AIMC Topic: Animals

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Fluo-Cast-Bright: a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live oocytes.

Communications biology
In mammalian oocytes, large-scale chromatin organization regulates transcription, nuclear architecture, and maintenance of chromosome stability in preparation for meiosis onset. Pre-ovulatory oocytes with distinct chromatin configurations exhibit pro...

Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.

Journal of neural engineering
Creating an intracortical brain computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI de...

Tailless control of a four-winged flapping-wing micro air vehicle with wing twist modulation.

Bioinspiration & biomimetics
This paper describes the tailless control system design of a flapping-wing micro air vehicle in a four-winged configuration, which can provide high control authority to be stable and agile in flight conditions from hovering to maneuvering flights. Th...

Optimizing gelation time for cell shape control through active learning.

Soft matter
Hydrogels are popular platforms for cell encapsulation in biomedicine and tissue engineering due to their soft, porous structures, high water content, and excellent tunability. Recent studies highlight that the timing of network formation can be just...

Leveraging machine learning to uncover multi-pathogen infection dynamics across co-distributed frog families.

PeerJ
BACKGROUND: Amphibians are experiencing substantial declines attributed to emerging pathogens. Efforts to understand what drives patterns of pathogen prevalence and differential responses among species are challenging because numerous factors related...

The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis.

PLoS computational biology
Theoretical neuroscientists and machine learning researchers have proposed a variety of learning rules to enable artificial neural networks to effectively perform both supervised and unsupervised learning tasks. It is not always clear, however, how t...

Machine learning reveals the dynamic importance of accessory sequences for outbreak clustering.

mBio
UNLABELLED: Bacterial typing at whole-genome scales is now feasible owing to decreasing costs in high-throughput sequencing and the recent advances in computation. The unprecedented resolution of whole-genome typing is achieved by genotyping the vari...

Machine-Learning-Based Activity Tracking for Individual Pig Monitoring in Experimental Facilities for Improved Animal Welfare in Research.

Sensors (Basel, Switzerland)
In experimental research, animal welfare should always be of the highest priority. Currently, physical in-person observations are the standard. This is time-consuming, and results are subjective. Video-based machine learning models for monitoring exp...

Marigold: a machine learning-based web app for zebrafish pose tracking.

BMC bioinformatics
BACKGROUND: High-throughput behavioral analysis is important for drug discovery, toxicological studies, and the modeling of neurological disorders such as autism and epilepsy. Zebrafish embryos and larvae are ideal for such applications because they ...

Recurrent neural networks with transient trajectory explain working memory encoding mechanisms.

Communications biology
Whether working memory (WM) is encoded by persistent activity using attractors or by dynamic activity using transient trajectories has been debated for decades in both experimental and modeling studies, and a consensus has not been reached. Even thou...