A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), in collaboration with Ava Robotics and the Greater Boston Food Bank (GBFB), designed a new robotic system that powerfully disinfects surfaces and neutralizes aerosolized forms of the coronavirus. Using a custom UV-C light fixture, the system can clean a warehouse floor in half an hour.

Why UV-C light?

UV-C light is a short-wavelength, ultraviolet light that breaks apart germ DNA, leaving it unable to function or reproduce. In other words, UV-C light is germicidal (UV-A and UV-B light are not). UV-C can even neutralize “superbugs” that have developed a resistance to antibiotics.

This new robot doesn’t require any human supervision. Instead, it uses a UV-C array for disinfecting surfaces. Specifically, the array uses short-wavelength ultraviolet light to kill microorganisms and disrupt their DNA in a process called ultraviolet germicidal irradiation.

Scientists tested this approach in GBFB’s warehouse. They found that the robot system is capable of mapping the space and navigating between waypoints and other specified areas.

Scientists specifically used a UV-C dosimeter to check whether the robot was delivering the expected dosage of UV-C light predicted by the model.

The robot drives by the pallets and storage aisles at a speed of roughly 0.22 miles per hour. At this speed, the robot could cover a 4,000-square-foot space in GBFB’s warehouse in just half an hour. The UV-C dosage delivered during this time can neutralize approximately 90 percent of coronaviruses on surfaces. For many surfaces, this dose will be higher, resulting in more of the virus neutralized.

Catherine D’Amato, president and CEO of the Greater Boston Food Bank, said, “Our 10-year-old warehouse is a relatively new food distribution facility with AIB-certified, state-of-the-art cleanliness and food safety standards. Covid-19 is a new pathogen that GBFB, and the rest of the world, was not designed to handle. We are pleased to have this opportunity to work with MIT CSAIL and Ava Robotics to innovate and advance our sanitation techniques to defeat this menace.”

Scientists initially teleported the robot to teach it the path around the warehouse. It is equipped with the autonomy to move around without requiring humans to navigate it remotely. It can go to defined waypoints on its map, such as going to the loading dock, the warehouse shipping floor, then returning to base.

Within GBFB, the team identified the warehouse shipping floor as a “high-importance area” for the robot to disinfect. Each day, workers stage aisles of products and arrange them for up to 50 pickups by partners and distribution trucks the next day. By focusing on the shipping area, it prioritizes disinfecting items leaving the warehouse to reduce Covid-19 spread out into the community.

Now, the team is searching for ways to use its onboard sensors to adapt to changes in the environment, such that in new territory, the robot would adjust its speed to ensure the recommended dosage is applied to new objects and surfaces.

For immediate next steps, the team is focused on increasing the capabilities of the robot at GBFB, as well as eventually implementing design upgrades. Their broader intention focuses on how to make these systems more capable of adapting to our world: how a robot can dynamically change its plan based on estimated UV-C dosages, how it can work in new environments, and how to coordinate teams of UV-C robots to work together.

CSAIL director and project lead Daniela Rus said, “We are excited to see the UV-C disinfecting robot support our community in this time of need. The insights we received from the work at GBFB has highlighted several algorithmic challenges. We plan to tackle these to extend the scope of autonomous UV disinfection in complex spaces, including dorms, schools, airplanes, and grocery stores.”

Alyssa Pierson, CSAIL research scientist and technical lead of the UV-C lamp assembly and CSAIL Ph.D. student Jonathan Romanishin worked alongside Hunter Hansen (software capabilities), Bryan Teague of MIT Lincoln Laboratory (who assisted with the UV-C lamp assembly), Igor Gilitschenski and Xiao Li (assisting with future autonomy research), MIT professors Daniela Rus and Saman Amarasinghe, and Ava leads Marcio Macedo and Youssef Saleh. 

The post A new robotic system that powerfully disinfects large surfaces in half an hour appeared first on Artificial Intelligence.

NEW YORK – A team from the National Library of Medicine, Broad Institute, and Massachusetts Institute of Technology has started tallying the genetic features that distinguish pathogenic coronaviruses — particularly the SARS-CoV-2 virus behind the ongoing COVID-19 pandemic and the Middle Eastern respiratory syndrome-causing MERS-CoV — from less dangerous coronaviruses.

“We were able to identify several features that are not found in less virulent coronaviruses and that could be relevant for pathogenicity in humans. The actual demonstration of the relevance of these findings will come from direct experiments that are currently getting under way,” senior author Eugene Koonin, a biotechnology information researcher at the National Library of Medicine, said in a statement.

For a paper published in the Proceedings of the National Academy of Sciences on Wednesday, the researchers relied on comparative genomics, phylogenetic analyses, and support vector-based machine learning to narrow in on suspicious features shared by the SARS-CoV-2 and MERS-CoV coronaviruses, which they classified as viruses with “high case fatality rate” (high-CFR) coronaviruses. They noted that the machine-learning strategy selected made it possible to pick up differences between these high-CFR viruses and “low-CFR” human coronaviruses that might be missed with genome alignment-based comparisons alone.

