Marc A. O'NEill

BACKGROUND: This paper describes the progress that we have made in assessing the feasibility of ‘fingerprinting’ using imaged SDS–PAGE gels of haemolymph proteins, to identify soft‐bodied wood‐boring insect larvae such as the Asian longhorn beetle, Anoplophora glabripennis (Motscholsky, 1853) (Coleoptera: Cerambycidae). Because of stringent import restrictions and difficulty in obtaining licences to work with these organisms, we opted to work with four species of scarab beetle, Mecynorhina polyphemus (Fabricius, 1781), Pachnoda sinuata (Fabricius, 1775), Eucidella shiratica (Csiki, 1909) and Eucidella shultzeorum (Kolbe, 1906) which have near identical larval morphologies.RESULTS: We show that this technology when combined with an advanced pattern matching system (Digital Automated Identification SYstem ‐ DAISY) can classify soft‐bodied insect larvae that are almost identical morphologically to species at a level of accuracy is in excess of 98%. The study also indicates that the technology copes well with noisy data and small training sets.CONCLUSION: The experience gained in undertaking this study gives us confidence that we will be able to develop a field deployable system in the medium term. We believe that as a high‐throughput identification tool, this technology is superior to competitor technologies (e.g. fingerprinting of imaged DNA gels) in terms of speed, cost and ease of use; and therefore, is suitable for low‐cost deployment in the field. © 2017 Society of Chemical Industry

Where possible, automation has been a common response of humankind to many activities that have to be repeated numerous times. The routine identification of specimens of previously described species has many of the characteristics of other activities that have been automated, and poses a major constraint on studies in many areas of both pure and applied biology. In this paper, we consider some of the reasons why automated species identification has not become widely employed, and whether it is a realistic option, addressing the notions that it is too difficult, too threatening, too different or too costly. Although recognizing that there are some very real technical obstacles yet to be overcome, we argue that progress in the development of automated species identification is extremely encouraging that such an approach has the potential to make a valuable contribution to reducing the burden of routine identifications. Vision and enterprise are perhaps more limiting at present than practical constraints on what might possibly be achieved.

Two hundred and thirty‐seven species of Macrolepidoptera were light trapped at Treborth Botanical Garden, Gwynedd, UK. Live adults were digitally imaged using a simple, inexpensive method suitable for field use, then released. Inconsistent lighting, variation in resting posture and inclusion of worn individuals produced image sets high in intraspecific variation. Thirty‐five common species were selected to provide training images for the Digital Automated Identification SYstem (DAISY). Twenty individuals per species were pre‐processed to standardize size and posture and to enhance features. The right forewing of each was highlighted manually and the pattern rendered polar and greyscale for DAISY analysis. Despite poor quality of some images, 83% of unknown species were identified correctly. The best species had 100% correct identification and the worst 35%. The most poorly identified images were those of moths that had lost scales or been unevenly illuminated. The precision with which the forewing was highlighted affected performance. When highlighted carefully, Laothoe populi was identified correctly twice as successfully as when the same image was highlighted poorly. Size of the training set was also important. Sets of 5, 10, 15 and 20 training images, plotted against performance produced a curve of diminishing returns. Colourimages and inclusion of size should improve accuracy.

Many problems in analytical biology, such as the classification of organisms, the modelling of macromolecules, or the structural analysis of metabolic or neural networks, involve complex relational data. Here, we describe a software environment, the portable UNIX programming system (PUPS), which has been developed to allow efficient computational representation and analysis of such data. The system can also be used as a general development tool for database and classification applications. As the complexity of analytical biology problems may lead to computation times of several days or weeks even on powerful computer hardware, the PUPS environment gives support for persistent computations by providing mechanisms for dynamic interaction and homeostatic protection of processes. Biological objects and their interrelations are also represented in a homeostatic way in PUPS. Object relationships are maintained and updated by the objects themselves, thus providing a flexible, scalable and current data representation. Based on the PUPS environment, we have developed an optimization package, CANTOR, which can be applied to a wide range of relational data and which has been employed in different analyses of neuroanatomical connectivity. The CANTOR package makes use of the PUPS system features by modifying candidate arrangements of objects within the system's database. This restructuring is carried out via optimization algorithms that are based on user–defined cost functions, thus providing flexible and powerful tools for the structural analysis of the database content. The use of stochastic optimization also enables the CANTOR system to deal effectively with incomplete and inconsistent data. Prototypical forms of PUPS and CANTOR have been coded and used successfully in the analysis of anatomical and functional mammalian brain connectivity, involving complex and inconsistent experimental data. In addition, PUPS has been used for solving multivariate engineering optimization problems and to implement the digital identification system (DAISY), a system for the automated classification of biological objects. PUPS is implemented in ANSI–C under the POSIX.1 standard and is to a great extent architecture– and operating–system independent. The software is supported by systems libraries that allow multi–threading (the concurrent processing of several database operations), as well as the distribution of the dynamic data objects and library operations over clusters of computers. These attributes make the system easily scalable, and in principle allow the representation and analysis of arbitrarily large sets of relational data. PUPS and CANTOR are freely distributed (http://www.pups.org.uk) as open–source software under the GNU license agreement.

