Integrated detection and population-genetic analysis of SNPs and copy number variation

Dissecting the genetic basis of disease risk requires measuring all forms of genetic variation, including SNPs and copy number variants (CNVs), and is enabled by accurate maps of their locations, frequencies and population-genetic properties. We designed a hybrid genotyping array (Affymetrix SNP 6.0) to simultaneously measure 906,600 SNPs and copy number at 1.8 million genomic locations. By characterizing 270 HapMap samples, we developed a map of human CNV (at 2-kb breakpoint resolution) informed by integer genotypes for 1,320 copy number polymorphisms (CNPs) that segregate at an allele frequency >1%. More than 80% of the sequence in previously reported CNV regions fell outside our estimated CNV boundaries, indicating that large (>100 kb) CNVs affect much less of the genome than initially reported. Approximately 80% of observed copy number differences between pairs of individuals were due to common CNPs with an allele frequency >5%, and more than 99% derived from inheritance rather than new mutation. Most common, diallelic CNPs were in strong linkage disequilibrium with SNPs, and most low-frequency CNVs segregated on specific SNP haplotypes.     

The Parallelogram Rule from Pseudo-Aristotle to Newton

The history of the Parallelogram Rule for composing physical quantities, such as motions and forces, is marked by conceptual difficulties leading to false starts and halting progress. In particular, authors resisted the required assumption that the magnitude and the direction of a quantity can interact and are jointly necessary to represent the quantity. Consequently, the origins of the Rule cannot be traced to Pseudo-Aristotle or Stevin, as commonly held, but to Fermat, Hobbes, and subsequent developments in the latter part of the seventeenth century.     

Structural disorder in plant proteins: where plasticity meets sessility

Plants are sessile organisms. This intriguing nature provokes the question of how they survive despite the continual perturbations caused by their constantly changing environment. The large amount of knowledge accumulated to date demonstrates the fascinating dynamic and plastic mechanisms, which underpin the diverse strategies selected in plants in response to the fluctuating environment. This phenotypic plasticity requires an efficient integration of external cues to their growth and developmental programs that can only be achieved through the dynamic and interactive coordination of various signaling networks. Given the versatility of intrinsic structural disorder within proteins, this feature appears as one of the leading characters of such complex functional circuits, critical for plant adaptation and survival in their wild habitats. In this review, we present information of those intrinsically disordered proteins (IDPs) from plants for which their high level of predicted structural disorder has been correlated with a particular function, or where there is experimental evidence linking this structural feature with its protein function. Using examples of plant IDPs involved in the control of cell cycle, metabolism, hormonal signaling and regulation of gene expression, development and responses to stress, we demonstrate the critical importance of IDPs throughout the life of the plant.     

Significance of the length of the shortest tree

The distribution of lengths of phylogenetic trees under the taxonomic principle of parsimony is compared with the distribution obtained by randomizing the characters of the sequence data. This comparison allows us to define a measure of the extent to which sequence data contain significant hierarchical information. We show how to calculate this measure exactly for up to 10 taxa, and provide a good approximation for larger sets of taxa. The measure is applied to test sequences on 10 and 15 taxa.     

Forecasting the recent behavior of US business fixed investment spending: an analysis of competing models

We evaluate forecasting models of US business fixed investment spending growth over the recent 1995:1–2004:2 out‐of‐sample period. The forecasting models are based on the conventional Accelerator, Neoclassical, Average Q, and Cash‐Flow models of investment spending, as well as real stock prices and excess stock return predictors. The real stock price model typically generates the most accurate forecasts, and forecast‐encompassing tests indicate that this model contains most of the information useful for forecasting investment spending growth relative to the other models at longer horizons. In a robustness check, we also evaluate the forecasting performance of the models over two alternative out‐of‐sample periods: 1975:1–1984:4 and 1985:1–1994:4. A number of different models produce the most accurate forecasts over these alternative out‐of‐sample periods, indicating that while the real stock price model appears particularly useful for forecasting the recent behavior of investment spending growth, it may not continue to perform well in future periods. Copyright © 2007 John Wiley & Sons, Ltd.     

Total systems intervention: A research program

The development of TSI represents a real advance in management science in our view. Further developments are necessary however. This Research Note has outlined what we currently consider to be key issues in this development. We welcome other criticisms and suggestions. We invite you to join with us in this project.