For centuries, scientists have trained themselves to look through microscopes and carefully study their structural and molecular features. But those long hours bent over a microscope poring over microscopic images could be less necessary in the years ahead. The job of analyzing cellular features could one day belong to specially trained computers.
In a new study published in the journal Cell, researchers trained computers by feeding them paired sets of fluorescently labeled and unlabeled images of brain tissue millions of times in a row . This allowed the computers to discern patterns in the images, form rules, and apply them to viewing future images. Using this so-called deep learning approach, the researchers demonstrated that the computers not only learned to recognize individual cells, they also developed an almost superhuman ability to identify the cell type and whether a cell was alive or dead. Even more remarkable, the trained computers made all those calls without any need for harsh chemical labels, including fluorescent dyes or stains, which researchers normally require to study cells. In other words, the computers learned to “see” the invisible!
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Tags: Alzheimer's disease, brain, Brain Bot, cell biology, cells, computer learning, computers, deep learning, Google, machine learning, microscopy, neurology, neuroscience, Parkinson's disease, schizophrenia
Research shows that the roots of autism spectrum disorder (ASD) generally start early—most likely in the womb. That’s one more reason, on top of a large number of epidemiological studies, why current claims about the role of vaccines in causing autism can’t be right. But how early is ASD detectable? It’s a critical question, since early intervention has been shown to help limit the effects of autism. The problem is there’s currently no reliable way to detect ASD until around 18–24 months, when the social deficits and repetitive behaviors associated with the condition begin to appear.
Several months ago, an NIH-funded team offered promising evidence that it may be possible to detect ASD in high-risk 1-year-olds by shifting attention from how kids act to how their brains have grown . Now, new evidence from that same team suggests that neurological signs of ASD might be detectable even earlier.
Tags: ASD, autism, Autism Spectrum Disorder, brain, brain connectivity, brain connectivity maps, brain development, brain scans, childhood disease, childhood vaccinations, computer learning, diagnosis, early detection, fMRI, Functional magnetic resonance imaging, imaging, infants, machine learning, neuroimaging, neurology, vaccines