Detection of catechol using mixed Langmuir-Blodgett films of a phospholipid and phthalocyanines as voltammetric sensors

The combination of metallic phthalocyanines (MPcs) and biomolecules has been explored in the literature either as mimetic systems to investigate molecular interactions or as supporting layers to immobilize biomolecules. Here, Langmuir-Blodgett (LB) films containing the phospholipid dimyristoyl phosphatidic acid (DMPA) mixed either with iron phthalocyanine (FePc) or with lutetium bisphthalocyanine (LuPc(2)) were applied as ITO modified-electrodes in the detection of catechol using cyclic voltammetry. The mixed Langmuir films of FePc + DMPA and LuPc(2) + DMPA displayed surface-pressure isotherms with no evidence of molecular-level interactions. The Fourier Transform Infrared (FTIR) spectra of the multilayer LB films confirmed the lack of interaction between the components. The DMPA and the FePc molecules were found to be oriented perpendicularly to the substrate, while LuPc(2) molecules were randomly organized. The phospholipid matrix induced a remarkable electrocatalytic effect on the phthalocyanines; as a result the mixed LB films deposited on ITO could be used to detect catechol with detection limits of 4.30 x 10(-7) and 3.34 x 10(-7) M for FePc + DMPA and LuPc(2) + DMPA, respectively. Results from kinetics experiments revealed that ion diffusion dominated the response of the modified electrodes. The sensitivity was comparable to that of other non-enzymatic sensors, which is sufficient to detect catechol in the food industry. The higher stability of the electrochemical response of the LB films and the ability to control the molecular architecture are promising for further studies with incorporation of biomolecules.

Dynamical Casimir effect for a massless scalar field between two concentric spherical shells with mixed boundary conditions

In this work we analyze the dynamical Casimir effect for a massless scalar field confined between two concentric spherical shells considering mixed boundary conditions. We thus generalize a previous result in literature [Phys. Rev. A 78, 032521 (2008)], where the same problem is approached for the field constrained to the Dirichlet-Dirichlet boundary conditions. A general expression for the average number of particle creation is deduced considering an arbitrary law of radial motion of the spherical shells. This expression is then applied to harmonic oscillations of the shells, and the number of particle production is analyzed and compared with the results previously obtained under Dirichlet-Dirichlet boundary conditions.

Treatment of Obese Adolescents: The Influence of Periodization Models and ACE Genotype

The aims of the present study were to compare the effects of two periodization models on metabolic syndrome risk factors in obese adolescents and verify whether the angiotensin-converting enzyme (ACE) genotype is important in establishing these effects. A total of 32 postpuberty obese adolescents were submitted to aerobic training (AT) and resistance training (RT) for 14 weeks. The subjects were divided into linear periodization (LP, n = 16) or daily undulating periodization (DUP, n = 16). Body composition, visceral and subcutaneous fat, glycemia, insulinemia, homeostasis model assessment of insulin resistance (HOMA-IR), lipid profiles, blood pressure, maximal oxygen consumption (VO(2max)), resting metabolic rate (RMR), muscular endurance were analyzed at baseline and after intervention. Both groups demonstrated a significant reduction in body mass, BMI, body fat, visceral and subcutaneous fat, total and low-density lipoprotein cholesterol, blood pressure and an increase in fat-free mass, VO(2max), and muscular endurance. However, only DUP promoted a reduction in insulin concentrations and HOMA-IR. It is important to emphasize that there was no statics difference between LP and DUP groups; however, it appears that there may be bigger changes in the DUP than LP group in some of the metabolic syndrome risk factors in obese adolescents with regard to the effect size (ES). Both periodization models presented a large effect on muscular endurance. Despite the limitation of sample size, our results suggested that the ACE genotype may influence the functional and metabolic characteristics of obese adolescents and may be considered in the future strategies for massive obesity control.

