A Novel Multiinstance Learning Approach for Liver Cancer Recognition on Abdominal CT Images Based on CPSO-SVM and IO

A novel multi-instance learning (MIL) method is proposed to recognize liver cancer with abdominal CT images based on instance optimization (IO) and support vector machine with parameters optimized by a combination algorithm of particle swarm optimization and local optimization (CPSO-SVM). Introducing MIL into liver cancer recognition can solve the problem of multiple regions of interest classification. The images we use in the experiments are liver CT images extracted from abdominal CT images. The proposed method consists of two main steps: (1) obtaining the key instances through IO by texture features and a classification threshold in classification of instances with CPSO-SVM and (2) predicting unknown samples with the key instances and the classification threshold. By extracting the instances equally based on the entire image, the proposed method can ignore the procedure of tumor region segmentation and lower the demand of segmentation accuracy of liver region. The normal SVM method and two MIL algorithms, Citation-kNN algorithm and WEMISVM algorithm, have been chosen as comparing algorithms. The experimental results show that the proposed method can effectively recognize liver cancer images from two kinds of cancer CT images and greatly improve the recognition accuracy

MLVA genotyping of Chinese human Brucella melitensis biovar 1, 2 and 3 isolates

<p>Abstract</p> <p>Background</p> <p>Since 1950, <it>Brucella melitensis </it>has been the predominant strain associated with human brucellosis in China. In this study we investigated the genotypic characteristics of <it>B. melitensis </it>isolates from China using a multiple-locus variable-number tandem-repeat analysis (MLVA) and evaluated the utility of MLVA with regards to epidemiological trace-back investigation.</p> <p>Results</p> <p>A total of 105 <it>B. melitensis </it>strains isolated from throughout China were divided into 69 MLVA types using MLVA-16. Nei's genetic diversity indices for the various loci ranged between 0.00 - 0.84. 12 out 16 loci were the low diversity with values < 0.2 and the most discriminatory markers were bruce16 and bruce30 with a diversity index of > 0.75 and containing 8 and 7 alleles, respectively. Many isolates were single-locus or double-locus variants of closely related <it>B. melitensis </it>isolates from different regions, including the north and south of China. Using panel 1, the majority of strains (84/105) were genotype 42 clustering to the 'East Mediterranean' <it>B. melitensis </it>group. Chinese <it>B. melitensis </it>are classified in limited number of closely related genotypes showing variation mainly at the panel 2B loci.</p> <p>Conclusion</p> <p>The MLVA-16 assay can be useful to reveal the predominant genotypes and strain relatedness in endemic or non-endemic regions of brucellosis. However it is not suitable for biovar differentiation of <it>B. melitensis</it>. Genotype 42 is widely distributed throughout China during a long time. Bruce 16 and bruce 30 in panel 2B markers are most useful for typing Chinese isolates.</p

Development of Novel Microsatellite Markers in the Omei Treefrog (Rhacophorus omeimontis)

Eleven novel microsatellite markers were developed and characterized for the Omei treefrog (Rhacophorus omeimontis) using the fast isolation by AFLP of sequences containing repeats method. Polymorphism of each locus was tested in 24 individuals from two wild populations. The number of alleles per locus ranged from 4 to 15, the average observed and expected heterozygosity per locus ranged from 0.250 to 0.839 and from 0.562 to 0.914, respectively. Two of the 11 microsatellite loci showed significant deviations from Hardy-Weinberg equilibrium. Two locus pairs showed significant linkage disequilibrium. Neither evidence of scoring error due to stuttering nor evidence of large allele dropout was found at all of the 11 loci, but evidence of null alleles was indicated at two loci because of general excess of homozygotes for most allele size classes. These polymorphic loci will be useful markers in studying mate choice of the Omei treefrog

A medium-shifted splitting iteration method for a diagonal-plus-Toeplitz linear system from spatial fractional Schrödinger equations

Abstract The centered difference discretization of the spatial fractional coupled nonlinear Schrödinger equations obtains a discretized linear system whose coefficient matrix is the sum of a real diagonal matrix D and a complex symmetric Toeplitz matrix T̃ which is just the symmetric real Toeplitz T plus an imaginary identity matrix iI. In this study, we present a medium-shifted splitting iteration method to solve the discretized linear system, in which the fast algorithm can be utilized to solve the Toeplitz linear system. Theoretical analysis shows that the new iteration method is convergent. Moreover, the new splitting iteration method naturally leads to a preconditioner. Analysis shows that the eigenvalues of the corresponding preconditioned matrix are tighter than those of the original coefficient matrix A. Finally, compared with the other algorithms by numerical experiments, the new method is more effective