Mesoscale Modeling of Amphiphilic Fluid Dynamics

Abstract

So-called “vector models”, in which surfactant molecules retain only translational and orientational degrees of freedom, have been used to study the equilibrium properties of amphiphilic fluids for nearly a decade now. We demonstrate that hydrodynamic lattice-gas automata provide an effective means of coupling the Hamiltonian of such vector models to hydrodynamic flow with conserved momentum, thereby providing a self-consistent treatment of the hydrodynamics of amphiphilic fluids. In this “talk”, we describe these hydrodynamic lattice-gas models in two and three dimensions, and present their application to problems of amphiphilic-fluid hydrodynamics, including the dynamics of phase separation and the shear-induced sponge-to-lamellar phase transition.     

Co-diversity and co-distribution in phyllostomid bats: Evaluating the relative roles of climate and niche conservatism

Explaining the causes of geographic gradients in biodiversity remains an elusive task. Traditionally, correlative approaches have been used to relate species richness with contemporary climate, without actually explaining the causal factors. Recent approaches propose simulation models as more appropriate tools for assessing potential causes of macroecological patterns. Here we developed stochastic models to assess the relative contribution of climate and niche conservatism in determining compositional similarity among sites (co-diversity) and geographic association among species (co-distribution) in the bat family Phyllostomidae. We used range-diversity plots and variance-ratio tests to describe and evaluate such patterns. Our results supported a strong effect of climate in determining cohesive ranges causing positive co-diversity and co-distribution. We also demonstrated a marginal effect of niche conservatism, as modeled here, among species in shaping these patterns. However, climate and niche conservatism are not sufficient and other processes are still required to explain observed patterns. Our study highlights the importance of historical processes and demonstrates the usefulness of a simulation framework in testing biogeographical hypothesis to understand the relationship between diversity and distribution.     

Multivariate frailty models for two types of recurrent events with a dependent terminal event: Application to breast cancer data

Individuals may experience more than one type of recurrent event and a terminal event during the life course of a disease. Follow‐up may be interrupted for several reasons, including the end of a study, or patients lost to follow‐up, which are noninformative censoring events. Death could also stop the follow‐up, hence, it is considered as a dependent terminal event. We propose a multivariate frailty model that jointly analyzes two types of recurrent events with a dependent terminal event. Two estimation methods are proposed: a semiparametrical approach using penalized likelihood estimation where baseline hazard functions are approximated by M‐splines, and another one with piecewise constant baseline hazard functions. Finally, we derived martingale residuals to check the goodness‐of‐fit. We illustrate our proposals with a real dataset on breast cancer. The main objective was to model the dependency between the two types of recurrent events (locoregional and metastatic) and the terminal event (death) after a breast cancer.     

Improving psychometric assessment of the Beck Depression Inventory using Multidimensional Item Response Theory

We studied the latent factor structure of the Beck Depression Inventory (BDI) under the light of Multidimensional Item Response Theory models. Under a Bayesian Markov chain Monte Carlo setting, we chose the most adequate model, estimated its parameters and verified its fit to the data. An evaluation of the inventory in terms of the assumed dimensions seems to agree with previous investigations in the factor structure of the BDI present in the literature. Cognitive and somatic‐affective latent traits were identified in the analysis making possible the interpretation of symptom evolution along these dimensions, in terms of probability of their appearance.     

Understanding telomerase in cancer stem cell biology

Comment on: Vicente-Dueñas C, et al. Oncotarget 2012; Epub ahead of print; PMID:22408137.     

Disruptions of Ultradian and Circadian Organization of Core Temperature in a Rat Model of African Trypanosomiasis Using Periodogram Techniques on Detrended Data

Periodogram techniques on detrended data were used to determine the incidence of Trypanosoma brucei brucei infection on the distribution of the core temperature of rats and the expression of temperature rhythms. In such an animal model, sudden episodic hypothermic bouts were described. These episodes of hypothermia are used here as temporal marks for the purpose of performing punctual comparisons on temperature organization. The experiment was conducted on 10 infected and 3 control Sprague‐Dawley rats reared under a 24 h light‐dark cycle. Core temperature was recorded continuously throughout the experiment, until the animals' death. Temperature distributions, analyzed longitudinally across the full duration of the experiment, exhibited a progressive shift from a bimodal to unimodal pattern, suggesting a weakening of the day/night core temperature differences. After hypothermic events, the robustness of the circadian rhythm substantially weakened, also affecting the ultradian components. The ultradian periods were reduced, suggesting fragmentation of temperature generation. Moreover, differences between daytime and nighttime ultradian patterns decreased during illness, confirming the weakening of the circadian component. The results of the experiments show that both core temperature distribution and temperature rhythm were disrupted during the infection. These disruptions worsened after each episode of hypothermia, suggesting an alteration of the temperature regulatory system.     

Adjusting for reporting delay in cancer incidence when combining different sets of cancer registries

Cancer registries collect cancer incidence data that can be used to calculate incidence rates in a population and track changes over time. For incidence rates to be accurate, it is critical that diagnosed cases be reported in a timely manner. Registries typically allow a fixed amount of time (e.g. two years) for diagnosed cases to be reported before releasing the initial case counts for a particular diagnosis year. Inevitably, however, additional cases are reported after the initial counts are released; these extra cases are included in subsequent releases that become more complete over time, while incidence rates based on earlier releases will underestimate the true rates. Statistical methods have been developed to estimate the distribution of reporting delay (the amount of time until a diagnosed case is reported) and to correct incidence rates for underestimation due to reporting delay. Since the observed reporting delays must be less than the length of time the registry has been collecting data, most methods estimate a truncated delay distribution. These methods can be applied to a group of registries that began collecting data in the same diagnosis year. In this paper, we extend the methods to two groups of registries that began collecting data in two different diagnosis years (so that the delay distributions are truncated at different times). We apply the proposed method to data from the National Cancer Institute's Surveillance Epidemiology and End Results (SEER) program, a consortium of U.S. cancer registries that includes nine registries with data collection beginning in 1981 and four registries with data collection beginning in 1992. We use the method to obtain delay‐adjusted incidence rates for melanoma, liver cancer, and Hodgkin lymphoma.