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Category: robotics/AI

Methylation definition at 5:05, 27:20 a lil about reprogramming, 32:00 q&a, 47:44 Aubrey chimes in, 57:00 Keith Comito(and other throughout)

Zoom transcription: https://otter.ai/u/AIIhn4i_p4DIXHAJx0ZaG0HUnAU

We will be joined by Morgan Levine, Yale University, to discuss the recent article “Underlying Features of Epigenetic Aging Clocks” she co-authored.

The talk will compare and contrast existing epigenetic clocks and describe how they can be deconstructed to facilitate our understanding of causes and consequences of epigenetic aging.

Article Abstract:

Epigenetic clocks, developed using DNA methylation data, have been widely used to quantify biological aging in multiple tissues/cells. However, many existing epigenetic clocks are weakly correlated with each other, suggesting they may capture different biological processes. We utilize multi‐omics data from diverse human tissue/cells to identify shared features across eleven existing epigenetic clocks. Despite the striking lack of overlap in CpGs, multi‐omics analysis suggested five clocks (Horvath1, Horvath2, Levine, Hannum, and Lin) share transcriptional associations conserved across purified CD14+ monocytes and dorsolateral prefrontal cortex. The pathways enriched in the shared transcriptional association suggested links between epigenetic aging and metabolism, immunity, and autophagy. Results from in vitro experiments showed that two clocks (Levine and Lin) were accelerated in accordance with two hallmarks of aging—cellular senescence and mitochondrial dysfunction. Finally, using multi‐tissue data to deconstruct the epigenetic clock signals, we developed a meta‐clock that demonstrated improved prediction for mortality and robustly related to hallmarks of aging in vitro than single clocks.

Morgan’s Bio:

Morgan Levine is a ladder-rank Assistant Professor in the Department of Pathology at the Yale School of Medicine and a member of both the Yale Combined Program in Computational Biology and Bioinformatics, and the Yale Center for Research on Aging. Her work relies on an interdisciplinary approach, integrating theories and methods from statistical genetics, computational biology, and mathematical demography to develop biomarkers of aging for humans and animal models using high-dimensional omics data. As PI or co-Investigator on multiple NIH-, Foundation-, and University-funded projects, she has extensive experience using systems-level and machine learning approaches to track epigenetic, transcriptomic, and proteomic changes with aging and incorporate this information to develop measures of risk stratification for major chronic diseases, such as cancer and Alzheimer’s disease. Her work also involves development of systems-level outcome measures of aging, aimed at facilitating evaluation for geroprotective interventions. A number of the existing biological aging measures she has developed are being applied in both basic and observational research.

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AI, Genetics, and Health-Tech / Wearables — 21st Century Technologies For Healthy Companion Animals.

Ira Pastor ideaXme life sciences ambassador interviews Dr. Angela Hughes, the Global Scientific Advocacy Relations Senior Manager and Veterinary Geneticist at Mars Petcare.

The global petcare industry is significantly expanding, with North America sales alone expected to hit US $300 billion by 2025. And while we may associate the Mars Corporation, the world’s largest candy company, with leading confectionary brands like Milky Way, M&M’s, Skittles, Snickers, Twix, etc. They also happen to be one of the world’s largest companies in pet care as well.

Dr. Angela Hughes, is the Global Scientific Advocacy Relations Senior Manager & Veterinary Geneticist at Mars Petcare. Dr. Hughes is both Doctor of Veterinary Medicine, and a PhD with a focus in Canine Genetics, both from the University of California, Davis. Dr. Hughes also serves as Veterinary Genetics Research Manager of Wisdom Health, a business unit of Mars Petcare, which has developed state-of-the-art genetic tests for companion animals, leading to revolutionary personalized petcare. She also serves as a Veterinary Geneticist of Hughes Veterinary Consulting, focused on small animal and equine genetics and with a special interest in small animal reproduction and pediatrics.

