Chromatic interaction between egg pigmentation and skin chromatophores in the nuptial coloration of female two-spotted gobies

In two-spotted gobies (Gobiusculus flavescens Fabricius 1779), females develop an orange belly as they approach sexual maturity. Bright belly coloration is preferred by males and has been suggested to act as a female ornament. This coloration is unusual in that it originates partly from pigmentation of the abdominal skin but also from strongly pigmented gonads directly visible through the skin. In addition, females have been observed to temporarily become more colourful during courtship and competition. To understand how gonad and skin pigmentation interact in this nuptial coloration, the potential for colour modification via regulation of skin chromatophores was investigated. Noradrenaline caused aggregation of chromatophore pigment and was used to experimentally reduce the contribution of skin chromatophores to the nuptial coloration. Chromatophore pigment aggregation caused bellies to become less colourful and abdominal skin biopsies to become less colourful and more transparent. There was a strong positive relationship between belly coloration and the coloration of the underlying gonads. This shows that belly coloration honestly reflects egg pigmentation, mainly because the transparency of the abdominal skin allows other fish to see the gonads directly. Interestingly, when noradrenaline caused pigment to aggregate and thereby increased the transparency of the skin, the relationship between belly and gonad coloration weakened. We conclude that female G. flavescens have a potential to use skin chromatophores to rapidly alter their nuptial coloration, thereby affecting the efficacy with which information about gonad coloration is conveyed.

Does female nuptial coloration reflect egg carotenoids and clutch quality in the Two-Spotted Goby (Gobiusculus flavescens, Gobiidae)?

Summary

1 - Carotenoid-based ornamentation has often been suggested to signal mate quality, and species with such ornaments have frequently been used in studies of sexual selection.

2 - Female Gobiusculus flavescens (Two-Spotted Goby) develop colourful orange bellies during the breeding season. Belly coloration varies among mature females, and previous work has shown that nest-holding males prefer females with more colourful bellies. Because males invest heavily in offspring during incubation, the evolution of this preference can be explained if colourful females provide males with eggs of higher quality.

3 - We tested this hypothesis by allowing males to spawn with ‘colourful’ and ‘drab’ females and comparing parameters including egg carotenoid concentration, clutch size, hatchability and larval viability between groups. We also investigated relationships between egg carotenoid concentration and clutch quality parameters.

4 - Eggs from colourful females had significantly higher concentrations of total carotenoids than drab females, and photographically quantified belly coloration was a good predictor of egg carotenoid concentration.

5 - Colourful females produced slightly larger clutches, but female belly coloration was not related to any measure of clutch quality. In addition, there were no significant relationships between egg carotenoids and clutch quality. Females with high levels of egg carotenoids spawned slightly earlier, however, possibly because they were more ready to spawn or because of male mate choice.

6 - Our results call into question the generality of a causal link between egg carotenoids and offspring quality.

Formation and dissolution of hen egg white lysozyme amyloid fibrils in protic ionic liquids

The formation of amyloid fibrils from non-disease-related proteins demonstrates that any protein can adopt this “rogue” form; we show that it is possible to use protic ionic liquids to fibrilize hen egg white lysozyme, and then subsequently to dissolve the fibrils with up to 72% restoration of enzymatic activity.

Structure–energy relations in hen egg white lysozyme observed during refolding from a quenched unfolded state

We use infrared spectroscopy to study the evolution of protein folding intermediate structures on arbitrarily slow time scales by rapidly quenching thermally unfolded hen egg white lysozyme in a glassy matrix, followed by reheating of the protein to refold; upon comparison with differential scanning calorimetric experiments, low-temperature structural changes that precede the formation of energetic native contacts are revealed.

The reliance on distant resources for egg formation in high Arctic breeding barnacle geese Branta leucopsis

Breeding in the high Arctic is time constrained and animals should therefore start with their annual reproduction as early as possible. To allow for such early reproduction in migratory birds, females arrive at the breeding grounds either with body stores or they try to rapidly develop their eggs after arrival using local resources. Svalbard breeding barnacle geese Branta leucopsis have to fly non-stop for about 1100 km from their last continental staging site to the archipelago making the transport of body stores costly. However, environmental conditions at the breeding grounds are highly unpredictable favouring residual body stores allowing for egg production after arrival on the breeding grounds. We estimated the reliance on southern continental resources, i.e. body stores for egg formation, in barnacle geese using stable isotope ratios in the geese's forage along the flyway and in their eggs. Females adopted mixed breeding strategies by using southern resources as well as local resources to varying extents for egg formation. Southern capital in lipid-free yolk averaged 41% (range: 23-65%), early laid eggs containing more southern capital than eggs laid late in the season. Yolk lipids and albumen did not vary over time and averaged a southern capital proportion of 54% (range: 32-73%) and 47% (range: 25-88%), respectively. Our findings indicate that female geese vary the use of southern resources when synthesising their eggs and this allocation also varies among egg tissues. Their mixed and flexible use of distant and local resources potentially allows for adaptive adjustments to environmental conditions encountered at the archipelago just before breeding.

Short term load forecasting using interval type-2 fuzzy logic systems

Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy may drop due to presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with extra degrees of freedom, are an excellent tool for handling prevailing uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models appropriately approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks used in this study.

Effect of speculative prefetching on network load in distributed systems

Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper on the other hand investigates the performance of speculative prefetching. When prefetching is performed speculatively, there is bound to be an increase in the network load. Furthermore, the prefetched items must compete for space with existing cache occupants. These two factors-increased load and eviction of potentially useful cache entries-are considered in the analysis. We obtain the following conclusion: to maximise the improvement in access time, prefetch exclusively all items with access probabilities exceeding a certain threshold.

