Detection of somatic changes in human cancer DNA by DNA fingerprint analysis.

Minisatellite DNA probes which can detect a large number of autosomal loci dispersed throughout the human genome were used to examine the constitutional and tumour DNA of 35 patients with a variety of cancers of which eight were of gastrointestinal origin. Somatic changes were seen in the tumour DNA in ten of the 35 cases. The changes included alterations in the relative intensities of hybridising DNA fragments, and, in three cases of cancers of gastrointestinal origin, the appearance of novel minisatellite fragments not seen in the corresponding constitutional DNA. The results of this preliminary study suggests that DNA fingerprint analysis provides a useful technique for identifying somatic changes in cancers

Generating mice with targeted mutations.

Journal ArticleMutational analysis is one of the most informative approaches available for the study of complex biological processes. It has been particularly successful in the analysis of the biology of bacteria, yeast, the nematode worm Caenorhabditis elegans and the fruit fly Drosophila melanogaster. Extension of this approach to the mouse, through informative, was far less successful relative to what has been achieved with these simpler model organisms. This is because it is not numerically practical in mice to use random mutagenesis to isolate mutations that affect a specified biological process of interest. Nonetheless, biological phenomena such as a sophisticated immune response, cancer, vascular disease or higher-order cognitive function, to mention just a few, must analyzed in organisms that show such phenomena, and for this reason geneticists and other researchers have turned to the mouse. Gene targeting, the means for creating mice with designed mutations in almost any gene, was developed as an alternative to the impractical use of random mutgenesis for pursing genetic analysis in the mouse. Now gene targeting has advanced the genomic manipulations possible in mice to a level that can be matched only in far simple organisms such as bacteria and yeast

The prevalence of mild cognitive impairment in diverse geographical and ethnocultural regions: The COSMIC Collaboration

Background Changes in criteria and differences in populations studied and methodology have produced a wide range of prevalence estimates for mild cognitive impairment (MCI). Methods Uniform criteria were applied to harmonized data from 11 studies from USA, Europe, Asia and Australia, and MCI prevalence estimates determined using three separate definitions of cognitive impairment. Results The published range of MCI prevalence estimates was 5.0%-36.7%. This was reduced with all cognitive impairment definitions: performance in the bottom 6.681% (3.2%-10.8%); Clinical Dementia Rating of 0.5 (1.8%-14.9%); Mini-Mental State Examination score of 24-27 (2.1%-20.7%). Prevalences using the first definition were 5.9% overall, and increased with age (P < .001) but were unaffected by sex or the main races/ethnicities investigated (Whites and Chinese). Not completing high school increased the likelihood of MCI (P = .01). Conclusion Applying uniform criteria to harmonized data greatly reduced the variation in MCI prevalence internationally

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject