Effect of sodium chloride on solutesolvent interactions in aqueous polyethylene glycolsodium sulfate two-phase systems

Partition behavior of eight small organic compounds and six proteins was examined in poly(ethylene glycol)-8000sodium sulfate aqueous two-phase systems containing 0.215 M NaCl and 0.5 M osmolyte (sorbitol, sucrose, TMAO) and poly(ethylene glycol)-10000sodium sulfate0.215 M NaCl system, all in 0.01 M sodium phosphate buffer, pH 6.8. The differences between the solvent properties of the coexisting phases (solvent dipolarity/polarizability, hydrogen bond donor acidity, and hydrogen bond acceptor basicity) were characterized with solvatochromic dyes using the solvatochromic comparison method. Differences between the electrostatic properties of the phases were determined by analysis of partitioning of sodium salts of dinitrophenylated (DNP-) amino acids with aliphatic alkyl side-chain. The partition coefficients of all compounds examined (including proteins) were described in terms of solutesolvent interactions. The results obtained in the study show that solutesolvent interactions of nonionic organic compounds and proteins in polyethylene glycolsodium sulfate aqueous two-phase system change in the presence of NaCl additive.

Modelos Mixtos-Aditivos Generalizados para el Estudio de Fenómenos Biológicos y Sociales Descriptos por Datos Agregados y/o Longitudinales. Generalized Mixed Additive Models for the Study of Biological and Social Phenomenons from Longitudinal and/or Aggregated Data.

Este proyecto propone extender y generalizar los procesos de estimación e inferencia de modelos aditivos generalizados multivariados para variables aleatorias no gaussianas, que describen comportamientos de fenómenos biológicos y sociales y cuyas representaciones originan series longitudinales y datos agregados (clusters). Se genera teniendo como objeto para las aplicaciones inmediatas, el desarrollo de metodología de modelación para la comprensión de procesos biológicos, ambientales y sociales de las áreas de Salud y las Ciencias Sociales, la condicionan la presencia de fenómenos específicos, como el de las enfermedades.Es así que el plan que se propone intenta estrechar la relación entre la Matemática Aplicada, desde un enfoque bajo incertidumbre y las Ciencias Biológicas y Sociales, en general, generando nuevas herramientas para poder analizar y explicar muchos problemas sobre los cuales tienen cada vez mas información experimental y/o observacional.Se propone, en forma secuencial, comenzando por variables aleatorias discretas (Yi, con función de varianza menor que una potencia par del valor esperado E(Y)) generar una clase unificada de modelos aditivos (paramétricos y no paramétricos) generalizados, la cual contenga como casos particulares a los modelos lineales generalizados, no lineales generalizados, los aditivos generalizados, los de media marginales generalizados (enfoques GEE1 -Liang y Zeger, 1986- y GEE2 -Zhao y Prentice, 1990; Zeger y Qaqish, 1992; Yan y Fine, 2004), iniciando una conexión con los modelos lineales mixtos generalizados para variables latentes (GLLAMM, Skrondal y Rabe-Hesketh, 2004), partiendo de estructuras de datos correlacionados. Esto permitirá definir distribuciones condicionales de las respuestas, dadas las covariables y las variables latentes y estimar ecuaciones estructurales para las VL, incluyendo regresiones de VL sobre las covariables y regresiones de VL sobre otras VL y modelos específicos para considerar jerarquías de variación ya reconocidas. Cómo definir modelos que consideren estructuras espaciales o temporales, de manera tal que permitan la presencia de factores jerárquicos, fijos o aleatorios, medidos con error como es el caso de las situaciones que se presentan en las Ciencias Sociales y en Epidemiología, es un desafío a nivel estadístico. Se proyecta esa forma secuencial para la construcción de metodología tanto de estimación como de inferencia, comenzando con variables aleatorias Poisson y Bernoulli, incluyendo los existentes MLG, hasta los actuales modelos generalizados jerárquicos, conextando con los GLLAMM, partiendo de estructuras de datos correlacionados. Esta familia de modelos se generará para estructuras de variables/vectores, covariables y componentes aleatorios jerárquicos que describan fenómenos de las Ciencias Sociales y la Epidemiología.

A comparative analysis of Cobb-Douglas model with additive and multiplicative error

This paper dis cusses the fitting of a Cobb-Doug las response curve Yi = αXβi, with additive error, Yi = αXβi + e i, instead of the usual multiplicative error Yi = αXβi (1 + e i). The estimation of the parameters A and B is discussed. An example is given with use of both types of error.

Overlapping, additive and counterregulatory effects of type II and I interferons on myeloid dendritic cell functions.