Determining the effect of grain size and maximum induction upon coercive field of electrical steels

Although theoretical models have already been proposed, experimental data is still lacking to quantify the influence of grain size upon coercivity of electrical steels. Some authors consider a linear inverse proportionality, while others suggest a square root inverse proportionality. Results also differ with regard to the slope of the reciprocal of grain size-coercive field relation for a given material. This paper discusses two aspects of the problem: the maximum induction used for determining coercive force and the possible effect of lurking variables such as the grain size distribution breadth and crystallographic texture. Electrical steel sheets containing 0.7% Si, 0.3% Al and 24 ppm C were cold-rolled and annealed in order to produce different grain sizes (ranging from 20 to 150 mu m). Coercive field was measured along the rolling direction and found to depend linearly on reciprocal of grain size with a slope of approximately 0.9 (A/m)mm at 1.0 T induction. A general relation for coercive field as a function of grain size and maximum induction was established, yielding an average absolute error below 4%. Through measurement of B(50) and image analysis of micrographs, the effects of crystallographic texture and grain size distribution breadth were qualitatively discussed. (C) 2011 Elsevier B.V. All rights reserved.

Log-modified Weibull regression models with censored data: Sensitivity and residual analysis

This paper proposes a regression model considering the modified Weibull distribution. This distribution can be used to model bathtub-shaped failure rate functions. Assuming censored data, we consider maximum likelihood and Jackknife estimators for the parameters of the model. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and we also present some ways to perform global influence. Besides, for different parameter settings, sample sizes and censoring percentages, various simulations are performed and the empirical distribution of the modified deviance residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for a martingale-type residual in log-modified Weibull regression models with censored data. Finally, we analyze a real data set under log-modified Weibull regression models. A diagnostic analysis and a model checking based on the modified deviance residual are performed to select appropriate models. (c) 2008 Elsevier B.V. All rights reserved.

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil

A total of 152,145 weekly test-day milk yield records from 7317 first lactations of Holstein cows distributed in 93 herds in southeastern Brazil were analyzed. Test-day milk yields were classified into 44 weekly classes of DIM. The contemporary groups were defined as herd-year-week of test-day. The model included direct additive genetic, permanent environmental and residual effects as random and fixed effects of contemporary group and age of cow at calving as covariable, linear and quadratic effects. Mean trends were modeled by a cubic regression on orthogonal polynomials of DIM. Additive genetic and permanent environmental random effects were estimated by random regression on orthogonal Legendre polynomials. Residual variances were modeled using third to seventh-order variance functions or a step function with 1, 6,13,17 and 44 variance classes. Results from Akaike`s and Schwarz`s Bayesian information criterion suggested that a model considering a 7th-order Legendre polynomial for additive effect, a 12th-order polynomial for permanent environment effect and a step function with 6 classes for residual variances, fitted best. However, a parsimonious model, with a 6th-order Legendre polynomial for additive effects and a 7th-order polynomial for permanent environmental effects, yielded very similar genetic parameter estimates. (C) 2008 Elsevier B.V. All rights reserved.

Effect of inulin on growth and acidification performance of different probiotic bacteria in co-cultures and mixed culture with Streptococcus thermophilus

Inulin was used as a prebiotic to improve the quality and consistency of skim milk fermented by Lactobacillus acidophilus (La), Lactobacillus rhamnosus (Lr), Lactobacillus bulgaricus (Lb) and Bifidobacterium lactis (BI) with Streptococcus thermophilus (St), either in binary co-cultures or in cocktail containing all microorganisms. We compared, either in the presence of 40 mg inulin g(-1) or not, the results of the maximum acidification rate (V(max)) and the times to reach it (t(max)), to reach pH 5.0 (t(PH5.0)) and to complete the fermentation (t(f)). Post-acidification, lactic acid formation and cell counts were also compared after either 1 day (D1) or 7 days of storage at 4 degrees C (N). In co-culture, inulin addition to the milk increased V(max), decreased t(max) and t(f), favored post-acidification and exerted a bifidogenic effect. S. thermophilus proved to stimulate the metabolism of the other lactic bacteria and enhanced the product features. After D7, a significant prebiotic effect of inulin was observed in all co-cultures. Either after D1 or D7, the enumerations of Lr and BI in mixed culture markedly decreased compared to their respective co-cultures because of greater competition for the same substrates. (C) 2008 Elsevier Ltd. All rights reserved.

