Going to Market with Deweyfish: The Journey from Partnership to Commercialisation

In 2016-7, The University of Western Australia Library partnered with researchers in the Australian Research Council’s Centre of Excellence in Plant Energy Biology to produce cropPAL2, a database providing the subcellular locations for proteins in crops significant for food production. The project was funded by the Australian National Data Service as part of its High Value Collections program, with the team consisting of computational biologists, software engineers and a librarian. The project involved many hours of manual article evaluation and data extraction by specialists in the plant species included in cropPAL, and the team decided that developing in-house software could make managing the process of article evaluation by multiple people much easier. Key software features were that it prevented assessing the same article twice, simplified finding and adding new articles to the database, provided real-time access by international group members, and the cut and drop function facilitated saving images and notes. Use of this software represented a 90% saving in time and therefore salaries. The team realised the in-house software could be applied across many areas of research. Known as Team DeweyFish, the group embarked on the CSIRO’s ON Prime program in 2018 to learn how to commercialise the software. This process involved the team generating and testing 15 hypotheses about researcher behaviour through conducting 66 one on one interviews with potential users. This data lead to some significant insights, clarifying the needs of various user groups and refining the software specifications. An initial target market has been selected, and the team is now working towards developing a commercialisable prototype. This paper will discuss the role of the Library as a key player in this collaboration, a first for the University of WA, both in the innovative process and as a key driver in directing the development towards the wider benefit of researchers at UWA and beyond

Importance of spatial and spectral data reduction in the detection of internal defects in food products.

Despite the importance of data reduction as part of the processing of reflection-based classifications, this study represents one of the first in which the effects of both spatial and spectral data reductions on classification accuracies are quantified. Furthermore, the effects of approaches to data reduction were quantified for two separate classification methods, linear discriminant analysis (LDA) and support vector machine (SVM). As the model dataset, reflection data were acquired using a hyperspectral camera in 230 spectral channels from 401 to 879 nm (spectral resolution of 2.1 nm) from field pea (Pisum sativum) samples with and without internal pea weevil (Bruchus pisorum) infestation. We deployed five levels of spatial data reduction (binning) and eight levels of spectral data reduction (40 datasets). Forward stepwise LDA was used to select and include only spectral channels contributing the most to the separation of pixels from non-infested and infested field peas. Classification accuracies obtained with LDA and SVM were based on the classification of independent validation datasets. Overall, SVMs had significantly higher classification accuracies than LDAs (P < 0.01). There was a negative association between pixel resolution and classification accuracy, while spectral binning equivalent to up to 98% data reduction had negligible effect on classification accuracies. This study supports the potential use of reflection-based technologies in the quality control of food products with internal defects, and it highlights that spatial and spectral data reductions can (1) improve classification accuracies, (2) vastly decrease computer constraints, and (3) reduce analytical concerns associated with classifications of large and high-dimensional datasets

Importance of spatial and spectral data reduction in the detection of internal defects in food products.

Despite the importance of data reduction as part of the processing of reflection-based classifications, this study represents one of the first in which the effects of both spatial and spectral data reductions on classification accuracies are quantified. Furthermore, the effects of approaches to data reduction were quantified for two separate classification methods, linear discriminant analysis (LDA) and support vector machine (SVM). As the model dataset, reflection data were acquired using a hyperspectral camera in 230 spectral channels from 401 to 879 nm (spectral resolution of 2.1 nm) from field pea (Pisum sativum) samples with and without internal pea weevil (Bruchus pisorum) infestation. We deployed five levels of spatial data reduction (binning) and eight levels of spectral data reduction (40 datasets). Forward stepwise LDA was used to select and include only spectral channels contributing the most to the separation of pixels from non-infested and infested field peas. Classification accuracies obtained with LDA and SVM were based on the classification of independent validation datasets. Overall, SVMs had significantly higher classification accuracies than LDAs (P < 0.01). There was a negative association between pixel resolution and classification accuracy, while spectral binning equivalent to up to 98% data reduction had negligible effect on classification accuracies. This study supports the potential use of reflection-based technologies in the quality control of food products with internal defects, and it highlights that spatial and spectral data reductions can (1) improve classification accuracies, (2) vastly decrease computer constraints, and (3) reduce analytical concerns associated with classifications of large and high-dimensional datasets

