Clinical pharmacokinetics of mycophenolic acid and its metabolites in solid organ transplant recipient

Mycophenolate mofetil (MMF), an ester prodrug of the immunosuppressant mycophenolic acid (MPA), is widely used for maintenance immunosuppressive therapy and prevention of renal allograft rejection in renal transplant recipients.MPA inhibits inosine monophosphate dehydrogenase (IMPDH), an enzyme involved in the “de novo” synthesis of purine nucleotides, thus suppressing both T-cell and B-cell proliferation. MPA shows a complex pharmacokinetics with considerable interand intra- patient by between- and within patient variabilities associated to MPA exposure. Several factors may contribute to it. The pharmacokinetic modeling according to the population pharmacokinetic approach with the non-linear mixed effects models has shown to be a powerful tool to describe the relationships between MMF doses and the MPA exposures and also to identify potential predictive patients’ demographic and clinical characteristics for dose tailoring during the post-transplant immunosuppresive treatment.

Clinical pharmacokinetics of mycophenolic acid and its metabolites in solid organ transplant recipient

Mycophenolate mofetil (MMF), an ester prodrug of the immunosuppressant mycophenolic acid (MPA), is widely used for maintenance immunosuppressive therapy and prevention of renal allograft rejection in renal transplant recipients.MPA inhibits inosine monophosphate dehydrogenase (IMPDH), an enzyme involved in the “de novo” synthesis of purine nucleotides, thus suppressing both T-cell and B-cell proliferation. MPA shows a complex pharmacokinetics with considerable interand intra- patient by between- and within patient variabilities associated to MPA exposure. Several factors may contribute to it. The pharmacokinetic modeling according to the population pharmacokinetic approach with the non-linear mixed effects models has shown to be a powerful tool to describe the relationships between MMF doses and the MPA exposures and also to identify potential predictive patients’ demographic and clinical characteristics for dose tailoring during the post-transplant immunosuppresive treatment.

Clinical pharmacokinetics of mycophenolic acid and its metabolites in solid organ transplant recipient

Mycophenolate mofetil (MMF), an ester prodrug of the immunosuppressant mycophenolic acid (MPA), is widely used for maintenance immunosuppressive therapy and prevention of renal allograft rejection in renal transplant recipients.MPA inhibits inosine monophosphate dehydrogenase (IMPDH), an enzyme involved in the “de novo” synthesis of purine nucleotides, thus suppressing both T-cell and B-cell proliferation. MPA shows a complex pharmacokinetics with considerable interand intra- patient by between- and within patient variabilities associated to MPA exposure. Several factors may contribute to it. The pharmacokinetic modeling according to the population pharmacokinetic approach with the non-linear mixed effects models has shown to be a powerful tool to describe the relationships between MMF doses and the MPA exposures and also to identify potential predictive patients’ demographic and clinical characteristics for dose tailoring during the post-transplant immunosuppresive treatment.

Forecasting tourism demand to Catalonia: neural networks vs. time series models

The increasing interest aroused by more advanced forecasting techniques, together with the requirement for more accurate forecasts of tourismdemand at the destination level due to the constant growth of world tourism, has lead us to evaluate the forecasting performance of neural modelling relative to that of time seriesmethods at a regional level. Seasonality and volatility are important features of tourism data, which makes it a particularly favourable context in which to compare the forecasting performance of linear models to that of nonlinear alternative approaches. Pre-processed official statistical data of overnight stays and tourist arrivals fromall the different countries of origin to Catalonia from 2001 to 2009 is used in the study. When comparing the forecasting accuracy of the different techniques for different time horizons, autoregressive integrated moving average models outperform self-exciting threshold autoregressions and artificial neural network models, especially for shorter horizons. These results suggest that the there is a trade-off between the degree of pre-processing and the accuracy of the forecasts obtained with neural networks, which are more suitable in the presence of nonlinearity in the data. In spite of the significant differences between countries, which can be explained by different patterns of consumer behaviour,we also find that forecasts of tourist arrivals aremore accurate than forecasts of overnight stays.

Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and pareto filters

Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the ethanol production in the fermentation of Saccharomyces cerevisiae.

Differential growth responses to water balance of coexisting deciduous tree species are linked to wood density in a Bolivian tropical dry forest

A seasonal period of water deficit characterizes tropical dry forests (TDFs). There, sympatric tree species exhibit a diversity of growth rates, functional traits, and responses to drought, suggesting that each species may possess different strategies to grow under different conditions of water availability. The evaluation of the long-term growth responses to changes in the soil water balance should provide an understanding of how and when coexisting tree species respond to water deficit in TDFs. Furthermore, such differential growth responses may be linked to functional traits related to water storage and conductance. We used dendrochronology and climate data to retrospectively assess how the radial growth of seven coexisting deciduous tree species responded to the seasonal soil water balance in a Bolivian TDF. Linear mixed-effects models were used to quantify the relationships between basal area increment and seasonal water balance. We related these relationships with wood density and sapwood production to assess if they affect the growth responses to climate. The growth of all species responded positively to water balance during the wet season, but such responses differed among species as a function of their wood density. For instance, species with a strong growth response to water availability averaged a low wood density which may facilitate the storage of water in the stem. By contrast, species with very dense wood were those whose growth was less sensitive to water availability. Coexisting tree species thus show differential growth responses to changes in soil water balance during the wet season. Our findings also provide a link between wood density, a trait related to the ability of trees to store water in the stem, and wood formation in response to water availability.

Beyond pure public and pure private management models: Partial privatization in the European airport industry

The use of private funding and management is enjoying an increasing trend in airports. The literature has not paid enough attention to the mixed management models in this industry, although many European airports take the form of mixed public-private companies, where ownership is shared between public and private sectors. We examine the determinants of the degree of private participation in the European airport sector. Drawing on a sample of the 100 largest European airports, we estimate a multivariate equation in order to determine the role of airport characteristics, fiscal variables, and political factors on the extent of private involvement. Our results confirm the alignment between public and private interests in partially privatized airports. Fiscal constraints and market attractiveness promote private participation. Integrated governance models and the share of network carriers prevent the presence of private ownership, while the degree of private participation appears to be pragmatic rather than ideological.

Alternative ways to estimate change points in multinomial sequences. An application to an authorship attribution problem

The statistical analysis of literary style is the part of stylometry that compares measurable characteristicsin a text that are rarely controlled by the author, with those in other texts. When thegoal is to settle authorship questions, these characteristics should relate to the author’s style andnot to the genre, epoch or editor, and they should be such that their variation between authors islarger than the variation within comparable texts from the same author.For an overview of the literature on stylometry and some of the techniques involved, see for exampleMosteller and Wallace (1964, 82), Herdan (1964), Morton (1978), Holmes (1985), Oakes (1998) orLebart, Salem and Berry (1998).Tirant lo Blanc, a chivalry book, is the main work in catalan literature and it was hailed to be“the best book of its kind in the world” by Cervantes in Don Quixote. Considered by writterslike Vargas Llosa or Damaso Alonso to be the first modern novel in Europe, it has been translatedseveral times into Spanish, Italian and French, with modern English translations by Rosenthal(1996) and La Fontaine (1993). The main body of this book was written between 1460 and 1465,but it was not printed until 1490.There is an intense and long lasting debate around its authorship sprouting from its first edition,where its introduction states that the whole book is the work of Martorell (1413?-1468), while atthe end it is stated that the last one fourth of the book is by Galba (?-1490), after the death ofMartorell. Some of the authors that support the theory of single authorship are Riquer (1990),Chiner (1993) and Badia (1993), while some of those supporting the double authorship are Riquer(1947), Coromines (1956) and Ferrando (1995). For an overview of this debate, see Riquer (1990).Neither of the two candidate authors left any text comparable to the one under study, and thereforediscriminant analysis can not be used to help classify chapters by author. By using sample textsencompassing about ten percent of the book, and looking at word length and at the use of 44conjunctions, prepositions and articles, Ginebra and Cabos (1998) detect heterogeneities that mightindicate the existence of two authors. By analyzing the diversity of the vocabulary, Riba andGinebra (2000) estimates that stylistic boundary to be near chapter 383.Following the lead of the extensive literature, this paper looks into word length, the use of the mostfrequent words and into the use of vowels in each chapter of the book. Given that the featuresselected are categorical, that leads to three contingency tables of ordered rows and therefore tothree sequences of multinomial observations.Section 2 explores these sequences graphically, observing a clear shift in their distribution. Section 3describes the problem of the estimation of a suden change-point in those sequences, in the followingsections we propose various ways to estimate change-points in multinomial sequences; the methodin section 4 involves fitting models for polytomous data, the one in Section 5 fits gamma modelsonto the sequence of Chi-square distances between each row profiles and the average profile, theone in Section 6 fits models onto the sequence of values taken by the first component of thecorrespondence analysis as well as onto sequences of other summary measures like the averageword length. In Section 7 we fit models onto the marginal binomial sequences to identify thefeatures that distinguish the chapters before and after that boundary. Most methods rely heavilyon the use of generalized linear models

