This work considers the problem of computing the canonical polyadic decomposition (CPD) of large tensors. Prior works mostly leverage data sparsity to handle this problem, which is not suitable for handling dense tensors that often arise in applications such as medical imaging, computer vision, and remote sensing. Stochastic optimization is known for its low memory cost and per-iteration complexity when handling dense data. However, exisiting stochastic CPD algorithms are not flexible enough to incorporate a variety of constraints/regularizations that are of interest in signal and data analytics. Convergence properties of many such algorithms are also unclear. In this work, we propose a stochastic optimization framework for large-scale CPD with constraints/regularizations. The framework works under a doubly randomized fashion, and can be regarded as a judicious combination of randomized block coordinate descent (BCD) and stochastic proximal gradient (SPG). The algorithm enjoys lightweight updates and small memory footprint. In addition, this framework entails considerable flexibility-many frequently used regularizers and constraints can be readily handled under the proposed scheme. The approach is also supported by convergence analysis. Numerical results on large-scale dense tensors are employed to showcase the effectiveness of the proposed approach.

The main role of CodY, a global regulatory protein in most low G + C gram-positive bacteria, is in transcriptional repression. To study the functions of CodY in Streptococcus suis serotype 2 (S. suis 2), a mutant codY clone named ∆codY was constructed to explore the phenotypic variation between ∆codY and the wild-type strain. The result showed that the codY mutation significantly inhibited cell growth, adherence and invasion ability of S. suis 2 to HEp-2 cells. The codY mutation led to decreased binding of the pathogen to the host cells, easier clearance by RAW264.7 macrophages and decreased growth ability in fresh blood of Cavia porcellus. The codY mutation also attenuated the virulence of S. suis 2 in BALB/c mice. Morphological analysis revealed that the codY mutation decreased the thickness of the capsule of S. suis 2 and changed the surface structures analylized by SDS-PAGE. Finally, the codY mutation altered the expressions of many virulence related genes, including sialic acid synthesis genes, leading to a decreased sialic acid content in capsule. Overall, mutation of codY modulated bacterial virulence by affecting the growth and colonization of S. suis 2, and at least via regulating sialic acid synthesis and capsule thickness.

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We used 135 genome-wide simple sequence repeat (SSR) markers to assess genetic diversity, population structure and linkage disequilibrium (LD) of 196 soybean landraces. On the basis of estimated population structure, we conducted association mapping for soybean resistance to common cutworm (CCW) using genomic-wide mapping strategies and detected the elite alleles of soybean resistance to CCW, along with their carriers. In addition to wide geographic origin, the population showed extensive genetic variation, and 17.9% of the SSR pairs were in LD (with D'>0, P<0.05).The extent of LD was > 6.61 cM with genetic distance of locus pairs for the loci in the same linkage group (LG). Association analysis revealed 7 SSRs associated with soybean resistance to CCW (P<0.01): 4 accounted for >10% of the total genetic variation for resistance; 6 located in the linkage groups reported to be related to soybean resistance to insects. Allele effect analysis revealed that the alleles related to larval weight of CCW mainly had a negative effect, the allele Sat_334-A208 showing the largest negative effect (43.9%). The alleles related to leaf consumption of single larva (LCL) and pupal weight (PW) of CCW mainly had a positive effect, the allele Satt199-A186 showing the largest positive effect for LCL (36.4%) and the allele Sat_320-A286, the largest positive effect for PW (31.4%).

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