Protective effect of an extract from Ascaris suum in experimental arthritis models

We investigated the effect of an extract from a helminth (Ascaris suum) in zymosan-induced arthritis (ZYA) or collagen-induced arthritis (CIA). Rats and mice, respectively, received 1 mg and 0.1 mg zymosan intra-articularly (i.a.). Test groups received an A. suum extract either per os (p.o.) or intraperitoneally (i.p.) 30 min prior to i.a. zymosan. Controls received saline. Hypernociception was measured using the articular incapacitation test. Cell influx, nitrite, and cytokine levels were assessed in joint exudates. The synovia and distal femoral extremities were used for histopathology. Cartilage damage was assessed through determining glycosaminoglycan (GAG) content. DBA/1J mice were subjected to CIA. The test group received A. suum extract i.p. 1 day after CIA became clinically detectable. Clinical severity and hypernociception were assessed daily. Neutrophil influx was determined using myeloperoxidase activity. The A. suum extract, either i.p. or p.o., significantly and dose-dependently inhibited cell influx and hypernociception in ZYA in addition to reducing GAG loss and ameliorating synovitis. The A. suum extract reduced i.a. levels of NO, interleukin-1 beta (IL-1 beta), and IL-10 but not tumor necrosis factor alpha (TNF-alpha) in rats subjected to ZYA while reducing i.a. IL-10, but not IL-1 beta or TNIT-alpha, levels in mice. Clinically, mice subjected to CIA treated with the A. suum extract had less severe arthritis. Hypernociception, myeloperoxidase activity, and synovitis severity were significantly reduced. These data show that a helminth extract given p.o. protects from arthritis severity in two classical arthritis models. This A. suum effect is species independent and functions orally and parenterally. The results show clinical and structural benefits when A. suum extract is given either prophylactically or therapeutically.

The neuroprotective effect of dental pulp cells in models of Alzheimer`s and Parkinson`s disease

Aim of the present study was to investigate the neuroprotective effect of dental pulp cells (DPCs) in in vitro models of Alzheimer and Parkinson disease. Primary cultures of hippocampal and ventral mesencephalic neurons were treated for 24 h with amyloid beta (A beta(1-42)) peptide 1-42 and 6-OHDA, respectively. DPCs isolated from adult rat incisors were previously cultured in tissue culture inserts and added to the neuron cultures 2 days prior to neurotoxin treatment. Cell viability was assessed by the MTT assay. The co-culture with DPCs significantly attenuated 6-OHDA and A beta(1-42)-induced toxicity in primary cultures of mesencephalic and hippocampal neurons, and lead to an increase in neuronal viability in untreated cultures, suggesting a neurotrophic effect in both models. Furthermore, human dental pulp cells expressed a neuronal phenotype and produced the neurotrophic factors NGF, GDNF, BDNF, and BMP2 shown by microarray screening and antibody staining for the representative proteins. DPCs protected primary neurons in in vitro models of Alzheimer`s and Parkinson`s disease and can be viewed as possible candidates for studies on cell-based therapy.

The suitability of different FEA models for studying root fractures caused by wedge effect

Finite element analysis (FEA) utilizing models with different levels of complexity are found in the literature to study the tendency to vertical root fracture caused by post intrusion (""wedge effect""). The objective of this investigation was to verify if some simplifications used in bi-dimensional FEA models are acceptable regarding the analysis of stresses caused by wedge effect. Three plane strain (PS) and two axisymmtric (Axi) models were studied. One PS model represented the apical third of the root entirely, in dentin (PS-nG). The other models included gutta-percha in the apical third, and differed regarding dentin-post relationship: bonded (PS-B and Axi-B) or nonbonded (PS-nB and Axi-nB). Mesh discretization and material properties were similar for all cases. Maximum principal stress (sigma(max)) was analyzed as a response to a 165 N longitudinal load. Stress magnitude and orientation varied widely (PS-nG: 10.3 MPa; PS-B: 0.8 MPa; PS-nB: 10.4 MPa; Axi-13: 0.2 MPa, Axi-nB: 10.8 MPa). Axi-nB was the only model where all (sigma(max) vectors at the apical third were perpendicular to the model plane. Therefore, it is adequate to demonstrate the tendency to vertical root fractures caused by wedge effect. Axi-13 showed only part of the (sigma(max) perpendicular to the model plane while PS models showed sigma(max) on the model plane. In these models, sigma(max) orientation did not represent a situation where vertical root fracture would occur due to wedge effect. Adhesion between post and dentin significantly reduced (c) 2007 Wiley Periodicals, Inc.

