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This paper presents a prototype of a portable and modular electrical impedance tomography (EIT) system for breast tumor detection. The proposed system uses MATLAB to generate three-dimensional representations of breast tissue. The modular architecture of the system allows for flexible customization and scalability. It consists of several interconnected modules. Each module can be easily replaced or upgraded, facilitating system maintenance and future enhancements. Testing of the prototype has shown promising results in preliminary screening based on experimental studies. Agar models were used for the experimental stage of this project. The 3D representations provide clinicians with valuable information for accurate diagnosis and treatment planning. Further research and refinement of the system is warranted to validate its performance in future clinical trials. [ABSTRACT FROM AUTHOR]

We revisit a deterministic model for studying the dynamics of allelopathy. The model is formulated in terms of a non-homogeneous linear system of differential equations whose forcing or source term is a piecewise constant function (square wave). To account for the inherent uncertainties present in this natural phenomenon, we reformulate the model as a system of random differential equations where all model parameters and the initial condition are assumed to be random variables, while the forcing term is a stochastic process. Taking extensive advantage of the so-called Random Variable Transformation (RVT) method, we obtain the solution of the randomized model by providing explicit expressions of the first probability density function of the solution under very general assumptions on the model data. We also determine the joint probability density function of the non-trivial equilibrium point, which is a random vector. If the source term is a time-dependent stochastic process, the RVT method might not be applicable since no explicit solution of the model is available. We then show an alternative approach to overcome this drawback by applying the Liouville–Gibbs partial differential equation. All the theoretical findings are illustrated through several examples, including the application of the randomized model to real-world data on alkaloid contents from leaching thornapple seed. [ABSTRACT FROM AUTHOR]

The Random Variable Transformation (RVT) technique has been applied in recent years to analyze a wide variety of dynamic models formulated via random differential equations. The applicability of this technique has usually been focused on problems where an explicit solution of the underlying deterministic problem is available. This fact limits the usefulness of the RVT method. This note aims to point out that the RVT technique can be successfully applied without this requirement by showing a wider range of potential applications including very general classes of single-species models. [ABSTRACT FROM AUTHOR]

Copyright of E-balonmano.com: Journal of Sports Science / Revista de Ciencias del Deporte is the property of Federacion Extremena de Balonmano and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We study, from a probabilistic standpoint, a full randomization of nonhomogeneous second‐order linear difference equations assuming that its data (initial conditions, coefficients, and forcing term) are random variables. Our analysis consists of computing the so‐called first probability density function of the solution, which is a stochastic process, and then analyzing the stability of the solution assuming that all data have an arbitrary joint probability density function. To achieve these goals, we take extensive advantage of the so‐called random variable transformation technique. The theoretical results extend their deterministic counterpart, and then, they have many applications in real‐world problems where uncertainty plays a key role. Our findings are first illustrated by means of several numerical examples, where different simulations are carried out, and, second, by means of a model belonging to biomathematics. [ABSTRACT FROM AUTHOR]

Fuzzy systems are widely used in most fields of science and engineering, mainly because the models they produce are robust, accurate, easy to evaluate and capture real-world uncertainty better than do the classical alternatives. We propose a new methodology for structure and parameter tuning of Takagi–Sugeno–Kang fuzzy models using several optimization techniques. The output parameters are determined analytically, by finding the minimum of the root-mean-square error (RMSE) for a properly defined error function. The membership functions are simplified by considering symmetry and equispacing, to reduce the optimization problem of finding their parameters, and allow it to be carried out using the numerical method of gradient descent. Both algorithms are fast enough to finally implement a strategy based on the hill climbing approach to finding the optimal structure (number and type of membership functions) of the fuzzy system. The effectiveness of the proposed strategy is shown by comparing its performance, using four case studies found in current relevant works, to the popular adaptive network-based fuzzy inference system (ANFIS), and to other recently published strategies based on evolutionary fuzzy models. In terms of the RMSE, performance was at least 28% better in all case studies. [ABSTRACT FROM AUTHOR]

We extend the study of the random Hermite second-order ordinary differential equation to the fractional setting. We first construct a random generalized power series that solves the equation in the mean square sense under mild hypotheses on the random inputs (coefficients and initial conditions). From this representation of the solution, which is a parametric stochastic process, reliable approximations of the mean and the variance are explicitly given. Then, we take advantage of the random variable transformation technique to go further and construct convergent approximations of the first probability density function of the solution. Finally, several numerically simulations are carried out to illustrate the broad applicability of our theoretical findings. [ABSTRACT FROM AUTHOR]

