Utah State University researchers and collaborators published RF-PHATE on June 30, 2026, in Nature Computational Science, presenting a supervised AI visualization method for interpreting high-dimensional biological datasets including multiple sclerosis progression, COVID-19 plasma profiles, lung cancer cell measurements, and RNA sequencing. The immediate news is a research paper, but the larger story is the steady migration of AI from spectacle to scientific instrumentation. RF-PHATE is not another chatbot wrapped around a lab notebook; it is a reminder that some of the most consequential AI systems will look like better maps of data humans could not otherwise see. For WindowsForum readers, the lesson is familiar: the next wave of AI infrastructure will not be judged only by model size, but by whether it can make messy, domain-specific data usable without flattening away the truth.
The public AI debate has spent the last three years orbiting language models, copilots, and the question of whether a machine can summarize a meeting without inventing a budget. RF-PHATE lives in a different part of the landscape. It belongs to the world of scientific machine learning, where the central problem is not generating plausible text but making sense of datasets so large and multidimensional that ordinary visual intuition collapses.
That distinction matters. In computational biology, a dataset may contain hundreds of thousands of observations, each with many variables tied to cellular behavior, treatment history, disease progression, or molecular response. A human researcher cannot simply “look” at that data in the way a radiologist looks at an X-ray or a sysadmin looks at a dashboard.
Visualization tools are the bridge between measurement and interpretation. They compress high-dimensional data into two or three dimensions so researchers can see clusters, gradients, trajectories, and outliers. The danger is that compression is never neutral. A pretty plot can clarify a hidden pattern, or it can manufacture one.
RF-PHATE is being introduced as a supervised visualization method that tries to preserve the relationships researchers actually care about. Instead of treating all structure in the dataset as equally important, it uses labels and expert knowledge to emphasize relationships relevant to the biological question being asked. In plain English, it is an attempt to build a scientific map that knows what kind of terrain the researcher is trying to navigate.
But every projection is a bargain. If you squash a high-dimensional dataset into a two-dimensional figure, something has to give. Distances, densities, continuity, and group relationships cannot all be perfectly preserved at once.
That trade-off is particularly dangerous in biology because the most important signal is not always the loudest signal. An unsupervised method may find the dominant structure in the data, but the dominant structure may be batch effects, measurement artifacts, patient demographics, or some other source of variation that is scientifically real but irrelevant to the question at hand. The plot may be accurate in a narrow mathematical sense while still leading the researcher away from the biological insight.
Supervised methods try to correct that by incorporating labels, outcomes, or expert annotations. But the RF-PHATE team argues that existing supervised approaches only partially solve the problem and may introduce new distortions. That is the niche this paper tries to occupy: not generic visualization, not pure prediction, but guided exploration.
The name itself is a clue to the architecture. RF-PHATE combines random forests with PHATE, short for Potential of Heat-diffusion for Affinity-based Trajectory Embedding. Random forests learn relationships between input features and labels; PHATE-style diffusion geometry helps convert those relationships into a lower-dimensional embedding. The result is a visualization meant to show label-relevant structure while suppressing noise and irrelevant variation.
Still, the MS example shows why this kind of tool matters. Multiple sclerosis is not a single uniform patient journey. Disease course, immune activity, progression, and treatment response can differ widely. If a visualization method can help distinguish meaningful subtypes in longitudinal clinical data, it could help researchers refine hypotheses about treatment selection and disease mechanisms.
The paper’s abstract describes RF-PHATE as being applied to longitudinal multiple sclerosis data, and the Utah State account says one test dataset included clinical measurements from MS patients, with large numbers of data points tied to disease progression at the cellular level, treatments, and outcomes. That is exactly the kind of data environment where ordinary statistical summaries can struggle. The relationships may be nonlinear, temporally uneven, and spread across many interacting variables.
Here, the point is not that RF-PHATE has “solved” MS classification. The more sober reading is that it gives researchers another way to interrogate complex clinical structure. In medicine, discovery often starts with a pattern that is visible before it is fully explained. RF-PHATE is trying to make those candidate patterns more faithful to the underlying biological question.
That difference between discovery support and clinical deployment is important. The former can be transformative inside research groups. The latter requires validation, reproducibility, regulatory scrutiny, and real-world performance across populations that are usually messier than research datasets. The AI industry has too often blurred that line. This paper is more useful if we do not.
