Discovery of Jupiter's 'gossamer' ring

Jupiter's ring system has previously been described as being composed of a 'bright' narrow ring and an interior, vertically-extended halo. The one image which reveals this morphology most clearly is Voyager 2's parting shot of the Jupiter system, a wide-angle (WA) view of the ring ansa in forward-scattered light (FDS 20693.02). The bright ring is plainly visible, and the halo appears after slight contrast enhancement. By further enhancement of this image we have discovered an additional ring, which is far fainter than either of the (already faint) components previously identified, extending to a radius of 210,000 km.     

Progressive field-state collapse and quantum non-demolition photon counting

The irreversible evolution of a microscopic system under measurement is a central feature of quantum theory. From an initial state generally exhibiting quantum uncertainty in the measured observable, the system is projected into a state in which this observable becomes precisely known. Its value is random, with a probability determined by the initial system's state. The evolution induced by measurement (known as 'state collapse') can be progressive, accumulating the effects of elementary state changes. Here we report the observation of such a step-by-step collapse by non-destructively measuring the photon number of a field stored in a cavity. Atoms behaving as microscopic clocks cross the cavity successively. By measuring the light-induced alterations of the clock rate, information is progressively extracted, until the initially uncertain photon number converges to an integer. The suppression of the photon number spread is demonstrated by correlations between repeated measurements. The procedure illustrates all the postulates of quantum measurement (state collapse, statistical results and repeatability) and should facilitate studies of non-classical fields trapped in cavities.     

PDGF signalling controls age-dependent proliferation in pancreatic β-cells

Determining the signalling pathways that direct tissue expansion is a principal goal of regenerative biology. Vigorous pancreatic β-cell replication in juvenile mice and humans declines with age, and elucidating the basis for this decay may reveal strategies for inducing β-cell expansion, a long-sought goal for diabetes therapy. Here we show that platelet-derived growth factor receptor (Pdgfr) signalling controls age-dependent β-cell proliferation in mouse and human pancreatic islets. With age, declining β-cell Pdgfr levels were accompanied by reductions in β-cell enhancer of zeste homologue 2 (Ezh2) levels and β-cell replication. Conditional inactivation of the Pdgfra gene in β-cells accelerated these changes, preventing mouse neonatal β-cell expansion and adult β-cell regeneration. Targeted human PDGFR-α activation in mouse β-cells stimulated Erk1/2 phosphorylation, leading to Ezh2-dependent expansion of adult β-cells. Adult human islets lack PDGF signalling competence, but exposure of juvenile human islets to PDGF-AA stimulated β-cell proliferation. The discovery of a conserved pathway controlling age-dependent β-cell proliferation indicates new strategies for β-cell expansion.     

Alternative stable states explain unpredictable biological control of Salvinia molesta in Kakadu

Suppression of the invasive plant Salvinia molesta by the salvinia weevil is an iconic example of successful biological control. However, in the billabongs (oxbow lakes) of Kakadu National Park, Australia, control is fitful and incomplete. By fitting a process-based nonlinear model to thirteen-year data sets from four billabongs, here we show that incomplete control can be explained by alternative stable states--one state in which salvinia is suppressed and the other in which salvinia escapes weevil control. The shifts between states are associated with annual flooding events. In some years, high water flow reduces weevil populations, allowing the shift from a controlled to an uncontrolled state; in other years, benign conditions for weevils promote the return shift to the controlled state. In most described ecological examples, transitions between alternative stable states are relatively rare, facilitated by slow-moving environmental changes, such as accumulated nutrient loading or climate change. The billabongs of Kakadu give a different manifestation of alternative stable states that generate complex and seemingly unpredictable dynamics. Because shifts between alternative stable states are stochastic, they present a potential management strategy to maximize effective biological control: when the domain of attraction to the state of salvinia control is approached, augmentation of the weevil population or reduction of the salvinia biomass may allow the lower state to trap the system.     

A disinhibitory microcircuit for associative fear learning in the auditory cortex

Learning causes a change in how information is processed by neuronal circuits. Whereas synaptic plasticity, an important cellular mechanism, has been studied in great detail, we know much less about how learning is implemented at the level of neuronal circuits and, in particular, how interactions between distinct types of neurons within local networks contribute to the process of learning. Here we show that acquisition of associative fear memories depends on the recruitment of a disinhibitory microcircuit in the mouse auditory cortex. Fear-conditioning-associated disinhibition in auditory cortex is driven by foot-shock-mediated cholinergic activation of layer 1 interneurons, in turn generating inhibition of layer 2/3 parvalbumin-positive interneurons. Importantly, pharmacological or optogenetic block of pyramidal neuron disinhibition abolishes fear learning. Together, these data demonstrate that stimulus convergence in the auditory cortex is necessary for associative fear learning to complex tones, define the circuit elements mediating this convergence and suggest that layer-1-mediated disinhibition is an important mechanism underlying learning and information processing in neocortical circuits.     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.