Ecological niche models and patterns of richness and endemism of the southern Andean genus Eurymetopum (Coleoptera, Cleridae)

Eurymetopum is an Andean clerid genus with 22 species. We modeled the ecological niches of 19 species with Maxent and used them as potential distributional maps to identify patterns of richness and endemicity. All modeled species maps were overlapped in a single map in order to determine richness. We performed an optimality analysis with NDM/VNDM in a grid of 1º latitude-longitude in order to identify endemism. We found a highly rich area, located between 32º and 41º south latitude, where the richest pixels have 16 species. One area of endemism was identified, located in the Maule and Valdivian Forest biogeographic provinces, which extends also to the Santiago province of the Central Chilean subregion, and contains four endemic species (E. parallelum, E. prasinum, E. proteus, and E. viride), as well as 16 non-endemic species. The sympatry of these phylogenetically unrelated species might indicate ancient vicariance processes, followed by episodes of dispersal. Based on our results, we suggest a close relationship between these provinces, with the Maule representing a complex area.

Pitfalls in the modeling of forward-looking price setting and investment decisions

The present paper makes progress in explaining the role of capital for inflation and output dynamics. We followWoodford (2003, Ch. 5) in assuming Calvo pricing combined with a convex capital adjustment cost at the firm level. Our main result is that capital accumulation affects inflation dynamics primarily through its impact on the marginal cost. This mechanism is much simpler than the one implied by the analysis in Woodford's text. The reason is that his analysis suffers from a conceptual mistake, as we show. The latter obscures the economic mechanism through which capital affects inflation and output dynamics in the Calvo model, as discussed in Woodford (2004).

Flexible multivariate GARCH modeling with an application to international stock markets

The goal of this paper is to estimate time-varying covariance matrices.Since the covariance matrix of financial returns is known to changethrough time and is an essential ingredient in risk measurement, portfolioselection, and tests of asset pricing models, this is a very importantproblem in practice. Our model of choice is the Diagonal-Vech version ofthe Multivariate GARCH(1,1) model. The problem is that the estimation ofthe general Diagonal-Vech model model is numerically infeasible indimensions higher than 5. The common approach is to estimate more restrictive models which are tractable but may not conform to the data. Our contributionis to propose an alternative estimation method that is numerically feasible,produces positive semi-definite conditional covariance matrices, and doesnot impose unrealistic a priori restrictions. We provide an empiricalapplication in the context of international stock markets, comparing thenew estimator to a number of existing ones.

Macroeconomic modeling for monetary policy evaluation

We describe some of the main features of the recent vintage macroeconomic models used for monetary policy evaluation. We point to some of the key differences with respect to the earlier generation ofmacro models, and highlight the insights for policy that these new frameworks have to offer. Our discussion emphasizes two key aspects of the new models: the significant role of expectations of future policy actions in the monetary transmission mechanism, and the importance for the central bank of tracking of the flexible price equilibrium values of the natural levels of output and the real interest rate. We argue that both features have important implications for the conduct of monetary policy.

Expanding molecular modeling and design tools to non-natural sidechains.

Protein-protein interactions encode the wiring diagram of cellular signaling pathways and their deregulations underlie a variety of diseases, such as cancer. Inhibiting protein-protein interactions with peptide derivatives is a promising way to develop new biological and therapeutic tools. Here, we develop a general framework to computationally handle hundreds of non-natural amino acid sidechains and predict the effect of inserting them into peptides or proteins. We first generate all structural files (pdb and mol2), as well as parameters and topologies for standard molecular mechanics software (CHARMM and Gromacs). Accurate predictions of rotamer probabilities are provided using a novel combined knowledge and physics based strategy. Non-natural sidechains are useful to increase peptide ligand binding affinity. Our results obtained on non-natural mutants of a BCL9 peptide targeting beta-catenin show very good correlation between predicted and experimental binding free-energies, indicating that such predictions can be used to design new inhibitors. Data generated in this work, as well as PyMOL and UCSF Chimera plug-ins for user-friendly visualization of non-natural sidechains, are all available at http://www.swisssidechain.ch. Our results enable researchers to rapidly and efficiently work with hundreds of non-natural sidechains.

