Friday, February 06, 2026

The internet names a new deep-sea species, Senckenberg researchers select a scientific name from over 8,000 suggestions.

Pensoft Publishers

Habitus of the newly found deep-sea chiton Ferreiraella populi — image:
Habitus of the newly found deep-sea chiton *Ferreiraella populi*
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Credit: © Senckenberg Ocean Species Alliance

The Senckenberg Ocean Species Alliance (SOSA), in partnership with the scientific publisher Pensoft Publishers and famous science YouTuber Ze Frank, have let the Internet name a newly discovered deep-sea chiton (a type of marine mollusk). The formal description of the species was published today in the open-access Biodiversity Data Journal.

From over 8,000 name suggestions submitted via social media, the research team responsible for describing the species selected the name Ferreiraella populi. The specific epithet populi is a Latin singular noun in the genitive case meaning “of the people”. Curiously, the name was independently suggested by 11 different contributors during the naming contest.

From a YouTube video to Taxonomy

It all began when Ze Frank featured the rare deep-sea chiton (genus Ferreiraella) in an episode of his "True Facts" YouTube series.

Equipped with an iron-clad radula (a rasping tongue) and eight protective shell plates, the chiton also hosts a tiny community of worms near its tail that feed on its excrements. Everyone was invited to propose a scientific name and justification; within a week the community responded with over 8,000 name suggestions.

“We were overwhelmed by the response and the massive number of creative name suggestions!” says Prof. Dr. Julia Sigwart, co-chair of SOSA at the Senckenberg Research Institute and Natural History Museum Frankfurt. “The name we chose, Ferreiraella populi, translates to “of the people”.

Other notable suggestions included Ferreiraella stellacadens meaning “Shooting star chiton” - named for its unique aesthete pattern and the fact that it “shot to fame” during the selection process. Another was Fereiraella ohmu in reference to a chiton-like creature from Studio Ghibli, providing a nod to Japan, where the species was discovered.

A Specialized Resident of the Deep

Originally discovered in 2024 within the Izu-Ogasawara Trench at a depth of 5,500 meters, the new deep-sea chiton Ferreiraella populi belongs to the genus Ferreiraella, a rare and specialized group of mollusks that live exclusively on sunken wood in the deep sea.

The new species represents an addition to a lineage of chitons that has been little researched to date and provides further evidence that deep-sea wood-fall ecosystems host highly specialized and still largely undiscovered communities,
- explains Sigwart.

Chitons are often described as a cross between a snail and a beetle. However, unlike common mollusks with a single shell, chitons possess eight separate shell plates (valves). This unique anatomy allows them to roll into a protective ball or cling to the irregular surfaces of deep-sea wood-falls. Found in both warm coastal waters and coral reefs as well as the deep sea, chitons can live at depths of up to 7,000 meters under extreme conditions and in absolute darkness.

How is a scientific name formed?

Ever wonder how a creature goes from "that deep-sea thing" to a formal scientific entry? Every newly discovered species is assigned a scientific name as part of its original taxonomic description. This follows Carl Linnaeus’s principle of binomial nomenclature and consists of two parts: the genus name (the first part, capitalized and italicised) and the specific epithet (the second part, lowercase and italicised). The name is assigned by the author(s) of the first description in a scientific publication, adhering to international codes such as the ICZN (zoology) or the ICN (botany). The name must be novel, unique, and latinized. Usually, epithets are often derived from characteristics like color or size, geographic locations, mythology, or personal names used to honor a specific individual.

Ferreiraella populi exemplifies the overwhelming biodiversity of the oceans, the vast majority of which remains unexplored. Many species go extinct before we even know they exist - this is especially true for marine invertebrates,
- says Sigwart.

“It can often take ten, if not twenty years, for a new species to be studied, scientifically described, named, and published. At SOSA, we have therefore made it our mission to streamline these processes while simultaneously engaging the public with these fascinating creatures. Finding a name for the chiton together on social media is a wonderful opportunity to do just that! Ferreiraella populi has now been described and given a scientific name only two years after its discovery. This is crucial for the conservation of marine diversity, especially in light of the threats it faces such as deep-sea mining!”