Unsupervised visualization has a certain philosophical appeal. Give the algorithm the data, let it reveal structure, and avoid injecting too much human bias. But in practical science, the idea of neutral exploration can be misleading. Researchers already bring hypotheses, labels, measurements, and domain knowledge to the table.
RF-PHATE formalizes that reality. It says, in effect, that the map should be shaped by the question. If the researcher wants to understand disease outcome, treatment response, cell state, or molecular trajectory, the visualization should not be dominated by unrelated variation merely because that variation is mathematically strong.
That is powerful, but it is also risky. Supervision can reveal relevant structure, but it can also reinforce the assumptions embedded in the labels. If the labels are incomplete, biased, noisy, or clinically contested, the visualization may inherit those problems. A supervised map is not free from subjectivity; it is subjectivity made computational.
This is where scientific AI differs sharply from consumer AI branding. The more expert knowledge a model incorporates, the more valuable it can become — and the more important it is to inspect the provenance, quality, and meaning of that knowledge. RF-PHATE’s promise rests on a careful balance: suppress irrelevant variation without erasing inconvenient biological complexity.
A researcher looking at an embedding is making judgments. Are these groups genuinely distinct? Is this trajectory continuous? Is this outlier meaningful or noise? Does this apparent separation reflect biology, measurement procedure, or the algorithm’s own geometry?
The RF-PHATE paper argues that common unsupervised tools can over-emphasize differences between groups and fail to preserve how those groups relate to one another. That critique should be familiar to anyone who has seen a dashboard turn a complicated operational system into a set of misleading red and green tiles. Visualization is persuasive precisely because it feels immediate.
In computational biology, that persuasiveness can be dangerous. A two-dimensional embedding may become the figure that anchors a lab meeting, a grant proposal, or a follow-up experiment. If the map exaggerates separation or hides continuity, downstream decisions can follow the distortion.
RF-PHATE’s contribution is to make the embedding more accountable to the analytic task. It is not promising omniscience. It is promising a visualization that better aligns with the labels and outcomes that matter for a given investigation. That is a narrower claim than “AI discovers biology,” and a more useful one.
For researchers, open implementation lowers the barrier to replication and extension. For data scientists outside biology, it makes the method easier to test on analogous problems: finance, industrial telemetry, cybersecurity, network behavior, materials science, or any other domain where high-dimensional data must be reduced into human-interpretable views.
That does not mean every Windows power user will be running RF-PHATE on a desktop tomorrow. The likely user base is specialized: computational biologists, statisticians, machine-learning researchers, and interdisciplinary labs with enough Python infrastructure to manage real datasets. But the direction of travel is relevant to a much wider technical audience.
AI tools that start in research environments often reshape expectations elsewhere. The enterprise dashboard, the SOC console, the observability stack, and the Windows admin portal all wrestle with versions of the same problem: too many signals, too much dimensionality, and too little confidence that the picture on the screen is the right one.
For IT administrators, tools like RF-PHATE are another reminder that “AI readiness” is not just about buying accelerators. It is about managing reproducible environments, package versions, data access, storage throughput, security boundaries, and collaboration workflows. The model may be the glamorous part, but the operational substrate decides whether the work is repeatable.
The Python ecosystem is both a strength and a headache here. It gives researchers fast access to cutting-edge tools, but it also creates dependency drift, environment sprawl, and fragile notebooks that work on one machine and fail on another. Windows shops supporting scientific users increasingly need a strategy that spans local workstations, WSL2, containers, remote GPU hosts, and identity-aware access to sensitive datasets.
There is also a governance angle. Biological and clinical datasets are not generic test files. They may contain protected health information, institutionally restricted records, or data governed by consent agreements. A visualization workflow that looks harmless from a software perspective can still create compliance risk if copied into unmanaged environments.
RF-PHATE fits that second version. It is not replacing the biologist or clinician. It is trying to improve the exploratory interface between expert knowledge and data complexity. That is a meaningful role, especially in fields where measurements have outpaced interpretation.
The paper’s case studies are deliberately broad: multiple sclerosis, COVID-19 patient plasma data, Raman spectral measurements in antioxidant-treated lung cancer cells, and RNA sequencing with simulated dropout. That breadth is meant to show that the method is not tuned to a single disease or dataset. It also signals the ambitions of modern computational biology, where methods increasingly travel across experimental modalities.
