As a record producer and administrator, Peter Andry has worked with many of the 20th century's greatest classical music artists and performers. Through his work with Decca, his years as president of EMI Classics, and his creation and direction of Warner Classics, he has collaborated with high-caliber artists such as Maria Callas, Yehudi Menuhin, and Herbert von Karajan. He associated with them in close quarters through times of work, play, stress, and relaxation. He has admired their talent, dedication, and charisma, as well as coped with their foibles, idiosyncrasies, and egos. In Inside the Recording Studio: Working with Callas, Rostropovich, Domingo, and the Classical Elite, Andry recounts his experiences with these exceptional talents, with whom he worked as a musician, a record producer, and a company executive. Andry presents intimate portraits of brilliant artists-such as Luciano Pavarotti, Joan Sutherland, Sir Thomas Beecham, Otto Klemperer, Sir Simon Rattle, Mstislav Rostropovich, Jacqueline du Pre, and Maxim Vengerov-juxtaposed with the dramatic changes occurring in the recording business during this time, a period that began with 78s and saw successively the advent of LPs, stereo sound, quadraphonic sound, audio cassettes, video, CDs, DVDs, and the growing importance of the internet. A foreword by Placido Domingo and more than 30 photos of the artists are included along with a discography of Andry's recordings with the three labels. These memoirs will be fascinating and exciting to anyone interested in the classical music and recording industries.

This book constitutes the refereed proceedings of the 20th International Conference on Computational Methods in Systems Biology, CMSB 2022, held in Bucharest, Romania, in September 2022.The 13 full papers and 4 tool papers were carefully reviewed and selected from 43 submissions. CMSB focuses on modeling, simulation, analysis, design and control of biological systems. The papers are arranged thematically as follows: Chemical reaction networks; Boolean networks; continuous and hybrid models; machine learning; software.