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.     

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.     

An enzyme combination assay for serum sphingomyelin: Improved specificity through avoiding the interference with lysophosphatidylcholine

Serum sphingomyelin (SM) has predictive value in the development of atherosclerosis. Furthermore, SM plays important roles in cell membrane structure, signal transduction pathways, and lipid raft formation. A convenient enzymatic method for SM is available for routine laboratory practice, but the enzyme specificity is not sufficient because of nonspecific reactions with lysophosphatidylcholine (LPC). Based on the differential specificity of selected enzymes toward choline-containing phospholipids, a two-step assay for measuring SM was constructed and its performance was evaluated using sera from healthy individuals on a Hitachi 7170 autoanalyzer. Results from this assay were highly correlated with theoretical serum SM concentrations estimated by subtracting phosphatidylcholine (PC) and LPC concentrations from that of total phospholipids determined using previously established methods. There was a good correlation between the results of SM assayed by the proposed method and the existing enzymatic method in sera from healthy individuals. Moreover, the proposed method was superior to the existing method in preventing nonspecific reactions with LPC present in sera. The proposed method does not require any pretreatment, uses 2.5 μl of serum samples, and requires only 10 min on an autoanalyzer. This high-throughput method can measure serum SM with sufficient specificity for clinical purposes and is applicable in routine laboratory practice.     

Phosphorylated TandeMBP: A unique protein substrate for protein phosphatase assay

To analyze a variety of protein phosphatases, we developed phosphorylated TandeMBP (P-TandeMBP), in which two different mouse myelin basic protein isoforms were fused in tandem, as a protein phosphatase substrate. P-TandeMBP was prepared efficiently in four steps: (1) phosphorylation of TandeMBP by a protein kinase mixture (Ca²⁺/calmodulin-dependent protein kinase Iδ, casein kinase 1δ, and extracellular signal-regulated kinase 2); (2) precipitation of both P-TandeMBP and protein kinases to remove ATP, Pi, and ADP; (3) acid extraction of P-TandeMBP with HCl to remove protein kinases; and (4) neutralization of the solution that contains P-TandeMBP with Tris. In combination with the malachite green assay, P-TandeMBP can be used to detect protein phosphatase activity without using radioactive materials. Moreover, P-TandeMBP served as an efficient substrate for PPM family phosphatases (PPM1A, PPM1B, PPM1D, PPM1F, PPM1G, PPM1H, PPM1K, and PPM1M) and PPP family phosphatase PP5. Various phosphatase activities were also detected with high sensitivity in gel filtration fractions from mouse brain using P-TandeMBP. These results indicate that P-TandeMBP might be a powerful tool for the detection of protein phosphatase activities.     

A taxonomic revision of <Emphasis Type="Italic">Bursera</Emphasis> subgen. <Emphasis Type="Italic">Bursera</Emphasis> in the Greater Antilles and the Bahamas,including a new species from Cuba

A revision of Bursera in the Greater Antilles and the Bahamas has confirmed that Commiphora does not occur in the region and that all but one species in the region belong to Bursera subgen. Bursera. Here we describe a new species, Bursera yaterensis; B. nashii is synonymized with B. glauca and B. ovata is synonymized with B. trinitensis; and we return five species from Commiphora to Bursera. A dichotomous key is provided using mostly vegetative characters due to the frequent lack of adequate reproductive material and the relative uniformity of most floral and fruit characters.