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Category: information science – Page 247

Harvard Professor Wants to Slow Down & Reverse Aging: David Sinclair’s Approach For a Longer Life

David Sinclair wants to slow down and ultimately reverse aging. Sinclair sees aging as a disease and he is convinced aging is caused by epigenetic changes, abnormalities that occur when the body’s cells process extra or missing pieces of DNA. This results in the loss of the information that keeps our cells healthy. This information also tells the cells which genes to read. David Sinclair’s book: “Lifespan, why we age and why we don’t have to”, he describes the results of his research, theories and scientific philosophy as well as the potential consequences of the significant progress in genetic technologies.

At present, researchers are only just beginning to understand the biological basis of aging even in relatively simple and short-lived organisms such as yeast. Sinclair however, makes a convincing argument for why the life-extension technologies will eventually offer possibilities of life prolongation using genetic engineering.

He and his team recently developed two artificial intelligence algorithms that predict biological age in mice and when they will die. This will pave the way for similar machine learning models in people.
The loss of epigenetic information is likely the root cause of aging. By analogy, If DNA is the digital information on a compact disc, then aging is due to scratches. What we are searching for, is the polish.

Every time a cell divides, the DNA strands at the ends of your chromosomes replicate in order to copy all the genetic information to each new cell, and this process is not perfect. Over time, however, the ends of your chromosomes can become scrambled.

However, the progress in genetic engineering has proved that these changes can be reversed even at the cellular level, and it is possible to restore the information in our cells, thus improving the functioning of our organs and slowing the aging process.

#Aging #DavidSinclair #Lifespan

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Using deep learning to control the unconsciousness level of patients in an anesthetic state

In recent years, researchers have been developing machine learning algorithms for an increasingly wide range of purposes. This includes algorithms that can be applied in healthcare settings, for instance helping clinicians to diagnose specific diseases or neuropsychiatric disorders or monitor the health of patients over time.

Researchers at Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital have recently carried out a study investigating the possibility of using learning to control the levels of unconsciousness of patients who require anesthesia for a medical procedure. Their paper, set to be published in the proceedings of the 2020 International Conference on Artificial Intelligence in Medicine, was voted the best paper presented at the conference.

“Our lab has made significant progress in understanding how anesthetic medications affect and now has a multidisciplinary team studying how to accurately determine anesthetic doses from neural recordings,” Gabriel Schamberg, one of the researchers who carried out the study, told TechXplore. “In our recent study, we trained a using the cross-entropy method, by repeatedly letting it run on simulated patients and encouraging actions that led to good outcomes.”

Microsoft’s camera-based AI app solves your math problems

Microsoft has made several quirky and useful apps that can help you with daily problems and its new app seeks to help you with math.

Microsoft Math Solver — available on both iOS and Android — can solve various math problems including quadratic equations, calculus, and statistics. The app can also show graphs for the equation to enhance your understanding of the subject.

Big Questions: The Multiverse, Cosmological Neural Networks and “Space Noodles”

Ira Pastor, ideaXme life sciences ambassador and founder of Bioquark interviews Dr Vitaly Vanchurin, PhD, Associate Professor, Theoretical Physics and Cosmology, Swenson College of Science and Engineering, at the University of Minnesota (UMN).

Dr Vanchurin’s big questions and the tools we need to answer them:

“What is the origin of our Universe? What determines our vacuum and the cosmological constant that is responsible for the observed accelerated expansion of space? What determines the onset of structure formation and the birth of galaxies in our Universe? Our innate curiosity about our beginnings has been, since time immemorial, and still is, at the heart of every human being. This age old question of our origin can now be addressed by applying the scientific method”.

Ira Pastor comments:

Today, we have a really exciting thought leader joining us on ideaXme who spends his time thinking about really BIG questions – Questions like: what is the origin of our Universe? What’s behind the cosmological constant (in Albert Einstein’s field equations of general relativity) that is responsible accelerated expansion of space? What determines the onset of structure formation and the birth of galaxies in our Universe? And many other fascinating topics.

Dr. Vitaly Vanchurin, is an Associate Professor, Theoretical Physics and Cosmology, Swenson College of Science and Engineering, at the University of Minnesota (UMN).