“[W]e trained multiple support vector machines across a sliding window to detect regions that confer clean separation between high- and low-CFR virus genomes,” the authors explained. “We evaluated the performance of each [support vector machine] via cross-validation and filtered for genomic regions that significantly distinguish the high- and low-CFR genomes.”

Based on analyses of more than 900 available coronavirus genomes, the team uncovered 11 seemingly distinct sites in the high-CFR SARS-CoV-2 and MERS-CoV genomes, including sequences coding for the nucleocapsid protein and the spike glycoprotein that interacts with host cell receptors.

When they took a closer look at these changes, the researchers saw signs that the high-CFR viruses produce a version of the nucleocapsid protein with an enhanced nuclear localization signal, while the spike protein for the potentially deadly SARS and MERS coronaviruses shared insertions not found in more mild-mannered, low-CFR coronaviruses.

“The enhancement of the NLS in the high-CFR coronaviruses nucleocapsids implies an important role of the sub-cellular localization of the nucleocapsid protein in coronavirus pathogenicity,” the authors suggested, adding that “insertions in the spike protein appear to have been acquired independently by the SARs and MERS clades of the high-CFR coronaviruses, in both the domain involved in virus-cell fusion and the domain mediating receptor recognition.”

While functional studies are needed to dig into the potential connections identified in their new analysis, the authors suggested that the features found so far “could be crucial contributors to coronavirus pathogenicity and possible targets for diagnostics, prognostication, and interventions.”

“These features correlate with the high fatality rate of these coronaviruses as well as their ability to switch hosts from animals to humans,” Koonin and co-authors explained. “The identified features could represent crucial elements of coronavirus virulence and allow for detecting animal coronaviruses that have the potential to make the jump to humans in the future.”

The post Coronavirus Pathogenicity Clues Uncovered Using Machine-Learning Approach appeared first on Artificial Intelligence.

Recently, a team of researchers at MIT discovered a powerful new antibiotic using machine learning algorithms. The antibiotic is named for the AI in 2001: A Space Odyssey, halicin. It successfully wiped out numerous bacterial strains, including some of the most dangerous drug-resistant bacteria on the World Health Organization’s most wanted list. After month-long efforts and experiments, E. coli bacteria also failed to develop resistance to halicin, in stark contrast to the existing antibiotic ciprofloxacin.

As noted by the study, “Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub—halicin—that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.”

According to a senior author on the study and computer science professor at MIT, Regina Barzilay, “In terms of antibiotic discovery, this is absolutely a first.”

Reportedly, the algorithm that discovered halicin was trained on the molecular features of 2,500 compounds. Nearly half were FDA-approved drugs, and another 800 naturally occurring. The researchers specifically tuned the algorithm to look for molecules with antibiotic properties but whose structures would differ from existing antibiotics (as halicin’s does). Using another machine learning program, they screened the results for those likely to be safe for humans.

Early study suggests halicin attacks the bacteria’s cell membranes, disrupting their ability to produce energy. Protecting the cell membrane from halicin might take more than one or two genetic mutations, which could account for its impressive ability to prevent resistance.

James Collins, an MIT professor of bioengineering and senior author said, “I think this is one of the more powerful antibiotics that has been discovered to date. It has remarkable activity against a broad range of antibiotic-resistant pathogens.”

Beyond tests in petri-dish bacterial colonies, the team also tested halicin in mice. The antibiotic cleared up infections of a strain of bacteria resistant to all known antibiotics in a day. The team plans further study in partnership with a pharmaceutical company or non-profit, and they hope to eventually prove it safe and effective for use in humans.

However, this last bit remains the trickiest step, given the cost of getting a new drug approved, but Collins hopes algorithms like theirs will help and they could dramatically reduce the cost required to get through clinical trials.

Moreover, Barzilay hopes the approach can find or even design novel antibiotics that kill bad bacteria with alacrity while sparing the good guys. In this way, a round of antibiotics would cure whatever ails people without taking out their whole gut microbiome in the process.

The discovery in itself a greater achievement yet the bigger picture implies the increasing use of machine learning and similar technologies in the long, expensive process of drug discovery. Apart from MIT researchers, other universities and industry players working in this arena are extensively deploying AI to produce a vast amount of drug-like compounds to thrive in the healthcare industry infused with new-age technologies.

Barzilay says, “There is still a question of whether machine-learning tools are really doing something intelligent in healthcare, and how we can develop them to be workhorses in the pharmaceutical industry. This shows how far you can adapt to this tool.”

The post MIT RESEARCHERS DEPLOYING MACHINE LEARNING TO DEVELOP EFFECTIVE ANTIBIOTICS appeared first on Artificial Intelligence.