Scale-dependent adaptive evolution and morphological convergence to climatic niche in Californian eriogonoids (Polygonaceae)

Aim Macroevolutionary patterns and processes change substantially depending on levels of taxonomic and ecological organization, and the resolution of environmental and spatial variability. In comparative methods, the resolution of environmental and spatial variability often defines the number of selective regimes used to test whether phenotypic characteristics are adaptively correlated with the environment. Here, we examine how investigator choice of the number of selective regimes, determined by varying the resolution of among-species variability in the species climatic niche (hereafter called ecological scale'), influences trait morphological diversification among Eriogonoideae species. We assess whether adaptive or neutral processes drive the evolution of several morphological traits in these species. Location South-western North America. Methods We applied a phylogenetic framework of three evolutionary models to four morphological traits and the climatic niches of Eriogonoideae (in the buckwheat family, Polygonaceae). We tested whether morphological traits evolve in relation to climate by adaptive or neutral process, and whether the resulting patterns of morphological variability are conserved or convergent across the clade. We inspected adaptive models of evolution under different levels of resolution of among-species variability of the climatic niche. Results We show that morphological traits and climate niches of Eriogonoideae species are not phylogenetically conserved. Further, adaptive evolution of phenotypic traits is specific to climatic niche occupancy across this clade. Finally, the likely evolutionary process and the level of detectable niche conservatism change depending on the resolution of environmental variability of the climatic niche. Main conclusions Our study demonstrates the need to consider both the resolution of environmental variability and alternative evolutionary models to understand the morphological diversification that accompanies divergent adaptive evolution of lineages to climatic conditions.

Comment on recent modeling studies of astrocyte-neuron metabolic interactions.

Recent years have seen a surge in mathematical modeling of the various aspects of neuron-astrocyte interactions, and the field of brain energy metabolism is no exception in that regard. Despite the advent of biophysical models in the field, the long-lasting debate on the role of lactate in brain energy metabolism is still unresolved. Quite the contrary, it has been ported to the world of differential equations. Here, we summarize the present state of this discussion from the modeler's point of view and bring some crucial points to the attention of the non-mathematically proficient reader.

Modeling complex wells with the multi-scale finite-volume method

In this paper, an extension of the multi-scale finite-volume (MSFV) method is devised, which allows to Simulate flow and transport in reservoirs with complex well configurations. The new framework fits nicely into the data Structure of the original MSFV method,and has the important property that large patches covering the whole well are not required. For each well. an additional degree of freedom is introduced. While the treatment of pressure-constraint wells is trivial (the well-bore reference pressure is explicitly specified), additional equations have to be solved to obtain the unknown well-bore pressure of rate-constraint wells. Numerical Simulations of test cases with multiple complex wells demonstrate the ability of the new algorithm to capture the interference between the various wells and the reservoir accurately. (c) 2008 Elsevier Inc. All rights reserved.

Forecasting the effects of global warming on biodiversity

The demand for accurate forecasting of the effects of global warming on biodiversity is growing, but current methods for forecasting have limitations. in this article, we compare and discuss the different uses of four forecasting methods: (1) models that consider species individually, (2) niche-theory models that group species by habitat (more specifically, by environmental conditions under which a species can persist or does persist), (3) general circulation models and coupled ocean-atmosphere-biosphere models, and (4) specics-area curve models that consider all species or large aggregates of species. After outlining the different uses and limitations of these methods, we make eight primary suggestions for improving forecasts. We find that greater use of the fossil record and of modern genetic studies would improve forecasting methods. We note a Quaternary conundrum: While current empirical and theoretical ecological results suggest that many species could be at risk from global warming, during the recent ice ages surprisingly few species became extinct. The potential resolution of this conundrum gives insights into the requirements for more accurate and reliable forecasting. Our eight suggestions also point to constructive synergies in the solution to the different problems.

