Development and application of functional gene arrays for microbial community analysis

功能的基因标记能关于功能的基因差异和微生物引起的社区的潜在的活动提供重要信息。尽管 microarray 技术成功地被使用了为纯文化学习基因表示，简单、人工的微生物引起的社区，改编如此的一种技术分析复杂微生物引起的社区仍然以设计，样品准备，和数据分析提出很多挑战。这个工作集中于功能的基因数组(FGA ) 的开发和应用程序为微生物引起的社区研究指向关键功能的基因标记。一些与 FGA 有关明确地给问题调音，例如 oligonucleotide 探查设计， nucleic 酸抽取和纯化，数据分析，特性，敏感，和量的能力详细被讨论。最近的研究证明了 FGA 能从许多自然环境关于微生物引起的社区提供特定、敏感、潜在地量的信息并且控制生态系统。这种技术被期望革命化微生物引起的社区的分析，并且连接微生物引起的结构到生态系统工作。

Classification analysis of microarray data based on ontological engineering

Background knowledge is important for data mining, especially in complicated situation. Ontological engineering is the successor of knowledge engineering. The sharable knowledge bases built on ontology can be used to provide background knowledge to direct the process of data mining. This paper gives a common introduction to the method and presents a practical analysis example using SVM (support vector machine) as the classifier. Gene Ontology and the accompanying annotations compose a big knowledge base, on which many researches have been carried out. Microarray dataset is the output of DNA chip. With the help of Gene Ontology we present a more elaborate analysis on microarray data than former researchers. The method can also be used in other fields with similar scenario.

核心素养下高三数学微专题探究——“构造等差数列解决数列问题”微专题教学实例及思考

本节微专题让学生深刻理解等差数列的定义及其等价形式,能选择有效的方法构造等差数列解决一般数列问题,难点是将非等差数列转化为等差数列,体现的核心素养是逻辑推理和数学运算。

On some difference sequence spaces defined by a sequence of Orlicz functions

The idea of difference sequence spaces was introduced in （Klzmaz, 1981） and this concept was generalized in （Et and Colak, 1995）. In this paper we define some difference sequence spaces by a sequence of Orlicz functions and establish some inclusion relations.

Normalization strategy of microarray gene expression data

Objective： To discuss strategies and methods of normalization on how to deal with and analyze data for different chips with the combination of statistics, mathematics and bioinformatics in order to find significant difference genes. Methods： With Excel and SPSS software, high or low density chips were analyzed through total intensity normalization （TIN） and locally weighted linear regression normalization （LWLRN）. Results： These methods effectively reduced systemic errors and made data more comparable and reliable. Conclusion： These methods can search the genes of significant difference, although normalization methods are being developed and need to be improved further. Great breakthrough will be obtained in microarray data normalization analysis and transformation with the development of non-linear technology, software and hardware of computer.

Casoratian Solutions to a Non-isospectral Differential-Difference Kadomtsev-Petviashvilli Equation

In this paper, a non-isospectrai differential-difference Kadomtsev-Petviashvilli equation （n-D△KPE） is presented. Then, the Casoratian solutions of the n-D△KPE are obtained by generalizing Casoratian conditions of the non-isospectrai D△KPE, single-soliton solution is also derived by using Hiorta＇s method.

An effective quadrilateral mesh adaptation

Accuracy of a simulation strongly depends on the grid quality. Here, quality means orthogonality at the boundaries and quasi-orthogonality within the critical regions, smoothness, bounded aspect ratios and solution adaptive behaviour. It is not recommended to refine the parts of the domain where the solution shows little variation. It is desired to concentrate grid points and cells in the part of the domain where the solution shows strong gradients or variations. We present a simple, effective and com- putationally efficient approach for quadrilateral mesh adaptation. Several numerical examples are presented for supporting our claim.

