Genome-wide comparative analysis of the codon usage patterns in plants

Codon usage analysis has been a classical area of study for decades and is important for evolution, mRNA translation, and new gene discovery. Recently, genome sequencing has made it possible to perform studies of the entire genome in plant kingdoms. The base composition of the coding sequence, codon usage pattern, codon pairs, and related indicators of relative synonymous codon usage (RSCU), including the Fop, Nc, RSCU, CAI and GC contents, were analyzed. We found that the GC content of single-celled algae is the highest, whereas dicotyledons are the lowest. Moreover, the base composition of plants is similar within the same family. In addition, the GC content of the second base of the codon is lower than the first and third base. In conclusion, the codon usage characteristics are opposite in Gramineae, single-celled algae, fern and dicotyledon, moss, and Pinaceae. Furthermore, the degree of codon usage bias is decreasing with evolution. Therefore, we hypothesize that the lower the plants, the more that they must optimize codons and that higher plants no longer need to optimize codons.     

Peptidylarginine Deiminase 3 (PAD3) Is Upregulated by Prolactin Stimulation of CID-9 Cells and Expressed in the Lactating Mouse Mammary Gland

Peptidylarginine deiminases (PADs) post-translationally convert arginine into neutral citrulline residues. Our past work shows that PADs are expressed in the canine and murine mammary glands; however, the mechanisms regulating PAD expression and the function of citrullination in the normal mammary gland are unclear. Therefore, the first objective herein was to investigate regulation of PAD expression in mammary epithelial cells. We first examined PAD levels in CID-9 cells, which were derived from the mammary gland of mid-pregnant mice. PAD3 expression is significantly higher than all other PAD isoforms and mediates protein citrullination in CID-9 cells. We next hypothesized that prolactin regulates PAD3 expression. To test this, CID-9 cells were stimulated with 5 μg/mL of prolactin for 48 hours which significantly increases PAD3 mRNA and protein expression. Use of a JAK2 inhibitor and a dominant negative (DN)-STAT5 adenovirus indicate that prolactin stimulation of PAD3 expression is mediated by the JAK2/STAT5 signaling pathway in CID-9 cells. In addition, the human PAD3 gene promoter is prolactin responsive in CID-9 cells. Our second objective was to investigate the expression and activity of PAD3 in the lactating mouse mammary gland. PAD3 expression in the mammary gland is highest on lactation day 9 and coincident with citrullinated proteins such as histones. Use of the PAD3 specific inhibitor, Cl4-amidine, indicates that PAD3, in part, can citrullinate proteins in L9 mammary glands. Collectively, our results show that upregulation of PAD3 is mediated by prolactin induction of the JAK2/STAT5 signaling pathway, and that PAD3 appears to citrullinate proteins during lactation.     

Inertia weight control strategies for particle swarm optimization

Particle swarm optimization (PSO) is a population-based, stochastic optimization technique inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control parameters, and thus, there has been a significant amount of research effort devoted to the dynamic adaptation of these parameters. The focus of the adaptive approaches has largely revolved around adapting the inertia weight as it exhibits the clearest relationship with the exploration/exploitation balance of the PSO algorithm. However, despite the significant amount of research efforts, many inertia weight control strategies have not been thoroughly examined analytically nor empirically. Thus, there are a plethora of choices when selecting an inertia weight control strategy, but no study has been comprehensive enough to definitively guide the selection. This paper addresses these issues by first providing an overview of 18 inertia weight control strategies. Secondly, conditions required for the strategies to exhibit convergent behaviour are derived. Finally, the inertia weight control strategies are empirically examined on a suite of 60 benchmark problems. Results of the empirical investigation show that none of the examined strategies, with the exception of a randomly selected inertia weight, even perform on par with a constant inertia weight.     

Turing learning: a metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for ‘tricking’ the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product—the classifiers—that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.