Carbon and oxygen isotope study of hydrothermal carbonates in the zinc-lead deposits of the San Vicente district, central Peru: A quantitative modeling on mixing processes and CO2 degassing

Carbon and oxygen isotope studies of the host and gangue carbonates of Mississippi Valley-type zinc-lead deposits in the San Vicente District hosted in the Upper Triassic to Lower Jurassic dolostones of the Pucara basin (central Peru) were used to constrain models of the ore formation. A mixing model between an incoming hot saline slightly acidic radiogenic (Pb, Sr) fluid and the native formation water explains the overall isotopic variation (delta(13)C = - 11.5 to + 2.5 parts per thousand relative to PDB and delta(18)O = + 18.0 to + 24.3 parts per thousand relative to SMOW) of the carbonate generations. The dolomites formed during the main ore stage show a narrower range (delta(13)C = - 0.1 to + 1.7 parts per thousand and delta(18)O = + 18.7 to + 23.4 parts per thousand) which is explained by exchange between the mineralizing fluids and the host carbonates combined with changes in temperature and pressure. This model of fluid-rock interaction explains the pervasive alteration of the host dolomite I and precipitation of sphalerite I. The open-space filling hydrothermal white sparry dolomite and the coexisting sphalerite II formed by prolonged fluid-host dolomite interaction and limited CO2 degassing. Late void-filling dolomite III (or calcite) and the associated sphalerite III formed as the consequence of CO2 degassing and concomitant pH increase of a slightly acidic ore fluid. Widespread brecciation is associated to CO2 outgassing. Consequently, pressure variability plays a major role in the ore precipitation during the late hydrothermal events in San Vicente. The presence of native sulfur associated with extremely carbon-light calcites replacing evaporitic sulfates (e.g., delta(13)C = - 11.5 parts per thousand), altered native organic matter and heavier hydrothermal bitumen (from - 27.0 to - 23.0 parts per thousand delta(13)C) points to thermochemical reduction of sulfate and/or thiosulfate. The delta(13)C- and delta(18)O-values of the altered host dolostone and hydrothermal carbonates, and the carbon isotope composition of the associated organic matter show a strong regional homogeneity. These results coupled with the strong mineralogical and petrographic similarities of the different MVT occurrences perhaps reflects the fact that the mineralizing processes were similar in the whole San Vicente belt, suggesting the existence of a common regional mineralizing hydrothermal system with interconnected plumbing.

Identification of optimal structural connectivity using functional connectivity and neural modeling.

The complex network dynamics that arise from the interaction of the brain's structural and functional architectures give rise to mental function. Theoretical models demonstrate that the structure-function relation is maximal when the global network dynamics operate at a critical point of state transition. In the present work, we used a dynamic mean-field neural model to fit empirical structural connectivity (SC) and functional connectivity (FC) data acquired in humans and macaques and developed a new iterative-fitting algorithm to optimize the SC matrix based on the FC matrix. A dramatic improvement of the fitting of the matrices was obtained with the addition of a small number of anatomical links, particularly cross-hemispheric connections, and reweighting of existing connections. We suggest that the notion of a critical working point, where the structure-function interplay is maximal, may provide a new way to link behavior and cognition, and a new perspective to understand recovery of function in clinical conditions.

