scholarly journals A Universal Automated Data-Driven Modeling Framework for Truck Traffic Volume Prediction

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Amirsaman Mahdavian ◽  
Alireza Shojaei ◽  
Milad Salem ◽  
Haluk Laman ◽  
Naveen Eluru ◽  
...  
Keyword(s):  
Traffic Volume ◽  
Data Driven ◽  
Truck Traffic ◽  
Modeling Framework ◽  
Data Driven Modeling

scholarly journals Data-driven modeling predicts gene regulatory network dynamics during the differentiation of multipotential progenitors

2021 ◽  
Author(s):  
Joanna Elzbieta Handzlik ◽  
Manu Manu
Keyword(s):  
Regulatory Networks ◽  
Data Driven ◽  
High Temporal Resolution ◽  
Mouse Bone Marrow ◽  
Modeling Framework ◽  
Dynamical Modeling ◽  
Gene Regulatory ◽  
Data Driven Modeling ◽  
Neutrophil Differentiation

Cellular differentiation during hematopoiesis is guided by gene regulatory networks (GRNs) thought to be organized as a hierarchy of bistable switches, with antagonism between Gata1 and PU.1 driving red- and white-blood cell differentiation. We utilized high temporal-resolution gene-expression data from in vitro erythrocyte-neutrophil differentiation and a predictive data-driven dynamical modeling framework to learn the architecture and dynamics of gene regulation in a comprehensive twelve-gene GRN. The inferred genetic architecture is densely interconnected rather than hierarchical. The analysis of model dynamics revealed that neutrophil differentiation is driven by C/EBPα and Gfi1 rather than PU.1. This prediction is supported by the sequence of gene upregulation in an independent mouse bone marrow scRNA-Seq dataset. These results imply that neutrophil differentiation is not driven by the Gata1-PU.1 switch but by neutrophil-specific genes instead. More generally, this work shows that data-driven dynamical modeling can uncover the causality of developmental events that might otherwise be obscured.

scholarly journals Fine-tuned control of stress priming and thermotolerance

2021 ◽  
Author(s):  
Benjamin Pfeuty ◽  
Emmanuel Courtade ◽  
Quentin Thommen
Keyword(s):  
Heat Stress ◽  
Heat Shock ◽  
Competitive Advantage ◽  
Data Driven ◽  
Adaptation To Stress ◽  
Stress Exposure ◽  
Modeling Framework ◽  
Predictive Tool ◽  
Data Driven Modeling ◽  
Prior Stress

AbstractA common signature of cell adaptation to stress is the improved resistance upon priming by prior stress exposure. In the context of hyperthermia, priming or preconditioning with sublethal heat shock can be a useful tool to confer thermotolerance and competitive advantage to cells. In the present study, we develop a data-driven modeling framework that is simple and generic enough to capture a broad set of adaptation behaviors to heat stress at both molecular and cellular levels. The model recovers the main features of thermotolerance and clarifies the tradeoff principles which maximize the thermotolerance effect. It therefore provides an effective predictive tool to design preconditioning and fractionation hyperthermia protocols for therapeutic purpose.

scholarly journals Are college campuses superspreaders? A data-driven modeling study

2021 ◽  
pp. 1-11
Author(s):  
Hannah Lu ◽  
Cortney Weintz ◽  
Joseph Pace ◽  
Dhiraj Indana ◽  
Kevin Linka ◽  
...  
Keyword(s):  
College Campuses ◽  
Data Driven ◽  
Data Driven Modeling ◽  
Modeling Study
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