Recent analyses of association fibre networks in the primate cerebral cortex have revealed a small number of densely intra–connected and hierarchically organized structural systems. Corresponding analyses of data on functional connectivity are required to establish the significance of these structural systems. W e therefore built up a relational database by systematically collating published data on the spread of activity after strychnine–induced disinhibition in the macaque cerebral cortex in vivo . After mapping these data to two different parcellation schemes, we used three independent methods of analysis which demonstrate that the cortical network of functional interactions is not homogeneous, but shows a clear segregation into functional assemblies of mutually interacting areas. The assemblies suggest a principal division of the cortex into visual, somatomotor and orbito–temporo–insular systems, while motor and somatosensory areas are inseparably interrelated. These results are largely compatible with corresponding analyses of structural data of mammalian cerebral cortex, and deliver the first functional evidence for ‘small–world’ architecture of primate cerebral cortex.

Neuroanatomists have described a large number of connections between the various structures of monkey and cat cortical sensory systems. Because of the complexity of the connection data, analysis is required to unravel what principles of organization they imply. To date, analysis of laminar origin and termination connection data to reveal hierarchical relationships between the cortical areas has been the most widely acknowledged approach. We programmed a network processor that searches for optimal hierarchical orderings of cortical areas given known hierarchical constraints and rules for their interpretation. For all cortical systems and all cost functions, the processor found a multitude of equally low–cost hierarchies. Laminar hierarchical constraints that are presently available in the anatomical literature were therefore insufficient to constrain a unique ordering for any of the sensory systems we analysed. Hierarchical orderings of the monkey visual system that have been widely reported, but which were derived by hand, were not among the optimal orderings. All the cortical systems we studied displayed a significant degree of hierarchical organization, and the anatomical constraints from the monkey visual and somatomotor systems were satisfied with very few constraint violations in the optimal hierarchies. The visual and somato–motor systems in that animal were therefore surprisingly strictly hierarchical. Most inconsistencies between the constraints and the hierarchical relationships in the optimal structures for the visual system were related to connections of area FST (fundus of superior temporal sulcus). W e found that the hierarchical solutions could be further improved by assuming that FSTconsists of two areas, which differ in the nature of their projections. Indeed, we found that perfect hierarchical arrangements of the primate visual system, without any violation of anatomical constraints, could be obtained under two reasonable conditions, namely the subdivision of FST into two distinct areas, whose connectivity we predict, and the abolition of at least one of the less reliable rule constraints. Our analyses showed that the future collection of the same type of laminar constraints, or the inclusion of new hierarchical constraints from thalamocortical connections, will not resolve the problem of multiple optimal hierarchical representations for the primate visual system. Further data, however, may help to specify the relative ordering of some more areas. This indeterminacy of the visual hierarchy is in part due to the reported absence of some connections between cortical areas. These absences are consistent with limited cross–talk between differentiated processing streams in the system. Hence, hierarchical representation of the visual system is affected by, and must take into account, other organizational features, such as processing streams.

The number of different cortical structures in mammalian brains and the number of extrinsic fibres linking these regions are both large. As with any complex system, systematic analysis is required to draw reliable conclusions about the organization of the complex neural networks comprising these numerous elements. One aspect of organization that has long been suspected is that cortical networks are organized into ‘streams’ or ‘systems’. Here we report computational analyses capable of showing whether clusters of strongly interconnected areas are aspects of the global organization of cortical systems in macaque and cat. We used two different approaches to analyse compilations of corticocortical connection data from the macaque and the cat. The first approach, optimal set analysis, employed an explicit definition of a neural ‘system’ or ‘stream’, which was based on differential connectivity. We defined a two–component cost function that described the cost of the global cluster arrangement of areas in terms of the areas‘ connectivity within and between candidate clusters. Optimal cluster arrangements of cortical areas were then selected computationally from the very many possible arrangements, using an evolutionary optimization algorithm. The second approach, non–parametric cluster analysis (NPCA), grouped cortical areas on the basis of their proximity in multidimensional scaling representations. We used non–metric multidimensional scaling to represent the cortical connectivity structures metrically in two and five dimensions. NPCA then analysed these representations to determine the nature of the clusters for a wide range of different cluster shape parameters. The results from both approaches largely agreed. They showed that macaque and cat cortices are organized into densely intra–connected clusters of areas, and identified the constituent members of the clusters. These clusters reflected functionally specialized sets of cortical areas, suggesting that structure and function are closely linked at this gross, systems level.