Pentoxifylline modifies three-dimensional collagen lattice model contraction and expression of collagen types I and III by human fibroblasts derived from post-burn hypertrophic scars and from normal skin

Fibroblasts are thought to be partially responsible for the persisting contractile forces that result in burn contractures. Using a monolayer cell culture and fibroblast populated collagen lattice (FPCL) three-dimensional model we subjected hypertrophic scar and non-cicatricial fibroblasts to the antifibrogenic agent pentoxifylline (PTF - 1 mg/mL) in order to reduce proliferation, collagen types I and III synthesis and model contraction. Fibroblasts were isolated from post-burn hypertrophic scars (HSHF) and non-scarred skin (NHF). Cells were grown in monolayers or incorporated into FPCL`s and exposed to PTF. In monolayer, cell number proliferation was reduced (46.35% in HSHF group and 37.73% in NHF group, p < 0.0001). PTF selectively inhibited collagen III synthesis in the HSHF group while inhibition was more evident to type I collagen synthesis in the NHF group. PTF also reduced contraction in both (HSHF and NHF) FPCL. (C) 2009 Elsevier Ltd and ISBI. All rights reserved.

Derivation and external validation of a simple prediction model for the diagnosis of type 2 Diabetes Mellitus in the Brazilian urban population

A risk score model was developed based in a population of 1,224 individuals from the general population without known diabetes aging 35 years or more from an urban Brazilian population sample in order to select individuals who should be screened in subsequent testing and improve the efficacy of public health assurance. External validation was performed in a second, independent, population from a different city ascertained through a similar epidemiological protocol. The risk score was developed by multiple logistic regression and model performance and cutoff values were derived from a receiver operating characteristic curve. Model`s capacity of predicting fasting blood glucose levels was tested analyzing data from a 5-year follow-up protocol conducted in the general population. Items independently and significantly associated with diabetes were age, BMI and known hypertension. Sensitivity, specificity and proportion of further testing necessary for the best cutoff value were 75.9, 66.9 and 37.2%, respectively. External validation confirmed the model`s adequacy (AUC equal to 0.72). Finally, model score was also capable of predicting fasting blood glucose progression in non-diabetic individuals in a 5-year follow-up period. In conclusion, this simple diabetes risk score was able to identify individuals with an increased likelihood of having diabetes and it can be used to stratify subpopulations in which performing of subsequent tests is necessary and probably cost-effective.

Modulation of effective connectivity during emotional processing by Delta(9)-tetrahydrocannabinol and cannabidiol

Cannabis sativa, the most widely used illicit drug, has profound effects on levels of anxiety in animals and humans. Although recent studies have helped provide a better understanding of the neurofunctional correlates of these effects, indicating the involvement of the amygdala and cingulate cortex, their reciprocal influence is still mostly unknown. In this study dynamic causal modelling (DCM) and Bayesian model selection (BMS) were used to explore the effects of pure compounds of C. sativa [600 mg of cannabidiol (CBD) and 10 mg Delta(9)-tetrahydrocannabinol (Delta(9)-THC)] on prefrontal-subcortical effective connectivity in 15 healthy subjects who underwent a double-blind randomized, placebo-controlled fMRI paradigm while viewing faces which elicited different levels of anxiety. In the placebo condition, BMS identified a model with driving inputs entering via the anterior cingulate and forward intrinsic connectivity between the amygdala and the anterior cingulate as the best fit. CBD but not Delta(9)-THC disrupted forward connectivity between these regions during the neural response to fearful faces. This is the first study to show that the disruption of prefrontal-subocrtical connectivity by CBD may represent neurophysiological correlates of its anxiolytic properties.

Hierarchical spherical model from a geometric point of view

A continuous version of the hierarchical spherical model at dimension d=4 is investigated. Two limit distributions of the block spin variable X(gamma), normalized with exponents gamma = d + 2 and gamma=d at and above the critical temperature, are established. These results are proven by solving certain evolution equations corresponding to the renormalization group (RG) transformation of the O(N) hierarchical spin model of block size L(d) in the limit L down arrow 1 and N ->infinity. Starting far away from the stationary Gaussian fixed point the trajectories of these dynamical system pass through two different regimes with distinguishable crossover behavior. An interpretation of this trajectories is given by the geometric theory of functions which describe precisely the motion of the Lee-Yang zeroes. The large-N limit of RG transformation with L(d) fixed equal to 2, at the criticality, has recently been investigated in both weak and strong (coupling) regimes by Watanabe (J. Stat. Phys. 115:1669-1713, 2004) . Although our analysis deals only with N = infinity case, it complements various aspects of that work.

rho(0) photoproduction in ultraperipheral relativistic heavy ion collisions at root s(NN)=200 GeV