But breadth should not be mistaken for universality. A method that performs well across several case studies still needs domain-specific validation. The shape of a biological dataset is not merely numerical; it reflects instruments, protocols, sample selection, missingness, and the historical assumptions embedded in labels. RF-PHATE may travel well, but it will not travel without baggage.
The stronger story is subtler. RF-PHATE is not exciting because it has the word AI attached to it. It is exciting because it addresses a specific weakness in how researchers visualize high-dimensional data. That weakness is not glamorous, but it is foundational.
Many AI announcements ask readers to marvel at outputs. This one asks a more important question: can we trust the map we use before we decide what to study next? In science, that upstream question can shape years of downstream work.
That is why the method’s interpretability angle matters. The paper suggests RF-PHATE can be used not only to explore biological datasets but also to develop and analyze more interpretable AI models. If true across broader use cases, that puts the work in a productive lane: AI tools that help humans interrogate other AI tools, rather than merely adding another opaque layer.
RF-PHATE’s supervised design may reduce certain distortions, but it cannot remove the need for statistical validation, experimental follow-up, and biological interpretation. A map can suggest that patient groups differ; it cannot by itself prove why they differ. It can highlight a possible subtype; it cannot by itself establish clinical utility.
That caution is not a criticism of the work. It is the condition under which this kind of work becomes useful. The best visualization tools do not end inquiry. They make better inquiry possible.
For administrators and technical decision-makers, the same lesson applies outside the lab. AI-assisted analytics tools should be judged by how they change decision quality, not how impressive their embeddings look in a demo. If the map cannot be audited, reproduced, or explained to domain experts, it may be a liability wearing the clothes of insight.
The next generation of useful AI tooling will not merely answer questions in natural language. It will help technical users see which relationships matter, which variations are noise, and which anomalies deserve attention. That requires systems that can incorporate expert labels without becoming hostage to them.
The caution is equally portable. If supervised visualization depends on labels, then label quality becomes infrastructure. In security, bad labels mean missed threats. In healthcare, bad labels can distort disease categories. In enterprise analytics, bad labels can turn organizational assumptions into automated “insight.”
This is why RF-PHATE should be read less as an isolated academic release and more as a case study in where AI is going. The problem is not just intelligence. The problem is representation. Whoever controls the representation controls what becomes visible.
The New AI Frontier Looks Less Like a Chatbot and More Like a Microscope
The public AI debate has spent the last three years orbiting language models, copilots, and the question of whether a machine can summarize a meeting without inventing a budget. RF-PHATE lives in a different part of the landscape. It belongs to the world of scientific machine learning, where the central problem is not generating plausible text but making sense of datasets so large and multidimensional that ordinary visual intuition collapses.That distinction matters. In computational biology, a dataset may contain hundreds of thousands of observations, each with many variables tied to cellular behavior, treatment history, disease progression, or molecular response. A human researcher cannot simply “look” at that data in the way a radiologist looks at an X-ray or a sysadmin looks at a dashboard.
Visualization tools are the bridge between measurement and interpretation. They compress high-dimensional data into two or three dimensions so researchers can see clusters, gradients, trajectories, and outliers. The danger is that compression is never neutral. A pretty plot can clarify a hidden pattern, or it can manufacture one.
RF-PHATE is being introduced as a supervised visualization method that tries to preserve the relationships researchers actually care about. Instead of treating all structure in the dataset as equally important, it uses labels and expert knowledge to emphasize relationships relevant to the biological question being asked. In plain English, it is an attempt to build a scientific map that knows what kind of terrain the researcher is trying to navigate.
Dimensionality Reduction Has Always Been a Deal With the Devil
The underlying problem RF-PHATE addresses is older than the current AI boom. Techniques such as t-SNE, UMAP, Isomap, and PHATE have become common tools for projecting complicated data into human-readable form. They are popular because they work well enough to become part of the daily language of modern data science.But every projection is a bargain. If you squash a high-dimensional dataset into a two-dimensional figure, something has to give. Distances, densities, continuity, and group relationships cannot all be perfectly preserved at once.
That trade-off is particularly dangerous in biology because the most important signal is not always the loudest signal. An unsupervised method may find the dominant structure in the data, but the dominant structure may be batch effects, measurement artifacts, patient demographics, or some other source of variation that is scientifically real but irrelevant to the question at hand. The plot may be accurate in a narrow mathematical sense while still leading the researcher away from the biological insight.
