Pheromone production, male abundance, body size, and the evolution of elaborate antennae in moths

The males of some species of moths possess elaborate feathery antennae. It is widely assumed that these striking morphological features have evolved through selection for males with greater sensitivity to the female sex pheromone, which is typically released in minute quantities. Accordingly, females of species in which males have elaborate (i.e., pectinate, bipectinate, or quadripectinate) antennae should produce the smallest quantities of pheromone. Alternatively, antennal morphology may be associated with the chemical properties of the pheromone components, with elaborate antennae being associated with pheromones that diffuse more quickly (i.e., have lower molecular weights). Finally, antennal morphology may reflect population structure, with low population abundance selecting for higher sensitivity and hence more elaborate antennae. We conducted a phylogenetic comparative analysis to test these explanations using pheromone chemical data and trapping data for 152 moth species. Elaborate antennae are associated with larger body size (longer forewing length), which suggests a biological cost that smaller moth species cannot bear. Body size is also positively correlated with pheromone titre and negatively correlated with population abundance (estimated by male abundance). Removing the effects of body size revealed no association between the shape of antennae and either pheromone titre, male abundance, or mean molecular weight of the pheromone components. However, among species with elaborate antennae, longer antennae were typically associated with lower male abundances and pheromone compounds with lower molecular weight, suggesting that male distribution and a more rapidly diffusing female sex pheromone may influence the size but not the general shape of male antennae.

Sexual selection and the evolution of complex color patterns in dragon lizards

Many species have elaborate and complex coloration and patterning, which often differ between the sexes. Sexual selection may increase the size or intensity of color patches (elaboration) in one sex or drive the evolution of novel signal elements (innovation). The latter potentially increases color pattern complexity. Color pattern complexity may also be influenced by ecological factors related to predation and environment; however, very few studies have investigated the effects of both sexual and natural selection on color pattern complexity across species. We used a phylogenetic comparative approach to examine these effects in 85 species and subspecies of Australian dragon lizards (family Agamidae). We quantified color pattern complexity by adapting the Shannon–Wiener diversity index. There were clear sex differences in color pattern complexity, which were positively correlated with both sexual dichromatism and sexual size dimorphism, consistent with the idea that sexual selection plays a significant role in the evolution of color pattern complexity. By contrast, we found little evidence of a link between environmental factors and color pattern complexity on body regions exposed to predators. Our results suggest that sexual selection rather than natural selection has led to increased color pattern complexity in males.

Brains and bravery: little evidence of a relationship between brain size and flightiness in shorebirds

The ability of birds to perceive, assess and appropriately respond to the presence of relatively novel threats is important to their survival. We hypothesized that the cognitive capacity of birds will influence their ability for accurate response to novelty. We used brain volume as a surrogate for cognitive capacity and postulated that larger brained birds would moderate their responses when presented with a benign, frequently occurring stimulus, such as a person, because they would habituate more readily. We conducted phylogenetic generalized least square regression to investigate the relationship between brain volume and flight initiation distance (FID; the distance to which a bird can be approached before initiating escape behaviour), while controlling for confounding factors including body size (body mass and wing length) and migration status. We compared seven different models using combinations of these parameters using Akaike's information criterion to determine the best approximating model(s) explaining FID. The two best-supported models included only wing length and only body mass with Akaike weights of 0.396 and 0.311 respectively. No model including brain volume had an Akaike weight greater than 0.083 and brain volume was poorly correlated with FID in models after controlling for body mass. Thus, brain volume does not appear to strongly relate to bravery among these shorebirds.

Modeling of a stereo vision system using a genetic algorithm based fuzzy linear regression

In this paper a fuzzy linear regression (FLR) model integrated with a genetic algorithm (GA) is proposed. The proposed GA-FLR model is applied to modeling of a stereo vision system. A set of empirical data from stereo vision object measurement is collected based on the full factorial design technique. Three regression models, namely ordinary least-squares regression (OLS), FLR, and GA-FLR, are developed, and with their performances compared. The results show that the proposed GA-FLR model performs better than OLS and FLR in modeling of a stereo vision system.

