Tyler O'Neal, Staff Editor ECONOMICS

Where do baby sea turtles go? New supercomputing technique may provide answers

The international group of scientists worked on the model, which will give communities, scientists and government agencies a tool to help conserve sea turtles.

A team of Florida researchers and their collaborators created a first-of-its-kind supercomputer model that tracks where sea turtle hatchlings go after they leave Florida’s shores, giving scientists a new tool to figure out where young turtles spend their “lost years.”

Nathan Putman, a biologist with LGL Ecological Research Assoc. based in Texas, led the study, which included 22 collaborators across Mexico, the southeastern United States, the Caribbean, and Europe. Co-authors include UCF Associate Professor Kate Mansfield, who leads UCF’s Marine Turtle Research Group, and UCF assistant research scientist Erin Seney.

“The model gives community groups, scientists, nonprofit agencies and governments across borders a tool to help inform conservation efforts and guide policies to protect sea turtle species and balance the needs of fisheries and other human activity,” Putman said. The team’s simulation model and findings were published this week in the online journal Ecography.

The model is built to predict loggerhead, green turtle and Kemp’s ridley abundance, according to the authors. To create the model, the team looked at ocean circulation data over the past 30 years. These data are known to be reliable and routinely used by the National Ocean and Atmospheric Administration and other agencies. The team also used sea turtle nesting and stranding data from various sources along the Caribbean, Gulf of Mexico and Florida coasts. The dataset includes more than 30 years of information from UCF, which has been monitoring sea turtle nests in east Central Florida since the late 1970s. Mansfield, Seney, and Putman previously worked together on other sea turtle studies in the Gulf of Mexico. A sea turtle hatchling heads to the ocean. CREDIT: G.Stahelin{module INSIDE STORY}

“The combination of big data is what made this supercomputer model so robust, reliable and powerful,” Putman said.

The group used U.S. and Mexico stranding data—information about where sea turtles washed ashore for a variety of reasons—to check if the supercomputer model was accurate, Putman said. The model also accounts for hurricanes and their impact on the ocean, but it does not take into consideration manmade threats such as the 2010 Deepwater Horizon oil spill in the Gulf of Mexico, which occurred during the years analyzed in the study.

The supercomputer model also predicts where the turtles go during their “lost years” – a period after the turtles break free from their eggs on the shoreline and head into the ocean in the Gulf of Mexico and northwest Atlantic. The turtles spend years among sargassum in the ocean, and any data about that time is scarce. Better data exist when they are larger juveniles and return to forage closer to coastlines. What young sea turtles do in between hatching and returned to nearshore waters takes place during what is called the “lost years” and is the foundation of sea turtle populations. Understanding where and when the youngest sea turtles go is critical to understanding the threats these young turtles may encounter, and for better-predicting population trends throughout the long lives of these species, said Mansfield.

This work was supported in part by a National Academy of Sciences gulf research program grant awarded to Mansfield, Seney and Putman to synthesize available sea turtle datasets across the Gulf of Mexico.

“While localized data collection and research projects are important for understanding species’ biology, health and ecology, the turtles studied in one location typically spend different parts of their lives in other places, including migrations from offshore to inshore waters, from juvenile to adult foraging grounds, and between foraging and nesting areas,” said Seney, who helped coordinate data compilation from the multiple locations. “Our extensive collaborations on this project allowed us to study the Gulf of Mexico’s three most abundant sea turtle species and to integrate nesting beach data for distant nesting populations that ended up having close connections to the 1- to 3-year-old turtles living and stranding along various portions of the U.S. Gulf Coast. Without the involvement of our Mexican and Costa Rican collaborators, a big piece of this picture would have been missing.”

This work was funded with support from the Gulf Research Program of the National Academy of Sciences under award number 2000006434 and from a Florida RESTORE Act Centers of Excellence Grant through the Florida Institute of Oceanography under sub‐agreement no. 4710‐1126‐00‐H. The content is the sole responsibility of the authors and does not necessarily reflect the views of the Gulf Research Program, the National Academy of Sciences or the Florida Institute of Oceanography.

German built artificial intelligence tracks down leukemia

Tyler O'Neal, Staff Editor ECONOMICS

Largest metastudy to date on acute myeloid leukemia

Artificial intelligence can detect one of the most common forms of blood cancer - acute myeloid leukemia (AML) - with high reliability. Researchers at the German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn have now shown this in a proof-of-concept study. Their approach is based on the analysis of the gene activity of cells found in the blood. Used in practice, this approach could support conventional diagnostics and possibly accelerate the beginning of therapy. The research results have been published in the journal "iScience."

Artificial intelligence is a much-discussed topic in medicine, especially in the field of diagnostics. "We aimed to investigate the potential on the basis of a specific example," explains Prof. Joachim Schultze, a research group leader at the DZNE and head of the Department for Genomics and Immunoregulation at the LIMES Institute of the University of Bonn. "Because this requires large amounts of data, we evaluated data on the gene activity of blood cells. Numerous studies have been carried out on this topic and the results are available through databases. Thus, there is an enormous data pool. We have collected virtually everything that is currently available." {module INSIDE STORY}

Fingerprint of Gene Activity

Schultze and his colleagues focused on the "transcriptome", which is a kind of fingerprint of gene activity. In each and every cell, depending on its condition, only certain genes are actually "switched on", which is reflected in their profiles of gene activity. Exactly such data - derived from cells in blood samples and spanning many thousands of genes - were analysed in the current study. "The transcriptome holds important information about the condition of cells. However, classical diagnostics is based on different data. We therefore wanted to find out what an analysis of the transcriptome can achieve using artificial intelligence, that is to say trainable algorithms," said Schultze, who is member of the Bonn-based "ImmunoSensation" cluster of excellence. "In the long term, we intend to apply this approach to further topics, in particular in the field of dementia."

