Priority of color over scent during flower visitation by adult <Emphasis Type="Italic">Vanessa indica</Emphasis> butterflies

Most flower visitors innately prefer a particular color and scent, and use them as cues for flower recognition and selection. However, in most cases, since color and scent serve as a combined signal, not only does the preference for an individual cue, but also the preference hierarchy among different cues, influence their flower visitation. In the present study, we attempted to reveal (1) the chromatic and (2) the olfactory cues that stimulate flower visiting, and (3) the preference hierarchy between these cues, using the naïve adult butterfly Vanessa indica. When we offered 12 different-colored (six chromatic and six achromatic) paper flower models, V. indica showed a color preference for yellow and blue. When we examined the proboscis extension reflex (PER) of V. indica towards 16 individual compounds identified in the floral scents from two nectar plants belonging to the family Compositae, Taraxacum officinale and Cirsium japonicum, six compounds were found to have relatively high PER-eliciting activities, including benzaldehyde, acetophenone, and (E+Z)-nerolidol. When we combined color and scent cues in two-choice bioassays, where butterflies were offered flower models that were purple (a relatively unattractive color), the models scented with these active compounds were significantly more attractive than the odorless controls. In addition, synthetic blends mimicking the floral scents of T. officinale and C. japonicum (at doses equivalent to that of ten flowers) enhanced the number of visits to the scented models. However, the effect of odorizing was not conspicuous in parallel bioassays when yellow flower models were used, and the butterflies also significantly preferred odorless yellow models to scented purple models. These results demonstrate that V. indica depends primarily on color and secondarily on scent during flower visitation.     

The Secondary Structure of Mammalian Mitochondrial 16S rRNA Molecules: Refinements Based on a Comparative Phylogenetic Approach

Eighty six complete 16S ribosomal RNA (rRNA) gene sequences representing every mammalian order (one monotreme, 33 marsupials, 52 placentals) were employed to establish a core secondary structure model for mammalian 16S rRNA. Starting with the Gutell et al. (1993) and De Rijk et al. (1999) models, we used the criteria of potential base-pairing and positional covariance to make refinements in these models for mammalian 16S rRNA molecules. Our results suggest a mammalian secondary structure model with deletions as well as additions to the Gutell et al. (1993) and De Rijk et al. (1999) models for cow. We recognize 53 stems, 41 of which show at least some positional covariance within Mammalia. In addition, we recognize four tertiary interactions. Stems and loops have distinctly different properties, including base composition and relative substitution rates. Accounting for these differences results in improved models of sequence evolution.     

Process,correlation and parameter fitting in species distribution models: a response to Kriticos et al

In a recent article (Dormann et al., 2012, Journal of Biogeography, 39, 2119–2131), we compared different approaches to species distribution modelling and depicted modelling approaches along an axis from purely ‘correlative’ to ‘forward process‐based’ models. In their correspondence, Kriticos et al. (2013, Journal of Biogeography, doi: 10.1111/j.1365‐2699.2012.02791.x ) challenge this view, claiming that our continuum representation neglects differences among models and does not consider the ability of fitted process‐based models to combine the advantages of both process‐based and correlative modelling approaches. Here we clarify that the continuum view resulted from recognition of the manifold differences between models. We also reinforce the point that the current trend towards combining different modelling approaches may lead not only to the desired combination of the advantages but also to the accumulation of the disadvantages of those approaches. This point has not been made sufficiently clear previously.     

Analytic steady-state space use patterns and rapid computations in mechanistic home range analysis

Mechanistic home range models are important tools in modeling animal dynamics in spatially complex environments. We introduce a class of stochastic models for animal movement in a habitat of varying preference. Such models interpolate between spatially implicit resource selection analysis (RSA) and advection-diffusion models, possessing these two models as limiting cases. We find a closed-form solution for the steady-state (equilibrium) probability distribution u* using a factorization of the redistribution operator into symmetric and diagonal parts. How space use is controlled by the habitat preference function w depends on the characteristic width of the animals’ redistribution kernel: when the redistribution kernel is wide relative to variation in w, u* ∝ w, whereas when it is narrow relative to variation in w, u* ∝ w ². In addition, we analyze the behavior at discontinuities in w which occur at habitat type boundaries, and simulate the dynamics of space use given two-dimensional prey-availability data, exploring the effect of the redistribution kernel width. Our factorization allows such numerical simulations to be done extremely fast; we expect this to aid the computationally intensive task of model parameter fitting and inverse modeling.      

