A Real Space Renormalization Group Approach to Field Evolution Equations

A new operator formalism for the reduction of degrees of freedom in the evolution of discrete partial differential equations (PDE) via real space Renormalization Group is introduced, in which cell-overlapping is the key concept. Applications to 1+1-dimensional PDEs are presented for linear and quadratic equations which are first order in time.Comment: 8 pages, 10 ps figures. Accepted for publication in Phys. Rev.

Nonperturbative real-space renormalization group scheme for the spin-1/2 XXX Heisenberg model

In this paper we apply an analytical real-space renormalization group formulation which is based on numerical concepts of the density-matrix renormalization group, Within a rigorous mathematical framework we construct nonperturbative renormalization group transformations for the spin-1/2 XXX Heisenberg model in the finite-temperature regime. The developed renormalization group scheme allows for calculating the renormalization group flow behavior in the temperature-dependent coupling constant. The constructed renormalization group transformations are applied within the ferromagnetic and the antiferromagnetic regime of the Heisenberg chain. The ferromagnetic fixed point is computed and compared to results derived by other techniques

Multicentre evaluation of the Boehringer Mannheim/Hitachi 917 analysis system

The new selective access analysis system BM/Hitachi 917 was evaluated in an international multicentre study, mainly according to the ECCLS protocol for the evaluation of analysers in clinical chemistry. Forty-three different analytes, covering 56 different methods enzymes, substrates, electrolytes, specific proteins, drugs and urine applications were tested in seven European clinical chemistry laboratories. Additionally, the practicability of the BM/ Hitachi 917 was tested according to a standardized questionnaire. Within-run CVs (median of 3 days) for enzymes, substrates and electrolytes were <2% except for creatine-kinase MB isoform and lipase at low concentration. For proteins, drugs and urine analytes the within-run CVs were < 4% except for digoxin and albumin in urine. Between-day median CVs were generally < 3% for enzymes, substrates and electrolytes, and < 6% for proteins, drugs and urine analytes, except for lipase, creatine kinase and MB isoform, D-dimer, glycosylated haemoglobin, rheumatoid factors, digoxin, digitoxin, theophylline and albumin in urine in some materials. Linearity was found according to the test specifications or better and there were no relevant effects seen in drift and carry-over testing. The interference results clearly show that also for the BM/Hitachi 917 interference exists sometimes, as could be expected because of the chemistries applied. It is a situation that can be found in equivalent analysers as well. The accuracy is acceptable regarding a 95–105% recovery in standard reference material, with the exception of the creatinine Jaffé method. Most of the 160 method comparisons showed acceptable agreement according to our criteria: enzymes, substrates, urine analytes deviation of slope ± 5%, electrolytes ± 3%, and proteins and drugs ± 10%. The assessment of practicability for 14 groups of attributes resulted in a grading of one–three scores better for the BM/Hitachi 917 than the present laboratory situation. In conclusion, the results of the study showed good analytical performance and confirmed the usefulness of the system as a consolidated workstation in medium-sized to large clinical chemistry laboratories

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes(1). Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel(2)) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries

Breast tumor classification and visualization with machine-learning approaches

no abstrac

Breas

Nattkemper TW, Degenhard A, Twellmann T. Breas. In: Hayat MA, ed. Cancer Imaging - Lung and breast carcinomas. Vol 1. Academic Press; 2007: 309-324