Supervised methods try to correct that by incorporating labels, outcomes, or expert annotations. But the RF-PHATE team argues that existing supervised approaches only partially solve the problem and may introduce new distortions. That is the niche this paper tries to occupy: not generic visualization, not pure prediction, but guided exploration.
The name itself is a clue to the architecture. RF-PHATE combines random forests with PHATE, short for Potential of Heat-diffusion for Affinity-based Trajectory Embedding. Random forests learn relationships between input features and labels; PHATE-style diffusion geometry helps convert those relationships into a lower-dimensional embedding. The result is a visualization meant to show label-relevant structure while suppressing noise and irrelevant variation.
The Multiple Sclerosis Example Is the Headline for a Reason
The most attention-grabbing claim in the university’s announcement is that RF-PHATE provided evidence of a previously suspected multiple sclerosis subtype. That is exactly the kind of claim that deserves both interest and restraint. A visualization method can expose a pattern, strengthen a hypothesis, and guide further analysis, but it is not by itself a clinical diagnostic standard.Still, the MS example shows why this kind of tool matters. Multiple sclerosis is not a single uniform patient journey. Disease course, immune activity, progression, and treatment response can differ widely. If a visualization method can help distinguish meaningful subtypes in longitudinal clinical data, it could help researchers refine hypotheses about treatment selection and disease mechanisms.
The paper’s abstract describes RF-PHATE as being applied to longitudinal multiple sclerosis data, and the Utah State account says one test dataset included clinical measurements from MS patients, with large numbers of data points tied to disease progression at the cellular level, treatments, and outcomes. That is exactly the kind of data environment where ordinary statistical summaries can struggle. The relationships may be nonlinear, temporally uneven, and spread across many interacting variables.
Here, the point is not that RF-PHATE has “solved” MS classification. The more sober reading is that it gives researchers another way to interrogate complex clinical structure. In medicine, discovery often starts with a pattern that is visible before it is fully explained. RF-PHATE is trying to make those candidate patterns more faithful to the underlying biological question.
That difference between discovery support and clinical deployment is important. The former can be transformative inside research groups. The latter requires validation, reproducibility, regulatory scrutiny, and real-world performance across populations that are usually messier than research datasets. The AI industry has too often blurred that line. This paper is more useful if we do not.
Supervision Is the Quiet Political Act Inside the Model
The most interesting part of RF-PHATE is not that it uses AI. Everything uses AI now, at least in the press release version of the world. The interesting part is that it makes supervision central to the visualization process.Unsupervised visualization has a certain philosophical appeal. Give the algorithm the data, let it reveal structure, and avoid injecting too much human bias. But in practical science, the idea of neutral exploration can be misleading. Researchers already bring hypotheses, labels, measurements, and domain knowledge to the table.
RF-PHATE formalizes that reality. It says, in effect, that the map should be shaped by the question. If the researcher wants to understand disease outcome, treatment response, cell state, or molecular trajectory, the visualization should not be dominated by unrelated variation merely because that variation is mathematically strong.
That is powerful, but it is also risky. Supervision can reveal relevant structure, but it can also reinforce the assumptions embedded in the labels. If the labels are incomplete, biased, noisy, or clinically contested, the visualization may inherit those problems. A supervised map is not free from subjectivity; it is subjectivity made computational.
This is where scientific AI differs sharply from consumer AI branding. The more expert knowledge a model incorporates, the more valuable it can become — and the more important it is to inspect the provenance, quality, and meaning of that knowledge. RF-PHATE’s promise rests on a careful balance: suppress irrelevant variation without erasing inconvenient biological complexity.
The Real Competition Is Not Between Algorithms, but Between Interpretations
It is tempting to frame RF-PHATE as a horse race against t-SNE, UMAP, and PHATE. That makes for tidy comparison charts, but it misses the deeper point. Visualization tools compete not merely on mathematical elegance but on the interpretations they enable.A researcher looking at an embedding is making judgments. Are these groups genuinely distinct? Is this trajectory continuous? Is this outlier meaningful or noise? Does this apparent separation reflect biology, measurement procedure, or the algorithm’s own geometry?
