The mutual dependencies of many plant species and their pollinators mean that the negative effects of climate change are exacerbated. As UZH researchers show, the total number of species threatened with extinction is therefore considerably higher than predicted in previous models.

The mutual dependencies of many plant species and their pollinators mean that the negative effects of climate change are exacerbated. As UZH researchers show, the total number of species threatened with extinction is therefore considerably higher than predicted in previous models.

Global climate change is threatening biodiversity. To predict the fate of species, ecologists use climatic models that consider individual species in isolation. This type of model, however, overlooks the fact that species are part of a giant network of mutual dependencies: For example, plants need insects to disperse their pollen and, in turn, insects depend on plants for food.

Seven pollination networks in Europe investigated

These types of mutually beneficial interactions have been very important in generating the diversity of life on Earth. But the interaction also has a negative knock-on effect when the extinction of one species causes other species that are dependent on it to also die out, an effect that is called co-extinction. Evolutionary biologists at the University of Zurich, together with ecologists from Spain, Great Britain and Chile, have now quantified how much more of an impact climate change has on biodiversity when these mutual dependencies between the species are taken into account. To this end, the researcher team analyzed the networks between flowering plants and their insect pollinators in seven different regions of Europe.


Extinction of the rock rose means the myrtle is also under threat

First author Jordi Bascompte gives a specific example to illustrate the results of the study: "In one of the networks situated in southern Spain, the sage-leaved rock rose has a 52 percent predicted probability of extinction caused by climate change in 2080. Should this happen, one of its pollinators, the small carpenter bee, would face a risk of co-extinction as a consequence of losing one of the resources it depends upon. Because the small carpenter bee also pollinates the myrtle, the latter is also under threat of extinction." Thus while the predicted extinction risk of the myrtle considered in isolation is 38 percent, the risk rises to around 62 percent when taking into account the network of interactions.

"If the interactions of individual species are also considered, the total number of species threatened with extinction rises," summarizes Bascompte. "Some species with a very low likelihood of climate-related extinction according to the traditional model are at high risk of extinction due to their dependencies."

Optical illusions reveal that regular waves of brain activity enable visual feature integration

Optical illusions reveal that regular waves of brain activity enable visual feature integration

Rhythmic waves of brain activity cause people to see or not see complex images that flash before our eyes. An image can become practically invisible if it flashes before our eyes at the same time as a low point of those brain waves. We can reset that brain wave rhythm with a simple voluntary action, like choosing to push a button.

The new results come from experts who use optical illusions to understand human vision. Human sight involves activity both in the eyes and in the brain. Optical illusions are formed by playing tricks on any part of our complex visual system.

"This is the first record of rhythmic brain activity used to achieve integrated visual perception," said Associate Professor Isamu Motoyoshi from the University of Tokyo and co-author of the recent research article.

NIH and radiology societies map path for translational research on AI in medical imaging Clinical emphasis rounds out foundational research focus of earlier report.

NIH and radiology societies map path for translational research on AI in medical imagingClinical emphasis rounds out foundational research focus of earlier report.

A new report, with contributions from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), part of the National Institutes of Health, provides a roadmap for translational research on artificial intelligence (AI) in medical imaging. The report, published in the May 28, 2019, Journal of the American College of Radiology, identifies research priorities that leverage big data, the cloud, and machine learning for augmenting clinicians’ image planning and use to make diagnoses or assess patients’ responses to therapy.

This report and a companion report published last month summarize conclusions from an August 2018 workshop co-organized by NIH, the Radiologic American College of Radiology (ACR), the Radiological Society of North America (RSNA), and The Academy for Radiology and Biomedical Imaging Research. The first report published April 16, 2019, maps a path forward for foundational research in AI and this second report focuses on translational research necessary to deliver AI to clinical practice.

“Radiology has transformed the practice of medicine in the past century, and AI has the potential to radically impact radiology in positive ways,” said Krishna Kandarpa, M.D., Ph.D., co-author of the report and director of research sciences and strategic directions at NIBIB. “This roadmap is a timely survey and analysis by experts at federal agencies and among our industry and professional societies that will help us take the best advantage of AI technologies as they impact the medical imaging field.”

The companion reports — co-authored by government, industry, academia and radiology specialty society leaders — have identified and prioritized initiatives to accelerate foundational and translational research in AI for medical imaging.

“This new roadmap paper gives guidance for the clinical translation of AI innovation,” said Curtis P. Langlotz, M.D., Ph.D., report co-author and RSNA Board Liaison for Information Technology and Annual Meeting. “Together, these two connected roadmaps show us how AI not only will transform the work of radiologists and other medical imagers, but also will enhance the delivery of care throughout the clinical environment.”

The authors suggest that the application of AI can impact the entire radiology process, from the clinical decision to perform diagnostic imaging, to preparation of patients for procedures, to conducting the scan, to interpretation of imaging results, and finally to the management of workflow in radiology departments. While most attention is drawn to the power of computers to aid in interpreting images, there is ongoing research on AI applications that address every aspect of the imaging process, according to the report. AI tools considered by this roadmap are algorithms for disease detection and classification, image optimization, radiation reduction, and workflow enhancement. The authors suggest that radiologists should take the lead in identifying the most important areas for AI development.

The authors identified key priorities:

• Structured AI use cases. In software development, use cases define who will use a system and for what specific goal. AI use cases should define and highlight clinical challenges potentially solvable by AI.
• Data sharing. Researchers should establish methods to encourage data sharing for training and testing AI algorithms to promote generalizability to widespread clinical practice and minimize unintended bias.
• Tools for validation and performance monitoring of AI algorithms to facilitate regulatory approval.
• Standards and common data elements for seamless integration of AI tools into existing clinical workflows.

“Although advances in foundational research are occurring rapidly, translation to routine clinical practice has been slower because we must ensure AI in medical imaging is useful, safe, effective, and easily integrated into existing radiology workflows before they can be used in routine patient care,” said Bibb Allen, M.D., report co-author and chief medical officer of the ACR Data Science Institute.

The report establishes that an important goal of the resulting roadmap is to grow an ecosystem—facilitated by professional societies, industry, and government agencies—that will allow robust collaborations between practicing clinicians and AI researchers to advance foundational and translational research relevant to medical imaging.

“This NIBIB sponsored workshop was an important step in coordinating private and government efforts related to AI implementation in medical imaging,” said Mitchell Schnall, M.D. Ph.D., Eugene P. Pendergrass Professor & Chair of Radiology, University of Pennsylvania, Philadelphia; vice president & DxCP Task Force Chair, Academy of Radiology & Biomedical Imaging Research. “It will take a true public-private partnership to realize the tremendous potential contribution of AI to transform medical imaging, and this roadmap is the first step in that direction.”

About the National Institute of Biomedical Imaging and Bioengineering:  NIBIB’s mission is to improve health by leading the development and accelerating the application of biomedical technologies. The Institute is committed to integrating the physical and engineering sciences with the life sciences to advance basic research and medical care. NIBIB supports emerging technology research and development within its internal laboratories and through grants, collaborations, and training. More information is available at the NIBIB website: https://www.nibib.nih.gov.

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.