Characterization of genic microsatellite markers derived from expressed sequence tags in Pacific abalone (Haliotis discus hannai)

Simple sequence repeat (SSR) markers were developed from the expressed sequence tags (ESTs) of Pacific abalone (Haliotis discus hannai).Repeat motifs were found in 4.95% of the ESTs at a frequency of one repeat every 10.04 kb of EST sequences,after redundancy elimination.Seventeen polymorphic EST-SSRs were developed.The number of alleles per locus varied from 2-17,with an average of 6.8 alleles per locus.The expected and observed heterozygosities ranged from 0.159 to 0.928 and from 0.132 to 0.922,respectively.Twelve of the 17 loci (70.6%) were successfully amplified in H.diversicolor.Seventeen loci segregated in three families,with three showing the presence of null alleles (17.6%).The adequate level of variability and low frequency of null alleles observed in H.discus hannai,together with the high rate of transportability across Haliotis species,make this set of EST-SSR markers an important tool for comparative mapping,marker-assisted selection,and evolutionary studies,not only in the Pacific abalone,but also in related species.

Meteorological Data Generation for the Numerical Simulation of Stratified Flow in the Joumine Reservoir, Tunisia

Dynamic numerical simulation of water conditions is useful for reservoir management. In remote semi-arid areas, however, meteorological and hydrological time-series data needed for computation are not frequently measured and must be obtained using other information. This paper presents a case study of data generation for the computation of thermal conditions in the Joumine Reservoir, Tunisia. Data from the Wind Finder web site and daily sunshine duration at the nearest weather stations were utilized to generate cloud cover and solar radiation data based on meteorological correlations obtained in Japan, which is located at the same latitude as Tunisia. A time series of inflow water temperature was estimated from air temperature using a numerical filter expressed as a linear second-order differential equation. A numerical simulation using a vertical 2-D （two-dimensional） turbulent flow model for a stratified water body with generated data successfully reproduced seasonal thermal conditions in the reservoir, which were monitored using a thermistor chain.

Effect of Gaussian Noise Amplitude on Translation Error Estimated Using Double-Wayland Algorithm

The Wayland algorithm has been improved in order to evaluate the degree of visible determinism for dynamical systems that generate time series. The objective of this study is to show that the Double-Wayland algorithm can distinguish between time series generated by a deterministic process and those generated by a stochastic process. The authors conducted numerical analysis of the van der Pol equation and a stochastic differential equation as a deterministic process and a Ganssian stochastic process, respectively. In case of large S/N ratios, the noise term did not affect the translation error derived from time series data, but affected that from the temporal differences of time series. In case of larger noise amplitudes, the translation error from the differences was calculated to be approximately 1 using the Double-Wayland algorithm, and it did not vary in magnitude. Furthermore, the translation error derived from the differenced sequences was considered stable against noise. This novel algorithm was applied to the detection of anomalous signals in some fields of engineering, such as the analysis of railway systems and bio-signals.

Cultivable Bacterial Diversity of the East China Sea

Bacterial diversity of 14 sites of the East China Sea was investigated by culture-dependent methods. The impact of human activities on marine bacteria was primarily studied and characteristics of bacteria communities in different areas were analyzed. A total of 396 strains were obtained. These strains belong to 4 phyla, 9 classes and 146 species according to 16S rDNA sequences alignment. For 32 strains, the 16S rDNA sequences similarities between isolated strains and their most closely related species were lower than 98%. The result indicated that there are abundant microbial diversity and a large number of unknown microbial resources in the East China Sea. Isolated strains were dominated byy-proteobacteria （64%）, ct-proteobacteria （18%） and Firmicutes （15%）. Actinobacteria and Bacteroidetes were less than 3%. Microbial community composition, diversity and abundance among areas with varies distances from land were different. The far the regions from the land, the lower the Shannon index （H＇） and the Margalef index （DMg） values were.

CONSTRUCTION OF A NEW CLASS OF ORTHOGONAL ARRAYS

By using the generalized Hadamard product, difference matrix and projection matrices, we present a class of orthogonal projection matrices and related orthogonal arrays of strength two. A new class of orthogonal arrays are constructed.

