Health & Medicine

As technologies like single-cell genomic sequencing, enhanced biomedical imaging, and medical "internet of things" devices proliferate, key discoveries about human health are increasingly found within vast troves of complex life science and health data. But drawing meaningful conclusions from that data is a difficult problem that can involve piecing together different data types and manipulating huge data sets in response to varying scientific inquiries. The problem is as much about computer science as it is about other areas of science. That's where Paradigm4 comes in. The company, founded by Marilyn Matz SM '80 and Turing Award winner and MIT Professor Michael Stonebraker, helps pharmaceutical companies, research institutes, and biotech companies turn data into insights.

Working at Royal Dutch Shell's Deepwater division in New Orleans gives Barbara Waelde a front-row seat to how the right data can unlock crucial information for the oil giant. So when her supervisor asked her last year if she was interested in a program that could sharpen her digital and data science capabilities, Waelde, 55, jumped at the chance. Since she began her online coursework, the seven-year Shell veteran has learned Python programming, supervised learning algorithms and data modeling, among other skills. Shell began making these online courses available to U.S. employees long before COVID-19 upended daily life. And according to the oil giant, there are no plans to halt or cancel any of them, despite the fact that on March 23 it announced plans to slash operating costs by $9 billion.

Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavio's report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio's comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

Automation Anywhere, a global leader in Robotic Process Automation (RPA), announced the launch of Bot Security, the industry's first security program to set the standard for securing software bots that enable business continuity. The magnitude of the coronavirus (Covid-19) outbreak has organizations around the world looking to technologies like RPA and intelligent automation to help mitigate disruptions and advance public health, keep global supply chains moving and governments afloat. The company introduced a flexible, multi-tiered framework to certify that bots built by customers, partners, and publishers of bots on Bot Store – the world's largest intelligent automation marketplace with more than 850 pre-built bots – are pre-certified and trusted to scale RPA more rapidly and securely. With Bot Security, users downloading ready-to-deploy intelligent software bots no longer have to compromise on security as they build RPA solutions to access critical data, track the virus's spread and direct citizens to vital information from trusted sources. Automation Anywhere leads the industry as the first vendor to offer a web-based, cloud-native RPA platform that is System and Organization Controls (SOC) 2 Type 1 certified.

Two graduates of the Data Science Institute (DSI) at Columbia University are using computational design to quickly discover treatments for the coronavirus. Andrew Satz and Brett Averso are chief executive officer and chief technology officer, respectively, of EVQLV, a startup creating algorithms capable of computationally generating, screening, and optimizing hundreds of millions of therapeutic antibodies. They apply their technology to discover treatments most likely to help those infected by the virus responsible for COVID-19.

Machine Learning in Python: Principal Component Analysis (PCA) for Handling High-Dimensional Data In this video, I will be showing you how to perform principal component analysis (PCA) in Python using the scikit-learn package. PCA represents a powerful learning approach that enables the analysis of high-dimensional data as well as reveal the contribution of descriptors in governing the distribution of data clusters. Particularly, we will be creating PCA scree plot, scores plot and loadings plot. This video is part of the [Python Data Science Project] series. If you're new here, it would mean the world to me if you would consider subscribing to this channel.

Tunisia deployed a police robot to patrol streets of the capital and enforce a lockdown imposed to contain coronavirus spread. Known as PGuard, the "robocop" which is remotely operated and is equipped with thermal imaging cameras is seen calling out to suspected violators in a video, "What are you doing? You don't know there's a lockdown?"

NEW YORK: Scientists have developed an artificial intelligence (AI) tool that may accurately predict which patients newly infected with the virus that causes Covid-19 would go on to develop severe respiratory disease. The study, published in the journal Computers, Materials & Continua, also revealed the best indicators of future severity, and found that they were not as expected. "While work remains to further validate our model, it holds promise as another tool to predict the patients most vulnerable to the virus, but only in support of physicians' hard-won clinical experience in treating viral infections," said Megan Coffee, a clinical assistant professor at New York University (NYU) in the US. "Our goal was to design and deploy a decision-support tool using AI capabilities -- mostly predictive analytics -- to flag future clinical coronavirus severity," said Anasse Bari, a clinical assistant professor at New York University. "We hope that the tool, when fully developed, will be useful to physicians as they assess which moderately ill patients really need beds, and who can safely go home, with hospital resources stretched thin," Bari said.

The readout of brain activity and audio of the spoken sentences were input to an algorithm, which learned to recognize how the parts of speech were formed. The initial results were highly inaccurate, for instance, interpreting brain activity from hearing the sentence "she wore warm fleecy woolen overalls" as "the oasis was a mirage." As the program learned over time, it was able to make translations with limited errors, such as interpreting brain activity in response to hearing "the ladder was used to rescue the cat and the man" as "which ladder will be used to rescue the cat and the man."

Researchers at the University of California, San Francisco have recently created an AI system that can produce text by analyzing a person's brain activity, essentially translating their thoughts into text. The AI takes neural signals from a user and decodes them, and it can decipher up to 250 words in real-time based on a set of between 30 to 50 sentences. As reported by the Independent, the AI model was trained on neural signals collected from four women. The participants in the experiment had electrodes implanted in their brains to monitor for the occurrence of epileptic seizures. The participants were instructed to read sentences aloud, and their neural signals were fed to the AI model.