How Do Price Minimizing Behaviors Impact Smoking Cessation? Findings from the International Tobacco Control (ITC) Four Country Survey

This paper examines how price minimizing behaviors impact efforts to stop smoking. Data on 4,988 participants from the International Tobacco Control Policy Evaluation (ITC) Four-Country Survey who were smokers at baseline (wave 5) and interviewed at a 1 year follow-up were used. We examined whether price minimizing behaviors at baseline predicted: (1) cessation, (2) quit attempts, and (3) successful quit attempts at one year follow up using multivariate logistic regression modeling. A subset analysis included 3,387 participants who were current smokers at waves 5 and 6 and were followed through wave 7 to explore effects of changing purchase patterns on cessation. Statistical tests for interaction were performed to examine the joint effect of SES and price/tax avoidance behaviors on cessation outcomes. Smokers who engaged in any price/tax avoidance behaviors were 28% less likely to report cessation. Persons using low/untaxed sources were less likely to quit at follow up, those purchasing cartons were less likely to make quit attempts and quit, and those using discount cigarettes were less likely to succeed, conditional on making attempts. Respondents who utilized multiple behaviors simultaneously were less likely to make quit attempts and to succeed. SES did not modify the effects of price minimizing behaviors on cessation outcomes. The data from this paper indicate that the availability of lower priced cigarette alternatives may attenuate public health efforts aimed at to reduce reducing smoking prevalence through price and tax increases among all SES groups

Endophytic actinomycetes from spontaneous plants of Algerian Sahara: indole-3-acetic acid production and tomato plants growth promoting activity

Twenty-seven endophytic actinomycete strains were isolated from five spontaneous plants well adapted to the poor sandy soil and arid climatic conditions of the Algerian Sahara. Morphological and chemotaxonomical analysis indicated that twenty-two isolates belonged to the Streptomyces genus and the remaining five were non- Streptomyces. All endophytic strains were screened for their ability to produce indole-3-acetic acid (IAA) in vitro on a chemically defined medium. Eighteen strains were able to produce IAA and the maximum production occurred with the Streptomyces sp. PT2 strain. The IAA produced was further extracted, partially purified and confirmed by thin layer chromatography (TLC) analysis. The 16S rDNA sequence analysis and phylogenetic studies indicated that strain PT2 was closely related to Streptomyces enissocaecilis NRRL B 16365T, Streptomyces rochei NBRC 12908T and Streptomyces plicatus NBRC 13071T, with 99.52 % similarity. The production of IAA was affected by cultural conditions such as temperature, pH, incubation period and L-tryptophan concentration. The highest level of IAA production (127 lg/ml) was obtained by cultivating the Streptomyces sp. PT2 strain in yeast extract-tryptone broth supplemented with 5 mg L-tryptophan/ ml at pH 7 and incubated on a rotary shaker (200 rpm) at 30°C for 5 days. Twenty-four-hour treatment of tomato cv. Marmande seeds with the supernatant culture of Streptomyces sp. PT2 that contained the crude IAA showed the maximum effect in promoting seed germination and root elongation

Low omega-6 vs. low omega-6 plus high omega-3 dietary intervention for Chronic Daily Headache: Protocol for a randomized clinical trial

<p>Abstract</p> <p>Background</p> <p>Targeted analgesic dietary interventions are a promising strategy for alleviating pain and improving quality of life in patients with persistent pain syndromes, such as chronic daily headache (CDH). High intakes of the omega-6 (n-6) polyunsaturated fatty acids (PUFAs), linoleic acid (LA) and arachidonic acid (AA) may promote physical pain by increasing the abundance, and subsequent metabolism, of LA and AA in immune and nervous system tissues. Here we describe methodology for an ongoing randomized clinical trial comparing the metabolic and clinical effects of a low n-6, average n-3 PUFA diet, to the effects of a low n-6 plus high n-3 PUFA diet, in patients with CDH. Our primary aim is to determine if: A) both diets reduce n-6 PUFAs in plasma and erythrocyte lipid pools, compared to baseline; and B) the low n-6 plus high n-3 diet produces a greater decline in n-6 PUFAs, compared to the low n-6 diet alone. Secondary clinical outcomes include headache-specific quality-of-life, and headache frequency and intensity.</p> <p>Methods</p> <p>Adults meeting the International Classification of Headache Disorders criteria for CDH are included. After a 6-week baseline phase, participants are randomized to a low n-6 diet, or a low n-6 plus high n-3 diet, for 12 weeks. Foods meeting nutrient intake targets are provided for 2 meals and 2 snacks per day. A research dietitian provides intensive dietary counseling at 2-week intervals. Web-based intervention materials complement dietitian advice. Blood and clinical outcome data are collected every 4 weeks.</p> <p>Results</p> <p>Subject recruitment and retention has been excellent; 35 of 40 randomized participants completed the 12-week intervention. Preliminary blinded analysis of composite data from the first 20 participants found significant reductions in erythrocyte n-6 LA, AA and %n-6 in HUFA, and increases in n-3 EPA, DHA and the omega-3 index, indicating adherence.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov <a href="http://www.clinicaltrials.gov/ct2/show/(NCT01157208)">(NCT01157208)</a></p

Streamflow forecasting using least-squares support vector machines

This paper investigates the ability of a least-squares support vector machine (LSSVM) model to improve the accuracy of streamflow forecasting. Cross-validation and grid-search methods are used to automatically determine the LSSVM parameters in the forecasting process. To assess the effectiveness of this model, monthly streamflow records from two stations, Tg Tulang and Tg Rambutan of the Kinta River in Perak, Peninsular Malaysia, were used as case studies. The performance of the LSSVM model is compared with the conventional statistical autoregressive integrated moving average (ARIMA), the artificial neural network (ANN) and support vector machine (SVM) models using various statistical measures. The results of the comparison indicate that the LSSVM model is a useful tool and a promising new method for streamflow forecasting.Editor D. Koutsoyiannis; Associate editor L. SeeCitation Shabri, A. and Suhartono, 2012. Streamflow forecasting using least-squares support vector machines. Hydrological Sciences Journal, 57 (7), 1275-1293