AIMC Topic: Time Factors

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Deep learning unlocks the true potential of organ donation after circulatory death with accurate prediction of time-to-death.

Scientific reports
Increasing the number of organ donations after circulatory death (DCD) has been identified as one of the most important ways of addressing the ongoing organ shortage. While recent technological advances in organ transplantation have increased their s...

Framingham Risk Score Prediction at 12 Months in the STANDFIRM Randomized Control Trial.

Journal of the American Heart Association
BACKGROUND: The STANDFIRM (Shared Team Approach Between Nurses and Doctors for Improved Risk Factor Management; ANZCTR registration ACTRN12608000166370) trial was designed to test the effectiveness of chronic disease care management for modifying the...

Depth-of-interaction encoding techniques for pixelated PET detectors enabled by machine learning methods and fast waveform digitization.

Physics in medicine and biology
. Pixelated detectors with single-ended readout are routinely used by commercial positron emission tomography scanners owing to their good energy and timing resolution and optimized manufacturing, but they typically do not provide depth-of-interactio...

Recognition of beef aging time using a miniaturized near-infrared spectrometer in tandem with support vector machine.

Food chemistry
Consumers increasingly demand sustainable production practices and high-quality standards. Near-infrared (NIR) spectroscopy presents a non-invasive and efficient tool for addressing these concerns. This study aimed to evaluate vacuum-aged beef across...

A fractional-order multi-delayed bicyclic crossed neural network: Stability, bifurcation, and numerical solution.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose a fractional-order bicyclic crossed neural network (NN) with multiple time delays consisting of two sharing neurons between rings. The given fractional-order NN is defined in terms of the Caputo fractional derivatives. We pr...

Source-free time series domain adaptation with wavelet-based multi-scale temporal imputation.

Neural networks : the official journal of the International Neural Network Society
Recent works on source-free domain adaptation (SFDA) for time series reveal the effectiveness of learning domain-invariant temporal dynamics on improving the cross-domain performance of the model. However, existing SFDA methods for time series mainly...

Improved prediction and risk stratification of major adverse cardiovascular events using an explainable machine learning approach combining plasma biomarkers and traditional risk factors.

Cardiovascular diabetology
BACKGROUND: Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality globally. Traditional risk models, primarily based on established risk factors, often lack the precision needed to accurately predict new-onset major advers...

Prediction of Chromatographic Retention Time of a Small Molecule from SMILES Representation Using a Hybrid Transformer-LSTM Model.

Journal of chemical information and modeling
Accurate retention time (RT) prediction in liquid chromatography remains a significant consideration in molecular analysis. In this study, we explore the use of a transformer-based language model to predict RTs by treating simplified molecular input ...

Stability of Jurkat cells during short-term liquid storage analyzed by flow imaging microscopy.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
The viability of cell-based medicinal products (CBMPs) is a critical quality attribute and must be assessed throughout the product lifecycle to contribute to a safe and potent drug product. In this study, we investigated the impact of short-term liqu...