Light-sensing phytochromes feel the heat

Plant phytochrome activity is governed not just by light, but also by prevailing temperature</jats:p

Self-repairing mobile robotic car using astrocyte-neuron networks

A self-repairing robot utilising a spiking astrocyte-neuron network is presented in this paper. It uses the output spike frequency of neurons to control the motor speed and robot activation. A software model of the astrocyte-neuron network previously demonstrated self-detection of faults and its self-repairing capability. In this paper the application demonstrator of mobile robotics is employed to evaluate the fault-tolerant capabilities of the astrocyte-neuron network when implemented in a hardware-based robotic car system. Results demonstrated that when 20% or less synapses associated with a neuron are faulty, the robot car can maintain system performance and complete the task of forward motion correctly. If 80% synapses are faulty, the system performance shows a marginal degradation, however this degradation is much smaller than that of conventional fault-tolerant techniques under the same levels of faults. This is the first time that astrocyte cells merged within spiking neurons demonstrates a self-repairing capabilities in the hardware system for a real application

Metalliferous sediments and the scavenging residence time of Nd near hydrothermal vents

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94674/1/grl5993.pd

Metal impact and vaporization on the Moon's surface: nano‐geochemical insights into the source of lunar metals

Millimeter-to-nanometer-sized iron- and nickel-rich metals are ubiquitous on the lunar surface. The proposed origin of these metals falls into two broad classes which should have distinct geochemical signatures—(1) the reduction of iron-bearing minerals or (2) the addition of metals from meteoritic sources. The metals measured here from the Apollo 16 regolith possess low Ni (2–6 wt%) and elevated Ge (80–350 ppm) suggesting a meteoritic origin. However, the measured Ni is lower, and the Ge higher than currently known iron meteorites. In comparison to the low Ni iron meteorites, the even lower Ni and higher Ge contents exhibited by these lunar metals are best explained by impact-driven volatilization and condensation of Ni-poor meteoritic metal during their impact and addition to the lunar surface. The presence of similar, low Ni-bearing metals in Apollo return samples from geographically distant sites suggests that this geochemical signature might not be restricted to just the Apollo 16 locality and that volatility-driven modification of meteoritic metals are a common feature of lunar regolith formation. The possibility of a significant low Ni/high Ge meteoritic component in the lunar regolith, and the observation of chemical fractionation during emplacement, has implications for the interpretation of both lunar remote-sensing data and bulk geochemical data derived from sample return material. Additionally, our observation of predominantly meteoritic sourced metals has implications for the prevalence of meteoritic addition on airless planetary bodies

Modelling the petrogenesis of high Rb/Sr silicic magmas

Rhyolites can be highly evolved with Sr contents as low as 0.1 ppm and Rb/Sr &gt; 2,000. In contrast, granite batholiths are commonly comprised of rocks with Rb/Sr 100. Mass-balance modelling of source compositions, differentiation and contamination using the trace-element geochemistry of granites are therefore commonly in error because of the failure to account for evolved differentiates that may have been erupted from the system. Rhyolitic magmas with very low Sr concentrations ([les]1 ppm) cannot be explained by any partial melting models involving typical crustal source compositions. The only plausible mechanism for the production of such rhyolites is Rayleigh fractional crystallization involving substantial volumes of cumulates. A variety of methods for modelling the differentiation of magmas with extremely high Rb/Sr is discussed. In each case it is concluded that the bulk partition coefficients for Sr have to be large. In the simplest models, the bulk DSr of the most evolved types is modelled as &gt; 50. Evidence from phenocryst/glass/whole-rock concentrations supports high Sr partition coefficients in feldspars from high silica rhyolites. However, the low modal abundance of plagioclase commonly observed in such rocks is difficult to reconcile with such simple fractionation models of the observed trace-element trends. In certain cases, this may be because the apparent trace-element trend defined by the suite of cognetic rhyolites is the product of different batches of magma with separate differentiation histories accumulating in the magma chamber roof zone.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29127/1/0000166.pd

Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks

This paper presents a hardware based implementation of a biologically-faithful astrocyte-based selfrepairing mechanism for Spiking Neural Networks. Spiking Astrocyte-neuron Networks (SANNs) are a new computing paradigm which capture the key mechanisms of how the human brain performs repairs. Using SANN in hardware affords the potential for realizing computing architecture that can self-repair. This paper demonstrates that Spiking Astrocyte Neural Network (SANN) in hardware have a resilience to significant levels of faults. The key novelty of the paper resides in implementing an SANN on FPGAs using fixed-point representation and demonstrating graceful performance degradation to different levels of injected faults via its self-repair capability. A fixed-point implementation of astrocyte, neurons and tripartite synapses are presented and compared against previous hardware floating-point and Matlab software implementations of SANN. All results are obtained from the SANN FPGA implementation and show how the reduced fixedpoint representation can maintain the biologically-realistic repair capability

Exploring Self-Repair in a Coupled Spiking Astrocyte Neural Network

It is now known that astrocytes modulate the activity at the tripartite synapses where indirect signaling via the retrograde messengers, endocannabinoids, leads to a localized self-repairing capability. In this paper, a self-repairing spiking astrocyte neural network (SANN) is proposed to demonstrate a distributed self-repairing capability at the network level. The SANN uses a novel learning rule that combines the spike-timing-dependent plasticity (STDP) and Bienenstock, Cooper, and Munro (BCM) learning rules (hereafter referred to as the BSTDP rule). In this learning rule, the synaptic weight potentiation is not only driven by the temporal difference between the presynaptic and postsynaptic neuron firing times but also by the postsynaptic neuron activity. We will show in this paper that the BSTDP modulates the height of the plasticity window to establish an input-output mapping (in the learning phase) and also maintains this mapping (via self-repair) if synaptic pathways become dysfunctional. It is the functional dependence of postsynaptic neuron firing activity on the height of the plasticity window that underpins how the proposed SANN self-repairs on the fly. The SANN also uses the coupling between the tripartite synapses and γ -GABAergic interneurons. This interaction gives rise to a presynaptic neuron frequency filtering capability that serves to route information, represented as spike trains, to different neurons in the subsequent layers of the SANN. The proposed SANN follows a feedforward architecture with multiple interneuron pathways and astrocytes modulate synaptic activity at the hidden and output neuronal layers. The self-repairing capability will be demonstrated in a robotic obstacle avoidance application, and the simulation results will show that the SANN can maintain learned maneuvers at synaptic fault densities of up to 80% regardless of the fault locations

Small-Scale Disequilibrium in a Magmatic Inclusion and its More Silicic Host

Basaltic andesite inclusions and their host dacite from the Purico-Chascon complex in northern Chile are isotopically distinct. Textural characteristics of the inclusions are typical of those resulting from magma mingling. Serial sectioning across the interface of an inclusion and its host dacite, complemented by microdrill sampling and detailed microprobe work, has enabled us to examine the scale of mixing and chemical (isotopic and trace element) disequilibrium. The results of this work show that (1) the composition of the inclusion is relatively homogeneous; (2) the dacite host is generally higher in ⁸⁷Sr/⁸⁶Sr and lower in ¹⁴³Nd/¹⁴⁴Nd than the enclave, but it is heterogeneous on a small scale, and probably a “hybrid”; (3) the isotopic composition in the marginal zone, apparently on both host and inclusion sides of the weakly chilled interface, actually shows the highest ⁸⁷Sr/⁸⁶Sr and lowest ¹⁴³Nd/¹⁴⁴Nd; (4) large plagioclase crystals in the inclusions and host are xenocrystic, with higher ⁸⁷Sr/⁸⁶Sr than any of the other samples. These observations are reconciled with a model of magma evolution in a crustal magma chamber. In such a scenario the mafic magma is overlain by a cap of rhyolite - a partial melt of the dacitic ignimbrites which now underlie the Purico-Chascon complex. Overturn of such a magma system gives rise to a hybrid dacite containing discrete mafic inclusions