Background-Although abnormal L-arginine NO signaling contributes to endothelial dysfunction in the aging cardiovascular system, the biochemical mechanisms remain controversial. L-arginine, the NO synthase (NOS) precursor, is also a substrate for arginase. We tested the hypotheses that arginase reciprocally regulates NOS by modulating L-arginine bioavailability and that arginase is upregulated in aging vasculature, contributing to depressed endothelial function. Methods and Results-Inhibition of arginase with (S)-(2-boronoethyl)-L-cysteine, HCl (BEC) produced vasodilation in aortic rings from young (Y) adult rats (maximum effect, 46.4Ϯ9.4% at 10 Ϫ5 mol/L, PϽ0.01). Similar vasorelaxation was elicited with the additional arginase inhibitors N-hydroxy-nor-L-arginine (nor-NOHA) and difluoromethylornithine (DFMO). This effect required intact endothelium and was prevented by 1H-oxadiazole quinoxalin-1-one (PϽ0.05 and PϽ0.001, respectively), a soluble guanylyl cyclase inhibitor. DFMO-elicited vasodilation was greater in old (O) compared with Y rat aortic rings (60Ϯ6% versus 39Ϯ6%, PϽ0.05). In addition, BEC restored depressed L-arginine (10 Ϫ4 mol/L)-dependent vasorelaxant responses in O rings to those of Y. Arginase activity and expression were increased in O rings, whereas NOS activity and cyclic GMP levels were decreased. BEC and DFMO suppressed arginase activity and restored NOS activity and cyclic GMP levels in O vessels to those of Y.Conclusions-These findings demonstrate that arginase modulates NOS activity, likely by regulating intracellular L-arginine availability. Arginase upregulation contributes to endothelial dysfunction of aging and may therefore be a therapeutic target.

This colloquium examines the theoretical modeling of nonequilibrium low-temperature ͑tens of thousands of degrees͒ plasmas, which involves a juxtaposition of three distinct fields: atomic and molecular physics, for the input of scattering cross sections; statistical mechanics, for the kinetic modeling; and electromagnetic theory, for the simultaneous solution of Maxwell's equations. Cross sections come either from single-scattering beam experiments or, at very low energies ͑Ͻ0.5 eV͒, from multiple-scattering experiments on "swarms" in gases-the free diffusion or large Debye length limit of a plasma, where they are embedded in transport coefficient data. The same Boltzmann kinetic theory that has been developed to a high level of sophistication over the past 50 years, specifically for the purpose of unfolding these transport data, can be employed for low-temperature plasmas with appropriate modification to allow for self-consistent rather than externally prescribed fields. A full kinetic treatment of low-temperature plasmas is, however, a daunting task and remains at the developmental level. Fortunately, since the accuracy requirements for modeling plasmas are generally much less stringent than for swarms, such a sophisticated phase-space treatment is not always necessary or desirable, and a computationally more efficient but correspondingly less accurate macroscopic theoretical model in configuration space at the fluid level is often considered sufficient. There has been a proliferation of such fluid modeling in recent times and this approach is now routinely used in the design and development of a large variety of plasma technologies, ranging from plasma display panels to plasma etching reactors for microelectronic device fabrication. However, many of these models have been developed empirically with specific applications in mind, and rigor and sophistication vary accordingly. In this colloquium, starting from the governing Boltzmann kinetic equation, a unified, general formulation of fluid equations is given for both ions and electrons in gaseous media with transparent and internally consistent approximations, all benchmarked against established results. Thereby a fluid model is obtained that is appropriate for practical application but at the same time is based on a firmer physical foundation. CONTENTS

We outline a new kinetic theory for positrons in soft matter, which blends together cross sections for positrons scattering from single molecules, with the structure function of the medium as a whole. Numerical results are presented for positrons in liquid argon, where negative differential conductivity arises from both positron formation and the structure of the medium.

Robotic weed control has seen increased research of late with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for croplands, ignoring the weed management problems facing rangeland stock farmers. Perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust classification of weed species in their natural environment. The unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the complex rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust classification methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification accuracy of 95.1% and 95.7%, respectively. We also demonstrate real time performance of the ResNet-50 architecture, with an average inference time of 53.4 ms per image. These strong results bode well for future field implementation of robotic weed control methods in the Australian rangelands.

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