Regulation of proteins mediating neurodegeneration in experimental autoimmune encephalomyelitis and multiple sclerosis

Multiple sclerosis is characterized by inflammatory demyelination and axonal loss as pathophysiological correlates of relapsing activity and progressive development of clinical disability. The molecular processes involved in this pathogenesis are still unclear as they are quite complex and heterogeneous. In this article we present protein expression analysis of brain and spinal cord tissues from different models of murine experimental autoimmune encephalomyelitis (EAE), the most commonly used animal model for multiple sclerosis. We observed a number of EAE-specific protein expression and PTM differences. Proteome analysis was extended to multiple sclerosis specimens in order to validate the EAE findings. Our findings suggest the regulation of a number of proteins that shed light on the molecular mechanisms of the disease processes taking place in EAE and multiple sclerosis. We found consistent modulation of proteins including serum amyloid P component, sirtuin 2, dihydropyrimidinase-related protein family proteins, stathmin 1, creatine kinase B and chloride intracellular channel protein 1. Functional classification of the proteins by database and the literature mining reveals their association with neuronal development and myelinogenesis, suggesting possible disease processes that mediate neurodegeneration.     

基于Toy模型蛋白质折叠预测的多种群微粒群优化算法研究

基于Toy模型的蛋白质折叠结构预测问题是一个典型的NP问题.提出了多种群微粒群优化算法用于计算蛋白质能量最小值.该算法采用了一种新的算法结构,在该结构中,每一代的种群被分为精英子种群、开采子种群和勘探子种群三部分,通过改善种群的局部开采能力和全局勘探能力来提高算法的性能.分别采用Fibonacci蛋白质测试序列和真实蛋白质序列进行了折叠结构预测的仿真实验.实验结果表明该算法能够更精确地进行蛋白质折叠结构预测,为生物科学研究提供了一条有效途径.     

Coating nanodiamonds with biocompatible shells for applications in biology and medicine

Use of nanodiamonds (NDs) as nontoxic nanoparticles for biological imaging, sensing, and drug delivery is expanding rapidly. The interest in NDs is triggered by their unique combination of optical properties. ND can accommodate nitrogen-vacancy color centers which provide stable fluorescence without photobleaching or photoblinking and their electronic structure is very sensitive to magnetic and electric fields. The limited options to control ND properties during synthesis or by direct surface functionalization leave room to be improved upon by employing surface coatings engineered precisely for a particular application. The major disadvantages of unmodified NDs are their limited colloidal stability and tendency to non-specifically adsorb biomolecules. This review aims to summarize recent advances in coating NDs (namely with silica and polymer shells), which addresses these disadvantages and enables the use of NDs in biological applications such as targeting of specific cells, drug delivery, and biological imaging.     

Surface energy-mediated fibronectin adsorption and osteoblast responses on nanostructured diamond

Nanostructured diamond have potential applications in many biomedical related fields and demonstrated extraordinary capacity to influence cellular responses. Studying the surface property of nanodiamond and its influence to protein adsorption and subsequent cellular responses along with the mechanism behind such capacity becomes more important. Here the role of surface energy associated with nanostructured diamond in modulating fibronectin and osteoblast(OB, bone forming cells) responses was investigated. Nanocrystalline diamond(NCD) and submicron crystalline diamond(SMCD) films with controllable surface energy were prepared by microwave-enhanced plasma chemical vapor deposition(MPCVD) techniques. Fibronectin adsorption on the diamond films with varied surface energy values was measured via the enzyme-linked immunosorbent assay(ELISA) and the relationship between the surface energy and fibronectin adsorption was studied. The result indicated that fibronectin adsorption on nanostructured surfaces was closely related to both surface energy and material microstructures. The spreading and migration of OB aggregates(each containing 30–50 cells) on the NCD with varied surface energy values were also studied. The result indicates a correlation between the cell spreading and migration on nanodiamond and the surface energy of nanostructured surface.     

