纳米材料的体内命运：纳米医学安全性评价中的重要挑战

纳米材料的物理化学特性、金属杂质及聚集状态等与纳米材料的生物效应和安全性息息相关。纳米材料在生物体内的释放、分布、转运、代谢特征及由此带来的潜在安全性问题是纳米医药领域实现产业转化最重要的基础科学问题。纳米药物需具备良好的生物安全性、靶向性及功能,但目前依然缺乏纳米材料与生物体系作用的分析方法、早期毒理学效应的生物医学评价指标与方法以及标准化和规范化的纳米安全性评价方法。基于纳米—生物体作用过程的特点,发展原位、非标记、高通量、超高灵敏、超高分辨和动态快速检测的新方法和新策略,实现纳米材料的精准定量和准确定位,并获取纳米材料的关键化学结构动态信息是未来研究需要关注的内容。     

DNA介导的功能性纳米组装体及其生物医学应用

利用表面配体对无机纳米材料进行修饰、组装而获得的功能性纳米组装体,具备不同于单分散纳米材料的特殊光学、电学、磁学和热力学等性质,在生物医药领域显示出巨大的应用价值。DNA具有精确的化学结构和精准的分子识别特性,使其作为一种配体在无机纳米材料表面修饰和组装调控方面展现出独特的优势。由DNA配体介导的功能性纳米组装体能够实现组装结构的精准可控调节,拓宽无机纳米材料在生物医药领域的应用前景,可有效提高肿瘤等重大疾病的诊疗效率,推动生命科学的进步和发展。本文对国内外构建DNA介导功能性纳米组装体及其在生物医药领域应用相关的代表性工作进行总结和对比,讨论我国在该领域面临的机遇和挑战,为今后的发展提出建议。     

纳米科学与酶

现代生命科学的发展尤其注重极端条件下生物体系的潜力。作为工业生物技术科学的一个分支,现代酶技术广泛探索如何极大限度地使酶在细胞外长期保持活性,并能有效地适应非生态环境的条件。纳米科学的迅速发展为酶的稳定和高效催化转化带来了新的机遇。纳米材料和酶技术结合可制备纳米酶催化剂,其纳米结构不仅能使酶在不同体系长期保持活性稳定,而且能提高水相、有机相、油．水界面的催化效率,并使多酶体系催化反应和辅酶再生成为可能。纳米颗粒的高曲率能降低酶固定化时的变构,纳米颗粒的布朗运动使纳米固定化酶和底物频繁碰撞,大幅度提高催化效率。同样,纳米纤维和纳米孔均能很好地保持酶的活性。用合适的纳米颗粒和纳米纤维修饰酶,可使酶自组装于油-水界面,不仅加速了油-水界面反应,而且使酶在油-水界面保持稳定。纳米孔还使伴随辅酶再生的多酶催化体系成为可能。深入研究纳米结构对酶稳定性的影响规律,从而根据酶的特性设计最佳的纳米结构是今后的挑战。利用多酶催化体系的工业生物技术是一个极具挑战性和前瞻性的发展方向。同时,微反应器的设计使纳米酶的回收利用成为可能,将带来更大的工业应用优势。     

潜心微观世界 立足化学前沿——记南京大学化学化工学院朱俊杰教授

纳米技术是研究结构尺寸在0．1～100纳米范围内材料的性质和应用的技术,它是一门交叉性很强的综合学科,研究内容涉及现代科技的广阔领域。纳米科学与技术主要包括：纳米体系物理学、纳米化学、纳米材料学、纳米生物学、纳米电子学、纳米加工学、纳米力学等。     

自然科学基金在促进纳米材料研究中的作用

纳米技术是一种通过在零到一百纳米这样极为精细的尺度下人为控制和制备纳米材料和纳米结构以实现特殊功能和智能作用的先进科学技术，被认为是将会带来21世纪工业革命的前沿科学技术。而纳米材料是纳米技术的重要基础。国家自然科学基金委员会工程与材料科学部无机非金属材料学科早在1987年就开始资助纳米材料领域的研究项目，在90年代已经资助了相当一批纳米科技方面的研究，形成了面上、重点、重大、人才基金等几个层次上的资助格局。主要研究领域有纳米粉体，纳米薄膜，一维纳米材料的制备、表征等，涉及到信息、能源、国防等等方面，研究人员取得了一些有显示性的成果，如作为非共识创新项目得到学部主任基金资助的范守善教授，获得基金连续资助的中科院金属所成会明研究员，从海外归来的中国科技大学候建国教授等先后在Na-ture、Science发表了文章。除此而外，还有经历了研究低谷取得了优异研究成果的南京大学鲍希茂教授。总结这些成果，我们可以看到各类基金项目的资助起着它特有的作用。     

特种纳米涂料

一、主要技术内容 "特种纳米涂料"系"九五"科技攻关成果项目. 纳米材料是由表面(界面)结构组元构成的材料,粒径介于原子团簌与常规粉体之间,一般不超过100纳米.一般根据纳米材料的不同需要,控制其粒子的形态就形成了以下三种纳米材料三维尺寸均为纳米量级的为三维纳米材料,如纳米粉体;某二维尺寸为纳米量级的为二维纳米材料,如层状纳米材料;某一维尺寸为纳米量级的为二维纳米材料,如纳米纤维.     

