Rapid discrimination and determination of polyunsaturated fatty acid composition in marine oils by FTIR Spectroscopy and Multivariate Data Analysis

A rapid analytical approach for discrimination and quantitative determination of polyunsaturated fatty acid (PUFA) contents, particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), in a range of oils extracted from marine resources has been developed by using attenuated total reflection Fourier transform infrared spectroscopy and multivariate data analysis. The spectral data were collected without any sample preparation; thus, no chemical preparation was involved, but data were rather processed directly using the developed spectral analysis platform, making it fast, very cost effective, and suitable for routine use in various biotechnological and food research and related industries. Unsupervised pattern recognition techniques, including principal component analysis and unsupervised hierarchical cluster analysis, discriminated the marine oils into groups by correlating similarities and differences in their fatty acid (FA) compositions that corresponded well to the FA profiles obtained from traditional lipid analysis based on gas chromatography (GC). Furthermore, quantitative determination of unsaturated fatty acids, PUFAs, EPA and DHA, by partial least square regression analysis through which calibration models were optimized specifically for each targeted FA, was performed in both known marine oils and totally independent unknown n - 3 oil samples obtained from an actual commercial product in order to provide prospective testing of the developed models towards actual applications. The resultant predicted FAs were achieved at a good accuracy compared to their reference GC values as evidenced through (1) low root mean square error of prediction, (2) good coefficient of determination close to 1 (i.e., R 2≥ 0.96), and (3) the residual predictive deviation values that indicated the predictive power at good and higher levels for all the target FAs. © 2014 Springer Science+Business Media New York.

Energy consumption and income in six Asian developing countries: a multivariate cointegration analysis

 This article examines the short- and long-run causal relationship between energy consumption and GDP of six emerging economies of Asia. Based on cointegration and vector error correction modeling the empirical results show that there exists unidirectional short- and long-run causality running from energy consumption to GDP for China, uni-directional short-run causality from output to energy consumption for India, whilst bi-directional short-run causality for Thailand. Neutrality between energy consumption and income is found for Indonesia, Malaysia and Philippines. Both the generalized variance decompositions and impulse response functions confirm the direction of causality. These findings have important policy implications for the countries concerned. The results suggest that while India may directly initiate energy conservation measures, China and Thailand may opt for a balanced combination of alternative polices.

Generalised 2D-correlation NMR analysis of a wine fermentation

A wine fermentation has been monitored on a daily basis by 1H NMR spectroscopy. Following data pre-processing that includes synthesis of the spectra to ensure all peaks are of constant half-width, the series of spectra were examined using generalised two-dimensional correlation techniques. Synchronous and asynchronous data maps have been generated and employed to interpret the changes in the fermentation process as a function of time. The results illustrate the potential of high resolution NMR with multivariate data analysis as a tool for process monitoring and the manner in which two-dimensional correlation mapping can aid in data interpretation.

Analysis of protein sequence and interaction data for candidate disease gene prediction

Linkage analysis is a successful procedure to associate diseases with specific genomic regions. These regions are often large, containing hundreds of genes, which make experimental methods employed to identify the disease gene arduous and expensive. We present two methods to prioritize candidates for further experimental study: Common Pathway Scanning (CPS) and Common Module Profiling (CMP). CPS is based on the assumption that common phenotypes are associated with dysfunction in proteins that participate in the same complex or pathway. CPS applies network data derived from protein–protein interaction (PPI) and pathway databases to identify relationships between genes. CMP identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype. Both algorithms use two forms of input data: known disease genes or multiple disease loci. When using known disease genes as input, our combined methods have a sensitivity of 0.52 and a specificity of 0.97 and reduce the candidate list by 13-fold. Using multiple loci, our methods successfully identify disease genes for all benchmark diseases with a sensitivity of 0.84 and a specificity of 0.63. Our combined approach prioritizes good candidates and will accelerate the disease gene discovery process.

Synchrotron-FTIR microspectroscopy enables the distinction of lipid accumulation in thraustochytrid strains through analysis of individual live cells

The superior characteristics of high photon flux and diffraction-limited spatial resolution achieved by synchrotron-FTIR microspectroscopy allowed molecular characterization of individual live thraustochytrids. Principal component analysis revealed distinct separation of the single live cell spectra into their corresponding strains, comprised of new Australasian thraustochytrids (AMCQS5-5 and S7) and standard cultures (AH-2 and S31). Unsupervised hierarchical cluster analysis (UHCA) indicated close similarities between S7 and AH-7 strains, with AMCQS5-5 being distinctly different. UHCA correlation conformed well to the fatty acid profiles, indicating the type of fatty acids as a critical factor in chemotaxonomic discrimination of these thraustochytrids and also revealing the distinctively high polyunsaturated fatty acid content as key identity of AMCQS5-5. Partial least squares discriminant analysis using cross-validation approach between two replicate datasets was demonstrated to be a powerful classification method leading to models of high robustness and 100% predictive accuracy for strain identification. The results emphasized the exceptional S-FTIR capability to perform real-time in vivo measurement of single live cells directly within their original medium, providing unique information on cell variability among the population of each isolate and evidence of spontaneous lipid peroxidation that could lead to deeper understanding of lipid production and oxidation in thraustochytrids for single-cell oil development.

