Generalized Grey Target Decision Method for Mixed Attributes Based on Connection Number

Grey target decision model for mixed attributes including real numbers, interval numbers, triangular fuzzy numbers, and trapezoidal fuzzy numbers is complex for its data processing in different ways and information distortion in handling fuzzy numbers. To solve these problems, the binary connection number proposed in set pair analysis is applied to unify different types of index values with their parameters’ average values and standard deviations as determinacy-uncertainty vectors. Then the target center index vectors are determined by the modules of index vectors of all alternatives under different attributes. So the similarity of each index vector and its target center index vector called nearness degree can be calculated. Following, all the nearness degrees are normalized in linear method in order to be compared with each other. Finally, the optimal alternative can be determined by the minimum of all integrated nearness degrees. Case study demonstrated that this approach can not only unify different types of numbers, and simplify the calculation but also reduce the information distortion in operating fuzzy numbers

Perspectives on the Application of Genome-Editing Technologies in Crop Breeding

Most conventional and modern crop-improvement methods exploit natural or artificially induced genetic variations and require laborious characterization of the progeny of multiple generations of time-consuming genetic crosses. Genome-editing systems, in contrast, provide the means to rapidly modify genomes in a precise and predictable way, making it possible to introduce improvements directly into elite varieties. Here, we describe the range of applications available to agricultural researchers using existing genome-editing tools. In addition to providing examples of genome-editing applications in crop breeding, we discuss the technical and social challenges faced by breeders using genome-editing tools for crop improvement

A Big Data Mining in Petroleum Exploration and Development

We take a well log in petroleum exploration and development as an example of the big data mining, and adopt three regression and two classification algorithms: the multiple regression analysis (MRA), the error back-propagation neural network (BPNN), the regression of support vector machine (R-SVM), the classification of support vector machine (C-SVM), and the Bayesian successive discrimination (BAYSD). It is well known that MRA, BPNN and R-SVM are regression algorithms while C-SVM and BAYSD are classification algorithms, and only MRA is linear algorithm whereas the other four algorithms are nonlinear algorithms. From this case study, we can draw the following five major conclusions: a) Since C-SVM is the best classifier, it is employed as a data cleaning tool. b) Since MRA is a linear algorithm, its total mean absolute relative residual R*(%) can express the nonlinearity of studied problem. For this case study, R*(%)=52.14 showing the nonlinearity of the studied problem is strong. c) Since both MRA and BAYD can establish the order of dependence between a dependent variable and independent variables, each of MRA and BAYD could serve as a pioneering dimension-reduction tool in data mining. In the case study, since the nonlinearity of the studied problem is strong, the nonlinear algorithm BAYSD can serve as a pioneering dimension-reduction tool, but the linear algorithm MRA cannot. d) Since the nonlinearity of the case study is strong, BPNN and R-SVM are not applicable though they are nonlinear algorithms, whereas other two nonlinear algorithms C-SVM and BAYSD are applicable, indicating the nonlinear ability of C-SVM and BAYSD is higher than that of BPNN and R-SVM. e) Comparing the two applicable algorithms C-SVM and BAYSD for this case study, it is seen that R*(%) of C-SVM is less than that of BAYSD; BAYSD can serve as a pioneering dimension-reduction tool, but C-SVM cannot; it is easy to code the BAYSD program whereas it is very complicated to code the C-SVM program, so BAYSD is a good software for this case study when C-SVM is not available.Key words: Big data mining; Well log; Data cleaning; Dimension-reduction; Regression; Classificatio

Moderate Dietary Protein Restriction Optimized Gut Microbiota and Mucosal Barrier in Growing Pig Model

Appropriate protein concentration is essential for animal at certain stage. This study evaluated the effects of different percentages of dietary protein restriction on intestinal health of growing pigs. Eighteen barrows were randomly assigned to a normal (18%), low (15%), and extremely low (12%) dietary protein concentration group for 30 days. Intestinal morphology and permeability, bacterial communities, expressions, and distributions of intestinal tight junction proteins, expressions of biomarkers of intestinal stem cells (ISCs) and chymous bacterial metabolites in ileum and colon were detected. The richness and diversity of bacterial community analysis with Chao and Shannon index were highest in the ileum of the 15% crude protein (CP) group. Ileal abundances of Streptococcaceae and Enterobacteriaceae decreased respectively, while beneficial Lactobacillaceae, Clostridiaceae_1, Actinomycetaceae, and Micrococcaceae increased their proportions with a protein reduction of 3 percentage points. Colonic abundances of Ruminococcaceae, Christensenellaceae, Clostridiaceae_1, Spirochaetaceae, and Bacterodales_S24-7_group declined respectively, while proportions of Lachnospiraceae, Prevotellaceae, and Veillonellaceae increased with dietary protein reduction. Concentrations of most bacterial metabolites decreased with decreasing dietary protein concentration. Ileal barrier function reflected by expressions of tight junction proteins (occludin, zo-3, claudin-3, and claudin-7) did not show significant decrease in the 15% CP group while sharply reduced in the 12% CP group compared to that in the 18% CP group. And in the 15% CP group, ileal distribution of claudin-3 mainly located in the cell membrane with complete morphological structure. In low-protein treatments, developments of intestinal villi and crypts were insufficient. The intestinal permeability reflected by serous lipopolysaccharide (LPS) kept stable in the 15% CP group while increased significantly in the 12% CP group. The expression of ISCs marked by Lgr5 slightly increased in ileum of the 15% CP group. Colonic expressions of tight junction proteins declined in extremely low protein levels. In conclusion, moderate protein restriction (15% CP) can optimize the ileal microbiota structure via strengthening beneficial microbial populations and suppressing harmful bacterial growth and altering the function of ileal tight junction proteins as well as epithelial cell proliferation

Novel Swine Influenza Virus Reassortants in Pigs, China

During swine influenza virus surveillance in pigs in China during 2006–2009, we isolated subtypes H1N1, H1N2, and H3N2 and found novel reassortment between contemporary swine and avian panzootic viruses. These reassortment events raise concern about generation of novel viruses in pigs, which could have pandemic potential