Two-scale modelling of Saponaria bellidifolia Sm. (Caryophyllaceae) abundance on limestone outcrops from its northern range periphery (Southeastern Carpathians)

We modelled the effect of habitat heterogeneity on the abundance of the submediterranean Saponaria bellidifolia, a red list species in Romania. The study was designed at two scales: 100 and 0.5 m². At larger scale, generalized additive models and canonical correspondence analysis were used to model the density of ramets, whereas at microscale, binomial logistic regression was employed to model the species’ occurrence. S. bellidifolia abundance responded sensitively to habitat type (classified as “grassy”, “rocky” and “scree”), rather than to microclimatic variables. At both scales, habitat type was the best predictor of ramet abundance, followed by slope and vegetation cover. At 0.5 m², soil depth was also a good predictor of species occurrence. The data revealed that screes are the most suitable habitats for hosting relatively large populations of this rare species, because of occasional natural disturbances and presumably lower interspecific competition.     

Metabolic Engineering with power-law and linear-logarithmic systems

Metabolic Engineering aims to improve the performance of biotechnological processes through rational manipulation rather than random mutagenesis of the organisms involved. Such a strategy can only succeed when a mathematical model of the target process is available. Simplifying assumptions are often needed to cope with the complexity of such models in an efficient way, and the choice of such assumptions often leads to models that fall within a certain structural template or formalism. The most popular formalisms can be grouped in two categories: power-law and linear-logarithmic. As optimization and analysis of a model strongly depends on its structure, most methods in Metabolic Engineering have been defined within a given formalism and never used in any other.In this work, the four most commonly used formalisms (two power-law and two linear-logarithmic) are placed in a common framework defined within Biochemical Systems Theory. This framework defines every model as matrix equations in terms of the same parameters, enabling the formulation of a common steady state analysis and providing means for translating models and methods from one formalism to another. Several Metabolic Engineering methods are analysed here and shown to be variants of a single equation. Particularly, two problem solving philosophies are compared: the application of the design equation and the solution of constrained optimization problems. Generalizing the design equation to all the formalisms shows it to be interchangeable with the direct solution of the rate law in matrix form. Furthermore, optimization approaches are concluded to be preferable since they speed the exploration of the feasible space, implement a better specification of the problem and exclude unrealistic results.Beyond consolidating existing knowledge and enabling comparison, the systematic approach adopted here can fill the gaps between the different methods and combine their strengths.     

Modeling baseline conditions of ecological indicators: Marine renewable energy environmental monitoring

Ecological indicators are often collected to detect and monitor environmental change. Statistical models are used to estimate natural variability, pre-existing trends, and environmental predictors of baseline indicator conditions. Establishing standard models for baseline characterization is critical to the effective design and implementation of environmental monitoring programs. An anthropogenic activity that requires monitoring is the development of Marine Renewable Energy sites. Currently, there are no standards for the analysis of environmental monitoring data for these development sites. Marine Renewable Energy monitoring data are used as a case study to develop and apply a model evaluation to establish best practices for characterizing baseline ecological indicator data. We examined a range of models, including six generalized regression models, four time series models, and three nonparametric models. Because monitoring data are not always normally distributed, we evaluated model ability to characterize normal and non-normal data using hydroacoustic metrics that serve as proxies for ecological indicator data. The nonparametric support vector regression and random forest models, and parametric state-space time series models generally were the most accurate in interpolating the normal metric data. Support vector regression and state-space models best interpolated the non-normally distributed data. If parametric results are preferred, then state-space models are the most robust for baseline characterization. Evaluation of a wide range of models provides a comprehensive characterization of the case study data, and highlights advantages of models rarely used in Marine Renewable Energy environmental monitoring. Our model findings are relevant for any ecological indicator data with similar properties, and the evaluation approach is applicable to any monitoring program.     

Acetylated tau exacerbates apoptosis by disturbing mitochondrial dynamics in HEK293 cells

An increase in tau acetylation at K274 and K281 and abnormal mitochondrial dynamics have been observed in the brains of Alzheimer's disease (AD) patients. Here, we constructed three types of tau plasmids, TauKQ (acetylated tau mutant, by mutating its K274/K281 into glutamine to mimic disease-associated lysine acetylation), TauKR (non-acetylated tau mutant, by mutating its K274/K281 into arginine), and TauWT (wild-type human full-length tau). By transfecting these tau plasmids in HEK293 cells, we found that TauWT and TauKR induced mitochondrial fusion by increasing the level of mitochondrial fusion proteins. Conversely, TauKQ induced mitochondrial fission by reducing mitochondrial fusion proteins, exacerbating mitochondrial dysfunction and apoptosis. BGP-15 ameliorated TauKQ-induced mitochondrial dysfunction and apoptosis by improving mitochondrial dynamics. Our findings suggest that acetylation of K274/281 represents an important post-translational modification site regulating mitochondrial dynamics, and that BGP-15 holds potential as a therapeutic agent for mitochondria-associated diseases such as AD.

