Lipidomic profiling in Crohn's disease: abnormalities in phosphatidylinositols, with preservation of ceramide, phosphatidylcholine and phosphatidylserine composition.

Crohn's disease is a chronic inflammatory condition largely affecting the terminal ileum and large bowel. A contributing cause is the failure of an adequate acute inflammatory response as a result of impaired secretion of pro-inflammatory cytokines by macrophages. This defective secretion arises from aberrant vesicle trafficking, misdirecting the cytokines to lysosomal degradation. Aberrant intestinal permeability is also well-established in Crohn's disease. Both the disordered vesicle trafficking and increased bowel permeability could result from abnormal lipid composition. We thus measured the sphingo- and phospholipid composition of macrophages, using mass spectrometry and stable isotope labelling approaches. Stimulation of macrophages with heat-killed Escherichia coli resulted in three main changes; a significant reduction in the amount of individual ceramide species, an altered composition of phosphatidylcholine, and an increased rate of phosphatidylcholine synthesis in macrophages. These changes were observed in macrophages from both healthy control individuals and patients with Crohn's disease. The only difference detected between control and Crohn's disease macrophages was a reduced proportion of newly-synthesised phosphatidylinositol 16:0/18:1 over a defined time period. Shotgun lipidomics analysis of macroscopically non-inflamed ileal biopsies showed a significant decrease in this same lipid species with overall preservation of sphingolipid, phospholipid and cholesterol composition

N-player quantum games in an EPR setting

The $N$-player quantum game is analyzed in the context of an Einstein-Podolsky-Rosen (EPR) experiment. In this setting, a player's strategies are not unitary transformations as in alternate quantum game-theoretic frameworks, but a classical choice between two directions along which spin or polarization measurements are made. The players' strategies thus remain identical to their strategies in the mixed-strategy version of the classical game. In the EPR setting the quantum game reduces itself to the corresponding classical game when the shared quantum state reaches zero entanglement. We find the relations for the probability distribution for $N$-qubit GHZ and W-type states, subject to general measurement directions, from which the expressions for the mixed Nash equilibrium and the payoffs are determined. Players' payoffs are then defined with linear functions so that common two-player games can be easily extended to the $N$-player case and permit analytic expressions for the Nash equilibrium. As a specific example, we solve the Prisoners' Dilemma game for general $N \ge 2$. We find a new property for the game that for an even number of players the payoffs at the Nash equilibrium are equal, whereas for an odd number of players the cooperating players receive higher payoffs.Comment: 26 pages, 2 figure

CpG islands or CpG clusters: how to identify functional GC-rich regions in a genome?

Background CpG islands (CGIs), clusters of CpG dinucleotides in GC-rich regions, are often located in the 5\u27 end of genes and considered gene markers. Hackenberg et al. (2006) recently developed a new algorithm, CpGcluster, which uses a completely different mathematical approach from previous traditional algorithms. Their evaluation suggests that CpGcluster provides a much more efficient approach to detecting functional clusters or islands of CpGs. Results We systematically compared CpGcluster with the traditional algorithm by Takai and Jones (2002). Our comparisons of (1) the number of islands versus the number of genes in a genome, (2) the distribution of islands in different genomic regions, (3) island length, (4) the distance between two neighboring islands, and (5) methylation status suggest that Takai and Jones\u27 algorithm is overall more appropriate for identifying promoter-associated islands of CpGs in vertebrate genomes. Conclusion The generation of genome sequence and DNA methylation data is expected to accelerate greatly. The information in this study is important for its extensive utility in gene feature analysis and epigenomics including gene prediction and methylation chip design in different genomes

Synthetic matrix enhances transplanted satellite cell engraftment in dystrophic and aged skeletal muscle with comorbid trauma

