Do leatherback turtles Dermochelys coriacea forage during the breeding season? A combination of data-logging devices provide new insights

Animals which undertake migrations from foraging grounds to suitable breeding areas must adopt strategies in these new conditions in order to minimise the rate at which body condition deteriorates (which will occur due to oogenesis or provisioning for young). For some animals this involves continuing foraging, whereas for others the optimal strategy is to fast during the breeding season. The leatherback turtle undertakes long-distance migrations from temperate zones to tropical breeding areas, and in some of these areas it has been shown to exhibit diving behaviour indicative of foraging. We used conventional time–depth recorders and a single novel mouth-opening sensor to investigate the foraging behaviour of leatherback turtles in the southern Caribbean. Diving behaviour suggested attempted foraging on vertically migrating prey with significantly more diving to a more consistent depth occurring during the night. No obvious prey manipulation was detected by the mouth sensor, but rhythmic mouth opening did occur during specific phases of dives, suggesting that the turtle was relying on gustatory cues to sense its immediate environment. Patterns of diving in conjunction with these mouth-opening activities imply that leatherbacks are attempting to forage during the breeding season and that gustatory cues are important to leatherbacks.

Variance-covariance based weighing for neural network ensembles

Neural network (NN) is a popular artificial intelligence technique for solving complicated problems due to their inherent capabilities. However generalization in NN can be harmed by a number of factors including parameter's initialization, inappropriate network topology and setting parameters of the training process itself. Forecast combinations of NN models have the potential for improved generalization and lower training time. A weighted averaging based on Variance-Covariance method that assigns greater weight to the forecasts producing lower error, instead of equal weights is practiced in this paper. While implementing the method, combination of forecasts is done with all candidate models in one experiment and with the best selected models in another experiment. It is observed during the empirical analysis that forecasting accuracy is improved by combining the best individual NN models. Another finding of this study is that reducing the number of NN models increases the diversity and, hence, accuracy.

Detection of fingertips based on the combination of color information and circle detection

Fingertips of human hand play an important role in hand-based interaction with computers. Therefore, identification of fingertips' positions on hand image is vital for developing a human computer interaction system. All most all of the research works for fingertips detection, initially isolate hand image from the background image. Most of these techniques develop color based segmentation methods because human skin color possess an exceptional characterises that can be used to isolate hand from the rest of the image quite easily. Sometimes color image segmentation becomes difficult due to illumination and background variations. To make it simple and reliable, this paper proposes a robust method for detecting fingertips of a hand image based on the combination of color segmentation and circle detection. Due to the characteristics of circularity of fingertips regions of hand boundary, any existing circle detection algorithms can be applied to detect circles at fingertips region. It is difficult to detect fingertips solely based on the circle detection method. For this reason, initially the proposed method detects all the circular regions on the image applying Circle Hough Transformation (CHT) then the fingertips are selected based on the color characteristics of the fingertips regions. Experimental results show that the proposed approach is promising.

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.

Synergistic antibacterial effects of theaflavin in combination with ampicillin against hospital isolates of Stenotrophomonas Maltophilia

Stenotrophomonas maltophilia is an important opportunistic nosocomial pathogen that shows intrinsic resistance to many antibiotics. This often limits treatment options and can cause lengthy hospital stays. Combination treatments are often used to combat resistance and using natural compounds such as polyphenols could give increased treatment options and even the reuse of antibiotics to which high levels of resistance have been observed. A checkerboard assay was used to determine if any synergy exists between ampicillin and the polyphenol theaflavin against 9 clinical isolates and one control isolate (NCTC 13014) of S. maltophilia. It was discovered that significant synergy (P  0.05) does exist between theaflavin and ampicillin, reducing the mean MIC of ampicillin from 12.5-22.9 µg/mL, in liquid culture, to 3.125-6.25 µg/mL. The FIC index was calculated to be 0.22-0.35 confirming synergy. From these results, significant potential for medical applications can be seen and further investigation is recommended.

A novel fuzzy multi-objective framework to construct optimal prediction intervals for wind power forecast

The forecasting behavior of the high volatile and unpredictable wind power energy has always been a challenging issue in the power engineering area. In this regard, this paper proposes a new multi-objective framework based on fuzzy idea to construct optimal prediction intervals (Pis) to forecast wind power generation more sufficiently. The proposed method makes it possible to satisfy both the PI coverage probability (PICP) and PI normalized average width (PINAW), simultaneously. In order to model the stochastic and nonlinear behavior of the wind power samples, the idea of lower upper bound estimation (LUBE) method is used here. Regarding the optimization tool, an improved version of particle swam optimization (PSO) is proposed. In order to see the feasibility and satisfying performance of the proposed method, the practical data of a wind farm in Australia is used as the case study.

An adjunctive antidepressant nutraceutical combination in treating major depression: study protocol and clinical considerations

Current treatment for major depressive disorder (MDD), a prevalent and disabling mental illness, is inadequate, with two-thirds of people treated with first-line antidepressants not achieving remission. MDD is for many a chronic condition, often requiring multiple treatment attempts, thus development of additional interventions is urgently required. An emerging approach to improve non-response to antidepressants is the use of adjunctive nutraceuticals. The pathophysiology of MDD is considered to involve a range of abnormalities (monoamine impairment, neuro-endocrinological changes, reduced brain-derived neurotrophic factor, and cytokine alterations). By targeting an array of these key neurobiological pathways via specific nutraceuticals (S-adenosyl methionine; [SAMe], 5-HTP [active tryptophan], folinic acid [active folic acid], omega-3 fatty acids, and zinc), there is the potential to provide a more comprehensive therapeutic biological approach to treat depression. We are currently conducting a National Health and Medical Research Council funded study in Australia (APP1048222). The clinical trial is phase II/III, multi-site, 3-arm, 8-week, randomised, double-blind, placebo-controlled study using SAMe + folinic acid versus a combination nutraceutical (SAMe, 5-HTP, folinic acid, omega-3, and zinc) or matching placebo in 300 currently depressed participants with diagnosed MDD who are non-responsive to current antidepressants (ANZCTR, protocol number: 12613001300763). The results may provide evidence for a novel adjunctive neurobiological approach for treating depression.