     

A note on type II error under random effects misspecification in generalized linear mixed models

Litière, Alonso, and Molenberghs (2007, Biometrics, 63, 1038-1044) presented the results of simulation studies that they claimed showed that misspecification of the shape of the random effects distribution can produce marked increases in Type II error (decreases in power) of tests based on fits of generalized linear mixed models. However, the article contains a logical fallacy that invalidates this claim. We present logically correct simulation studies that demonstrate little increase in Type II error, consistent with the earlier work that shows little effect due to misspecification.     

Generalized causal mediation analysis

The goal of mediation analysis is to assess direct and indirect effects of a treatment or exposure on an outcome. More generally, we may be interested in the context of a causal model as characterized by a directed acyclic graph (DAG), where mediation via a specific path from exposure to outcome may involve an arbitrary number of links (or "stages"). Methods for estimating mediation (or pathway) effects are available for a continuous outcome and a continuous mediator related via a linear model, while for a categorical outcome or categorical mediator, methods are usually limited to two-stage mediation. We present a method applicable to multiple stages of mediation and mixed variable types using generalized linear models. We define pathway effects using a potential outcomes framework and present a general formula that provides the effect of exposure through any specified pathway. Some pathway effects are nonidentifiable and their estimation requires an assumption regarding the correlation between counterfactuals. We provide a sensitivity analysis to assess the impact of this assumption. Confidence intervals for pathway effect estimates are obtained via a bootstrap method. The method is applied to a cohort study of dental caries in very low birth weight adolescents. A simulation study demonstrates low bias of pathway effect estimators and close-to-nominal coverage rates of confidence intervals. We also find low sensitivity to the counterfactual correlation in most scenarios.     

How the molecular structure determines the flow of excitation energy in plant light-harvesting complex II

Excitation energy transfer in the light-harvesting complex II of higher plants is modeled using excitonic couplings and local transition energies determined from structure-based calculations recently (Müh et al., 2010). A theory is introduced that implicitly takes into account protein induced dynamic localization effects of the exciton wavefunction between weakly coupled optical and vibronic transitions of different pigments. Linear and non-linear optical spectra are calculated and compared with experimental data reaching qualitative agreement. High-frequency intramolecular vibrational degrees of freedom are found important for ultrafast subpicosecond excitation energy transfer between chlorophyll (Chl) b and Chla, since they allow for fast dissipation of the excess energy. The slower ps component of this transfer is due to the monomeric excited state of Chlb 605. The majority of exciton relaxation in the Chla spectral region is characterized by slow ps exciton equilibration between the Chla domains within one layer and between the lumenal and stromal layers in the 10-20 ps time range. Subpicosecond exciton relaxation in the Chla region is only found within the terminal emitter domain (Chls a 610/611/612) and within the Chla 613/614 dimer. Deviations between measured and calculated exciton state life times are obtained for the intermediate spectral region between the main absorbance bands of Chla and Chlb that indicate that besides Chlb 608 another pigment should absorb there. Possible candidates, so far not identified by structure-based calculations, but by fitting of optical spectra and mutagenesis studies, are discussed. Additional mutagenesis studies are suggested to resolve this issue.     

Loss of the m‐AAA protease subunit AFG3L2 causes mitochondrial transport defects and tau hyperphosphorylation

The m‐AAA protease subunit AFG3L2 is involved in degradation and processing of substrates in the inner mitochondrial membrane. Mutations in AFG3L2 are associated with spinocerebellar ataxia SCA28 in humans and impair axonal development and neuronal survival in mice. The loss of AFG3L2 causes fragmentation of the mitochondrial network. However, the pathogenic mechanism of neurodegeneration in the absence of AFG3L2 is still unclear. Here, we show that depletion of AFG3L2 leads to a specific defect of anterograde transport of mitochondria in murine cortical neurons. We observe similar transport deficiencies upon loss of AFG3L2 in OMA1‐deficient neurons, indicating that they are not caused by OMA1‐mediated degradation of the dynamin‐like GTPase OPA1 and inhibition of mitochondrial fusion. Treatment of neurons with antioxidants, such as N‐acetylcysteine or vitamin E, or decreasing tau levels in axons restored mitochondrial transport in AFG3L2‐depleted neurons. Consistently, tau hyperphosphorylation and activation of ERK kinases are detected in mouse neurons postnatally deleted for Afg3l2. We propose that reactive oxygen species signaling leads to cytoskeletal modifications that impair mitochondrial transport in neurons lacking AFG3L2.