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Drug cocktail seems to reverse biological signs of ageing in people

New Scientist

Is this the world's first anti-ageing drug? Scientists have made people younger for the first time, or so they think. Nine men took a year-long drug regime that appeared to reverse the ageing process, leaving them one-and-a-half years younger – biologically – than when they started. The clinical trial was the first to investigate the possibility that a drug might be able to reverse the biological signs of ageing, increasing lifespan. However, the results are limited by the fact that this was a feasibility study without a placebo.


The dad who found out he wasn't his kids' biological father

BBC News

Mr Mason's ex-wife has been ordered to pay him £250,000 for paternity fraud, but the legal case has allowed her to keep the identity of the real father a secret.


How I found my biological mother

BBC News

Andre Kuik was four months old when he was adopted by a Dutch family, 40 years later he is flying back to Indonesia to meet his birth mother.


Machine learning predicts behavior of biological circuits: Neural networks cut modeling times of complex biological circuits to enable new insights into their inner workings

#artificialintelligence

In the new study, the researchers trained a neural network to predict the circular patterns that would be created by a biological circuit embedded into a bacterial culture. The system worked 30,000 times faster than the existing computational model. To further improve accuracy, the team devised a method for retraining the machine learning model multiple times to compare their answers. Then they used it to solve a second biological system that is computationally demanding in a different way, showing the algorithm can work for disparate challenges. The results appear online on September 25 in the journal Nature Communications.


Machine Learning Predicts Behavior of Biological Circuits

#artificialintelligence

Biomedical engineers at Duke University have devised a machine learning approach to modeling the interactions between complex variables in engineered bacteria that would otherwise be too cumbersome to predict. Their algorithms are generalizable to many kinds of biological systems. In the new study, the researchers trained a neural network to predict the circular patterns that would be created by a biological circuit embedded into a bacterial culture. The system worked 30,000 times faster than the existing computational model. To further improve accuracy, the team devised a method for retraining the machine learning model multiple times to compare their answers.