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As the issue of agricultural household economic well‐being begins to resurface, we use custom data sets to compare the financial position of agricultural households to the general Australian population. We find that agricultural households tend to have higher levels of wealth compared to households in the average Australian population, yet experience more income volatility on average. Our study brings attention to the important well‐being issue while also highlighting the need to collect robust farm household data in order to better understand the complex dynamics of household well‐being in the Australian agricultural sector. [ABSTRACT FROM AUTHOR]

The past four decades have witnessed unprecedented economic growth and rapidly rising food demand in China. This paper provides an introduction to readers with useful information summarising the development of China's agricultural sector and the transformation of its rural economy over the 40 years of economic reform. It is, however, impossible to cover all aspects of this recent and rich history in a single journal special issue. Nevertheless, we are of the view that these papers address the most fundamentally important and insightful topics including: land reform and rural development; technology progress and productivity growth; changing food consumption patterns; rural education and human capital accumulation; and poverty alleviation. [ABSTRACT FROM AUTHOR]

In order to develop a secure and competent technique to express the human immune gene for fighting infections, we cloned and expressed the BD2/3 using VR1020 (a eukaryotic expression plasmid). BD2/3 contains human β-defensin 2 (BD2) and human BD3. To explore safe and effective DNA delivery molecules in vitro and in vivo, the fusion genes of BD2/3 were used as an immune-labelled gene to verify transfection effectivness of modified chitosan (CS). Plasmid of VR1020-BD2/3 was packed with biomaterials: CS, average molecular weight: 25000D; polyethylene glycol-O-chitosan-polyethylenimine (PEG-O-CS-PEI); liposomes (LP); polyamine cationic liposomes (PCL); polyamine cationic liposomes of protamine (PCL-protamine) by ionotropic gelation. We observed that BD2/3 fusion gene showed high bioactivity in vitro and in vivo. The BD2/3 fusion protein inhibited the proliferation of bacteria (S. aureus, S. pneumoniae, P. aeruginosa and E. coli). The Kunming mice were immune to these nanoparticles and we analyzed their delivery efficiency and gene expression effect. BD2/3 results in multiple changes of innate and required immune system of mice. BD2/3 increases expression of IgG, IgG1, IgG2a, IL-2, IL-6, IFN-γ, as well as of lymphocytes and monocytes. Following challenge with virulent E. coli, CD4+ and CD8+ positive T-cell counts were highly elevated in the BD2/3 immunized mice, resulting in higher survival rates of mice. These results indicate that nanoparticles containing modified CS and BD2/3 are potentially safe and effective drugs in vivo to improve the immunity against bacterial infection and enhance innate immunity and adaptive immunity against infectious diseases. [ABSTRACT FROM AUTHOR]

This paper uses the growth accounting approach to estimate total factor productivity in the Australian agriculture industry between 1949 and 2012. To shed light on an unresolved debate on quantifying the roles of capital and labor, we compare the 'ex-ante' and 'ex-post' approaches to the estimation of returns to capital and labor, and find the former performs better than the latter in the context of the agricultural production account. We also demonstrate how the measurement of agricultural productivity may be improved by accounting for heterogeneity in output and input quality. Finally, our estimates are distinct from existing statistics in both the time length and industry coverage and provide new information about the long-term trend of agriculture productivity in Australia. [ABSTRACT FROM AUTHOR]

Adversarial Training is a practical approach for improving the robustness of deep neural networks against adversarial attacks. Although bringing reliable robustness, the performance towards clean examples is negatively affected after Adversarial Training, which means a trade-off exists between accuracy and robustness. Recently, some studies have tried to use knowledge distillation methods in Adversarial Training, achieving competitive performance in improving the robustness but the accuracy for clean samples is still limited. In this paper, to mitigate the accuracy-robustness trade-off, we introduce the Balanced Multi-Teacher Adversarial Robustness Distillation (B-MTARD) to guide the model's Adversarial Training process by applying a strong clean teacher and a strong robust teacher to handle the clean examples and adversarial examples, respectively. During the optimization process, to ensure that different teachers show similar knowledge scales, we design the Entropy-Based Balance algorithm to adjust the teacher's temperature and keep the teachers' information entropy consistent. Besides, to ensure that the student has a relatively consistent learning speed from multiple teachers, we propose the Normalization Loss Balance algorithm to adjust the learning weights of different types of knowledge. A series of experiments conducted on three public datasets demonstrate that B-MTARD outperforms the state-of-the-art methods against various adversarial attacks.

Adversarial attacks have been proven to be potential threats to Deep Neural Networks (DNNs), and many methods are proposed to defend against adversarial attacks. However, while enhancing the robustness, the accuracy for clean examples will decline to a certain extent, implying a trade-off existed between the accuracy and adversarial robustness. In this paper, to meet the trade-off problem, we theoretically explore the underlying reason for the difference of the filters' weight distribution between standard-trained and robust-trained models and then argue that this is an intrinsic property for static neural networks, thus they are difficult to fundamentally improve the accuracy and adversarial robustness at the same time. Based on this analysis, we propose a sample- wise dynamic network architecture named Adversarial Weight-Varied Network (AW-Net), which focuses on dealing with clean and adversarial examples with a "divide and rule" weight strategy. The AW-Net adaptively adjusts the network's weights based on regulation signals generated by an adversarial router, which is directly influenced by the input sample. Benefiting from the dynamic network architecture, clean and adversarial examples can be processed with different network weights, which provides the potential to enhance both accuracy and adversarial robustness. A series of experiments demonstrate that our AW-Net is architecture-friendly to handle both clean and adversarial examples and can achieve better trade-off performance than state-of-the-art robust models.

Various computational methodologies can be applied to enzymological studies on enzymes in the fatty acid, polyketide, and non-ribosomal peptide biosynthetic pathways. These multi-domain complexes are called fatty acid synthases, polyketide synthases, and non-ribosomal peptide synthetases. These mega-synthases biosynthesize chemically diverse and complex bioactive molecules, with the intermediates being chauffeured between catalytic partners via a carrier protein. Recent efforts have been made to engineer these systems to expand their product diversity. A major stumbling block is our poor understanding of the transient protein-protein and protein-substrate interactions between the carrier protein and its many catalytic partner domains and product intermediates. The innate reactivity of pathway intermediates in two major classes of polyketide synthases has frustrated our mechanistic understanding of these interactions during the biosynthesis of these natural products, ultimately impeding the engineering of these systems for the generation of engineered natural products. Computational techniques described in this chapter can aid data interpretation or used to generate testable models of these experimentally intractable transient interactions, thereby providing insight into key interactions that are difficult to capture otherwise, with the potential to expand the diversity in these systems., (© 2019 Elsevier Inc. All rights reserved.)