The transient and constitutive inflammatory signaling in tumorigenesis

Comment on: Rokavec M, et al. Mol Cell 2012; 45:777-89.     

The unpredictability of favourability: condition assessment and protected areas in England

The probability that protected areas will deliver their potential for maintaining or enhancing biodiversity is likely to be maximised if they are appropriately and effectively managed. As a result, governments and conservation agencies are devoting much attention to the management of protected areas. In the U.K., the demand for performance accountability has resulted in Public Service Agreements (PSA) that set out targets for government departments to deliver results in return for investments being made. One such target for England is to ensure that all nationally important wildlife sites are in favourable condition by 2010. Here, we tested the hypothesis, of potential strategic importance, that the ecological condition of these sites is predictable from relationships with a range of physical, environmental and demographic variables. We used binary logistic regression to investigate these relationships, using the results of English Nature’s 1997–2003 condition assessment exercise. Generally, sites in unfavourable condition tend to be larger in area, located at higher elevations, but with higher human population density and are more spatially isolated from units of the same habitat. However, despite the range of different parameters included in our models, the extent to which the condition of any given site could be predicted was low. Our results have implications for the delivery of PSA targets, funding allocation, and the location of new protected areas.     

MBAT: A scalable informatics system for unifying digital atlasing workflows

Background  

Digital atlases provide a common semantic and spatial coordinate system that can be leveraged to compare, contrast, and correlate data from disparate sources. As the quality and amount of biological data continues to advance and grow, searching, referencing, and comparing this data with a researcher's own data is essential. However, the integration process is cumbersome and time-consuming due to misaligned data, implicitly defined associations, and incompatible data sources. This work addressing these challenges by providing a unified and adaptable environment to accelerate the workflow to gather, align, and analyze the data.     

A predictive model for freshwater bioassessment (Mondego River,Portugal)

We sampled macroinvertebrates at 75 locations in the Mondego river catchment, Central Portugal, and developed a predictive model for water quality assessment of this basin, based on the Reference Condition Approach. Sampling was done from June to September 2001. Fifty-five sites were identified as “Reference sites” and 20 sites were used as “Test sites” to test the model. At each site we also measured 40 habitat variables to characterize water physics and chemistry, habitat type, land use, stream hydrology and geographic location. Macroinvertebrates were generally identified to species or genus level; a total of 207 taxa were found. By Unweighted Pair Group Method with Arithmetic mean (UPGMA) clustering and analysis of species contribution to similarities percentage (SIMPER), two groups of reference sites were established. Using Discriminant Analysis (stepwise forward), four variables correctly predicted 78% of the reference sites to the appropriate group: stream order, pool quality, substrate quality and current velocity. Test sites’ environmental quality was established from their relative distance to reference sites, in MDS ordination space, using a series of bands (BEAST methodology). The model performed well at upstream sites, but at downstream sites it was compromised by the lack of reference sites. As with the English RIVPACS predictive model, the Mondego model should be continually improved with the addition of new reference sites. The adaptation of the Mondego model methodology to the Water Framework Directive is possible and would consist mainly of the integration of the WFD typology and increasing the number of ellipses that define quality bands. Handling editor: K. Martens     

Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma

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Emerging infectious diseases and animal social systems

Emerging infectious diseases threaten a wide diversity of animals, and important questions remain concerning disease emergence in socially structured populations. We developed a spatially explicit simulation model to investigate whether—and under what conditions—disease-related mortality can impact rates of pathogen spread in populations of polygynous groups. Specifically, we investigated whether pathogen-mediated dispersal (PMD) can occur when females disperse after the resident male dies from disease, thus carrying infections to new groups. We also examined the effects of incubation period and virulence, host mortality and rates of background dispersal, and we used the model to investigate the spread of the virus responsible for Ebola hemorrhagic fever, which currently is devastating African ape populations. Output was analyzed using regression trees, which enable exploration of hierarchical and non-linear relationships. Analyses revealed that the incidence of disease in single-male (polygynous) groups was significantly greater for those groups containing an average of more than six females, while the total number of infected hosts in the population was most sensitive to the number of females per group. Thus, as expected, PMD occurs in polygynous groups and its effects increase as harem size (the number of females) increases. Simulation output further indicated that population-level effects of Ebola are likely to differ among multi-male–multi-female chimpanzees and polygynous gorillas, with larger overall numbers of chimpanzees infected, but more gorilla groups becoming infected due to increased dispersal when the resident male dies. Collectively, our results highlight the importance of social system on the spread of disease in wild mammals.     

Flood Risk Analysis Based on Information Diffusion Theory

Due to the fact that the flood data series of small drainage basins is relatively short, available data are often not sufficient for flood risk analysis. This presents the problem of risk analysis using very small data samples. One method that can be applied is to regard the available small samples as fuzzy information and optimize them using information diffusion technology to yield analytical results with greater reliability. In this article a risk analysis method based on information diffusion theory is applied to create a new flood risk analysis model. Application of the model is illustrated taking the Jinhuajiang and Qujiang drainage basins as examples. This is a new attempt at applying information diffusion theory in flood risk analysis. Computations based on this analytical flood risk model can yield an estimated flood damage value that is relatively accurate. This study indicates that the aforementioned model exhibits fairly stable analytical results, even when using a small set of sample data. The results also indicate that information diffusion technology is highly capable of extracting useful information and therefore improves system recognition accuracy. This method can be easily applied and the analytical results produced are easy to understand. Results are accurate enough to act as a guide in disaster situations.