The adaptive buffered force QM/MM method in the CP2K and AMBER software packages.

The implementation and validation of the adaptive buffered force (AdBF) quantum-mechanics/molecular-mechanics (QM/MM) method in two popular packages, CP2K and AMBER are presented. The implementations build on the existing QM/MM functionality in each code, extending it to allow for redefinition of the QM and MM regions during the simulation and reducing QM-MM interface errors by discarding forces near the boundary according to the buffered force-mixing approach. New adaptive thermostats, needed by force-mixing methods, are also implemented. Different variants of the method are benchmarked by simulating the structure of bulk water, water autoprotolysis in the presence of zinc and dimethyl-phosphate hydrolysis using various semiempirical Hamiltonians and density functional theory as the QM model. It is shown that with suitable parameters, based on force convergence tests, the AdBF QM/MM scheme can provide an accurate approximation of the structure in the dynamical QM region matching the corresponding fully QM simulations, as well as reproducing the correct energetics in all cases. Adaptive unbuffered force-mixing and adaptive conventional QM/MM methods also provide reasonable results for some systems, but are more likely to suffer from instabilities and inaccuracies.N.B. acknowledges funding for this work by the Office of Naval Research through the Naval Research Laboratory's basic research program, and computer time at the AFRL DoD Supercomputing Resource Center through the DoD High Performance Computing Modernization Program (subproject NRLDC04253428). B.L. was supported by EPSRC (grant no. EP/G036136/1) and the Scottish Funding Council. G.C. and B.L. acknowledge support form EPSRC under grant no. EP/J01298X/1. R.C.W. and A.W.G. acknowledge financial support by the National Institutes of Health (R01 GM100934), A.W.G. acknowledges financial support by the Department of Energy (DE-AC36-99GO-10337). This work was partially supported by National Science Foundation (grant no. OCI-1148358) and used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant no. ACI-1053575. Computer time was provided by the San Diego Supercomputer Center through XSEDE award TG-CHE130010.This is the author accepted version of the article. The final published version is available from Wiley at http://onlinelibrary.wiley.com/doi/10.1002/jcc.23839/full

Complex regional pain syndrome - phenotypic characteristics and potential biomarkers

Complex regional pain syndrome (CRPS) is a pain condition that usually affects a single limb, often following an injury. The underlying pathophysiology seems to be complex and probably varies between patients. Clinical diagnosis is based on internationally agreed-upon criteria, which consider the reported symptoms, presence of signs and exclusion of alternative causes. Research into CRPS biomarkers to support patient stratification and improve diagnostic certainty is an important scientific focus, and recent progress in this area provides an opportunity for an up-to-date topical review of measurable disease-predictive, diagnostic and prognostic parameters. Clinical and biochemical attributes of CRPS that may aid diagnosis and determination of appropriate treatment are delineated. Findings that predict the development of CRPS and support the diagnosis include trauma-related factors, neurocognitive peculiarities, psychological markers, and local and systemic changes that indicate activation of the immune system. Analysis of signatures of non-coding microRNAs that could predict the treatment response represents a new line of research. Results from the past 5 years of CRPS research indicate that a single marker for CRPS will probably never be found; however, a range of biomarkers might assist in clinical diagnosis and guide prognosis and treatment

Global ecological patterns in uncultured archaea

9 páginas, 5 figuras.We have applied a global analytical approach to uncultured Archaea that for the first time reveals well-defined community patterns along broad environmental gradients and habitat types. Phylogenetic patterns and the environmental factors governing the creation and maintenance of these patterns were analyzed for c. 2000 archaeal 16S rRNA gene sequences from 67 globally distributed studies. The sequences were dereplicated at 97% identity, grouped into seven habitat types, and analyzed with both Unifrac (to explore shared phylogenetic history) and multivariate regression tree (that considers the relative abundance of the lineages or taxa) approaches. Both phylogenetic and taxon-based approaches showed salinity and not temperature as one of the principal driving forces at the global scale. Hydrothermal vents and planktonic freshwater habitats emerged as the largest reservoirs of archaeal diversity and consequently are promising environments for the discovery of new archaeal lineages. Conversely, soils were more phylogenetically clustered and archaeal diversity was the result of a high number of closely related phylotypes rather than different lineages. Applying the ecological concept of ‘indicator species’, we detected up to 13 indicator archaeal lineages for the seven habitats prospected. Some of these lineages (that is, hypersaline MSBL1, marine sediment FCG1 and freshwater plSA1), for which ecological importance has remained unseen to date, deserve further attention as they represent potential key archaeal groups in terms of distribution and ecological processes. Hydrothermal vents held the highest number of indicator lineages, suggesting it would be the earliest habitat colonized by Archaea. Overall, our approach provided ecological support for the often arbitrary nomenclature within uncultured Archaea, as well as phylogeographical clues on key ecological and evolutionary aspects of archaeal biology.This research was supported by grant CRENYC CGL2006- 12058 to EOC from the Spanish Ministerio de Educacio´n y Ciencia (MEC), and CONSOLIDER-INGENIO 2010 project GRACCIE CSD2007-00004. JCA benefits from a Juan de la Cierva-MEC postdoctoral fellow, and AB is supported by an FPU-MEC predoctoral scholarship.Peer reviewe

Quantitative DNA Analyses for Airborne Birch Pollen

Birch trees produce large amounts of highly allergenic pollen grains that are distributed by wind and impact human health by causing seasonal hay fever, pollen-related asthma, and other allergic diseases. Traditionally, pollen forecasts are based on conventional microscopic counting techniques that are labor-intensive and limited in the reliable identification of species. Molecular biological techniques provide an alternative approach that is less labor-intensive and enables identification of any species by its genetic fingerprint. A particularly promising method is quantitative Real-Time polymerase chain reaction (qPCR), which can be used to determine the number of DNA copies and thus pollen grains in air filter samples. During the birch pollination season in 2010 in Mainz, Germany, we collected air filter samples of fine (<3 mu m) and coarse air particulate matter. These were analyzed by qPCR using two different primer pairs: one for a single-copy gene (BP8) and the other for a multi-copy gene (ITS). The BP8 gene was better suitable for reliable qPCR results, and the qPCR results obtained for coarse particulate matter were well correlated with the birch pollen forecasting results of the regional air quality model COSMO-ART. As expected due to the size of birch pollen grains (similar to 23 mu m), the concentration of DNA in fine particulate matter was lower than in the coarse particle fraction. For the ITS region the factor was 64, while for the single-copy gene BP8 only 51. The possible presence of so-called sub-pollen particles in the fine particle fraction is, however, interesting even in low concentrations. These particles are known to be highly allergenic, reach deep into airways and cause often severe health problems. In conclusion, the results of this exploratory study open up the possibility of predicting and quantifying the pollen concentration in the atmosphere more precisely in the future