Group motivational intervention in overweight/obese patients in primary prevention of cardiovascular disease in the primary healthcare area

Background The global mortality caused by cardiovascular disease increases with weight. The Framingham study showed that obesity is a cardiovascular risk factor independent of other risks such as type 2 diabetes mellitus, dyslipidemia and smoking. Moreover, the main problem in the management of weight-loss is its maintenance, if it is achieved. We have designed a study to determine whether a group motivational intervention, together with current clinical practice, is more efficient than the latter alone in the treatment of overweight and obesity, for initial weight loss and essentially to achieve maintenance of the weight achieved; and, secondly, to know if this intervention is more effective for reducing cardiovascular risk factors associated with overweight and obesity. Methods This 26-month follow up multi-centre trial, will include 1200 overweight/obese patients. Random assignment of the intervention by Basic Health Areas (BHA): two geographically separate groups have been created, one of which receives group motivational intervention (group intervention), delivered by a nurse trained by an expert phsychologist, in 32 group sessions, 1 to 12 fortnightly, and 13 to 32, monthly, on top of their standard program of diet, exercise, and the other (control group), receiving the usual follow up, with regular visits every 3 months. Discussion By addressing currently unanswered questions regarding the maintenance in weight loss in obesity/overweight, upon the expected completion of participant follow-up in 2012, the IMOAP trial should document, for the first time, the benefits of a motivational intervention as a treatment tool of weight loss in a primary care setting.

Time-delayed fronts from biased random walks

We generalize a previous model of time-delayed reaction–diffusion fronts (Fort and Méndez 1999 Phys. Rev. Lett. 82 867) to allow for a bias in the microscopic random walk of particles or individuals. We also present a second model which takes the time order of events (diffusion and reproduction) into account. As an example, we apply them to the human invasion front across the USA in the 19th century. The corrections relative to the previous model are substantial. Our results are relevant to physical and biological systems with anisotropic fronts, including particle diffusion in disordered lattices, population invasions, the spread of epidemics, etc

Efficiency of informational transfer in regular and complex networks

We analyze the process of informational exchange through complex networks by measuring network efficiencies. Aiming to study nonclustered systems, we propose a modification of this measure on the local level. We apply this method to an extension of the class of small worlds that includes declustered networks and show that they are locally quite efficient, although their clustering coefficient is practically zero. Unweighted systems with small-world and scale-free topologies are shown to be both globally and locally efficient. Our method is also applied to characterize weighted networks. In particular we examine the properties of underground transportation systems of Madrid and Barcelona and reinterpret the results obtained for the Boston subway network.

On the convergence of EM-like algorithms for image segmentation using Markov random fields.

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.

Comparison of cannabinoid concentrations in oral fluid and whole blood between occasional and regular cannabis smokers prior to and after smoking a cannabis joint.

A cross-over controlled administration study of smoked cannabis was carried out on occasional and heavy smokers. The participants smoked a joint (11 % Δ9-tetrahydrocannabinol (THC)) or a matching placebo on two different occasions. Whole blood (WB) and oral fluid (OF) samples were collected before and up to 3.5 h after smoking the joints. Pharmacokinetic analyses were obtained from these data. Questionnaires assessing the subjective effects were administered to the subjects during each session before and after the smoking time period. THC, 11-hydroxy-THC (11-OH-THC) and 11-nor-9-carboxy-THC (THCCOOH) were analyzed in the blood by gas chromatography or liquid chromatography (LC)-tandem mass spectrometry (MS/MS). The determination of THC, THCCOOH, cannabinol (CBN), and Δ9-tetrahydrocannabinolic acid A (THC-A) was carried out on OF only using LC-MS/MS. In line with the widely accepted assumption that cannabis smoking results in a strong contamination of the oral cavity, we found that THC, and also THC-A, shows a sharp, high concentration peak just after smoking, with a rapid decrease in these levels within 3 h. No obvious differences were found between both groups concerning THC median maximum concentrations measured either in blood or in OF; these levels were equal to 1,338 and 1,041 μg/L in OF and to 82 and 94 μg/L in WB for occasional and heavy smokers, respectively. The initial WB THCCOOH concentration was much higher in regular smokers than in occasional users. Compared with the occasional smokers, the sensation of confusion felt by the regular smokers was much less while the feeling of intoxication remained almost unchanged.