Invertebrate models of lysosomal storage disease: what have we learned so far?

The lysosomal storage diseases (LSDs) collectively account for death in 1 in 8,000 children. Although some forms are treatable, they are essentially incurable and usually are lethal in the first decade of life. The most intractable forms of LSD are those with neuronal involvement. In an effort to identify the pathological signaling driving pathology in the LSDs, invertebrate models have been developed. In this review, we outline our current understanding of LSDs and recent findings using invertebrate models. We outline strategies and pitfalls for the development of such models. Available models of LSD in Drosophila and Caenorhabditis elegans are uncovering roles for LSD-related proteins with previously unknown function using both gain-of-function and loss-of-function strategies. These models of LSD in Drosophila and C. elegans have identified potential pathogenic signaling cascades that are proving critical to our understanding of these lethal diseases.     

Differential expression of parvalbumin in neonatal phencyclidine‐treated rats and socially isolated rats

Decreased parvalbumin expression is a hallmark of the pathophysiology of schizophrenia and has been associated with abnormal cognitive processing and decreased network specificity. It is not known whether this decrease is due to reduced expression of the parvalbumin protein or degeneration of parvalbumin‐positive interneurons (PV⁺ interneurons). In this study, we examined PV⁺ expression in two rat models of cognitive dysfunction in schizophrenia: the environmental social isolation (SI) and pharmacological neonatal phencyclidine (neoPCP) models. Using a stereological method, the optical fractionator, we counted neurons, PV⁺ interneurons, and glial cells in the medial prefrontal cortex (mPFC) and hippocampus (HPC). In addition, we quantified the mRNA level of parvalbumin in the mPFC. There was a statistically significant reduction in the number of PV⁺ interneurons (p = 0.021) and glial cells (p = 0.024) in the mPFC of neonatal phencyclidine rats, but not in SI rats. We observed no alterations in the total number of neurons, hippocampal PV⁺ interneurons, parvalbumin mRNA expression or volume of the mPFC or HPC in the two models. Thus, as the total number of neurons remains unchanged following phencyclidine (PCP) treatment, we suggest that the decreased number of counted PV⁺ interneurons represents a reduced parvalbumin protein expression below immunohistochemical detection limit rather than a true cell loss. Furthermore, these results indicate that the effect of neonatal PCP treatment is not limited to neuronal populations.     

Modelling carbon stocks and fluxes in the wood product sector: a comparative review

In addition to forest ecosystems, wood products are carbon pools that can be strategically managed to mitigate climate change. Wood product models (WPMs) simulating the carbon balance of wood production, use and end of life can complement forest growth models to evaluate the mitigation potential of the forest sector as a whole. WPMs can be used to compare scenarios of product use and explore mitigation strategies. A considerable number of WPMs have been developed in the last three decades, but there is no review available analysing their functionality and performance. This study analyses and compares 41 WPMs. One surprising initial result was that we discovered the erroneous implementation of a few concepts and assumptions in some of the models. We further described and compared the models using six model characteristics (bucking allocation, industrial processes, carbon pools, product removal, recycling and substitution effects) and three model‐use characteristics (system boundaries, model initialization and evaluation of results). Using a set of indicators based on the model characteristics, we classified models using a hierarchical clustering technique and differentiated them according to their increasing degrees of complexity and varying levels of user support. For purposes of simulating carbon stock in wood products, models with a simple structure may be sufficient, but to compare climate change mitigation options, complex models are needed. The number of models has increased substantially over the last ten years, introducing more diversity and accuracy. Calculation of substitution effects and recycling has also become more prominent. However, the lack of data is still an important constraint for a more realistic estimation of carbon stocks and fluxes. Therefore, if the sector wants to demonstrate the environmental quality of its products, it should make it a priority to provide reliable life cycle inventory data, particularly regarding aspects of time and location.     