研究利用纳米材料建设节约型社会

纳米技术、信息技术及生物技术将成为新世纪社会经济发展的三大支柱.纳米科技的兴起,对我国提出了严峻的挑战,同时也为我国实现跨越式发展提供了难得的机遇.纳米材料是纳米科技的基础,功能纳米材料是纳米材料科学中最富有活力的领域,它对信息、生物、能源、环境、宇航等高科技领域,将产生深远的影响并具有广阔的应用前景.     

尺寸、形貌和结构可控的球形二氧化硅、及其纳米复合二氧化钛、银球颗粒的研发

纳米材料可控制备技术是目前国内外引人注目的一项高新技术。它利用一系列的化学方法，制备适应不同产品所需要的特定尺寸、结构、形状的纳米材料，改变纳米材料研究中的盲目性、随机性，按照人们的意志来设计、组装、构建纳米功能材料。     

中国发展纳米科技的关键战略

本世纪纳米科技将在两方面影响人们的生活,即纳米材料和纳米电子技术.就纳米材料而言,它包含两方面:一是材料在某一维上的尺寸小于100纳米,但又不仅仅界定在100纳米.二是当材料达到纳米尺寸时,应产生区别于微观的分子或原子,也区别于宏观物体的超常规物性.具备这两个条件,才可称为纳米材料.纳米材料主要包括结构材料和功能材料.     

液体-固体-溶液界面相转移和相分离——一种通用的纳米晶体合成策略

如何在纳米介观尺度范围内实现对材料结构与性能的调控,是纳米材料功能化及其应用的关键。单分散纳米晶指尺寸及形状均一、且在特定介质中具有良好分散能力的纳米材料。基于纳米粒子自身的尺寸效应、表面效应、量子效应,载流子在纳米粒子限制维度空间内的传输具有不同于其它维度材料的特性,展现出许多独特的光学、电学等物理化学性质。同时形状及尺寸严格均一的单分散纳米晶可以通过各种物理化学相互作用进行组装,在纳米器件、量子点激光器、非线性光学、磁介质、催化、功能材料及纳米生物技术等方面具有极为广阔的应用前景。然而由于不同化…     

Theoretical and Conceptual Analysis of the Celebrated 4<Emphasis Type="Italic">π</Emphasis>-Symmetry Neutron Interferometry Experiments

In 1975, two experimental groups have independently observed the \(4\pi \)-symmetry of neutrons’ spin, when passing through a static magnetic field, using a three-blade interferometer made from a single perfect Si-crystal (analogous to the Mach Zehnder interferometer of light optics). In this article, we provide a complete analysis of the experiment, both from a theoretical and conceptual point of view. Firstly, we solve the Schrödinger equation in the weak potential approximation, to obtain the amplitude of the refracted and forward refracted beams, produced by the passage of neutrons through one of the three plates of the LLL interferometer. Secondly, we analyze their passage through a static magnetic field region. This allows us to find explicit expressions for the intensities of the four beams exiting the interferometer, two of which will be interfering and show a typical \(4\pi \)-symmetry, when the strength of the magnetic field is varied. In the last part of the article, we provide a conceptual analysis of the experiment, showing that a neutron’s phase change, when passing through the magnetic field, is due to a longitudinal Stern–Gerlach effect, and not to a Larmor precession. We also emphasize that these experiments do not prove the observability of the sign change of the wave function, when a neutron is \(2\pi \) rotated, but strongly indicate that the latter, like any other elementary “particle,” would be a genuinely non-spatial entity.     

谢瓦莱在类域论方面的贡献

回顾了类域论的早期发展,尤其是希尔伯特的研究;以谢瓦莱引入的伊代尔概念为核心,深刻挖掘伊代尔概念的重要性,重新评价了谢瓦莱类域论工作的历史价值。指出正是借助伊代尔的概念,谢瓦莱摒弃了解析工具,完成了类域论的算术化过程,也建立了伊代尔叙述的整体类域论,更重要的是,将拓扑、泛函与类域论结合起来,促进了类域论的发展。这些研究对我们更全面、深刻的认识谢瓦莱的工作有借鉴意义。     

Axiomatization and Models of Scientific Theories

In this paper we discuss two approaches to the axiomatization of scientific theories in the context of the so called semantic approach, according to which (roughly) a theory can be seen as a class of models. The two approaches are associated respectively to Suppes’ and to da Costa and Chuaqui’s works. We argue that theories can be developed both in a way more akin to the usual mathematical practice (Suppes), in an informal set theoretical environment, writing the set theoretical predicate in the language of set theory itself or, more rigorously (da Costa and Chuaqui), by employing formal languages that help us in writing the postulates to define a class of structures. Both approaches are called internal, for we work within a mathematical framework, here taken to be first-order ZFC. We contrast these approaches with an external one, here discussed briefly. We argue that each one has its strong and weak points, whose discussion is relevant for the philosophical foundations of science.     