In recent years, invasive fungal infections have emerged as a common source of infections in immunosuppressed patients. All fungal cells are surrounded by a cell wall that is essential for cell integrity and survival. It prevents cell death and lysis resulting from high internal turgor pressure. Since the cell wall is not present in animal cells, it is an ideal target for selective invasive fungal infection treatments. The antifungal family known as echinocandins, which specifically inhibit the synthesis of the cell wall β(13)glucan, has been established as an alternative treatment for mycoses. To explore the mechanism of action of these antifungals, we analyzed the cell morphology and glucan synthases localization in Schizosaccharomyces pombe cells during the initial times of growth in the presence of the echinocandin drug caspofungin. S. pombe are rod-shaped cells that grow at the poles and divide by a central division septum. The cell wall and septum are formed by different glucans, which are synthesized by four essential glucan synthases: Bgs1, Bgs3, Bgs4, and Ags1. Thus, S. pombe is not only a perfect model for studying the synthesis of the fungal β(1-3)glucan, but also it is ideal for examining the mechanisms of action and resistance of cell wall antifungals. Herein, we examined the cells in a drug susceptibility test in the presence of either lethal or sublethal concentrations of caspofungin, finding that exposure to the drug for long periods at high concentrations (>10 µg/mL) induced cell growth arrest and the formation of rounded, swollen, and dead cells, whereas low concentrations (<10 µg/mL) permitted cell growth with a mild effect on cell morphology. Interestingly, short-term treatments with either high or low concentrations of the drug induced effects contrary to those observed in the susceptibility tests. Thus, low drug concentrations induced a cell death phenotype that was not observed at high drug concentrations, which caused transient fungistatic cell growth arrest. After 3 h, high concentrations of the drug caused the following: (i) a decrease in the GFP-Bgs1 fluorescence level; (ii) altered locations of Bgs3, Bgs4, and Ags1; and (iii) a simultaneous accumulation of cells with calcofluor-stained incomplete septa, which at longer times resulted in septation uncoupling from plasma membrane ingression. The incomplete septa revealed with calcofluor were found to be complete when observed via the membrane-associated GFP-Bgs or Ags1-GFP. Finally, we found that the accumulation of incomplete septa depended on Pmk1, the last kinase of the cell wall integrity pathway. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Derecho de la Seguridad Social, Laborum is the property of Ediciones Laborum S.L. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper addresses the probabilistic analysis of the deflection of a cantilever beam by means of a randomization of the classical governing fourth-order differential equation with null boundary conditions. The probabilistic study is based on the calculation of the first probability density function of the solution, which is a stochastic process, as well as the density function of further quantities of interest associated with this engineering problem such as the maximum slope and deflection at the free end of the cantilever beam, that are treated as random variables. In addition, the probability density function of the bending moment and the shear force will also be computed. The study takes extensive advantage of the so called Random Variable Transformation method, also known as Probability Transformation Method, that allows us to fully unify the probabilistic analysis in three relevant cases commonly studied in the deterministic setting. All the theoretical findings are illustrated via detailed numerical examples corresponding to each one of the three scenarios. [ABSTRACT FROM AUTHOR]

Zearalenone (ZON), zearalanone (ZAN) and their phase I metabolites: α-zearalenol (α-ZOL), β-zearalenol (β-ZOL), α-zearalalanol (α-ZAL) and β-zearalalanol (β-ZAL) are compounds with estrogenic activity that are metabolized and distributed by the circulatory system in animals and can access the food chain through meat products from livestock. Furthermore, biomonitoring of zearalenones in biological matrices can provide useful information to directly assess mycotoxin exposure; therefore, their metabolites may be suitable biomarkers. The aim of this study was to determine the presence of ZON, ZAN and their metabolites in alternative biological matrices, such as liver, from three different animals: chicken, pig and lamb, in order to evaluate their exposure. A solid–liquid extraction procedure coupled to a GC-MS/MS analysis was performed. The results showed that 69% of the samples were contaminated with at least one mycotoxin or metabolite at varying levels. The highest value (max. 152.62 ng/g of β-ZOL) observed, and the most contaminated livers (42%), were the chicken liver samples. However, pig liver samples presented a high incidence of ZAN (33%) and lamb liver samples presented a high incidence of α-ZOL (40%). The values indicate that there is exposure to these mycotoxins and, although the values are low (ranged to 0.11–152.6 ng/g for α-ZOL and β-ZOL, respectively), analysis and continuous monitoring are necessary to avoid exceeding the regulatory limits and to control the presence of these mycotoxins in order to protect animal and human health. [ABSTRACT FROM AUTHOR]

Nowadays, the bakery industry includes different bioactive ingredients to enrich the nutritional properties of its products, such as betalains from red beetroot (Beta vulgaris). However, cereal products are considered a major route of exposure to many mycotoxins, both individually and in combination, due to their daily consumption, if the cereals used contain these toxins. Only the fraction of the contaminant that is released from the food is bioaccessible and bioavailable to produce toxic effects. Foods with bioactive compounds vary widely in chemical structure and function, and some studies have demonstrated their protective effects against toxics. In this study the bioaccessibility and bioavailability of three legislated mycotoxins (AFB1, OTA and ZEN), individual and combined, in two breads, one with wheat flour and the other with wheat flour enriched with 20% Beta vulgaris, were evaluated. Bioaccessibility of these three mycotoxins from wheat bread and red beet bread enriched individually at 100 ng/g was similar between the breads: 16% and 14% for AFB1, 16% and 17% for OTA and 26% and 22% for ZEN, respectively. Whereas, when mycotoxins were co-present these values varied with a decreasing tendency: 9% and 15% for AFB1, 13% and 9% for OTA, 4% and 25% for ZEN in wheat bread and in red beet bread, respectively. These values reveal that the presence of other components and the co-presence of mycotoxins can affect the final bioavailability; however, it is necessary to assess this process with in vivo studies to complete the studies. [ABSTRACT FROM AUTHOR]

