Copper complexes of dinucleating octa-azamacrocyclic ligands

The synthesis of mono and dinucleating ligands and their copper complexes are described. Three types of dinacleating tetraimine macrocycles have been prepared from 4,7-diaza-2,3;8,9-dibenzodecane-l,10-dione by condensation with the appropriate polyamine; I, large-ring octa-aza macrocycles e.g. the 28—membered ring compound 5,6,7,8,15,16,23,24,25,26,33,34— dodecahydrotetrabenzo(e,m,s,a’) (1,4,11,15,18,22,25) octaaza- cyclooctacosine and related 30- and 36- membered ring compounds; II, the "fused" bis(tetra-azamacrocycle) 5,6,7,8,22,23,24,25-octahydrotetrabenzo (f,f’1,1’) benzo (1,2-b:4,5-b’)- bis(1,4,8,)tetraazacyclotetradecine; III, the "Linked" bis(tetra-azamacrocycle) 5,6,7,8,24,25,26,27-octahydrotetrabenzo (f,f’,1,1)diphenyl(3,4-b:3’,4’-b’)bis(l,4,8,11)tetraazacyclotetradecine. For the type I and III ligands reduction of the imine linkages yielded the related octa-amines. The preparation of copper complexes is described. For many of the neutral copper complexes (formed by deprotonation of anilino nitrogen atoms) a novel synthetic route had to be used to overcome problems associated with the very low solubility of both ligand and complex

Targeted agents for the treatment of metastatic melanoma

In the last year, the armamentarium of melanoma therapeutics has radically changed. Recent discoveries in melanoma biology and immunology have led to novel therapeutics targeting known oncogenes and immunotherapeutic antibodies. Phase III clinical trials of these agents have reported measurable and meaningful benefits to patients with metastatic disease. In this article, we review recent findings and discuss their significance in melanoma therapy. As our understanding of melanoma biology grows, this initial therapeutic success may be enhanced through the use of molecular markers to select patients, and new targeted immunotherapies in sequential or combination drug regimens

On the impulse criterion for entrainment of coarse grains in air

River hydrodynamicsTurbulent open channel flow and transport phenomen

Judicial nominees who have confirmation hearings during divided government are much more likely to face ideological questions

While the U.S. Senate is now unable to make use of the filibuster to delay judicial nominees to federal circuit and district courts, they must still undergo a hearing before the Senate Judiciary Committee. New research from Logan Dancey, Kjersten R. Nelson and Eve M. Ringsmuth finds that the political environment is a better predictor of the hearing’s content and questions than the characteristics of the nominee under scrutiny. They write that nominees who face confirmation hearings when the presidency and Senate are controlled by different parties are more likely to face questions on crime, abortion, civil rights and on their judicial philosophy

Instantaneous pressure measurements on a spherical grain under threshold flow conditions

River morphodynamics and sediment transportMechanics of sediment transpor

Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees

Deep Reinforcement Learning (DRL) has achieved impressive success in many applications. A key component of many DRL models is a neural network representing a Q function, to estimate the expected cumulative reward following a state-action pair. The Q function neural network contains a lot of implicit knowledge about the RL problems, but often remains unexamined and uninterpreted. To our knowledge, this work develops the first mimic learning framework for Q functions in DRL. We introduce Linear Model U-trees (LMUTs) to approximate neural network predictions. An LMUT is learned using a novel on-line algorithm that is well-suited for an active play setting, where the mimic learner observes an ongoing interaction between the neural net and the environment. Empirical evaluation shows that an LMUT mimics a Q function substantially better than five baseline methods. The transparent tree structure of an LMUT facilitates understanding the network's learned knowledge by analyzing feature influence, extracting rules, and highlighting the super-pixels in image inputs.Comment: This paper is accepted by ECML-PKDD 201