Environmental response in gene expression and DNA methylation reveals factors influencing the adaptive potential of Arabidopsis lyrata

Abstract Understanding what factors influence plastic and genetic variation is valuable for predicting how organisms respond to changes in the selective environment. Here, using gene expression and DNA methylation as molecular phenotypes, we study environmentally induced variation among Arabidopsis lyrata plants grown at lowland and alpine field sites. Our results show that gene expression is highly plastic, as many more genes are differentially expressed between the field sites than between populations. These environmentally responsive genes evolve under strong selective constraint — the strength of purifying selection on the coding sequence is high, while the rate of adaptive evolution is low. We find, however, that positive selection on cis-regulatory variants has likely contributed to the maintenance of genetically variable environmental responses, but such variants segregate only between distantly related populations. In contrast to gene expression, DNA methylation at genic regions is largely insensitive to the environment, and plastic methylation changes are not associated with differential gene expression. Besides genes, we detect environmental effects at transposable elements (TEs): TEs at the high-altitude field site have higher expression and methylation levels, suggestive of a broad-scale TE activation. Compared to the lowland population, plants native to the alpine environment harbor an excess of recent TE insertions, and we observe that specific TE families are enriched within environmentally responsive genes. Our findings provide insight into selective forces shaping plastic and genetic variation. We also highlight how plastic responses at TEs can rapidly create novel heritable variation in stressful conditions

SUBA4:the interactive data analysis centre for Arabidopsis subcellular protein locations

Abstract The SUBcellular location database for Arabidopsis proteins (SUBA4, http://suba.live) is a comprehensive collection of manually curated published data sets of large-scale subcellular proteomics, fluorescent protein visualization, protein-protein interaction (PPI) as well as subcellular targeting calls from 22 prediction programs. SUBA4 contains an additional 35 568 localizations totalling more than 60 000 experimental protein location claims as well as 37 new suborganellar localization categories. The experimental PPI data has been expanded to 26 327 PPI pairs including 856 PPI localizations from experimental fluorescent visualizations. The new SUBA4 user interface enables users to choose quickly from the filter categories: ‘subcellular location’, ‘protein properties’, ‘protein–protein interaction’ and ‘affiliations’ to build complex queries. This allows substantial expansion of search parameters into 80 annotation types comprising 1 150 204 new annotations to study metadata associated with subcellular localization. The ‘BLAST’ tab contains a sequence alignment tool to enable a sequence fragment from any species to find the closest match in Arabidopsis and retrieve data on subcellular location. Using the location consensus SUBAcon, the SUBA4 toolbox delivers three novel data services allowing interactive analysis of user data to provide relative compartmental protein abundances and proximity relationship analysis of PPI and coexpression partners from a submitted list of Arabidopsis gene identifiers

Unraveling the Role of MIXL1 Activation in Endoderm Differentiation of Isogenic Human Induced Pluripotent Stem Cells

International audienceHuman induced pluripotent stem cells (hiPSC) possess the ability to differentiate into a multitude of tissue types but display heterogeneity in the propensity of differentiation into specific tissue lineages. An examination of isogenic hiPSC lines revealed variations in the endoderm propensity under directed differentiation conditions. Characterization of the transcriptome and proteome of the hiPSC lines showed that MIXL1 activity at the early differentiation stage correlated with the efficacy of generating definitive endoderm and further differentiation into endoderm derivatives. Enforced expression of MIXL1 in the endoderm-incompetent hiPSCs enhanced the propensity of endoderm differentiation, suggesting that modulation of key drivers of lineage differentiation can re-wire hiPSC to the desired lineage propensity for stem cell products