In-Situ Biofilm Formation in Hyper Alkaline Environments

Lime manufacture in the UK has resulted in the generation of a number of alkaline sites (>pH 11.0) with complex indigenous microbial populations. Within the present study, retrievable cotton samples were used to investigate the fate of cellulose, the primary carbon source, within three sites aged from ≈25 to 140 years. Following 3 months incubation in situ, biofilms had formed on all cotton samples in these extreme pH conditions; with matrices comprised of carbohydrates, proteins, lipids and eDNA. Biofilms from the older sites contained greater amounts of eDNA, a structural component that aids the production of a denser biofilm. The age of the sites correlated with a shift from polysaccharides composed of β 1,4 and β 1,3 linked sugars to those composed of pyranosyl sugars within the older sites. These changes were reflected in the active biofilm communities which shifted from being Clostridiales dominated in the youngest site to Proteobacteria dominated in the older sites. The study demonstrates that the microbial communities resident in anthropogenic alkaline sites are able to form biofilms at pH values > pH 11.0 and that these biofilms evolve toward Proteobacteria dominated communities employing eDNA and pyranosyl sugar based polysaccharides to build the biofilm matrix

A 15.65 solar mass black hole in an eclipsing binary in the nearby spiral galaxy Messier 33

Stellar-mass black holes are discovered in X-ray emitting binary systems, where their mass can be determined from the dynamics of their companion stars. Models of stellar evolution have difficulty producing black holes in close binaries with masses >10 solar masses, which is consistent with the fact that the most massive stellar black holes known so all have masses within 1 sigma of 10 solar masses. Here we report a mass of 15.65 +/- 1.45 solar masses for the black hole in the recently discovered system M33 X-7, which is located in the nearby galaxy Messier 33 (M33) and is the only known black hole that is in an eclipsing binary. In order to produce such a massive black hole, the progenitor star must have retained much of its outer envelope until after helium fusion in the core was completed. On the other hand, in order for the black hole to be in its present 3.45 day orbit about its 70.0 +/- 6.9 solar mass companion, there must have been a ``common envelope'' phase of evolution in which a significant amount of mass was lost from the system. We find the common envelope phase could not have occured in M33 X-7 unless the amount of mass lost from the progenitor during its evolution was an order of magnitude less than what is usually assumed in evolutionary models of massive stars.Comment: To appear in Nature October 18, 2007. Four figures (one color figure degraded). Differs slightly from published version. Supplementary Information follows in a separate postin

Plugging a hole and lightening the burden: A process evaluation of a practice education team

Aim: To investigate the perceptions of clinical and senior managers about the role of Practice Educators employed in one acute hospital in the UK. Background: Producing nurses who are fit for practice, purpose and academic award is a key issue for nurse education partnership providers in the UK. Various new models for practice learning support structures and new roles within health care institutions have been established. To sustain funding and policy support for these models, there is a need for evaluation research. Design: A process evaluation methodology was employed to determine the current value of a practice education team and to provide information to guide future direction. Methods: Data were collected through semi-structured telephone interviews using a previously designed schedule. All senior nurse managers (N=5) and a purposive sample of clinical managers (n=13) who had personal experience of and perceptions about the role of practice educators provided the data. Interview notes were transcribed, coded and a thematic framework devised to present the results. Results: A number of key themes emerged including: qualities needed for being a successful practice educator; visibility and presence of practice educators; providing a link with the university; ‘plugging a hole’ in supporting learning needs; providing relief to practitioners in dealing with ‘the burden of students’; alleviating the ‘plight of students’; and effects on student attrition. Conclusions: Findings provided evidence for the continued funding of the practice educator role with improvements to be made in dealing with stakeholder expectations and outcomes. Relevance to clinical practice: In the UK, there still remain concerns about the fitness for practice of newly registered nurses, prompting a recent national consultation by the professional regulating body. Despite fiscal pressures, recommendations for further strengthening of all systems that will support the quality of practice learning may continue to sustain practice learning support roles

Cell Encapsulation in Sub-mm Sized Gel Modules Using Replica Molding

For many types of cells, behavior in two-dimensional (2D) culture differs from that in three-dimensional (3D) culture. Among biologists, 2D culture on treated plastic surfaces is currently the most popular method for cell culture. In 3D, no analogous standard method—one that is similarly convenient, flexible, and reproducible—exists. This paper describes a soft-lithographic method to encapsulate cells in 3D gel objects (modules) in a variety of simple shapes (cylinders, crosses, rectangular prisms) with lateral dimensions between 40 and 1000 μm, cell densities of 105 – 108 cells/cm3, and total volumes between 1×10−7 and 8×10−4 cm3. By varying (i) the initial density of cells at seeding, and (ii) the dimensions of the modules, the number of cells per module ranged from 1 to 2500 cells. Modules were formed from a range of standard biopolymers, including collagen, Matrigel™, and agarose, without the complex equipment often used in encapsulation. The small dimensions of the modules allowed rapid transport of nutrients by diffusion to cells at any location in the module, and therefore allowed generation of modules with cell densities near to those of dense tissues (108 – 109 cells/cm3). This modular method is based on soft lithography and requires little special equipment; the method is therefore accessible, flexible, and well suited to (i) understanding the behavior of cells in 3D environments at high densities of cells, as in dense tissues, and (ii) developing applications in tissue engineering

Discovering context-specific relationships from biological literature by using multi-level context terms

