The mPer2 gene encodes a functional component of the mammalian circadian clock.

Circadian rhythms are driven by endogenous biological clocks that regulate many biochemical, physiological and behavioural processes in a wide range of life forms. In mammals, there is a master circadian clock in the suprachiasmatic nucleus of the anterior hypothalamus. Three putative mammalian homologues (mPer1, mPer2 and mPer3) of the Drosophila circadian clock gene period (per) have been identified. The mPer genes share a conserved PAS domain (a dimerization domain found in Per, Arnt and Sim) and show a circadian expression pattern in the suprachiasmatic nucleus. To assess the in vivo function of mPer2, we generated and characterized a deletion mutation in the PAS domain of the mouse mPer2 gene. Here we show that mice homozygous for this mutation display a shorter circadian period followed by a loss of circadian rhythmicity in constant darkness. The mutation also diminishes the oscillating expression of both mPer1 and mPer2 in the suprachiasmatic nucleus, indicating that mPer2 may regulate mPer1 in vivo. These data provide evidence that an mPer gene functions in the circadian clock, and define mPer2 as a component of the mammalian circadian oscillator.     

Insect behaviour: arboreal ants build traps to capture prey

To meet their need for nitrogen in the restricted foraging environment provided by their host plants, some arboreal ants deploy group ambush tactics in order to capture flying and jumping prey that might otherwise escape. Here we show that the ant Allomerus decemarticulatus uses hair from the host plant's stem, which it cuts and binds together with a purpose-grown fungal mycelium, to build a spongy 'galleried' platform for trapping much larger insects. Ants beneath the platform reach through the holes and immobilize the prey, which is then stretched, transported and carved up by a swarm of nestmates. To our knowledge, the collective creation of a trap as a predatory strategy has not been described before in ants.     

Magnetic-field-induced charge-stripe order in the high-temperature superconductor YBa2Cu3Oy

Electronic charges introduced in copper-oxide (CuO(2)) planes generate high-transition-temperature (T(c)) superconductivity but, under special circumstances, they can also order into filaments called stripes. Whether an underlying tendency towards charge order is present in all copper oxides and whether this has any relationship with superconductivity are, however, two highly controversial issues. To uncover underlying electronic order, magnetic fields strong enough to destabilize superconductivity can be used. Such experiments, including quantum oscillations in YBa(2)Cu(3)O(y) (an extremely clean copper oxide in which charge order has not until now been observed) have suggested that superconductivity competes with spin, rather than charge, order. Here we report nuclear magnetic resonance measurements showing that high magnetic fields actually induce charge order, without spin order, in the CuO(2) planes of YBa(2)Cu(3)O(y). The observed static, unidirectional, modulation of the charge density breaks translational symmetry, thus explaining quantum oscillation results, and we argue that it is most probably the same 4a-periodic modulation as in stripe-ordered copper oxides. That it develops only when superconductivity fades away and near the same 1/8 hole doping as in La(2-x)Ba(x)CuO(4) (ref.?1) suggests that charge order, although visibly pinned by CuO chains in YBa(2)Cu(3)O(y), is an intrinsic propensity of the superconducting planes of high-T(c) copper oxides.     

Forecasting carbon price using a multi-objective least squares support vector machine with mixture kernels

For improving forecasting accuracy and trading performance, this paper proposes a new multi-objective least squares support vector machine with mixture kernels to forecast asset prices. First, a mixture kernel function is introduced into taking full use of global and local kernel functions, which is adaptively determined following a data-driven procedure. Second, a multi-objective fitness function is proposed by incorporating level forecasting and trading performance, and particle swarm optimization is used to synchronously search the optimal model selections of least squares support vector machine with mixture kernels. Taking CO₂ assets as examples, the results obtained show that compared with the popular models, the proposed model can achieve higher forecasting accuracy and higher trading performance. The advantages of the mixture kernel function and the multi-objective fitness function can improve the forecasting ability of the asset price. The findings also show that the models with a high-level forecasting accuracy cannot always have a high trading performance of asset price forecasting. In contrast, high directional forecasting usually means a high trading performance.