Obesity and diabetes gene discovery approaches

New treatments are currently required for the common metabolic diseases obesity and type 2 diabetes. The identification of physiological and  biochemical factors that underlie the metabolic disturbances observed in obesity and type 2 diabetes is a key step in developing better therapeutic outcomes. The discovery of new genes and pathways involved in the  pathogenesis of these diseases is critical to this process, however  identification of genes that contribute to the risk of developing these diseases represents a significant challenge as obesity and type 2 diabetes are complex diseases with many genetic and environmental causes. A number of diverse approaches have been used to discover and validate potential new targets for obesity and diabetes. To date, DNA-based approaches using candidate gene and genome-wide linkage analysis have had limited success in identifying genomic regions or genes involved in the development of these diseases. Recent advances in the ability to evaluate linkage analysis data from large family pedigrees using variance components based linkage analysis show great promise in robustly identifying genomic regions associated with the development of obesity and diabetes. RNA-based technologies such as cDNA microarrays have identified many genes differentially expressed in tissues of healthy and diseased subjects. Using a combined approach, we are endeavouring to focus attention on differentially expressed genes located in chromosomal regions previously linked with obesity and / or diabetes. Using this strategy, we have identified Beacon as a potential new target for obesity and diabetes.

Prediction of alcohol-related harm from controlled drinking stragegies and alcohol consumption trajectories

Aims To establish predictors of age 21 alcohol-related harm from prior drinking patterns, current levels of alcohol consumption and use of controlled drinking strategies.
Participants One thousand, five hundred and ninety-six students recruited from an initial sample of 3300 during their final year of high school in 1993.
Design Longitudinal follow-up across five waves of data collection.
Setting Post high school in Victoria, Australia.
Measurements Self-administered surveys examining a range of health behaviours, including alcohol consumption patterns and related behaviour.
Findings Drinking behaviours at age 21 were found to be strongly predicted by drinking trajectories established through the transition from high school. Multivariate regression analysis revealed that alcohol-related harms at age 21 were reduced where current levels of alcohol use fell within limits recommended in Australian national guidelines. After controlling for this effect it was found that the range of strategies employed by participants to control alcohol use maintained a small protective influence. Post-high-school drinking trajectories continued to demonstrate a significant effect after controlling for current behaviours. Findings revealed that over one quarter of males and females drank alcohol, but on a less-than-weekly basis. This pattern of alcohol use demonstrated considerable stability through the post-school transition and was associated with a low level of subsequent harm at age 21.
Conclusions Future research should investigate whether encouraging more Australian adolescents to drink alcohol on a less-than-weekly basis may be a practical intervention target for reducing alcohol-related harms.

Narcotics Anonymous participation and changes in substance use and social support

In Victoria (a southern Australian state) in 1995, Narcotics Anonymous had a small but growing membership providing an opportunity to study the early experience of new self-help members. Ninety-one new members were interviewed and 62 (68%) were reinterviewed after 12 months. Three measures of self-help participation were examined: service role involvement, step work, and stable meeting attendance. Lower prior involvement in treatment services and greater participation in self-help predicted subsequent self-help participation. Higher levels of secondary school education predicted service role involvement and longer periods in stable meeting attendance. Higher self-help participation through the 12 months prior to follow-up was associated with lower levels of hazardous alcohol use and higher emotional support at reinterview. Multivariate regression analysis suggested stable self-help meeting attendance and step work continued to predict reductions in hazardous alcohol use and improvements in social support, after controlling for a range of alternative predictors.

Prediction of alcohol-related harm from controlled drinking strategies and alcohol consumption trajectories

Aims To establish predictors of age 21 alcohol-related harm from prior drinking patterns, current levels of alcohol consumption and use of controlled drinking strategies.

Participants One thousand, five hundred and ninety-six students recruited from an initial sample of 3300 during their final year of high school in 1993.

Design Longitudinal follow-up across five waves of data collection.

Setting Post high school in Victoria, Australia.

Measurements Self-administered surveys examining a range of health behaviours, including alcohol consumption patterns and related behaviour.

Findings Drinking behaviours at age 21 were found to be strongly predicted by drinking trajectories established through the transition from high school. Multivariate regression analysis revealed that alcohol-related harms at age 21 were reduced where current levels of alcohol use fell within limits recommended in Australian national guidelines. After controlling for this effect it was found that the range of strategies employed by participants to control alcohol use maintained a small protective influence. Post-high-school drinking trajectories continued to demonstrate a significant effect after controlling for current behaviours. Findings revealed that over one quarter of males and females drank alcohol, but on a less-than-weekly basis. This pattern of alcohol use demonstrated considerable stability through the post-school transition and was associated with a low level of subsequent harm at age 21.

Conclusions Future research should investigate whether encouraging more Australian adolescents to drink alcohol on a less-than-weekly basis may be a practical intervention target for reducing alcohol-related harms.

