A widely tunable few electron droplet

Quasi-static transport measurements are employed to characterize a few electron quantum dot electrostatically defined in a GaAs/AlGaAs heterostructure. The gate geometry allows observations on one and the same electron droplet within a wide range of coupling strengths to the leads. The weak coupling regime is described by discrete quantum states. At strong interaction with the leads Kondo phenomena are observed as a function of a magnetic field. By varying gate voltages the electron droplet can, in addition, be distorted into a double quantum dot with a strong interdot tunnel coupling while keeping track of the number of trapped electrons.Comment: 11 pages, 5 figure

Negative frequency tuning of a carbon nanotube nano-electromechanical resonator

A suspended, doubly clamped single wall carbon nanotube is characterized as driven nano-electromechanical resonator at cryogenic temperatures. Electronically, the carbon nanotube displays small bandgap behaviour with Coulomb blockade oscillations in electron conduction and transparent contacts in hole conduction. We observe the driven mechanical resonance in dc-transport, including multiple higher harmonic responses. The data shows a distinct negative frequency tuning at finite applied gate voltage, enabling us to electrostatically decrease the resonance frequency to 75% of its maximum value. This is consistently explained via electrostatic softening of the mechanical mode.Comment: 4 pages, 4 figures; submitted for the IWEPNM 2013 conference proceeding

Impact of boundary layer flow velocity on oxygen utilisation in coastal sediments

Small pressure gradients generated by boundary flow-topography interactions cause advective pore water flows in permeable sediments. Advective pore water exchange enhances the flux of solutes between the sediment and the overlying water, thus generating conditions for an increased utilisation of oxygen. We compared a less permeable (k = 5 x 10(-12) m(2)) with a permeable sediment (k = 5 x 10(-11) m(2)) typical for coastal and shelf sediments. Total oxygen utilisation (TOU) in incubated sediment cores was measured in 10 laboratory experiments using recirculating flow tanks (33 runs). TOU was a function of now velocity in permeable sediment where advective pore water now occurred. TOU increased with the increasing volume of sediment flushed with oxygenated water. We found that TOU increased by 91 +/- 23% in coarse sand when now increased from 3 to 14 cm s(-1) (38 mounds m(-2) height 10 to 30 mm, now measured 8 cm above the sediment). Addition of fresh algal material caused a stronger stimulation of TOU in the coarse sand than in the fine sand (4 additional flume runs). After the addition, intensive oxygen consumption reduced the oxygen penetration depth in the advectively flushed zone of the coarse sediment. However, counteracting this process, advective flow maintained an oxic sediment volume still larger than that in the less permeable sediment. Flow-enhanced oxygen utilisation is potentially effective in permeable beds of coastal and shelf regions, in contrast to the situation in cohesive sediments limited by predominantly diffusive oxygen supply

The oxygen-independent metabolism of cyclic monoterpenes in Castellaniella defragrans 65Phen

BACKGROUND: The facultatively anaerobic betaproteobacterium Castellaniella defragrans 65Phen utilizes acyclic, monocyclic and bicyclic monoterpenes as sole carbon source under oxic as well as anoxic conditions. A biotransformation pathway of the acyclic β-myrcene required linalool dehydratase-isomerase as initial enzyme acting on the hydrocarbon. An in-frame deletion mutant did not use myrcene, but was able to grow on monocyclic monoterpenes. The genome sequence and a comparative proteome analysis together with a random transposon mutagenesis were conducted to identify genes involved in the monocyclic monoterpene metabolism. Metabolites accumulating in cultures of transposon and in-frame deletion mutants disclosed the degradation pathway. RESULTS: Castellaniella defragrans 65Phen oxidizes the monocyclic monoterpene limonene at the primary methyl group forming perillyl alcohol. The genome of 3.95 Mb contained a 70 kb genome island coding for over 50 proteins involved in the monoterpene metabolism. This island showed higher homology to genes of another monoterpene-mineralizing betaproteobacterium, Thauera terpenica 58Eu(T), than to genomes of the family Alcaligenaceae, which harbors the genus Castellaniella. A collection of 72 transposon mutants unable to grow on limonene contained 17 inactivated genes, with 46 mutants located in the two genes ctmAB (cyclic terpene metabolism). CtmA and ctmB were annotated as FAD-dependent oxidoreductases and clustered together with ctmE, a 2Fe-2S ferredoxin gene, and ctmF, coding for a NADH:ferredoxin oxidoreductase. Transposon mutants of ctmA, B or E did not grow aerobically or anaerobically on limonene, but on perillyl alcohol. The next steps in the pathway are catalyzed by the geraniol dehydrogenase GeoA and the geranial dehydrogenase GeoB, yielding perillic acid. Two transposon mutants had inactivated genes of the monoterpene ring cleavage (mrc) pathway. 2-Methylcitrate synthase and 2-methylcitrate dehydratase were also essential for the monoterpene metabolism but not for growth on acetate. CONCLUSIONS: The genome of Castellaniella defragrans 65Phen is related to other genomes of Alcaligenaceae, but contains a genomic island with genes of the monoterpene metabolism. Castellaniella defragrans 65Phen degrades limonene via a limonene dehydrogenase and the oxidation of perillyl alcohol. The initial oxidation at the primary methyl group is independent of molecular oxygen