Photoproduction reactions occur when the electromagnetic field of a relativistic heavy ion interacts with another heavy ion. The STAR Collaboration presents a measurement of rho(0) and direct pi(+)pi(-) photoproduction in ultraperipheral relativistic heavy ion collisions at root s(NN) = 200 GeV. We observe both exclusive photoproduction and photoproduction accompanied by mutual Coulomb excitation. We find a coherent cross section of sigma(AuAu -> Au*Au*rho(0)) = 530 +/- 19(stat.) +/- 57(syst.) mb, in accord with theoretical calculations based on a Glauber approach, but considerably below the predictions of a color dipole model. The rho 0 transverse momentum spectrum (p(T)(2)) is fit by a double exponential curve including both coherent and incoherent coupling to the target nucleus; we find sigma(inc)/sigma(coh) = 0.29 +/- 0.03 (stat.) +/- 0.08 (syst.). The ratio of direct pi(+)pi(-) to rho(0) production is comparable to that observed in gamma(p) collisions at HERA and appears to be independent of photon energy. Finally, the measured rho(0) spin helicity matrix elements agree within errors with the expected s-channel helicity conservation.

TESTING THE DISTANCE-DUALITY RELATION WITH GALAXY CLUSTERS AND TYPE Ia SUPERNOVAE

In this Letter, we propose a new and model-independent cosmological test for the distance-duality (DD) relation, eta = D(L)(z)(1 + z)(-2)/D(A)(z) = 1, where D(L) and D(A) are, respectively, the luminosity and angular diameter distances. For D(L) we consider two sub-samples of Type Ia supernovae (SNe Ia) taken from Constitution data whereas D(A) distances are provided by two samples of galaxy clusters compiled by De Filippis et al. and Bonamente et al. by combining Sunyaev-Zeldovich effect and X-ray surface brightness. The SNe Ia redshifts of each sub-sample were carefully chosen to coincide with the ones of the associated galaxy cluster sample (Delta z < 0.005), thereby allowing a direct test of the DD relation. Since for very low redshifts, D(A)(z) approximate to D(L)(z), we have tested the DD relation by assuming that. is a function of the redshift parameterized by two different expressions: eta(z) = 1 + eta(0)z and eta(z) = 1 +eta(0)z/(1 + z), where eta(0) is a constant parameter quantifying a possible departure from the strict validity of the reciprocity relation (eta(0) = 0). In the best scenario (linear parameterization), we obtain eta(0) = -0.28(-0.44)(+0.44) (2 sigma, statistical + systematic errors) for the De Filippis et al. sample (elliptical geometry), a result only marginally compatible with the DD relation. However, for the Bonamente et al. sample (spherical geometry) the constraint is eta(0) = -0.42(-0.34)(+0.34) (3 sigma, statistical + systematic errors), which is clearly incompatible with the duality-distance relation.

Inflation targeting in emerging economies: What do the data say?

In a recent thought-provoking paper, Ball and Sheridan [Ball, L., Sheridan, N., 2005. Does inflation targeting matter? In: Bernanke, B.S., Woodford, M. (Eds.), The Inflation-Targeting Debate, University of Chicago Press] show that the available evidence for a group of developed economies does not lend credence to the belief that adopting an inflation targeting regime (IT) was instrumental in bringing inflation and inflation volatility down. Here, we extend Ball and Sheridan`s analysis for a subset of 36 emerging market economies and find that, for them, the story is quite different. Compared to non-targeters, developing countries adopting the IT regime not only experienced greater drops in inflation, but also in growth volatility, thus corroborating the view that the regime`s ""constrained flexibility"" to deal with adverse shocks delivered concrete welfare gains. (c) 2006 Elsevier B.V. All rights reserved.

Shared structural determinants for the calcium-independent liposome membrane permeabilization and sarcolemma depolarization in Bothropstoxin-I, a LYS49-PLA(2) from the venom of Bothrops jararacussu

The structural determinants of myotoxicity of bothropstoxin-I (BthTX-I), a Lys49 phospholipase A(2) from Bothrops jararacussu venom, were studied by measuring the resting membrane potential in the mouse phrenic nerve-diaphragm preparation. This method proved to be around 100-fold more sensitive than the creatine kinase release assay, and was used to evaluate a total of 31 site-directed BthTX-I alanine scanning mutants. Mutants that reduced the resting membrane potential were located in a surface patch defined by residues in the C-terminal loop (residues 115-129), positions 37-39 in the membrane interfacial recognition surface (Y46 and K54), and residue K93. These results expand the known structural determinants of the biological activity as evaluated by previous creatine kinase release experiments. Furthermore, a strong correlation is observed between the structural determinants of sarcolemma depolarization and calcium-independent disruption of liposome membranes, suggesting that a common mechanism of action underlies the permeabilization of the biological and model membranes. (C) 2009 Elsevier Ltd. All rights reserved.

