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Fighting Covid-19 Brought These Lasting Breakthroughs to Science and Medicine




2020 was the year of the pandemic. But the arrival of Covid-19 in January not only threw an Earth-sized wrench into our lives, it also dictated the course of scientific discovery. Never before have so much attention, investment, and passion been devoted to one scientific problem. Never before have pre-print servers exploded in popularity, allowing scientists to share discoveries at lightning speed. And never before have we managed to build an arsenal to beat back a life form entirely novel to us, massively accelerating vaccine development by months, if not years—a true paradigm shift not just in vaccinology, but also in how science is done and communicated under fire.

Yet I don’t want to focus solely on Covid-19. We’re now in the end game. Last week, the FDA and its Canadian and British equivalents approved the Pfizer-BioNTech mRNA vaccine for emergency use. Moderna’s mRNA vaccine is hot on its heels, also boasting a success rate of over 90 percent. Front-line workers are receiving the jab all over the country. And dozens of other vaccines are still in the rat race.

There’s no escaping Covid-19 in an end-year retrospective. But there’s good reason to look ahead—the biotech and camaraderie that created an entirely new type of vaccine in record pace isn’t confined to the pandemic, vaccine research, or infectious diseases. They have the power to completely overhaul medicine.

mRNA Vaccines Enter Center Stage

You might have heard that mRNA vaccines have never previously been approved by the FDA. Yet the science behind them is decades long, courtesy of a young Hungarian-born biologist behind a key mRNA discovery—one so novel and groundbreaking it precipitated the death of her career.

Nearly all lifeforms are built by and run on proteins. But the instructions for building proteins are saved in our genetic material. Think of DNA as a library, and the cell’s protein-building factory as a far-off facility speaking a different language. mRNA, short for messenger RNA, is the translator that literally moves between our cells’ DNA library and the protein factory.

In other words, our bodies listen to mRNA to decide which proteins to build. If we could design and synthesize artificial mRNA and deliver them into cells, it’s possible in theory to hijack our cells’ own protein-building system to make any protein we want—even those that are foreign, such as viral proteins.

That’s the reasoning behind both Pfizer-BioNTech and Moderna’s vaccines. By delivering the mRNA of a viral part into our cells, our bodies will make these proteins. Because these proteins are basically “alien invaders,” our immune system learns to recognize them and creates a memory of those foes. When it encounters a real infection, the entire immune military of trained antibodies and killer cells can then rapidly spur to life, nixing the invader before they have a chance to spread or reproduce.

There’s a reason mRNA vaccines are so desirable. Compared to traditional protein-based ones, such as those involving dead viruses that need to be grown in chicken embryos (not kidding), mRNA is incredibly easy to scale in production with low costs. This also makes it possible to screen through candidates at super-sonic speed—and in a pandemic, speed is everything.

At least, that’s the theory on paper. Thanks to recent advances in biotech and Covid-19 lighting science’s behind on fire, mRNA drugs have finally become a widely successful reality.

A Recipe for mRNA Vaccines

Broadly speaking, three main technologies have propelled mRNA vaccines to success in the Covid-19 race: whole-genome reading, mRNA design and packaging, and mRNA synthesis.

The first step to combating any viral foe is to know thy enemy. By January 11, Chinese scientists had deposited parts of the virus’s genetic blueprint onto GenBank, a highly popular online database for genetic information. Whole-genome sequences soon followed, “digitizing” the virus and allowing comparisons between its genetic blueprint and other known viruses. Within a month, we knew that the virus belonged to the coronavirus family, allowing scientists to draw upon previous experience with similar viruses—SARS, MERS—to hone in on the newcomer’s surface “spike proteins,” named after their jagged shapes, as a potential vaccine target.

Genetic sequencing soon took the reins. As an offshoot of synthetic biology, a field that reshuffles the building blocks of life, the cost of making artificial genetic sequences has dropped dramatically—so much so that it’s now simple to order these molecules through commercial companies at dollars a pop.

It’s also made it possible to recreate an entire genome from scratch halfway across the world. A Swiss group, for example, used China’s data to synthesize SARS-CoV-2’s entire genome in the lab, essentially instantly teleporting it into their hands without having to wait for physical samples. Other teams reproduced only the spike protein to analyze for portions that are especially incendiary towards our immune system, which could spark a larger immune response. In early February, long before the world realized we’d be in the midst of a pandemic, scientists had already nailed down the sequence and shape of the protein that eventually spurred the development of our newfangled mRNA vaccines.

