AIMC Topic: Animals

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Manual versus deep learning measurements to evaluate cumulus expansion of bovine oocytes and its relationship with embryo development in vitro.

Computers in biology and medicine
Cumulus expansion is an important indicator of oocyte maturation and has been suggested to be indicative of greater oocyte developmental capacity. Although multiple methods have been described to assess cumulus expansion, none of them is considered a...

Integrating artificial intelligence and wing geometric morphometry to automate mosquito classification.

Acta tropica
Mosquitoes (Diptera: Culicidae) comprise over 3500 global species, primarily in tropical regions, where the females act as disease vectors. Thus, identifying medically significant species is vital. In this context, Wing Geometric Morphometry (WGM) em...

HostNet: improved sequence representation in deep neural networks for virus-host prediction.

BMC bioinformatics
BACKGROUND: The escalation of viruses over the past decade has highlighted the need to determine their respective hosts, particularly for emerging ones that pose a potential menace to the welfare of both human and animal life. Yet, the traditional me...

Deep learning structural insights into heterotrimeric alternatively spliced P2X7 receptors.

Purinergic signalling
P2X7 receptors (P2X7Rs) are membrane-bound ATP-gated ion channels that are composed of three subunits. Different subunit structures may be expressed due to alternative splicing of the P2RX7 gene, altering the receptor's function when combined with th...

Detection of paralytic shellfish toxins by near-infrared spectroscopy based on a near-Bayesian SVM classifier with unequal misclassification costs.

Journal of the science of food and agriculture
BACKGROUND: Paralytic shellfish poisoning caused by human consumption of shellfish fed on toxic algae is a public health hazard. It is essential to implement shellfish monitoring programs to minimize the possibility of shellfish contaminated by paral...

MaTPIP: A deep-learning architecture with eXplainable AI for sequence-driven, feature mixed protein-protein interaction prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Protein-protein interaction (PPI) is a vital process in all living cells, controlling essential cell functions such as cell cycle regulation, signal transduction, and metabolic processes with broad applications that include ...

Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision.

Advanced materials (Deerfield Beach, Fla.)
The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal-oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shu...

Learning Skill Characteristics From Manipulations.

IEEE transactions on neural networks and learning systems
Percutaneous coronary intervention (PCI) has increasingly become the main treatment for coronary artery disease. The procedure requires high experienced skills and dexterous manipulations. However, there are few techniques to model PCI skill so far. ...

Dual-Stream Spatiotemporal Networks with Feature Sharing for Monitoring Animals in the Home Cage.

Sensors (Basel, Switzerland)
This paper presents a spatiotemporal deep learning approach for mouse behavioral classification in the home-cage. Using a series of dual-stream architectures with assorted modifications for optimal performance, we introduce a novel approach that joi...

FastCellpose: A Fast and Accurate Deep-Learning Framework for Segmentation of All Glomeruli in Mouse Whole-Kidney Microscopic Optical Images.

Cells
Automated evaluation of all glomeruli throughout the whole kidney is essential for the comprehensive study of kidney function as well as understanding the mechanisms of kidney disease and development. The emerging large-volume microscopic optical ima...