Fuzzy linguistic model for bioelectrical impedance vector analysis

Backgrounds Ea aims: The boundaries between the categories of body composition provided by vectorial analysis of bioimpedance are not well defined. In this paper, fuzzy sets theory was used for modeling such uncertainty. Methods: An Italian database with 179 cases 18-70 years was divided randomly into developing (n = 20) and testing samples (n = 159). From the 159 registries of the testing sample, 99 contributed with unequivocal diagnosis. Resistance/height and reactance/height were the input variables in the model. Output variables were the seven categories of body composition of vectorial analysis. For each case the linguistic model estimated the membership degree of each impedance category. To compare such results to the previously established diagnoses Kappa statistics was used. This demanded singling out one among the output set of seven categories of membership degrees. This procedure (defuzzification rule) established that the category with the highest membership degree should be the most likely category for the case. Results: The fuzzy model showed a good fit to the development sample. Excellent agreement was achieved between the defuzzified impedance diagnoses and the clinical diagnoses in the testing sample (Kappa = 0.85, p < 0.001). Conclusions: fuzzy linguistic model was found in good agreement with clinical diagnoses. If the whole model output is considered, information on to which extent each BIVA category is present does better advise clinical practice with an enlarged nosological framework and diverse therapeutic strategies. (C) 2012 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Connections between the Sznajd model with general confidence rules and graph theory

The Sznajd model is a sociophysics model that is used to model opinion propagation and consensus formation in societies. Its main feature is that its rules favor bigger groups of agreeing people. In a previous work, we generalized the bounded confidence rule in order to model biases and prejudices in discrete opinion models. In that work, we applied this modification to the Sznajd model and presented some preliminary results. The present work extends what we did in that paper. We present results linking many of the properties of the mean-field fixed points, with only a few qualitative aspects of the confidence rule (the biases and prejudices modeled), finding an interesting connection with graph theory problems. More precisely, we link the existence of fixed points with the notion of strongly connected graphs and the stability of fixed points with the problem of finding the maximal independent sets of a graph. We state these results and present comparisons between the mean field and simulations in Barabasi-Albert networks, followed by the main mathematical ideas and appendices with the rigorous proofs of our claims and some graph theory concepts, together with examples. We also show that there is no qualitative difference in the mean-field results if we require that a group of size q > 2, instead of a pair, of agreeing agents be formed before they attempt to convince other sites (for the mean field, this would coincide with the q-voter model).

Quantum critical scaling at a Bose-glass/superfluid transition: Theory and experiment for a model quantum magnet

In this paper we investigate the quantum phase transition from magnetic Bose Glass to magnetic Bose-Einstein condensation induced by amagnetic field in NiCl2 center dot 4SC(NH2)(2) (dichloro-tetrakis-thiourea-nickel, or DTN), doped with Br (Br-DTN) or site diluted. Quantum Monte Carlo simulations for the quantum phase transition of the model Hamiltonian for Br-DTN, as well as for site-diluted DTN, are consistent with conventional scaling at the quantum critical point and with a critical exponent z verifying the prediction z = d; moreover the correlation length exponent is found to be nu = 0.75(10), and the order parameter exponent to be beta = 0.95(10). We investigate the low-temperature thermodynamics at the quantum critical field of Br-DTN both numerically and experimentally, and extract the power-law behavior of the magnetization and of the specific heat. Our results for the exponents of the power laws, as well as previous results for the scaling of the critical temperature to magnetic ordering with the applied field, are incompatible with the conventional crossover-scaling Ansatz proposed by Fisher et al. [Phys. Rev. B 40, 546 (1989)]. However they can all be reconciled within a phenomenological Ansatz in the presence of a dangerously irrelevant operator.

The Aurora A and B kinases are up-regulated in bone marrow-derived chronic lymphocytic leukemia cells and represent potential therapeutic targets