Evaluating the ability of regional models to predict local avian abundance

Spatial modeling over broad scales can potentially direct conservation efforts to areas with high species-specific abundances. We examined the performance of regional models for predicting bird abundance at spatial scales typically addressed in conservation planning. Specifically, we used point count data on wood thrush (Hylocichla mustelina) and blue-winged warbler (Vermivora cyanoptera) from 2 time periods (1995–1998 and 2006–2007) to evaluate the ability of regional models derived via Bayesian hierarchical techniques to predict bird abundance. We developed models for each species within Bird Conservation Region (BCR) 23 in the upper midwestern United States at 800-ha, 8,000-ha, and approximately 80,000-ha scales. We obtained count data from the Breeding Bird Survey and land cover data from the National Land Cover Dataset (1992). We evaluated predictions from the best models, as defined by an information-theoretic criterion, using point count data collected within an ecological subregion of BCR 23 at 131 count stations in the 1990s and again in 2006–2007. Competing (Deviance Information Criteria <5) blue-winged warbler models accounted for 67% of the variability and suggested positive associations with forest edge and proportion of forest at the 8,000-ha scale, and negative associations with forest patch area (800 ha) and wetness (800 ha and 80,000 ha). The regional model performed best for blue-winged warbler predicted abundances from point counts conducted in Iowa during 1995–1996 (r_s = 0.57; P = 0.14), the survey period that most closely aligned with the time period of data used for regional model construction. Wood thrush models exhibited positive correlations with point count data for all survey areas and years combined (r_s = 0.58, P ≤ 0.001). In comparison, blue-winged warbler models performed worse as time increased between the point count surveys and vintage of the model building data (r_s = 0.03, P = 0.92 for Iowa and r_s = 0.13, P = 0.51 for all areas, 2006–2007), likely related to the ephemeral nature of their preferred early successional habitat. Species abundance and sensitivity to changing habitat conditions seems to be an important factor in determining the predictive ability of regional models. Hierarchical models can be a useful tool for concentrating efforts at the scale of management units and should be one of many tools used by land managers, but we caution that the utility of such models may decrease over time for species preferring relatively ephemeral habitats if model inputs are not updated accordingly. © 2012 The Wildlife Society.     

Fast optimization of statistical potentials for structurally constrained phylogenetic models

Background  

Statistical approaches for protein design are relevant in the field of molecular evolutionary studies. In recent years, new, so-called structurally constrained (SC) models of protein-coding sequence evolution have been proposed, which use statistical potentials to assess sequence-structure compatibility. In a previous work, we defined a statistical framework for optimizing knowledge-based potentials especially suited to SC models. Our method used the maximum likelihood principle and provided what we call the joint potentials. However, the method required numerical estimations by the use of computationally heavy Markov Chain Monte Carlo sampling algorithms.     

A mathematical analysis for the Brownian dynamics of a DNA tether

In the single-particle tracking experiment, the internal motion of a single DNA or polymer molecule whose one end is attached to a microsphere (optical marker) and the other end is anchored to a substratum is studied (Finzi and Gelles, 1995). The stochastic Brownian dynamics of the sphere reflect the spontaneous fluctuations, thus the physical characteristics, of the DNA or polymer molecule (Qian and Elson, 1999, Qian, 2000). In this paper, two continuous models of polymer molecules, a flexible elastic string and a weakly bentable elastic rod, are analyzed. Both models are cast mathematically in terms of linear stochastic differential equations. Based on Fourier analyses, we calculate the mean square displacement (MSD) of the particle motion, the key observable in the experiment. We obtain for both models the short-time asymptotics for the MSD, as well as the long-time behavior in terms of the smallest non-zero eigenvalues. It is shown that: (i) the long-time dynamics of continuous elastic string model quantitatively agree with that of the discrete bead-spring model. (ii) The short-time MSD of both models are controlled by the tethered particle, with linear dependence on t. (iii) The two models show characteristic difference for long-time behavior: The longest relaxation time is proportional to L ² for long elastic string and to L for short elastic string, but is proportional to L ⁴ for both long and short weakly bentable rod. Received: 26 March 1998 / Revised version: 9 June 2000 / Published online: 14 September 2000     

Where will species go? Incorporating new advances in climate modelling into projections of species distributions