Dendritic cells (DCs) are central player in immunity by bridging the innate and adaptive arms of the immune system (IS). Interferons (IFNs) are one of the most important factors that regulate both innate and adaptive immunity too. Thus, the understanding of how type II and I IFNs modulate the immune-regulatory properties of DCs is a central issue in immunology. In this paper, we will address this point in the light of the most recent literature, also highlighting the controversial data reported in the field. According to the wide literature available, type II as well as type I IFNs appear, at the same time, to collaborate, to induce additive effects or overlapping functions, as well as to counterregulate each one's effects on DC biology and, in general, the immune response. The knowledge of these effects has important therapeutic implications in the treatment of infectious/autoimmune diseases and cancer and indicates strategies for using IFNs as vaccine adjuvants and in DC-based immune therapeutic approaches.

The additive genetic variance after bottlenecks is affected by the number of loci involved in epistatic interactions.

We investigated the role of the number of loci coding for a neutral trait on the release of additive variance for this trait after population bottlenecks. Different bottleneck sizes and durations were tested for various matrices of genotypic values, with initial conditions covering the allele frequency space. We used three different types of matrices. First, we extended Cheverud and Routman's model by defining matrices of "pure" epistasis for three and four independent loci; second, we used genotypic values drawn randomly from uniform, normal, and exponential distributions; and third we used two models of simple metabolic pathways leading to physiological epistasis. For all these matrices of genotypic values except the dominant metabolic pathway, we find that, as the number of loci increases from two to three and four, an increase in the release of additive variance is occurring. The amount of additive variance released for a given set of genotypic values is a function of the inbreeding coefficient, independently of the size and duration of the bottleneck. The level of inbreeding necessary to achieve maximum release in additive variance increases with the number of loci. We find that additive-by-additive epistasis is the type of epistasis most easily converted into additive variance. For a wide range of models, our results show that epistasis, rather than dominance, plays a significant role in the increase of additive variance following bottlenecks.

Partitioning of reproduction in animal societies.

A key feature differentiating cooperative animal societies Is the apportionment of reproduction among individuals. Only recently have studies started to focus on intraspecific variability in the distribution of reproduction within animal societies, and the available data suggest that this variability might be greater than previously suspected. How can one account for intra-and interspecific variability in partitioning of reproduction? This Is one of the most intriguing problems in the study of social behaviour, and understanding the factors underlying this variability is one of the keys to understanding the properties of complex animal societies.

Additive effects of LPL, APOA5 and APOE variant combinations on triglyceride levels and hypertriglyceridemia: results of the ICARIA genetic sub-study.

BACKGROUND Hypertriglyceridemia (HTG) is a well-established independent risk factor for cardiovascular disease and the influence of several genetic variants in genes related with triglyceride (TG) metabolism has been described, including LPL, APOA5 and APOE. The combined analysis of these polymorphisms could produce clinically meaningful complementary information. METHODS A subgroup of the ICARIA study comprising 1825 Spanish subjects (80% men, mean age 36 years) was genotyped for the LPL-HindIII (rs320), S447X (rs328), D9N (rs1801177) and N291S (rs268) polymorphisms, the APOA5-S19W (rs3135506) and -1131T/C (rs662799) variants, and the APOE polymorphism (rs429358; rs7412) using PCR and restriction analysis and TaqMan assays. We used regression analyses to examine their combined effects on TG levels (with the log-transformed variable) and the association of variant combinations with TG levels and hypertriglyceridemia (TG > or = 1.69 mmol/L), including the covariates: gender, age, waist circumference, blood glucose, blood pressure, smoking and alcohol consumption. RESULTS We found a significant lowering effect of the LPL-HindIII and S447X polymorphisms (p < 0.0001). In addition, the D9N, N291S, S19W and -1131T/C variants and the APOE-epsilon4 allele were significantly associated with an independent additive TG-raising effect (p < 0.05, p < 0.01, p < 0.001, p < 0.0001 and p < 0.001, respectively). Grouping individuals according to the presence of TG-lowering or TG-raising polymorphisms showed significant differences in TG levels (p < 0.0001), with the lowest levels exhibited by carriers of two lowering variants (10.2% reduction in TG geometric mean with respect to individuals who were homozygous for the frequent alleles of all the variants), and the highest levels in carriers of raising combinations (25.1% mean TG increase). Thus, carrying two lowering variants was protective against HTG (OR = 0.62; 95% CI, 0.39-0.98; p = 0.042) and having one single raising polymorphism (OR = 1.20; 95% CI, 1.39-2.87; p < 0.001) or more (2 or 3 raising variants; OR = 2.90; 95% CI, 1.56-5.41; p < 0.001) were associated with HTG. CONCLUSION Our results showed a significant independent additive effect on TG levels of the LPL polymorphisms HindIII, S447X, D9N and N291S; the S19W and -1131T/C variants of APOA5, and the epsilon4 allele of APOE in our study population. Moreover, some of the variant combinations studied were significantly associated with the absence or the presence of hypertriglyceridemia.

