AIMC Topic: Machine Learning

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Machine learning discriminates a movement disorder in a zebrafish model of Parkinson's disease.

Disease models & mechanisms
Animal models of human disease provide an system that can reveal molecular mechanisms by which mutations cause pathology, and, moreover, have the potential to provide a valuable tool for drug development. Here, we have developed a zebrafish model of...

Detecting and Responding to Information Overload With an Adaptive User Interface.

Human factors
OBJECTIVE: The objective of this study was to develop and evaluate an adaptive user interface that could detect states of operator information overload and calibrate the amount of information on the screen.

An Investigation of Speech Features, Plant System Alarms, and Operator-System Interaction for the Classification of Operator Cognitive Workload During Dynamic Work.

Human factors
OBJECTIVE: To investigate speech features, human-machine alarms, and operator-system interaction for the estimation of cognitive workload in full-scale realistic simulated scenarios.

Artificial Intelligence (AI) to the Rescue: Deploying Machine Learning to Bridge the Biorelevance Gap in Antioxidant Assays.

SLAS technology
Oxidative stress induced by excessive levels of reactive oxygen species (ROS) underlies several diseases. Therapeutic strategies to combat oxidative damage are, therefore, a subject of intense scientific investigation to prevent and treat such diseas...

Machine Learning-Guided Three-Dimensional Printing of Tissue Engineering Scaffolds.

Tissue engineering. Part A
Various material compositions have been successfully used in 3D printing with promising applications as scaffolds in tissue engineering. However, identifying suitable printing conditions for new materials requires extensive experimentation in a time ...

Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms.

Scientific reports
Nondestructive techniques for estimating nitrogen (N) status are essential tools for optimizing N fertilization input and reducing the environmental impact of agricultural N management, especially in green tea cultivation, which is notably problemati...

Topaz-Denoise: general deep denoising models for cryoEM and cryoET.

Nature communications
Cryo-electron microscopy (cryoEM) is becoming the preferred method for resolving protein structures. Low signal-to-noise ratio (SNR) in cryoEM images reduces the confidence and throughput of structure determination during several steps of data proces...

Dyspnea, effort and muscle pain during exercise in lung transplant recipients: an analysis of their association with cardiopulmonary function parameters using machine learning.

Respiratory research
BACKGROUND: Despite improvement in lung function, most lung transplant (LTx) recipients show an unexpectedly reduced exercise capacity that could be explained by persisting peripheral muscle dysfunction of multifactorial origin. We analyzed the cours...

An evaluation of DistillerSR's machine learning-based prioritization tool for title/abstract screening - impact on reviewer-relevant outcomes.

BMC medical research methodology
BACKGROUND: Systematic reviews often require substantial resources, partially due to the large number of records identified during searching. Although artificial intelligence may not be ready to fully replace human reviewers, it may accelerate and re...

Extracting medication information from unstructured public health data: a demonstration on data from population-based and tertiary-based samples.

BMC medical research methodology
BACKGROUND: Unstructured data from clinical epidemiological studies can be valuable and easy to obtain. However, it requires further extraction and processing for data analysis. Doing this manually is labor-intensive, slow and subject to error. In th...