The effects of herbivory on paternal fitness in scarlet gilia: better moms also make better pops

Abstract Whether plants can benefit from the direct effects of herbivory has been contentious among ecologists and evolutionary biologists. Although previous studies have provided experimental evidence of enhanced maternal fitness following herbivory in a natural system, an accurate depiction of plant-herbivore interactions must include the effects of herbivory on male as well as female fitness. Here we show that ungulate herbivory on scarlet gilia results in an increase in paternal as well as maternal fitness. This study represents the first evidence of overcompensation in a natural system where both paternal and maternal components of fitness have been assessed.     

Plasticity in Skeletal, Cardiac, and Smooth Muscle: Selected Contribution: Mechanisms underlying increased force generation by rat diaphragm muscle fibers during development

It has beenfound that maximum specific force (F_max; force percross-sectional area) of rat diaphragm muscle doubles from birth to 84 days (adult). We hypothesize that this developmental change inF_max reflects an increase in myosin heavy chain (MHC) content per half-sarcomere (an estimate of the number of cross bridgesin parallel) and/or a greater force per cross bridge in fibersexpressing fast MHC isoforms compared with slow and neonatal MHCisoforms (MHC_slow and MHC_neo, respectively).Single Triton 100-X-permeabilized fibers were activated at a pCa of4.0. MHC isoform expression was determined by SDS-PAGE. MHC content per half-sarcomere was determined by densitometric analysis and comparison to a standard curve of known MHC concentrations. MHC content per half-sarcomere progressively increased during early postnatal development. When normalized for MHC content per half-sarcomere, fibersexpressing MHC_slow and coexpressing MHC_neoproduced less force than fibers expressing fast MHC isoforms. Weconclude that lower force per cross bridge in fibers expressingMHC_slow and MHC_neo contributes to the lowerF_max seen in early postnatal development.

     

Software Review of Analytica 1.2

Analytica is an easy-to-learn, easy-to-use modeling tool that allows modelers to represent what they know through influence diagrams. These diagrams show which model quantities are derived from which others and indicate by shape and color the roles that different nodes play in the model, e.g., decision variables, chance variables, outcome variables, deterministic functions, or abstractions of sub-models. A wide variety of built-in probability distributions allow uncertainties about input values to be painlessly specified and propagated through the model via a fast, professional Monte-Carlo simulation engine. Resulting uncertainties and sensitivities about any quantity in the model can be viewed with admirable ease and flexibility by selecting among probability density, cumulative distribution, confidence band, sensitivity analysis, and other displays. Analytica features clever hierarchical model management and navigation features that serious model-builders will appreciate and that novice modelers will learn from as they are led to develop well-structured, well-documented models. Simple continuous (compartmental-flow) and Markov chain dynamic simulation models can be built by paying some detailed attention to arrays and indices, although Analytica does not support true discrete-event simulation. Within its chosen domain—uncertainty propagation through influence diagram models—Analytica is by far the easiest and best tool that we have seen.     

Phytoplankton community structure of a deep subalpine Italian Lake (Lake Orta,N. Italy) four years after the recovery from acidification by liming

The phytoplankton succession in Lake Orta, heavy metals and ammonium polluted for decades and subjected to liming intervention in 1989–90, was studied during 1994. The existence of three main succession stages, with different structures of the community emerged. Chlorophyta are the most important taxonomic group in every season. The spring community was dominated by chlorophytes, Dinobryon sertularia and Kephyrion sp., the summer by Synedra sp. and Westella botryoides, the autumn by Rhizosolenia eriensis and W. botryoides, the winter by Synedra sp., R. eriensis and Synechococcus sp. The abundance of Chlorophyta, less important in other deep subalpine lakes, and the dominance of taxa able to thrive in polluted environments, could be related to the presence of trace metals in the sediments.     