Impedance measuring acquisition systems focused on breast tumor detection, as well as image processing techniques for 3D imaging, are reviewed in this paper in order to define potential opportunity areas for future research. The description of reported works using electrical impedance tomography (EIT)-based techniques and methodologies for 3D bioimpedance imaging of breast tissues with tumors is presented. The review is based on searching and analyzing related works reported in the most important research databases and is structured according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) parameters and statements. Nineteen papers reporting breast tumor detection and location using EIT were systematically selected and analyzed in this review. Clinical trials in the experimental stage did not produce results in most of analyzed proposals (about 80%), wherein statistical criteria comparison was not possible, such as specificity, sensitivity and predictive values. A 3D representation of bioimpedance is a potential tool for medical applications in malignant breast tumors detection being capable to estimate an ap-proximate the tumor volume and geometric location, in contrast with a tumor area computing capacity, but not the tumor extension depth, in a 2D representation. [ABSTRACT FROM AUTHOR]

An epidemiological model is proposed to study the transmission dynamics of the herpes virus type 2, a sexually transmitted infectious disease. This model considers two states, susceptible and infected, divides the population into sexes, assumes only heterosexual contacts and includes different transmission rates depending on whether the transmission is woman–man or man–woman. Reported and prevalence series data are retrieved from several sources. We consider the inherent data survey errors and the sensitivity of the diagnosis tests (data uncertainty). To calibrate the model to the available data and their uncertainty, a novel technique is proposed in two steps: (1) the application of the estimation of distribution algorithm (EDA) to find sets of model parameter values close to the data uncertainty and (2) the application of a selection algorithm to get a reduced number of model parameter values whose model outputs capture accurately the data uncertainty. Then, we check its robustness, and we provide a prediction of the evolution of the infected over the next 4 years. From the technical point of view, we conclude that the proposed technique to calibrate probabilistically the model is reliable and robust. Also, it is able to provide confidence intervals for the model parameter values and the predictions. From the medical point of view, the model returns that the transmission woman–man is higher than the man–woman, according to recent literature, and there is a mild increasing trend in the number of infected people over the next years. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to deal with the randomness associated to ecological modelling. Data variability makes it necessary to analyse the impact of random perturbations on the fitted model parameters. We conduct such analysis for the logistic growth model with a certain sigmoid functional form of the carrying capacity, which was proposed in the literature for the study of parasite growth during infection. We show how the probability distributions of the parameters are set via the maximum entropy principle. Then the random variable transformation method allows for computing the density function of the population. [ABSTRACT FROM AUTHOR]

This paper deals with the random wave equation on a bounded domain with Dirichlet boundary conditions. Randomness arises from the velocity wave, which is a positive random variable, and the two initial conditions, which are regular stochastic processes. The aleatory nature of the inputs is mainly justified from data errors when modeling the motion of a vibrating string. Uncertainty is propagated from these inputs to the output, so that the solution becomes a smooth random field. We focus on the mean square contextualization of the problem. Existence and uniqueness of the exact series solution, based upon the classical method of separation of variables, are rigorously established. Exact series for the mean and the variance of the solution process are obtained, which converge at polynomial rate. Some numerical examples illustrate these facts. [ABSTRACT FROM AUTHOR]

In this work, we consider control problems represented by a linear differential equation assuming that all the coefficients are random variables and with an additive control that is a stochastic process. Specifically, we will work with controllable problems in which the initial condition and the final target are random variables. The probability density function of the solution and the control has been calculated. The theoretical results have been applied to study, from a probabilistic standpoint, a damped oscillator. [ABSTRACT FROM AUTHOR]

In this work we consider a particular randomized kinetic model for reaction–deactivation of hydrogen peroxide decomposition. We apply the random variable transformation technique to obtain the first probability density function of the solution stochastic process under general conditions. From the first probability density function, we can obtain fundamental statistical information, such as the mean and the variance of the solution, at every instant time. The transformation considered in the application of the random variable transformation technique is not unique. Then, the first probability density function can take different expressions, although essentially equivalent in terms of computing probabilistic information. To motivate this fact, we consider in our analysis two different mappings. Several numerical examples show the capability of our approach and of the obtained results as well. We show, through simulations, that the choice of the transformation, that permits computing the first probability density function, is a crucial issue regarding the computational time. [ABSTRACT FROM AUTHOR]

Copyright of Revista Internacional y Comparada de Relaciones Laborales y Derecho del Empleo is the property of ADAPT University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The variability of the data and the incomplete knowledge of the true physics require the incorporation of randomness into the formulation of mathematical models. In this setting, the deterministic numerical methods cannot capture the propagation of the uncertainty from the inputs to the model output. For some problems, such as the Burgers' equation (simplification to understand properties of the Navier–Stokes equations), a small variation in the parameters causes nonnegligible changes in the output. Thus, suitable techniques for uncertainty quantification must be used. The generalized polynomial chaos (gPC) method has been successfully applied to compute the location of the transition layer of the steady‐state solution, when a small uncertainty is incorporated into the boundary. On the contrary, the classical perturbation method does not give reliable results, due to the uncertainty magnitude of the output. We propose a modification of the perturbation method that converges and is comparable with the gPC approach in terms of efficiency and rate of convergence. The method is even applicable when the input random parameters are dependent random variables. [ABSTRACT FROM AUTHOR]

