Published by John Wiley & Sons, Ltd.”
“Heart transplantation (HTx) has been a successful therapy for patients with end-stage heart failure. Since 1987, we have performed 288 HTx. Thirty-si x subjects needed mechanical support prior to HTx. We use anti-thymocyte Epigenetics inhibitor globulin (ATG) as induction

therapy and low-dose immunosuppressive agents for maintenance treatment. In June 1996, we performed combined heart and kidney transplantation after bridging for 14 days with an indigenous total artificial heart (TAH). The patient is still well. Our actuarial survival rates at 1, 5, and 10 years are 86%, 76%, and 61%, respectively. One recipient who voluntarily discontinued all treatment at 4 years after HTx is still alive and free of rejection in his ninth posttransplantation year. The longest surviving recipient is in her 18(th) posttransplantation year. We also have used many suboptimal donor hearts, most with satisfactory outcomes. A 14-year-old boy had full Selleck LXH254 recovery of heart function after receiving a donor heart after 13 hours of ischemia in 2003. Standard biatrial anastomotic technique is Still Our first choice. The incidence of tricuspid regurgitation (TR) and conduction

disturbances is not higher than the bicaval technique reported by others. With low-dose therapy, our short-term and long-term results of HTx are satisfactory. The use of suboptimal donor hearts may expand the donor pool and save more patients’ lives. A biatrial anastomosis remains our surgical technique.”
“We consider the stochastic shortest path problem, a classical finite-state Markovian decision problem with a termination state, and we propose new convergent Q-learning algorithms that combine elements of policy iteration and classical Q-learning/value iteration. These algorithms are related to the ones introduced by the authors for discounted problems in Bertsekas and Yu (Math. Oper. Res. 37(1):66-94, 2012). The main difference from the standard policy iteration approach is in the policy evaluation phase: instead of solving a linear system of equations, our algorithm solves an optimal stopping problem inexactly with

a finite number of value iterations. The main advantage over the standard Q-learning approach is lower overhead: most iterations do not require a minimization over all 10058-F4 supplier controls, in the spirit of modified policy iteration. We prove the convergence of asynchronous deterministic and stochastic lookup table implementations of our method for undiscounted, total cost stochastic shortest path problems. These implementations overcome some of the traditional convergence difficulties of asynchronous modified policy iteration, and provide policy iteration-like alternative Q-learning schemes with as reliable convergence as classical Q-learning. We also discuss methods that use basis function approximations of Q-factors and we give an associated error bound.