Application of meshfree methods to numerically simulate microscale deformations of different plant food materials during drying

Plant food materials have a very high demand in the consumer market and therefore, improved food products and efficient processing techniques are concurrently being researched in food engineering. In this context, numerical modelling and simulation techniques have a very high potential to reveal fundamentals of the underlying mechanisms involved. However, numerical modelling of plant food materials during drying becomes quite challenging, mainly due to the complexity of the multiphase microstructure of the material, which undergoes excessive deformations during drying. In this regard, conventional grid-based modelling techniques have limited applicability due to their inflexible grid-based fundamental limitations. As a result, meshfree methods have recently been developed which offer a more adaptable approach to problem domains of this nature, due to their fundamental grid-free advantages. In this work, a recently developed meshfree based two-dimensional plant tissue model is used for a comparative study of microscale morphological changes of several food materials during drying. The model involves Smoothed Particle Hydrodynamics (SPH) and Discrete Element Method (DEM) to represent fluid and solid phases of the cellular structure. Simulation are conducted on apple, potato, carrot and grape tissues and the results are qualitatively and quantitatively compared and related with experimental findings obtained from the literature. The study revealed that cellular deformations are highly sensitive to cell dimensions, cell wall physical and mechanical properties, middle lamella properties and turgor pressure. In particular, the meshfree model is well capable of simulating critically dried tissues at lower moisture content and turgor pressure, which lead to cell wall wrinkling. The findings further highlighted the potential applicability of the meshfree approach to model large deformations of the plant tissue microstructure during drying, providing a distinct advantage over the state of the art grid-based approaches.

Numerical simulation of micro-scale morphological changes of plant food materials during drying - a meshfree approach

This thesis developed a high preforming alternative numerical technique to investigate microscale morphological changes of plant food materials during drying. The technique is based on a novel meshfree method, and is more capable of modeling large deformations of multiphase problem domains, when compared with conventional grid-based numerical modeling techniques. The developed cellular model can effectively replicate dried tissue morphological changes such as shrinkage and cell wall wrinkling, as influenced by moisture reduction and turgor loss.

Combining passive sampling and toxicity testing for evaluation of mixtures of polar organic chemicals in sewage treatment plant effluent

Effluent from sewage treatment plants has been associated with a range of pollutant effects. Depending on the influent composition and treatment processes the effluent may contain a myriad of different chemicals which makes monitoring very complex. In this study we aimed to monitor relatively polar organic pollutant mixtures using a combination of passive sampling techniques and a set of biochemistry based assays covering acute bacterial toxicity (Microtox™), phytotoxicity (Max-I-PAM assay) and genotoxicity (umuC assay). The study showed that all of the assays were able to detect effects in the samples and allowed a comparison of the two plants as well as a comparison between the two sampling periods. Distinct improvements in water quality were observed in one of the plants as result of an upgrade to a UV disinfection system, which improved from 24× sample enrichment required to induce a 50% response in the Microtox™ assay to 84×, from 30× sample enrichment to induce a 50% reduction in photosynthetic yield to 125×, and the genotoxicity observed in the first sampling period was eliminated. Thus we propose that biochemical assay techniques in combination with time integrated passive sampling can substantially contribute to the monitoring of polar organic toxicants in STP effluents.

Biological monitoring in workers in a nitrobenzene reduction plant : Haemoglobin versus serum albumin adducts

The high priority of monitoring workers exposed to nitrobenzene is a consequence of clear findings of experimental carcinogenicity of nitrobenzene and the associated evaluations by the International Agency for Research on Cancer. Eighty male employees of a nitrobenzene reduction plant, with potential skin contact with nitrobenzene and aniline, participated in a current medical surveillance programme. Blood samples were routinely taken and analysed for aniline, 4-aminodiphenyl (4-ADP) and benzidine adducts of haemoglobin (Hb) and human serum albumin (HSA). Also, levels of methaemoglobin (Met-Hb) and of carbon monoxide haemoglobin (CO-Hb) were monitored. Effects of smoking were straightforward. Using the rank sum test of Wilcoxon, we found that very clear-cut and statistically significant smoking effects (about 3-fold increases) were apparent on CO-Hb (P = 0.00085) and on the Hb adduct of 4-ADP (P = 0.0006). The mean aniline-Hb adduct level in smokers was 1.5 times higher than in non-smokers; the significance (P = 0.05375) was close to the 5% level. The strongest correlation was evident between the Hb and HSA adducts of aniline (rs = 0.846). Less pronounced correlations (but with P values < 0.02) appeared between aniline-Hb and 4-ADP-Hb adducts (rs = 0.388), between 4-ADP and 4-ADP-HSA adducts (rs = 0.373), and between 4-ADP-Hb and aniline-HSA adducts (rs = 0.275). In view of the proposal for additional use of the aniline-HSA adduct for biological monitoring, particularly in cases of acute overexposures or poisonings, the strong correlation of the Hb and HSA conjugates is noteworthy; the ratio aniline-HSA:aniline-Hb was 1:42 for the entire cohort.

