Host plant quality,selection history and trade-offs shape the immune responses of Manduca sexta

Immune defences are an important component of fitness. Yet susceptibility to pathogens is common, suggesting the presence of ecological and evolutionary limitations on immune defences. Here, we use structural equation modelling to quantify the direct effects of resource quality and selection history, and their indirect effects mediated via body condition prior to an immune challenge on encapsulation and melanization immune defences in the tobacco hornworm, Manduca sexta. We also investigate allocation trade-offs among immune defences and growth rate following an immune challenge. We found considerable variation in the magnitude and direction of the direct effects of resource quality and selection history on immune defences and their indirect effects mediated via body condition and allocation trade-offs. Greater resource quality and evolutionary exposure to pathogens had positive direct effects on encapsulation and melanization. The indirect effect of resource quality on encapsulation mediated via body condition was substantial, whereas indirect effects on melanization were negligible. Individuals in better condition prior to the immune challenge had greater encapsulation; however, following the immune challenge, greater encapsulation traded off with slower growth rate. Our study demonstrates the importance of experimentally and analytically disentangling the relative contributions of direct and indirect effects to understand variation in immune defences.     

Impacts of imperfect reference data on the apparent accuracy of species presence–absence models and their predictions

Aim To explore the impacts of imperfect reference data on the accuracy of species distribution model predictions. The main focus is on impacts of the quality of reference data (labelling accuracy) and, to a lesser degree, data quantity (sample size) on species presence–absence modelling. Innovation The paper challenges the common assumption that some popular measures of model accuracy and model predictions are prevalence independent. It highlights how imperfect reference data may impact on a study and the actions that may be taken to address problems. Main conclusions The theoretical independence of prevalence of popular accuracy measures, such as sensitivity, specificity, true skills statistics (TSS) and area under the receiver operating characteristic curve (AUC), is unlikely to occur in practice due to reference data error; all of these measures of accuracy, together with estimates of species occurrence, showed prevalence dependency arising through the use of a non‐gold‐standard reference. The number of cases used also had implications for the ability of a study to meet its objectives. Means to reduce the negative effects of imperfect reference data in study design and interpretation are suggested.     

Respiratory system dynamical mechanical properties: modeling in time and frequency domain

The mechanical properties of the respiratory system are important determinants of its function and can be severely compromised in disease. The assessment of respiratory system mechanical properties is thus essential in the management of some disorders as well as in the evaluation of respiratory system adaptations in response to an acute or chronic process. Most often, lungs and chest wall are treated as a linear dynamic system that can be expressed with differential equations, allowing determination of the system’s parameters, which will reflect the mechanical properties. However, different models that encompass nonlinear characteristics and also multicompartments have been used in several approaches and most specifically in mechanically ventilated patients with acute lung injury. Additionally, the input impedance over a range of frequencies can be assessed with a convenient excitation method allowing the identification of the mechanical characteristics of the central and peripheral airways as well as lung periphery impedance. With the evolution of computational power, the airway pressure and flow can be recorded and stored for hours, and hence continuous monitoring of the respiratory system mechanical properties is already available in some mechanical ventilators. This review aims to describe some of the most frequently used models for the assessment of the respiratory system mechanical properties in both time and frequency domain.     

Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering

A Bayesian model-based clustering approach is proposed for identifying differentially expressed genes in meta-analysis. A Bayesian hierarchical model is used as a scientific tool for combining information from different studies, and a mixture prior is used to separate differentially expressed genes from non-differentially expressed genes. Posterior estimation of the parameters and missing observations are done by using a simple Markov chain Monte Carlo method. From the estimated mixture model, useful measure of significance of a test such as the Bayesian false discovery rate (FDR), the local FDR (Efron et al., 2001), and the integration-driven discovery rate (IDR; Choi et al., 2003) can be easily computed. The model-based approach is also compared with commonly used permutation methods, and it is shown that the model-based approach is superior to the permutation methods when there are excessive under-expressed genes compared to over-expressed genes or vice versa. The proposed method is applied to four publicly available prostate cancer gene expression data sets and simulated data sets.     

Protein Sequence Modules

Abstract

Conserved protein sequence segments are commonly believed to correspond to functional sites in the protein sequence. A novel approach is proposed to profile the changing degree of conservation along the protein sequence, by evaluating the occurrence frequencies of all short oligopeptides of the given sequence in a large proteome database. Thus, a protein sequence conservation profile can be plotted for every protein. The profile indicates where along the sequences the potential functional (conserved) sites are located. The corresponding oligopeptides belonging to the sites are very frequent across many prokaryotic species. Analysis of a representative set of such profiles reveals a common feature of all examined proteins: they consist of sequence modules represented by the peaks of conservation. Typical size of the modules (peak-to-peak distance) is 25–30 amino acid residues.     