Wavelet packets for lighting-effects determination

This paper presents a system to determine lighting effiects within face images. The theories of multivariate discriminant analysis and wavelet packets transform are utilised to develop the proposed system. An extensive set of face images of different poses, illuminated from different angles, are used to train the system. The performance of the proposed system is evaluated by conducting experiments on different test sets, and by comparing its results against those of some existing counterparts.

Optimization of laccase production from Trametes versicolor by solid fermentation

The regulation of culture conditions, especially the optimization of substrate constituents, is crucial for laccase production by solid fermentation. To develop an inexpensive optimized substrate formulation to produce high-activity laccase, a uniform design formulation experiment was devised. The solid fermentation of Trametes versicolor was performed with natural aeration, natural substrate pH (about 6.5), environmental humidity of 60% and two different temperature stages (at 37 °C for 3 days, and then at 30 °C for the next 17 days). From the experiment, a regression equation for laccase activity, in the form of a second-degree polynomial model, was constructed using multivariate regression analysis and solved with unconstrained optimization programming. The optimized substrate formulation for laccase production was then calculated. Tween 80 was found to have a negative effect on laccase production in solid fermentation; the optimized solid substrate formulation was 10.8% glucose, 27.7% wheat bran, 9.0% (NH4)2SO4, and 52.5% water. In a scaled-up verification of solid fermentation at a 10 kg scale, laccase activity from T. versicolor in the optimized substrate formulation reached 110.9 IU/g of dry mass.

Pectinase production by solid fermentation from Aspergillus niger by a new prescription experiment

The Ordos Plateau in China is covered with up to 300,000 ha of peashrub (Caragana) which is the dominant natural vegetation and ideal for fodder production. To exploit peashrub fodder, it is crucially important to optimize the culture conditions, especially culture substrate to produce pectinase complex. In this study, a new prescription process was developed. The process, based on a uniform experimental design, first optimizes the solid substrate and second, after incubation, applies two different temperature treatments (30 °C for the first 30 h and 23°C for the second 42 h) in the fermentation process. A multivariate regression analysis is applied to a number of independent variables (water, wheat bran, rice dextrose, ammonium sulfate, and Tween 80) to develop a predictive model of pectinase activity. A second-degree polynomial model is developed which accounts for an excellent proportion of the explained variation (R² = 97:7%). Using unconstrained mathematical programming, an optimized substrate prescription for pectinase production is subsequently developed. The mathematical analysis revealed that the optimal formula for pectinase production from Aspergillus niger by solid fermentation under the conditions of natural aeration, natural substrate pH (about 6.5), and environmental humidity of 60% is rice dextrose 8%, wheat bran 24%, ammonium sulfate ((NH₄)₂SO₄) 6%, and water 61%. Tween 80 was found to have a negative effect on the production of pectinase in solid substrate. With this substrate prescription, pectinase produced by solid fermentation of A. niger reached 36.3 IU/(g DM). Goats fed on the pectinase complex obtain an incremental increase of 0:47 kg day^-1 during the initial 25 days of feeding, which is a very promising new feeding prospect for the local peashrub. It is concluded that the new formula may be very useful for the sustainable development of arid and semiarid pastures such as those of the Ordos Plateau.

Coastal marine habitat mapping using multi-beam acoustics, video data and a decision tree classifier on the temperate south-east Australian continental shelf

In this habitat mapping study, multi-beam acoustic data are integrated with extensive, precisely geo-referenced video validation data in a GIS environment to classify benthic substrates and biota at a 33km2 site in the near shore waters of Victoria, Australia. Using an automated decision-tree classification method, 5 representative biotic groups were identified in the Cape Nelson survey area using a combination of multi-beam bathymetry, backscatter and derivative products. Rigorous error assessment of derived, classified maps produced high overall accuracies (>85%) for all mapping products. In addition, a discrete multivariate analysis technique (kappa analysis) was used to assess classification accuracy. High-resolution (2.5m cell-size) representation of sea floor morphology and textural characteristics provided by multi-beam bathymetry and backscatter datasets, allowed the interpretation of benthic substrates of the Cape Nelson site and the communities of sessile organisms that populate them. Non-parametric multivariate statistical analysis (ANOSIM) revealed a significant difference in biotic composition between depth strata, and between substrate types. Incorporated with other descriptive measures, these results indicate that depth and substrate are important factors in the distributional ecology of the biotic communities at the Cape Nelson study site. BIOENV analysis indicates that derivatives of both multi-beam datasets (bathymetry and backscatter) are correlated with distribution and density of biotic communities. Results from this study provide new tools for research and management of the coastal zone.

Object classification under ignorance using CaRBS : the case of the European barn swallow

This article investigates the potential of a novel technique for object classification, called Classification and Ranking Belief Simplex (CaRBS), which is based on the Dempster-Shafer theory of evidence. As such, the classification of objects and the evidence from their characteristics have a level of ignorance associated with them. Its potential is exposited in the application of the classification of European barn swallows according to their gender. The classification of biological data in the presence of ignorance about such data sets is a common problem in biology. Comparisons of the results from CaRBS with those from multivariate discriminant analysis and neural networks are made. Also shown throughout the investigation is the interpretability of the results with the utilisation of the simplex plot method of representing data