Physics Meets Data: A New Approach to Computer Vision

Physics Meets Data: A New Approach to Computer Vision

Computer vision has a long history of research and development dating back to the 1960s at MIT, when pioneers of artificial intelligence sought to mimic the human visual system. The intended goal of such projects was to enable computers to “describe what they saw” from digital images or videos. However, this proved to be a much harder problem than anticipated, requiring significant strides in mathematics, physics, statistics, and learning theory that ultimately slowed interest in the field. Computer vision has since experienced a resurgence.

How Computational Immunology has Redefined Drug Development

How Computational Immunology has Redefined Drug Development

Thanks to new practices of applying statistical modeling to artificial intelligence via computers, disease detection has become more efficient and accurate. This has given rise to the field of computational immunology, defined as a discipline that uses mathematical models and statistical techniques to understand patterns behind immune systems and effectively develop drugs. New methods of sequencing genomes and understanding proteomics –the study of protein structures and functions– have increased the amount of data available for research, which scientists can study to glean more information about immune responses.

Cellular Senescence and Aging: Reduction of Biological Age Through Senotherapeutic Peptides

Cellular Senescence and Aging: Reduction of Biological Age Through Senotherapeutic Peptides

Aging scientists have been exploring a cell growth arrest phenomenon, cellular senescence, as a possible cause of biological aging. Senotherapeutic treatments have been discovered to reduce biological aging through the clearance of these growth-arrested “senescent” cells, resulting in mechanisms to increase biological age longevity. Could senotherapeutics be the path to human lifespan expansion? 

Examining The Growing Need To Make Blood Pressure Prediction More Equitable

Examining The Growing Need To Make Blood Pressure Prediction More Equitable

When you consider health conditions that can affect a large number of people, it becomes clear why diversity is so important in study. Take high blood pressure, commonly known as hypertension. It's a major deal and, for some people, even potentially fatal. Mendelian randomization, a technique that uses gene variation to assess cause and effect relationships, has revealed a relationship between high blood pressure and cardiovascular illness.

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