A contrast: The idiomatic development of the cello from the eighteenth to the twentieth centuries as shown by the prelude and saraband of Johann Sebastian Bach\u27s Suite No. 2 in D minor for Unaccompanied Cello, BWY 1008 and Dmitri Shostakovich\u27s Sonata in D minor for Cello and Piano, OP. 40.

The idiomatic development of the cello from the eighteenth to the twentieth centuries can be traced through its compositions. When Johann Sebastian Bach wrote the Six Suites for Unaccompanied Cello, BWV 1008, he wrote for a Baroque instrument that was considerably different from cello which had evolved at the time Dmitri Shostakovich wrote his Sonata for Violoncello and Piano in D Minor. op. 40 in the twentieth century. The physical modifications to the instrument itself, most notably in the tension, created the timbre and brilliance of the modern cello. The unique qualities of the Suites are explored through the Prelude and Sarabande of the Suite II. No other composer prior to Bach had been able to compose such beautiful music with a single line, broken chords, and a few double stops. The Sonata for Violoncello and Piano in D Minor. op. 40 is a hallmark piece of the twentieth century duo sonata literature. It is a virtuoso piece with extre es in register, tempi, and harmony. Like Bach, Shostakovich conceived the cello in a novel way, and expanded the capabilities of the cello repertoire

Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk