Effect of Growth Performance and Survival Rate of Goldfish (Carassius auratus L. 1758) Fed Garlic (Allium sativum L.) Supplemented Diets

This research evaluated the effect of different garlic (Allium sativum) levels on the nutritional characteristics, growth, and survival rate of goldfish (Carassius auratus).  Five different commercial experimental diets were prepared garlic was added at levels of 0 (Control), 5, 10, 15, and 20 g·kg-1. The experiment was carried out for 84 days. Diet 4 group, in which 20 g·kg-1 garlic was added to the trial feed, showed the best growth performance compared to Diet 0 (Control) and other groups (Diet 1, Diet 2, Diet 3) (p<0.05). The best growth in parameters such as body weight gain, percent live weight increase, and specific growth rate was in the Diet 4 group (p<0.05). It was determined that the statistical difference between the groups for the condition factor and survival rate parameters was insignificant. The highest feed conversion rate was determined in the Diet 0 group with the lowest Diet 4 group (p<0.05).  As a result, it has been determined that feeding goldfish with the addition of garlic up to 20 g·kg-1 (2%) in its diet will have a positive effect on growth

Towards accurate and precise T1 and extracellular volume mapping in the myocardium: a guide to current pitfalls and their solutions

Mapping of the longitudinal relaxation time (T1) and extracellular volume (ECV) offers a means of identifying pathological changes in myocardial tissue, including diffuse changes that may be invisible to existing T1-weighted methods. This technique has recently shown strong clinical utility for pathologies such as Anderson- Fabry disease and amyloidosis and has generated clinical interest as a possible means of detecting small changes in diffuse fibrosis; however, scatter in T1 and ECV estimates offers challenges for detecting these changes, and bias limits comparisons between sites and vendors. There are several technical and physiological pitfalls that influence the accuracy (bias) and precision (repeatability) of T1 and ECV mapping methods. The goal of this review is to describe the most significant of these, and detail current solutions, in order to aid scientists and clinicians to maximise the utility of T1 mapping in their clinical or research setting. A detailed summary of technical and physiological factors, issues relating to contrast agents, and specific disease-related issues is provided, along with some considerations on the future directions of the field. Towards accurate and precise T1 and extracellular volume mapping in the myocardium: a guide to current pitfalls and their solutions. Available from: https://www.researchgate.net/publication/317548806_Towards_accurate_and_precise_T1_and_extracellular_volume_mapping_in_the_myocardium_a_guide_to_current_pitfalls_and_their_solutions [accessed Jun 13, 2017]

Identification of the site of oxidase substrate binding in Scytalidium thermophilum catalase.

The catalase from Scytalidium thermophilum is a homotetramer containing a heme d in each active site. Although the enzyme has a classical monofunctional catalase fold, it also possesses oxidase activity towards a number of small organics, including catechol and phenol. In order to further investigate this, the crystal structure of the complex of the catalase with the classical catalase inhibitor 3-amino-1,2,4-triazole (3TR) was determined at 1.95 Å resolution. Surprisingly, no binding to the heme site was observed; instead, 3TR occupies a binding site corresponding to the NADPH-binding pocket in mammalian catalases at the entrance to a lateral channel leading to the heme. Kinetic analysis of site-directed mutants supports the assignment of this pocket as the binding site for oxidase substrates

A Rescorla-Wagner Drift-Diffusion Model of Conditioning and Timing

Computational models of classical conditioning have made significant contributions to the theoretic understanding of associative learning, yet they still struggle when the temporal aspects of conditioning are taken into account. Interval timing models have contributed a rich variety of time representations and provided accurate predictions for the timing of responses, but they usually have little to say about associative learning. In this article we present a unified model of conditioning and timing that is based on the influential Rescorla-Wagner conditioning model and the more recently developed Timing Drift-Diffusion model. We test the model by simulating 10 experimental phenomena and show that it can provide an adequate account for 8, and a partial account for the other 2. We argue that the model can account for more phenomena in the chosen set than these other similar in scope models: CSC-TD, MS-TD, Learning to Time and Modular Theory. A comparison and analysis of the mechanisms in these models is provided, with a focus on the types of time representation and associative learning rule used

Of monkeys and men:Impatience in perceptual decision-making

For decades sequential sampling models have successfully accounted for human and monkey decision-making, relying on the standard assumption that decision makers maintain a pre-set decision standard throughout the decision process. Based on the theoretical argument of reward rate maximization, some authors have recently suggested that decision makers become increasingly impatient as time passes and therefore lower their decision standard. Indeed, a number of studies show that computational models with an impatience component provide a good fit to human and monkey decision behavior. However, many of these studies lack quantitative model comparisons and systematic manipulations of rewards. Moreover, the often-cited evidence from single-cell recordings is not unequivocal and complimentary data from human subjects is largely missing. We conclude that, despite some enthusiastic calls for the abandonment of the standard model, the idea of an impatience component has yet to be fully established; we suggest a number of recently developed tools that will help bring the debate to a conclusive settlement

Dynamic excitatory and inhibitory gain modulation can produce flexible, robust and optimal decision-making

<div><p>Behavioural and neurophysiological studies in primates have increasingly shown the involvement of urgency signals during the temporal integration of sensory evidence in perceptual decision-making. Neuronal correlates of such signals have been found in the parietal cortex, and in separate studies, demonstrated attention-induced gain modulation of both excitatory and inhibitory neurons. Although previous computational models of decision-making have incorporated gain modulation, their abstract forms do not permit an understanding of the contribution of inhibitory gain modulation. Thus, the effects of co-modulating both excitatory and inhibitory neuronal gains on decision-making dynamics and behavioural performance remain unclear. In this work, we incorporate time-dependent co-modulation of the gains of both excitatory and inhibitory neurons into our previous biologically based decision circuit model. We base our computational study in the context of two classic motion-discrimination tasks performed in animals. Our model shows that by simultaneously increasing the gains of both excitatory and inhibitory neurons, a variety of the observed dynamic neuronal firing activities can be replicated. In particular, the model can exhibit winner-take-all decision-making behaviour with higher firing rates and within a significantly more robust model parameter range. It also exhibits short-tailed reaction time distributions even when operating near a dynamical bifurcation point. The model further shows that neuronal gain modulation can compensate for weaker recurrent excitation in a decision neural circuit, and support decision formation and storage. Higher neuronal gain is also suggested in the more cognitively demanding reaction time than in the fixed delay version of the task. Using the exact temporal delays from the animal experiments, fast recruitment of gain co-modulation is shown to maximize reward rate, with a timescale that is surprisingly near the experimentally fitted value. Our work provides insights into the simultaneous and rapid modulation of excitatory and inhibitory neuronal gains, which enables flexible, robust, and optimal decision-making.</p></div