Temporal evolution of measured climate forcing agents at South Pole, Antarctica

Greenhouse gas (GHG; mainly CO 2, CH 4, N 2O, CFC-11 and CFC-12) measurements for 22 years (1983-2004) have been analysed to evaluate the radiative forcing (RF) and temporal evolution at the South Pole. About 20 increase in growth rate of CO 2 has been observed during 1992-2004 compared to 1983-91. However, remarkable deceleration in the growth rate of CH 4, CFC-11 and CFC-12 has been observed. CO 2 radiative forcing has increased by ~49 during 2004 for 10 increase in CO 2 concentration during the last 22 years. RF due to CH 4 was found to be 0.47 Wm -2 in 1999 and since then has remained almost constant through 2004. The net RF has been observed to increase by 0.7 Wm -2 during 2004 compared to 1983, which corresponds to ~38 increase in the last 22 years. Growth rate of net RF decreased by ~22 during 1990-2004, compared to the growth rate during 1983-90. A global warming simulation made using the EdGCM model shows an increase in surface air temperature and sea surface temperature of about 1.7 oC and 1 oC respectively, in 2050 compared to 1958. In response to change in GHGs from 1958 to 2050, warming over the higher latitudes is greater than in the tropics and also increase in minimum temperature is greater than the increase in maximum temperature. Similarly, up to 50 change in snow-ice cover over some of the regions in the higher latitudes is observed with this simulation

Impact of motorboats on fish embryos depends on engine type

This is the final version of the article. Available from Oxford University Press via the DOI in this record.Human generated noise is changing the natural underwater soundscapes worldwide. The most pervasive sources of underwater anthropogenic noise are motorboats, which have been found to negatively affect several aspects of fish biology. However, few studies have examined the effects of noise on early life stages, especially the embryonic stage, despite embryo health being critical to larval survival and recruitment. Here, we used a novel setup to monitor heart rates of embryos from the staghorn damselfish (Amblyglyphidodon curacao) in shallow reef conditions, allowing us to examine the effects ofin situboat noise in context with real-world exposure. We found that the heart rate of embryos increased in the presence of boat noise, which can be associated with the stress response. Additionally, we found 2-stroke outboard-powered boats had more than twice the effect on embryo heart rates than did 4-stroke powered boats, showing an increase in mean individual heart rate of 1.9% and 4.6%, respectively. To our knowledge this is the first evidence suggesting boat noise elicits a stress response in fish embryo and highlights the need to explore the ecological ramifications of boat noise stress during the embryo stage. Also, knowing the response of marine organisms caused by the sound emissions of particular engine types provides an important tool for reef managers to mitigate noise pollution.Research was funded by the ARC Center of Excellence for Coral Reef Studies (EI140100117), an International Postgraduate Research Scholarship awarded to S.J.S. from James Cook University and a UK Natural Environment Research Council grant to S.D.S. (NE/P001572/1)

Transference of Transport Anisotropy to Composite Fermions

When interacting two-dimensional electrons are placed in a large perpendicular magnetic field, to minimize their energy, they capture an even number of flux quanta and create new particles called composite fermions (CFs). These complex electron-flux-bound states offer an elegant explanation for the fractional quantum Hall effect. Furthermore, thanks to the flux attachment, the effective field vanishes at a half-filled Landau level and CFs exhibit Fermi-liquid-like properties, similar to their zero-field electron counterparts. However, being solely influenced by interactions, CFs should possess no memory whatever of the electron parameters. Here we address a fundamental question: Does an anisotropy of the electron effective mass and Fermi surface (FS) survive composite fermionization? We measure the resistance of CFs in AlAs quantum wells where electrons occupy an elliptical FS with large eccentricity and anisotropic effective mass. Similar to their electron counterparts, CFs also exhibit anisotropic transport, suggesting an anisotropy of CF effective mass and FS.Comment: 5 pages, 5 figure

