SPAnDX: a process-based population dynamics model to explore management and climate change impacts on an invasive alien plant, Acacia nilotica

This paper describes a process-based metapopulation dynamics and phenology model of prickly acacia, Acacia nilotica, an invasive alien species in Australia. The model, SPAnDX, describes the interactions between riparian and upland sub-populations of A. nilotica within livestock paddocks, including the effects of extrinsic factors such as temperature, soil moisture availability and atmospheric concentrations of carbon dioxide. The model includes the effects of management events such as changing the livestock species or stocking rate, applying fire, and herbicide application. The predicted population behaviour of A. nilotica was sensitive to climate. Using 35 years daily weather datasets for five representative sites spanning the range of conditions that A. nilotica is found in Australia, the model predicted biomass levels that closely accord with expected values at each site. SPAnDX can be used as a decision-support tool in integrated weed management, and to explore the sensitivity of cultural management practices to climate change throughout the range of A. nilotica. The cohort-based DYMEX modelling package used to build and run SPAnDX provided several advantages over more traditional population modelling approaches (e.g. an appropriate specific formalism (discrete time, cohort-based, process-oriented), user-friendly graphical environment, extensible library of reusable components, and useful and flexible input/output support framework). (C) 2003 Published by Elsevier Science B.V.

Epidermis recreation in spongy-like hydrogels: New opportunities to explore epidermis-like analogues

[Excerpt] On the road to successfully achieving skin regeneration, 3D matrices/scaffolds that provide the adequate physico-chemical and biological cues to recreate the ideal healing environment are believed to be a key element [1], [2] and [3]. Numerous polymeric matrices derived from both natural [4] and [5] and synthetic [6], [7] and [8] sources have been used as cellular supports; nowadays, fewer matrices are simple carriers, and more and more are ECM analogues that can actively participate in the healing process. Therefore, the attractive characteristics of hydrogels, such as high water content, tunable elasticity and facilitated mass transportation, have made them excellent materials to mimic cells’ native environment [9]. Moreover, their hygroscopic nature [10] and possibility of attaining soft tissues-like mechanical properties mean they have potential for exploitation as wound healing promoters [11], [12], [13] and [14]. Nonetheless, hydrogels lack natural cell adhesion sites [15], which limits the maximization of their potential in the recreation of the cell niche. This issue has been tackled through the use of a range of sophisticated approaches to decorate the hydrogels with adhesion sequences such as arginine-glycine-aspartic acid (RGD) derived from fibronectin [16], [17] and [18], and tyrosine-isoleucine-glycine-serine-arginine (YIGSR) derived from laminin [18] and [19], which not only aim to modulate cell adhesion, but also influencing cell fate and survival [18]. Nonetheless, its widespread use is still limited by significant costs associated with the use of recombinant bioactive molecules.

Using life strategies to explore the vulnerability of ecosystem services to invasion by alien plants

Invasive plants can have different effects of ecosystem functioning and on the provision of ecosystem services, from strongly deleterious impacts to positive effects. The nature and intensity of such effects will depend on the service and ecosystem being considered, but also on features of life strategies of invaders that influence their invasiveness as well as their influence of key processes of receiving ecosystems. To address the combined effect of these various factors we developed a robust and efficient methodological framework that allows to identify areas of possible conflict between ecosystem services and alien invasive plants, considering interactions between landscape invasibility and species invasiveness. Our framework combines the statistical robustness of multi-model inference, efficient techniques to map ecosystem services, and life strategies as a functional link between invasion, functional changes and potential provision of services by invaded ecosystems. The framework was applied to a test region in Portugal, for which we could successfully predict the current patterns of plant invasion, of ecosystem service provision, and finally of probable conflict (expressing concern for negative impacts, and value for positive impacts on services) between alien species richness (total and per plant life strategy) and the potential provision of selected services. Potential conflicts were identified for all combinations of plant strategy and ecosystem service, with an emphasis for those concerning conflicts with carbon sequestration, water regulation and wood production. Lower levels of conflict were obtained between invasive plant strategies and the habitat for biodiversity supporting service. The added value of the proposed framework in the context of landscape management and planning is discussed in perspective of anticipation of conflicts, mitigation of negative impacts, and potentiation of positive effects of plant invasions on ecosystems and their services.

GO! Explore the World of Transportation, January - February 2007

Newsletter produced by the Iowa Department of Transport.

Explore Aviation in Iowa, March 21, 2003

This brochure gives you all the addresses to the aviation museum that Iowa has to offer.

A highly sensitive in vitro infection assay to explore early stages of mouse mammary tumor virus infection.

Mouse mammary tumor virus (MMTV) infection of adult mice induces a strong response to superantigen (Sag) in their draining lymph nodes, which results from the presentation of Sag by MMTV-infected B cells to Sag-reactive T cells. To date, infection with physiologically relevant doses of MMTV can be detected in vivo only after several days of Sag-mediated T-cell-dependent amplification of infected B cells. Furthermore, no efficient in vitro system of detecting MMTV infection is available. Such a model would allow the dissection of the early phase of infection, the assessment of the contributions of different cell types, and the screening of large panels of molecules for their potential roles in infection and Sag response. For these reasons, we have established an in vitro model for detecting infection which is as sensitive and reproducible as the in vivo model. We found that the viral envelope (Env) protein is crucial for target cell infection but not for presentation of Sag. Furthermore, we show that infection of purified B cells with MMTV induces entry of Sag-responsive T cells into the cell cycle, while other professional antigen-presenting cells, such as dendritic cells, are much less efficient in inducing a response.

Iowa guide to long-term care insurance : the time to explore your insurance options is now, 2014

This is a guide to the Senior Health Insurance Information Program (SHIIP)

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Using Pareto optimality to explore the topology and dynamics of the human connectome.

Graph theory has provided a key mathematical framework to analyse the architecture of human brain networks. This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performance. An exploration of these interacting factors and driving forces may reveal salient network features that are critically important for shaping and constraining the brain's topological organization and its evolvability. Several studies have pointed to an economic balance between network cost and network efficiency with networks organized in an 'economical' small-world favouring high communication efficiency at a low wiring cost. In this study, we define and explore a network morphospace in order to characterize different aspects of communication efficiency in human brain networks. Using a multi-objective evolutionary approach that approximates a Pareto-optimal set within the morphospace, we investigate the capacity of anatomical brain networks to evolve towards topologies that exhibit optimal information processing features while preserving network cost. This approach allows us to investigate network topologies that emerge under specific selection pressures, thus providing some insight into the selectional forces that may have shaped the network architecture of existing human brains.