Additive functions in boolean models of gene regulatory network modules.

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.

Energy partitioning for "fuzzy" atoms

The total energy of molecule in terms of 'fuzzy atoms' presented as sum of one- and two-atomic energy components is described. The divisions of three-dimensional physical space into atomic regions exhibit continuous transition from one to another. The energy components are on chemical energy scale according to proper definitions. The Becke's integration scheme and weight function determines realization of method which permits effective numerical integrations

One- and two-center physical space partitioning of the energy in the density functional theory

A conceptually new approach is introduced for the decomposition of the molecular energy calculated at the density functional theory level of theory into sum of one- and two-atomic energy components, and is realized in the "fuzzy atoms" framework. (Fuzzy atoms mean that the three-dimensional physical space is divided into atomic regions having no sharp boundaries but exhibiting a continuous transition from one to another.) The new scheme uses the new concept of "bond order density" to calculate the diatomic exchange energy components and gives them unexpectedly close to the values calculated by the exact (Hartree-Fock) exchange for the same Kohn-Sham orbitals

Partitioning of genetic (Rapd) variability among sexes and populations of the Barn Owl (Tyto alba) in Europe

The white Barn Owl subspecies (Tyto alba alba) is found in southern Europe and the reddish-brown subspecies (T a. guttata) in northern and eastern Europe. In central Europe, the two subspecies interbreed producing a large range of phenotypic variants. Because of the different ratios of the subspecies in different geographic regions, we predict that genetic variation should be greater in Switzerland than in Hungary. We tested this hypothesis by measuring genetic variation with the RAPD method. As predicted, the genetic differentiation within a Swiss population of Barn Owls was significantly greater than the variation within a Hungarian population. This suggests that gene flow is greater in central Europe than at the eastern limit of the Barn Owl distribution in Hungary. In both countries genetic variation was more pronounced in females than in males. As in other birds, this is probably because female Barn Owls are less philopatric than males. The number of migrants between Hungary and Switzerland is ca. 1 individual per generation; if calculated separately for the sexes, then 0.525 for males and ca. I for females (Nm values). The difference in the number of migrants between genders again is likely a consequence of higher male philopatry. The sexual differentiation is greater in the Swiss population than in the Hungarian and the genetic substructuring of the populations of the species is substantial. The reason for the considerable population substructuring could be the nonmigratory behavior and socially monogamous pairing of the species, as well as the geographical barriers (Alps) between the populations examined.

Generalized linear and generalized additive models in studies of species distributions: setting the scene

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001.We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling.

Partitioning of reproduction in mother-daughter versus sibling associations : a test of optimal skew theory

A critical feature of cooperative animal societies is the reproductive skew, a shorthand term for the degree to which a dominant individual monopolizes overall reproduction in the group. Our theoretical analysis of the evolutionarily stable skew in matrifilial (i.e., mother-daughter) societies, in which relatednesses to offspring are asymmetrical, predicts that reproductive skews in such societies should tend to be greater than those of semisocial societies (i.e., societies composed of individuals of the same generation, such as siblings), in which relatednesses to offspring are symmetrical. Quantitative data on reproductive skews in semisocial and matrifilial associations within the same species for 17 eusocial Hymenoptera support this prediction. Likewise, a survey of reproductive partitioning within 20 vertebrate societies demonstrates that complete reproductive monopoly is more likely to occur in matrifilial than in semisocial societies, also as predicted by the optimal skew model.

ParA2, a Vibrio cholerae chromosome partitioning protein, forms left-handed helical filaments on DNA.

Most bacterial chromosomes contain homologs of plasmid partitioning (par) loci. These loci encode ATPases called ParA that are thought to contribute to the mechanical force required for chromosome and plasmid segregation. In Vibrio cholerae, the chromosome II (chrII) par locus is essential for chrII segregation. Here, we found that purified ParA2 had ATPase activities comparable to other ParA homologs, but, unlike many other ParA homologs, did not form high molecular weight complexes in the presence of ATP alone. Instead, formation of high molecular weight ParA2 polymers required DNA. Electron microscopy and three-dimensional reconstruction revealed that ParA2 formed bipolar helical filaments on double-stranded DNA in a sequence-independent manner. These filaments had a distinct change in pitch when ParA2 was polymerized in the presence of ATP versus in the absence of a nucleotide cofactor. Fitting a crystal structure of a ParA protein into our filament reconstruction showed how a dimer of ParA2 binds the DNA. The filaments formed with ATP are left-handed, but surprisingly these filaments exert no topological changes on the right-handed B-DNA to which they are bound. The stoichiometry of binding is one dimer for every eight base pairs, and this determines the geometry of the ParA2 filaments with 4.4 dimers per 120 A pitch left-handed turn. Our findings will be critical for understanding how ParA proteins function in plasmid and chromosome segregation.