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Computer Simulation

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The critical role of neutrophil-endothelial cell interactions in sepsis: new synergistic approaches employing organ-on-chip, omics, immune cell phenotyping and modeling to identify new therapeutics.

Frontiers in cellular and infection microbiology
Sepsis is a global health concern accounting for more than 1 in 5 deaths worldwide. Sepsis is now defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis can develop from bacterial (gram negative or g...

Reinforcement learning-based consensus control for MASs with intermittent constraints.

Neural networks : the official journal of the International Neural Network Society
In this article, an adaptive optimal consensus control problem is studied for multiagent systems in the strict-feedback structure with intermittent constraints (the constraints appear intermittently). More specifically, by designing a novel switch-li...

Counterfactual formulation of patient-specific root causes of disease.

Journal of biomedical informatics
OBJECTIVE: Root causes of disease intuitively correspond to root vertices of a causal model that increase the likelihood of a diagnosis. This description of a root cause nevertheless lacks the rigorous mathematical formulation needed for the developm...

Supervised diagnostic classification of cognitive attributes using data augmentation.

PloS one
Over recent decades, machine learning, an integral subfield of artificial intelligence, has revolutionized diverse sectors, enabling data-driven decisions with minimal human intervention. In particular, the field of educational assessment emerges as ...

Repeated Decision Stumping Distils Simple Rules from Single-Cell Data.

Journal of computational biology : a journal of computational molecular cell biology
Single-cell data afford unprecedented insights into molecular processes. But the complexity and size of these data sets have proved challenging and given rise to a large armory of statistical and machine learning approaches. The majority of approache...

The emergence of machine learning force fields in drug design.

Medicinal research reviews
In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, ma...

Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning.

IEEE transactions on medical imaging
Automated volumetric meshing of patient-specific heart geometry can help expedite various biomechanics studies, such as post-intervention stress estimation. Prior meshing techniques often neglect important modeling characteristics for successful down...

Deep residual networks for crystallography trained on synthetic data.

Acta crystallographica. Section D, Structural biology
The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and tra...

A hybrid deep learning approach to improve real-time effluent quality prediction in wastewater treatment plant.

Water research
Wastewater treatment plant (WWTP) operation is usually intricate due to large variations in influent characteristics and nonlinear sewage treatment processes. Effective modeling of WWTP effluent water quality can provide valuable decision-making supp...

Effect of Feedback Modality on Simulated Surgical Skills Learning Using Automated Educational Systems- A Four-Arm Randomized Control Trial.

Journal of surgical education
OBJECTIVE: To explore optimal feedback methodologies to enhance trainee skill acquisition in simulated surgical bimanual skills learning during brain tumor resections.