Compositional analysis of bivariate discrete probabilities

A joint distribution of two discrete random variables with finite support can be displayed as a two way table of probabilities adding to one. Assume that this table hasn rows and m columns and all probabilities are non-null. This kind of table can beseen as an element in the simplex of n · m parts. In this context, the marginals areidentified as compositional amalgams, conditionals (rows or columns) as subcompositions. Also, simplicial perturbation appears as Bayes theorem. However, the Euclideanelements of the Aitchison geometry of the simplex can also be translated into the tableof probabilities: subspaces, orthogonal projections, distances.Two important questions are addressed: a) given a table of probabilities, which isthe nearest independent table to the initial one? b) which is the largest orthogonalprojection of a row onto a column? or, equivalently, which is the information in arow explained by a column, thus explaining the interaction? To answer these questionsthree orthogonal decompositions are presented: (1) by columns and a row-wise geometric marginal, (2) by rows and a columnwise geometric marginal, (3) by independenttwo-way tables and fully dependent tables representing row-column interaction. Animportant result is that the nearest independent table is the product of the two (rowand column)-wise geometric marginal tables. A corollary is that, in an independenttable, the geometric marginals conform with the traditional (arithmetic) marginals.These decompositions can be compared with standard log-linear models.Key words: balance, compositional data, simplex, Aitchison geometry, composition,orthonormal basis, arithmetic and geometric marginals, amalgam, dependence measure,contingency table

Leontief versus Ghosh: two faces of the same coin

In this paper we explore the sectoral and aggregate implications of some endogeneization rules (i.e. on value-added and final demand) which have been common in the Leontief model and have been recently proposed in the Ghosh model. We detect that these rules may give rise in both models to some allegedly pathological behavior in the sense that sectoral or aggregate output, very often, may not follow the logical and economically expected direct relationship with some underlying endogenous variables—namely, output and value-added in the Ghosh model and output and consumption in the Leontief model. Because of the common mathematical structure, whatever is or seems to be pathological in the Ghosh model also has a symmetric counterpart in the Leontief model. These would not be good news for the inner consistency of these linear models. To avoid such possible inconsistencies, we propose new and simple endogeneization rules that have a sound economic interpretation.