Diverse urban plantings managed with sufficient resource availability can increase plant productivity and arthropod diversity

Buildings structures and surfaces are explicitly being used to grow plants, and these “urban plantings” are generally designed for aesthetic value. Urban plantings also have the potential to contribute significant “ecological values” by increasing urban habitat for animals such as arthropods and by increasing plant productivity. In this study, we evaluated how the provision of these additional ecological values is affected by plant species richness; the availability of essential resources for plants, such as water, light, space; and soil characteristics. We sampled 33 plantings located on the exterior of three buildings in the urban center of Brisbane, Australia (subtropical climatic region) over 2, 6 week sampling periods characterized by different temperature and rainfall conditions. Plant cover was estimated as a surrogate for productivity as destructive sampling of biomass was not possible. We measured weekly light levels (photosynthetically active radiation), plant CO2 assimilation, soil CO2 efflux, and arthropod diversity. Differences in plant cover were best explained by a three-way interaction of plant species richness, management water regime and sampling period. As the richness of plant species increased in a planter, productivity and total arthropod richness also increased significantly—likely due to greater habitat heterogeneity and quality. Overall we found urban plantings can provide additional ecological values if essential resources are maintained within a planter such as water, light and soil temperature. Diverse urban plantings that are managed with these principles in mind can contribute to the attraction of diverse arthropod communities, and lead to increased plant productivity within a dense urban context.

Plant diversity predicts beta but not alpha diversity of soil microbes across grasslands worldwide

Aboveground–belowground interactions exert critical controls on the composition and function of terrestrial ecosystems, yet the fundamental relationships between plant diversity and soil microbial diversity remain elusive. Theory predicts predominantly positive associations but tests within single sites have shown variable relationships, and associations between plant and microbial diversity across broad spatial scales remain largely unexplored. We compared the diversity of plant, bacterial, archaeal and fungal communities in one hundred and forty-five 1 m2 plots across 25 temperate grassland sites from four continents. Across sites, the plant alpha diversity patterns were poorly related to those observed for any soil microbial group. However, plant beta diversity (compositional dissimilarity between sites) was significantly correlated with the beta diversity of bacterial and fungal communities, even after controlling for environmental factors. Thus, across a global range of temperate grasslands, plant diversity can predict patterns in the composition of soil microbial communities, but not patterns in alpha diversity.

Denhaminols A–H, dihydro-β-agarofurans from the endemic Australian rainforest plant Denhamia celastroides

Eight new dihydro-β-agarofurans, denhaminols A–H (1–8), were isolated from the leaves of the Australian rainforest tree Denhamia celastroides. The chemical structures of 1–8 were elucidated following analysis of 1D/2D NMR and MS data. The absolute configuration of denhaminol A (1) was determined by single-crystal X-ray crystallography. All compounds were evaluated for cytotoxic activity against the human prostate cancer cell line LNCaP, using live-cell imaging and metabolic assays. Denhaminols A (1) and G (7) were also tested for their effects on the lipid content of LNCaP cells. This is the first report of secondary metabolites from D. celastroides.

Productivity is a poor predictor of plant species richness

For more than 30 years, the relationship between net primary productivity and species richness has generated intense debate in ecology about the processes regulating local diversity. The original view, which is still widely accepted, holds that the relationship is hump-shaped, with richness first rising and then declining with increasing productivity. Although recent meta-analyses questioned the generality of hump-shaped patterns, these syntheses have been criticized for failing to account for methodological differences among studies. We addressed such concerns by conducting standardized sampling in 48 herbaceous-dominated plant communities on five continents. We found no clear relationship between productivity and fine-scale (meters−2) richness within sites, within regions, or across the globe. Ecologists should focus on fresh, mechanistic approaches to understanding the multivariate links between productivity and richness.

Green-fluorescent protein facilitates rapid in vivo detection of genetically transformed plant cells

Early detection of plant transformation events is necessary for the rapid establishment and optimization of plant transformation protocols. We have assessed modified versions of the green fluorescent protein (GFP) from Aequorea victoria as early reporters of plant transformation using a dissecting fluorescence microscope with appropriate filters. Gfp-expressing cells from four different plant species (sugarcane, maize, lettuce, and tobacco) were readily distinguished, following either Agrobacterium-mediated or particle bombardment-mediated transformation. The identification of gfp-expressing sugarcane cells allowed for the elimination of a high proportion of non-expressing explants and also enabled visual selection of dividing transgenic cells, an early step in the generation of transgenic organisms. The recovery of transgenic cell clusters was streamlined by the ability to visualize gfp-expressing tissues in vitro.

Fine-grained plant classification using convolutional neural networks for feature extraction

We present an overview of the QUT plant classification system submitted to LifeCLEF 2014. This system uses generic features extracted from a convolutional neural network previously used to perform general object classification. We examine the effectiveness of these features to perform plant classification when used in combination with an extremely randomised forest. Using this system, with minimal tuning, we obtained relatively good results with a score of 0:249 on the test set of LifeCLEF 2014.

Optimal eradication: When to stop looking for an invasive plant

The notion of being sure that you have completely eradicated an invasive species is fanciful because of imperfect detection and persistent seed banks. Eradication is commonly declared either on an ad hoc basis, on notions of seed bank longevity, or on setting arbitrary thresholds of 1% or 5% confidence that the species is not present. Rather than declaring eradication at some arbitrary level of confidence, we take an economic approach in which we stop looking when the expected costs outweigh the expected benefits. We develop theory that determines the number of years of absent surveys required to minimize the net expected cost. Given detection of a species is imperfect, the optimal stopping time is a trade-off between the cost of continued surveying and the cost of escape and damage if eradication is declared too soon. A simple rule of thumb compares well to the exact optimal solution using stochastic dynamic programming. Application of the approach to the eradication programme of Helenium amarum reveals that the actual stopping time was a precautionary one given the ranges for each parameter. © 2006 Blackwell Publishing Ltd/CNRS.