Physiologically based pharmacokinetic modeling of arsenic in the mouse

A remarkable feature of the carcinogenicity of inorganic arsenic is that while human exposures to high concentrations of inorganic arsenic in drinking water are associated with increases in skin, lung, and bladder cancer, inorganic arsenic has not typically caused tumors in standard laboratory animal test protocols. Inorganic arsenic administered for periods of up to 2 yr to various strains of laboratory mice, including the Swiss CD-1, Swiss CR:NIH(S), C57Bl/6p53(+/-), and C57Bl/6p53(+/+), has not resulted in significant increases in tumor incidence. However, Ng et al. (1999) have reported a 40% tumor incidence in C57Bl/6J mice exposed to arsenic in their drinking water throughout their lifetime, with no tumors reported in controls. In order to investigate the potential role of tissue dosimetry in differential susceptibility to arsenic carcinogenicity, a physiologically based pharmacokinetic (PBPK) model for inorganic arsenic in the rat, hamster, monkey, and human (Mann et al., 1996a, 1996b) was extended to describe the kinetics in the mouse. The PBPK model was parameterized in the mouse using published data from acute exposures of B6C3F1 mice to arsenate, arsenite, monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA) and validated using data from acute exposures of C57Black mice. Predictions of the acute model were then compared with data from chronic exposures. There was no evidence of changes in the apparent volume of distribution or in the tissue-plasma concentration ratios between acute and chronic exposure that might support the possibility of inducible arsenite efflux. The PBPK model was also used to project tissue dosimetry in the C57Bl/6J study, in comparison with tissue levels in studies having shorter duration but higher arsenic treatment concentrations. The model evaluation indicates that pharmacokinetic factors do not provide an explanation for the difference in outcomes across the various mouse bioassays. Other possible explanations may relate to strain-specific differences, or to the different durations of dosing in each of the mouse studies, given the evidence that inorganic arsenic is likely to be active in the later stages of the carcinogenic process. [Authors]

Understanding the ecological niche to elucidate spatial strategies of the southernmost Tupinambis lizards

Understanding factors that shape ranges of species is central in evolutionary biology. Species distribution models have become important tools to test biogeographical, ecological and evolutionary hypotheses. Moreover, from an ecological and evolutionary perspective, these models help to elucidate the spatial strategies of species at a regional scale. We modelled species distributions of two phylogenetically, geographically and ecologically close Tupinambis species (Teiidae) that occupy the southernmost area of the genus distribution in South America. We hypothesized that similarities between these species might have induced spatial strategies at the species level, such as niche differentiation and divergence of distribution patterns at a regional scale. Using logistic regression and MaxEnt we obtained species distribution models that revealed interspecific differences in habitat requirements, such as environmental temperature, precipitation and altitude. Moreover, the models obtained suggest that although the ecological niches of Tupinambis merianae and T. rufescens are different, these species might co-occur in a large contact zone. We propose that niche plasticity could be the mechanism enabling their co-occurrence. Therefore, the approach used here allowed us to understand the spatial strategies of two Tupinambis lizards at a regional scale.

Positional information by differential endocytosis splits auxin response to drive Arabidopsis root meristem growth.

In the Arabidopsis root meristem, polar auxin transport creates a transcriptional auxin response gradient that peaks at the stem cell niche and gradually decreases as stem cell daughters divide and differentiate [1-3]. The amplitude and extent of this gradient are essential for both stem cell maintenance and root meristem growth [4, 5]. To investigate why expression of some auxin-responsive genes, such as the essential root meristem growth regulator BREVIS RADIX (BRX) [6], deviates from this gradient, we combined experimental and computational approaches. We created cellular-level root meristem models that accurately reproduce distribution of nuclear auxin activity and allow dynamic modeling of regulatory processes to guide experimentation. Expression profiles deviating from the auxin gradient could only be modeled after intersection of auxin activity with the observed differential endocytosis pattern and positive autoregulatory feedback through plasma-membrane-to-nucleus transfer of BRX. Because BRX is required for expression of certain auxin response factor targets, our data suggest a cell-type-specific endocytosis-dependent input into transcriptional auxin perception. This input sustains expression of a subset of auxin-responsive genes across the root meristem's division and transition zones and is essential for meristem growth. Thus, the endocytosis pattern provides specific positional information to modulate auxin response.