Background: The fungal cell wall is an essential and robust external structure that protects the cell from the environment. It is mainly composed of polysaccharides with different functions, some of which are necessary for cell integrity. Thus, the process of fractionation and analysis of cell wall polysaccharides is useful for studying the function and relevance of each polysaccharide, as well as for developing a variety of practical and commercial applications. This method can be used to study the mechanisms that regulate cell morphogenesis and integrity, giving rise to information that could be applied in the design of new antifungal drugs. Nonetheless, for this method to be reliable, the availability of trustworthy commercial recombinant cell wall degrading enzymes with non-contaminating activities is vital. Results: Here we examined the efficiency and reproducibility of 12 recombinant endo-β(1,3)-d-glucanases for specifically degrading the cell wall β(1,3)-d-glucan by using a fast and reliable protocol of fractionation and analysis of the fission yeast cell wall. This protocol combines enzymatic and chemical degradation to fractionate the cell wall into the four main polymers: galactomannoproteins, α-glucan, β(1,3)-d-glucan and β(1,6)-d-glucan. We found that the GH16 endo-β(1,3)-d-glucanase PfLam16A from Pyrococcus furiosus was able to completely and reproducibly degrade β(1,3)-d-glucan without causing the release of other polymers. The cell wall degradation caused by PfLam16A was similar to that of Quantazyme, a recombinant endo-β(1,3)-d-glucanase no longer commercially available. Moreover, other recombinant β(1,3)-d-glucanases caused either incomplete or excessive degradation, suggesting deficient access to the substrate or release of other polysaccharides. Conclusions: The discovery of a reliable and efficient recombinant endo-β(1,3)-d-glucanase, capable of replacing the previously mentioned enzyme, will be useful for carrying out studies requiring the digestion of the fungal cell wall β(1,3)-d-glucan. This new commercial endo-β(1,3)-d-glucanase will allow the study of the cell wall composition under different conditions, along the cell cycle, in response to environmental changes or in cell wall mutants. Furthermore, this enzyme will also be greatly valuable for other practical and commercial applications such as genome research, chromosomes extraction, cell transformation, protoplast formation, cell fusion, cell disruption, industrial processes and studies of new antifungals that specifically target cell wall synthesis. [ABSTRACT FROM AUTHOR]

We study a class of single-degree-of-freedom oscillators whose restoring function is affected by small nonlinearities and excited by stationary Gaussian stochastic processes. We obtain, via the stochastic perturbation technique, approximations of the main statistics of the steady state, which is a random variable, including the first moments, and the correlation and power spectral functions. Additionally, we combine this key information with the principle of maximum entropy to construct approximations of the probability density function of the steady state. We include two numerical examples where the advantages and limitations of the stochastic perturbation method are discussed with regard to certain general properties that must be preserved. [ABSTRACT FROM AUTHOR]

The Baranyi--Roberts model describes the dynamics of the volumetric densities of two interacting cell populations. We randomize this model by considering that the initial conditions are random variables whose distributions are determined by using sample data and the principle of maximum entropy. Subsequenly, we obtain the Liouville--Gibbs partial differential equation for the probability density function of the two-dimensional solution stochastic process. Because the exact solution of this equation is unaffordable, we use a finite volume scheme to numerically approximate the aforementioned probability density function. From this key information, we design an optimization procedure in order to determine the best growth rates of the Baranyi--Roberts model, so that the expectation of the numerical solution is as close as possible to the sample data. The results evidence good fitting that allows for performing reliable predictions. [ABSTRACT FROM AUTHOR]

Taking into account the public health importance of the human papillomavirus (HPV) control in the future, it is mandatory to assess the effect of the vaccination campaigns on the control of HPV spread and the associated diseases using reliable mathematical models. We propose a computational random network model with the aim of studying the transmission dynamics of HPV infections. This model reflects the herd immunity effect in the heterosexual network more accurately than the classical models. We perform a sensitivity analysis of the sexual behavior changes consisting of increasing the number of men who have sex with men (MSM), increasing the frequency of the intercourses and increasing the number of sexual partners. We find that large changes in the sexual behavior, in some extent, only have minor effects on the decline of the HPV infections in women and men in the current vaccination campaign in Spain (vaccination of young girls with a coverage of 70%). Therefore, the current vaccination program in Spain is robust for the heterosexuals. However, we cannot say the same for MSM, where they do not benefit by the herd immunity effect of the vaccination of girls, and consequently, the circulation of the virus among them remains unchanged. A consequence of the present study is that the effect of other external factors that may affect the transmission dynamics of the HPV, for instance, the tourism or the immigration, does not influence the protection provided by the current Spanish vaccination program. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the randomized generalized diffusion equation with delay: ut(t, x) = a2uxx(t, x) + b2uxx(t − τ, x), t > τ, 0 ≤ x ≤ l; u(t,0)=u(t,l)=0, t ≥ 0; u(t,x)=φ(t,x), 0 ≤ t ≤ τ, 0 ≤ x ≤ l. Here, τ > 0 and l > 0 are constant. The coefficients a2 and b2 are nonnegative random variables, and the initial condition φ(t, x) and the solution u(t, x) are random fields. The separation of variables method develops a formal series solution. We prove that the series satisfies the delay diffusion problem in the random Lebesgue sense rigorously. By truncating the series, the expectation and the variance of the random‐field solution can be approximated. [ABSTRACT FROM AUTHOR]

