In this lecture, Gerald Joyce focuses on the perpetuation of genetic information as a defining characteristic of life. He draws a connection between digital computers (von Neumann machines), especially those with the capacity to self-reproduce, and molecular Darwinian systems that maintain heritable ‘bits’ of information, which are refined through evolution.
In this lecture, Gerald Joyce focuses on the perpetuation of genetic information as a defining characteristic of life. He draws a connection between digital computers (von Neumann machines), especially those with the capacity to self-reproduce, and molecular Darwinian systems that maintain heritable ‘bits’ of information, which are refined through evolution.
In this lecture, Gerald Joyce focuses on the perpetuation of genetic information as a defining characteristic of life. He draws a connection between digital computers (von Neumann machines), especially those with the capacity to self-reproduce, and molecular Darwinian systems that maintain heritable ‘bits’ of information, which are refined through evolution.
A central goal in neuroscience is determining the genetic basis of neurological disorders — from autism to brain tumors. Many of these pathological states result from defects in gene regulatory programs that are fundamental to all cell types but lead to dysfunction specifically within the nervous system. Gail Mandel investigates the basis of this phenomenon and has identified cell-cell interactions between neurons and glia involved in pathological states of brain development. Mandel has ameliorated the neuropathology of one autism spectrum disorder, Rett syndrome, by genetically replacing the defective MeCP2 gene with a good copy of the gene in astrocytes – glia cells in the brain. She is now exploring the underlying mechanisms crucial for neuronal signaling.
A central goal in neuroscience is determining the genetic basis of neurological disorders — from autism to brain tumors. Many of these pathological states result from defects in gene regulatory programs that are fundamental to all cell types but lead to dysfunction specifically within the nervous system. Gail Mandel investigates the basis of this phenomenon and has identified cell-cell interactions between neurons and glia involved in pathological states of brain development. Mandel has ameliorated the neuropathology of one autism spectrum disorder, Rett syndrome, by genetically replacing the defective MeCP2 gene with a good copy of the gene in astrocytes – glia cells in the brain. She is now exploring the underlying mechanisms crucial for neuronal signaling.
In recent decades, physicists and astronomers have discovered two beautiful standard models, one for the quantum world of extremely short distances and one for the universe as a whole. Both models have had spectacular success, but there are also strong arguments for new physics beyond these models.
In recent decades, physicists and astronomers have discovered two beautiful standard models, one for the quantum world of extremely short distances and one for the universe as a whole. Both models have had spectacular success, but there are also strong arguments for new physics beyond these models.
Dr. Schadt provides an overview of how his team organizes very large scale data across many different types, and then integrates these data using sophisticated mathematical algorithms to construct predictive network models of disease, and discusses the application of this type of modeling in the cancer arena.
Probabilistic topic models provide a suite of tools for analyzing large collections of electronic documents. A traditional topic model analyzes a collection of documents to discover its hidden themes. These themes can be used to organize, visualize, summarize and navigate the collection. Many collections are associated with corresponding reader behavior data, which is useful both for making predictions about readers (such as which articles they will like) and in understanding patterns in how they read.
Probabilistic topic models provide a suite of tools for analyzing large collections of electronic documents. A traditional topic model analyzes a collection of documents to discover its hidden themes. These themes can be used to organize, visualize, summarize and navigate the collection. Many collections are associated with corresponding reader behavior data, which is useful both for making predictions about readers (such as which articles they will like) and in understanding patterns in how they read.
