Pharmacokinetics of a novel formulation of ivermectin after administration to goats

González, Aranzazu; Sahagún, Ana M.; Díez, María José; Fernández, Nélida; Sierra, Matilde; García, José Alejos

doi:10.2460/ajvr.67.2.323

Cited by 361 publications

(10 citation statements)

References 21 publications

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“…The mean elimination half-lives in the present study was 6.4 days in sheep following SC route of administration of Noromectin ® . These findings of mean elimination half -lives could be corroborated with (7.4 days) in goats (25) .…”

Section: Resultssupporting

confidence: 76%

“…The value of T max in the present study was 3.3 days in sheep following SC administration of Noromectin ® . These findings could be well corroborated with T max (3.4 days) in cattle (24) and goats (3 days; (25) ).…”

Section: Resultssupporting

confidence: 74%

“…A lower volume of distribution (1.2 L.kg -1 ) compared to present study has been reported in cattle (34) , goats (2.8 L.kg -1 ; (25) , pigs (2.7 L.kg -1 ; (31) ) and sheep (3 L.kg -1 ; (30) ). However, higher volume of distribution compared to present study has been reported (8.8 L.kg -1 ) in sheep (15) , and goats (12.8 L.kg -1 , (25) ).…”

Section: Resultscontrasting

confidence: 60%

See 2 more Smart Citations

Pharmacokinetics of Ivermectin (Noromectin®) Following Single Dose Subcutaneous Administration in Sheep

Sakthikarthikeyan¹,

Ahmad²,

Kannan³

2020

Int.J.Curr.Microbiol.App.Sci

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Section: Resultssupporting

confidence: 76%

Section: Resultssupporting

confidence: 74%

See 1 more Smart Citation

Pharmacokinetics of Ivermectin (Noromectin®) Following Single Dose Subcutaneous Administration in Sheep

Sakthikarthikeyan¹,

Ahmad²,

Kannan³

2020

Int.J.Curr.Microbiol.App.Sci

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“…Inverse reinforcement learning (IRL) (Choi and Kim, 2011;Ng and Russell, 2000;Ziebart et al, 2008) is a framework for learning a reward function from the actions of an expert, often a human demonstrator. In one famous example, Abbeel et al (2007) taught an agent acrobatic helicopter flight by observing the actions of a human pilot.…”

Section: Value Specificationmentioning

confidence: 99%

AGI Safety Literature Review

Everitt

Lea

Hutter

2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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The development of Artificial General Intelligence (AGI) promises to be a major event. Along with its many potential benefits, it also raises serious safety concerns (Bostrom, 2014). The intention of this paper is to provide an easily accessible and up-to-date collection of references for the emerging field of AGI safety. A significant number of safety problems for AGI have been identified. We list these, and survey recent research on solving them. We also cover works on how best to think of AGI from the limited knowledge we have today, predictions for when AGI will first be created, and what will happen after its creation. Finally, we review the current public policy on AGI.A major challenge for AGI safety research is to find the right conceptual models for plausible AGIs. This is especially challenging since we can only guess at the technology, algorithms, and structure that will be used. Indeed, even if we had the blueprint of an AGI system, understanding and predicting its behavior might still be hard: Both its design and its be-

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“…Thus some animal's behavioral strategies are likely to be associated with reward in its neural system. Inverse reinforcement learning (IRL) is a recently-developed machine learning framework that can solve an inverse problem of RL (blue arrow in Fig 1A) and estimate reward-based strategy from behavioral timeseries [8,9]. One engineering application of IRL is apprenticeship learning.…”

Section: Introductionmentioning

confidence: 99%

Identification of Animal Behavioral Strategies by Inverse Reinforcement Learning

Yamaguchi

Naoki

Ikeda

et al. 2017

Preprint

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Animals are able to flexibly adapt to new environments by controlling different behavioral patterns. Identification of the behavioral strategy used for this control is important for understanding animals' decision-making, but methods available for quantifying such behavioral strategies have not been fully established. In this study, we developed an inverse reinforcement-learning (IRL) framework to identify an animal's behavioral strategy from behavioral time-series data. As a particular target, we applied this framework to thermotactic behavior in C.elegans. After identifying the behavioral strategy dependent on thermosensory state, we found it comprised mixture of two strategies: directed migration (DM) and isothermal migration (IM). First, the DM is a strategy that the worms efficiently reach to specific temperature, which not only explained observation that the worms migrate toward the cultivated temperature, but also clarifies how the worms control thermosensory state through the 10 migration. Second, the IM is a strategy that the worms track along a constant temperature, which reflects isothermal tracking well observed in previous studies. By further applying our method to starved worm and thermosensory neuron-deficient worms, we identified the neural basis underlying the strategies. Consequently, we interpreted behavioral strategies in terms of control theory. Therefore, this study validates and presents a novel approach that should propel the development of new, more effective experiments to identify behavioral strategies and decision-making in animals. Author SummaryUnderstanding animal decision-making has been a fundamental problem in neuroscience and behavioral 20 ecology. Many studies analyze actions that represent decision-making in behavioral tasks, in which rewards are artificially designed with specific objectives. However, it is impossible to extend this artificially designed experiment to a natural environment, because in a natural environment, the rewards for freely-behaving animals cannot be clearly defined. To this end, we must reverse the current paradigm so that rewards are identified from behavioral data. Here, we propose a new reverse-engineering approach (inverse reinforcement learning) that can estimate a behavioral strategy from time-series data of freely-behaving animals. By applying this technique with thermotaxis in C. elegans, we successfully identified the rewardbased behavioral strategy. 30not peer-reviewed)

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Pharmacokinetics of a novel formulation of ivermectin after administration to goats

Cited by 361 publications

References 21 publications

Pharmacokinetics of Ivermectin (Noromectin®) Following Single Dose Subcutaneous Administration in Sheep

Pharmacokinetics of Ivermectin (Noromectin®) Following Single Dose Subcutaneous Administration in Sheep

AGI Safety Literature Review

Identification of Animal Behavioral Strategies by Inverse Reinforcement Learning

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