Simple SVM based whole-genome segmentation

We present a support vector machine (SVM) based framework for DNA segmentation into binary classes. Two applications are explored: transcription start site prediction and transcription factor binding prediction. Experiments demonstrate our approach has significantly better performance than other methods on both tasks

Effect of soil moisture stress on growth and flowering of carnations, The

Includes bibliographical references (pages [106]-115).December, 1967.The effect of differences in soil moisture stress, provided by the use of different soils and depths of soil, on yield and quality of carnations was investigated. A technique that would offer a better indication of when to water carnations under greenhouse conditions was also evaluated. The values of bulk density, moisture content at all suctions and total pore space of the best soils were an average of the extremes of all soils compared. Reduction of soil depth from 8 to 4 inches increased problems that result from too much or insufficient water. Yield and grade were best on plants grown in 8-inch soil. Raw field soil had a decreased yield due to an aeration problem when placed in a greenhouse bench. The effect of stress was most noticeable in the flowering of the second crop which was delayed up to 5 weeks under high stress. Indications were that some stress may be essential for production of higher grade carnations. The number of stomatal and epidermal cells per unit area increased as either solar radiation or soil moisture stress increased. Stomata on leaves from plants grown under high stress adapted to the unfavorable growing conditions by having a greater resistance to transpiration. The use of stomatal index was not beneficial in understanding stomatal distribution. A higher correlation was found between transpiration rate and stomatal aperture than transpiration rate and solar radiation. Although the lithium chloride hygrometer was easy to use, it was not sensitive enough to be used in a greenhouse as an indication of when to water. The measurement of stomatal apertures by the use of silicon rubber impressions was too laborious to be used as a practical field technique

Czarny humor w twórczości Władysława Szlengla ze szczególnym uwzględnieniem wiersza „Mała stacja Treblinki”

Black humor in Władysław Szlengel works, with particular focus on Mała stacja Treblinki (A small station called Treblinki) Władysław Szlengel (1914–1943), was a Jewish poet writing in Polish. His works are the best example of the use of black humor in Polish poetry of World War II. War caused him to change his worldview, which is reflected in the change of humor in his works. The shift was so powerful that in fact Szlengel-commentator replaced Szlengel-satirist. He did not hesitate to use the sharpest irony both against his enemies and against other victims of the system. His poem A Small Station Called Treblinki is the most shocking instance of black humor. Key words: Władyslaw Szlengel; black humour; holocaust; humour; risus sardonicus

Daleko od nigdzie

Far away from nowhere Abstract The person of and texts of Joseph Roth were used by Claudio Magris in his work to showa complex analysis of the Jewish intellectual culture of the beginning of 20th century – thewriting of Roth is a counterpoint to general considerations of writings of other Jewishintellectualists and to indicate differences and similarities between them. Especially WalterBenjamin is the one of the most recalled name in the work of Magris. An essayistic style of thewriter places his work among scientific work and artistic text. Keywords: Mitteleuropa, Claudio Magris, Józef Rot

Precision-mapping and statistical validation of quantitative trait loci by machine learning

<p>Abstract</p> <p>Background</p> <p>We introduce a QTL-mapping algorithm based on Statistical Machine Learning (SML) that is conceptually quite different to existing methods as there is a strong focus on generalisation ability. Our approach combines ridge regression, recursive feature elimination, and estimation of generalisation performance and marker effects using bootstrap resampling. Model performance and marker effects are determined using independent testing samples (individuals), thus providing better estimates. We compare the performance of SML against Composite Interval Mapping (CIM), Bayesian Interval Mapping (BIM) and single Marker Regression (MR) on synthetic datasets and a multi-trait and multi-environment dataset of the progeny for a cross between two barley cultivars.</p> <p>Results</p> <p>In an analysis of the synthetic datasets, SML accurately predicted the number of QTL underlying a trait while BIM tended to underestimate the number of QTL. The QTL identified by SML for the barley dataset broadly coincided with known QTL locations. SML reported approximately half of the QTL reported by either CIM or MR, not unexpected given that neither CIM nor MR incorporates independent testing. The latter makes these two methods susceptible to producing overly optimistic estimates of QTL effects, as we demonstrate for MR. The QTL resolution (peak definition) afforded by SML was consistently superior to MR, CIM and BIM, with QTL detection power similar to BIM. The precision of SML was underscored by repeatedly identifying, at ≤ 1-cM precision, three QTL for four partially related traits (heading date, plant height, lodging and yield). The set of QTL obtained using a 'raw' and a 'curated' version of the same genotypic dataset were more similar to each other for SML than for CIM or MR.</p> <p>Conclusion</p> <p>The SML algorithm produces better estimates of QTL effects because it eliminates the optimistic bias in the predictive performance of other QTL methods. It produces narrower peaks than other methods (except BIM) and hence identifies QTL with greater precision. It is more robust to genotyping and linkage mapping errors, and identifies markers linked to QTL in the absence of a genetic map.</p

Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context

<p>Abstract</p> <p>Background</p> <p>Different microarray studies have compiled gene lists for predicting outcomes of a range of treatments and diseases. These have produced gene lists that have little overlap, indicating that the results from any one study are unstable. It has been suggested that the underlying pathways are essentially identical, and that the expression of gene sets, rather than that of individual genes, may be more informative with respect to prognosis and understanding of the underlying biological process.</p> <p>Results</p> <p>We sought to examine the stability of prognostic signatures based on gene sets rather than individual genes. We classified breast cancer cases from five microarray studies according to the risk of metastasis, using features derived from predefined gene sets. The expression levels of genes in the sets are aggregated, using what we call a set statistic. The resulting prognostic gene sets were as predictive as the lists of individual genes, but displayed more consistent rankings via bootstrap replications within datasets, produced more stable classifiers across different datasets, and are potentially more interpretable in the biological context since they examine gene expression in the context of their neighbouring genes in the pathway. In addition, we performed this analysis in each breast cancer molecular subtype, based on ER/HER2 status. The prognostic gene sets found in each subtype were consistent with the biology based on previous analysis of individual genes.</p> <p>Conclusions</p> <p>To date, most analyses of gene expression data have focused at the level of the individual genes. We show that a complementary approach of examining the data using predefined gene sets can reduce the noise and could provide increased insight into the underlying biological pathways.</p

A Deployment Process for Strategic Measurement Systems

Explicitly linking software-related activities to an organisation's higher-level goals has been shown to be critical for organizational success. GQM+Strategies provides mechanisms for explicitly linking goals and strategies, based on goal-oriented strategic measurement systems. Deploying such strategic measurement systems in an organization is highly challenging. Experience has shown that a clear deployment strategy is needed for achieving sustainable success. In particular, an adequate deployment process as well as corresponding tool support can facilitate the deployment. This paper introduces the systematical GQM+Strategies deployment process and gives an overview of GQM+Strategies modelling and associated tool support. Additionally, it provides an overview of industrial applications and describes success factors and benefits for the usage of GQM+Strategies.Comment: 12 pages. Proceedings of the 8th Software Measurement European Forum (SMEF 2011