Greenhouse gas emissions from soils under organic management

This report was presented at the UK Organic Research 2002 Conference. Land emissions of N2O, CO2 and NH3 have been subject to little study under organic systems, yet form important aspects of sustainability of such systems. We describe innovative methods developed at SAC to assess trace gas emission using both automatic closed chamber systems (intensive, short term monitoring) and manually-operated closed chamber systems (occasional, long term monitoring). Long-term data were collected from organic ley-arable rotation trials in North-east of Scotland. Short term data were collected to show the effect of timing and depth of ploughing-out of the ley phase on gas emissions. Ploughing gave a shortterm stimulation of CO2 and, more markedly, of N2O emission. Emissions of N2O from organic grass-clover leys were considerably lower than from conventional grass. However, some N2O emissions from organic arable are higher than from conventional systems, particularly in the first year after ploughing out ley. Ammonia emissions after spreading manure on grass were significant in the summer, though only short-lived

Hearing in Drosophila

The dissection of the Drosophila auditory system has revealed multiple parallels between fly and vertebrate hearing. Recent studies have analyzed the operation of auditory sensory cells and the processing of sound in the fly's brain. Neuronal responses to sound have been characterized, and novel classes of auditory neurons have been defined; transient receptor potential (TRP) channels were implicated in auditory transduction, and genetic and environmental causes of auditory dysfunctions have been identified. This review discusses the implications of these recent advances on our understanding of how hearing happens in the fly

When The Sun Goes Down In Dixie : And The Moon Begains To Rise

https://digitalcommons.library.umaine.edu/mmb-vp/2731/thumbnail.jp

Curly Head

https://digitalcommons.library.umaine.edu/mmb-vp/5581/thumbnail.jp

What Kind of an American Are You?

https://digitalcommons.library.umaine.edu/mmb-vp/2653/thumbnail.jp

Power gain exhibited by motile mechanosensory neurons in Drosophila ears

In insects and vertebrates alike, hearing is assisted by the motility of mechanosensory cells. Much like pushing a swing augments its swing, this cellular motility is thought to actively augment vibrations inside the ear, thus amplifying the ear's mechanical input. Power gain is the hallmark of such active amplification, yet whether and how much energy motile mechanosensory cells contribute within intact auditory systems has remained uncertain. Here, we assess the mechanical energy provided by motile mechanosensory neurons in the antennal hearing organs of Drosophila melanogaster by analyzing the fluctuations of the sound receiver to which these neurons connect. By using dead WT flies and live mutants (tilB(2), btv(5P1), and nompA(2)) with defective neurons as a background, we show that the intact, motile neurons do exhibit power gain. In WT flies, the neurons lift the receiver's mean total energy by 19 zJ, which corresponds to 4.6 times the energy of the receiver's Brownian motion. Larger energy contributions (200 zJ) associate with self-sustained oscillations, suggesting that the neurons adjust their energy expenditure to optimize the receiver's sensitivity to sound. We conclude that motile mechanosensory cells provide active amplification; in Drosophila, mechanical energy contributed by these cells boosts the vibrations that enter the ear

Transducer-Based Force Generation Explains Active Process in Drosophila Hearing

BACKGROUND: Like vertebrate hair cells, Drosophila auditory neurons are endowed with an active, force-generating process that boosts the macroscopic performance of the ear. The underlying force generator may be the molecular apparatus for auditory transduction, which, in the fly as in vertebrates, seems to consist of force-gated channels that occur in series with adaptation motors and gating springs. This molecular arrangement explains the active properties of the sensory hair bundles of inner-ear hair cells, but whether it suffices to explain the active macroscopic performance of auditory systems is unclear.Results: To relate transducer dynamics and auditory-system behavior, we have devised a simple model of the Drosophila hearing organ that consists only of transduction modules and a harmonic oscillator that represents the sound receiver. In vivo measurements show that this model explains the ear's active performance, quantitatively capturing displacement responses of the fly's antennal sound receiver to force steps, this receiver's free fluctuations, its response to sinusoidal stimuli, nonlinearity, and activity and cycle-by-cycle amplification, and properties of electrical compound responses in the afferent nerve.Conclusions: Our findings show that the interplay between transduction channels and adaptation motors accounts for the entire macroscopic phenomenology of the active process in the Drosophila auditory system, extending transducer-based amplification from hair cells to fly ears and demonstrating that forces generated by transduction modules can suffice to explain active processes in ears

Activity driven modeling of time varying networks

Network modeling plays a critical role in identifying statistical regularities and structural principles common to many systems. The large majority of recent modeling approaches are connectivity driven. The structural patterns of the network are at the basis of the mechanisms ruling the network formation. Connectivity driven models necessarily provide a time-aggregated representation that may fail to describe the instantaneous and fluctuating dynamics of many networks. We address this challenge by defining the activity potential, a time invariant function characterizing the agents' interactions and constructing an activity driven model capable of encoding the instantaneous time description of the network dynamics. The model provides an explanation of structural features such as the presence of hubs, which simply originate from the heterogeneous activity of agents. Within this framework, highly dynamical networks can be described analytically, allowing a quantitative discussion of the biases induced by the time-aggregated representations in the analysis of dynamical processes.Comment: 10 pages, 4 figure