Food preference in the cultured species, Penaeus monodon Fabricius (Crustacea: Decapoda)

The study of food preference is necessitated by the need to promote coastal culture of shrimps in Pakistan. The cultured Penaeus monodon was selected for study. Food preferences have been examined through the analysis of the gut contents. The shrimp shows a seasonal variation in its preference to food and feeding.

Effects of scopolamine on morphine-induced conditioned place preference in mice

It is well known that the cholinergic system plays a crucial role in learning and memory. Psychopharmacological studies in humans and animals have shown that a systemic cholinergic blockade may induce deficits in learning and memory. Accumulated studies h

Pentylenetetrazole-induced status epilepticus following training does not impair expression of morphine-induced conditioned place preference

Learning and memory play an important role in morphine addiction. Status epilepticus (SE) can impair the spatial and emotional learning and memory. However, little is known about the effects of SE on morphine-induced conditioned place preference (CPP). Th

Irregular morphine administration affects the retention but not acquisition of conditioned place preference in rats

Drug addiction is increasingly viewed as the expression of abnormal associative learning following repeated exposures to the drugs of abuse Previous I studies have demonstrated that the patterns of repetition such as frequency and spacing are important to many kinds of learning and memory retention We hypothesized that drug repetition pattern might affect the reward-related learning although the total doses of the drug were the same. In the present study, we tested morphine-induced place preference following either regular or irregular pattern of morphine pairing in rats Regular morphine group received morphine administration daily at a regular time with the same dose Irregular morphine groups received morphine administration either at the same time but irregular doses, irregular time but same dose, or irregular time and irregular doses. We found that rats, who received irregular morphine pairing, exhibited similar acquisition of peace preference but different preference retentions compared with regular morphine-treated rats after the same total dose of morphine Rats, who received morphine administration at the same time but irregular doses and at irregular time and irregular doses, showed rapid disruption of place preference than the regular morphine group. Rats, who received morphine at irregular time but the same dose, showed similar retention of place preference to regular morphine group Our results suggest that the pattern of drug pairing plays an important role in the retention of reward-related memory This study may provide new evidence to broaden our understanding of the development and maintenance of drug craving (C) 2009 Elsevier B V. All rights reserved

Morphine conditioned place preference depends on glucocorticoid receptors in both hippocampus and nucleus accumbens

Learned association between drugs of abuse and context is essential for the formation of drug conditioned place preference (CPP), which is believed to engage many brain regions including hippocampus, and nucleus accumbens (NAc). The underlying mechanisms

Bayesian Active Learning for Classification and Preference Learning

Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with nonparametric models, the optimal solution is harder to compute. Current approaches make approximations to achieve tractability. We propose an approach that expresses information gain in terms of predictive entropies, and apply this method to the Gaussian Process Classifier (GPC). Our approach makes minimal approximations to the full information theoretic objective. Our experimental performance compares favourably to many popular active learning algorithms, and has equal or lower computational complexity. We compare well to decision theoretic approaches also, which are privy to more information and require much more computational time. Secondly, by developing further a reformulation of binary preference learning to a classification problem, we extend our algorithm to Gaussian Process preference learning.

Predictive neural coding of reward preference involves dissociable responses in human ventral midbrain and ventral striatum.

Food preferences are acquired through experience and can exert strong influence on choice behavior. In order to choose which food to consume, it is necessary to maintain a predictive representation of the subjective value of the associated food stimulus. Here, we explore the neural mechanisms by which such predictive representations are learned through classical conditioning. Human subjects were scanned using fMRI while learning associations between arbitrary visual stimuli and subsequent delivery of one of five different food flavors. Using a temporal difference algorithm to model learning, we found predictive responses in the ventral midbrain and a part of ventral striatum (ventral putamen) that were related directly to subjects' actual behavioral preferences. These brain structures demonstrated divergent response profiles, with the ventral midbrain showing a linear response profile with preference, and the ventral striatum a bivalent response. These results provide insight into the neural mechanisms underlying human preference behavior.

Effect of several feeding stimulants on diet preference by juvenile gibel carp (Carassius auratus gibelio), fed diets with or without partial replacement of fish meal by meat and bone meal

The objectives of this work were to study the effects of several feeding stimulants on gibel carp fed diets with or without replacement of fish meal by meat and bone meal (MBM). The feeding stimulants tested were betaine, glycine, L-lysine, L-methionine, L-phenylalanine, and a commercial squid extract. Three inclusion levels were tested for each stimulant (0.18, 0.5%, and 1% for betaine and 0.1, 0.25 and 0.5% for the other stimulants). Two basal diets (40% crude protein) were used. one with 26% fish meal (FM), and the other with 21% fish meal and 6% MBM, Betaine at 0.1% in the fish meal group and at 0.5% in the meat and bone meal group was used in all experiments for comparison among stimulants. In the experiment on each stimulant, six tanks of fish were equally divided into two groups, one fed the FM diet, and the other fed the MBM diet. After 7 days' adaptation to the basal diet, in which the fish were fed to satiation twice a day, the fish were fed for another 7 days an equal mixture of diets containing varying levels of stimulants. Each diet contained a unique rare earth oxide as inert marker (Y2O3, Yb2O3, La2O3, Sm2O3 or Nd2O3). During the last 3 days of the experiment, faeces from each tank were collected. Preference for each diet was estimated based on the relative concentration of each marker in the faeces. Gibel carp fed the FM diet had higher intake than those fed the MBM diet, but the difference was significant only in the experiments on betaine, glycine and L-methionine. None of the feeding stimulants tested showed feeding enhancing effects in FM diets. All feeding stimulants showed feeding enhancing effects in MBM diets. and the optimum inclusion level was 0.5% for betaine, 0.1% for glycine, 0.25% for L-lysine, 0.1% for L-methionine. 0.25% For L-phenylalanine. and 0.1% for squid extract. The squid extract had the strongest stimulating effect among all the stimulants tested. (C) 2001 Elsevier Science B.V. All rights reserved.

