High-affinity NO(3-)-H+ cotransport in the fungus Neurospora:induction and control by pH and membrane voltage

High-affinity nitrate transport was examined in intact hyphae of Neurospora crassa using electrophysiological recordings to characterize the response of the plasma membrane to NO3- challenge and to quantify transport activity. The NO3(-)-associated membrane current was determined using a three electrode voltage clamp to bring membrane voltage under experimental control and to compensate for current dissipation along the longitudinal cell axis. Nitrate transport was evident in hyphae transferred to NO3(-)-free, N-limited medium for 15 hr, and in hyphae grown in the absence of a nitrogen source after a single 2-min exposure to 100 microM NO3-. In the latter, induction showed a latency of 40-80 min and rose in scalar fashion with full transport activity measurable approx. 100 min after first exposure to NO3-; it was marked by the appearance of a pronounced sensitivity of membrane voltage to extracellular NO3- additions which, after induction, resulted in reversible membrane depolarizations of (+)54-85 mV in the presence of 50 microM NO3-; and it was suppressed when NH4+ was present during the first, inductive exposure to NO3-. Voltage clamp measurements carried out immediately before and following NO3- additions showed that the NO3(-)-evoked depolarizations were the consequence of an inward-directed current that appeared in parallel with the depolarizations across the entire range of accessible voltages (-400 to +100 mV). Measurements of NO3- uptake using NO3(-)-selective macroelectrodes indicated a charge stoichiometry for NO3- transport of 1(+):1(NO3-) with common K(m) and Jmax values around 25 microM and 75 pmol NO3- cm-2sec-1, respectively, and combined measurements of pHo and [NO3-]o showed a net uptake of approx. 1 H+ with each NO3- anion. Analysis of the NO3- current demonstrated a pronounced voltage sensitivity within the normal physiological range between -300 and -100 mV as well as interactions between the kinetic parameters of membrane voltage, pHo and [NO3-]o. Increasing the bathing pH from 5.5 to 8.0 reduced the current and the associated membrane depolarizations 2- to 4-fold. At a constant pHo of 6.1, driving the membrane voltage from -350 to -150 mV resulted in an approx. 3-fold reduction in the maximum current and a 5-fold rise in the apparent affinity for NO3-. By contrast, the same depolarization effected an approx. 20% fall in the K(m) for transport as a function in [H+]o. These, and additional results are consistent with a charge-coupling stoichiometry of 2(H+) per NO3- anion transported across the membrane, and implicate a carrier cycle in which NO3- binding is kinetically adjacent to the rate-limiting step of membrane charge transit. The data concur with previous studies demonstrating a pronounced voltage-dependence to high-affinity NO3- transport system in Arabidopsis, and underline the importance of voltage as a kinetic factor controlling NO3- transport; finally, they distinguish metabolite repression of NO3- transport induction from its sensitivity to metabolic blockade and competition with the uptake of other substrates that draw on membrane voltage as a kinetic substrate.

Hardware elliptic curve cryptographic processor over GF(p)

A novel hardware architecture for elliptic curve cryptography (ECC) over GF(p) is introduced. This can perform the main prime field arithmetic functions needed in these cryptosystems including modular inversion and multiplication. This is based on a new unified modular inversion algorithm that offers considerable improvement over previous ECC techniques that use Fermat's Little Theorem for this operation. The processor described uses a full-word multiplier which requires much fewer clock cycles than previous methods, while still maintaining a competitive critical path delay. The benefits of the approach have been demonstrated by utilizing these techniques to create a field-programmable gate array (FPGA) design. This can perform a 256-bit prime field scalar point multiplication in 3.86 ms, the fastest FPGA time reported to date. The ECC architecture described can also perform four different types of modular inversion, making it suitable for use in many different ECC applications. © 2006 IEEE.

On the Condition Number Distribution of Complex Wishart Matrices

This paper investigates the distribution of the condition number of complex Wishart matrices. Two closely related measures are considered: the standard condition number (SCN) and the Demmel condition number (DCN), both of which have important applications in the context of multiple-input multipleoutput (MIMO) communication systems, as well as in various branches of mathematics. We first present a novel generic framework for the SCN distribution which accounts for both central and non-central Wishart matrices of arbitrary dimension. This result is a simple unified expression which involves only a single scalar integral, and therefore allows for fast and efficient computation. For the case of dual Wishart matrices, we derive new exact polynomial expressions for both the SCN and DCN distributions. We also formulate a new closed-form expression for the tail SCN distribution which applies for correlated central Wishart matrices of arbitrary dimension and demonstrates an interesting connection to the maximum eigenvalue moments of Wishart matrices of smaller dimension. Based on our analytical results, we gain valuable insights into the statistical behavior of the channel conditioning for various MIMO fading scenarios, such as uncorrelated/semi-correlated Rayleigh fading and Ricean fading. © 2010 IEEE.

