Effectiveness of Switching Smoking-Cessation Medications Following Relapse

Introduction—Nicotine dependence is a chronic disorder often characterized by multiple failed quit attempts (QAs). Yet, little is known about the sequence of methods used across multiple QAs or how this may impact future ability to abstain from smoking. This prospective cohort study examines the effectiveness of switching smoking-cessation medications (SCMs) across multiple QAs. Methods—Adult smokers (aged ≥ 18 years) participating in International Tobacco Control surveys in the United Kingdom, U.S., Canada, and Australia (N=795) who: (1) completed two consecutive surveys between 2006 and 2011; (2) initiated a QA at least 1 month before each survey; and (3) provided data for the primary predictor (SCM use during most recent QA), outcome (1-month point prevalence abstinence), and relevant covariates. Analyses were conducted in 2016. Results—Five SCM user classifications were identified: (1) non-users (43.5%); (2) early users (SCM used for initial, but not subsequent QA; 11.4%); (3) later users (SCM used for subsequent, but not initial QA; 18.4%); (4) repeaters (same SCM used for both QAs; 10.7%); and (5) switchers (different SCM used for each QA; 14.2%). Abstinence rates were lower for non-users (15.9%, OR=0.48, p=0.002), early users (16.6%, OR=0.27, p=0.03), and repeaters (12.4%, OR=0.36, p=0.004) relative to switchers (28.5%). Conclusions—Findings suggest smokers will be more successful if they use a SCM in QAs and vary the SCM they use across time. That smokers can increase their odds of quitting by switching SCMs is an important message that could be communicated to smokers

Methods of the ITC Four Country Smoking and Vaping Survey, wave 1 (2016)

AIM: To describe the methods of the 2016 International Tobacco Control (ITC) Four Country Smoking and Vaping (4CV) Survey, conducted in 2016 in Australia (AU), Canada (CA), England (EN) and the United States (US). METHODS: The respondents were cigarette smokers, former smokers (quit within the previous 2 years), and at-least-weekly vapers, aged 18 years and older. Eligible cohort members from the ITC Four Country Survey (4C) were retained. New respondents were sampled by commercial firms from their panels. Where possible, ages 18-24 and vapers were oversampled. Data were collected online, and respondents were remunerated. Survey weights were calibrated to benchmarks from nationally representative surveys. RESULTS: Response rates by country for new recruits once invited ranged from 15.2 to 49.6%. Sample sizes for smokers/former smokers were 1504 in AU, 3006 in CA, 3773 in EN and 2239 in the US. Sample sizes for additional vapers were 727 in CA, 551 in EN and 494 in the US. CONCLUSION: The International Tobacco Control Four Country Smoking and Vaping Survey design and data collection methods allow analyses to examine prospectively the use of cigarettes and nicotine vaping products in jurisdictions with different regulatory policies. The effects on the sampling designs and response quality of recruiting the respondents from commercial panels are mitigated by the use of demographic and geographic quotas in sampling; by quality control measures; and by the construction of survey weights taking into account smoking/vaping status, sex, age, education and geography

Critical mutation rate has an exponential dependence on population size for eukaryotic-length genomes with crossover

The critical mutation rate (CMR) determines the shift between survival-of-the-fittest and survival of individuals with greater mutational robustness (“flattest”). We identify an inverse relationship between CMR and sequence length in an in silico system with a two-peak fitness landscape; CMR decreases to no more than five orders of magnitude above estimates of eukaryotic per base mutation rate. We confirm the CMR reduces exponentially at low population sizes, irrespective of peak radius and distance, and increases with the number of genetic crossovers. We also identify an inverse relationship between CMR and the number of genes, confirming that, for a similar number of genes to that for the plant Arabidopsis thaliana (25,000), the CMR is close to its known wild-type mutation rate; mutation rates for additional organisms were also found to be within one order of magnitude of the CMR. This is the first time such a simulation model has been assigned input and produced output within range for a given biological organism. The decrease in CMR with population size previously observed is maintained; there is potential for the model to influence understanding of populations undergoing bottleneck, stress, and conservation strategy for populations near extinction

Students' Models of Newton's Second Law in Mechanics and Electromagnetism

We investigated students' use of Newton's second law in mechanics and electromagnetism contexts by interviewing students in a two-semester calculus-based physics course. We observed that students' responses are consistent with three mental models. These models appeard in mechanics contexts and were transferred to electromagnetism contexts. We developed an inventory to help instructors identify these models and direct students towards the correct one.Comment: 15 pages, 3 figues and 4 table

