Thermodynamics of natural selection

It is shown that biological-natural-selection-like behavior can occur, as a general type of time evolution, in a statistical system where detailed balance is violated owing to the presence of metastable energy states. A model of a non-equilibrium phase transition corresponding to the spontaneous origin of self-reproduction in the system is suggested. After a phase transition, the system passes from one quasistationary distribution of self-reproducing subsystems to another, with an increase in the total organization, as long as the growth of the energy flow through the system or a reduction of energy dissipation in the system is possible. The entropy production is calculated for this process in terms of selective values of Eigen's theory for self-organization in autocatalytic systems. Correspondence of the extremal principle of Eigen's theory with the criterion of evolution in Prigogine's thermodynamics is established.     

STUDIES ON PREPARATION OF INACTIVATED VACCINES FROM CELL CULTURES OF MINK ENTERITIS VIRUS (MEV) AND THEIR IMMUNITY

In this paper, superhigh reproductive rate strains of MEV with titre more than HA8192* or TCID50 log9.7 10 have been achieved both by cultivation in cell lines with different susceptibility to MEV and by isolating and identifying in field by the author. The systematic tests proved that S18 and L12 strains of MEV are the best strains for vaccine preparation. In this study, the best means for the tissue cultivation of MEV and the most advanced technological process for the production and detection of serum-free cell-cultured MEV fluids with super-high HA titre in batches in large quantities have been established for the first time. Optimum conditions for MEV inactivation were determined, and safe and effective inactivated vaccines with mineral oil or A1(OH)3 gel adjuvant were successfully prepared with serum-free cell-cultured MEV fluids. Both vaccines with different adjuvants can be manufactured in batches in large quantities and have been widely used all over China since 1986. The change laws of the imm     

Improving (Q)SAR predictions by examining bias in the selection of compounds for experimental testing

ABSTRACT

Existing data on structures and biological activities are limited and distributed unevenly across distinct molecular targets and chemical compounds. The question arises if these data represent an unbiased sample of the general population of chemical-biological interactions. To answer this question, we analyzed ChEMBL data for 87,583 molecules tested against 919 protein targets using supervised and unsupervised approaches. Hierarchical clustering of the Murcko frameworks generated using Chemistry Development Toolkit showed that the available data form a big diffuse cloud without apparent structure. In contrast hereto, PASS-based classifiers allowed prediction whether the compound had been tested against the particular molecular target, despite whether it was active or not. Thus, one may conclude that the selection of chemical compounds for testing against specific targets is biased, probably due to the influence of prior knowledge. We assessed the possibility to improve (Q)SAR predictions using this fact: PASS prediction of the interaction with the particular target for compounds predicted as tested against the target has significantly higher accuracy than for those predicted as untested (average ROC AUC are about 0.87 and 0.75, respectively). Thus, considering the existing bias in the data of the training set may increase the performance of virtual screening.     

Packings and stationary phases for biopolymer separations by HPLC

Summary Packings and stationary phases applied to high resolution separations of proteins, enzymes, and nucleic acids must satisfy a series of distinct criteria that are different from those usually required by HPLC of low molecular weight non-biologically active analytes. These requirements have been met through substantial improvements in classical gel media together with novel developments in silica supports, and have led to a family of products with tailor-made and reproducible properties. Supports consisting of cross-linked organic gels, and inorganic materials (mostly silicas) are now available with graduated particle sizes, pore sizes, porosities and surface areas as well as non-porous beads. A whole range of stationary phases, such as reversed phase, hydrophobic interaction, ion exchanger and affinity packings, were designed for application as chemical sensors for biopolymer recognition in adsorptive chromatography. The phase systems are operated in the gradient mode, giving high resolution and high peak capacities. In addition, aqueous liquid-liquid partitioning systems have been developed for the fractionation of proteins and nucleic acids. Size exclusion media complete the set of HPLC variants enabling a discrimination of proteins according to their size and shape in an isocratic elution mode. Basically, protein purification and isolation is a multistage process where-by the HPLC variants are combined in a logistic sequence, utilizing the different selectivities of the phase systems and thus maximising resolution, speed and throughput.     

Lipschitzian inverse functions,directional derivatives,and applications inC 1,1 optimization

The paper shows that Thibault's limit sets allow an iff-characterization of local Lipschitzian invertibility in finite dimension. We consider these sets as directional derivatives and extend the calculus in a way that can be used to clarify whether critical points are strongly stable inC ^1,1 optimization problems.Many fruitful discussions with colleagues D. Klatte and K. Tammer as well as with H. Th. Jongen and F. Nozicka have influenced the present investigations in a very constructive manner. For the original papers concerning the sets f(x; u), the author is indebted to Prof. L. Thibault.     

Global optimization of univariate Lipschitz functions: II. New algorithms and computational comparison

We consider the following global optimization problems for a Lipschitz functionf implicitly defined on an interval [a, b]. Problem P: find a globally-optimal value off and a corresponding point; Problem Q: find a set of disjoint subintervals of [a, b] containing only points with a globally-optimal value and the union of which contains all globally optimal points. A two-phase algorithm is proposed for Problem P. In phase I, this algorithm obtains rapidly a solution which is often globally-optimal. Moreover, a sufficient condition onf for this to be the case is given. In phase II, the algorithm proves the-optimality of the solution obtained in phase I or finds a sequence of points of increasing value containing one with a globally-optimal value. The new algorithm is empirically compared (on twenty problems from the literature) with a best possible algorithm (for which the optimal value is assumed to be known), with a passive algorithm and with the algorithms of Evtushenko, Galperin, Shen and Zhu, Piyavskii, Timonov and Schoen. For small, the new algorithm requires only a few percent more function evaluations than the best possible one. An extended version of Piyavskii's algorithm is proposed for problem Q. A sufficient condition onf is given for the globally optimal points to be in one-to-one correspondance with the obtained intervals. This result is achieved for all twenty test problems.The research of the authors has been supported by AFOSR grants 0271 and 0066 to Rutgers University. Research of the second author has been also supported by NSERC grant GP0036426, FCAR grant 89EQ4144 and partially by AFOSR grant 0066. We thank Nicole Paradis for her help in drawing the figures.     

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.     

改进的PNC方法及其在非线性偏微分方程多解问题中的应用

2017年,李昭祥等提出了一种偏牛顿-校正法（Partial Newton-Correction Method,简记为PNC方法）,并利用它成功地计算出了三类非线性偏微分方程的多重不稳定解.本文在PNC方法的基础上,提出并发展了一种改进的PNC方法.首先,利用Nehari流形$\mathcal{N}$与零平凡解的可分离性,建立并证明了$\mathcal{N}$的某特殊子流形$\mathcal{M}$上的全局分离定理及其推广（即局部分离定理）.全局分离定理只跟非线性偏微分算子或相应的非线性泛函本身有关,而与具体的计算方法无关.对一些典型的非线性偏微分方程多解问题（比如,Henon方程问题）,该全局分离定理的分离条件,经验证是成立的.另一个方面,通过修改或补充原辅助变换的定义,去掉了原辅助变换的奇异性;接着建立并证明了某些非线性偏微分方程问题的新未知解与该非线性偏微分算子零核空间的密切关系;在证明中,去掉了在原奇异变换下所需的标准收敛（standard convergence）假设.最后,计算实例与数值结果验证了改进的PNC方法的可行性和有效性;同时表明子流形$\mathcal{M}$与已知解的可分离性是PNC方法和本文新方法能成功找到多解的关键.