Comparison of lagrangian and eulerian simulations of slurry flows in a sudden expansion

From a review of technical literature, it was not apparent if the Lagrangian or the Eulerian dispersed phase modeling approach was more valid to simulate dilute erosive slurry flow. In this study, both modeling approaches were employed and a comparative analysis of performances and accuracy between the two models was carried out. Due to an impossibility to define, for the Eulerian model already implemented in FLUENT, a set of boundary conditions consistent with the Lagrangian impulsive equations, an Eulerian dispersed phase model was integrated in the FLUENT code using subroutines and user-defined scalar equations. Numerical predictions obtained from the two different approaches for two-phase flow in a sudden expansion were compared with the measured data. Excellent agreement was attained between the predicted and observed fluid and particle velocity in the axial direction and for the kinetic energy. Erosion profiles in a sudden expansion computed using the Lagrangian scheme yielded good qualitative agreement with measured data and predicted a maximum impact angle of 29 deg at the fluid reattachment point. The Eulerian model was adversely affected by the reattachment of the fluid phase to the wall and the simulated erosion profiles were not in agreement with the Lagrangian or measured data. Furthermore, the Eulerian model under-predicted the Lagrangian impact angle at all locations except the reattachment point. © 2010 American Society of Mechanical Engineers.

The Biospheric Project - Manchester International Festival 2013:Technical food systems

The Biospheric Project is a nested multi-scalar urban agriculture project that aims to develop sustainable food systems in disadvantaged communities, though not only physical interventions, such as the urban masterplan and neighbourhood design to the building and its roof and façade, but also through social and commercial interventions, such as community involvement, businesses and a distribution system.

The project is focused around the Biospheric Foundation, a community interest company and research think-tank whose aim is to hasten our transition to a closed cycle, low-carbon economy. Its home is Irwell house, that houses a large-scale aquaponic-based food production system, which is directly linked to a whole-food shop (78 Steps, named after the distance from the productive system) and a whole food distribution system (the Whole Box). The building sits within a post-industrial landscape which is being developed into a new productive landscape, utilizing the the technologies developed by the Biospheric Foundation and Prof Greg Keeffe of Queens University Belfast. The collaboration links designer, academics and activists across the disciplines of Urban design, Architecture, Permaculture, landscape design, environmental science and business and community.

The development of Temporal Cognition

Time features in two key ways in cognition, each of which is discussed in turn in this chapter: time is processed as a dimension of stimuli or events, and time is represented as a framework in which events can be located. Section 1 examines the first of these from a developmental perspective, by reviewing research on age-related changes in the accuracy of duration processing. The Piagetian approach linked changes in duration processing to the development of a concept of time as a dimension of events separable from other event dimensions. This is contrasted with recent research conducted within the framework of Scalar Expectancy Theory, which models development in terms of changes in components of specialized timing mechanisms. Section 2 discusses developmental changes in the temporal frameworks that children use to represent the locations of events. Although as adults, we represent times as locations on a linear framework stretching from the past, to the present, and into the future, this way of representing time is not developmentally basic. A model is proposed of developmental stages in the acquisition of a mature temporal framework. The chapter concludes by considering two themes that cut across Section 1 and 2: the issue of whether there are both qualitative and quantitative change in children’s temporal abilities, and the link between temporal and spatial cognition.

IPPro: FPGA based Image Processing Processor

The paper presents IPPro which is a high performance, scalable soft-core processor targeted for image processing applications. It has been based on the Xilinx DSP48E1 architecture using the ZYNQ Field Programmable Gate Array and is a scalar 16-bit RISC processor that operates at 526MHz, giving 526MIPS of performance. Each IPPro core uses 1 DSP48, 1 Block RAM and 330 Kintex-7 slice-registers, thus making the processor as compact as possible whilst maintaining flexibility and programmability. A key aspect of the approach is in reducing the application design time and implementation effort by using multiple IPPro processors in a SIMD mode. For different applications, this allows us to exploit different levels of parallelism and mapping for the specified processing architecture with the supported instruction set. In this context, a Traffic Sign Recognition (TSR) algorithm has been prototyped on a Zedboard with the colour and morphology operations accelerated using multiple IPPros. Simulation and experimental results demonstrate that the processing platform is able to achieve a speedup of 15 to 33 times for colour filtering and morphology operations respectively, with a reduced design effort and time.