Research paper:

(SOSA) SOSA, Chen C, Frank H, Kraniotis L, Nakadera Y, Schwabe E, Sigwart JD, Trautwein B, Vončina K (2026) Ocean Species Discoveries 28–30 — new species of chitons (Mollusca, Polyplacophora) and a public naming competition. Biodiversity Data Journal 14: e180491. https://doi.org/10.3897/BDJ.14.e180491

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About The Senckenberg Society for Nature Research

The Senckenberg Society for Nature Research is an institution of the Leibniz Association and has been researching the “Earth System” worldwide for over 200 years-investigating the past, the present, and providing projections for the future. We conduct integrative “geobiodiversity research” with the goal of understanding nature and its infinite diversity in order to preserve it as the basis of life for future generations and to use it sustainably.

In addition, Senckenberg communicates research findings in a variety of ways, primarily through its three natural history museums in Frankfurt, Görlitz, and Dresden. The Senckenberg Natural History Museums are places of learning and wonder, serving as open platforms for the dialogue between science and society-inclusive, participatory, and international. More information is available at www.senckenberg.de.

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About True Facts

Launched in December 2012, the True Facts series is an ongoing educational comedy project led by Ze Frank. The series utilizes a distinct mockumentary format, characterized by fast-paced narration and comedic style. By pairing bizarre wildlife trivia with legitimate scientific facts, Ze Frank has created a unique brand of combining education and entertainment that explores the eccentricities of the natural world.

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About Pensoft Publishers

Founded in 1992 “by scientists, for scientists”, the academic open-access publishing company is well known worldwide for its novel cutting-edge publishing tools, workflows and methods for text and data publishing of journals, books and conference materials. Back in 2010, Pensoft became the first scientific publisher to introduce semantic enrichments in scholarly publications. Through its Research and Technical Development department, the company is involved in various research and technology projects.

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About Biodiversity Data Journal

The Biodiversity Data Journal (BDJ) is an open-access, community-reviewed platform designed to accelerate the publication and sharing of all biodiversity-related data, regardless of scale or taxon. BDJ ensures that everything from ancient fossils to modern genomes is instantly accessible and machine-readable for the global scientific community, turning static research into a dynamic, reusable resource for the future of biology.

Journal

Biodiversity Data Journal

DOI

10.3897/BDJ.14.e180491

Article Title

Ocean Species Discoveries 28–30 — new species of chitons (Mollusca, Polyplacophora) and a public naming competition

Article Publication Date

6-Feb-2026

Deep-sea sample extraction [VIDEO]

Ferreiraella populi in its natural habitat [VIDEO]

Ferreiraella populi on woodfall

Two individuals of the newly described deep-sea chiton on woodfall.

Credit

© Chong Chen/JAMSTEC

Close up photo of the worms on the tail valve of Ferreiraella populiCredit

AI model can read and diagnose a brain MRI in seconds

Researchers say the technology has potential applications beyond neurological diagnoses

Michigan Medicine - University of Michigan

An AI-powered model developed at University of Michigan can read a brain MRI and diagnose a person in seconds, a study suggests.

The model detected neurological conditions with up to 97.5% accuracy and predicted how urgently a patient required treatment.

Researchers say the first-of-its-kind technology could transform neuroimaging at health systems across the United States.

The results are published in Nature Biomedical Engineering.

“As the global demand for MRI rises and places significant strain our physicians and health systems, our AI model has potential to reduce burden by improving diagnosis and treatment with fast, accurate information,” said senior author Todd Hollon, M.D., a neurosurgeon at University of Michigan Health and assistant professor of neurosurgery at U-M Medical School.

Hollon calls the invention Prima. He and his research team tested the technology on more than 30,000 MRI studies over the course of a year.

Across more than 50 radiologic diagnoses from major neurological disorders, Prima outperformed other state-of-the-art AI models on diagnostic performance.

The model also succeeded in determining which cases should take higher priority.

Some neurological conditions, such as brain hemorrhages or strokes, require immediate medical attention. In such cases, Prima can automatically alert providers so rapid action can be taken, Hollon says.

