life sciences

How neural networks are learning to decode information transmitted along neurons


They say their decoder significantly outperforms existing approaches. These included a Long Short Term Memory Network, a recurrent neural network, and a feedforward neural network. "For instance, for all of the three brain areas, a Long Short Term Memory Network decoder explained over 40% of the unexplained variance from a Wiener filter," they say. But Glaser and co deliberately reduced the amount of training data they fed to the algorithms and found the neural nets still outperformed the conventional techniques.

Instagram photos reveal predictive markers of depression


Instagram posts made by individuals diagnosed with depression can be reliably distinguished from posts made by healthy controls, using only measures extracted computationally from posted photos and associated metadata. In studies associating mood, color, and mental health, healthy individuals identified darker, grayer colors with negative mood, and generally preferred brighter, more vivid colors [16–19]. Instagram posts made by depressed individuals prior to the date of first clinical diagnosis can be reliably distinguished from posts made by healthy controls. The authors analyzed 118 studies that evaluated general practitioners' abilities to correctly diagnose depression in their patients, without assistance from scales, questionnaires, or other measurement instruments.

Veritas Genomics Scoops Up an AI Company to Sort Out Its *DNA*


On August 3, sequencing company Veritas Genomics bought one of the most influential: seven-year old Curoverse. In a step forward, the company also hopes to use things like natural language processing and deep learning to help customers query their genetic data on demand. He points to a 2013 study that used polygenic testing to predict heart disease using the Framingham Heart Study data--about as good as you can get, when it comes to health data and heart disease. "They authors showed that yes, given polygenic risk score, and blood levels, and lipid levels, and family history, you can predict within 10 years if someone will develop heart disease," says Butte.

Phytoplankton and chips

MIT News

The Darwin Project, an alliance between oceanographers and microbiologists in the MIT Department of Earth, Atmospheric and Planetary Sciences (EAPS) and the Parsons Lab in the MIT Department of Civil and Environmental Engineering, was conceived as an initiative to "advance the development and application of novel models of marine microbes and microbial communities, identifying the relationships of individuals and communities to their environment, connecting cellular-scale processes to global microbial community structure" with the goal of coupling "state of the art physical models of global ocean circulation with biogeochemistry and genome-informed models of microbial processes." The boost in computational infrastructure the award provides for will advance several linked areas of research, including the capacity to model marine microbial systems in more detail, enhanced fidelity of the modeled fluid dynamical environment, support for state of the art data analytics including machine learning techniques, and accelerating and extending genomic data processing capabilities. As an initiative to advance our understanding of the biology, ecology, and biogeochemistry of microbial processes that dominate Earth's largest biome -- the global ocean -- SCOPE seeks to measure, model, and conduct experiments at a model ecosystem site located 100 km north of the Hawaiian island of Oahu that is representative of a large portion of the North Pacific Ocean. Steady growth in available large-scale metagenomic and single-cell genomic data resulting from genetics data activities in the Chisholm Lab are also driving additional computational processing resource needs.

AI : The cure the pharmaceutical industry has been waiting for - ET HealthWorld


By Subhro Mallik Vice President, Life Sciences Infosys Today, the pharmaceutical and life science industry leads in AI adoption, according to a research, Amplifying Human Potential – Towards Purposeful AI, conducted by Infosys. Similarly, complex AI algorithms are being put to work by the Center for Rare Childhood Disorder to analyse large amounts of molecular and genetic data to identify medical conditions and create personalized treatment plans. Enhancing Human Decision Making Though AI systems cannot be allowed to work completely without human intervention, their analytics based insights can be used to augment decisions across the organization. Similar medical marvels are now possible because of the new AI technologies and analytics tools that have the capability to analyse large quantities of patient health and genetic data, and medical image analysis.

AI in healthcare: The unevenly distributed future is here


Early this year, I wrote a piece that discussed how emerging technologies such as artificial intelligence (AI) and blockchain will drive precision medicine this year. AI represents a $150 billion savings opportunity for healthcare, across a wide range of applications: robot-assisted surgery, clinical diagnosis and treatment options, and operational efficiencies, to name a few. Health plans: There is considerable traction today applying RPA tools and AI technologies for improving productivity and efficiencies in health plans. As healthcare transitions from a fee-for-service to a value-based care era, the need for advanced technologies for everything from precision medicine to increased operational efficiencies and improved patient engagement will drive the adoption rates for these technologies.

Scientists know how to make mice angry--but mice know how to keep their cool

Popular Science

Researchers at Stanford University Medical Center have taken a closer look at the roots of this rage in the mouse brain, and in a study published today in Neuron, they pinpoint the brain cells that give rise to male territorial aggression. "It's a needle in a haystack compared to the 80 million neurons in the mouse brain," says Nirao Shah, senior study author and a professor at Stanford University. When scientists activated their clusters of VMH neurons, the mice still aggressively defended their cage against intruders. But when placed in a different mouse's cage, they didn't attack, even when the VMH neurons were activated--these mice knew they were guests in someone else's home.

Featured video: A self-driving wheelchair

MIT News

Singapore and MIT have been at the forefront of autonomous vehicle development. Now, leveraging similar technology, MIT and Singaporean researchers have developed and deployed a self-driving wheelchair at a hospital. Spearheaded by Daniela Rus, the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science and director of MIT's Computer Science and Artificial Intelligence Laboratory, this autonomous wheelchair is an extension of the self-driving scooter that launched at MIT last year -- and it is a testament to the success of the Singapore-MIT Alliance for Research and Technology, or SMART – a collaboration between researchers at MIT and in Singapore. Rus, who is also the principal investigator of the SMART Future Urban Mobility research group, says this newest innovation can help nurses focus more on patient care as they can get relief from logistics work which includes searching for wheelchairs and wheeling patients in the complex hospital network.

For Computers, Too, It's Hard to Learn to Speak Chinese

MIT Technology Review

In China today, voice assistant technology works by turning a user's voice commands into text and generating a response based on the meaning of the text. They will also have to understand emotions, since humans' decision making is not based solely on logic, notes Jia Jia, an associate professor at Tsinghua University who studies social affective computing. As of the end of 2016, Baidu claimed 665 million monthly active mobile users, and as of March this year, Alibaba had 507 million mobile monthly active users. For example, to train a neural network to understand texts in sports medicine, you could draw upon data from sports and data from medicine.

Google's AI guru says that great artificial intelligence must build on neuroscience


Currently, most AI systems are based on layers of mathematics that are only loosely inspired by the way the human brain works. Building AI that can perform general tasks, rather than niche ones, is a long-held desire in the world of machine learning. It argues that deep learning, which uses layers of artificial neurons to understand inputs, and reinforcement learning, where systems learn by trial and error, both owe a great deal to neuroscience. The solution, Hassabis and his colleagues argue, is a renewed "exchange of ideas between AI and neuroscience [that] can create a'virtuous circle' advancing the objectives of both fields."