Dr Hughes is published in multiple academic journals, including the Journal of the American Veterinary Medical Association and has contributed chapters for publication in Veterinary Clinics of North America Small Animal Practice: Pediatrics and Large Animal Internal Medicine.

On this ideaXme episode we will hear from Dr. Hughes about:

-Her background — how she developed an interest in veterinary medicine and animal genetics, and how she arrived at Mars Petcare.

-Her role as the senior manager of Global Scientific Advocacy Relations at Mars Petcare.

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Computational molecular physics (CMP) aims to leverage the laws of physics to understand not just static structures but also the motions and actions of biomolecules. Applying CMP to proteins has required either simplifying the physical models or running simulations that are shorter than the time scale of the biological activity. Brini et al. reviewed advances that are moving CMP to time scales that match biological events such as protein folding, ligand unbinding, and some conformational changes. They also highlight the role of blind competitions in driving the field forward. New methods such as deep learning approaches are likely to make CMP an increasingly powerful tool in describing proteins in action.

Science, this issue p.

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The biggest computer chip in the world is so fast and powerful it can predict future actions “faster than the laws of physics produce the same result.”

That’s according to a post by Cerebras Systems, a that made the claim at the online SC20 supercomputing conference this week.

Working with the U.S. Department of Energy’s National Energy Technology Laboratory, Cerebras designed what it calls “the world’s most powerful AI compute system.” It created a massive chip 8.5 inch-square chip, the Cerebras CS-1, housed in a refrigerator-sized computer in an effort to improve on deep-learning training models.

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Code Unto Caesar

Durendal’s algorithm wrote scripture about three topics: “the plague,” “Caesar,” and “the end of days.” So it’s not surprising that things took a grim turn. The full text is full of glitches characteristic of AI-written texts, like excerpts where over half of the nouns are “Lord.” But some passages are more coherent and read like bizarre doomsday prophecies.

For example, from the plague section: “O LORD of hosts, the God of Israel; When they saw the angel of the Lord above all the brethren which were in the wilderness, and the soldiers of the prophets shall be ashamed of men.”

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Whole-body positron emission tomography combined with computed tomography (PET/CT) is a cornerstone in the management of lymphoma (cancer in the lymphatic system). PET/CT scans are used to diagnose disease and then to monitor how well patients respond to therapy. However, accurately classifying every single lymph node in a scan as healthy or cancerous is a complex and time-consuming process. Because of this, detailed quantitative treatment monitoring is often not feasible in clinical day-to-day practice.

Researchers at the University of Wisconsin-Madison have recently developed a deep-learning model that can perform this task automatically. This could free up valuable physician time and make quantitative PET/CT treatment monitoring possible for a larger number of patients.

To acquire PET/CT scans, patients are injected with a sugar molecule marked with radioactive fluorine-18 (18 F-fluorodeoxyglucose). When the fluorine atom decays, it emits a positron that instantly annihilates with an electron in its immediate vicinity. This annihilation process emits two back-to-back photons, which the scanner detects and uses to infer the location of the radioactive decay.

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Artificial intelligence is being developed that can analyze whether it’s own decision or prediction is reliable.

…An AI that is aware/determine or analyze it’s own weaknesses. Basically, it should help doctors or passengers of the AI know quickly the risk involved.

How might The Terminator have played out if Skynet had decided it probably wasn’t responsible enough to hold the keys to the entire US nuclear arsenal? As it turns out, scientists may just have saved us from such a future AI-led apocalypse, by creating neural networks that know when they’re untrustworthy.

These deep learning neural networks are designed to mimic the human brain by weighing up a multitude of factors in balance with each other, spotting patterns in masses of data that humans don’t have the capacity to analyse.

While Skynet might still be some way off, AI is already making decisions in fields that affect human lives like autonomous driving and medical diagnosis, and that means it’s vital that they’re as accurate as possible. To help towards this goal, this newly created neural network system can generate its confidence level as well as its predictions.

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