The next step was finding a weapon against the virus—and getting it inside a cell. Thanks to computational alignment tools, figuring out the genetic code for the spike protein was a piece of cake. The harder part was designing mRNA candidates, the “instructions,” to encode for the spike protein. One frustrating reason why mRNA vaccines have previously failed is because these molecules are extremely fragile. The body, with its relatively high heat and multitudes of molecular-digesting proteins, is a hostile place.

The hostility also goes the other way. Synthetic mRNAs are very foreign to our bodies. Without care, they can trigger the immune system to go into overdrive—a dangerous condition that could result in serious problems.

Here’s where new tech stood on the shoulders of age-old research. With hopes of making mRNA drugs a reality, scientists have long worked to change their basic components—“letters” very similar to DNA’s familiar quad squad of A, T, C, and G—with slightly chemically-improved ones to increase their stability. Other swaps fine-tune the mRNA’s efficacy so that it triggers a Goldilocks-like immune response—not too much, not too little.

Finally, naked mRNA needs to get inside a cell to work. But once it does, it’s almost instantaneously chopped up. Without mRNA sticking around, our bodies can’t make the viral spike protein, hence no immunity. To deliver it into cells, scientists relied on fatty bubbles—also known as lipid nanoparticles—to form a vessel around the mRNA strands. These cellular spaceships are also a gift from the past: back in 2018, the FDA approved their use for delivering another type of RNA molecules. Pfizer-BioNTech and Moderna’s results provide some of the strongest evidence that they also work well with mRNAs.

The success is indisputable: Moderna went from analyzing the virus’s genetic sequence to an experimental jab in the arm in just 63 days. Pfizer-BioNTech broke lightspeed with its vaccine for emergency use in less than a year.

Now What?

The biotechnologies that made Covid-19 mRNA vaccines are here to stay. So are the fountains of knowledge we’ve gained from this terrifying trial by fire. From the ins and outs of immune responses to what makes mRNA more stable, less toxic, and easier to deliver, to advances in synthetic biology and seamless global collaboration, the battle against Covid-19 highlights how a decade-long scientific dream just blossomed to fruition.

Covid-19 is only one foe. A similar strategy could now be used, with far more confidence, on our long-battled enemies such as HIV. Even novel vaccines are just a small slice of what’s possible. mRNA is the body’s “guidebook” for building protein—any protein. A synthetic mRNA strand that recognizes certain types of cancer could lead to highly-specific “cancer vaccines.” BioNTech, for example, reported in 2017 that a vaccine against melanoma, tailor-made to each of its 13 participants’ unique cancer genetic profile, had higher immunity against their tumors and reduced the chance of spread. Synthetic mRNA could artificially produce missing or defective proteins in the body, such as those critical for normal eyesight or nerve function.

The dream of mRNA therapeutics has been alive since the 90s. One just came true. Keep your eyes peeled for others in 2021.

Image Credit: Felipe Esquivel Reed/Wikimedia Commons



Aite survey: Financial institutions will invest more to automate loan process




Financial institutions plan to increase their spend on automations and collections management solutions for their loan processes. Fresh results on consumer lending practice from research and advisory firm Aite Group indicate lenders plan to invest more heavily in their collections processes, said Leslie Parrish, senior analyst for the Aite Group’s consumer lending practice. Parrish shared […]

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Facial recognition, other ‘risky’ AI set for constraints in EU




Facial recognition and other high-risk artificial intelligence applications will face strict constraints under new rules unveiled by the European Union that threaten hefty fines for companies that don’t comply.

The European Commission, the bloc’s executive body, proposed measures on Wednesday that would ban certain AI applications in the EU, including those that exploit vulnerable groups, deploy subliminal techniques or score people’s social behavior.

The use of facial recognition and other real-time remote biometric identification systems by law enforcement would also be prohibited, unless used to prevent a terror attack, find missing children or tackle other public security emergencies.

Facial recognition is a particularly controversial form of AI. Civil liberties groups warn of the dangers of discrimination or mistaken identities when law enforcement uses the technology, which sometimes misidentifies women and people with darker skin tones. Digital rights group EDRI has warned against loopholes for public security exceptions use of the technology.

Other high-risk applications that could endanger people’s safety or legal status—such as self-driving cars, employment or asylum decisions — would have to undergo checks of their systems before deployment and face other strict obligations.