Muscle satellite cells (MuSCs) play a central role in muscle regeneration, but their quantity and function decline with comorbidity of trauma, aging, and muscle diseases. Although transplantation of MuSCs in traumatically injured muscle in the comorbid context of aging or pathology is a strategy to boost muscle regeneration, an effective cell delivery strategy in these contexts has not been developed. We engineered a synthetic hydrogel-based matrix with optimal mechanical, cell-adhesive, and protease-degradable properties that promotes MuSC survival, proliferation, and differentiation. Furthermore, we establish a biomaterial-mediated cell delivery strategy for treating muscle trauma, where intramuscular injections may not be applicable. Delivery of MuSCs in the engineered matrix significantly improved in vivo cell survival, proliferation, and engraftment in nonirradiated and immunocompetent muscles of aged and dystrophic mice compared to collagen gels and cell-only controls. This platform may be suitable for treating craniofacial and limb muscle trauma, as well as postoperative wounds of elderly and dystrophic patients.Research reported in this publication was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the NIH under award numbers R21AR072287 (to Y.C.J.) and R01AR062368 (to A.J.G.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work was also funded by the Parker H. Petit Institute for Bioengineering and Bioscience Seed Grant Program (to A.J.G. and Y.C.J.)

Synthetic matrix enhances transplanted satellite cell engraftment in dystrophic and aged skeletal muscle with comorbid trauma

Muscle satellite cells (MuSCs) play a central role in muscle regeneration, but their quantity and function decline with comorbidity of trauma, aging, and muscle diseases. Although transplantation of MuSCs in traumatically injured muscle in the comorbid context of aging or pathology is a strategy to boost muscle regeneration, an effective cell delivery strategy in these contexts has not been developed. We engineered a synthetic hydrogel-based matrix with optimal mechanical, cell-adhesive, and protease-degradable properties that promotes MuSC survival, proliferation, and differentiation. Furthermore, we establish a biomaterial-mediated cell delivery strategy for treating muscle trauma, where intramuscular injections may not be applicable. Delivery of MuSCs in the engineered matrix significantly improved in vivo cell survival, proliferation, and engraftment in nonirradiated and immunocompetent muscles of aged and dystrophic mice compared to collagen gels and cell-only controls. This platform may be suitable for treating craniofacial and limb muscle trauma, as well as postoperative wounds of elderly and dystrophic patients.Research reported in this publication was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the NIH under award numbers R21AR072287 (to Y.C.J.) and R01AR062368 (to A.J.G.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work was also funded by the Parker H. Petit Institute for Bioengineering and Bioscience Seed Grant Program (to A.J.G. and Y.C.J.)

Discovery of the progenitor of the type Ia supernova 2007on

Type Ia supernovae are exploding stars that are used to measure the accelerated expansion of the Universe and are responsible for most of the iron ever produced. Although there is general agreement that the exploding star is a white dwarf in a binary system, the exact configuration and trigger of the explosion is unclear, which could hamper their use for precision cosmology. Two families of progenitor models have been proposed. In the first, a white dwarf accretes material from a companion until it exceeds the Chandrasekhar mass, collapses and explodes. Alternatively, two white dwarfs merge, again causing catastrophic collapse and an explosion. It has hitherto been impossible to determine if either model is correct. Here we report the discovery of an object in pre-supernova archival X-ray images at the position of the recent type Ia supernova (2007on) in the elliptical galaxy NGC 1404. Deep optical images (also archival) show no sign of this object. From this we conclude that the X-ray source is the progenitor of the supernova, which favours the accretion model for this supernova, although the host galaxy is older (6-9 Gyr) than the age at which the explosions are predicted in the accreting models.Comment: Published in Nature See also the two follow-up papers: Roelofs, Bassa, Voss, Nelemans Nelemans, Voss, Roelofs, Bassa both on astro-ph 02/15/0

