University of Erlangen-Nuremberg

About: University of Erlangen-Nuremberg is a education organization based out in Erlangen, Bayern, Germany. It is known for research contribution in the topics: Population & Immune system. The organization has 42405 authors who have published 85600 publications receiving 2663922 citations.

Systematic Aortic and Pelvic Lymphadenectomy Versus Resection of Bulky Nodes Only in Optimally Debulked Advanced Ovarian Cancer: A Randomized Clinical Trial

Abstract: Background: The role of systematic aortic and pelvic lymphadenectomy in patients with optimally debulked advanced ovarian cancer is unclear and has not been addressed by randomized studies. We conducted a randomized clinical trial to determine whether systematic aortic and pelvic lymphadenectomy improves progression-free and overall survival compared with resection of bulky nodes only. Methods: From January 1991 through May 2003, 427 eligible patients with International Federation of Gynecology and Obstetrics (FIGO) stage IIIB-C and IV epithelial ovarian carcinoma were randomly assigned to undergo systematic pelvic and para-aortic lymphadenectomy (n = 216) or resection of bulky nodes only (n = 211). Progression-free survival and overall survival were analyzed using a log-rank statistic and a Cox multivariable regression analysis. All statistical tests were two-sided. Results: After a median follow-up of 68.4 months, 292 events (i.e., recurrences or deaths) were observed, and 202 patients had died. Sites of first recurrences were similar in both arms. The adjusted risk for first event was statistically significantly lower in the systematic lymphadenectomy arm (hazard ratio [HR] =.75, 95% confidence interval [CI] = 0.59 to 0.94; P = .01) than in the no-lymphadenectomy arm, corresponding to 5-year progression-free survival rates of 31.2 and 21.6% in the systematic lymphadenectomy and control arms, respectively (difference = 9.6%, 95% CI = 1.5% to 21.6%), and to median progression-free survival of 29.4 and 22.4 months, respectively (difference = 7 months, 95% CI = 1.0 to 14.4 months). The risk of death was similar in both arms (HR = 0.97, 95% CI = 0.74 to 1.29; P = .85), corresponding to 5-year overall survival rates of 48.5 and 47%, respectively (difference = 1.5%, 95% CI = -8.4% to 10.6%), and to median overall survival of 58.7 and 56.3 months, respectively (difference = 2.4 months, 95% CI = -11.8 to 21.0 months). Median operating time was longer, and the percentage of patients requiring blood transfusions was higher in the systematic lymphadenectomy arm than in the no-lymphadenectomy arm (300 versus 210 minutes, P<.001, and 72% versus 59%; P = .006, respectively). Conclusion: Systematic lymphadenectomy improves progression-free but not overall survival in women with optimally debulked advanced ovarian carcinoma.

Structure of the Tet repressor-tetracycline complex and regulation of antibiotic resistance

Abstract: The most frequently occurring resistance of Gram-negative bacteria against tetracyclines is triggered by drug recognition of the Tet repressor. This causes dissociation of the repressor-operator DNA complex and enables expression of the resistance protein TetA, which is responsible for active efflux of tetracycline. The 2.5 angstrom resolution crystal structure of the homodimeric Tet repressor complexed with tetracycline-magnesium reveals detailed drug recognition. The orientation of the operator-binding helix-turn-helix motifs of the repressor is inverted in comparison with other DNA binding proteins. The repressor-drug complex is unable to interact with DNA because the separation of the DNA binding motifs is 5 angstroms wider than usually observed.

Quantum approximate optimization of non-planar graph problems on a planar superconducting processor

Abstract: Faster algorithms for combinatorial optimization could prove transformative for diverse areas such as logistics, finance and machine learning. Accordingly, the possibility of quantum enhanced optimization has driven much interest in quantum technologies. Here we demonstrate the application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA). Like past QAOA experiments, we study performance for problems defined on the planar connectivity graph native to our hardware; however, we also apply the QAOA to the Sherrington–Kirkpatrick model and MaxCut, non-native problems that require extensive compilation to implement. For hardware-native problems, which are classically efficient to solve on average, we obtain an approximation ratio that is independent of problem size and observe that performance increases with circuit depth. For problems requiring compilation, performance decreases with problem size. Circuits involving several thousand gates still present an advantage over random guessing but not over some efficient classical algorithms. Our results suggest that it will be challenging to scale near-term implementations of the QAOA for problems on non-native graphs. As these graphs are closer to real-world instances, we suggest more emphasis should be placed on such problems when using the QAOA to benchmark quantum processors. It is hoped that quantum computers may be faster than classical ones at solving optimization problems. Here the authors implement a quantum optimization algorithm over 23 qubits but find more limited performance when an optimization problem structure does not match the underlying hardware.