AIMC Topic: Dogs

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Direct Comparison of the Prediction of the Unbound Brain-to-Plasma Partitioning Utilizing Machine Learning Approach and Mechanistic Neuropharmacokinetic Model.

The AAPS journal
The mechanistic neuropharmacokinetic (neuroPK) model was established to predict unbound brain-to-plasma partitioning (K) by considering in vitro efflux activities of multiple drug resistance 1 (MDR1) and breast cancer resistance protein (BCRP). Herei...

Machine-learning based prediction of Cushing's syndrome in dogs attending UK primary-care veterinary practice.

Scientific reports
Cushing's syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing's syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-...

A remote management system for control and surveillance of echinococcosis: design and implementation based on internet of things.

Infectious diseases of poverty
BACKGROUND: As a neglected cross-species parasitic disease transmitted between canines and livestock, echinococcosis remains a global public health concern with a heavy disease burden. In China, especially in the epidemic pastoral communities on the ...

Dog galloping on rough terrain exhibits similar limb co-ordination patterns and gait variability to that on flat terrain.

Bioinspiration & biomimetics
Understanding how animals regulate their gait during locomotion can give biological insight and inspire controllers for robots. Why animals use the gallop at the highest speeds remains incompletely explained. Hypothesized reasons for galloping includ...

Further evaluation and validation of the VETSCAN IMAGYST: in-clinic feline and canine fecal parasite detection system integrated with a deep learning algorithm.

Parasites & vectors
BACKGROUND: Fecal examinations in pet cats and dogs are key components of routine veterinary practice; however, their accuracy is influenced by diagnostic methodologies and the experience level of personnel performing the tests. The VETSCAN IMAGYST s...

Human-dog relationships as a working framework for exploring human-robot attachment: a multidisciplinary review.

Animal cognition
Robotic agents will be life-long companions of humans in the foreseeable future. To achieve such successful relationships, people will likely attribute emotions and personality, assign social competencies, and develop a long-lasting attachment to rob...

Machine-Learning-Based Approach to Decode the Influence of Nanomaterial Properties on Their Interaction with Cells.

ACS applied materials & interfaces
In an nanotoxicity system, cell-nanoparticle (NP) interaction leads to the surface adsorption, uptake, and changes into nuclei/cell phenotype and chemistry, as an indicator of oxidative stress, genotoxicity, and carcinogenicity. Different types of n...

Relationship between Machine-Learning Image Classification of T-Weighted Intramedullary Hypointensity on 3 Tesla Magnetic Resonance Imaging and Clinical Outcome in Dogs with Severe Spinal Cord Injury.

Journal of neurotrauma
Early prognostic information in cases of severe spinal cord injury can aid treatment planning and stratification for clinical trials. Analysis of intraparenchymal signal change on magnetic resonance imaging has been suggested to inform outcome predic...

AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer Learning.

Neural networks : the official journal of the International Neural Network Society
Transfer learning enables solving a specific task having limited data by using the pre-trained deep networks trained on large-scale datasets. Typically, while transferring the learned knowledge from source task to the target task, the last few layers...