A Nonparametric Approach to Cognitive Diagnosis by Proximity to Ideal Response Patterns

A trend in educational testing is to go beyond unidimensional scoring and provide a more complete profile of skills that have been mastered and those that have not. To achieve this, cognitive diagnosis models have been developed that can be viewed as restricted latent class models. Diagnosis of class membership is the statistical objective of these models. As an alternative to latent class modeling, a nonparametric procedure is introduced that only requires specification of an item-by-attribute association matrix, and classifies according to minimizing a distance measure between observed responses, and the ideal response for a given attribute profile that would be implied by the item-by-attribute association matrix. This procedure requires no statistical parameter estimation, and can be used on a sample size as small as 1. Heuristic arguments are given for why the nonparametric procedure should be effective under various possible cognitive diagnosis models for data generation. Simulation studies compare classification rates with parametric models, and consider a variety of distance measures, data generation models, and the effects of model misspecification. A real data example is provided with an analysis of agreement between the nonparametric method and parametric approaches.     

The Deluge of Spurious Correlations in Big Data

Very large databases are a major opportunity for science and data analytics is a remarkable new field of investigation in computer science. The effectiveness of these tools is used to support a “philosophy” against the scientific method as developed throughout history. According to this view, computer-discovered correlations should replace understanding and guide prediction and action. Consequently, there will be no need to give scientific meaning to phenomena, by proposing, say, causal relations, since regularities in very large databases are enough: “with enough data, the numbers speak for themselves”. The “end of science” is proclaimed. Using classical results from ergodic theory, Ramsey theory and algorithmic information theory, we show that this “philosophy” is wrong. For example, we prove that very large databases have to contain arbitrary correlations. These correlations appear only due to the size, not the nature, of data. They can be found in “randomly” generated, large enough databases, which—as we will prove—implies that most correlations are spurious. Too much information tends to behave like very little information. The scientific method can be enriched by computer mining in immense databases, but not replaced by it.     

基于Protégé的人工影响天气术语本体知识库设计与实现

基于本体编辑工具Protégé,设计了人工影响天气术语本体知识库。按照本体构造的“七步法”,定义了术语类,明确了术语类层次关系,定义了术语类的对象属性和数据属性;基于自定义的对象属性,利用HermiT推理机对本体进行了一致性检查。文章系统组织了人工影响天气领域的专业术语,为该领域术语研究、人机翻译、基于语义的信息检索奠定了基础。     

Model Similarity and Rank-Order Based Classification of Bayesian Networks

Suppose that we rank-order the conditional probabilities for a group of subjects that are provided from a Bayesian network (BN) model of binary variables. The conditional probability is the probability that a subject has a certain attribute given an outcome of some other variables and the classification is based on the rank-order. Under the condition that the class sizes are equal across the class levels and that all the variables in the model are positively associated with each other, we compared the classification results between models of binary variables which share the same model structure. In the comparison, we used a BN model, called a similar BN model, which was constructed under some rule based on a set of BN models satisfying certain conditions. Simulation results indicate that the agreement level of the classification between a set of BN models and their corresponding similar BN model is considerably high with the exact agreement for about half of the subjects or more and the agreement up to one-class-level difference for about 90% or more.     

最小作用量原理之统计解释

本文提出最小作用量原理之统计解释,将作用量等价于物理场背后潜在统计系统之自由能。由此导出系统之绝对温度正比于位能,提出真空具有相应于其温度的真空辐射及真空位能。并对量子隧道效应提出了不同于海森伯测不准原理的新解释,预言粒子穿透隧道所需时间正比于隧道长度之平方,建议用隧道二极管做实验加以验证。     

Dimensionally Reduced Model-Based Clustering Through Mixtures of Factor Mixture Analyzers

Dimensionally reduced model-based clustering methods are recently receiving a wide interest in statistics as a tool for performing simultaneously clustering and dimension reduction through one or more latent variables. Among these, Mixtures of Factor Analyzers assume that, within each component, the data are generated according to a factor model, thus reducing the number of parameters on which the covariance matrices depend. In Factor Mixture Analysis clustering is performed through the factors of an ordinary factor analysis which are jointly modelled by a Gaussian mixture. The two approaches differ in genesis, parameterization and consequently clustering performance. In this work we propose a model which extends and combines them. The proposed Mixtures of Factor Mixture Analyzers provide a unified class of dimensionally reduced mixture models which includes the previous ones as special cases and could offer a powerful tool for modelling non-Gaussian latent variables.