AIMC Topic: Animals

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Research perspectives on animal health in the era of artificial intelligence.

Veterinary research
Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host...

Tunabot Flex: a tuna-inspired robot with body flexibility improves high-performance swimming.

Bioinspiration & biomimetics
Tunas are flexible, high-performance open ocean swimmers that operate at high frequencies to achieve high swimming speeds. Most fish-like robotic systems operate at low frequencies (≤3 Hz) resulting in low swim speeds (≤1.5 body lengths per second), ...

Machine learning-based classification of mitochondrial morphology in primary neurons and brain.

Scientific reports
The mitochondrial network continually undergoes events of fission and fusion. Under physiologic conditions, the network is in equilibrium and is characterized by the presence of both elongated and punctate mitochondria. However, this balanced, homeos...

HistoNet: A Deep Learning-Based Model of Normal Histology.

Toxicologic pathology
We introduce HistoNet, a deep neural network trained on normal tissue. On 1690 slides with rat tissue samples from 6 preclinical toxicology studies, tissue regions were outlined and annotated by pathologists into 46 different tissue classes. From the...

Monolithic Three-Dimensional Functionally Graded Hydrogels for Bioinspired Soft Robots Fabrication.

Soft robotics
Bioinspired soft robotics aims at reproducing the complex hierarchy and architecture of biological tissues within artificial systems to achieve the typical motility and adaptability of living organisms. The development of suitable fabrication approac...

Using deep neural networks to evaluate object vision tasks in rats.

PLoS computational biology
In the last two decades rodents have been on the rise as a dominant model for visual neuroscience. This is particularly true for earlier levels of information processing, but a number of studies have suggested that also higher levels of processing su...

Deep learning approaches for challenging species and gender identification of mosquito vectors.

Scientific reports
Microscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learni...

Dactyls and inward gripping stance for amphibious crab-like robots on sand.

Bioinspiration & biomimetics
Sandy beaches are areas that challenge robots of all sizes, especially smaller scale robots. Sand can hinder locomotion and waves apply hydrodynamic forces which can displace, reorient, or even invert the robot. Crab-like legs and gaits are well suit...

DNN-m6A: A Cross-Species Method for Identifying RNA N6-Methyladenosine Sites Based on Deep Neural Network with Multi-Information Fusion.

Genes
As a prevalent existing post-transcriptional modification of RNA, N6-methyladenosine (m6A) plays a crucial role in various biological processes. To better radically reveal its regulatory mechanism and provide new insights for drug design, the accurat...

Deep learning differentiates between healthy and diabetic mouse ears from optical coherence tomography angiography images.

Annals of the New York Academy of Sciences
We trained a deep learning algorithm to use skin optical coherence tomography (OCT) angiograms to differentiate between healthy and type 2 diabetic mice. OCT angiograms were acquired with a custom-built OCT system based on an akinetic swept laser at ...