Bioclimatic models are the primary tools for simulating the impact of climate change on species distributions. Part of the uncertainty in the output of these models results from uncertainty in projections of future climates. To account for this, studies often simulate species responses to climates predicted by more than one climate model and/or emission scenario. One area of uncertainty, however, has remained unexplored: internal climate model variability. By running a single climate model multiple times, but each time perturbing the initial state of the model slightly, different but equally valid realizations of climate will be produced. In this paper, we identify how ongoing improvements in climate models can be used to provide guidance for impacts studies. In doing so we provide the first assessment of the extent to which this internal climate model variability generates uncertainty in projections of future species distributions, compared with variability between climate models. We obtained data on 13 realizations from three climate models (three from CSIRO Mark2 v3.0, four from GISS AOM, and six from MIROC v3.2) for two time periods: current (1985–1995) and future (2025–2035). Initially, we compared the simulated values for each climate variable (P, T_max, T_min, and T_mean) for the current period to observed climate data. This showed that climates simulated by realizations from the same climate model were more similar to each other than to realizations from other models. However, when projected into the future, these realizations followed different trajectories and the values of climate variables differed considerably within and among climate models. These had pronounced effects on the projected distributions of nine Australian butterfly species when modelled using the BIOCLIM component of DIVA-GIS. Our results show that internal climate model variability can lead to substantial differences in the extent to which the future distributions of species are projected to change. These can be greater than differences resulting from between-climate model variability. Further, different conclusions regarding the vulnerability of species to climate change can be reached due to internal model variability. Clearly, several climate models, each represented by multiple realizations, are required if we are to adequately capture the range of uncertainty associated with projecting species distributions in the future.     

A Generalized Logistic Model for Quantal Response Bioassay

In bioassay, where different levels of the stimulus may represent different doses of a drug, the binary response is the death or survival of an individual receiving a specified dose. In such applications, it is common to model the probability of a positive response P at the stimulus level x by P = F(x′β), where F is a cumulative distribution function and β is a vector of unknown parameters which characterize the response function. The two most popular models used for modelling binary response bioassay involve the probit model [BLISS (1935), FINNEY (1978)], and the logistic model [BERKSON (1944), BROWN (1982)]. However, these models have some limitations. The use of the probit model involves the inverse of the standard normal distribution function, making it rather intractable. The logistic model has a simple form and a closed expression for the inverse distribution function, however, neither the logistic nor the probit can provide a good fit to response functions which are not symmetric or are symmetric but have a steeper or gentler incline in the central probability region. In this paper we introduce a more realistic model for the analysis of quantal response bioassay. The proposed model, which we refer to it as the generalized logistic model, is a family of response curves indexed by shape parameters m₁ and m₂. This family is rich enough to include the probit and logistic models as well as many others as special cases or limiting distributions. In particular, we consider the generalized logistic three parameter model where we assume that m₁ = m, m is a positive real number, and m₂ = 1. We apply this model to various sets of data, comparing the fit results to those obtained previously by other dose-response curves such as the logistic and probit, and showing that the fit can be improved by using the generalized logistic.     

Molecular hydrogen enhances osteogenesis in Danio rerio embryos

Molecular hydrogen (H₂) has been reported to have important biological effects on bone tissue in several in vitro and in vivo models. Danio rerio (zebrafish) embryo is a good model to study osteogenesis because of its transparency, size and rapid development. In zebrafish embryo, hydrogen-rich water (HRW) does not affect vitality or growth rate up to 15%. In addition, 7% HRW treatment enhances zebrafish embryo osteogenesis, increasing the vertebral mineralization rate. In conclusion, we demonstrated the beneficial effects of hydrogen on anabolic bone functions in vivo.     

The distribution of <Emphasis Type="Italic">F</Emphasis><Subscript><Emphasis Type="Italic">st</Emphasis></Subscript> and other genetic statistics for a class of population structure models

