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Cyclotides are plant-derived, cyclic miniproteins with three interlocking disulfide bonds that have attracted great interests because of their excellent stability and potential as peptide therapeutics. In this study, we characterize the cyclotides of the medicinal plant Clitoria ternatea (butterfly pea) and investigate their biological activities. Using a combined proteomic and transcriptomic method, we identified 41 novel cyclotide sequences, which we named cliotides, making C. ternatea one of the richest cyclotide-producing plants to date. Selected members of the cationic cliotides display potent antibacterial activity specifically against Gram-negative bacteria with minimal inhibitory concentrations as low as 0.5 μ m. Remarkably, they also possess prominent immunostimulating activity. At a concentration of 1 μ m, cationic cliotides are capable of augmenting the secretion of various cytokines and chemokines in human monocytes at both resting and lipopolysaccharide-stimulated states. Chemokines such as macrophage inflammatory proteins 1α and 1β, interferon γ-induced protein 10, interleukin 8 and tumor necrosis factor α were among the most upregulated with up to 129-fold increase in secretion level. These findings suggest cyclotides can serve as potential candidates for novel immunomodulating therapeutics. Database The protein sequences reported in this paper (cT13-cT21) are available in the UniProt Knowledgebase under the accession numbers , , , , , , , and , respectively. The transcriptome data in this paper are available at the Sequence Read Archive database ( NCBI) under accession number . The protein precursors reported in this paper (ctc13, ctc15, ctc17-ctc19, ctc21-ctc53) are available at GenBank under the accession numbers , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and , respectively. [ABSTRACT FROM AUTHOR]

Graph mining methods have become quite popular and a timely challenge is to discover dynamic properties in evolving graphs or networks. We consider the so-called relational dynamic oriented graphs that can be encoded as n-ary relations with n ⩾ 3 and thus represented by Boolean tensors. Two dimensions are used to encode the graph adjacency matrices and at least one other denotes time. We design the pattern domain of multi-dimensional association rules, i.e., non trivial extensions of the popular association rules that may involve subsets of any dimensions in their antecedents and their consequents. First, we design new objective interestingness measures for such rules and it leads to different approaches for measuring the rule confidence. Second, we must compute collections of a priori interesting rules. It is considered here as a post-processing of the closed patterns that can be extracted efficiently from Boolean tensors. We propose optimizations to support both rule extraction scalability and non redundancy. We illustrate the added-value of this new data mining task to discover patterns from a real-life relational dynamic graph. [ABSTRACT FROM AUTHOR]