The RF-PHATE paper argues that common unsupervised tools can over-emphasize differences between groups and fail to preserve how those groups relate to one another. That critique should be familiar to anyone who has seen a dashboard turn a complicated operational system into a set of misleading red and green tiles. Visualization is persuasive precisely because it feels immediate.
In computational biology, that persuasiveness can be dangerous. A two-dimensional embedding may become the figure that anchors a lab meeting, a grant proposal, or a follow-up experiment. If the map exaggerates separation or hides continuity, downstream decisions can follow the distortion.
RF-PHATE’s contribution is to make the embedding more accountable to the analytic task. It is not promising omniscience. It is promising a visualization that better aligns with the labels and outcomes that matter for a given investigation. That is a narrower claim than “AI discovers biology,” and a more useful one.
Open Code Turns a Research Claim Into a Testable Tool
One encouraging detail is that a Python implementation of RF-PHATE is available for academic use. In the world of computational science, code availability is not a courtesy; it is part of the evidence. A method that cannot be inspected, run, broken, benchmarked, and adapted is closer to marketing than science.For researchers, open implementation lowers the barrier to replication and extension. For data scientists outside biology, it makes the method easier to test on analogous problems: finance, industrial telemetry, cybersecurity, network behavior, materials science, or any other domain where high-dimensional data must be reduced into human-interpretable views.
That does not mean every Windows power user will be running RF-PHATE on a desktop tomorrow. The likely user base is specialized: computational biologists, statisticians, machine-learning researchers, and interdisciplinary labs with enough Python infrastructure to manage real datasets. But the direction of travel is relevant to a much wider technical audience.
AI tools that start in research environments often reshape expectations elsewhere. The enterprise dashboard, the SOC console, the observability stack, and the Windows admin portal all wrestle with versions of the same problem: too many signals, too much dimensionality, and too little confidence that the picture on the screen is the right one.
Windows Workstations Still Sit in the Middle of Scientific AI
Scientific AI is often discussed as if it lives only in cloud clusters and Linux workstations. That is only partly true. Many labs still operate in mixed environments where Windows desktops, institutional file shares, Python environments, Jupyter notebooks, WSL, and GPU-enabled workstations coexist in varying states of harmony.For IT administrators, tools like RF-PHATE are another reminder that “AI readiness” is not just about buying accelerators. It is about managing reproducible environments, package versions, data access, storage throughput, security boundaries, and collaboration workflows. The model may be the glamorous part, but the operational substrate decides whether the work is repeatable.
The Python ecosystem is both a strength and a headache here. It gives researchers fast access to cutting-edge tools, but it also creates dependency drift, environment sprawl, and fragile notebooks that work on one machine and fail on another. Windows shops supporting scientific users increasingly need a strategy that spans local workstations, WSL2, containers, remote GPU hosts, and identity-aware access to sensitive datasets.
There is also a governance angle. Biological and clinical datasets are not generic test files. They may contain protected health information, institutionally restricted records, or data governed by consent agreements. A visualization workflow that looks harmless from a software perspective can still create compliance risk if copied into unmanaged environments.
The AI-for-Science Pitch Is Maturing Into Infrastructure
Kevin Moon’s framing places RF-PHATE within the broader AI for Science movement, which aims to use machine learning to accelerate research, analyze massive datasets, and simulate complex systems. That movement is not new, but it is becoming more concrete. The early rhetoric around AI in science often sounded like a promise that models would simply discover truths at scale. The better version is more modest and more durable: AI becomes part of the scientific instrument stack.RF-PHATE fits that second version. It is not replacing the biologist or clinician. It is trying to improve the exploratory interface between expert knowledge and data complexity. That is a meaningful role, especially in fields where measurements have outpaced interpretation.
The paper’s case studies are deliberately broad: multiple sclerosis, COVID-19 patient plasma data, Raman spectral measurements in antioxidant-treated lung cancer cells, and RNA sequencing with simulated dropout. That breadth is meant to show that the method is not tuned to a single disease or dataset. It also signals the ambitions of modern computational biology, where methods increasingly travel across experimental modalities.
But breadth should not be mistaken for universality. A method that performs well across several case studies still needs domain-specific validation. The shape of a biological dataset is not merely numerical; it reflects instruments, protocols, sample selection, missingness, and the historical assumptions embedded in labels. RF-PHATE may travel well, but it will not travel without baggage.
