AIMC Topic: Animals

Clear Filters Showing 2741 to 2750 of 8866 articles

Effects of caudal fin stiffness on optimized forward swimming and turning maneuver in a robotic swimmer.

Bioinspiration & biomimetics
In animal and robot swimmers of body and caudal fin (BCF) form, hydrodynamic thrust is mainly produced by their caudal fins, the stiffness of which has profound effects on both thrust and efficiency of swimming. Caudal fin stiffness also affects the ...

Embodied essentialism in the reconstruction of the animal sign in robot animal design.

Bio Systems
Robot animals are important for the interpretation of the biological world. In this paper, I show that specific design solutions for robot animal signs usually privilege the overall perception of biological systems (and animal signs in general) as ma...

Deep learning-based label-free imaging of lymphatics and aqueous veins in the eye using optical coherence tomography.

Scientific reports
We demonstrate an adaptation of deep learning for label-free imaging of the micro-scale lymphatic vessels and aqueous veins in the eye using optical coherence tomography (OCT). The proposed deep learning-based OCT lymphangiography (DL-OCTL) method wa...

Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors.

Stem cell research & therapy
BACKGROUND: Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long...

Bayesian inference is facilitated by modular neural networks with different time scales.

PLoS computational biology
Various animals, including humans, have been suggested to perform Bayesian inferences to handle noisy, time-varying external information. In performing Bayesian inference by the brain, the prior distribution must be acquired and represented by sampli...

End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging.

PLoS computational biology
Deep learning is a powerful tool for neural decoding, broadly applied to systems neuroscience and clinical studies. Interpretable and transparent models that can explain neural decoding for intended behaviors are crucial to identifying essential feat...

Deepm6A-MT: A deep learning-based method for identifying RNA N6-methyladenosine sites in multiple tissues.

Methods (San Diego, Calif.)
N6-methyladenosine (m6A) is the most prevalent, abundant, and conserved internal modification in the eukaryotic messenger RNA (mRNAs) and plays a crucial role in the cellular process. Although more than ten methods were developed for m6A detection ov...

Predicting lncRNA-protein interactions through deep learning framework employing multiple features and random forest algorithm.

BMC bioinformatics
RNA-protein interaction (RPI) is crucial to the life processes of diverse organisms. Various researchers have identified RPI through long-term and high-cost biological experiments. Although numerous machine learning and deep learning-based methods fo...

An accurately supervised motion-aware deep network for non-contact pain assessment of trigeminal neuralgia mouse model.

Journal of oral & facial pain and headache
Pain assessment in trigeminal neuralgia (TN) mouse models is essential for exploring its pathophysiology and developing effective analgesics. However, pain assessment methods for TN mouse models have not been widely studied, resulting in a critical g...

Time series classification of multi-channel nerve cuff recordings using deep learning.

PloS one
Neurostimulation and neural recording are crucial to develop neuroprostheses that can restore function to individuals living with disabilities. While neurostimulation has been successfully translated into clinical use for several applications, it rem...