AIMC Topic: Larva
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The CPGs for Limbed Locomotion-Facts and Fiction.
International journal of molecular sciences
May 30, 2021
The neuronal networks that generate locomotion are well understood in swimming animals such as the lamprey, zebrafish and tadpole. The networks controlling locomotion in tetrapods remain, however, still enigmatic with an intricate motor pattern requi...
Deep learning-enhanced light-field imaging with continuous validation.
Nature methods
May 7, 2021
Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effect...
Towards Detecting Red Palm Weevil Using Machine Learning and Fiber Optic Distributed Acoustic Sensing.
Sensors (Basel, Switzerland)
Feb 25, 2021
Red palm weevil (RPW) is a detrimental pest, which has wiped out many palm tree farms worldwide. Early detection of RPW is challenging, especially in large-scale farms. Here, we introduce the combination of machine learning and fiber optic distribute...
Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network.
Scientific reports
Jan 12, 2021
Closed-cycle aquaculture using hatchery produced seed stocks is vital to the sustainability of endangered species such as Pacific bluefin tuna (Thunnus orientalis) because this aquaculture system does not depend on aquaculture seeds collected from th...
The acoustic near-field measurement of aye-ayes' biological auditory system utilizing a biomimetic robotic tap-scanning.
Bioinspiration & biomimetics
Jul 7, 2020
The aye-aye (Daubentonia madagascariensis) is best known for its unique acoustic-based foraging behavior called 'tap-scanning' or 'percussive foraging'. The tap-scanning is a unique behavior allowing aye-aye to locate small cavities beneath tree bark...
CytoCensus, mapping cell identity and division in tissues and organs using machine learning.
eLife
May 19, 2020
A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends conv...
Microscopy, Video
Organoids
Drosophila melanogaster
Cell Division
Tissue Culture Techniques
Time-Lapse Imaging
Embryo, Mammalian
Animals, Genetically Modified
Automation
Male
Larva
Time Factors
Image Processing, Computer-Assisted
Mice
Mutation
Retina
Zebrafish
Machine Learning
Phenotype
Brain
Female
Reproducibility of Results
Animals
Identification of the Species Constituents of Maggot Populations Feeding on Decomposing Remains-Facilitation of the Determination of Post Mortem Interval and Time Since Tissue Infestation through Application of Machine Learning and Direct Analysis in Real Time-Mass Spectrometry.
Analytical chemistry
Mar 12, 2020
The utilization of entomological specimens such as larvae (maggots) for the estimation of time since oviposition (i.e., egg laying) for post mortem interval determination, or for estimation of time since tissue infestation (in investigations of elder...
DeTerm: Software for automatic detection of neuronal dendritic branch terminals via an artificial neural network.
Genes to cells : devoted to molecular & cellular mechanisms
May 28, 2019
Dendrites of neurons receive and process synaptic or sensory inputs. The Drosophila class IV dendritic arborization (da) neuron is an established model system to explore molecular mechanisms of dendrite morphogenesis. The total number of dendritic br...
Anthelmintic effect and tissue alterations induced in vitro by hydrolysable tannins on the adult stage of the gastrointestinal nematode Haemonchus contortus.
Veterinary parasitology
Dec 24, 2018
Haemonchus contortus constitutes a severe problem for ruminant's production, it is the most frequent nematode parasite both in template and tropical regions, induces economical losses, and shows increasing resistance to currently available anthelmint...
High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology.
Nature communications
Dec 3, 2018
Technologies for mapping the spatial and temporal patterns of neural activity have advanced our understanding of brain function in both health and disease. An important application of these technologies is the discovery of next-generation neurotherap...
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