商业诽谤犯罪中网络服务提供者行为的技术与法律分析

在本案中,刑法及其司法解释完全从商业诽谤犯罪实行行为的角度进行解释,没有重视网络信息服务提供者的行为特点,实际上导致难以根据刑法以及司法解释分析网络服务提供者在网络商业诽谤违法犯罪中的责任。在商业诽谤刑事案件中,网络服务提供者不仅有义务向侦查机关提供有关资料,而且由于其在信息监管责任缺位,必须对其设置责任追究的机制,从而推动其履行商业诽谤信息审查义务。     

Embedding HMMs-based models in a Euclidean space: the topological hidden Markov models

Current extensions of hidden Markov models such as structural, hierarchical, coupled, and others have the power to classify complex and highly organized patterns. However, one of their major limitations is the inability to cope with topology: When applied to a visible observation (VO) sequence, the traditional HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the VO sequence. To fulfill this need, we propose a novel paradigm named “topological hidden Markov models” (THMMs) that classifies VO sequences by embedding the nodes of an HMM state transition graph in a Euclidean space. This is achieved by modeling the noise embedded in the shape generated by the VO sequence. We cover the first and second level topological HMMs. We describe five basic problems that are assigned to a second level topological hidden Markov model: (1) sequence probability evaluation, (2) statistical decoding, (3) structural decoding, (4) topological decoding, and (5) learning. To show the significance of this research, we have applied the concept of THMMs to: (i) predict the ASCII class assigned to a handwritten numeral, and (ii) map protein primary structures to their 3D folds. The results show that the second level THMMs outperform the SHMMs and the multi-class SVM classifiers significantly.     

The effect of nonhuman’s versus human’s external regulation on children’s speech use, manifested self-regulation, and satisfaction during learning tasks

Because of several analytical and methodological critiques on the findings and contexts of children’s private speech (PS), self-regulation learning (SRL), and thinking aloud (TA), the present study was conducted to shed new light on the effect of the nonhuman’s/computer’s versus human’s/teacher’s intervention (C-Condition versus T-Condition) on young children’s speech use, SRL, and satisfaction during learning tasks. Four developmental measurements with novel criteria were used to measure: (1) speech analysis, (3) SRL as a function of task level selection, (3) SRL as a function of task precision, and (4) a friendly-chat questionnaire to measure children’s satisfaction. Two types of intervention (enacted versus verbal encouragement) were applied through computer-based learning environment and investigated by forty preschool children divided by their teachers between the two conditions equivalently. It was hypothesized that children who acted alone (C-Condition) were more PS productive, manifested higher SRL, task performance, and satisfaction. The results confirmed the hypothesis with no significant differential effect of the gender on performance, showed that the injudicious use of encouragement hindered the children’s regulation behavior, and proved that PS and TA elicitation were fully different. However, the results were not confirmed Vygotsky’s view and simultaneously not fully inline with Piaget’s view of self-regulation development.     

Monte Carlo simulations of systems with complex energy landscapes

Non-traditional Monte Carlo simulations are a powerful approach to the study of systems with complex energy landscapes. After reviewing several of these specialized algorithms we shall describe the behavior of typical systems including spin glasses, lattice proteins, and models for “real” proteins. In the Edwards-Anderson spin glass it is now possible to produce probability distributions in the canonical ensemble and thermodynamic results of high numerical quality. In the hydrophobic-polar (HP) lattice protein model Wang-Landau sampling with an improved move set (pull-moves) produces results of very high quality. These can be compared with the results of other methods of statistical physics. A more realistic membrane protein model for Glycophorin A is also examined. Wang-Landau sampling allows the study of the dimerization process including an elucidation of the nature of the process.     

A novel, electrically protein-manipulated microcantilever biosensor for enhancement of capture antibody immobilization

This study demonstrated a microcantilever biosensor for enhancement of capture antibody immobilization. The electrically protein-manipulated, microcantilever biosensor is featured with enhanced capture antibody immobilization, miniaturization, and high sensitivity. Thanks to the electric property of biological substances in a real environment, given charged proteins can be manipulated with attraction in solution under an electric field. It is evident that higher amount of capture antibody molecules immobilized onto sensing surfaces captures or detects specific molecules, indicating greater deflection and stresses as well. This however leads to significant cost in biosensors. With the merit of MEMS technique that allows highly fabrication-compatible integration into microcantilever biosensors, sparsely distributed antibody molecules in solution are attracted in focus onto a sensing solid surface under electric fields. As the sensing element of the gold-coated, V-shaped silicon nitride microcantilever also serves as an electrode, the electric fields are applied in a channel of flowing microfluidics by locally in-plane electrodes or by a top electrode arranged for three-dimensional fields. As expected, most charged proteins distributed in solution are effectively attracted onto the sensing area within the electric fields. This improves the efficiency of capture antibody immobilization and achieves an eight-fold reduction over the necessary amount of capture antibodies without applying electric fields. With such a successful manipulation of charged proteins, the novel microcantilever biosensor exhibits efficient use of capture antibodies in solution.     

A novel ACO–GA hybrid algorithm for feature selection in protein function prediction

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.