The Press Release Is Right to Be Excited, but the Paper Is More Interesting Than the Hype
The Mirage News item largely republishes the university account, which is common in science-news distribution. The headline emphasizes a “novel AI tool” for complex biological data. That is accurate enough, but it risks making RF-PHATE sound like another generalized AI breakthrough in a media ecosystem already saturated with them.The stronger story is subtler. RF-PHATE is not exciting because it has the word AI attached to it. It is exciting because it addresses a specific weakness in how researchers visualize high-dimensional data. That weakness is not glamorous, but it is foundational.
Many AI announcements ask readers to marvel at outputs. This one asks a more important question: can we trust the map we use before we decide what to study next? In science, that upstream question can shape years of downstream work.
That is why the method’s interpretability angle matters. The paper suggests RF-PHATE can be used not only to explore biological datasets but also to develop and analyze more interpretable AI models. If true across broader use cases, that puts the work in a productive lane: AI tools that help humans interrogate other AI tools, rather than merely adding another opaque layer.
A Better Map Does Not Eliminate the Need for Skepticism
Every visualization method invites over-reading. Clusters look like categories. Gradients look like trajectories. Empty space looks like separation. Human pattern recognition is powerful, but it is not always disciplined.RF-PHATE’s supervised design may reduce certain distortions, but it cannot remove the need for statistical validation, experimental follow-up, and biological interpretation. A map can suggest that patient groups differ; it cannot by itself prove why they differ. It can highlight a possible subtype; it cannot by itself establish clinical utility.
That caution is not a criticism of the work. It is the condition under which this kind of work becomes useful. The best visualization tools do not end inquiry. They make better inquiry possible.
For administrators and technical decision-makers, the same lesson applies outside the lab. AI-assisted analytics tools should be judged by how they change decision quality, not how impressive their embeddings look in a demo. If the map cannot be audited, reproduced, or explained to domain experts, it may be a liability wearing the clothes of insight.
The Practical Lessons Hide in the Shape of the Data
RF-PHATE’s debut is a biology story, but the operational implications are broader. The method lands at a moment when organizations are drowning in high-dimensional telemetry: endpoints, identity systems, network flows, application logs, vulnerability data, user behavior, and cloud configuration states. The specific algorithm may not be headed for your Windows admin console, but the design philosophy almost certainly is.The next generation of useful AI tooling will not merely answer questions in natural language. It will help technical users see which relationships matter, which variations are noise, and which anomalies deserve attention. That requires systems that can incorporate expert labels without becoming hostage to them.
The caution is equally portable. If supervised visualization depends on labels, then label quality becomes infrastructure. In security, bad labels mean missed threats. In healthcare, bad labels can distort disease categories. In enterprise analytics, bad labels can turn organizational assumptions into automated “insight.”
This is why RF-PHATE should be read less as an isolated academic release and more as a case study in where AI is going. The problem is not just intelligence. The problem is representation. Whoever controls the representation controls what becomes visible.
The Mapmakers Are Moving Closer to the Lab Bench
The concrete facts are simple, but their implications are larger than a single paper.- RF-PHATE was published on June 30, 2026, in Nature Computational Science as a supervised visualization method for high-dimensional biological data.
- The method combines random forests with PHATE-style diffusion geometry to emphasize label-relevant structure in low-dimensional embeddings.
- The paper reports case studies involving multiple sclerosis data, COVID-19 plasma measurements, Raman spectral data from antioxidant-treated lung cancer cells, and RNA sequencing data.
- Utah State University’s account says the MS analysis provided evidence for a previously suspected subtype, a finding that should be treated as research evidence rather than clinical proof.
- A Python implementation is available for academic use, making the method testable by researchers with suitable data and infrastructure.
- The larger significance is that AI-for-science tools are becoming practical instruments for interpretation, not just headline-grabbing generators of text, images, or predictions.
References
- Primary source: Mirage News
Published: Wed, 01 Jul 2026 00:26:00 GMT
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www.miragenews.com - Independent coverage: Utah State University
Published: 2026-06-30T22:30:19.046647
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www.usu.edu - Related coverage: phys.org
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phys.org - Related coverage: neuroscience.stanford.edu
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neuroscience.stanford.edu - Related coverage: researchgate.net
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www.researchgate.net - Related coverage: publications.sci.utah.edu
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publications.sci.utah.edu