Linking bacterial community structure to advection and environmental impact along a coast-fjord gradient of the Sognefjord, western Norway

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A single dividing cell population with imbalanced fate drives oesophageal tumour growth.

Understanding the cellular mechanisms of tumour growth is key for designing rational anticancer treatment. Here we used genetic lineage tracing to quantify cell behaviour during neoplastic transformation in a model of oesophageal carcinogenesis. We found that cell behaviour was convergent across premalignant tumours, which contained a single proliferating cell population. The rate of cell division was not significantly different in the lesions and the surrounding epithelium. However, dividing tumour cells had a uniform, small bias in cell fate so that, on average, slightly more dividing than non-dividing daughter cells were generated at each round of cell division. In invasive cancers induced by Kras(G12D) expression, dividing cell fate became more strongly biased towards producing dividing over non-dividing cells in a subset of clones. These observations argue that agents that restore the balance of cell fate may prove effective in checking tumour growth, whereas those targeting cycling cells may show little selectivity.Cancer Research UK (Grant ID: C609/A17257), Medical Research Council (Grant-in-Aid), DFG (Research Fellowship), Engineering and Physical Sciences Research Council (Critical Mass Grant), Wellcome Trust (Grant ID: 098357/Z/12/Z)This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/ncb340

Factors Associated with Revision Surgery after Internal Fixation of Hip Fractures

Background: Femoral neck fractures are associated with high rates of revision surgery after management with internal fixation. Using data from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial evaluating methods of internal fixation in patients with femoral neck fractures, we investigated associations between baseline and surgical factors and the need for revision surgery to promote healing, relieve pain, treat infection or improve function over 24 months postsurgery. Additionally, we investigated factors associated with (1) hardware removal and (2) implant exchange from cancellous screws (CS) or sliding hip screw (SHS) to total hip arthroplasty, hemiarthroplasty, or another internal fixation device. Methods: We identified 15 potential factors a priori that may be associated with revision surgery, 7 with hardware removal, and 14 with implant exchange. We used multivariable Cox proportional hazards analyses in our investigation. Results: Factors associated with increased risk of revision surgery included: female sex, [hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.25-2.50; P = 0.001], higher body mass index (fo

Optimal defense strategies in an idealized microbial food web under trade-off between competition and defense

Trophic mechanisms that can generate biodiversity in food webs include bottom-up (growth rate regulating) and top-down (biomass regulating) factors. The top-down control has traditionally been analyzed using the concepts of “Keystone Predation” (KP) and “Killing-the-Winner” (KtW), predominately occuring in discussions of macro- and micro-biological ecology, respectively. Here we combine the classical diamond-shaped food web structure frequently discussed in KP analyses and the KtW concept by introducing a defense strategist capable of partial defense. A formalized description of a trade-off between the defense-strategist's competitive and defensive ability is included. The analysis reveals a complex topology of the steady state solution with strong relationships between food web structure and the combination of trade-off, defense strategy and the system's nutrient content. Among the results is a difference in defense strategies corresponding to maximum biomass, production, or net growth rate of invading individuals. The analysis thus summons awareness that biomass or production, parameters typically measured in field studies to infer success of particular biota, are not directly acted upon by natural selection. Under coexistence with a competition specialist, a balance of competitive and defensive ability of the defense strategist was found to be evolutionarily stable, whereas stronger defense was optimal under increased nutrient levels in the absence of the pure competition specialist. The findings of success of different defense strategies are discussed with respect to SAR11, a highly successful bacterial clade in the pelagic ocean

Mass balance equations and equilibrium solutions for competition specialist (C), defense strategist (D), predator (P) and free nutrients (N) for original and modified KtW with partial defense.

<p>Mass balance equations and equilibrium solutions for competition specialist (C), defense strategist (D), predator (P) and free nutrients (N) for original and modified KtW with partial defense.</p

Biomass distributions at steady state as a function of defense strategy and trade-off parameter .

<p>Steady-state biomass distributions for the predator (P*, top), the defense strategist (D*, middle) and the competition specialist (C*, bottom) with respect to the defense strategy and trade-off parameter for three limiting nutrient contents (20, left, 50, middle, and 80, right). Other parameters as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101415#pone-0101415-t002" target="_blank">Table 2</a>.</p

Symbols and parameter values used including trade-off functions for defensive and competitive abilities of the defense strategist.

<p>Symbols and parameter values used including trade-off functions for defensive and competitive abilities of the defense strategist.</p

Optimal defense strategies with respect to maximum biomass, maximum production and evolutionarily stable strategy (ESS).

<p>Defense strategies corresponding to defense strategist's maximum biomass (blue), maximum production (defined as , green) and ESS (red) are shown as a function of the trade-off parameter for different nutrient contents. The ESS is defined by the maximum net growth rate of a invading mutant, which is found by critical point analysis of the first partial derivative of the net growth rate with respect to strategy (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101415#pone.0101415.s002" target="_blank">Appendix S1</a>). Different contours show the effect of the total nutrient content on the maximizing strategies. Other parameters as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101415#pone-0101415-t002" target="_blank">Table 2</a>.</p