Use of nicotine substitute prescribed at hourly plus ab libitum intake or ad libitum for heavy smokers willing to quit: a randomized controlled trial.

OBJECTIVE: To assess the impact of instructional guidance in the regular use of use nicotine nasal spray (NNS) on the true use of NNS during the first three weeks of smoking cessation for heavy smokers who are willing to quit. METHODS: This randomized, open, controlled trial included 50 patients who were heavy smokers, were willing to quit, and attending an academic outpatient clinic in Western Switzerland. Patients were randomised to instruction on NNS use as "ad libitum" (administration whenever cravings appear; control group) or to use NNS when craving appears and at least every hour when awake (intervention group). Intakes were monitored using an electronic device fixed in the spray unit (MDILog) during the first three weeks of use. Self reported abstinence from smoking at six months was confirmed by expired-air carbon monoxide. Using intention-to-treat analysis, random-effect GLS regression was used to calculate the mean difference of daily doses between groups controlling for lack of independence between measures from the same individual. RESULTS: One patient was lost to follow-up. At baseline randomization, the group receiving instruction to use NNS hourly included more women, patients with previous desires to quit, and patients with more psychiatric comorbidities and less somatic complaints compared to the group instructed to use NNS with cravings (group imbalance). Both groups self-administered more than the daily recommended dosage of 8 uses. Mean daily usage was 13.6 dose/day and 11.1 dose/day for the group instructed to use NNS hourly and with cravings, respectively. Adjusting for baseline imbalance, the increased daily doses in the intervention group (hourly use) remained nonsignificant compared to ad libitum use (-0.5 dose/day; CI 95% -6.2; 5.3, from day 1 to day 7; and 2.3 dose/day; CI 95% -5.4; 10.0, from day 8 to day 21). Instructing patients to use the NNS daily had no effect on smoking cessation at six months (RR = 0.69; CI 95% 0.34; 1.39). CONCLUSION: Heavy smokers willing to quit use NNS frequently, regardless of the instructions given. Recommending the use of NNS only when craving appears for heavy smokers willing to quit seems acceptable compared to prescribing hourly administration. TRIAL REGISTRATION: ClinicalTrials.gov: NCT00861276.

Evolution of specialization: a phylogenetic study of host range in the red milkweed beetle (Tetraopes tetraophthalmus).

Specialization is common in most lineages of insect herbivores, one of the most diverse groups of organisms on earth. To address how and why specialization is maintained over evolutionary time, we hypothesized that plant defense and other ecological attributes of potential host plants would predict the performance of a specialist root-feeding herbivore (the red milkweed beetle, Tetraopes tetraophthalmus). Using a comparative phylogenetic and functional trait approach, we assessed the determinants of insect host range across 18 species of Asclepias. Larval survivorship decreased with increasing phylogenetic distance from the true host, Asclepias syriaca, suggesting that adaptation to plant traits drives specialization. Among several root traits measured, only cardenolides (toxic defense chemicals) correlated with larval survival, and cardenolides also explained the phylogenetic distance effect in phylogenetically controlled multiple regression analyses. Additionally, milkweed species having a known association with other Tetraopes beetles were better hosts than species lacking Tetraopes herbivores, and milkweeds with specific leaf area values (a trait related to leaf function and habitat affiliation) similar to those of A. syriaca were better hosts than species having divergent values. We thus conclude that phylogenetic distance is an integrated measure of phenotypic and ecological attributes of Asclepias species, especially defensive cardenolides, which can be used to explain specialization and constraints on host shifts over evolutionary time.

Effects of computing parameters and measurement locations on the estimation of 3D NPS in non-stationary MDCT images.

The goal of this study was to investigate the impact of computing parameters and the location of volumes of interest (VOI) on the calculation of 3D noise power spectrum (NPS) in order to determine an optimal set of computing parameters and propose a robust method for evaluating the noise properties of imaging systems. Noise stationarity in noise volumes acquired with a water phantom on a 128-MDCT and a 320-MDCT scanner were analyzed in the spatial domain in order to define locally stationary VOIs. The influence of the computing parameters in the 3D NPS measurement: the sampling distances bx,y,z and the VOI lengths Lx,y,z, the number of VOIs NVOI and the structured noise were investigated to minimize measurement errors. The effect of the VOI locations on the NPS was also investigated. Results showed that the noise (standard deviation) varies more in the r-direction (phantom radius) than z-direction plane. A 25 × 25 × 40 mm(3) VOI associated with DFOV = 200 mm (Lx,y,z = 64, bx,y = 0.391 mm with 512 × 512 matrix) and a first-order detrending method to reduce structured noise led to an accurate NPS estimation. NPS estimated from off centered small VOIs had a directional dependency contrary to NPS obtained from large VOIs located in the center of the volume or from small VOIs located on a concentric circle. This showed that the VOI size and location play a major role in the determination of NPS when images are not stationary. This study emphasizes the need for consistent measurement methods to assess and compare image quality in CT.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.