Characterization of <Emphasis Type="Italic">Arabidopsis thaliana</Emphasis> regrowth patterns suggests a trade-off between undamaged fitness and damage tolerance

Metacommunity ecology has rapidly become a dominant framework through which ecologists understand the natural world. Unfortunately, persistent misunderstandings regarding metacommunity theory and the methods for evaluating hypotheses based on the theory are common in the ecological literature. Since its beginnings, four major paradigms—species sorting, mass effects, neutrality, and patch dynamics—have been associated with metacommunity ecology. The Big 4 have been misconstrued to represent the complete set of metacommunity dynamics. As a result, many investigators attempt to evaluate community assembly processes as strictly belonging to one of the Big 4 types, rather than embracing the full scope of metacommunity theory. The Big 4 were never intended to represent the entire spectrum of metacommunity dynamics and were rather examples of historical paradigms that fit within the new framework. We argue that perpetuation of the Big 4 typology hurts community ecology and we encourage researchers to embrace the full inference space of metacommunity theory. A related, but distinct issue is that the technique of variation partitioning is often used to evaluate the dynamics of metacommunities. This methodology has produced its own set of misunderstandings, some of which are directly a product of the Big 4 typology and others which are simply the product of poor study design or statistical artefacts. However, variation partitioning is a potentially powerful technique when used appropriately and we identify several strategies for successful utilization of variation partitioning.     

Forecasting infectious disease emergence subject to seasonal forcing

Background

Despite high vaccination coverage, many childhood infections pose a growing threat to human populations. Accurate disease forecasting would be of tremendous value to public health. Forecasting disease emergence using early warning signals (EWS) is possible in non-seasonal models of infectious diseases. Here, we assessed whether EWS also anticipate disease emergence in seasonal models.

Methods

We simulated the dynamics of an immunizing infectious pathogen approaching the tipping point to disease endemicity. To explore the effect of seasonality on the reliability of early warning statistics, we varied the amplitude of fluctuations around the average transmission. We proposed and analyzed two new early warning signals based on the wavelet spectrum. We measured the reliability of the early warning signals depending on the strength of their trend preceding the tipping point and then calculated the Area Under the Curve (AUC) statistic.

Results

Early warning signals were reliable when disease transmission was subject to seasonal forcing. Wavelet-based early warning signals were as reliable as other conventional early warning signals. We found that removing seasonal trends, prior to analysis, did not improve early warning statistics uniformly.

Conclusions

Early warning signals anticipate the onset of critical transitions for infectious diseases which are subject to seasonal forcing. Wavelet-based early warning statistics can also be used to forecast infectious disease.

     

A habitat-based model for the spread of hantavirus between reservoir and spillover species

New habitat-based models for spread of hantavirus are developed which account for interspecies interaction. Existing habitat-based models do not consider interspecies pathogen transmission, a primary route for emergence of new infectious diseases and reservoirs in wildlife and man. The modeling of interspecies transmission has the potential to provide more accurate predictions of disease persistence and emergence dynamics. The new models are motivated by our recent work on hantavirus in rodent communities in Paraguay. Our Paraguayan data illustrate the spatial and temporal overlaps among rodent species, one of which is the reservoir species for Jabora virus and others which are spillover species. Disease transmission occurs when their habitats overlap. Two mathematical models, a system of ordinary differential equations (ODE) and a continuous-time Markov chain (CTMC) model, are developed for spread of hantavirus between a reservoir and a spillover species. Analysis of a special case of the ODE model provides an explicit expression for the basic reproduction number, , such that if , then the pathogen does not persist in either population but if , pathogen outbreaks or persistence may occur. Numerical simulations of the CTMC model display sporadic disease incidence, a new behavior of our habitat-based model, not present in other models, but which is a prominent feature of the seroprevalence data from Paraguay. Environmental changes that result in greater habitat overlap result in more encounters among various species that may lead to pathogen outbreaks and pathogen establishment in a new host.     

Synthesis and activity of a new class of pathway-selective estrogen receptor ligands: hydroxybenzoyl-3,4-dihydroquinoxalin-2(1H)-ones

The anti-inflammatory activity of non-selective estrogens has been attributed to their ability to antagonize the activity of nuclear factor kappaB (NF-kappaB), a known mediator of inflammatory responses. Here we report the identification of a potent new class of pathway-selective ER ligands that selectively antagonize NF-kappaB functional activity, while exhibiting a lack of classical estrogenic effect.