Non-Monomial Permutations with Differential Uniformity Six

In this paper, a family of non-monomial permutations over the finite field F2n with differential uniformity at most 6 is proposed, where n is a positive integer. The algebraic degree of these functions is also determined.

On the Essential Conditions of Difference Information and Class Ratio Dispersion of Several Kinds of Data Processing Methods Reducing in Grey Predicting Modelling

In this paper, author presents the essential conditions of difference information and class ratio dispersion reducing by logarithm and root sequence. They also point out that although new data is in the range suitable of the model, the error after it returns to original state might be great.

Cancer classification based on microarray gene expression data using a principal component accumulation method

The classification of cancer is a major research topic in bioinformatics. The nature of high dimensionality and small size associated with gene expression data,however,makes the classification quite challenging. Although principal component analysis (PCA) is of particular interest for the high-dimensional data,it may overemphasize some aspects and ignore some other important information contained in the richly complex data,because it displays only the difference in the first twoor three-dimensional PC subspaces. Based on PCA,a principal component accumulation (PCAcc) method was proposed. It employs the information contained in multiple PC subspaces and improves the class separability of cancers. The effectiveness of the present method was evaluated by four commonly used gene expression datasets,and the results show that the method performs well for cancer classification.

A Network Partition Algorithm for Mining Gene Functional Modules of Colon Cancer from DNA Microarray Data

Computational analysis is essential for transforming the masses of microarray datainto a mechanistic understanding of cancer. Here we present a method for findinggene functional modules of cancer from microarray data and have applied it tocolon cancer. First, a colon cancer gene network and a normal colon tissue genenetwork were constructed using correlations between the genes. Then the modulesthat tended to have a homogeneous functional composition were identified by split-ting up the network. Analysis of both networks revealed that they are scale-free.Comparison of the gene functional modules for colon cancer and normal tissuesshowed that the modules’ functions changed with their structures.

Design of a fractional PI~λD~μ controller using the cohort intelligence method

The cohort intelligence （CI） method has recently evolved as an optimization method based on artificial intelligence. We use the CI method for the first time to optimize the parameters of the fractional proportional- integral-derivative （PID） controller. The performance of the CI method in designing the fractional PID controller was validated and compared with those of some other popular algorithms such as particle swarm optimization, the genetic algorithm, and the improved electromagnetic algorithm. The CI method yielded improved solutions in terms of the cost function, computing time, and function evaluations in comparison with the other three algorithms. In addition, the standard deviations of the CI method demonstrated the robustness of the proposed algorithm in solving control problems.

Determinant Solutions to a (3+1)-Dimensional Generalized KP Equation with Variable Coefficients

1 Introduction Although partial differential equations that govern the motion of solitons are nonlinear, many of them can be put into the bilinear form. Hirota, in 1971, developed an ingenious method to obtain exact solutions to nonlinear partial differential equations in the soliton theory, such as the KdV equation, the Boussinesq equation and the KP equation （see [1-2]）.

Lateral migration of dual droplet trains in a double spiral microchannel

Microfluidic droplets have emerged as novel platforms for chemical and biological applications. Manipulation of droplets has thus attracted increasing attention. Different from solid particles, deformable droplets cannot be efficiently controlled by inertia-driven approaches. Here, we report a study on the lateral migration of dual droplet trains in a double spiral microchannel at low Reynolds numbers. The dominant driving mechanism is elucidated as wall effect originated from the droplet deformation. Three types of migration modes are observed with varying Reynolds numbers and the size-dependent mode is intensively investigated. We obtain empirical formulas by relating the migration to Reynolds numbers and droplet sizes. The effect of droplet deformability on the migration and the detailed migration behavior along the double spiral channel are discussed. Numerical simulations are also performed and yielded in qualitative agreement with the experiments. could be a promising alternative to existing inertia-driven approaches bio-particles. This proposed low Re approach based on lateral migration especially concerning deformable entities and susceptible