Exhaustive exercise training enhances aerobic capacity in American alligator (Alligator mississippiensis)

The oxygen transport system in mammals is extensively remodelled in response to repeated bouts of activity, but many reptiles appear to be ‘metabolically inflexible’ in response to exercise training. A recent report showed that estuarine crocodiles (Crocodylus porosus) increase their maximum metabolic rate in response to exhaustive treadmill training, and in the present study, we confirm this response in another crocodilian, American alligator (Alligator mississippiensis). We further specify the nature of the crocodilian training response by analysing effects of training on aerobic [citrate synthase (CS)] and anaerobic [lactate dehydrogenase (LDH)] enzyme activities in selected skeletal muscles, ventricular and skeletal muscle masses and haematocrit. Compared to sedentary control animals, alligators regularly trained for 15 months on a treadmill (run group) or in a flume (swim group) exhibited peak oxygen consumption rates higher by 27 and 16%, respectively. Run and swim exercise training significantly increased ventricular mass (~11%) and haematocrit (~11%), but not the mass of skeletal muscles. However, exercise training did not alter CS or LDH activities of skeletal muscles. Similar to mammals, alligators respond to exercise training by increasing convective oxygen transport mechanisms, specifically heart size (potentially greater stroke volume) and haematocrit (increased oxygen carrying-capacity of the blood). Unlike mammals, but similar to squamate reptiles, alligators do not also increase citrate synthase activity of the skeletal muscles in response to exercise

Long-Branch Attraction Bias and Inconsistency in Bayesian Phylogenetics

Bayesian inference (BI) of phylogenetic relationships uses the same probabilistic models of evolution as its precursor maximum likelihood (ML), so BI has generally been assumed to share ML's desirable statistical properties, such as largely unbiased inference of topology given an accurate model and increasingly reliable inferences as the amount of data increases. Here we show that BI, unlike ML, is biased in favor of topologies that group long branches together, even when the true model and prior distributions of evolutionary parameters over a group of phylogenies are known. Using experimental simulation studies and numerical and mathematical analyses, we show that this bias becomes more severe as more data are analyzed, causing BI to infer an incorrect tree as the maximum a posteriori phylogeny with asymptotically high support as sequence length approaches infinity. BI's long branch attraction bias is relatively weak when the true model is simple but becomes pronounced when sequence sites evolve heterogeneously, even when this complexity is incorporated in the model. This bias—which is apparent under both controlled simulation conditions and in analyses of empirical sequence data—also makes BI less efficient and less robust to the use of an incorrect evolutionary model than ML. Surprisingly, BI's bias is caused by one of the method's stated advantages—that it incorporates uncertainty about branch lengths by integrating over a distribution of possible values instead of estimating them from the data, as ML does. Our findings suggest that trees inferred using BI should be interpreted with caution and that ML may be a more reliable framework for modern phylogenetic analysis

Transcriptome Analysis of Synaptoneurosomes Identifies Neuroplasticity Genes Overexpressed in Incipient Alzheimer's Disease

In Alzheimer's disease (AD), early deficits in learning and memory are a consequence of synaptic modification induced by toxic beta-amyloid oligomers (oAβ). To identify immediate molecular targets downstream of oAβ binding, we prepared synaptoneurosomes from prefrontal cortex of control and incipient AD (IAD) patients, and isolated mRNAs for comparison of gene expression. This novel approach concentrates synaptic mRNA, thereby increasing the ratio of synaptic to somal mRNA and allowing discrimination of expression changes in synaptically localized genes. In IAD patients, global measures of cognition declined with increasing levels of dimeric Aβ (dAβ). These patients also showed increased expression of neuroplasticity related genes, many encoding 3′UTR consensus sequences that regulate translation in the synapse. An increase in mRNA encoding the GluR2 subunit of the α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) was paralleled by elevated expression of the corresponding protein in IAD. These results imply a functional impact on synaptic transmission as GluR2, if inserted, maintains the receptors in a low conductance state. Some overexpressed genes may induce early deficits in cognition and others compensatory mechanisms, providing targets for intervention to moderate the response to dAβ

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of diseas

Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods - recursive partitioning and regression - to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios >1.5; Pcombined = 2.01 × 10-19 and 2.35 × 10-13, respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous studies, we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes. ©2007 Nature Publishing Group