Lifeboat Foundation

Researchers at the university of chicago.

Founded in 1,890, the University of Chicago (UChicago, U of C, or Chicago) is a private research university in Chicago, Illinois. Located on a 217-acre campus in Chicago’s Hyde Park neighborhood, near Lake Michigan, the school holds top-ten positions in various national and international rankings. UChicago is also well known for its professional schools: Pritzker School of Medicine, Booth School of Business, Law School, School of Social Service Administration, Harris School of Public Policy Studies, Divinity School and the Graham School of Continuing Liberal and Professional Studies, and Pritzker School of Molecular Engineering.

https://www.youtube.com/watch?v=tR5TiVn5PYk

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Scientists hope the findings could lead to treatments for the memory-destroying disease.

Eye drops that have been contaminated with a highly drug-resistant strain of bacteria have now been linked to four deaths and 14 cases of blindness, according to an update from the Centers for Disease Control and Prevention (CDC).

The CDC said in a post on its website that it has identified 81 patients from 18 states who have been infected with VIM-GES-CRPA, a rare strain of a drug-resistant bacterium called P. aeruginosa. This includes 13 additional patients from the agency’s previous update in March.

Alzheimer’s disease (AD) is a complex neurodegenerative illness with genetic and environmental origins. Females experience faster cognitive decline and cerebral atrophy than males, while males have greater mortality rates. Using a new machine-learning method they developed called “Evolutionary Action Machine Learning (EAML),” researchers at Baylor College of Medicine and the Jan and Dan Duncan Neurological Research Institute (Duncan NRI) at Texas Children’s Hospital have discovered sex-specific genes and molecular pathways that contribute to the development and progression of this condition. The study was published in Nature Communications.

“We have developed a unique machine-learning software that uses an advanced computational predictive metric called the evolutionary action (EA) score as a feature to identify genetic factors that influence AD risk separately in males and females,” Dr. Olivier Lichtarge, MD, Ph.D., professor of biochemistry and molecular biology at Baylor College of Medicine, said. “This approach lets us exploit a massive amount of evolutionary data efficiently, so we can now probe with greater accuracy smaller cohorts and identify genes involved in sex-specific differences in AD.”

EAML is an ensemble computational approach that includes nine machine learning algorithms to analyze the functional impact of non-synonymous coding variants, defined as DNA mutations that affect the structure and function of the resulting protein, and estimates their deleterious effect on biological processes using the evolutionary action (EA) score.

Aubrey: 50% chance to LEV in 12–15 years, and a variety of topics from Rey Kurzweil to A.I. to Singularity, and so on.

In this podcast, Aubrey de Grey discusses his work as President and CSO at Lev Foundation and co-founder at Sense Research Foundation in the field of longevity. He explains how the Foundation’s focus is to combine rejuvenation and damage repair interventions to have greater efficacy in postponing aging and saving lives. De Grey believes that within 12 to 15 years, they have a 50% chance of achieving longevity escape velocity, which is postponing aging and rejuvenating the body faster than time passes. De Grey acknowledges the limitations of traditional approaches like exercise and diet in postponing aging and feels that future breakthroughs will come from high-tech approaches like skin and cell therapies. He discusses the potential of AI and machine learning in drug discovery and the possibility of using it to accelerate scientific experimentation to optimize decisions about which experiments to do next. De Gray cautions that the quality of conclusions from AI depends on the quality and quantity of input data and that the path towards defeating aging would require a symbiotic partnership between humans and AI. Finally, he discusses his excitement about the possibilities of hardware and devices like Apple Watch and Levels in tracking blood sugar levels and their potential to prolong life.

Language impairment is comorbid in most children with Autism Spectrum Disorder (ASD) but its neural basis is poorly understood. Using structural magnetic resonance imaging (MRI), the present study provides the whole-brain comparison of both volume-and surface-based characteristics between groups of children with and without ASD and investigates the relationships between these characteristics in language-related areas and the language abilities of children with ASD measured with standardized tools. A total of 36 school-aged children participated in the study: 18 children with ASD and 18 age-and sex-matched typically developing controls. The results revealed that multiple regions differed between groups of children in gray matter volume, gray matter thickness, gyrification, and cortical complexity (fractal dimension).

Optical imaging and metrology techniques are key tools for research rooted in biology, medicine and nanotechnology. While these techniques have recently become increasingly advanced, the resolutions they achieve are still significantly lower than those attained by methods using focused beams of electrons, such as atomic-scale transmission electron spectroscopy and cryo-electron tomography.

Researchers at University of Southampton and Nanyang Technological University have recently introduced a non-invasive approach for optical measurements with atomic-scale resolution. Their proposed approach, outlined in Nature Materials, could open exciting new possibilities for research in a variety of fields, allowing scientists to characterize systems or phenomena at the scale of a fraction of a billionth of a meter.

“Since the nineteen century, improvements of spatial resolution of microscopy has been a major trend in science that has been marked with at least seven Nobel Prizes,” Nicolay I. Zheludev, one of the researchers who carried out the study told Phys.org. “Our dream was to develop technology that can detect atomic scale events with light, and we have been working on this for the last three years.”

A machine learning-based method developed by a Mount Sinai research team allows medical facilities to forecast the mortality risk for certain cardiac surgery patients. The new method is the first institution-specific model for determining the risk of a cardiac patient before surgery and was developed using vast amounts of Electronic Health Data (EHR).

Comparing the data-driven approach to the current population-derived models reveals a considerable performance improvement.