Background The malignant B cells in chronic lymphocytic leukemia receive signals from the bone marrow and lymph node microenvironments which regulate their survival and proliferation. Characterization of these signals and the pathways that propagate them to the interior of the cell is important for the identification of novel potential targets for therapeutic intervention. Design and Methods We compared the gene expression profiles of chronic lymphocytic leukemia B cells purified from bone marrow and peripheral blood to identify genes that are induced by the bone marrow microenvironment. Two of the differentially expressed genes were further studied in cell culture experiments and in an animal model to determine whether they could represent appropriate therapeutic targets in chronic lymphocytic leukemia. Results Functional classification analysis revealed that the majority of differentially expressed genes belong to gene ontology categories related to cell cycle and mitosis. Significantly up-regulated genes in bone marrow-derived tumor cells included important cell cycle regulators, such as Aurora A and B, survivin and CDK6. Down-regulation of Aurora A and B by RNA interference inhibited proliferation of chronic lymphocytic leukemia-derived cell lines and induced low levels of apoptosis. A similar effect was observed with the Aurora kinase inhibitor VX-680 in primary chronic lymphocytic leukemia cells that were induced to proliferate by CpG-oligonucleotides and interleukin-2. Moreover, VX-680 significantly blocked leukemia growth in a mouse model of chronic lymphocytic leukemia. Conclusions Aurora A and B are up-regulated in proliferating chronic lymphocytic leukemia cells and represent potential therapeutic targets in this disease.

Thermodynamics of Ising model with infinite-range interactions by generalized canonical ensemble

In this work we present the idea of how generalized ensembles can be used to simplify the operational study of non-additive physical systems. As alternative of the usual methods of direct integration or mean-field theory, we show how the solution of the Ising model with infinite-range interactions is obtained by using a generalized canonical ensemble. We describe how the thermodynamical properties of this model in the presence of an external magnetic field are founded by simple parametric equations. Without impairing the usual interpretation, we obtain an identical critical behaviour as observed in traditional approaches.

A log-Birnbaum-Saunders regression model with asymmetric errors

This paper introduces a skewed log-Birnbaum-Saunders regression model based on the skewed sinh-normal distribution proposed by Leiva et al. [A skewed sinh-normal distribution and its properties and application to air pollution, Comm. Statist. Theory Methods 39 (2010), pp. 426-443]. Some influence methods, such as the local influence and generalized leverage, are presented. Additionally, we derived the normal curvatures of local influence under some perturbation schemes. An empirical application to a real data set is presented in order to illustrate the usefulness of the proposed model.

MODEL PARTITIONS AND COMPACT TEST CASE SUITES

We present a generalized test case generation method, called the G method. Although inspired by the W method, the G method, in contrast, allows for test case suite generation even in the absence of characterization sets for the specification models. Instead, the G method relies on knowledge about the index of certain equivalences induced at the implementation models. We show that the W method can be derived from the G method as a particular case. Moreover, we discuss some naturally occurring infinite classes of FSM models over which the G method generates test suites that are exponentially more compact than those produced by the W method.

Cannabidiol, a non-psychotropic plant-derived cannabinoid, decreases inflammation in a murine model of acute lung injury: Role for the adenosine A(2A) receptor

Acute lung injury is an inflammatory condition for which treatment is mainly supportive because effective therapies have not been developed. Cannabidiol, a non-psychotropic cannabinoid component of marijuana (Cannabis sativa), has potent immunosuppressive and anti-inflammatory properties. Therefore, we investigated the possible anti-inflammatory effect of cannabidiol in a murine model of acute lung injury. Analysis of total inflammatory cells and differential in bronchoalveolar lavage fluid was used to characterize leukocyte migration into the lungs; myeloperoxidase activity of lung tissue and albumin concentration in the bronchoalveolar lavage fluid were analyzed by colorimetric assays; cytokine/chemokine production in the bronchoalveolar lavage fluid was also analyzed by Cytometric Bead Arrays and Enzyme-Linked Immunosorbent Assay (ELISA). A single dose of cannabidiol (20 mg/kg) administered prior to the induction of LPS (lipopolysaccharide)-induced acute lung injury decreases leukocyte (specifically neutrophil) migration into the lungs, albumin concentration in the bronchoalveolar lavage fluid, myeloperoxidase activity in the lung tissue, and production of pro-inflammatory cytokines (TNF and IL-6) and chemokines (MCP-1 and MIP-2) 1, 2, and 4 days after the induction of LPS-induced acute lung injury. Additionally, adenosine A(2A) receptor is involved in the anti-inflammatory effects of cannabidiol on LPS-induced acute lung injury because ZM241385 (4-(2[7-Amino-2-(2-furyl)[1,2,4] triazolo[2,3-a][1,3,5]triazin-5-ylamino]ethyl) phenol) (a highly selective antagonist of adenosine A(2A) receptor) abrogated all of the anti-inflammatory effects of cannabidiol previously described. Thus, we show that cannabidiol has anti-inflammatory effects in a murine model of acute lung injury and that this effect is most likely associated with an increase in the extracellular adenosine offer and signaling through adenosine A(2A) receptor. (C) 2012 Elsevier B. V. All rights reserved.

Formaldehyde inhalation reduces respiratory mechanics in a rat model with allergic lung inflammation by altering the nitric oxide/cyclooxygenase-derived products relationship

Bronchial hyperresponsiveness is a hallmark of asthma and many factors modulate bronchoconstriction episodes. A potential correlation of formaldehyde (FA) inhalation and asthma has been observed; however, the exact role of FA remains controversial. We investigated the effects of FA inhalation on Ovalbumin (OVA) sensitisation using a parameter of respiratory mechanics. The involvement of nitric oxide (NO) and cyclooxygenase-derived products were also evaluated. The rats were submitted, or not, to FA inhalation (1%, 90 min/day, 3 days) and were OVA-sensitised and challenged 14 days later. Our data showed that previous FA exposure in allergic rats reduced bronchial responsiveness, respiratory resistance (Rrs) and elastance (Ers) to methacholine. FA exposure in allergic rats also increased the iNOS gene expression and reduced COX-1. L-NAME treatment exacerbated the bronchial hyporesponsiveness and did not modify the Ers and Rrs, while Indomethacin partially reversed all of the parameters studied. The L-NAME and Indomethacin treatments reduced leukotriene B4 levels while they increased thromboxane B2 and prostaglandin E2. In conclusion, FA exposure prior to OVA sensitisation reduces the respiratory mechanics and the interaction of NO and PGE2 may be representing a compensatory mechanism in order to protect the lung from bronchoconstriction effects.

Liquid-liquid equilibria in binary solutions formed by [Pyridinium-Derived][F4B] ionic liquids and alkanols:new experimental data and validation of a multiparametric model for correlating LLE data

[EN]Experimental solubility data are presented for a set of binary systems composed of ionic liquids (IL) derived from pyridium, with the tetrafluoroborate anion, and normal alcohols ranging from ethanol to decanol, in the temperature interval of 275 420 K, at atmospheric pressure. For each case, the miscibility curve and the upper critical solubility temperature (UCST) values are presented. The effects of the ILs on the behavior of solutions with alkanols are analyzed, paying special attention to the pyridine derivatives, and considering a series of structural characteristics of the compounds involved.

Non-Markovian stochastic processes and their applications: from anomalous diffusion to time series analysis

This work provides a forward step in the study and comprehension of the relationships between stochastic processes and a certain class of integral-partial differential equation, which can be used in order to model anomalous diffusion and transport in statistical physics. In the first part, we brought the reader through the fundamental notions of probability and stochastic processes, stochastic integration and stochastic differential equations as well. In particular, within the study of H-sssi processes, we focused on fractional Brownian motion (fBm) and its discrete-time increment process, the fractional Gaussian noise (fGn), which provide examples of non-Markovian Gaussian processes. The fGn, together with stationary FARIMA processes, is widely used in the modeling and estimation of long-memory, or long-range dependence (LRD). Time series manifesting long-range dependence, are often observed in nature especially in physics, meteorology, climatology, but also in hydrology, geophysics, economy and many others. We deepely studied LRD, giving many real data examples, providing statistical analysis and introducing parametric methods of estimation. Then, we introduced the theory of fractional integrals and derivatives, which indeed turns out to be very appropriate for studying and modeling systems with long-memory properties. After having introduced the basics concepts, we provided many examples and applications. For instance, we investigated the relaxation equation with distributed order time-fractional derivatives, which describes models characterized by a strong memory component and can be used to model relaxation in complex systems, which deviates from the classical exponential Debye pattern. Then, we focused in the study of generalizations of the standard diffusion equation, by passing through the preliminary study of the fractional forward drift equation. Such generalizations have been obtained by using fractional integrals and derivatives of distributed orders. In order to find a connection between the anomalous diffusion described by these equations and the long-range dependence, we introduced and studied the generalized grey Brownian motion (ggBm), which is actually a parametric class of H-sssi processes, which have indeed marginal probability density function evolving in time according to a partial integro-differential equation of fractional type. The ggBm is of course Non-Markovian. All around the work, we have remarked many times that, starting from a master equation of a probability density function f(x,t), it is always possible to define an equivalence class of stochastic processes with the same marginal density function f(x,t). All these processes provide suitable stochastic models for the starting equation. Studying the ggBm, we just focused on a subclass made up of processes with stationary increments. The ggBm has been defined canonically in the so called grey noise space. However, we have been able to provide a characterization notwithstanding the underline probability space. We also pointed out that that the generalized grey Brownian motion is a direct generalization of a Gaussian process and in particular it generalizes Brownain motion and fractional Brownain motion as well. Finally, we introduced and analyzed a more general class of diffusion type equations related to certain non-Markovian stochastic processes. We started from the forward drift equation, which have been made non-local in time by the introduction of a suitable chosen memory kernel K(t). The resulting non-Markovian equation has been interpreted in a natural way as the evolution equation of the marginal density function of a random time process l(t). We then consider the subordinated process Y(t)=X(l(t)) where X(t) is a Markovian diffusion. The corresponding time-evolution of the marginal density function of Y(t) is governed by a non-Markovian Fokker-Planck equation which involves the same memory kernel K(t). We developed several applications and derived the exact solutions. Moreover, we considered different stochastic models for the given equations, providing path simulations.