Researchers designed the model to recommend which subspecialty provider should be alerted, such as a stroke neurologist or neurosurgeon, with feedback available immediately after a patient completes imaging.

“Accuracy is paramount when reading a brain MRI, but quick turnaround times are critical for timely diagnosis and improved outcomes,” said Yiwei Lyu, M.S., co-first author and postdoctoral fellow of Computer Science and Engineering at U-M.

“At key steps in the process, our results show how Prima can improve workflows and streamline clinical care without abandoning accuracy.”

What is Prima?

Prima is a vision language model (VLM), an AI system that can simultaneously process video, images and text in real time.

It’s not the first attempt to apply AI to MRI and other forms of neuroimaging, but the approach is unique.

Past models rely on manually curated subsets of MRI data to achieve specific tasks, such detecting lesions or predicting dementia risk.

When designing Prima, Hollon’s team trained the system on every MRI — over 200,000 studies and 5.6 million sequences — taken since radiology digitization began University of Michigan Health decades ago.

Researchers also input patients’ clinical histories and the physicians’ reasons for ordering medical imaging study into the model.

“Prima works like a radiologist by integrating information regarding the patient’s medical history and imaging data to produce a comprehensive understanding of their health,” said co-first author Samir Harake, a data scientist in Hollon’s Machine Learning in Neurosurgery Lab.

“This enables better performance across a broad range of prediction tasks.”

Millions of MRI studies are performed globally each year, with a significant portion focused on neurological diseases.

This demand, researchers say, outpaces the availability of neuroradiology services and leads to significant challenges, including workforce shortages and diagnostic errors.

Depending on where you get a scan, it can take days, or even longer, to get a result.

“Whether you are receiving a scan at a larger health system that is facing increasing volume or a rural hospital with limited resources, innovative technologies are needed to improve access to radiology services,” said Vikas Gulani, M.D. Ph.D., co-author and chair of the Department of Radiology at U-M Health.

“Our teams at University of Michigan have collaborated to develop a cutting-edge solution to this problem with tremendous, scalable potential.”

The future of AI and imaging

While Prima performed well, the research is in its initial stage of evaluation.

The research team’s future work will explore integrating more detailed patient information and electronic medical record data for more accurate diagnosis. This strategy closely emulates how radiologists and physicians interpret MRIs and other radiology studies.

Health care providers, systems and policymakers are still determining how to appropriately integrate artificial intelligence into practice, yet most systems currently used are for narrow medical tasks.

What Hollon describes as “ChatGPT for medical imaging” has broader potential — and could one day be adapted for other imaging modalities, such as mammograms, chest X-rays and ultrasounds.

“Like the way AI tools can help draft an email or provide recommendations, Prima aims to be a co-pilot for interpreting medical imaging studies,” Hollon said.

“We believe that Prima exemplifies the transformative potential of integrating health systems and AI-driven models to improve health care through innovation.”

Additional authors: Asadur Chowdury, M.S., Soumyanil Banerjee, M.S., Rachel Gologorsky, Shixuan Liu, Anna-Katharina Meissner, M.D., Akshay Rao, Chenhui Zhao, Akhil Kondepudi, Cheng Jiang, Xinhai Hou, Rushikesh S. Joshi, M.D., Volker Neuschmelting, M.D., Ashok Srinivasan, M.D., Dawn Kleindorfer, M.D., Brian Athey, Ph.D., Aditya Pandey, M.D., and Honglak Lee, Ph.D., all of University of Michigan.

Funding/disclosures: This work was supported in part by the National Institute of Neurological Disorders and Stroke (K12NS080223) of the National Institutes of Health.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

This work was also supported by the Chan Zuckerberg Initiative (CZI), Frankel Institute for Heart and Brain Health, the Mark Trauner Brain Research Fund, the Zenkel Family Foundation, Ian’s Friends Foundation and the UM Precision Health Investigators Awards grant program.

Michigan Research Core(s): UM Advanced Research Computing

Paper cited: “Learning neuroimaging models from health system-scale data,” Nature Biomedical Engineering. DOI: 10.1038/s41551-025-01608-0