AIMC Topic: Animals

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Deep-learning for seizure forecasting in canines with epilepsy.

Journal of neural engineering
OBJECTIVE: This paper introduces a fully automated, subject-specific deep-learning convolutional neural network (CNN) system for forecasting seizures using ambulatory intracranial EEG (iEEG). The system was tested on a hand-held device (Mayo Epilepsy...

Machine Learning Based Real-Time Image-Guided Cell Sorting and Classification.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Cell classification based on phenotypical, spatial, and genetic information greatly advances our understanding of the physiology and pathology of biological systems. Technologies derived from next generation sequencing and fluorescent activated cell ...

Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery.

Sensors (Basel, Switzerland)
Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast a...

A mode-of-action ontology model for safety evaluation of chemicals: Outcome of a series of workshops on repeated dose toxicity.

Toxicology in vitro : an international journal published in association with BIBRA
Repeated dose toxicity evaluation aims at assessing the occurrence of adverse effects following chronic or repeated exposure to chemicals. Non-animal approaches have gained importance in the last decades because of ethical considerations as well as d...

A supervised machine learning approach to characterize spinal network function.

Journal of neurophysiology
Spontaneous activity is a common feature of immature neuronal networks throughout the central nervous system and plays an important role in network development and consolidation. In postnatal rodents, spontaneous activity in the spinal cord exhibits ...

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels.

BMC genomics
BACKGROUND: Although software tools abound for the comparison, analysis, identification, and classification of genomic sequences, taxonomic classification remains challenging due to the magnitude of the datasets and the intrinsic problems associated ...

Semi-supervised deep learning of brain tissue segmentation.

Neural networks : the official journal of the International Neural Network Society
Brain image segmentation is of great importance not only for clinical use but also for neuroscience research. Recent developments in deep neural networks (DNNs) have led to the application of DNNs to brain image segmentation, which required extensive...

Low-Invasive Cell Injection based on Rotational Microrobot.

Advanced biosystems
The advancement of cell injections has created a need for accurate, efficient, and low-invasive injections. However, the conventional approaches to reduce cell damage during penetration, mainly optimization of micropipette tips and vision-based autom...

A Transition Control Mechanism for Artificial Bee Colony (ABC) Algorithm.

Computational intelligence and neuroscience
Artificial Bee Colony (ABC) algorithm inspired by the complex search and foraging behaviors of real honey bees is one of the most promising implementations of the Swarm Intelligence- (SI-) based optimization algorithms. Due to its robust and phase-di...

Automatic localization and identification of mitochondria in cellular electron cryo-tomography using faster-RCNN.

BMC bioinformatics
BACKGROUND: Cryo-electron tomography (cryo-ET) enables the 3D visualization of cellular organization in near-native state which plays important roles in the field of structural cell biology. However, due to the low signal-to-noise ratio (SNR), large ...