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.     

Factors that Affect Pancreatic Islet Cell Autophagy in Adult Rats: Evaluation of a Calorie-Restricted Diet and a High-Fat Diet

Aging may be a risk factor for type 2 diabetes in the elderly. Dietary intervention can affect glucose tolerance in adults, which may be due to body composition and islet cell autophagy. The aim of this study was to determine the effects of various dietary interventions on islet cell autophagy. Pancreatic tissue and blood samples were collected from Sprague Dawley rats (14–16 months old, n = 15 for each group) that received a normal diet (ND), a high-fat diet (HFD), or a calorie-restricted diet (CRD). The body weight (BW), visceral fat, serum lipid levels, fasting serum glucose, insulin levels, and β/α cell area were determined in 14-16-(0-w), 16-18-(8-w), and 18-20(16-w)-month-old rats. Pancreatic islet autophagy (LC3B and LAMP2), AP (Acid Phosphatase) and apoptosis (apoptosis index, AI (TUNEL assay) and cleaved caspase-3) were detected using immunohistochemistry, ELISA and western blot. At 16 weeks, the expressions of LC3B, LAMP2 and AP markedly increased in both the HFD (P<0.01) and CRD (P<0.05) groups; however, an increase in the AI (P<0.05), cleaved caspase-3 and Beclin1 expression and a decrease in the expressions of BCL2 and BCLXL (P<0.05) were observed in only the HFD group. FFA, triglyceride levels, HOMA-IR, insulin levels and glucagon levels were significantly increased in the HFD group but decreased in the CRD group at 16 weeks (P<0.05). The degree of islet cell autophagy was potentially regulated by the levels of FFA and islet cell insulin and glucagon, which may have been due to the effects of Beclin1/BCL2.     

In Vitro Analysis of Metabolites Secreted during Infection of Lung Epithelial Cells by Cryptococcus neoformans

Cryptococcus neoformans is an encapsulated basidiomycetous yeast commonly associated with pigeon droppings and soil. The opportunistic pathogen infects humans through the respiratory system and the metabolic implications of C. neoformans infection have yet to be explored. Studying the metabolic profile associated with the infection could lead to the identification of important metabolites associated with pulmonary infection. Therefore, the aim of the study was to simulate cryptococcal infection at the primary site of infection, the lungs, and to identify the metabolic profile and important metabolites associated with the infection at low and high multiplicity of infections (MOI). The culture supernatant of lung epithelial cells infected with C. neoformans at MOI of 10 and 100 over a period of 18 hours were analysed using gas chromatography mass spectrometry. The metabolic profiles obtained were further analysed using multivariate analysis and the pathway analysis tool, MetaboAnalyst 2.0. Based on the results from the multivariate analyses, ten metabolites were selected as the discriminatory metabolites that were important in both the infection conditions. The pathways affected during early C. neoformans infection of lung epithelial cells were mainly the central carbon metabolism and biosynthesis of amino acids. Infection at a higher MOI led to a perturbance in the β-alanine metabolism and an increase in the secretion of pantothenic acid into the growth media. Pantothenic acid production during yeast infection has not been documented and the β-alanine metabolism as well as the pantothenate and CoA biosynthesis pathways may represent underlying metabolic pathways associated with disease progression. Our study suggested that β-alanine metabolism and the pantothenate and CoA biosynthesis pathways might be the important pathways associated with cryptococcal infection.     

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.