The time evolution of microorganisms, such as bacteria, is of great interest in biology. In the article by D. Stanescu et al. [Electronic Transactions on Numerical Analysis, 34, 44–58 (2009)], a logistic model was proposed to model the growth of anaerobic photosynthetic bacteria. In the laboratory experiment, actual data for two species of bacteria were considered: Rhodobacter capsulatus and Chlorobium vibrioforme. In this paper, we suggest a new nonlinear model by assuming that the population growth rate is not proportional to the size of the bacteria population, but to the number of interactions between the microorganisms, and by taking into account the beginning of the death phase in the kinetic curve. Stanescu et al. evaluated the effect of randomness into the model coefficients by using generalized polynomial chaos (gPC) expansions, by setting arbitrary distributions without taking into account the likelihood of the data. By contrast, we utilize a Bayesian inverse approach for parameter estimation to obtain reliable posterior distributions for the random input coefficients in both the logistic and our new model. Since our new model does not possess an explicit solution, we use gPC expansions to construct the Bayesian model and to accelerate the Markov chain Monte Carlo algorithm for the Bayesian inference. [ABSTRACT FROM AUTHOR]

In this work, we study the full randomized versions of Airy, Hermite and Laguerre differential equations, which depend on a random variable appearing as an equation coefficient as well as two random initial conditions. In previous contributions, the mean square stochastic solutions to the aforementioned random differential equations were constructed via the Fröbenius method, under the assumption of exponential growth of the absolute moments of the equation coefficient, which is equivalent to its essential boundedness. In this paper we aim at relaxing the boundedness hypothesis to allow more general probability distributions for the equation coefficient. We prove that the equations are solvable in the mean square sense when the equation coefficient has finite moment-generating function in a neighborhood of the origin. A thorough discussion of the new hypotheses is included. [ABSTRACT FROM AUTHOR]

Copyright of Colombian Journal of Anesthesiology / Revista Colombiana de Anestesiología is the property of Sociedad Colombiana de Anestesiologia y Reanimacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we address the problem of approximating the probability density function of the following random logistic differential equation: P′(t,ω)=A(t,ω)(1−P(t,ω))P(t,ω), t∈[t0,T], P(t0,ω)=P0(ω), where ω is any outcome in the sample space Ω. In the recent contribution [Cortés, JC, et al. Commun Nonlinear Sci Numer Simulat 2019; 72: 121–138], the authors imposed conditions on the diffusion coefficient A(t) and on the initial condition P0 to approximate the density function f1(p,t) of P(t): A(t) is expressed as a Karhunen–Loève expansion with absolutely continuous random coefficients that have certain growth and are independent of the absolutely continuous random variable P0, and the density of P0, fP0, is Lipschitz on (0,1). In this article, we tackle the problem in a different manner, by using probability tools that allow the hypotheses to be less restrictive. We only suppose that A(t) is expanded on L2([t0,T]×Ω), so that we include other expansions such as random power series. We only require absolute continuity for P0, so that A(t) may be discrete or singular, due to a modified version of the random variable transformation technique. For fP0, only almost everywhere continuity and boundedness on (0,1) are needed. We construct an approximating sequence {f1N(p,t)}N=1∞ of density functions in terms of expectations that tends to f1(p,t) pointwise. Numerical examples illustrate our theoretical results. [ABSTRACT FROM AUTHOR]

The article examines that several contributions are offered for a study of social protection from different perspectives: economic, sociological but especially legal. Topics include considered that the new mechanisms of social protection against poverty and social exclusion are analyzed but also analyzing the deficits and problems with respect to social protection with respect to certain groups integrated into the labor market.

The study of the dynamics of the size of a population via mathematical modelling is a problem of interest and widely studied. Traditionally, continuous deterministic methods based on differential equations have been used to deal with this problem. However, discrete versions of some models are also available and sometimes more adequate. In this paper, we randomize the Pielou logistic equation in order to include the inherent uncertainty in modelling. Taking advantage of the method of transformation of random variables, we provide a full probabilistic description to the randomized Pielou logistic model via the computation of the probability density functions of the solution stochastic process, the steady state, and the time until a certain level of population is reached. The theoretical results are illustrated by means of two examples: The first one consists of a numerical experiment and the second one shows an application to study the diffusion of a technology using real data. [ABSTRACT FROM AUTHOR]

This paper deals with the randomized heat equation defined on a general bounded interval [L1, L2] and with nonhomogeneous boundary conditions. The solution is a stochastic process that can be related, via changes of variable, with the solution stochastic process of the random heat equation defined on [0,1] with homogeneous boundary conditions. Results in the extant literature establish conditions under which the probability density function of the solution process to the random heat equation on [0,1] with homogeneous boundary conditions can be approximated. Via the changes of variable and the Random Variable Transformation technique, we set mild conditions under which the probability density function of the solution process to the random heat equation on a general bounded interval [L1, L2] and with nonhomogeneous boundary conditions can be approximated uniformly or pointwise. Furthermore, we provide sufficient conditions in order that the expectation and the variance of the solution stochastic process can be computed from the proposed approximations of the probability density function. Numerical examples are performed in the case that the initial condition process has a certain Karhunen‐Loève expansion, being Gaussian and non‐Gaussian. [ABSTRACT FROM AUTHOR]