Modeling of hybrid systems by piecewise decomposition

Piecewise linear models systems arise as mathematical models of systems in many practical applications, often from linearization for nonlinear systems. There are two main approaches of dealing with these systems according to their continuous or discrete-time aspects. We propose an approach which is based on the state transformation, more particularly the partition of the phase portrait in different regions where each subregion is modeled as a two-dimensional linear time invariant system. Then the Takagi-Sugeno model, which is a combination of local model is calculated. The simulation results show that the Alpha partition is well-suited for dealing with such a system

Internal test sets studies in a group of antimalarials

Topological indices have been applied to build QSAR models for a set of 20 antimalarial cyclic peroxy cetals. In order to evaluate the reliability of the proposed linear models leave-n-out and Internal Test Sets (ITS) approaches have been considered. The proposed procedure resulted in a robust and consensued prediction equation and here it is shown why it is superior to the employed standard cross-validation algorithms involving multilinear regression models

Prenatal exposure to traffic-related air pollution and ultrasound measures of fetal growth in the INMA Sabadell cohort

Background: Few studies have used longitudinal ultrasound measurements to assess the effect of traffic-related air pollution on fetal growth.Objective: We examined the relationship between exposure to nitrogen dioxide (NO2) and aromatic hydrocarbons [benzene, toluene, ethylbenzene, m/p-xylene, and o-xylene (BTEX)] on fetal growth assessed by 1,692 ultrasound measurements among 562 pregnant women from the Sabadell cohort of the Spanish INMA (Environment and Childhood) study.Methods: We used temporally adjusted land-use regression models to estimate exposures to NO2 and BTEX. We fitted mixed-effects models to estimate longitudinal growth curves for femur length (FL), head circumference (HC), abdominal circumference (AC), biparietal diameter (BPD), and estimated fetal weight (EFW). Unconditional and conditional SD scores were calculated at 12, 20, and 32 weeks of gestation. Sensitivity analyses were performed considering time–activity patterns during pregnancy.Results: Exposure to BTEX from early pregnancy was negatively associated with growth in BPD during weeks 20–32. None of the other fetal growth parameters were associated with exposure to air pollution during pregnancy. When considering only women who spent 2 hr/day in nonresidential outdoor locations, effect estimates were stronger and statistically significant for the association between NO2 and growth in HC during weeks 12–20 and growth in AC, BPD, and EFW during weeks 20–32.Conclusions: Our results lend some support to an effect of exposure to traffic-related air pollutants from early pregnancy on fetal growth during mid-pregnancy.

Emotion and reason in everyday risk perception

Although research has documented the importance of emotion in risk perception, little is knownabout its prevalence in everyday life. Using the Experience Sampling Method, 94 part-timestudents were prompted at random via cellular telephones to report on mood state and threeemotions and to assess risk on thirty occasions during their working hours. The emotions valence, arousal, and dominance were measured using self-assessment manikins (Bradley &Lang, 1994). Hierarchical linear models (HLM) revealed that mood state and emotions explainedsignificant variance in risk perception. In addition, valence and arousal accounted for varianceover and above reason (measured by severity and possibility of risks). Six risks were reassessedin a post-experimental session and found to be lower than their real-time counterparts.The study demonstrates the feasibility and value of collecting representative samples of data withsimple technology. Evidence for the statistical consistency of the HLM estimates is provided inan Appendix.

On ignoring scientific evidence: The bumpy road to enlightenment

It is well accepted that people resist evidence that contradicts their beliefs.Moreover, despite their training, many scientists reject results that are inconsistent withtheir theories. This phenomenon is discussed in relation to the field of judgment anddecision making by describing four case studies. These concern findings that clinical judgment is less predictive than actuarial models; simple methods have proven superiorto more theoretically correct methods in times series forecasting; equal weighting ofvariables is often more accurate than using differential weights; and decisions cansometimes be improved by discarding relevant information. All findings relate to theapparently difficult-to-accept idea that simple models can predict complex phenomenabetter than complex ones. It is true that there is a scientific market place for ideas.However, like its economic counterpart, it is subject to inefficiencies (e.g., thinness,asymmetric information, and speculative bubbles). Unfortunately, the market is only correct in the long-run. The road to enlightenment is bumpy.