Quantifying rates of landscape evolution and tectonic processes by thermochronology and numerical modeling of crustal heat transport using PECUBE

PECUBE is a three-dimensional thermal-kinematic code capable of solving the heat production-diffusion-advection equation under a temporally varying surface boundary condition. It was initially developed to assess the effects of time-varying surface topography (relief) on low-temperature thermochronological datasets. Thermochronometric ages are predicted by tracking the time-temperature histories of rock-particles ending up at the surface and by combining these with various age-prediction models. In the decade since its inception, the PECUBE code has been under continuous development as its use became wider and addressed different tectonic-geomorphic problems. This paper describes several major recent improvements in the code, including its integration with an inverse-modeling package based on the Neighborhood Algorithm, the incorporation of fault-controlled kinematics, several different ways to address topographic and drainage change through time, the ability to predict subsurface (tunnel or borehole) data, prediction of detrital thermochronology data and a method to compare these with observations, and the coupling with landscape-evolution (or surface-process) models. Each new development is described together with one or several applications, so that the reader and potential user can clearly assess and make use of the capabilities of PECUBE. We end with describing some developments that are currently underway or should take place in the foreseeable future. (C) 2012 Elsevier B.V. All rights reserved.

Modeling AIDS vaccines: the cellular level

This paper discuses current strategies for the development of AIDS vaccines wich allow immunzation to disturb the natural course of HIV at different detailed stages of its life cycle. Mathematical models describing the main biological phenomena (i.e. virus and vaccine induced T4 cell growth; virus and vaccine induced activation latently infected T4 cells; incremental changes immune response as infection progress; antibody dependent enhancement and neutralization of infection) and allowing for different vaccination strategies serve as a backgroud for computer simulations. The mathematical models reproduce updated information on the behavior of immune cells, antibody concentrations and free viruses. The results point to some controversial outcomes of an AIDS vaccine such as an early increase in virus concentration among vaccinated when compared to nonvaccinated individuals.

Cell-based computational modeling of vascular morphogenesis using Tissue Simulation Toolkit.

Computational modeling has become a widely used tool for unraveling the mechanisms of higher level cooperative cell behavior during vascular morphogenesis. However, experimenting with published simulation models or adding new assumptions to those models can be daunting for novice and even for experienced computational scientists. Here, we present a step-by-step, practical tutorial for building cell-based simulations of vascular morphogenesis using the Tissue Simulation Toolkit (TST). The TST is a freely available, open-source C++ library for developing simulations with the two-dimensional cellular Potts model, a stochastic, agent-based framework to simulate collective cell behavior. We will show the basic use of the TST to simulate and experiment with published simulations of vascular network formation. Then, we will present step-by-step instructions and explanations for building a recent simulation model of tumor angiogenesis. Demonstrated mechanisms include cell-cell adhesion, chemotaxis, cell elongation, haptotaxis, and haptokinesis.

Neural Wiskott-Aldrich syndrome protein modulates Wnt signaling and is required for hair follicle cycling in mice.

The Rho family GTPases Cdc42 and Rac1 are critical regulators of the actin cytoskeleton and are essential for skin and hair function. Wiskott-Aldrich syndrome family proteins act downstream of these GTPases, controlling actin assembly and cytoskeletal reorganization, but their role in epithelial cells has not been characterized in vivo. Here, we used a conditional knockout approach to assess the role of neural Wiskott-Aldrich syndrome protein (N-WASP), the ubiquitously expressed Wiskott-Aldrich syndrome-like (WASL) protein, in mouse skin. We found that N-WASP deficiency in mouse skin led to severe alopecia, epidermal hyperproliferation, and ulceration, without obvious effects on epidermal differentiation and wound healing. Further analysis revealed that the observed alopecia was likely the result of a progressive and ultimately nearly complete block in hair follicle (HF) cycling by 5 months of age. N-WASP deficiency also led to abnormal proliferation of skin progenitor cells, resulting in their depletion over time. Furthermore, N-WASP deficiency in vitro and in vivo correlated with decreased GSK-3beta phosphorylation, decreased nuclear localization of beta-catenin in follicular keratinocytes, and decreased Wnt-dependent transcription. Our results indicate a critical role for N-WASP in skin function and HF cycling and identify a link between N-WASP and Wnt signaling. We therefore propose that N-WASP acts as a positive regulator of beta-catenin-dependent transcription, modulating differentiation of HF progenitor cells.

Evaluating the influence of spatial uncertainty in locality points for species distributional modeling

1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.

Neural activity of the anterior insula in emotional processing depends on the individuals' emotional susceptibility

Differences in personality factors between individuals may manifest themselves with different patterns of neural activity while individuals process stimuli with emotional content. We attempted to verify this hypothesis by investigating emotional susceptibility (ES), a specific emotional trait of the human personality defined as the tendency to "experience feelings of discomfort, helplessness, inadequacy and vulnerability" after exposure to stimuli with emotional valence. By administering a questionnaire evaluating the individuals' ES, we selected two groups of participants with high and low ES respectively. Then, we used functional magnetic resonance imaging to investigate differences between the groups in the neural activity involved while they were processing emotional stimuli in an explicit (focusing on the content of the stimuli) or an incidental (focusing on spatial features of the stimuli, irrespectively of their content) way. The results showed a selective difference in brain activity between groups only in the explicit processing of the emotional stimuli: bilateral activity of the anterior insula was present in subjects with high ES but not in subjects with low ES. This difference in neural activity within the anterior insula proved to be purely functional since no brain morphological differences were found between groups, as assessed by a voxel-based morphometry analysis. Although the role of the anterior insula in the processing of contexts perceived as emotionally salient is well established, the present study provides the first evidence of a modulation of the insular activity depending on the individuals' ES trait of personality.

Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.

Complementarities between mathematical and computational modeling: the case of the repeated Prisoners' Dilemma

We study the properties of the well known Replicator Dynamics when applied to a finitely repeated version of the Prisoners' Dilemma game. We characterize the behavior of such dynamics under strongly simplifying assumptions (i.e. only 3 strategies are available) and show that the basin of attraction of defection shrinks as the number of repetitions increases. After discussing the difficulties involved in trying to relax the 'strongly simplifying assumptions' above, we approach the same model by means of simulations based on genetic algorithms. The resulting simulations describe a behavior of the system very close to the one predicted by the replicator dynamics without imposing any of the assumptions of the mathematical model. Our main conclusion is that mathematical and computational models are good complements for research in social sciences. Indeed, while computational models are extremely useful to extend the scope of the analysis to complex scenarios hard to analyze mathematically, formal models can be useful to verify and to explain the outcomes of computational models.

The stabilizing effects of the acquired immunity on the schistosomiasis transmission modeling - the sensitivity Analysis

A mathematical model is proposed to analyze the effects of acquired immunity on the transmission of schistosomiasis in the human host. From this model the prevalence curve dependent on four parameters can be obtained. These parameters were estimated fitting the data by the maximum likelihood method. The model showed a good retrieving capacity of real data from two endemic areas of schistosomiasis: Touros, Brazil (Schistosoma mansoni) and Misungwi, Tanzania (S. haematobium). Also, the average worm burden per person and the dispersion of parasite per person in the community can be obtained from the model. In this paper, the stabilizing effects of the acquired immunity assumption in the model are assessed in terms of the epidemiological variables as follows. Regarded to the prevalence curve, we calculate the confidence interval, and related to the average worm burden and the worm dispersion in the community, the sensitivity analysis (the range of the variation) of both variables with respect to their parameters is performed.

Synchronous versus asynchronous modeling of gene regulatory networks.

MOTIVATION: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. RESULTS: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. AVAILABILITY: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Expression of the gene encoding the high-Km glucose transporter 2 by the early postimplantation mouse embryo is essential for neural tube defects associated with diabetic embryopathy.

AIMS/HYPOTHESIS: Excess glucose transport to embryos during diabetic pregnancy causes congenital malformations. The early postimplantation embryo expresses the gene encoding the high-Km GLUT2 (also known as SLC2A2) glucose transporter. The hypothesis tested here is that high-Km glucose transport by GLUT2 causes malformations resulting from maternal hyperglycaemia during diabetic pregnancy. MATERIALS AND METHODS: Glut2 mRNA was assayed by RT-PCR. The Km of embryo glucose transport was determined by measuring 0.5-20 mmol/l 2-deoxy[3H]glucose transport. To test whether the GLUT2 transporter is required for neural tube defects resulting from maternal hyperglycaemia, Glut2+/- mice were crossed and transient hyperglycaemia was induced by glucose injection on day 7.5 of pregnancy. Embryos were recovered on day 10.5, and the incidence of neural tube defects in wild-type, Glut2+/- and Glut2-/- embryos was scored. RESULTS: Early postimplantation embryos expressed Glut2, and expression was unaffected by maternal diabetes. Moreover, glucose transport by these embryos showed Michaelis-Menten kinetics of 16.19 mmol/l, consistent with transport mediated by GLUT2. In pregnancies made hyperglycaemic on day 7.5, neural tube defects were significantly increased in wild-type embryos, but Glut2+/- embryos were partially protected from neural tube defects, and Glut2-/- embryos were completely protected from these defects. The frequency of occurrence of wild-type, Glut2+/- and Glut2-/- embryos suggests that the presence of Glut2 alleles confers a survival advantage in embryos before day 10.5. CONCLUSIONS/INTERPRETATIONS: High-Km glucose transport by the GLUT2 glucose transporter during organogenesis is responsible for the embryopathic effects of maternal diabetes.

Distinction of males of the Lutzomyia intermedia (Lutz & Neiva, 1912) species complex by ratios between dimensions and by an artificial neural network (Diptera: Psychodidae, Phlebotominae)

The females of the two species of the Lutzomyia intermedia complex can be easily distinguished, but the males of each species are quite similar. The ratios between the extra-genital and the genital structures of L. neivai are larger than those of L. intermedia s. s., according to ANOVA. An artificial neural network was trained with a set of 300 examples, randomly taken from a sample of 358 individuals. The input vectors consisted of several ratios between some structures of each insect. The model was tested on the remaining 58 insects, 56 of which (96.6%) were correctly identified. This ratio of success can be considered remarkable if one takes into account the difficulty of attaining comparable results using traditional statistical techniques.