Multiscale landscape genomic models to detect signatures of selection in the alpine plant Biscutella laevigata

Plant species are known to adapt locally to their environment, particularly in mountainous areas where conditions can vary drastically over short distances. The climate of such landscapes being largely influenced by topography, using fine‐scale models to evaluate environmental heterogeneity may help detecting adaptation to micro‐habitats. Here, we applied a multiscale landscape genomic approach to detect evidence of local adaptation in the alpine plant Biscutella laevigata. The two gene pools identified, experiencing limited gene flow along a 1‐km ridge, were different in regard to several habitat features derived from a very high resolution (VHR) digital elevation model (DEM). A correlative approach detected signatures of selection along environmental gradients such as altitude, wind exposure, and solar radiation, indicating adaptive pressures likely driven by fine‐scale topography. Using a large panel of DEM‐derived variables as ecologically relevant proxies, our results highlighted the critical role of spatial resolution. These high‐resolution multiscale variables indeed indicate that the robustness of associations between genetic loci and environmental features depends on spatial parameters that are poorly documented. We argue that the scale issue is critical in landscape genomics and that multiscale ecological variables are key to improve our understanding of local adaptation in highly heterogeneous landscapes.     

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.     

Structural and functional domains of cellobiohydrolase I from trichoderma reesei

Limited proteolysis (papain) of the cellobiohydrolase I (CBH I, 65 kDa) from Trichoderma reesei led to the seperation of two functional domains: a core protein (55 kDa) containing the active site, and a C-terminal glycopeptide (10 kDa) implicated in binding to the insoluble matrix (cellulose). The quaternary structures of the intact CBH I and its core in solution are now compared by small angle X-ray scattering (SAXS) measurements. The molecular parameters derived for the core (Rg=2.09 nm, D_max=6.5 nm) and for the intact enzyme (Rg=4.27 nm, D_max=18 nm) indicate very different shapes. The resulting models show a tadpole-like structure for the intact enzyme where the isotropic part coincides with the core protein and the flexible tail part should be identified with the C-terminal glycopeptide. Thus in this enzyme, functional differentiation is reflected in structural peculiarities.Abbreviations SAXS small angle X-ray scattering - SDS-PAGE SDS-polyacrylamide gel electrophoresis - IEF-PAG polyacrylamide gel isoelectric focusing; cellobiohydrolase (CBH, 1,4--glucan cellobio hydrolase (E.C.3.2.1.91)) - D_max maximum diameter - Rg radius of gyration     

Increasing in vitro microrhizome production of ginger (Zingiber officinale Roscoe)

In this study, using the quadratic saturation 310 D-optimal design method, we examined the effect of kinetin (KT), gibberellic acid (GA), and naphthalene acetic acid (NAA) on microrhizome production in ginger. The effect of GA on rhizome induction was larger than that of KT or NAA. Using simulation and optimality selection for tissue culture, we found that concentrations of GA, KT, and NAA of 1.33–2.35, 0.49–0.66, and 0.62 g/l, respectively, gave a microrhizome weight of over 0.25 g. The optimal conditions for microrhizome production were 80 g/l sucrose, 2 × MS macro-elements, and 1 × MS micro-elements, with a photoperiod of 24L:0D (light/dark). At the same time, 100% survival could be achieved on transfer of the in vitro ginger plantlets with microrhizomes to soil.     

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.