Interval type-2 fuzzy logic systems for load forecasting : a comparative study

Accurate short term load forecasting (STLF) is essential for a variety of decision-making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with additional degrees of freedom, are an excellent tool for handling uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models precisely approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks and traditional type-1 Takagi-Sugeno-Kang (TSK) FLSs.

Short term load forecasting using a hybrid neural network

Short Term Load Forecasting (STLF) is very important from the power systems grid operation point of view. STLF involves forecasting load demand in a short term time frame. The short term time frame may consist of half hourly prediction up to weekly prediction. Accurate forecasting would benefit the utility in terms of reliability and stability of the grid ensuring adequate supply is present to meet with the load demand. Apart from that it would also affect the financial performance of the utility company. An accurate forecast would result in better savings while maintaining the security of the grid. This paper outlines the STLF using a novel hybrid online learning neural network, known as the Gaussian Regression (GR). This new hybrid neural network is a combination of two existing online learning neural networks which are the Gaussian Adaptive Resonance Theory (GA) and the Generalized Regression Neural Network (GRNN). Both GA and GRNN implemented online learning, but each of them suffers from limitation. Originally GA is used for unsupervised clustering by compressing the training samples into several categories. A supervised version of GA is available, namely Gaussian ARTMAP (GAM). However, the GAM is still not capable on solving regression problem. On the other hand, GRNN is designed for solving real value estimation (regression) problem, but the learning process would involve of memorizing all training samples, hence high computational cost. The hybrid GR is considered an enhanced version of GRNN with compression ability while still maintains online learning properties. Simulation results show that GR has comparable prediction accuracy and has less prototype as compared to the original GRNN as well as the Support Vector Regression.

Quantification of physical load in elite junior Australian football players

Results from this thesis provide insights into the physical loads experienced by the elite junior Australian footballer. The information presented can assist in the facilitation of best practice advice for player management and training prescription through the use of training diaries and GPS TMA and HR device technologies.

Reproductive development, GnRHa-induced spawning and egg quality of wild meagre (Argyrosomus regius) acclimatised to captivity

The objective of the study was to acclimatise wild-caught meagre (Argyrosomus regius) to captivity to produce viable eggs for aquaculture production. Twelve meagre (3 males and 9 females, mean weight = 20 ± 7 kg) were caught and transported to a land-based facility on 26 October 2006. During, March to June 2007, all three males were spermiating and five of the nine females were in vitellogenesis with mean maximum oocyte diameter ≥550 μm. No spontaneous spawning was observed. Two hormone treatments, either a single injection of gonadotropin-releasing hormone agonist (GnRHa, 20 μg kg−1 for females and 10 μg kg−1 for males) or a slow-release implant loaded with the same GnRHa (50 μg kg−1 for females and 25 μg kg−1 for males), were used to induce spawning on three different dates on 26 March 2007, 4 May 2007 and 18 April 2008. From each spawning event, the following parameters were determined: fecundity, number of floating eggs, egg size, fertilisation and hatching success, unfed larval survival, and proximal composition and fatty acid profile of the eggs. In 2007, two females that were injected on 26 March and 4 May spawned a total of 5 times producing 9,019,300 floating eggs and a relative fecundity of 198,200 eggs kg−1 and two different females that were implanted on the same dates spawned 14 times producing 12,430,000 floating eggs and a relative fecundity of 276,200 eggs kg−1. In 2008, a pair that was implanted spawned five times producing a total of 10,211,900 floating eggs and a relative fecundity of 527,380 eggs kg−1. The latency period was 48–72 h. Parameters were compared between hormone treatments, date of hormone induction and parents determined by microsatellites. Percentage hatch and egg size were 70 ± 0.3% and 0.99 ± 0.02 mm, respectively, for GnRHa-implanted fish and were significantly higher (P < 0.05) compared to 30 ± 0.3% and 0.95 ± 0.03 mm, respectively, for injected fish. Few differences were observed in proximal composition and fatty acid profile and for all spawns mean (% dry weight) lipid content was 17.3 ± 3.0%, carbohydrate was 4.4 ± 1.9% and protein was 31.5 ± 6.4% and the essential fatty acids: Arachidonic acid (ARA, 20:4n-6) ranged between 0.9 and 1% (of total fatty acids), eicosapentaenoic acid (EPA 20:5n-3) 7.7–10.4% and docosahexaenoic acid (DHA 22:6n-3), 28.6–35.4%. All good quality spawns were obtained in the second and/or third spawn after GnRHa treatment, whereas all bad quality spawns were obtained either on the first spawn or after the fifth spawn. Both spawning protocols gave commercially viable (1,000,000+) numbers of good quality eggs that could form the basis of a hatchery production.

Precocious egg development in the blowfly Calliphora vicina : implications for developmental studies and post-mortem interval estimation

The occurrence of precocious egg development in forensically important calliphorid species has previously been reported; however, the frequency of occurrence in both wild and captive colonies, and the consequent effects on developmental studies and post-m

Improving load forecasting accuracy through combination of best forecasts

Neural network (NN) models have been widely used in the literature for short-term load forecasting. Their popularity is mainly due to their excellent learning and approximation capability. However, their forecasting performance significantly depends on several factors including initializing parameters, training algorithm, and NN structure. To minimize negative effects of these factors, this paper proposes a practically simple, yet effective and an efficient method to combine forecasts generated by NN models. The proposed method includes three main phases: (i) training NNs with different structures, (ii) selecting best NN models based on their forecasting performance for a validation set, and (iii) combination of forecasts for selected best NNs. Forecast combination is performed through calculating the mean of forecasts generated by best NN models. The performance of the proposed method is examined using real world data set. Comparative studies demonstrate that the accuracy of combined forecasts is significantly superior to those obtained from individual NN models.