In this paper, we retrieve data about the frequent users of electronic commerce during the period 2011–2016 from the Spanish National Institute of Statistics. These data, coming from surveys, have intrinsic uncertainty that we describe using appropriate random variables. Then, we propose a stochastic model to study the dynamics of frequent users of electronic commerce. The goal of this paper is to solve the inverse problem that consists of determining the model parameters as suitable parametric random variables, in such a way the model output be capable of capturing the data uncertainty, at the time instants where sample data are available, via adequate probability density functions. To achieve the aforementioned goal, we propose a computational procedure that involves building a nonlinear objective function, based on statistical moment measures, to be minimized using a variation of the particle swarm optimization algorithm. [ABSTRACT FROM AUTHOR]

Population dynamics models consisting of nonlinear difference equations allow us to get a better understanding of the processes involved in epidemiology. Usually, these mathematical models are studied under a deterministic approach. However, in order to take into account the uncertainties associated with the measurements of the model input parameters, a more realistic approach would be to consider these inputs as random variables. In this paper, we study the random time‐discrete epidemiological models SIS, SIR, SIRS, and SEIR using a powerful unified approach based upon the so‐called adaptive generalized polynomial chaos (gPC) technique. The solution to these random difference equations is a stochastic process in discrete time, which represents the number of susceptible, infected, recovered, etc individuals at each time step. We show, via numerical experiments, how adaptive gPC permits quantifying the uncertainty for the solution stochastic process of the aforementioned random time‐discrete epidemiological model and obtaining accurate results at a cheap computational expense. We also highlight how adaptive gPC can be applied in practice, by means of an example using real data. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to study insulin pump therapy and accurate monitoring of glucose levels in diabetic patients, which are current research trends in diabetology. Both problems have a wide margin for improvement and promising applications in the control of parameters and levels involved.Design/methodology/approach The authors have registered data for the levels of glucose in diabetic patients throughout a day with a temporal resolution of 5 minutes, the amount and time of insulin administered and time of ingestion. The estimated quantity of carbohydrates is also monitored. A mathematical model for Type 1 patients was fitted piecewise to these data and the evolution of the parameters was analyzed.Findings They have found that the parameters for the model change abruptly throughout a day for the same patient, but this set of parameters account with precision for the evolution of the glucose levels in the test patients. This fitting technique could be used to personalize treatments for specific patients and predict the glucose-level variations in terms of hours or even shorter periods of time. This way more effective insulin pump therapies could be developed.Originality/value The proposed model could allow for the development of improved schedules on insulin pump therapies. [ABSTRACT FROM AUTHOR]

Population dynamics models consisting of nonlinear difference equations allow us to get a better understanding of the processes involved in epidemiology. Usually, these mathematical models are studied under a deterministic approach. However, in order to take into account the uncertainties associated with the measurements of the model input parameters, a more realistic approach would be to consider these inputs as random variables. In this paper, we study the random time‐discrete epidemiological models SIS, SIR, SIRS, and SEIR using a powerful unified approach based upon the so‐called adaptive generalized polynomial chaos (gPC) technique. The solution to these random difference equations is a stochastic process in discrete time, which represents the number of susceptible, infected, recovered, etc individuals at each time step. We show, via numerical experiments, how adaptive gPC permits quantifying the uncertainty for the solution stochastic process of the aforementioned random time‐discrete epidemiological model and obtaining accurate results at a cheap computational expense. We also highlight how adaptive gPC can be applied in practice, by means of an example using real data. [ABSTRACT FROM AUTHOR]

It is widely accepted in eukaryotes that the cleavage furrow only initiates after mitosis completion. In fission yeast, cytokinesis requires the synthesis of a septum tightly coupled to cleavage furrow ingression. The current cytokinesis model establishes that simultaneous septation and furrow ingression only initiate after spindle breakage and mitosis exit. Thus, this model considers that although Cdk1 is inactivated at early-anaphase, septation onset requires the long elapsed time until mitosis completion and full activation of the Hippo-like SIN pathway. Here, we studied the precise timing of septation onset regarding mitosis by exploiting both the septum-specific detection with the fluorochrome calcofluor and the high-resolution electron microscopy during anaphase and telophase. Contrarily to the existing model, we found that both septum and cleavage furrow start to ingress at early anaphase B, long before spindle breakage, with a slow ingression rate during anaphase B, and greatly increasing after telophase onset. This shows that mitosis and cleavage furrow ingression are not concatenated but simultaneous events in fission yeast. We found that the timing of septation during early anaphase correlates with the cell size and is regulated by the corresponding levels of SIN Etd1 and Rho1. Cdk1 inactivation was directly required for timely septation in early anaphase. Strikingly the reduced SIN activity present after Cdk1 loss was enough to trigger septation by immediately inducing the medial recruitment of the SIN kinase complex Sid2-Mob1. On the other hand, septation onset did not depend on the SIN asymmetry establishment, which is considered a hallmark for SIN activation. These results recalibrate the timing of key cytokinetic events in fission yeast; and unveil a size-dependent control mechanism that synchronizes simultaneous nuclei separation with septum and cleavage furrow ingression to safeguard the proper chromosome segregation during cell division. [ABSTRACT FROM AUTHOR]

