The role of neutral Rh(PONOP)H, free NMe2H, boronium and ammonium salts in the dehydrocoupling of dimethylamine-borane using the cationic pincer [Rh(PONOP)(η2-H2)]+ catalyst

The σ-amine-borane pincer complex [Rh(PONOP)(η1-H3B·NMe3)][BArF4] [2, PONOP = κ3-NC5H3-2,6-(OPtBu2)2] is prepared by addition of H3B·NMe3 to the dihydrogen precursor [Rh(PONOP)(η2-H2)][BArF4], 1. In a similar way the related H3B·NMe2H complex [Rh(PONOP)(η1-H3B·NMe2H)][BArF4], 3, can be made in situ, but this undergoes dehydrocoupling to reform 1 and give the aminoborane dimer [H2BNMe2]2. NMR studies on this system reveal an intermediate neutral hydride forms, Rh(PONOP)H, 4, that has been prepared independently. 1 is a competent catalyst (2 mol%, ∼30 min) for the dehydrocoupling of H3B·Me2H. Kinetic, mechanistic and computational studies point to the role of NMe2H in both forming the neutral hydride, via deprotonation of a σ-amine-borane complex and formation of aminoborane, and closing the catalytic cycle by reprotonation of the hydride by the thus-formed dimethyl ammonium [NMe2H2]+. Competitive processes involving the generation of boronium [H2B(NMe2H)2]+ are also discussed, but shown to be higher in energy. Off-cycle adducts between [NMe2H2]+ or [H2B(NMe2H)2]+ and amine-boranes are also discussed that act to modify the kinetics of dehydrocoupling

When does a physical system compute?

Computing is a high-level process of a physical system. Recent interest in non-standard computing systems, including quantum and biological computers, has brought this physical basis of computing to the forefront. There has been, however, no consensus on how to tell if a given physical system is acting as a computer or not; leading to confusion over novel computational devices, and even claims that every physical event is a computation. In this paper, we introduce a formal framework that can be used to determine whether a physical system is performing a computation. We demonstrate how the abstract computational level interacts with the physical device level, in comparison with the use of mathematical models in experimental science. This powerful formulation allows a precise description of experiments, technology, computation and simulation, giving our central conclusion: physical computing is the use of a physical system to predict the outcome of an abstract evolution. We give conditions for computing, illustrated using a range of non-standard computing scenarios. The framework also covers broader computing contexts, where there is no obvious human computer user. We introduce the notion of a ‘computational entity’, and its critical role in defining when computing is taking place in physical systems

Multicenter Study of Epidemiological Cutoff Values and Detection of Resistance in Candida spp. to Anidulafungin, Caspofungin, and Micafungin Using the Sensititre YeastOne Colorimetric Method

Neither breakpoints (BPs) nor epidemiological cutoff values (ECVs) have been established for Candida spp. with anidulafungin, caspofungin, and micafungin when using the Sensititre YeastOne (SYO) broth dilution colorimetric method. In addition, reference caspofungin MICs have so far proven to be unreliable. Candida species wild-type (WT) MIC distributions (for microorganisms in a species/drug combination with no detectable phenotypic resistance) were established for 6,007 Candida albicans, 186 C. dubliniensis, 3,188 C. glabrata complex, 119 C. guilliermondii, 493 C. krusei, 205 C. lusitaniae, 3,136 C. parapsilosis complex, and 1,016 C. tropicalis isolates. SYO MIC data gathered from 38 laboratories in Australia, Canada, Europe, Mexico, New Zealand, South Africa, and the United States were pooled to statistically define SYO ECVs. ECVs for anidulafungin, caspofungin, and micafungin encompassing >97.5% of the statistically modeled population were, respectively, 0.12,0.25, and 0.06 μg/ml for C. albicans, 0.12, 0.25, and 0.03 μg/ml for C. glabrata complex, 4, 2, and 4 μg/ml for C. parapsilosis complex, 0.5,0.25, and 0.06 μg/ml for C. tropicalis, 0.25,1, and 0.25 μg/ml for C. krusei, 0.25,1, and 0.12 μg/ml for C. lusitaniae, 4,2, and 2 μg/ml for C. guilliermondii, and 0.25,0.25, and 0.12 μg/ml for C. dubliniensis. Species-specific SYO ECVs for anidulafungin, caspofungin, and micafungin correctly classified 72 (88.9%), 74 (91.4%), 76 (93.8%), respectively, of 81 Candida isolates with identified fks mutations. SYO ECVs may aid in detecting non-WT isolates with reduced susceptibility to anidulafungin, micafungin, and especially caspofungin, since testing the susceptibilities of Candida spp. to caspofungin by reference methodologies is not recommended. Copyright © 2015, American Society for Microbiology. All Rights Reserved