Derivative-free optimization and neural networks for robust regression

Large outliers break down linear and nonlinear regression models. Robust regression methods allow one to filter out the outliers when building a model. By replacing the traditional least squares criterion with the least trimmed squares (LTS) criterion, in which half of data is treated as potential outliers, one can fit accurate regression models to strongly contaminated data. High-breakdown methods have become very well established in linear regression, but have started being applied for non-linear regression only recently. In this work, we examine the problem of fitting artificial neural networks (ANNs) to contaminated data using LTS criterion. We introduce a penalized LTS criterion which prevents unnecessary removal of valid data. Training of ANNs leads to a challenging non-smooth global optimization problem. We compare the efficiency of several derivative-free optimization methods in solving it, and show that our approach identifies the outliers correctly when ANNs are used for nonlinear regression.

Effects of entrepreneurial knowledge on entrepreneurial intentions: a longitudinal study of selected South-east Asian business students

Drawing on the theory of planned behaviour, this study examines the direct and indirect effects of knowledge gained from a formal entrepreneurship education programme on an individual’s entrepreneurial intentions (EI). It tracks the changes in students’ entrepreneurial knowledge (EK), perceptions of desirability of, and self-efficacy in, engaging in entrepreneurship and the impact of those changes on students’ EI upon completion of an entrepreneurship course. It uses longitudinal survey data of 245 business students in a Philippine university. Using cross-lagged panel method and partial-least squares-based structural equation modelling, the study builds and tests the measurement and structural models to examine the hypothesised interactions of EK, perceived desirability of, self-efficacy towards entrepreneurship, and EI. The findings underscore the importance of developing knowledge to nurture students’ self-confidence and attitudinal propensity to engage in entrepreneurship.

Improved 2D‐HPLC of red wine by incorporating pre‐process signal‐smoothing algorithms

Twomultidimensional HPLC separations of an Australian red wine are presented, >70% of the available separation space was used. A porous graphitic carbon (PGC) stationary phase was used as the first dimension in both separations with both RP core–shell and hydrophilic interaction chromatography fully porous columns used separately in the second dimension. To overcome peak analysis problems caused by signal noise and low detection limits, the data were pre-processed with penalised least-squares smoothing. The PGC × RP combination separated 85 peaks with a spreading angle of 71 and the PGC × hydrophilic interaction chromatography separated 207 peaks with a spreading angle of 80. Both 2D-HPLC steps were completed in 76 min using a comprehensive stop-and-go approach. A smoothing step was added to peak-picking processes and was able to greatly reduce the number of false peaks present due to noise in the chromatograms. The required thresholds were not able to ignore the noise because of the small magnitude of the peaks; 1874 peaks were located in the non-smoothed PGC × RP separation that reduced to 227 peaks after smoothing was included.

Organisational and professional commitment of early career accountants: do mentoring and organisational ethical climate matter?

This study assesses the effects of mentoring and organisational ethical climate (OEC) on the organisational and professional commitment (PC) of early career accountants (ECAs) (i.e. accounting graduate recruits with three or less years of working experience). The empirical data are based on a questionnaire survey from 86 ECAs in Australian public accounting firms, and hypothesis testing utilises partial least squares analysis. Our results indicate when a career development style of mentoring is adopted there is greater organisational as well as PC. By contrast, a social support mentoring style has no significant impact on organisational commitment (OC) and a negative effect on PC. Further, our data also reveal OEC to be positively associated with OC, and OC in turn having a positive impact on PC. The results imply that fostering a career-focused mentoring environment and an OEC can increase an ECA's OC and PC. These results have various implications for human resource management at both the accounting firm and professional levels.

Comparative analysis of classic brain component sizes in relation to flightiness in birds

Increased encephalization has been linked to a range of behavioural traits and scenarios. However, studies of whole brain size in this context have been criticised for ignoring the role of specific brain areas in controlling behaviour. In birds, the response to potential threats is one such behaviour that may relate to the way in which the brain processes sensory information. We used a phylogenetic generalised least squares (PGLS) analyses, based on five different phylogenetic hypotheses, to analyse the relationship of relative sizes of whole brain and brain components with Flight-Initiation Distance (FID), the distance at which birds flee from an approaching human, for 41 bird species. Starting distance (the distance at which an approach to a bird commences), body mass and eye size have elsewhere been shown to be positively associated with FID, and consequently were included as covariates in our analysis. Starting distance and body mass were by far the strongest predictors of FID. Of all brain components, cerebellum size had the strongest predictor weight and was negatively associated with FID but the confidence intervals on the average estimate included zero and the overall predictor weight was low. Models featuring individual brain components were generally more strongly weighted than models featuring whole brain size. The PGLS analyses estimated there to be no phylogenetic signal in the regression models, and hence produced results equivalent to ordinary least squares regression analysis. However analyses that assumed strong phylogenetic signal produced substantially different results with each phylogeny, and overall suggest a negative relationship between forebrain size and FID. Our analyses suggest that the evolutionary assumptions of the comparative analysis, and consideration of starting distance make a profound difference to the interpretation of the effect of brain components on FID in birds.