Two-stage analyses of sequence variants in association with quantitative traits

We propose a two-stage design for the analysis of sequence variants in which a proportion of genes that show some evidence of association are identified initially and then followed up in an independent data set. We compare two different approaches. In both approaches the same summary measure (total number of minor alleles) is used for each gene in the initial analysis. In the first (simple) approach the same summary measure is used in the analysis of the independent data set. In the second (alternative) approach a more specific hypothesis is formed for the second stage; the summary measure used is the count of minor alleles in only those variants that in the initial data showed the same direction of association as was seen overall. We applied the methods to the simulated quantitative traits of Genetic Analysis Workshop 17, blind to the simulation model, and then evaluated their performance once the underlying model was known. Performance was similar for most genes, but the simple strategy considerably out-performed the alternative strategy for one gene, where most of the effect was due to very rare variants; this suggests that the alternative approach would not be advisable when the effect is seen in very rare variants. Further simulations are needed to investigate the potential superior power of the alternative method when some variants within a gene have opposing effects. Overall, the power to detect associations was low; this was also true when using a more powerful joint analysis that combined the two stages of the study

Non-local effects in the mean-field disc dynamo. II. Numerical and asymptotic solutions

The thin-disc global asymptotics are discussed for axisymmetric mean-field dynamos with vacuum boundary conditions allowing for non-local terms arising from a finite radial component of the mean magnetic field at the disc surface. This leads to an integro-differential operator in the equation for the radial distribution of the mean magnetic field strength, $Q(r)$ in the disc plane at a distance $r$ from its centre; an asymptotic form of its solution at large distances from the dynamo active region is obtained. Numerical solutions of the integro-differential equation confirm that the non-local effects act similarly to an enhanced magnetic diffusion. This leads to a wider radial distribution of the eigensolution and faster propagation of magnetic fronts, compared to solutions with the radial surface field neglected. Another result of non-local effects is a slowly decaying algebraic tail of the eigenfunctions outside the dynamo active region, $Q(r)\sim r^{-4}$, which is shown to persist in nonlinear solutions where $\alpha$-quenching is included. The non-local nature of the solutions can affect the radial profile of the regular magnetic field in spiral galaxies and accretion discs at large distances from the centre.Comment: Revised version, as accepted; Geophys. Astrophys. Fluid Dyna

BETULA: Numerically Stable CF-Trees for BIRCH Clustering

BIRCH clustering is a widely known approach for clustering, that has influenced much subsequent research and commercial products. The key contribution of BIRCH is the Clustering Feature tree (CF-Tree), which is a compressed representation of the input data. As new data arrives, the tree is eventually rebuilt to increase the compression. Afterward, the leaves of the tree are used for clustering. Because of the data compression, this method is very scalable. The idea has been adopted for example for k-means, data stream, and density-based clustering. Clustering features used by BIRCH are simple summary statistics that can easily be updated with new data: the number of points, the linear sums, and the sum of squared values. Unfortunately, how the sum of squares is then used in BIRCH is prone to catastrophic cancellation. We introduce a replacement cluster feature that does not have this numeric problem, that is not much more expensive to maintain, and which makes many computations simpler and hence more efficient. These cluster features can also easily be used in other work derived from BIRCH, such as algorithms for streaming data. In the experiments, we demonstrate the numerical problem and compare the performance of the original algorithm compared to the improved cluster features

Use of Bayesian networks to dissect the complexity of genetic disease: application to the Genetic Analysis Workshop 17 simulated data

Complex diseases are often the downstream event of a number of risk factors, including both environmental and genetic variables. To better understand the mechanism of disease onset, it is of great interest to systematically investigate the crosstalk among various risk factors. Bayesian networks provide an intuitive graphical interface that captures not only the association but also the conditional independence and dependence structures among the variables, resulting in sparser relationships between risk factors and the disease phenotype than traditional correlation-based methods. In this paper, we apply a Bayesian network to dissect the complex regulatory relationships among disease traits and various risk factors for the Genetic Analysis Workshop 17 simulated data. We use the Bayesian network as a tool for the risk prediction of disease outcome