Persistence of Dispersal-limited Species in Structured Dynamic Landscapes

Dynamic landscape models have generally assumed random distributions of habitat although real landscapes show spatial organization at many scales. To explore the role of spatial structure in determining the frequency of dispersal-limited forest species, we used a cellular landscape model divided into two zones. Zones were distributed in a random, clustered, or regular spatial pattern. Within each zone habitat cells were randomly destroyed and regenerated, and habitat density and turnover rate were systematically varied. A hypothetical habitat-limited species dispersed between adjacent habitat cells. All trials showed a reduced species frequency relative to a static landscape. Reduction was greater at low habitat density (P = 0.30) than at high density (0.90) suggesting the importance of habitat connectivity in controlling species frequency. The greatest reduction occurred when habitat was concentrated in a small, regularly distributed zone at low habitat density reflecting the enforced isolation of individual habitat cells. Very little reduction was observed when habitat cells were packed into a small clustered zone, a situation promoting connectivity between cells. Moderate–severe frequency reduction occurred when habitat turnover was concentrated in a clustered zone at high habitat density, but little was observed when turnover was widely distributed in a regular or random pattern. These results can be interpreted in terms of a source-sink function in which spatial pattern controlled the degree of contact between landscape zones and determined opportunities for dispersal between habitat cells. We conclude that clustering of forest habitat has the potential to maintain herb species frequency in sparsely forested landscapes. Conversely, clustering of forest disturbance in heavily forested regions, or regular distribution of forest stands (as often occurs in agricultural regions) creates areas which are difficult to colonize, and should be avoided.     

Differential susceptibility epidemic models

We formulate compartmental differential susceptibility (DS) susceptible-infective-removed (SIR) models by dividing the susceptible population into multiple subgroups according to the susceptibility of individuals in each group. We analyze the impact of disease-induced mortality in the situations where the number of contacts per individual is either constant or proportional to the total population. We derive an explicit formula for the reproductive number of infection for each model by investigating the local stability of the infection-free equilibrium. We further prove that the infection-free equilibrium of each model is globally asymptotically stable by qualitative analysis of the dynamics of the model system and by utilizing an appropriately chosen Liapunov function. We show that if the reproductive number is greater than one, then there exists a unique endemic equilibrium for all of the DS models studied in this paper. We prove that the endemic equilibrium is locally asymptotically stable for the models with no disease-induced mortality and the models with contact numbers proportional to the total population. We also provide sufficient conditions for the stability of the endemic equilibrium for other situations. We briefly discuss applications of the DS models to optimal vaccine strategies and the connections between the DS models and predator-prey models with multiple prey populations or host-parasitic interaction models with multiple hosts are also given.This research was partially supported by the Department of Energy under contracts W-7405-ENG-36 and the Applied Mathematical Sciences Program KC-07-01-01.     

Structural and spectroscopic studies of a model for catechol oxidase

A binuclear copper complex, [Cu₂(BPMP)(OAc)₂][ClO₄]·H₂O, has been prepared using the binucleating ligand 2,6-bis[bis(pyridin-2-ylmethylamino)methyl]-4-methylphenol (H-BPMP). The X-ray crystal structure reveals the copper centers to have a five-coordinate square pyramidal geometry, with the acetate ligands bound terminally. The bridging phenolate occupies the apical position of the square-based pyramids and magnetic susceptibility, electron paramagnetic resonance (EPR) and variable-temperature variable-field magnetic circular dichroism (MCD) measurements indicate that the two centers are very weakly antiferromagnetically coupled (J = −0.6 cm⁻¹). Simulation of the dipole–dipole-coupled EPR spectrum showed that in solution the Cu–O–Cu angle was increased from 126° to 160° and that the internuclear distance was larger than that observed crystallographically. The high-resolution spectroscopic information obtained has been correlated with a detailed ligand-field analysis to gain insight into the electronic structure of the complex. Symmetry arguments have been used to demonstrate that the sign of the MCD is characteristic of the tetragonally elongated environment. The complex also displays catecholase activity (k _cat = 15 ± 1.5 min⁻¹, K _M = 6.4 ± 1.8 mM), which is compared with other dicopper catechol oxidase models. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Confidence bands for cumulative incidence curves under the additive risk model

In the context of competing risks, the cumulative incidence function is often used to summarize the cause-specific failure-time data. As an alternative to the proportional hazards model, the additive risk model is used to investigate covariate effects by specifying that the subject-specific hazard function is the sum of a baseline hazard function and a regression function of covariates. Based on such a formulation, we present an approach to constructing simultaneous confidence intervals for the cause-specific cumulative incidence function of patients with given risk factors. A melanoma data set is used for the purpose of illustration.