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.     

Psychometric Properties of the Japanese Version of the STarT Back Tool in Patients with Low Back Pain

Background and Objective

The STarT Back Tool uses prognostic indicators to classify patients with low back pain into three risk groups to guide early secondary prevention in primary care. The present study aimed to evaluate the psychometric properties of the Japanese version of the tool (STarT-J).

Methods

An online survey was conducted among Japanese patients with low back pain aged 20–64 years. Reliability was assessed by examining the internal consistency of the overall and psychosocial subscales using Cronbach’s alpha coefficients. Spearman’s correlation coefficients were used to evaluate the concurrent validity between the STarT-J total score/psychosocial subscore and standard reference questionnaires. Discriminant validity was evaluated by calculating the area under the curves (AUCs) for the total and psychosocial subscale scores against standard reference cases. Known-groups validity was assessed by examining the relationship between low back pain-related disability and STarT-J scores.

Results

The analysis included data for 2000 Japanese patients with low back pain; the mean (standard deviation [SD]) age was 47.7 (9.3) years, and 54.1% were male. The mean (SD) STarT-J score was 2.2 (2.1). The Cronbach’s alpha coefficient was 0.75 for the overall scale and 0.66 for the psychosocial subscale. Spearman’s correlation coefficients ranged from 0.30 to 0.59, demonstrating moderate to strong concurrent validity. The AUCs for the total score ranged from 0.65 to 0.83, mostly demonstrating acceptable discriminative ability. For known-groups validity, participants with more somatic symptoms had higher total scores. Those in higher STarT-J risk groups had experienced more low back pain-related absences.

Conclusions

The overall STarT-J scale was internally consistent and had acceptable concurrent, discriminant, and known-groups validity. The STarT-J can be used with Japanese patients with low back pain.     

Association between Smoking Status and Obesity in a Nationwide Survey of Japanese Adults

Objective

A positive association between the number of cigarettes smoked per day and obesity has been reported, whereas how other smoking-related indices, such as pack-years and duration of smoking, are related with obesity has been less investigated. We analyzed the age-adjusted cross-sectional association between smoking and obesity in a general Japanese population.

Methods

We used data from a nationwide epidemiological study of Japanese adults (N = 23,106). We compared the prevalence of obesity (defined as body mass index ≥ 25kg/m²) among groups classified by smoking behavior, pack-years, number of cigarettes per day, duration of smoking, and duration and time of smoking cessation.

Results

In men, current smokers had a lower odds ratio (OR) for obesity of 0.80 (95% confidence interval (CI): 0.72–0.88) compared to non-smokers, whereas past smokers had a higher OR of 1.23 (95% CI: 1.09–1.37) compared to current smokers. In women, there were no differences in obesity between the three groups classified by smoking behavior. However, in both sexes, the prevalence of obesity tended to increase with pack-years and the number of cigarettes per day, but not with duration of smoking in current and past smokers. Further, in male smokers, the risks for obesity were markedly higher in short-term heavy smokers compared with long-term light smokers, even with the same number of pack-years. Regarding the impact of smoking cessation, female past smokers who quit smoking at an age > 55-years had an elevated OR of 1.60 (95% CI:1.05–2.38) for obesity.

Conclusions

In a general Japanese population, obesity is progressively associated with pack-years and number of cigarettes per day, but not with the duration of smoking. When investigating the association between obesity and cigarette smoking, the daily smoking burden and the duration of smoking require to be independently considered.     

Identification of Novel HLA-A*24:02-Restricted Epitope Derived from a Homeobox Protein Expressed in Hematological Malignancies

The homeobox protein, PEPP2 (RHOXF2), has been suggested as a cancer/testis (CT) antigen based on its expression pattern. However, the peptide epitope of PEPP2 that is recognized by cytotoxic T cells (CTLs) is unknown. In this study, we revealed that PEPP2 gene was highly expressed in myeloid leukemia cells and some other hematological malignancies. This gene was also expressed in leukemic stem-like cells. We next identified the first reported epitope peptide (PEPP2^271-279). The CTLs induced by PEPP2^271-279 recognized PEPP2-positive target cells in an HLA-A*24:02-restricted manner. We also found that a demethylating agent, 5-aza-2’-deoxycytidine, could enhance PEPP2 expression in leukemia cells but not in blood mononuclear cells from healthy donors. The cytotoxic activity of anti-PEPP2 CTL against leukemic cells treated with 5-aza-2’-deoxycytidine was higher than that directed against untreated cells. These results suggest a clinical rationale that combined treatment with this novel antigen-specific immunotherapy together with demethylating agents might be effective in therapy-resistant myeloid leukemia patients.     

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.