Theoretical investigation on the phase stability of Nd2Fe14B and site preference of V, Cr, Mn, Zr and Nb

The stability of the excellent permanent magnetic compound Nd2Fe14B and substitution of Fe in the compound by V, Cr, Mn, Zr and Nb are investigated by using interatomic pair potentials which are converted from lattice-inversion method. Calculation shows that the substitution always makes the cell volume larger, and the increase of the volume is almost linear with substituent concentration. The calculated cohesive energy shows that the preferential order of substitution of Fe is Nb, V, Cr, Mn, Zr. Nevertheless, all the five substituting elements should most preferentially replace Fe in the j(2)' site, which has the greatest space among all six Fe sites. (C) 2005 Elsevier B.V. All rights reserved.

Phase transition to color-flavor-locked matter and condensation of Goldstone bosons in neutron star matter

Deconfinement phase transition and condensation of Goldstone bosons in neutron star matter are investigated in a chiral hadronic model (also referred as to the FST model) for the hadronic phase (HP) and in the color-flavor-locked (CFL) quark model for the deconfined quark phase. It is shown that the hadronic-CFL mixed phase (MP) exists in the center of neutron stars with a small bag constant, while the CFL quark matter cannot appear in neutron stars when a large bag constant is taken. Color superconductivity softens the equation of state (EOS) and decreases the maximum mass of neutron stars compared with the unpaired quark matter. The K-0 condensation in the CFL phase has no remarkable contribution to the EOS and properties of neutron star matter. The EOS and the properties of neutron star matter are sensitive to the bag constant B, the strange quark mass m(s) and the color superconducting gap Delta. Increasing B and m(s) or decreasing Delta can stiffen the EOS which results in the larger maximum masses of neutron stars.

Natural Language Syntax and First Order Preference

We have argued elsewhere that first order inference can be made more efficient by using non-standard syntax for first order logic. In this paper we show how a fragment of English syntax under Montague semantics provides the foundation of a new inference procedure. This procedure seems more effective than corresponding procedures based on either classical syntax of our previously proposed taxonomic syntax. This observation may provide a functional explanation for some of the syntactic structure of English.

Finding optimal alternatives based on efficient comparative preference inference

Choosing the right or the best option is often a demanding and challenging task for the user (e.g., a customer in an online retailer) when there are many available alternatives. In fact, the user rarely knows which offering will provide the highest value. To reduce the complexity of the choice process, automated recommender systems generate personalized recommendations. These recommendations take into account the preferences collected from the user in an explicit (e.g., letting users express their opinion about items) or implicit (e.g., studying some behavioral features) way. Such systems are widespread; research indicates that they increase the customers' satisfaction and lead to higher sales. Preference handling is one of the core issues in the design of every recommender system. This kind of system often aims at guiding users in a personalized way to interesting or useful options in a large space of possible options. Therefore, it is important for them to catch and model the user's preferences as accurately as possible. In this thesis, we develop a comparative preference-based user model to represent the user's preferences in conversational recommender systems. This type of user model allows the recommender system to capture several preference nuances from the user's feedback. We show that, when applied to conversational recommender systems, the comparative preference-based model is able to guide the user towards the best option while the system is interacting with her. We empirically test and validate the suitability and the practical computational aspects of the comparative preference-based user model and the related preference relations by comparing them to a sum of weights-based user model and the related preference relations. Product configuration, scheduling a meeting and the construction of autonomous agents are among several artificial intelligence tasks that involve a process of constrained optimization, that is, optimization of behavior or options subject to given constraints with regards to a set of preferences. When solving a constrained optimization problem, pruning techniques, such as the branch and bound technique, point at directing the search towards the best assignments, thus allowing the bounding functions to prune more branches in the search tree. Several constrained optimization problems may exhibit dominance relations. These dominance relations can be particularly useful in constrained optimization problems as they can instigate new ways (rules) of pruning non optimal solutions. Such pruning methods can achieve dramatic reductions in the search space while looking for optimal solutions. A number of constrained optimization problems can model the user's preferences using the comparative preferences. In this thesis, we develop a set of pruning rules used in the branch and bound technique to efficiently solve this kind of optimization problem. More specifically, we show how to generate newly defined pruning rules from a dominance algorithm that refers to a set of comparative preferences. These rules include pruning approaches (and combinations of them) which can drastically prune the search space. They mainly reduce the number of (expensive) pairwise comparisons performed during the search while guiding constrained optimization algorithms to find optimal solutions. Our experimental results show that the pruning rules that we have developed and their different combinations have varying impact on the performance of the branch and bound technique.