Learning from time series:Supervised Aggregative Feature Extraction

Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings (e.g. uniformly sampled time series of constant length), and fail to deliver a thorough methodology to deal with noisy data. In this paper a methodology based on functional learning is proposed to overcome the aforementioned problems; the proposed Supervised Aggregative Feature Extraction (SAFE) approach allows to derive continuous, smooth estimates of time series data (yielding aggregate local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The SAFE paradigm enjoys several properties like closed form solution, incorporation of first and second order derivative information into the regressor matrix, interpretability of the generated functional predictor and the possibility to exploit Reproducing Kernel Hilbert Spaces setting to yield nonlinear predictive models. Simulation studies are provided to highlight the strengths of the new methodology w.r.t. standard unsupervised feature selection approaches. © 2012 IEEE.

Rescaling the Governance of Renewable Energy: Lessons from the UK Devolution Experience

Efforts to rescale governance arrangements to foster sustainable development are rarely simple in their consequences, an out-turn examined in this paper through an analysis of how the governance of renewable energy in the UK has been impacted by the devolution of power to Northern Ireland, Scotland and Wales. Theoretically, attention is given to the ways in which multiple modes of governing renewable energy, and the interactions between modes and objects of governance, together configure the scalar organization of renewable energy governance. Our findings show how the devolved governments have created new, sub-national renewable energy strategies and targets, yet their effectiveness largely depends on UK-wide systems of subsidy. Moreover, shared support for particular objects of governance—large-scale, commercial electricity generation facilities—has driven all the devolved government to centralize and expedite the issuing of consents. This leads to a wider conclusion. While the level at which environmental problems are addressed can affect how they are governed, what key actors believe about the objects of governance can mediate the effects of any rescaling processes.

High-affinity NO3 --H+ cotransport in the fungus Neurospora:Induction and control by pH and membrane voltate

High-affinity nitrate transport was examined in intact hyphae of Neurospora crassa using electrophysiological recordings to characterize the response of the plasma membrane to NO₃ ^- challenge and to quantify transport activity. The NO₃ ^--associated membrane current was determined using a three electrode voltage clamp to bring membrane voltage under experimental control and to compensate for current dissipation along the longitudinal cell axis. Nitrate transport was evident in hyphae transferred to NO₃ ^--free, N-limited medium for 15 hr, and in hyphae grown in the absence of a nitrogen source after a single 2-min exposure to 100 μM NO₃ ^-. In the latter, induction showed a latency of 40-80 min and rose in scalar fashion with full transport activity mensurable approx. 100 min after first exposure to NO₃ ^-; it was marked by the appearance of a pronounced sensitivity of membrane voltage to extracellular NO₃ ^- additions which, after induction, resulted in reversible membrane depolarizations of (+)54-85 mV in the presence of 50 μM NO₃ ^-; and it was suppressed when NH₄ ⁺, was present during the first, inductive exposure to NO₃ ^-. Voltage clamp measurements carried out immediately before and following NO₃ ^- additions showed that the NO₃ ^--evoked depolarizations were the consequence of an inward-directed current that appeared in parallel with the depolarizations across the entire range of accessible voltages -400 to +100 mV). Measurements of NO₃ ^- uptake using NO₃ ^--selective macroelectrodes indicated a charge stoichiometry for NO₃ ^- transport of 1(+):1(NO₃ ^-) with common K(m) and J(max) values around 25 μM and 75 pmol NO₃ ^- cm^-2sec^-1, respectively, and combined measurements of pH(o) and [NO₃ ^-](o) showed a net uptake of approx. 1 H⁺ with each NO₃ ^- anion. Analysis of the NO₃ ^- current demonstrated a pronounced voltage sensitivity within the normal physiological range between -300 and -100 mV as well as interactions between the kinetic parameters of membrane voltage, pH(o) and [NO₃ ^-](o). Increasing the bathing pH from 5.5 to 8.0 reduced the current and the associated membrane depolarizations 2- to 4-fold. At a constant pH(o) of 6.1, driving the membrane voltage from -350 to -150 mV resulted in an approx. 3-fold reduction in the maximum current and a 5-fold rise in the apparent affinity for NO₃ ^-. By contrast, the same depolarization effected an approx. 20% fall in the K(m) for transport as a function in [H⁺](o). These, and additional results are consistent with a charge-coupling stoichiometry of 2(H⁺) per NO anion transported across the membrane, and implicate a carrier cycle in which NO binding is kinetically adjacent to the rate-limiting step of membrane charge transit. The data concur with previous studies demonstrating a pronounced voltage-dependence to high-affinity NO₃ ^- transport system in Arabidopsis, and underline the importance of voltage as a kinetic factor controlling NO₃ ^- transport; finally, they distinguish metabolite repression of NO₃ ^- transport induction from its sensitivity to metabolic blockade and competition with the uptake of other substrates that draw on membrane voltage as a kinetic substrate.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.