AIMC Topic: Kinetics

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Deep Learning Unravels Differences Between Kinematic and Kinetic Gait Cycle Time Series from Two Control Samples of Healthy Children Assessed in Two Different Gait Laboratories.

Sensors (Basel, Switzerland)
We investigate the application of deep learning in comparing gait cycle time series from two groups of healthy children, each assessed in different gait laboratories. Both laboratories used similar gait analysis protocols with minimal differences in ...

Combining Group Contribution Method and Semisupervised Learning to Build Machine Learning Models for Predicting Hydroxyl Radical Rate Constants of Water Contaminants.

Environmental science & technology
Machine learning is an effective tool for predicting reaction rate constants for many organic compounds with the hydroxyl radical (HO). Previously reported models have achieved relatively good performance, but due to scarce data (<1400 records), the ...

Integrating Chemical Mechanisms and Feature Engineering in Machine Learning Models: A Novel Approach to Analyzing HONO Budget.

Environmental science & technology
Nitrous acid (HONO) serves as the primary source of OH radicals in the atmosphere, exerting significant impacts on atmospheric secondary pollution. The heterogeneous reactions of NO on surfaces and photolysis of particulate nitrate or adsorbed nitric...

High-throughput Kinetics using capillary Electrophoresis and Robotics (HiKER) platform used to study T7, T3, and Sp6 RNA polymerase misincorporation.

PloS one
T7 RNA Polymerase (RNAP) is a widely used enzyme with recent applications in the production of RNA vaccines. For over 50 years denaturing sequencing gels have been used as key analysis tools for probing the nucleotide addition mechanisms of T7 RNAP a...

Design and synthesis of a new recyclable nanohydrogel based on chitosan for Deltamethrin removal from aqueous solutions: Optimization and modeling by RSM-ANN.

International journal of biological macromolecules
In this study, a new magnetic biocompatible hydrogel was synthesized as an adsorbent for Deltamethrin pesticide removal. The optimal conditions and adsorption process of Deltamethrin by chitosan/polyacrylic acid/FeO nanocomposite hydrogel was studied...

A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems.

Scientific reports
The use of time lapse systems (TLS) in In Vitro Fertilization (IVF) labs to record developing embryos has paved the way for deep-learning based computer vision algorithms to assist embryologists in their morphokinetic evaluation. Today, most of the l...

Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning.

Nature communications
mRNA degradation is a central process that affects all gene expression levels, though it remains challenging to predict the stability of a mRNA from its sequence, due to the many coupled interactions that control degradation rate. Here, we carried ou...

Biosorption of cobalt and chromium from wastewater using manganese dioxide and iron oxide nanoparticles loaded on cellulose-based biochar: Modeling and optimization with machine learning (artificial neural network).

International journal of biological macromolecules
In this study, two nanomaterials with excellent adsorption capacities were developed to remove heavy metals efficiently from wastewater. Manganese dioxide MnO nanoparticles and iron oxide FeO nanoparticles were successfully synthesized using cassava ...

Uncertainty Qualification for Deep Learning-Based Elementary Reaction Property Prediction.

Journal of chemical information and modeling
The prediction of the thermodynamic and kinetic properties of elementary reactions has shown rapid improvement due to the implementation of deep learning (DL) methods. While various studies have reported the success in predicting reaction properties,...

Association between embryo development and early pregnancy loss revealed by artificial-intelligence-annotated kinetic events.

Reproductive biomedicine online
RESEARCH QUESTION: Can artificial intelligence (AI)-powered annotation of numerous biological events help to uncover an association between embryonic kinetics and early pregnancy loss?