AIMC Topic: Animals

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A computational framework for optimal control of a self-adjustive neural system with activity-dependent and homeostatic plasticity.

NeuroImage
The control of the brain system has received increasing attention in the domain of brain science. Most brain control studies have been conducted to explore the brain network's graph-theoretic properties or to produce the desired state based on neural...

SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.

International journal of molecular sciences
Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently show...

DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection.

Communications biology
In general, animal behavior can be described as the neuronal-driven sequence of reoccurring postures through time. Most of the available current technologies focus on offline pose estimation with high spatiotemporal resolution. However, to correlate ...

Further evaluation and validation of the VETSCAN IMAGYST: in-clinic feline and canine fecal parasite detection system integrated with a deep learning algorithm.

Parasites & vectors
BACKGROUND: Fecal examinations in pet cats and dogs are key components of routine veterinary practice; however, their accuracy is influenced by diagnostic methodologies and the experience level of personnel performing the tests. The VETSCAN IMAGYST s...

Machine learning approaches reveal subtle differences in breathing and sleep fragmentation in -derived astrocytes ablated mice.

Journal of neurophysiology
Modern neurophysiology research requires the interrogation of high-dimensionality data sets. Machine learning and artificial intelligence (ML/AI) workflows have permeated into nearly all aspects of daily life in the developed world but have not been ...

Using machine learning improves predictions of herd-level bovine tuberculosis breakdowns in Great Britain.

Scientific reports
In the United Kingdom, despite decades of control efforts, bovine tuberculosis (bTB) has not been controlled and currently costs ~ £100 m annually. Critical in the failure of control efforts has been the lack of a sufficiently sensitive diagnostic te...

Denoising non-steady state dynamic PET data using a feed-forward neural network.

Physics in medicine and biology
The quality of reconstructed dynamic PET images, as well as the statistical reliability of the estimated pharmacokinetic parameters is often compromised by high levels of statistical noise, particularly at the voxel level. Many denoising strategies h...

Sparse deep predictive coding captures contour integration capabilities of the early visual system.

PLoS computational biology
Both neurophysiological and psychophysical experiments have pointed out the crucial role of recurrent and feedback connections to process context-dependent information in the early visual cortex. While numerous models have accounted for feedback effe...

Integrated functional neuronal network analysis of 3D silk-collagen scaffold-based mouse cortical culture.

STAR protocols
Bioengineered 3D tunable neuronal constructs are a versatile platform for studying neuronal network functions, offering numerous advantages over existing technologies and providing for the discovery of new biological insights. Functional neural netwo...

Exploring convolutional neural networks and spatial video for on-the-ground mapping in informal settlements.

International journal of health geographics
BACKGROUND: The health burden in developing world informal settlements often coincides with a lack of spatial data that could be used to guide intervention strategies. Spatial video (SV) has proven to be a useful tool to collect environmental and soc...