The current study focused on AML. Without adequate treatment, this form of leukemia leads to death within weeks. AML is associated with the proliferation of pathologically altered bone marrow cells, which can ultimately enter the bloodstream. Ultimately both healthy cells and tumor cells drift in the blood. All these cells exhibit typical gene activity patterns, which were all considered in the analysis. Data from more than 12,000 blood samples - these came from 105 different studies - were taken into account: the largest dataset to date for a metastudy on AML. Approximately 4,100 of these blood samples derived from individuals diagnosed with AML, the remaining ones had been taken from individuals with other diseases or from healthy individuals.

High Hit Rate

The scientists fed their algorithms parts of this data set. The input included information about whether a sample came from an AML patient or not. "The algorithms then searched the transcriptome for disease-specific patterns. This is a largely automated process. It's called machine learning," said Schultze. Based on this pattern recognition, further data was analysed and classified by the algorithms, i.e. categorized into samples with AML and without AML. "Of course, we knew the classification as it was listed in the original data, but the software did not. We then checked the hit rate. It was above 99 percent for some of the applied methods. In fact, we tested various methods from the repertoire of machine learning and artificial intelligence. There was actually one algorithm that was particularly good, but the others were close behind."

Application in Practice?

Put into application, this method could support conventional diagnostics and help save costs, said Schultze. "In principle, a blood sample taken by the family doctor and sent to a laboratory for analysis could suffice. I guess that the cost would be less than 50 euros." Classical AML diagnostics includes a variety of methods. Some of these cost a few hundred euros per run, Schultze noted. "However, we have not yet developed a workable test. We have only shown that the approach works in principle. So we have laid the groundwork for developing a test."

Schultze emphasised that the diagnosis of AML will continue to require specialised physicians in the future. "The aim is to provide the experts with a tool that supports them in their diagnosis. In addition, many patients go through a real odyssey until they finally end up with a specialist and get a diagnosis." Because in the early stages the symptoms of AML can resemble those of a bad cold. However, AML is a life-threatening disease that should be treated as quickly as possible. "With a blood test, as it seems possible on the basis of our study, it is conceivable that the family doctor would already clarify a suspicion of AML. And when the suspicion is confirmed, the patient is referred to a specialist. Possibly, the diagnosis would then happen earlier than it does now and therapy could start earlier."

National Academy of Medicine publishes special report on AI's future potential hinges on consensus

Tyler O'Neal, Staff Editor ECONOMICS

The role of artificial intelligence, or machine learning, will be pivotal as the industry wrestles with a gargantuan amount of data that could improve -- or muddle -- health and cost priorities, according to a National Academy of Medicine Special Publication on the use of AI in health care.

Yet, the current explosion of investment and development is happening without an underpinning of consensus of responsible, transparent deployment, which potentially constrains its potential.

The new report is designed to be a comprehensive reference for organizational leaders, health care professionals, data analysts, model developers and those who are working to integrate machine learning into health care, said Vanderbilt University Medical Center's Michael Matheny, MD, MS, MPH, Associate Professor in the Department of Biomedical Informatics, and co-editor of AI in Healthcare: The Hope, The Hype, The Promise, The Peril.

"It's critical for the health care community to learn from both the successes, but also the challenges and recent failures in the use of these tools. We set out to catalog important examples in health care AI, highlight best practices around AI development and implementation, and offer key points that need to be discussed for consensus to be achieved on how to address them as an AI community and society," said Matheny. AI and Health Care cover PREPUB FINAL scaled 990b0{module INSIDE STORY}

Matheny underscores the applications in health care look nothing like the mass-market imagery of self-driving cars that is often synonymous with machine learning or tech-driven systems.

For the immediate future, in health care, AI should be thought of as a tool to support and complement the decision-making of highly trained professionals in delivering care in collaboration with patients and their goals, Matheny said.

Recent advances in deep learning and related technologies have met with great success in imaging interpretations, such as radiology and retina exams, which have spurred a rush toward AI development that brought first, venture capital funding, and then industry giants. However, some of the tools have had problems with bias from the populations they were developed from, or from the choice of an inappropriate target. Data analysts and developers need to work toward increased data access and standardization as well as thoughtful development so algorithms aren't biased against already marginalized patients.

The editors hope this report can contribute to the dialog of patient inclusivity and fairness in the use of AI tools, and the need for careful development, implementation, and surveillance of them to optimize their chance of success, Matheny said.

Matheny along with Stanford University School of Medicine's Sonoo Thadaney Israni, MBA, and Mathematica Policy Research's Danielle Whicher, PhD, MS, penned an accompanying piece for JAMA Network about the watershed moment in which the industry finds itself.

"AI has the potential to revolutionize health care. However, as we move into a future supported by technology together, we must ensure high data quality standards, that equity and inclusivity are always prioritized, that transparency is use-case-specific, that new technologies are supported by appropriate and adequate education and training, and that all technologies are appropriately regulated and supported by specific and tailored legislation," the National Academy of Medicine wrote in a release.

"I want people to use this report as a foil to hone the national discourse on a few key areas including education, equity in AI, uses that support human cognition rather than replacing it, and separating out AI transparency into data, algorithmic, and performance transparency," said Matheny.