Effect of repeated torque/mechanical loading cycles on two different abutment types in implants with internal tapered connections: an in vitro study

Internal tapered connections were developed to improve biomechanical properties and to reduce mechanical problems found in other implant connection systems. The purpose of this study was to evaluate the effects of mechanical loading and repeated insertion/removal cycles on the torque loss of abutments with internal tapered connections. Sixty-eight conical implants and 68 abutments of two types were used. They were divided into four groups: groups 1 and 3 received solid abutments, and groups 2 and 4 received two-piece abutments. In groups 1 and 2, abutments were simply installed and uninstalled; torque-in and torque-out values were measured. In groups 3 and 4, abutments were installed, mechanically loaded and uninstalled; torque-in and torque-out values were measured. Under mechanical loading, two-piece abutments were frictionally locked into the implant; thus, data of group 4 were catalogued under two subgroups (4a: torque-out value necessary to loosen the fixation screw; 4b: torque-out value necessary to remove the abutment from the implant). Ten insertion/removal cycles were performed for every implant/abutment assembly. Data were analyzed with a mixed linear model (P <= 0.05). Torque loss was higher in groups 4a and 2 (over 30% loss), followed by group 1 (10.5% loss), group 3 (5.4% loss) and group 4b (39% torque gain). All the results were significantly different. As the number of insertion/removal cycles increased, removal torques tended to be lower. It was concluded that mechanical loading increased removal torque of loaded abutments in comparison with unloaded abutments, and removal torque values tended to decrease as the number of insertion/removal cycles increased. To cite this article:Ricciardi Coppede A, de Mattos MdaGC, Rodrigues RCS, Ribeiro RF. Effect of repeated torque/mechanical loading cycles on two different abutment types in implants with internal tapered connections: an in vitro study.Clin. Oral Impl. Res. 20, 2009; 624-632.doi: 10.1111/j.1600-0501.2008.01690.x.

THE ROLE OF DIFFUSIVITY QUENCHING IN FLUX-TRANSPORT DYNAMO MODELS

In the nonlinear phase of a dynamo process, the back-reaction of the magnetic field upon the turbulent motion results in a decrease of the turbulence level and therefore in a suppression of both the magnetic field amplification (the alpha-quenching effect) and the turbulent magnetic diffusivity (the eta-quenching effect). While the former has been widely explored, the effects of eta-quenching in the magnetic field evolution have rarely been considered. In this work, we investigate the role of the suppression of diffusivity in a flux-transport solar dynamo model that also includes a nonlinear alpha-quenching term. Our results indicate that, although for alpha-quenching the dependence of the magnetic field amplification with the quenching factor is nearly linear, the magnetic field response to eta-quenching is nonlinear and spatially nonuniform. We have found that the magnetic field can be locally amplified in this case, forming long-lived structures whose maximum amplitude can be up to similar to 2.5 times larger at the tachocline and up to similar to 2 times larger at the center of the convection zone than in models without quenching. However, this amplification leads to unobservable effects and to a worse distribution of the magnetic field in the butterfly diagram. Since the dynamo cycle period increases when the efficiency of the quenching increases, we have also explored whether the eta-quenching can cause a diffusion-dominated model to drift into an advection-dominated regime. We have found that models undergoing a large suppression in eta produce a strong segregation of magnetic fields that may lead to unsteady dynamo-oscillations. On the other hand, an initially diffusion-dominated model undergoing a small suppression in eta remains in the diffusion-dominated regime.

Influence diagnostics in nonlinear mixed-effects elliptical models

In this work we propose and analyze nonlinear elliptical models for longitudinal data, which represent an alternative to gaussian models in the cases of heavy tails, for instance. The elliptical distributions may help to control the influence of the observations in the parameter estimates by naturally attributing different weights for each case. We consider random effects to introduce the within-group correlation and work with the marginal model without requiring numerical integration. An iterative algorithm to obtain maximum likelihood estimates for the parameters is presented, as well as diagnostic results based on residual distances and local influence [Cook, D., 1986. Assessment of local influence. journal of the Royal Statistical Society - Series B 48 (2), 133-169; Cook D., 1987. Influence assessment. journal of Applied Statistics 14 (2),117-131; Escobar, L.A., Meeker, W.Q., 1992, Assessing influence in regression analysis with censored data, Biometrics 48, 507-528]. As numerical illustration, we apply the obtained results to a kinetics longitudinal data set presented in [Vonesh, E.F., Carter, R.L., 1992. Mixed-effects nonlinear regression for unbalanced repeated measures. Biometrics 48, 1-17], which was analyzed under the assumption of normality. (C) 2009 Elsevier B.V. All rights reserved.