We examine genetic statistics used in the study of structured populations. In a 1999 paper, Wakeley observed that the coalescent process associated with the finite island model can be decomposed into a scattering phase and a collecting phase. This decomposition becomes exact in the large population limit with the coalescent at the end of the scattering phase converging to the Ewens sampling formula and the coalescent during the collecting phase converging to the Kingman coalescent. In this paper we introduce a class of limiting models, which we refer to as G/KC models, that generalize Wakeley’s decomposition. G in G/KC represents a completely general limit for the scattering phase, while KC represents a Kingman coalescent limit for the collecting phase. We show that both the island and two-dimensional stepping stone models converge to G/KC models in the large population limit. We then derive the distribution of the statistic F _st for all G/KC models under a large sample limit for the cases of strong or weak mutation, thereby deriving the large population, large sample limiting distribution of F _st for the island and two-dimensional stepping stone models as a special case of a general formula. Our methods allow us to take the large population and large sample limits simultaneously. In the context of large population, large sample limits, we show that the variance of F _st in the presence of weak mutation collapses as O(\frac1logd){O(\frac{1}{\log d})} where d is the number of demes sampled. Further, we show that this O(\frac1logd){O(\frac{1}{\log d})} is caused by a heavy tail in the distribution of F _st . Our analysis of F _st can be extended to an entire class of genetic statistics, and we use our approach to examine homozygosity measures. Our analysis uses coalescent based methods.     

Mass,phylogeny, and temperature are sufficient to explain differences in metabolic scaling across mammalian orders?

Whether basal metabolic rate‐body mass scaling relationships have a single exponent is highly discussed, and also the correct statistical model to establish relationships. Here, we aimed (1) to identify statistically best scaling models for 17 mammalian orders, Marsupialia, Eutheria and all mammals, and (2) thereby to prove whether correcting for differences in species’ body temperature and their shared evolutionary history improves models and their biological interpretability. We used the large dataset from Sieg et al. (The American Naturalist 174 , 2009, 720) providing species’ body mass (BM), basal metabolic rate (BMR) and body temperature (T). We applied different statistical approaches to identify the best scaling model for each taxon: ordinary least squares regression analysis (OLS) and phylogenetically informed analysis (PGLS), both without and with controlling for T. Under each approach, we tested linear equations (log‐log‐transformed data) estimating scaling exponents and normalization constants, and such with a variable normalization constant and a fixed exponent of either ? or ¾, and also a curvature. Only under temperature correction, an additional variable coefficient modeled the influence of T on BMR. Except for Pholidata and Carnivora, in all taxa studied linear models were clearly supported over a curvature by AICc. They indicated no single exponent at the level of orders or at higher taxonomic levels. The majority of all best models corrected for phylogeny, whereas only half of them included T. When correcting for T, the mathematically expected correlation between the exponent (b) and the normalization constant (a) in the standard scaling model y = a x ^b was removed, but the normalization constant and temperature coefficient still correlated strongly. In six taxa, T and BM correlated positively or negatively. All this hampers a disentangling of the effect of BM, T and other factors on BMR, and an interpretation of linear BMR‐BM scaling relationships in the mammalian taxa studied.     

Evolution of a structured cell population endowed with plasticity of traits under constraints on and between the traits

Homeostasis represents the idea that a feature may remain invariant despite changes in some external parameters. We establish a connection between homeostasis and injectivity for reaction network models. In particular, we show that a reaction network cannot exhibit homeostasis if a modified version of the network (which we call homeostasis-associated network) is injective. We provide examples of reaction networks which can or cannot exhibit homeostasis by analyzing the injectivity of their homeostasis-associated networks.

     

Traveling wave solutions from microscopic to macroscopic chemotaxis models

In this paper, we study the existence and nonexistence of traveling wave solutions for the one-dimensional microscopic and macroscopic chemotaxis models. The microscopic model is based on the velocity jump process of Othmer et al. (SIAM J Appl Math 57:1044–1081, 1997). The macroscopic model, which can be shown to be the parabolic limit of the microscopic model, is the classical Keller–Segel model, (Keller and Segel in J Theor Biol 30:225–234; 377–380, 1971). In both models, the chemosensitivity function is given by the derivative of a potential function, Φ(v), which must be unbounded below at some point for the existence of traveling wave solutions. Thus, we consider two examples: F(v) = lnv{\Phi(v) = \ln v} and F(v) = ln[v/(1-v)]{\Phi(v) = \ln[v/(1-v)]}. The mathematical problem reduces to proving the existence or nonexistence of solutions to a nonlinear boundary value problem with variable coefficient on \mathbb R{\mathbb R}. The main purpose of this paper is to identify the relationships between the two models through their traveling waves, from which we can observe how information are lost, retained, or created during the transition from the microscopic model to the macroscopic model. Moreover, the underlying biological implications of our results are discussed.     