In this work, a commercial hybrid maize was used, and the effect of selected mineral nutrients [sulfur (S), phosphorus (P), iron (Fe), and nitrogen (N)] on the chromium (Cr) captured by plants was analysed. Seeds were germinated in vitro, and plants were transferred to pots with soil supplemented with a growth inhibitory Cr concentration and different mineral nutrient combinations. Plants were grown for 13 weeks and irrigated with water. In the harvested plants, total Cr was determined considering the Cr concentration in tissues and the biomass as dry weight. The soil supplemented with phosphorus (P) or Fe improved the total Cr captured by plants by a quarter. Bioconcentration values in plant tissues were similar to those of other plants grown in soil with Cr. This information will be useful either for Cr phytoremediation or to protect commercial maize crops cultivated in Cr contaminated soils. [ABSTRACT FROM AUTHOR]

The mobile applications business is a really big market, growing constantly. In app marketing, a key issue is to predict future app installations. The influence of the peers seems to be very relevant when downloading apps. Therefore, the study of the evolution of mobile apps spread may be approached using a proper network model that considers the influence of peers. Influence of peers and other social contagions have been successfully described using models of epidemiological type. Hence, in this paper we propose an epidemiological random network model with realistic parameters to predict the evolution of downloads of apps. With this model, we are able to predict the behavior of an app in the market in the short term looking at its evolution in the early days of its launch. The numerical results provided by the proposed network are compared with data from real apps. This comparison shows that predictions improve as the model is fed back. Marketing researchers and strategy business managers can benefit from the proposed model since it can be helpful to predict app behavior over the time anticipating the spread of an app. [ABSTRACT FROM AUTHOR]

This paper deals with the construction of mean square analytic-numerical solution of parabolic partial differential problems where both initial condition and coefficients are stochastic processes. By using a random Fourier transform, an in finite integral form of the solution stochastic process is firstly obtained. Afterwards, explicit expressions for the expectation and standard deviation of the solution are obtained. Since these expressions depend upon random improper integrals, which are not computable in an exact manner, random Gauss-Hermite quadrature formulae are introduced throughout an illustrative example. [ABSTRACT FROM AUTHOR]

Introduction: Frailty is a major health concern associated with aging and with an increased vulnerability to adverse outcomes. The rising health care costs associated with fragile patients represents a threat to National Healthcare Systems due to the limited resources. One approach to this challenge is to consider the development of artificial intelligence (AI) based tools aimed at the redesigning care for frail older people, stratified by Rockwood grade [1]. This study aimed to the development of the SmartChronic platform that integrates a set of data-driven tools to both proactively identify frail older patients at risk of non-programmed hospital readmission and predict hospital clinical pathways for optimal use of limited healthcare resources. Methodology: Retrospective data of the present study came from the electronic healthcare record of La Fe Health Department (Valencia, Spain) that has deployed a Real-World Data analysis platform based on SAS® Software Analytics infrastructure, effectively organized reaching stage 6 in the eight-stage (0-7) EMRAM maturity model. Currently, the data lake layer includes structured and semi-structured records updated daily, coming from several information systems involving clinical activity, and is composed by the aggregation of 22 DataMart comprising 750 million rows, 84 tables and 4.064 columns. Gradient Boosted Trees has been used to develop the 30-day hospital readmission risk model and logistic regression for the development of the pathway prediction model. The study was approved in 2020 by the Ethical Clinical Research Committee of La FE Hospital (Reference number 2020-138-1). Results: The 30-day hospital readmission risk model shows the value as a risk percentage, with reasonable value of 0.665 for the area under the ROC curve. Regarding the pathway prediction model, the algorithm implemented can be applied iteratively at evolutive states of the clinical pathway. These states are determined by a sequence of transition events, that once validated, establish the starting point of a new step of processing. This way, we have developed a novel framework for modelling patient flow through various hospital services. The Accuracy value of the model is 0.40. Both models are integrated into a global SmartChronic platform. Conclusions: A moderately discriminative real-time 30-day readmission and patient pathway predictive models can aid the development, implementation, description and evaluation of integrated care programs for frailty patients with chronic diseases. The successful development of these tools required the establishment of an interdisciplinary collaboration among academia, industry and healthcare providers. Implications for applicability: This technology allows studying big database of patients, contributing to the development of the concept of personalized medicine and to optimize integrated care. Platforms such as SmartChronic would also improve the decision-making of health policy makers. This study has been carried out in a single hospital, it would have to be analyzed if the results can be ex-trapolated to other hospitals. Funding: This research was funded by the Valencian Innovation Agency, Generalitat Valenciana (INNEST/2020/47). [ABSTRACT FROM AUTHOR]

A probabilistic model is proposed to study the transmission dynamics of the cocaine consumption in Spain during the period of 1995–2011. Using the so-called probabilistic fitting technique, we study if the model is able to capture the data uncertainty coming from surveys. The proposed model is formulated through a nonlinear system of difference equations whose coefficients are treated as stochastic processes. A discussion regarding the usefulness and limitations of probabilistic fitting technique in order to capture the data uncertainty of the proposed model is presented. [ABSTRACT FROM AUTHOR]

The bomb attacks in Madrid three days before the general elections of March 14, 2004, and their possible influence on the victory of PSOE (Spanish Workers Socialist Party), defeating PP (Popular Party), have been a matter of study from several points of view (i.e., sociological, political, or statistical). In this paper, we present a dynamic model based on a system of differential equations such that it, using data from Spanish CIS (National Center of Sociological Research), describes the evolution of voting intention of the Spanish people over time. Using this model, we conclude that the probability is very low that the PSOE would have won had the attack not happened. Moreover, after the attack, the PSOE increased an average of 5.6% in voting on March 14 and an average of 11.2% of the Spanish people changed their vote between March 11 and March 14. These figures are in accordance with other studies. [ABSTRACT FROM AUTHOR]

