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.     

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.     

Inoculation of selenium hyperaccumulator Stanleya pinnata and related non‐accumulator Stanleya elata with hyperaccumulator rhizosphere fungi—investigation of effects on Se accumulation and speciation

Little is known about how fungi affect elemental accumulation in hyperaccumulators (HAs). Here, two rhizosphere fungi from selenium (Se) HA Stanleya pinnata, Alternaria seleniiphila (A1) and Aspergillus leporis (AS117), were used to inoculate S. pinnata and related non‐HA Stanleya elata. Growth and Se and sulfur (S) accumulation were analyzed. Furthermore, X‐ray microprobe analysis was used to investigate elemental distribution and speciation. Growth of S. pinnata was not affected by inoculation or by Se. Stanleya elata growth was negatively affected by AS117 and by Se, but combination of both did not reduce growth. Selenium translocation was reduced in inoculated S. pinnata, and inoculation reduced S translocation in both species. Root Se distribution and speciation were not affected by inoculation in either species; both species accumulated mainly (90%) organic Se. Sulfur, in contrast, was present equally in organic and inorganic forms in S. pinnata roots. Thus, these rhizosphere fungi can affect growth and Se and/or S accumulation, depending on host species. They generally enhanced root accumulation and reduced translocation. These effects cannot be attributed to altered plant Se speciation but may involve altered rhizosphere speciation, as these fungi are known to produce elemental Se. Reduced Se translocation may be useful in applications where toxicity to herbivores and movement of Se into the food chain is a concern. The finding that fungal inoculation can enhance root Se accumulation may be useful in Se biofortification or phytoremediation using root crop species.     

Engineering of Bacillus subtilis Strains To Allow Rapid Characterization of Heterologous Diguanylate Cyclases and Phosphodiesterases

Microbial processes, including biofilm formation, motility, and virulence, are often regulated by changes in the available concentration of cyclic dimeric guanosine monophosphate (c-di-GMP). Generally, high c-di-GMP concentrations are correlated with decreased motility and increased biofilm formation and low c-di-GMP concentrations are correlated with an increase in motility and activation of virulence pathways. The study of c-di-GMP is complicated, however, by the fact that organisms often encode dozens of redundant enzymes that synthesize and hydrolyze c-di-GMP, diguanylate cyclases (DGCs), and c-di-GMP phosphodiesterases (PDEs); thus, determining the contribution of any one particular enzyme is challenging. In an effort to develop a facile system to study c-di-GMP metabolic enzymes, we have engineered a suite of Bacillus subtilis strains to assess the effect of individual heterologously expressed proteins on c-di-GMP levels. As a proof of principle, we characterized all 37 known genes encoding predicted DGCs and PDEs in Clostridium difficile using parallel readouts of swarming motility and fluorescence from green fluorescent protein (GFP) expressed under the control of a c-di-GMP-controlled riboswitch. We found that 27 of the 37 putative C. difficile 630 c-di-GMP metabolic enzymes had either active cyclase or phosphodiesterase activity, with agreement between our motility phenotypes and fluorescence-based c-di-GMP reporter. Finally, we show that there appears to be a threshold level of c-di-GMP needed to inhibit motility in Bacillus subtilis.     

Mouse Wnt9b transforming activity,tissue-specific expression,and evolution

The members of the Wnt family of secreted factors have oncogenic potential and important roles as developmental regulators. We report an analysis of mouse Wnt9b (also called Wnt15 and Wnt14b), including its cDNA sequence, chromosomal mapping, epithelial cell transforming activity, adult and embryonic tissue expression patterns, and evolution. We also deduced the full-length amino acid sequence of its close relative, Wnt9a (also called Wnt14), from unannotated genomic DNA sequences in GenBank. Full-length comparisons among Wnt amino acid sequences provide evidence that Wnt9b and Wnt9a are close paralogs of each other and are orthologs of Wnt9 genes from shark and hagfish. Mapping Wnt9b to The Jackson Laboratory BSS interspecific backcross panel places it at 63.0 cM on chromosome 11. Sequence comparisons of two pairs of linked Wnt genes (the Wnt9a-Wnt3a pair and the Wnt9b-Wnt3 pair) suggest that they arose from the relatively recent duplication of a single ancestral Wnt gene pair, confirming the close paralogous relationship of Wnt9a and Wnt9b. Wnt9b expression is primarily restricted to the kidney in the adult mouse, with lower levels detected in the preputial gland, liver, and mammary gland. Testing of staged whole mouse embryos from 9.5 to 17.5 days of gestation showed expression at all stages with a peak at day 10.5. In situ hybridization analysis showed expression in most but not all tissues of the 16.5-day embryo. No significant elevation of Wnt9b expression was detected in 29 mouse mammary tumor virus-induced tumors. Overexpression of Wnt9b in C57MG mammary epithelial cells caused small transformed foci in cell monolayers and a moderate morphological transformation in pooled colonies compared with Wnt1.