AIMC Topic: Animals

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Training deep neural density estimators to identify mechanistic models of neural dynamics.

eLife
Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challen...

Understanding mass spectrometry images: complexity to clarity with machine learning.

Biopolymers
The application of artificial intelligence and machine learning to hyperspectral mass spectrometry imaging (MSI) data has received considerable attention over recent years. Various methodologies have shown great promise in their ability to handle the...

Group-level patterns emerge from individual speed as revealed by an extremely social robotic fish.

Biology letters
Understanding the emergence of collective behaviour has long been a key research focus in the natural sciences. Besides the fundamental role of social interaction rules, a combination of theoretical and empirical work indicates individual speed may b...

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT.

Sensors (Basel, Switzerland)
Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control ...

Cross Lingual Sentiment Analysis: A Clustering-Based Bee Colony Instance Selection and Target-Based Feature Weighting Approach.

Sensors (Basel, Switzerland)
The lack of sentiment resources in poor resource languages poses challenges for the sentiment analysis in which machine learning is involved. Cross-lingual and semi-supervised learning approaches have been deployed to represent the most common ways t...

Deep Learning for Automated Liver Segmentation to Aid in the Study of Infectious Diseases in Nonhuman Primates.

Academic radiology
With the advent of deep learning, convolutional neural networks (CNNs) have evolved as an effective method for the automated segmentation of different tissues in medical image analysis. In certain infectious diseases, the liver is one of the more hig...

Computational methods and next-generation sequencing approaches to analyze epigenetics data: Profiling of methods and applications.

Methods (San Diego, Calif.)
Epigenetics is mainly comprised of features that regulate genomic interactions thereby playing a crucial role in a vast array of biological processes. Epigenetic mechanisms such as DNA methylation and histone modifications influence gene expression b...

Neurodynamic modeling of the fruit fly Drosophila melanogaster.

Bioinspiration & biomimetics
This manuscript describes neuromechanical modeling of the fruit fly Drosophila melanogaster in the form of a hexapod robot, Drosophibot, and an accompanying dynamic simulation. Drosophibot is a testbed for real-time dynamical neural controllers model...

Sensitivity analysis based on the random forest machine learning algorithm identifies candidate genes for regulation of innate and adaptive immune response of chicken.

Poultry science
Two categories of immune responses-innate and adaptive immunity-have both polygenic backgrounds and a significant environmental component. The goal of the reported study was to define candidate genes and mutations for the immune traits of interest in...

Myelin detection in fluorescence microscopy images using machine learning.

Journal of neuroscience methods
BACKGROUND: The myelin sheath produced by glial cells insulates the axons, and supports the function of the nervous system. Myelin sheath degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS). There are no therapies for MS ...