<p>Abstract</p> <p>Background</p> <p>The Swanson's ABC model is powerful to infer hidden relationships buried in biological literature. However, the model is inadequate to infer relations with context information. In addition, the model generates a very large amount of candidates from biological text, and it is a semi-automatic, labor-intensive technique requiring human expert's manual input. To tackle these problems, we incorporate context terms to infer relations between AB interactions and BC interactions.</p> <p>Methods</p> <p>We propose 3 steps to discover meaningful hidden relationships between drugs and diseases: 1) multi-level (gene, drug, disease, symptom) entity recognition, 2) interaction extraction (drug-gene, gene-disease) from literature, 3) context vector based similarity score calculation. Subsequently, we evaluate our hypothesis with the datasets of the "Alzheimer's disease" related 77,711 PubMed abstracts. As golden standards, PharmGKB and CTD databases are used. Evaluation is conducted in 2 ways: first, comparing precision of the proposed method and the previous method and second, analysing top 10 ranked results to examine whether highly ranked interactions are truly meaningful or not.</p> <p>Results</p> <p>The results indicate that context-based relation inference achieved better precision than the previous ABC model approach. The literature analysis also shows that interactions inferred by the context-based approach are more meaningful than interactions by the previous ABC model.</p> <p>Conclusions</p> <p>We propose a novel interaction inference technique that incorporates context term vectors into the ABC model to discover meaningful hidden relationships. By utilizing multi-level context terms, our model shows better performance than the previous ABC model.</p

External validation suggests Integrin beta 3 as prognostic biomarker in serous ovarian adenocarcinomas

<p>Abstract</p> <p>Background</p> <p>The majority of women with ovarian cancer are diagnosed in late stages, and the mortality rate is high. The use of biomarkers as prognostic factors may improve the treatment and clinical outcome of these patients. We performed an external validation of the potential biomarkers CLU, ITGB3, CAPG, and PRAME to determine if the expression levels are relevant to use as prognostic factors.</p> <p>Methods</p> <p>We analysed the gene expression of CLU, ITGB3, CAPG, and PRAME in 30 advanced staged serous adenocarcinomas with quantitative real-time polymerase chain reaction (QPCR) and the protein levels were analysed in 98 serous adenocarcinomas with western blot for semiquantitative analysis. Statistical differences in mRNA and protein expressions between tumours from survivors and tumours from deceased patients were evaluated using the Mann-Whitney U test.</p> <p>Results</p> <p>The gene and protein ITGB3 (Integrin beta 3) were significantly more expressed in tumours from survivors compared to tumours from deceased patients, which is in concordance with our previous results. However, no significant differences were detected for the other three genes or proteins CLU, CAPG, and PRAME.</p> <p>Conclusion</p> <p>The loss of ITGB3 expression in tumours from deceased patients and high expression in tumours from survivors could be used as a biomarker for patients with advanced serous tumours.</p

Equal fitness paradigm explained by a trade-off between generation time and energy production rate

Most plant, animal and microbial species of widely varying body size and lifestyle are nearly equally fit as evidenced by their coexistence and persistence through millions of years. All organisms compete for a limited supply of organic chemical energy, derived mostly from photosynthesis, to invest in the two components of fitness: survival and production. All organisms are mortal because molecular and cellular damage accumulates over the lifetime; life persists only because parents produce offspring. We call this the equal fitness paradigm. The equal fitness paradigm occurs because: (1) there is a trade-off between generation time and productive power, which have equal-but-opposite scalings with body size and temperature; smaller and warmer organisms have shorter lifespans but produce biomass at higher rates than larger and colder organisms; (2) the energy content of biomass is essentially constant, ~22.4 kJ g−1 dry body weight; and (3) the fraction of biomass production incorporated into surviving offspring is also roughly constant, ~10–50%. As organisms transmit approximately the same quantity of energy per gram to offspring in the next generation, no species has an inherent lasting advantage in the struggle for existence. The equal fitness paradigm emphasizes the central importance of energy, biological scaling relations and power–time trade-offs in life history, ecology and evolution

A computational model of liver iron metabolism

Iron is essential for all known life due to its redox properties; however, these same properties can also lead to its toxicity in overload through the production of reactive oxygen species. Robust systemic and cellular control are required to maintain safe levels of iron, and the liver seems to be where this regulation is mainly located. Iron misregulation is implicated in many diseases, and as our understanding of iron metabolism improves, the list of iron-related disorders grows. Recent developments have resulted in greater knowledge of the fate of iron in the body and have led to a detailed map of its metabolism; however, a quantitative understanding at the systems level of how its components interact to produce tight regulation remains elusive. A mechanistic computational model of human liver iron metabolism, which includes the core regulatory components, is presented here. It was constructed based on known mechanisms of regulation and on their kinetic properties, obtained from several publications. The model was then quantitatively validated by comparing its results with previously published physiological data, and it is able to reproduce multiple experimental findings. A time course simulation following an oral dose of iron was compared to a clinical time course study and the simulation was found to recreate the dynamics and time scale of the systems response to iron challenge. A disease state simulation of haemochromatosis was created by altering a single reaction parameter that mimics a human haemochromatosis gene (HFE) mutation. The simulation provides a quantitative understanding of the liver iron overload that arises in this disease. This model supports and supplements understanding of the role of the liver as an iron sensor and provides a framework for further modelling, including simulations to identify valuable drug targets and design of experiments to improve further our knowledge of this system