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating

Quantifying biologically and physically induced flow and tracer dynamics in permeable sediments

International audienceInsight in the biogeochemistry and ecology of sandy sediments crucially depends on a quantitative description of pore water flow and the associated transport of various solutes and particles. Here, we compare and analyse existing models of tracer dynamics in permeable sediments. We show that all models can be derived from a generic backbone, consisting of the same flow and tracer equations. The principal difference between model applications concerns the geometry of the sediment-water interface and the pressure conditions that are specified along this boundary. We illustrate this commonality with four different case studies. These include biologically and physically induced pore water flows, as well as simplified laboratory set-ups versus more complex field-like conditions: [1] lugworm bio-irrigation in laboratory set-up, [2] interaction of bio-irrigation and groundwater seepage on a tidal flat, [3] pore water flow induced by rotational stirring in benthic chambers, and [4] pore water flow induced by unidirectional flow over a ripple sequence. To illustrate the potential of the generic model approach, the same two example simulations are performed in all four cases: (a) the time-dependent spreading of an inert tracer in the pore water, and (b) the computation of the steady-state distribution of oxygen in the sediment. Overall, our model comparison indicates that model development is promising, but within an early stage. Clear challenges remain in terms of model development, model validation, and model implementation

Towards tunable consensus clustering for studying functional brain connectivity during affective processing

In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results. To validate the method, we applied it to a complex fMRI experiment involving affective processing of hundreds of music clips. We found that brain structures related to visual, reward, and auditory processing have intrinsic spatial patterns of coherent neuroactivity during affective processing. The comparisons between the results obtained from our method and those from each individual clustering algorithm demonstrate that our paradigm has notable advantages over traditional single clustering algorithms in being able to evidence robust connectivity patterns even with complex neuroimaging data involving a variety of stimuli and affective evaluations of them. The consensus clustering method is implemented in the R package “UNCLES” available on http://cran.r-project.org/web/packages/UNCLES/index.html

Under pressure: Response urgency modulates striatal and insula activity during decision-making under risk

When deciding whether to bet in situations that involve potential monetary loss or gain (mixed gambles), a subjective sense of pressure can influence the evaluation of the expected utility associated with each choice option. Here, we explored how gambling decisions, their psychophysiological and neural counterparts are modulated by an induced sense of urgency to respond. Urgency influenced decision times and evoked heart rate responses, interacting with the expected value of each gamble. Using functional MRI, we observed that this interaction was associated with changes in the activity of the striatum, a critical region for both reward and choice selection, and within the insula, a region implicated as the substrate of affective feelings arising from interoceptive signals which influence motivational behavior. Our findings bridge current psychophysiological and neurobiological models of value representation and action-programming, identifying the striatum and insular cortex as the key substrates of decision-making under risk and urgency

The Affective Impact of Financial Skewness on Neural Activity and Choice

Few finance theories consider the influence of “skewness” (or large and asymmetric but unlikely outcomes) on financial choice. We investigated the impact of skewed gambles on subjects' neural activity, self-reported affective responses, and subsequent preferences using functional magnetic resonance imaging (FMRI). Neurally, skewed gambles elicited more anterior insula activation than symmetric gambles equated for expected value and variance, and positively skewed gambles also specifically elicited more nucleus accumbens (NAcc) activation than negatively skewed gambles. Affectively, positively skewed gambles elicited more positive arousal and negatively skewed gambles elicited more negative arousal than symmetric gambles equated for expected value and variance. Subjects also preferred positively skewed gambles more, but negatively skewed gambles less than symmetric gambles of equal expected value. Individual differences in both NAcc activity and positive arousal predicted preferences for positively skewed gambles. These findings support an anticipatory affect account in which statistical properties of gambles—including skewness—can influence neural activity, affective responses, and ultimately, choice