Parton Fragmentation within an Identified Jet at NNLL

The fragmentation of a light parton i to a jet containing a light energetic hadron h, where the momentum fraction of this hadron as well as the invariant mass of the jet is measured, is described by "fragmenting jet functions". We calculate the one-loop matching coefficients J_{ij} that relate the fragmenting jet functions G_i^h to the standard, unpolarized fragmentation functions D_j^h for quark and gluon jets. We perform this calculation using various IR regulators and show explicitly how the IR divergences cancel in the matching. We derive the relationship between the coefficients J_{ij} and the quark and gluon jet functions. This provides a cross-check of our results. As an application we study the process e+ e- to X pi+ on the Upsilon(4S) resonance where we measure the momentum fraction of the pi+ and restrict to the dijet limit by imposing a cut on thrust T. In our analysis we sum the logarithms of tau=1-T in the cross section to next-to-next-to-leading-logarithmic accuracy (NNLL). We find that including contributions up to NNLL (or NLO) can have a large impact on extracting fragmentation functions from e+ e- to dijet + h.Comment: expanded introduction, typos fixed, journal versio

Non-global Structure of the O({\alpha}_s^2) Dijet Soft Function

High energy scattering processes involving jets generically involve matrix elements of light- like Wilson lines, known as soft functions. These describe the structure of soft contributions to observables and encode color and kinematic correlations between jets. We compute the dijet soft function to O({\alpha}_s^2) as a function of the two jet invariant masses, focusing on terms not determined by its renormalization group evolution that have a non-separable dependence on these masses. Our results include non-global single and double logarithms, and analytic results for the full set of non-logarithmic contributions as well. Using a recent result for the thrust constant, we present the complete O({\alpha}_s^2) soft function for dijet production in both position and momentum space.Comment: 55 pages, 8 figures. v2: extended discussion of double logs in the hard regime. v3: minor typos corrected, version published in JHEP. v4: typos in Eq. (3.33), (3.39), (3.43) corrected; this does not affect the main result, numerical results, or conclusion

Observation of the Fractional Quantum Hall Effect in Graphene

When electrons are confined in two dimensions and subjected to strong magnetic fields, the Coulomb interactions between them become dominant and can lead to novel states of matter such as fractional quantum Hall liquids. In these liquids electrons linked to magnetic flux quanta form complex composite quasipartices, which are manifested in the quantization of the Hall conductivity as rational fractions of the conductance quantum. The recent experimental discovery of an anomalous integer quantum Hall effect in graphene has opened up a new avenue in the study of correlated 2D electronic systems, in which the interacting electron wavefunctions are those of massless chiral fermions. However, due to the prevailing disorder, graphene has thus far exhibited only weak signatures of correlated electron phenomena, despite concerted experimental efforts and intense theoretical interest. Here, we report the observation of the fractional quantum Hall effect in ultraclean suspended graphene, supporting the existence of strongly correlated electron states in the presence of a magnetic field. In addition, at low carrier density graphene becomes an insulator with an energy gap tunable by magnetic field. These newly discovered quantum states offer the opportunity to study a new state of matter of strongly correlated Dirac fermions in the presence of large magnetic fields

The origin of large molecules in primordial autocatalytic reaction networks

Large molecules such as proteins and nucleic acids are crucial for life, yet their primordial origin remains a major puzzle. The production of large molecules, as we know it today, requires good catalysts, and the only good catalysts we know that can accomplish this task consist of large molecules. Thus the origin of large molecules is a chicken and egg problem in chemistry. Here we present a mechanism, based on autocatalytic sets (ACSs), that is a possible solution to this problem. We discuss a mathematical model describing the population dynamics of molecules in a stylized but prebiotically plausible chemistry. Large molecules can be produced in this chemistry by the coalescing of smaller ones, with the smallest molecules, the `food set', being buffered. Some of the reactions can be catalyzed by molecules within the chemistry with varying catalytic strengths. Normally the concentrations of large molecules in such a scenario are very small, diminishing exponentially with their size. ACSs, if present in the catalytic network, can focus the resources of the system into a sparse set of molecules. ACSs can produce a bistability in the population dynamics and, in particular, steady states wherein the ACS molecules dominate the population. However to reach these steady states from initial conditions that contain only the food set typically requires very large catalytic strengths, growing exponentially with the size of the catalyst molecule. We present a solution to this problem by studying `nested ACSs', a structure in which a small ACS is connected to a larger one and reinforces it. We show that when the network contains a cascade of nested ACSs with the catalytic strengths of molecules increasing gradually with their size (e.g., as a power law), a sparse subset of molecules including some very large molecules can come to dominate the system.Comment: 49 pages, 17 figures including supporting informatio

New insights into the classification and nomenclature of cortical GABAergic interneurons.

A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts' assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus

Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model

Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 µg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures

FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysis

Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