Building a global alliance of biofoundries (vol 10, 2040, 2019)

The original version of this Comment contained errors in the legend of Figure 2, in which the locations of the fifteenth and sixteenth GBA members were incorrectly given as '(15) Australian Genome Foundry, Macquarie University; (16) Australian Foundry for Advanced Biomanufacturing, University of Queensland.'. The correct version replaces this with '(15) Australian Foundry for Advanced Biomanufacturing (AusFAB), University of Queensland and (16) Australian Genome Foundry, Macquarie University'. This has been corrected in both the PDF and HTML versions of the Comment

Building the UK's industrial base in engineering biology

Abstract The paper describes the strategy and components that have been put in place to build the UK's research and industrial base in Engineering Biology. The initial section of the paper provides a brief historical overview of the development of the field in the United Kingdom. This comprised, principally, a major report by the Royal Academy of Engineering and a strategic roadmap for synthetic biology, together with the establishment of six new synthetic biology research centres, a national centre for the industrial translation of synthetic biology and five biofoundries. The next section of the paper describes the UK government’s policy for the field. Important elements of the implementation of the policy comprises people, Infrastructure, Business Environment and place. In this context, a number of important areas are addressed—including industrial translation; building an expert workforce and nucleating, incubating and accelerating a new engineering biology industry in the United Kingdom. The final portion of the paper addresses the author's view of the way forward. This comprises placing the development of the field, both nationally and internationally, in the context of the development of the Bioeconomy and Climate Change. The final section of the text addresses a specific strategic approach and the implications for the United Kingdom in relation to the development of its industrial base in Engineering Biology

Addressing the post‐COVID era through engineering biology

Abstract Currently, the world is faced with two fundamental issues of great importance, namely climate change and the coronavirus pandemic. These are intimately involved with the need to control climate change and the need to switch from high carbon, unsustainable economies to low carbon economies. Inherent in this approach are the concepts of the bioeconomy and the Green Industrial Revolution. The article addresses both issues, but it, principally, focusses on the development of the bioeconomy. It considers how nations are divided in relation to the use of biotechnology and synthetic biology in terms of their bioeconomy strategies. The article addresses, as a central theme, the nature and role of engineering biology in these developments. Engineering biology is addressed in terms of BioDesign, coupled with high levels of automation (including AI and machine learning) to increase reproducibility and reliability to meet industrial standards. This lends itself to distributed manufacturing of products in a range of fields. Engineering biology is a platform technology that can be applied in a range of sectors. The bioeconomy, as an engine for economic growth is addressed—in terms of moving from oil‐based economies to bio‐based economies—using biomass, for example, using selected lignocellulosic waste as a feedstock

Single-trial EEG source reconstruction for brain-computer interface.

A new way to improve the classification rate of an EEG-based brain-computer interface (BCI) could be to reconstruct the brain sources of EEG and to apply BCI methods to these derived sources instead of raw measured electrode potentials. EEG source reconstruction methods are based on electrophysiological information that could improve the discrimination between BCI tasks. In this paper, we present an EEG source reconstruction method for BCI. The results are compared with results from raw electrode potentials to enable direct evaluation of the method. Features are based on frequency power change and Bereitschaft potential. The features are ranked with mutual information before being fed to a proximal support vector machine. The dataset IV of the BCI competition II and data from four subjects serve as test data. Results show that the EEG inverse solution improves the classification rate and can lead to results comparable to the best currently known methods

An evolutionary data-conscious artificial immune recognition system

Artificial Immune Recognition System (AIRS) algorithm offers a promising methodology for data classification. It is an immune-inspired supervised learning algorithm that works efficiently and has shown comparable performance with respect to other classifier algorithms. For this reason, it has received escalating interests in recent years. However, the full potential of the algorithm was yet unleashed. We proposed a novel algorithm called the evolutionary data-conscious AIRS (EDC-AIRS) algorithm that accentuates and capitalizes on 3 additional immune mechanisms observed from the natural immune system. These mechanisms are associated to the phenomena exhibited by the antibodies in response to the concentration, location and type of foreign antigens. Bio-mimicking these observations empower EDC-AIRS algorithm with the ability to robustly adapt to the different density, distribution and characteristics exhibited by each data class. This provides competitive advantages for the algorithm to better characterize and learn the underlying pattern of the data. Experiments on four widely used benchmarking datasets demonstrated promising results -- outperforming several state-of-the-art classification algorithms evaluated. This signifies the importance of integrating these immune mechanisms as part of the learning process

A Discussion on the Evaluation of A New Automatic Liver Volume Segmentation Method for Specified CT Image Datasets

Abstract. This paper presents discussions on experimental result evaluation outcomes of a new liver volume segmentation method developed for 10 specified CT image datasets. Precise liver surface segmentation is the first step and one of the major tasks in individual surgical resection virtual reality simulations. There are five major difficulties: Firstly, the automatic initialization of liver detection is often unreliable. Secondly, the liver surface shows great anatomical variations amongst patients and even in the same patient over time; Thirdly, the intensities of liver and kidney are similar in certain scanning planes, which makes their contacting boundaries weaker and fuzzier than most hepatic vessel or portal vein walls; Fourthly, in some cases the liver tail and the apex of the heart can easily be confused. Finally, the boundary between subcostal fat tissue and the liver in many cases is ambiguous. This paper presents a new automatic strategic active contour method for accurate and reproducible liver volume segmentation, which contains different ways to tackle different difficulties. Our method integrates a rotational template matching, and k-means clustering followed by rib cage area local edge enhancement, with a GVF (Gradient Vector Flow) geometric Snake. This proposed method has been trained by 20 specified CT image datasets, and implemented and evaluated on another 10 testing datasets.

Opportunities for microfluidic technologies in synthetic biology

We introduce microfluidics technologies as a key foundational technology for synthetic biology experimentation. Recent advances in the field of microfluidics are reviewed and the potential of such a technological platform to support the rapid development of synthetic biology solutions is discussed