Characterization of Developmental- and Stress-Mediated Expression of Cinnamoyl-CoA Reductase in Kenaf ( Hibiscus cannabinus

Cinnamoyl-CoA reductase (CCR) is an important enzyme for lignin biosynthesis as it catalyzes the first specific committed step in monolignol biosynthesis. We have cloned a full length coding sequence of CCR from kenaf (Hibiscus cannabinus L.), which contains a 1,020-bp open reading frame (ORF), encoding 339 amino acids of 37.37 kDa, with an isoelectric point (pI) of 6.27 (JX524276, HcCCR2). BLAST result found that it has high homology with other plant CCR orthologs. Multiple alignment with other plant CCR sequences showed that it contains two highly conserved motifs: NAD(P) binding domain (VTGAGGFIASWMVKLLLEKGY) at N-terminal and probable catalytic domain (NWYCYGK). According to phylogenetic analysis, it was closely related to CCR sequences of Gossypium hirsutum (ACQ59094) and Populus trichocarpa (CAC07424). HcCCR2 showed ubiquitous expression in various kenaf tissues and the highest expression was detected in mature flower. HcCCR2 was expressed differentially in response to various stresses, and the highest expression was observed by drought and NaCl treatments

Salinomycin enhances doxorubicin-induced cytotoxicity in multidrug resistant MCF-7/MDR human breast cancer cells via decreased efflux of doxorubicin

Salinomycin is a monocarboxylic polyether antibiotic, which is widely used as an anticoccidial agent. The anticancer property of salinomycin has been recognized and is based on its ability to induce apoptosis in human multidrug resistance (MDR). The present study investigated whether salinomycin reverses MDR towards chemotherapeutic agents in doxorubicin-resistant MCF-7/MDR human breast cancer cells. The results demonstrated that doxorubicin-mediated cytotoxicity was significantly enhanced by salinomycin in the MCF-7/MDR cells, and this occurred in a dose-dependent manner. This finding was consistent with subsequent observations made under a confocal microscope, in which the doxorubicin fluorescence signals of the salinomycin-treated cells were higher compared with the cells treated with doxorubicin alone. In addition, flow cytometric analysis revealed that salinomycin significantly increased the net cellular uptake and decreased the efflux of doxorubicin. The expression levels of MDR-1 and MRP-1 were not altered at either the mRNA or protein levels in the cells treated with salinomycin. These results indicated that salinomycin was mediated by its ability to increase the uptake and decrease the efflux of doxorubicin in MCF-7/MDR cells. Salinomycin reversed the resistance of doxorubicin, suggesting that chemotherapy in combination with salinomycin may benefit MDR cancer therapyopen

Characterization of Developmental-and Stress-Mediated Expression of Cinnamoyl-CoA Reductase in Kenaf (Hibiscus cannabinus L.)

Cinnamoyl-CoA reductase (CCR) is an important enzyme for lignin biosynthesis as it catalyzes the first specific committed step in monolignol biosynthesis. We have cloned a full length coding sequence of CCR from kenaf (Hibiscus cannabinus L.), which contains a 1,020-bp open reading frame (ORF), encoding 339 amino acids of 37.37 kDa, with an isoelectric point (pI) of 6.27 (JX524276, HcCCR2). BLAST result found that it has high homology with other plant CCR orthologs. Multiple alignment with other plant CCR sequences showed that it contains two highly conserved motifs: NAD(P) binding domain (VTGAGGFIASWMVKLLLEKGY) at N-terminal and probable catalytic domain (NWYCYGK). According to phylogenetic analysis, it was closely related to CCR sequences of Gossypium hirsutum (ACQ59094) and Populus trichocarpa (CAC07424). HcCCR2 showed ubiquitous expression in various kenaf tissues and the highest expression was detected in mature flower. HcCCR2 was expressed differentially in response to various stresses, and the highest expression was observed by drought and NaCl treatments

Efficient Decomposition-Based Multiclass and Multilabel Classification

Decomposition-based methods are widely used for multiclass and multilabel classification. These approaches transform or reduce the original task to a set of smaller possibly simpler problems and allow thereby often to utilize many established learning algorithms, which are not amenable to the original task. Even for directly applicable learning algorithms, the combination with a decomposition-scheme may outperform the direct approach, e.g., if the resulting subproblems are simpler (in the sense of learnability). This thesis addresses mainly the efficiency of decomposition-based methods and provides several contributions improving the scalability with respect to the number of classes or labels, number of classifiers and number of instances. Initially, we present two approaches improving the efficiency of the training phase of multiclass classification. The first of them shows that by minimizing redundant learning processes, which can occur in decomposition-based approaches for multiclass problems, the number of operations in the training phase can be significantly reduced. The second approach is tailored to Naive Bayes as base learner. By a tight coupling of Naive Bayes and arbitrary decompositions, it allows an even higher reduction of the training complexity with respect to the number of classifiers. Moreover, an approach improving the efficiency of the testing phase is also presented. It is capable of reducing testing effort with respect to the number of classes independently of the base learner. Furthermore, efficient decomposition-based methods for multilabel classification are also addressed in this thesis. Besides proposing an efficient prediction method, an approach rebalancing predictive performance, time and memory complexity is presented. Aside from the efficiency-focused methods, this thesis contains also a study about a special case of the multilabel classification setting, which is elaborated, formalized and tackled by a prototypical decomposition-based approach

Efficient Prediction Algorithms for Binary Decomposition Techniques

Binary decomposition methods transform multiclass learning problems into a series of two-class learning problems that can be solved with simpler learning algorithms. As the number of such binary learning problems often grows super-linearly with the number of classes, we need efficient methods for computing the predictions. In this paper, we discuss an efficient algorithm that queries only a dynamically determined subset of the trained classifiers, but still predicts the same classes that would have been predicted if all classifiers had been queried. The algorithm is first derived for the simple case of pairwise classification, and then generalized to arbitrary pairwise decompositions of the learning problem in the form of ternary error-correcting output codes under a variety of different code designs and decoding strategies

Efficient Pairwise Classification

Abstract. Pairwise classification is a class binarization procedure that converts a multi-class problem into a series of two-class problems, one problem for each pair of classes. While it can be shown that for training, this procedure is more efficient than the more commonly used oneagainst-all approach, it still has to evaluate a quadratic number of classifiers when computing the predicted class for a given example. In this paper, we propose a method that allows a faster computation of the predicted class when weighted or unweighted voting are used for combining the predictions of the individual classifiers. While its worst-case complexity is still quadratic in the number of classes, we show that even in the case of completely random base classifiers, our method still outperforms the conventional pairwise classifier. For the more practical case of well-trained base classifiers, its asymptotic computational complexity seems to be almost linear.