State dependent asymmetric loss and the consensus forecast of real U.S. GDP growth

It has been well documented that the consensus forecast from surveys of professional forecasters shows a bias that varies over time. In this paper, we examine whether this bias may be due to forecasters having an asymmetric loss function. In contrast to previous research, we account for the time variation in the bias by making the loss function depend on the state of the economy. The asymmetry parameter in the loss function is specified to depend on set state variables which may cause forecaster to intentionally bias their forecasts. We consider both the Lin–Ex and asymmetric power loss functions. For the commonly used Lin–Ex and Lin–Lin loss functions, we show the model can be easily estimated by least squares. We apply our methodology to the consensus forecast of real U.S. GDP growth from the Survey of Professional Forecasters. We find that forecast uncertainty has an asymmetric effect on the asymmetry parameter in the loss function dependent upon whether the economy is in expansion or contraction. When the economy is in expansion, forecaster uncertainty is related to an overprediction in the median forecast of real GDP growth. In contrast, when the economy is in contraction, forecaster uncertainty is related to an underprediction in the median forecast of real GDP growth. Our results are robust to the particular loss function that is employed in the analysis.

Development and evaluation of a raman flow cell for monitoring continuous flow reactions

We show how in-line Raman spectroscopy can be used to monitor both reactant and product concentrations for a heterogeneously catalysed Suzuki cross reaction operating in continuous flow. The flow system consisted of an HPLC pump to drive a homogeneous mixture of the reactants (4-bromobenzonitrile, phenylboronic acid, and potassium carbonate) through an oven heated (80°C) palladium catalyst immobilised on a silica monolith. A custom built PTFE in-line flow cell with a quartz window enabled the coupling of an Ocean Optics Raman spectrometer probe to monitor both the reactants and product (4-cyanobiphenyl). Calibration was based on obtaining multivariate spectral data in the range 1530 cm–1 and 1640 cm–1 and using partial least-squares regression (PLSR) to obtain a calibration model which was validated using gas chromatography–mass spectrometry (GCMS) analysis. In-line Raman monitoring of the reactant and product concentrations enable (i) determination of reaction kinetic information such as the empirical rate law and associated rate constant and (ii) optimisation of either the product conversion (61 % at 0.02 mL min–1 generating 17 g h–1) or product yield (14 % at 0.24 mL min–1 generating 53 g h–1).

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.

FTIR microspectroscopy for rapid screening and monitoring of polyunsaturated fatty acid production in commercially valuable marine yeasts and protists

The increase in polyunsaturated fatty acid (PUFA) consumption has prompted research into alternative resources other than fish oil. In this study, a new approach based on focal-plane-array Fourier transform infrared (FPA-FTIR) microspectroscopy and multivariate data analysis was developed for the characterisation of some marine microorganisms. Cell and lipid compositions in lipid-rich marine yeasts collected from the Australian coast were characterised in comparison to a commercially available PUFA-producing marine fungoid protist, thraustochytrid. Multivariate classification methods provided good discriminative accuracy evidenced from (i) separation of the yeasts from thraustochytrids and distinct spectral clusters among the yeasts that conformed well to their biological identities, and (ii) correct classification of yeasts from a totally independent set using cross-validation testing. The findings further indicated additional capability of the developed FPA-FTIR methodology, when combined with partial least squares regression (PLSR) analysis, for rapid monitoring of lipid production in one of the yeasts during the growth period, which was achieved at a high accuracy compared to the results obtained from the traditional lipid analysis based on gas chromatography. The developed FTIR-based approach when coupled to programmable withdrawal devices and a cytocentrifugation module would have strong potential as a novel online monitoring technology suited for bioprocessing applications and large-scale production.

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.