Censored Initiating and Terminating Times: A Missing-Data Approach

Survival data with censored initiating and terminating times have surfaced in some recent epidemiologic studies. Unlike standard survival analysis with known initiating times, analysis of data with both censored initiating and terminating times requires maximization of a complicated bivariate likelihood, which is often difficult to carry out. This article considers a missing-data formulation of the problem and focuses on the use of EM-type algorithms to simplify the computation of maximum likelihood estimates. This approach provides a feasible way of performing regression analysis with such bivariate survival data. Several illustrative examples are provided, including a real-data analysis application involving a cohort of HIV-infected hemophiliac patients.     

ML Estimation of the MultivariatetDistribution and the EM Algorithm

Maximum likelihood estimation of the multivariatetdistribution, especially with unknown degrees of freedom, has been an interesting topic in the development of the EM algorithm. After a brief review of the EM algorithm and its application to finding the maximum likelihood estimates of the parameters of thetdistribution, this paper provides new versions of the ECME algorithm for maximum likelihood estimation of the multivariatetdistribution from data with possibly missing values. The results show that the new versions of the ECME algorithm converge faster than the previous procedures. Most important, the idea of this new implementation is quite general and useful for the development of the EM algorithm. Comparisons of different methods based on two datasets are presented.