Are Indian stock returns predictable?

In this paper we show that Indian stock returns, based on industry portfolios, portfolios sorted on book-to-market, and on size, are predictable. While we discover that this predictability holds both in in-sample and out-of-sample tests, predictability is not homogenous. Some predictors are important than others and some industries and portfolios of stocks are more predictable and, therefore, more profitable than others. We also discover that a mean combination forecast approach delivers significant out-of-sample performance. Our results survive a battery of robustness tests.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

Microencapsulation of coupled folate and chitosan nanoparticles for targeted delivery of combination drugs to colon

Folate-chitosan nanoparticles, co-loaded with 5-fluourouacil (5-FU) and leucovorin (LV) and prepared by ionic gelation technology were physically microencapsulated by enteric polymer using a solvent evaporation method. Average particle size of the microencapsulated particles was in the range of 15 to 35 µm. High drug encapsulation efficiency was obtained for both 5-FU and LV in the microencapsulated particles. Both drugs were in amorphous state in the microencapsulated particles. By enteric coating, excellent pH-dependent release profile was achieved and no drug release was observed in simulated gastric and intestinal fluids. However, when the pH value reached the soluble threshold of Eudragit S-100, a constant and slow drug release was observed. The results indicated that these microencapsulated particles are a promising vehicle for selectively targeting drugs to colon in the chemotherapy of colon cancer.

Progesterone and testosterone in combination act in the hypothalamus of castrated rams to regulate the secretion of LH

We tested the hypotheses that progesterone enhances the negative feedback actions of testosterone in rams and that this occurs through actions at the hypothalamus. In the first part of this study, blood samples were collected every 10 min for 12 h before and after 7 days of treatment (i.m.) of castrated Romney Marsh rams (n=5 per group) with vehicle, progesterone (4 mg/12 h), testosterone (4 mg/12 h) or a combination of progesterone (4 mg/12 h) and testosterone (4 mg/12 h). In the second part of this study the brains of four gonad-intact Romney Marsh rams were collected, the hypothalamus was sectioned and in situ hybridisation of mRNA for progesterone receptors conducted. After 7 days of treatment with vehicle or progesterone or testosterone alone, there were no changes in the secretion of LH. In contrast, treatment with a combination of progesterone and testosterone resulted in a significant (P<0.01, repeated measures ANOVA) decrease in mean plasma concentrations of LH, the number of LH pulses per hour and the pre-LH pulse nadir and a significant (P<0.01) increase in the inter-LH pulse interval. We found cells containing mRNA for progesterone receptors throughout the hypothalamus, including the preoptic area (where most GnRH neurons are located in sheep), the periventricular, ventromedial and arcuate nuclei and the bed nucleus of the stria terminalis. This study shows that progesterone is capable of acting centrally with testosterone to suppress the secretion of LH in castrated rams and that cells containing mRNA for progesterone receptors are located in the hypothalamus of rams in the vicinity of GnRH neurons.

Hybrid controller with the combination of FLC and neural network-based IMC for nonlinear processes

This work presents a hybrid controller based on the combination of fuzzy logic control (FLC) mechanism and internal model-based control (IMC). Neural network-based inverse and forward models are developed for IMC. After designing the FLC and IMC independently, they are combined in parallel to produce a single control signal. Mean averaging mechanism is used to combine the prediction of both controllers. Finally, performance of the proposed hybrid controller is studied for a nonlinear numerical plant model (NNPM). Simulation result shows the proposed hybrid controller outperforms both FLC and IMC.

Combination of calcium and magnesium ions prevents substrate inhibition and promotes biomass and lipid production in thraustochytrids under higher glycerol concentration

Effect of calcium and magnesium ions was studied in detail in batch mode in shake flask cultures of two fast growing strains of thraustochytrids (Aurantiochytrium sp. DBTIOC-18 and Schizochytrium sp. DBTIOC-1) for biomass and lipid production. These strains were previously isolated from Indian marine biodiversity. Screening of these two strains on different carbon and nitrogen sources revealed the suitability of glycerol over glucose and sodium nitrate over yeast extract for the cultivation of these strains. The presence of higher concentration of glycerol in the medium inhibited the glycerol utilization by the cell thus resulting in lower biomass and lipid production in both the strains. Supplementing media with calcium and magnesium ions promoted glycerol utilization thus resulted in a substantial rise in volumetric production of biomass (55.12 g L-1, 48.12 g L-1), fatty acid for biodiesel (27.14 g L-1, 22.15 g L-1) and docosahexaenoic acid (14.57 g L-1, 10.12 g L-1) with both strains Aurantiochytrium sp. DBTIOC-18 and Schizochytrium sp. DBTIOC-1, respectively. Growth profile study of these two strains showed further improvement in production of biomass, fatty acid for biodiesel and docosahexaenoic acid when cultures were extended up to 7 days. Finding of this work underlines the importance of calcium and magnesium salts in designing new fermentation strategies to prevent substrate inhibition and achieve high cell density culture under high nutrient concentration especially carbon sources.