Copyright of Revista Biomedica is the property of Centro de Investigaciones Regionales Dr. Hideyo Noguchi; Facultad de Medicina, UADY and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fission yeast contains three essential β(1,3)-D-glucan synthases (GSs), Bgs1, Bgs3, and Bgs4, with non-overlapping roles in cell integrity and morphogenesis. Only the bgs4+ mutants pbr1-8 and pbr1-6 exhibit resistance to GS inhibitors, even in the presence of the wild-type (WT) sequences of bgs1+ and bgs3+. Thus, Bgs1 and Bgs3 functions seem to be unaffected by those GS inhibitors. To learn more about echinocandins' mechanism of action and resistance, cytokinesis progression and cell death were examined by time-lapse fluorescence microscopy in WT and pbr1-8 cells at the start of treatment with sublethal and lethal concentrations of anidulafungin, caspofungin, and micafungin. In WT, sublethal concentrations of the three drugs caused abundant cell death that was either suppressed (anidulafungin and micafungin) or greatly reduced (caspofungin) in pbr1-8 cells. Interestingly, the lethal concentrations induced differential phenotypes depending on the echinocandin used. Anidulafungin and caspofungin were mostly fungistatic, heavily impairing cytokinesis progression in both WT and pbr1-8. As with sublethal concentrations, lethal concentrations of micafungin were primarily fungicidal in WT cells, causing cell lysis without impairing cytokinesis. The lytic phenotype was suppressed again in pbr1-8 cells. Our results suggest that micafungin always exerts its fungicidal effect by solely inhibiting Bgs4. In contrast, lethal concentrations of anidulafungin and caspofungin cause an early cytokinesis arrest, probably by the combined inhibition of several GSs. [ABSTRACT FROM AUTHOR]

Cytokinesis is a crucial event in the cell cycle of all living cells. In fungal cells, it requires co-ordinated contraction of an actomyosin ring and synthesis of both plasmatic membrane and a septum structure that will constitute the new cell wall end. Schizosaccharomyces pombe contains four essential putative (1,3)β-d-glucan synthase catalytic subunits, Bgs1p to Bgs4p. Here we examined the function of Bgs1p in septation by studying the lethal phenotypes of bgs1+ shut-off and bgs1Δ cells and demonstrated that Bgs1p is responsible and essential for linear (1,3)β-d-glucan and primary septum formation. bgs1+ shut-off generates a more than 300-fold Bgs1p reduction, but the septa still present large amounts of disorganized linear (1,3)β-d-glucan and partial primary septa. Conversely, both structures are absent in bgs1Δ cells, where there is no Bgs1p. The septum analysis of bgs1+ -repressed cells indicates that linear (1,3)β-d-glucan is necessary but not sufficient for primary septum formation. Linear (1,3)β-d-glucan is the polysaccharide that specifically interacts with the fluorochrome Calcofluor white in fission yeast. We also show that in the absence of Bgs1p abnormal septa are formed, but the cells cannot separate and eventually die. [ABSTRACT FROM AUTHOR]

Schizosaccharomyces pombe contains four putative (1,3)β-D-glucan synthase (GS) catalytic subunits, Bgs1p-4p. In this work, we cloned bgs4+ and show that Bgs4p is the only subunit found to be a part of the GS enzyme and essential for maintaining cell integrity during cytokinesis and polarized growth. Here we show that bgs4+, cwg1+ (cwg1-1 shows reduced cell-wall β-glucan and GS catalytic activity) and orb11+ (orb11-59 is defective in cell morphogenesis) are the same gene. bgs4+ is essential for spore germination and bgs4+ shut-off produces cell lysis at growing poles and mainly at the septum prior to cytokinesis, suggesting that Bgs4p is essential for cell wall growth and to compensate for an excess of cell wall degradation during cytokinesis. Shut-off and overexpression analysis suggest that Bgs4p forms part of a GS catalytic multiprotein complex and that Bgs4p-promoted cell-wall β-glucan alterations induce compensatory mechanisms from other Bgs subunits and (1,3)α-D-glucan synthase. Physiological localization studies showed that Bgs4p localizes to the growing ends, the medial ring and septum, and at each stage of wall synthesis or remodeling that occurs during sexual differentiation: mating, zygote and spore formation, and spore germination. Bgs4p timing and requirements for proper positioning during cytokinesis and its localization pattern during spore maturation differ from those of Bgs1p. Bgs4p localizes overlapping the contractile ring once Bgs1p is present and a Calcofluor white-stained septum material is detected, suggesting that Bgs4p is involved in a late process of secondary or general septum synthesis. Unlike Bgs1p, Bgs4p needs the medial ring but not the septation initiation network proteins to localize with the other septation components. Furthermore, Bgs4p localization depends on the polarity establishment proteins. Finally, F-actin is necessary for Bgs4p delocalization from and relocalization to the growing regions, but it is not needed for the stable maintenance of Bgs4p at the growing sites, poles and septum. All these data show for the first time an essential role for a Bgs subunit in the synthesis of a (1,3)β-D-glucan necessary to preserve cell integrity when cell wall synthesis or repair are needed. [ABSTRACT FROM AUTHOR]