Asia differs substantially among and within its regions populated by diverse ethnic groups, which maintain their own respective cultures and dietary habits. To address the diversity in their gut microbiota, we characterized the bacterial community in fecal samples obtained from 303 school-age children living in urban or rural regions in five countries spanning temperate and tropical areas of Asia. The microbiota profiled for the 303 subjects were classified into two enterotype-like clusters, each driven by Prevotella (P-type) or Bifidobacterium/Bacteroides (BB-type), respectively. Majority in China, Japan and Taiwan harbored BB-type, whereas those from Indonesia and Khon Kaen in Thailand mainly harbored P-type. The P-type microbiota was characterized by a more conserved bacterial community sharing a greater number of type-specific phylotypes. Predictive metagenomics suggests higher and lower activity of carbohydrate digestion and bile acid biosynthesis, respectively, in P-type subjects, reflecting their high intake of diets rich in resistant starch. Random-forest analysis classified their fecal species community as mirroring location of resident country, suggesting eco-geographical factors shaping gut microbiota. In particular, children living in Japan harbored a less diversified microbiota with high abundance of Bifidobacterium and less number of potentially pathogenic bacteria, which may reflect their living environment and unique diet.

Proteomic and transcriptomic platforms both play important roles in cancer research, with differing strengths and limitations. Here, we describe a proteo-transcriptomic integrative strategy for discovering novel cancer biomarkers, combining the direct visualization of differentially expressed proteins with the high-throughput scale of gene expression profiling. Using breast cancer as a case example, we generated comprehensive two-dimensional electrophoresis (2DE)/mass spectrometry (MS) proteomic maps of cancer (MCF-7 and HCC-38) and control (CCD-1059Sk) cell lines, identifying 1724 expressed protein spots representing 484 different protein species. The differentially expressed cell-line proteins were then mapped to mRNA transcript databases of cancer cell lines and primary breast tumors to identify candidate biomarkers that were concordantly expressed at the gene expression level. Of the top nine selected biomarker candidates, we reidentified ANX1, a protein previously reported to be differentially expressed in breast cancers and normal tissues, and validated three other novel candidates, CRAB, 6PGL, and CAZ2, as differentially expressed proteins by immunohistochemistry on breast tissue microarrays. In total, close to half (4/9) of our protein biomarker candidates were successfully validated. Our study thus illustrates how the systematic integration of proteomic and transcriptomic data from both cell line and primary tissue samples can prove advantageous for accelerating cancer biomarker discovery.

Summary In this paper, we study a class of weakly nonlinear complementarity problems arising from the discretization of free boundary problems. By reformulating the complementarity problems as implicit fixed‐point equations based on splitting of the system matrices, we propose a class of modulus‐based matrix splitting algorithms. We show their convergence by assuming that the system matrix is positive definite. Moreover, we give several kinds of typical practical choices of the modulus‐based matrix splitting iteration methods based on the different splitting of the system matrix. Numerical experiments on two model problems are presented to illustrate the theoretical results and examine the numerical effectiveness of our modulus‐based matrix splitting algorithms. Copyright © 2016 John Wiley & Sons, Ltd.