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Ventilator-Associated Pneumonia: Diagnosis, Treatment, and Prevention

While critically ill patients experience a life-threatening illness, they commonly contract ventilator-associated pneumonia. This nosocomial infection increases morbidity and likely mortality as well as the cost of health care. This article reviews the literature with regard to diagnosis, treatment, and prevention. It provides conclusions that can be implemented in practice as well as an algorithm for the bedside clinician and also focuses on the controversies with regard to diagnostic tools and approaches, treatment plans, and prevention strategies.

Patients in the intensive care unit (ICU) are at risk for dying not only from their critical illness but also from secondary processes such as nosocomial infection. Pneumonia is the second most common nosocomial infection in critically ill patients, affecting 27% of all critically ill patients (170). Eighty-six percent of nosocomial pneumonias are associated with mechanical ventilation and are termed ventilator-associated pneumonia (VAP). Between 250,000 and 300,000 cases per year occur in the United States alone, which is an incidence rate of 5 to 10 cases per 1,000 hospital admissions (134, 170). The mortality attributable to VAP has been reported to range between 0 and 50% (10, 41, 43, 96, 161).

Neuroscience study finds ‘hidden’ thoughts in visual part of brain

How much control do you have over your thoughts? What if you were specifically told not to think of something—like a pink elephant?

A recent study led by UNSW psychologists has mapped what happens in the brain when a person tries to suppress a . The neuroscientists managed to ‘decode’ the complex brain activity using functional brain imaging (called fMRI) and an imaging algorithm.

The findings suggest that even when a person succeeds in ignoring a thought, like the pink elephant, it can still exist in another part of the brain—without them being aware of it.

NASA to test precision automated landing system designed for the moon and Mars on upcoming Blue Origin mission

NASA is going to be testing a new precision landing system designed for use on the tough terrain of the moon and Mars for the first time during an upcoming mission of Blue Origin’s New Shepard reusable suborbital rocket. The “Safe and Precise Landing – Integrated Capabilities Evolution” (SPLICE) system is made up of a number of lasers, an optical camera and a computer to take all the data collected by the sensors and process it using advanced algorithms, and it works by spotting potential hazards, and adjusting landing parameters on the fly to ensure a safe touchdown.

SPLICE will get a real-world test of three of its four primary subsystems during a New Shepard mission to be flown relatively soon. The Jeff Bezos –founded company typically returns its first-stage booster to Earth after making its trip to the very edge of space, but on this test of SPLICE, NASA’s automated landing technology will be operating on board the vehicle the same way they would when approaching the surface of the moon or Mars. The elements tested will include “terrain relative navigation,” Doppler radar and SPLICE’s descent and landing computer, while a fourth major system — lidar-based hazard detection — will be tested on future planned flights.

Currently, NASA already uses automated landing for its robotic exploration craft on the surface of other planets, including the Perseverance rover headed to Mars. But a lot of work goes into selecting a landing zone with a large area of unobstructed ground that’s free of any potential hazards in order to ensure a safe touchdown. Existing systems can make some adjustments, but they’re relatively limited in that regard.

Playing with Realistic Neural Talking Head Models

Researchers at the Samsung AI Center in Moscow (Russia) have recently presented interesting work called Living portraits: they made Mona Lisa and other subjects of photos and art alive using video of real people. They presented a framework for meta-learning of adversarial generative models called “Few-Shot Adversarial Learning”.

You can read more about details in the original paper.

Here we review this great implementation of the algorithm in PyTorch. The author of this implementation is Vincent Thévenin — research worker in De Vinci Innovation Center.

C-MIMI: Use of AI in radiology is evolving

September 14, 2020 — The use of artificial intelligence (AI) in radiology to aid in image interpretation tasks is evolving, but many of the old factors and concepts from the computer-aided detection (CAD) era still remain, according to a Sunday talk at the Conference on Machine Intelligence in Medical Imaging (C-MIMI).

A lot has changed as the new era of AI has emerged, such as faster computers, larger image datasets, and more advanced algorithms — including deep learning. Another thing that’s changed is the realization of additional reasons and means to incorporate AI into clinical practice, according to Maryellen Giger, PhD, of the University of Chicago. What’s more, AI is also being developed for a broader range of clinical questions, more imaging modalities, and more diseases, she said.

At the same time, many of the issues are the same as those faced in the era of CAD. There are the same clinical tasks of detection, diagnosis, and response assessment, as well as the same concern of “garbage in, garbage out,” she said. What’s more, there’s the same potential for off-label use of the software, and the same methods for statistical evaluations.

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