Pharmacokinetics of a Single Feeding of Pelleted Cannabidiol in Horses

Claims about cannabidiol (CBD) supplementation improving health and behavior are extensive, but research is lacking. Some studies have shown decreased anxiety behavior in rats, and increased activity in osteoarthritic dogs supplemented with CBD, but even less research exists on horses. This study monitored pharmacokinetics and short-term safety for 3 CBD dosages. Eighteen Quarter Horse geldings were randomly assigned to 3 treatment groups: 50 mg (TXT1), 100 mg (TXT2), and 250 mg (TXT3). Dosage was derived from manufacturer recommendations and existing literature on other species. Horses were fed a single dose of CBD pellets. Blood was collected pre- and post-treatment at 0.5, 1, 2, 4 and 12 hr. Serum was analyzed for CBD and serum chemistry, and plasma was analyzed for a complete blood chemistry (CBC) evaluation. Statistics were completed on serum chemistry using PROC MIXED procedure of SAS. Serum chemistry and CBC results were within normal parameters; however, treatment differences were observed for BUN (TXT1=15.50, TXT2=16.52, TXT3=18.61; P≤0.03) and creatinine (TXT1=1.41, TXT2=1.22, TXT3=1.49; P≤0.01). In other species, peak CBD concentrations occur approximately 2 hr post treatment. Peak serum concentrations were detected in 1 of 6 TXT2 horses and 5 of 6 TXT3 horses at 2 hr post treatment. This data can be used to support further research to determine correct and safe doses of CBD in horses

Mathematical properties of neuronal TD-rules and differential Hebbian learning: a comparison

A confusingly wide variety of temporally asymmetric learning rules exists related to reinforcement learning and/or to spike-timing dependent plasticity, many of which look exceedingly similar, while displaying strongly different behavior. These rules often find their use in control tasks, for example in robotics and for this rigorous convergence and numerical stability is required. The goal of this article is to review these rules and compare them to provide a better overview over their different properties. Two main classes will be discussed: temporal difference (TD) rules and correlation based (differential hebbian) rules and some transition cases. In general we will focus on neuronal implementations with changeable synaptic weights and a time-continuous representation of activity. In a machine learning (non-neuronal) context, for TD-learning a solid mathematical theory has existed since several years. This can partly be transfered to a neuronal framework, too. On the other hand, only now a more complete theory has also emerged for differential Hebb rules. In general rules differ by their convergence conditions and their numerical stability, which can lead to very undesirable behavior, when wanting to apply them. For TD, convergence can be enforced with a certain output condition assuring that the δ-error drops on average to zero (output control). Correlation based rules, on the other hand, converge when one input drops to zero (input control). Temporally asymmetric learning rules treat situations where incoming stimuli follow each other in time. Thus, it is necessary to remember the first stimulus to be able to relate it to the later occurring second one. To this end different types of so-called eligibility traces are being used by these two different types of rules. This aspect leads again to different properties of TD and differential Hebbian learning as discussed here. Thus, this paper, while also presenting several novel mathematical results, is mainly meant to provide a road map through the different neuronally emulated temporal asymmetrical learning rules and their behavior to provide some guidance for possible applications

Real-time estimation of horizontal gaze angle by saccade integration using in-ear electrooculography

The manuscript proposes and evaluates a real-time algorithm for estimating eye gaze angle based solely on single-channel electrooculography (EOG), which can be obtained directly from the ear canal using conductive ear moulds. In contrast to conventional high-pass filtering, we used an algorithm that calculates absolute eye gaze angle via statistical analysis of detected saccades. The estimated eye positions of the new algorithm were still noisy. However, the performance in terms of Pearson product-moment correlation coefficients was significantly better than the conventional approach in some instances. The results suggest that in-ear EOG signals captured with conductive ear moulds could serve as a basis for lightweight and portable horizontal eye gaze angle estimation suitable for a broad range of applications. For instance, for hearing aids to steer the directivity of microphones in the direction of the user’s eye gaze

The bashful and the boastful : prestigious leaders and social change in Mesolithic Societies

The creation and maintenance of influential leaders and authorities is one of the key themes of archaeological and historical enquiry. However the social dynamics of authorities and leaders in the Mesolithic remains a largely unexplored area of study. The role and influence of authorities can be remarkably different in different situations yet they exist in all societies and in almost all social contexts from playgrounds to parliaments. Here we explore the literature on the dynamics of authority creation, maintenance and contestation in egalitarian societies, and discuss the implications for our interpretation and understanding of the formation of authorities and leaders and changing social relationships within the Mesolithic

Archaeology, Architecture, and the Postcolonial Critique

Peer reviewe

Health literacy and public health: A systematic review and integration of definitions and models

<p>Abstract</p> <p>Background</p> <p>Health literacy concerns the knowledge and competences of persons to meet the complex demands of health in modern society. Although its importance is increasingly recognised, there is no consensus about the definition of health literacy or about its conceptual dimensions, which limits the possibilities for measurement and comparison. The aim of the study is to review definitions and models on health literacy to develop an integrated definition and conceptual model capturing the most comprehensive evidence-based dimensions of health literacy.</p> <p>Methods</p> <p>A systematic literature review was performed to identify definitions and conceptual frameworks of health literacy. A content analysis of the definitions and conceptual frameworks was carried out to identify the central dimensions of health literacy and develop an integrated model.</p> <p>Results</p> <p>The review resulted in 17 definitions of health literacy and 12 conceptual models. Based on the content analysis, an integrative conceptual model was developed containing 12 dimensions referring to the knowledge, motivation and competencies of accessing, understanding, appraising and applying health-related information within the healthcare, disease prevention and health promotion setting, respectively.</p> <p>Conclusions</p> <p>Based upon this review, a model is proposed integrating medical and public health views of health literacy. The model can serve as a basis for developing health literacy enhancing interventions and provide a conceptual basis for the development and validation of measurement tools, capturing the different dimensions of health literacy within the healthcare, disease prevention and health promotion settings.</p

Learning the Optimal Control of Coordinated Eye and Head Movements

Various optimality principles have been proposed to explain the characteristics of coordinated eye and head movements during visual orienting behavior. At the same time, researchers have suggested several neural models to underly the generation of saccades, but these do not include online learning as a mechanism of optimization. Here, we suggest an open-loop neural controller with a local adaptation mechanism that minimizes a proposed cost function. Simulations show that the characteristics of coordinated eye and head movements generated by this model match the experimental data in many aspects, including the relationship between amplitude, duration and peak velocity in head-restrained and the relative contribution of eye and head to the total gaze shift in head-free conditions. Our model is a first step towards bringing together an optimality principle and an incremental local learning mechanism into a unified control scheme for coordinated eye and head movements

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards