AIMC Topic: Caenorhabditis elegans
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Multiview motion tracking based on a cartesian robot to monitor Caenorhabditis elegans in standard Petri dishes.
Scientific reports
Feb 2, 2022
Data from manual healthspan assays of the nematode Caenorhabditis elegans (C. elegans) can be complex to quantify. The first attempts to quantify motor performance were done manually, using the so-called thrashing or body bends assay. Some laboratori...
Using Convolutional Neural Networks to Measure the Physiological Age of Caenorhabditis elegans.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 8, 2021
Caenorhabditis elegans (C. elegans) is a popular and excellent model for studies of aging due to its short lifespan. Methods for precisely measuring the physiological age of C. elegans are critically needed, especially for antiaging drug screening an...
Multiview confocal super-resolution microscopy.
Nature
Nov 26, 2021
Confocal microscopy remains a major workhorse in biomedical optical microscopy owing to its reliability and flexibility in imaging various samples, but suffers from substantial point spread function anisotropy, diffraction-limited resolution, depth-d...
Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans.
PLoS computational biology
Nov 9, 2021
Nervous systems extract and process information from the environment to alter animal behavior and physiology. Despite progress in understanding how different stimuli are represented by changes in neuronal activity, less is known about how they affect...
Probabilistic generative modeling and reinforcement learning extract the intrinsic features of animal behavior.
Neural networks : the official journal of the International Neural Network Society
Oct 18, 2021
It is one of the ultimate goals of ethology to understand the generative process of animal behavior, and the ability to reproduce and control behavior is an important step in this field. However, it is not easy to achieve this goal in systems with co...
Cross-species behavior analysis with attention-based domain-adversarial deep neural networks.
Nature communications
Sep 17, 2021
Since the variables inherent to various diseases cannot be controlled directly in humans, behavioral dysfunctions have been examined in model organisms, leading to better understanding their underlying mechanisms. However, because the spatial and tem...
Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods.
Nature communications
Aug 12, 2021
Efficient and precise base editors (BEs) for C-to-G transversion are highly desirable. However, the sequence context affecting editing outcome largely remains unclear. Here we report engineered C-to-G BEs of high efficiency and fidelity, with the seq...
Towards Lifespan Automation for Based on Deep Learning: Analysing Convolutional and Recurrent Neural Networks for Dead or Live Classification.
Sensors (Basel, Switzerland)
Jul 20, 2021
The automation of lifespan assays with in standard Petri dishes is a challenging problem because there are several problems hindering detection such as occlusions at the plate edges, dirt accumulation, and worm aggregations. Moreover, determining wh...
Fast deep neural correspondence for tracking and identifying neurons in using semi-synthetic training.
eLife
Jul 14, 2021
We present an automated method to track and identify neurons in , called 'fast Deep Neural Correspondence' or fDNC, based on the transformer network architecture. The model is trained once on empirically derived semi-synthetic data and then predicts ...
Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity.
Nature communications
Jun 10, 2021
In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LC) form toxic aggregates and amyloid fibrils in target organs. Prompt diagnosis is crucial to avoid permanent organ damage, but delayed diagnosis is common ...
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