Month: March 2020

Umeå University in Sweden is leading a team of researchers across Europe in the development of a coronavirus simulation framework that can support decision makers to experiment and evaluate possible interventions and their combined effects, in a simulated controlled world.

“No one can predict the future but with the ASSOCC framework you can accurately explore a wide range of possible scenarios, gain an understanding of the connections between health, economy and well-being, and therefore be better prepared to take decisions on the policies to implement,” says Frank Dignum, team leader and professor at the Department of Computing science at Umeå University in Sweden.

The coronavirus pandemic is the biggest crisis of our time. In their efforts to limit the spread of the virus, decision makers are struggling to balance their responses to the health situation with the needs of societies and economies. The interactions are complex and contextual and short-term steps can have large long-term consequences.

“We have developed a few initial scenarios based on theoretical models of epidemics, social behaviour and economics as an illustration of the framework. We invite decision makers and researchers to apply ASSOCC to their specific questions and empirical data,” says Frank Dignum.

“We have gathered a group of experienced researchers from all over Europe to work on this project. All are contributing their time and expertise voluntarily and without any funding. It is heart-warming to see the commitment and results from all team members who are working day and night since two weeks,” continues Frank Dignum.

At this moment, the team is already applying the framework to specific questions from the UK, Australia and the Netherlands. At the same time, they are working on the release

An estimated 18,000 people in the United States will die of brain and spinal cord tumors in 2020. To help doctors differentiate between the severity of cancers in the brain, an international team of researchers led by Dr. Murat Günel, Chair of Neurosurgery at Yale School of Medicine, and Nixdorff-German Professor of Neurosurgery, built a machine learning model that uses complex mathematics to learn how various types of brain tumors look in the brain. The model is designed to “learn” from this gathered data to make predictions and help doctors diagnose the stage of brain cancers faster and more accurately.

To test their artificial learning method, the team used 229 patients with brain tumors along a spectrum of how likely they are to become malignant from lower-grade gliomas, which are relatively slow-growing tumors that originate from glial cells of the brain—to glioblastomas, the highly aggressive counterpart to gliomas.

“Our machine learning models used to differentiate the tumor types were very accurate,” said Hang Cao, a medical student from Xiangya Hospital working with Dr. Gunel, and the lead author of the study published in European Radiology.

The researchers compiled data from a public tumor machine resonance imaging (MRI) database called The Cancer Imaging Archive. Board-certified neuro-radiologists then identified and selected glioma cases, which the researchers used for their model.

The team found significant differences in how the cancers looked, their volumes in various regions of the brain, and their locations. When taken together, the model could predict which tumors were lower-grade gliomas or glioblastomas with a high degree of accuracy.

The timeline for using such a model in a clinical setting is not known at this time. Although it would be possible to implement now as a stand-alone evaluation, the process is not yet integrated into