The measures are the latest attempt by the bloc to leverage the power of its vast, developed market to set global standards that companies around the world are forced to follow, much like with its General Data Protection Regulation.

The U.S. and China are home to the biggest commercial AI companies — Google and Microsoft Corp., Beijing-based Baidu, and Shenzhen-based Tencent — but if they want to sell to Europe’s consumers or businesses, they may be forced to overhaul operations.

Key Points:

  • Fines of 6% of revenue are foreseen for companies that don’t comply with bans or data requirements
  • Smaller fines are foreseen for companies that don’t comply with other requirements spelled out in the new rules
  • Legislation applies both to developers and users of high-risk AI systems
  • Providers of risky AI must subject it to a conformity assessment before deployment
  • Other obligations for high-risk AI includes use of high quality datasets, ensuring traceability of results, and human oversight to minimize risk
  • The criteria for ‘high-risk’ applications includes intended purpose, the number of potentially affected people, and the irreversibility of harm
  • AI applications with minimal risk such as AI-enabled video games or spam filters are not subject to the new rules
  • National market surveillance authorities will enforce the new rules
  • EU to establish European board of regulators to ensure harmonized enforcement of regulation across Europe
  • Rules would still need approval by the European Parliament and the bloc’s member states before becoming law, a process that can take years

—Natalia Drozdiak (Bloomberg Mercury)

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Artificial Intelligence

Prioritizing Artificial Intelligence and Machine Learning in a Pandemic




AI and ML
Illustration: © IoT For All

Artificial Intelligence (AI) and Machine Learning (ML) give companies the one thing humans can’t – scalability. Over time, humans limit a businesses’ ability to scale; they can only work so many hours at a given efficiency. On the other hand, AI and ML can work around the clock with the sole focus on a given project. As organizations navigate through COVID-19’s impact and the future of a remote workforce, scalability and efficiency can be the key to an organization’s successful recovery.

Implementation Challenges

The benefits of AI and ML don’t come without their own challenges; however, the top challenges are a lack of skills and time for proper implementation. In July, Deloitte found in a survey that 69% of respondents said the skills gap for AI implementation ranged from moderate to major to extreme. Simultaneously, many companies overlook the investment it takes to build the processes and infrastructure needed for successfully training, testing, deploying, and maintaining AI and ML in their enterprise.

Such challenges often cause companies to de-prioritize AI and ML projects, especially in times of uncertainty. That has been increasingly obvious throughout the COVID-19 pandemic. But while some organizations have drawn back on their efforts, the current global state demands the greater need for AI and ML to support critical business processes. This is especially true today given the growing remote workforce, considerations for returning to the workplace and work happening in silos worldwide.

Though challenging, it is not impossible to properly implement AI and ML. In this evolving COVID-influenced business landscape, four steps are key to effectively implementing a strong AI and ML system that helps streamline critical business processes despite uncertainty and limited resources.

Identify the Problem to Be Solved

Some companies mistakenly view AI and ML projects as a ‘silver bullet’ to solve all their problems. This often results in overinflated expectations, an unfocused approach, and unsatisfactory results. Instead, companies should identify those specific problems that will have the biggest impact from implementing AI and ML solutions and be hyper-focused on solving those problems.

Select Your Data

The second step in creating a strong AI and ML algorithm is to select the source data that your algorithm will be training on. There are two main options: training on your own data or training on a larger scale data set. Based on experience, training your algorithm on your own data puts you at a disadvantage. By training on a larger scale data set, the likelihood of success increases because your data is more representative and varied. Through advanced concepts such as transfer learning, companies can use semi-trained models based on larger data sets and then train the “last mile” using their own specific content unique to their business.

Clean House

The standby rules of data management apply here – garbage in, garbage out. Ultimately, the quality and accuracy of machine learning models depend on being representative. AI and ML – fed with the right data – can streamline operations and increase the benefit of companies’ DX and cloud migration journeys.

When you’re kicking off an AI or ML project, the most critical step is to clean up the data that your algorithm will be training on, especially if you’re using your own data or models.

Make Room for Training

AI and ML are all about probability. When you ask it a question, for example, “Is this a cat?,” the results you receive are the algorithm saying, “Out of the three buckets I was trained on, the likelihood of this image being a cat is .91, the likelihood of this image being a dog is .72 and the likelihood of this image being a bird is .32.”