Monte Carlo study of a kinetic lattice model with random diffusion of disorder

We report Monte Carlo results for a nonequilibrium Ising-like model in two and three dimensions. Nearest-neighbor interactions J change sign randomly with time due to competing kinetics. There follows a fast and random, i.e., spin-configuration-independent diffusion of Js, of the kind that takes place in dilute metallic alloys when magnetic ions diffuse. The system exhibits steady states of the ferromagnetic (antiferromagnetic) type when the probability p that J>0 is large (small) enough. No counterpart to the freezing phenomena found in quenched spin glasses occurs. We compare our results with existing mean-field and exact ones, and obtain information about critical behavior.

A random linear functional approach to efficiency bounds

This paper suggests a method for obtaining efficiency bounds in models containing either only infinite-dimensional parameters or both finite- and infinite-dimensional parameters (semiparametric models). The method is based on a theory of random linear functionals applied to the gradient of the log-likelihood functional and is illustrated by computing the lower bound for Cox's regression model

Via-configurable transistor array: a regular design technique to improve ICs yield

Process variations are a major bottleneck for digital CMOS integrated circuits manufacturability and yield. That iswhy regular techniques with different degrees of regularity are emerging as possible solutions. Our proposal is a new regular layout design technique called Via-Configurable Transistors Array (VCTA) that pushes to the limit circuit layout regularity for devices and interconnects in order to maximize regularity benefits. VCTA is predicted to perform worse than the Standard Cell approach designs for a certain technology node but it will allow the use of a future technology on an earlier time. Ourobjective is to optimize VCTA for it to be comparable to the Standard Cell design in an older technology. Simulations for the first unoptimized version of our VCTA of delay and energy consumption for a Full Adder circuit in the 90 nm technology node are presented and also the extrapolation for Carry-RippleAdders from 4 bits to 64 bits.

Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

A note on the degree sequences of graphs with restrictions

Degree sequences of some types of graphs will be studied and characterizedin this paper.

Challenging the "inoffensiveness" of regular cannabis use by its associations with other current risky substance use--a census of 20-year-old Swiss men.

3,537 men enrolling in 2007 for mandatory army recruitment procedures were assessed for the co-occurrence of risky licit substance use among risky cannabis users. Risky cannabis use was defined as at least twice weekly; risky alcohol use as 6+ drinks more than once/monthly, or more than 20 drinks per week; and risky tobacco use as daily smoking. Ninety-five percent of all risky cannabis users reported other risky use. They began using cannabis earlier than did non-risky users, but age of onset was unrelated to other risky substance use. A pressing public health issue among cannabis users stems from risky licit substance use warranting preventive efforts within this age group.

Self-avoiding random walks and Olbers' paradox

In this paper, we prove that a self-avoiding walk of infinite length provides a structure that would resolve Olbers' paradox. That is, if the stars of a universe were distributed like the vertices of an infinite random walk with each segment length of about a parsec, then the night sky could be as dark as actually observed on the Earth. Self-avoiding random walk structure can therefore resolve the Olbers' paradox even in a static universe.

Scaling behavior of random knots.

Using numerical simulations we investigate how overall dimensions of random knots scale with their length. We demonstrate that when closed non-self-avoiding random trajectories are divided into groups consisting of individual knot types, then each such group shows the scaling exponent of approximately 0.588 that is typical for self-avoiding walks. However, when all generated knots are grouped together, their scaling exponent becomes equal to 0.5 (as in non-self-avoiding random walks). We explain here this apparent paradox. We introduce the notion of the equilibrium length of individual types of knots and show its correlation with the length of ideal geometric representations of knots. We also demonstrate that overall dimensions of random knots with a given chain length follow the same order as dimensions of ideal geometric representations of knots.

On balanced CSPs with high treewidth

Tractable cases of the binary CSP are mainly divided in two classes: constraint language restrictions and constraint graph restrictions. To better understand and identify the hardest binary CSPs, in this work we propose methods to increase their hardness by increasing the balance of both the constraint language and the constraint graph. The balance of a constraint is increased by maximizing the number of domain elements with the same number of occurrences. The balance of the graph is defined using the classical definition from graph the- ory. In this sense we present two graph models; a first graph model that increases the balance of a graph maximizing the number of vertices with the same degree, and a second one that additionally increases the girth of the graph, because a high girth implies a high treewidth, an important parameter for binary CSPs hardness. Our results show that our more balanced graph models and constraints result in harder instances when compared to typical random binary CSP instances, by several orders of magnitude. Also we detect, at least for sparse constraint graphs, a higher treewidth for our graph models.