Stereological and allometric studies on neurons and axo-dendritic synapses in the superior cervical ganglia of rats, capybaras and horses

The superior cervical ganglion (SCG) in mammals varies in structure according to developmental age, body size, gender, lateral asymmetry, the size and nuclear content of neurons and the complexity and synaptic coverage of their dendritic trees. In small and medium-sized mammals, neuron number and size increase from birth to adulthood and, in phylogenetic studies, vary with body size. However, recent studies on larger animals suggest that body weight does not, in general, accurately predict neuron number. We have applied design-based stereological tools at the light-microscopic level to assess the volumetric composition of ganglia and to estimate the numbers and sizes of neurons in SCGs from rats, capybaras and horses. Using transmission electron microscopy, we have obtained design-based estimates of the surface coverage of dendrites by postsynaptic apposition zones and model-based estimates of the numbers and sizes of synaptophysin-labelled axo-dendritic synaptic disks. Linear regression analysis of log-transformed data has been undertaken in order to establish the nature of the relationships between numbers and SCG volume (V(scg)). For SCGs (five per species), the allometric relationship for neuron number (N) is N=35,067xV (scg) (0.781) and that for synapses is N=20,095,000xV (scg) (1.328) , the former being a good predictor and the latter a poor predictor of synapse number. Our findings thus reveal the nature of SCG growth in terms of its main ingredients (neurons, neuropil, blood vessels) and show that larger mammals have SCG neurons exhibiting more complex arborizations and greater numbers of axo-dendritic synapses.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

The effect of the negative coupling parameter on the spectrum of a trapped Bose gas

The interest in attractive Bose-Einstein Condensates arises due to the chemical instabilities generate when the number of trapped atoms is above a critical number. In this case, recombination process promotes the collapse of the cloud. This behavior is normally geometry dependent. Within the context of the mean field approximation, the system is described by the Gross-Pitaevskii equation. We have considered the attractive Bose-Einstein condensate, confined in a nonspherical trap, investigating numerically and analytically the solutions, using controlled perturbation and self-similar approximation methods. This approximation is valid in all interval of the negative coupling parameter allowing interpolation between weak-coupling and strong-coupling limits. When using the self-similar approximation methods, accurate analytical formulas were derived. These obtained expressions are discussed for several different traps and may contribute to the understanding of experimental observations.

Temperature effects on a network of dissipative quantum harmonic oscillators: collective damping and dispersion processes

In this paper we extend the results presented in (de Ponte, Mizrahi and Moussa 2007 Phys. Rev. A 76 032101) to treat quantitatively the effects of reservoirs at finite temperature in a bosonic dissipative network: a chain of coupled harmonic oscillators whatever its topology, i.e., whichever the way the oscillators are coupled together, the strength of their couplings and their natural frequencies. Starting with the case where distinct reservoirs are considered, each one coupled to a corresponding oscillator, we also analyze the case where a common reservoir is assigned to the whole network. Master equations are derived for both situations and both regimes of weak and strong coupling strengths between the network oscillators. Solutions of these master equations are presented through the normal ordered characteristic function. These solutions are shown to be significantly involved when temperature effects are considered, making difficult the analysis of collective decoherence and dispersion in dissipative bosonic networks. To circumvent these difficulties, we turn to the Wigner distribution function which enables us to present a technique to estimate the decoherence time of network states. Our technique proceeds by computing separately the effects of dispersion and the attenuation of the interference terms of the Wigner function. A detailed analysis of the dispersion mechanism is also presented through the evolution of the Wigner function. The interesting collective dispersion effects are discussed and applied to the analysis of decoherence of a class of network states. Finally, the entropy and the entanglement of a pure bipartite system are discussed.

Community structure and dynamics in climate networks

We consider climate networks constructed from observed and model simulated fields of three climate variables and investigate their community structure. We find that for all fields the number of effective communities is rather small (four to five). We are able to trace the origin of these communities to certain dynamical properties of climate. Our results suggest that the complete complexity of the climate system condenses beyond the `weather` time scales into a small number of low-dimensional interacting components and provide clues as to the nature of the climate subsystems underlying these components.

Sampling, WLS, and Mixed Models

Mixed models may be defined with or without reference to sampling, and can be used to predict realized random effects, as when estimating the latent values of study subjects measured with response error. When the model is specified without reference to sampling, a simple mixed model includes two random variables, one stemming from an exchangeable distribution of latent values of study subjects and the other, from the study subjects` response error distributions. Positive probabilities are assigned to both potentially realizable responses and artificial responses that are not potentially realizable, resulting in artificial latent values. In contrast, finite population mixed models represent the two-stage process of sampling subjects and measuring their responses, where positive probabilities are only assigned to potentially realizable responses. A comparison of the estimators over the same potentially realizable responses indicates that the optimal linear mixed model estimator (the usual best linear unbiased predictor, BLUP) is often (but not always) more accurate than the comparable finite population mixed model estimator (the FPMM BLUP). We examine a simple example and provide the basis for a broader discussion of the role of conditioning, sampling, and model assumptions in developing inference.