Effects of evaluation on creative production

Despite considerable interest in the problem of creativity, both in psychology and in other fields, there is still no standard definition of the term. Guilford (1 9 6 7 ) defines creativity by making a distinction between convergent thinking and divergent thinking. In convergent thinking, according to Guilford, there is a single right answer or best answer to a problem, while in divergent thinking there is not. He uses the term divergent thinking interchangeably with creativity. Ilis research indicates that of the factors identified as making up divergent thinking, ideational fluency, which Guilford defines as fairly rapid generation of units of verbal or semantic information, has least variance in common with intelligence. The correlation between ideational fluency and convergent thinking is .01 (Guilford, Frick, Christensen, & Merrifield, 1957)* Since a number of studies have indicated fairly low correlations (around .3 ) between I.Q, scores and scores of creativity, at least in children (e.g., Getzels & Jackson, 1962), it would seem that ideational fluency may be an important component of creativity. Other writers approach the term somewhat differently. Parnes and Meadow (1959) use the criteria of uniqueness and usefulness as determiners of good ideas in their studies on creativity. Wallach and Kogan (19&5) speak of total number of ideas generated as well as their uniqueness within a given subject sample. Though ideational output includes qualitative considerations as well as quantitative ones, the present study is concerned only with the quantitative — number of ideas produced in a divergent thinking situation

Aptamer-based multiplexed proteomic technology for biomarker discovery

Interrogation of the human proteome in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 [mu]L of serum or plasma). Our current assay allows us to measure ~800 proteins with very low limits of detection (1 pM average), 7 logs of overall dynamic range, and 5% average coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding DNA aptamer concentration signature, which is then quantified with a DNA microarray. In essence, our assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to discover unique protein signatures characteristic of various disease states. More generally, we describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine

Weak temperature dependence of P (+) H A (-) recombination in mutant Rhodobacter sphaeroides reaction centers

International audienceIn contrast with findings on the wild-type Rhodobacter sphaeroides reaction center, biexponential P (+) H A (-) → PH A charge recombination is shown to be weakly dependent on temperature between 78 and 298 K in three variants with single amino acids exchanged in the vicinity of primary electron acceptors. These mutated reaction centers have diverse overall kinetics of charge recombination, spanning an average lifetime from ~2 to ~20 ns. Despite these differences a protein relaxation model applied previously to wild-type reaction centers was successfully used to relate the observed kinetics to the temporal evolution of the free energy level of the state P (+) H A (-) relative to P (+) B A (-) . We conclude that the observed variety in the kinetics of charge recombination, together with their weak temperature dependence, is caused by a combination of factors that are each affected to a different extent by the point mutations in a particular mutant complex. These are as follows: (1) the initial free energy gap between the states P (+) B A (-) and P (+) H A (-) , (2) the intrinsic rate of P (+) B A (-) → PB A charge recombination, and (3) the rate of protein relaxation in response to the appearance of the charge separated states. In the case of a mutant which displays rapid P (+) H A (-) recombination (ELL), most of this recombination occurs in an unrelaxed protein in which P (+) B A (-) and P (+) H A (-) are almost isoenergetic. In contrast, in a mutant in which P (+) H A (-) recombination is relatively slow (GML), most of the recombination occurs in a relaxed protein in which P (+) H A (-) is much lower in energy than P (+) H A (-) . The weak temperature dependence in the ELL reaction center and a YLH mutant was modeled in two ways: (1) by assuming that the initial P (+) B A (-) and P (+) H A (-) states in an unrelaxed protein are isoenergetic, whereas the final free energy gap between these states following the protein relaxation is large (~250 meV or more), independent of temperature and (2) by assuming that the initial and final free energy gaps between P (+) B A (-) and P (+) H A (-) are moderate and temperature dependent. In the case of the GML mutant, it was concluded that the free energy gap between P (+) B A (-) and P (+) H A (-) is large at all times

Impact-Ionization Coefficient in Silicon at High Fields – A Parametric Approach

The impact-ionization coefficient at high fields is derived in terms of the electric field E and lattice temperature TL, without introducing a priori relations among the parameters. An asymptotic analysis leads to simplifications that validate closed-form expressions of the impact-ionization coefficient. The role of the relaxation times in determining the slope is discussed, along with the meaning of the critical field

Impact-ionization coefficient in silicon at high fields: \u2013 A parametric approach

The impact-ionization coefficient a_n at high fields is derived in terms of the electric fields E and lattice temperature T, without introducing a priori relations among the parameters. An asymptotic analysis leads to simplifications that validate closed-form expressions of a_n. The role of the relaxation times in determining the slope of a_n(E) is discussed, along with the meaning of the critical field