The Libor Market Model: from theory to calibration

This thesis is focused on the financial model for interest rates called the LIBOR Market Model. In the appendixes, we provide the necessary mathematical theory. In the inner chapters, firstly, we define the main interest rates and financial instruments concerning with the interest rate models, then, we set the LIBOR market model, demonstrate its existence, derive the dynamics of forward LIBOR rates and justify the pricing of caps according to the Black’s formula. Then, we also present the Swap Market Model, which models the forward swap rates instead of the LIBOR ones. Even this model is justified by a theoretical demonstration and the resulting formula to price the swaptions coincides with the Black’s one. However, the two models are not compatible from a theoretical point. Therefore, we derive various analytical approximating formulae to price the swaptions in the LIBOR market model and we explain how to perform a Monte Carlo simulation. Finally, we present the calibration of the LIBOR market model to the markets of both caps and swaptions, together with various examples of application to the historical correlation matrix and the cascade calibration of the forward volatilities to the matrix of implied swaption volatilities provided by the market.

A soft-tetramer model for diblock copolymer melts: structure formation and geometric characterization

The ability of block copolymers to spontaneously self-assemble into a variety of ordered nano-structures not only makes them a scientifically interesting system for the investigation of order-disorder phase transitions, but also offers a wide range of nano-technological applications. The architecture of a diblock is the most simple among the block copolymer systems, hence it is often used as a model system in both experiment and theory. We introduce a new soft-tetramer model for efficient computer simulations of diblock copolymer melts. The instantaneous non-spherical shape of polymer chains in molten state is incorporated by modeling each of the two blocks as two soft spheres. The interactions between the spheres are modeled in a way that the diblock melt tends to microphase separate with decreasing temperature. Using Monte Carlo simulations, we determine the equilibrium structures at variable values of the two relevant control parameters, the diblock composition and the incompatibility of unlike components. The simplicity of the model allows us to scan the control parameter space in a completeness that has not been reached in previous molecular simulations.The resulting phase diagram shows clear similarities with the phase diagram found in experiments. Moreover, we show that structural details of block copolymer chains can be reproduced by our simple model.We develop a novel method for the identification of the observed diblock copolymer mesophases that formalizes the usual approach of direct visual observation,using the characteristic geometry of the structures. A cluster analysis algorithm is used to determine clusters of each component of the diblock, and the number and shape of the clusters can be used to determine the mesophase.We also employ methods from integral geometry for the identification of mesophases and compare their usefulness to the cluster analysis approach.To probe the properties of our model in confinement, we perform molecular dynamics simulations of atomistic polyethylene melts confined between graphite surfaces. The results from these simulations are used as an input for an iterative coarse-graining procedure that yields a surface interaction potential for the soft-tetramer model. Using the interaction potential derived in that way, we perform an initial study on the behavior of the soft-tetramer model in confinement. Comparing with experimental studies, we find that our model can reflect basic features of confined diblock copolymer melts.

Multidimensional item response theory models with general and specific latent traits for ordinal data

The aim of the thesis is to propose a Bayesian estimation through Markov chain Monte Carlo of multidimensional item response theory models for graded responses with complex structures and correlated traits. In particular, this work focuses on the multiunidimensional and the additive underlying latent structures, considering that the first one is widely used and represents a classical approach in multidimensional item response analysis, while the second one is able to reflect the complexity of real interactions between items and respondents. A simulation study is conducted to evaluate the parameter recovery for the proposed models under different conditions (sample size, test and subtest length, number of response categories, and correlation structure). The results show that the parameter recovery is particularly sensitive to the sample size, due to the model complexity and the high number of parameters to be estimated. For a sufficiently large sample size the parameters of the multiunidimensional and additive graded response models are well reproduced. The results are also affected by the trade-off between the number of items constituting the test and the number of item categories. An application of the proposed models on response data collected to investigate Romagna and San Marino residents' perceptions and attitudes towards the tourism industry is also presented.