In vivo role of checkpoint kinase 2 in signaling telomere dysfunction

Checkpoint kinase 2 (CHK2) is a downstream effector of the DNA damage response (DDR). Dysfunctional telomeres, either owing to critical shortening or disruption of the shelterin complex, activate a DDR, which eventually results in cell cycle arrest, senescence and/or apoptosis. Successive generations of telomerase‐deficient (Terc) mice show accelerated aging and shorter lifespan due to tissue atrophy and impaired organ regeneration associated to progressive telomere shortening. In contrast, mice deficient for the shelterin component TRF1 in stratified epithelia show a rapid and massive induction of DDR, leading to perinatal lethality and severe skin defects. In both mouse models, p53 deficiency can rescue survival. Here, we set to address the role of CHK2 in signaling telomere dysfunction in both mouse models. To this end, we generated mice doubly deficient for Chk2 and either Terc (Chk2^?/? Terc^?/?) or Trf1 (Trf1^Δ/Δ K5Cre Chk2^?/?). We show that Chk2 deletion improves Terc‐associated phenotypes, including lifespan and age‐associated pathologies. Similarly, Chk2 deficiency partially rescues perinatal mortality and attenuates degenerative pathologies of Trf1^Δ/Δ K5Cre mice. In both cases, we show that the effects are mediated by a significant attenuation of p53/p21 signaling pathway. Our results represent the first demonstration of a role for CHK2 in the in vivo signaling of dysfunctional telomeres.     

A Primer on Interpreting Regression Models

Abstract: We perceive a need for more complete interpretation of regression models published in the wildlife literature to minimize the appearance of poor models and to maximize the extraction of information from good models. Accordingly, we offer this primer on interpretation of parameters in single- and multi-variable regression models. Using examples from the wildlife literature, we illustrate how to interpret linear zero-intercept, simple linear, semi-log, log-log, and polynomial models based on intercepts, coefficients, and shapes of relationships. We show how intercepts and coefficients have biological and management interpretations. We examine multiple linear regression models and show how to use the signs (+, -) of coefficients to assess the merit and meaning of a derived model. We discuss 3 methods of viewing the output of 3-dimensional models (y, x₁, x₂) in 2-dimensional space (sheet of paper) and illustrate graphical model interpretation with a 4-dimensional logistic regression model. Statistical significance or Akaike best-ness does not prevent the appearance of implausible regression models. We recommend that members of the peer review process be sensitive to full interpretation of regression models to forestall bad models and maximize information retrieval from good models     

The dynamic stress-induced “<Emphasis Type="Italic">O</Emphasis>-GlcNAc-ome” highlights functions for <Emphasis Type="Italic">O</Emphasis>-GlcNAc in regulating DNA damage/repair and other cellular pathways

The modification of nuclear, mitochondrial, and cytoplasmic proteins by O-linked β-N-acetylglucosamine (O-GlcNAc) is a dynamic and essential post-translational modification of metazoans. Numerous forms of cellular injury lead to elevated levels of O-GlcNAc in both in vivo and in vitro models, and elevation of O-GlcNAc levels before, or immediately after, the induction of cellular injury is protective in models of heat stress, oxidative stress, endoplasmic reticulum (ER) stress, hypoxia, ischemia reperfusion injury, and trauma hemorrhage. Together, these data suggest that O-GlcNAc is a regulator of the cellular stress response. However, the molecular mechanism(s) by which O-GlcNAc regulates protein function leading to enhanced cell survival have not been identified. In order to determine how O-GlcNAc modulates stress tolerance in these models we have used stable isotope labeling with amino acids in cell culture to determine the identity of proteins that undergo O-GlcNAcylation in response to heat shock. Numerous proteins with diverse functions were identified, including NF-90, RuvB-like 1 (Tip49α), RuvB-like 2 (Tip49β), and several COPII vesicle transport proteins. Many of these proteins bind double-stranded DNA-dependent protein kinase (PK), or double-stranded DNA breaks, suggesting a role for O-GlcNAc in regulating DNA damage signaling or repair. Supporting this hypothesis, we have shown that DNA-PK is O-GlcNAc modified in response to numerous forms of cellular stress.