This is why training on varied data is so important. If your training data only includes images of cats, dogs, and birds and you ask the algorithm to analyze the picture of a crocodile, it will only respond based on the buckets it’s been trained on – cats, dogs, and birds.

If you’ve properly selected and cleaned your data, training should be an easy last step, but it’s also an opportunity to go back to the first two steps and further refine based on your training.

The front end of training an AI and ML algorithm can be time-intensive, but following these four steps can make it easier to achieve significant outcomes. Across industries, AI and ML can quickly show ROI. For example, in the insurance industry, AI and ML can help insurers quickly search contracts, so employees aren’t sifting through contracts and repositories around the globe to answer simple questions. This means time efficiencies for an industry that COVID-19 has heavily impacted.

Even better, working with a SaaS provider with experience in your industry can make this process much easier and less costly. SaaS platforms allow companies to take advantage of having all of the infrastructure, security, and pre-trained models in place to reduce the overall effort and time to value. Many platforms allow users to uptrain the predefined models with unique customer data, reducing the training effort needed for model creation. Companies can then focus on integration with their ecosystem and workflows rather than model creation itself.

Bigger Picture

Overall, businesses can soften the impact of COVID by focusing on the bigger picture with AI and ML. Implementing AI and ML projects increase business productivity despite these times of uncertainty. As we continue on the road to recovery, we need tools like AI and ML to stay focused on the bigger picture, mission-critical tasks.

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ProGlove promotes worker well-being with human digital twin technology




Join Transform 2021 this July 12-16. Register for the AI event of the year.

ProGlove, the company behind an ergonomic barcode scanner, has developed new tools for analyzing human processes to build a human digital twin.

“We have always been driven to have our devices narrate the story of what is really happening on the shop floor, so we added process analytics capabilities that allow for time-motion studies, visualization of the shop floor, and more,” ProGlove CEO Andreas Koenig told VentureBeat.

The company’s newest process analytics tools can complement the typical top-down perspective of applications by adding a process-as-seen view to the conventional process-as-wanted view. Most importantly, it can also provide insights that improve well-being.

Koenig said, “We are building an ecosystem that empowers the human worker to make their businesses stronger.”

ProGlove CEO Andreas Koenig

Above: ProGlove CEO Andreas Koenig

Image Credit: ProGlove

The market for barcode scanning is still going strong and is often taken for granted, given how old it is. “You have technologies like RFID that have been celebrated for being the next big thing, and yet their impact thus far hasn’t been anywhere near where most pundits expected it,” Koenig said.

Companies like Zebra, Honeywell, and Datalogic have lasted for decades by building out an ecosystem of tools to address industry needs. “What sets us apart is that we looked beyond the obvious and started with the human worker in mind,” Koenig said.

Not only is the company providing a form factor designed to meet requirements for rugged tools, this shift to analytics could further promote efficiency, quality, and ergonomics on the shop floor.

How a human digital twin works

ProGlove’s cofounders participated in Intel’s Make It Wearable Challenge, with the idea of designing a smart glove for industries. Today, ProGlove’s MARK scanner can collect six-axis motion data, including pitch, yaw, roll, and acceleration, along with timestamps, a step count, and camera data (such as barcode reading speed and the scanner ID).

Koenig’s vision goes beyond selling a product to establish the right balance between businesses’ need for profits and their obligation to ensure worker well-being. Koenig estimates that human hands deliver 70% of added value in factories and on warehouse floors. “There is no doubt that they are your most valuable resource that needs protection. Even more so since we are way more likely to experience a shortage of human workers in the warehouses across the world than having them replaced by robots, automation, or AI.”

ProGlove Insight contextualizes the collected data and lets users compare workstations and measure the workload and effort necessary to complete the tasks. Users can also visualize their shop floor, look at heatmaps, and identify best practices or efficiency blockers. After a recent smart factory lab experiment with users, DPD and Asics realized efficiency gains by as much as 20%, Koenig said.

ProGlove’s vision of the human digital twin is built on three pillars: a digital representation of onsite workers, a visualization of the shop floor, and an industrial process engineer. “The human digital twin is all about striking the right balance between businesses’ needs for profitability, efficiency, and worker well-being,” Koenig said. At the same time, it is important that the human digital twin complies with data privacy regulations and provides transparency to frontline workers around what data is being transmitted.


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