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New CRISPR platform expands RNA editing capabilities

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CRISPR-based tools have revolutionized our ability to target disease-linked genetic mutations. CRISPR technology comprises a growing family of tools that can manipulate genes and their expression, including by targeting DNA with the enzymes Cas9 and Cas12 and targeting RNA with the enzyme Cas13. This collection offers different strategies for tackling mutations. Targeting disease-linked mutations in RNA, which is relatively short-lived, would avoid making permanent changes to the genome. In addition, some cell types, such as neurons, are difficult to edit using CRISPR/Cas9-mediated editing, and new strategies are needed to treat devastating diseases that affect the brain.

McGovern Institute Investigator and Broad Institute of MIT and Harvard core member Feng Zhang and his team have now developed one such strategy, called RESCUE (RNA Editing for Specific C to U Exchange), described in the journal Science.

Zhang and his team, including first co-authors Omar Abudayyeh and Jonathan Gootenberg (both now McGovern fellows), made use of a deactivated Cas13 to guide RESCUE to targeted cytosine bases on RNA transcripts, and used a novel, evolved, programmable enzyme to convert unwanted cytosine into uridine — thereby directing a change in the RNA instructions. RESCUE builds on REPAIR, a technology developed by Zhang’s team that changes adenine bases into inosine in RNA.

RESCUE significantly expands the landscape that CRISPR tools can target to include modifiable positions in proteins, such as phosphorylation sites. Such sites act as on/off switches for protein activity and are notably found in signaling molecules and cancer-linked pathways.

“To treat the diversity of genetic changes that cause disease, we need an array of precise technologies to choose from. By developing this new enzyme and combining it with the programmability and precision of CRISPR, we were able to fill a critical gap in the toolbox,” says Zhang, the James and Patricia Poitras Professor of Neuroscience at MIT. Zhang has appointments in MIT’s departments of Brain and Cognitive Sciences and Biological Engineering.

Expanding the reach of RNA editing to new targets

The previously developed REPAIR platform used the RNA-targeting CRISPR/Cas13 to direct the active domain of an RNA editor, ADAR2, to specific RNA transcripts where it could convert the nucleotide base adenine to inosine, or letters A to I. Zhang and colleagues took the REPAIR fusion and evolved it in the lab until it could change cytosine to uridine, or C to U.

RESCUE can be guided to any RNA of choice, then perform a C-to-U edit through the evolved ADAR2 component of the platform. The team took the new platform into human cells, showing that they could target natural RNAs in the cell, as well as 24 clinically relevant mutations in synthetic RNAs. They then further optimized RESCUE to reduce off-target editing, while minimally disrupting on-target editing.

New targets in sight

Expanded targeting by RESCUE means that sites regulating activity and function of many proteins through post-translational modifications, such as phosphorylation, glycosylation, and methylation, can now be more readily targeted for editing.

A major advantage of RNA editing is its reversibility, in contrast to changes made at the DNA level, which are permanent. Thus, RESCUE could be deployed transiently in situations where a modification may be desirable temporarily, but not permanently. To demonstrate this, the team showed that in human cells, RESCUE can target specific sites in the RNA encoding β-catenin, that are known to be phosphorylated on the protein product, leading to a temporary increase in β-catenin activation and cell growth. If such a change were made permanent, it could predispose cells to uncontrolled cell growth and cancer, but by using RESCUE, transient cell growth could potentially stimulate wound healing in response to acute injuries.

The researchers also targeted a pathogenic gene variant, APOE4. The APOE4 allele has consistently emerged as a genetic risk factor for the development of late-onset Alzheimer’s disease. Isoform APOE4 differs from APOE2, which is not a risk factor, by just two differences (both C in APOE4 versus U in APOE2). Zhang and colleagues introduced the risk-associated APOE4 RNA into cells and showed that RESCUE can convert its signature Cs to an APOE2 sequence, essentially converting a risk to a non-risk variant.

To facilitate additional work that will push RESCUE toward the clinic, as well as enable researchers to use RESCUE as a tool to better understand disease-causing mutations, the Zhang lab plans to share the RESCUE system broadly, as they have with previously developed CRISPR tools. The technology will be freely available for academic research through the nonprofit plasmid repository Addgene. Additional information can be found on the Zhang lab’s webpage.

Support for the study was provided by The Phillips Family; J. and P. Poitras; the Poitras Center for Psychiatric Disorders Research; Hock E. Tan and K. Lisa Yang Center for Autism Research; Robert Metcalfe; David Cheng; and a Natinoal Institutes of Heatlh grant to Omar Abudayyeh. Feng Zhang is a New York Stem Cell Foundation–Robertson Investigator. Feng Zhang is supported by the National Institutes of Health; the Howard Hughes Medical Institute; the New York Stem Cell Foundation, and G. Harold and Leila Mathers Foundations.


Topics: McGovern Institute, Broad Institute, Brain and cognitive sciences, Biological engineering, School of Science, CRISPR, School of Engineering, RNA, DNA, Genetics, Research

Source: http://news.mit.edu/2019/new-crispr-platform-expands-rna-editing-capabilities-0711

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Listen: OakNorth CIO shares automation trends in commercial lending

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Commercial banks have been automating aspects of the lending and decisioning process, primarily at the lower end of the commercial lending spectrum, but hesitate to automate for loans more than $1 million. This means commercial banks have kept automations focused on loans of less than $1 million, explains Sean Hunter in this podcast discussion with […]

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Source: https://bankautomationnews.com/allposts/corp-bank/listen-oaknorth-cio-shares-automation-trends-in-commercial-lending/

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Predictive Maintenance is a Killer AI App 

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Predictive maintenance resulting from IoT and AI working together has been identified as a killer app, with a track record of ROI. (Credit: Getty Images) 

By John P. Desmond, AI Trends Editor 

Predictive maintenance (PdM) has emerged as a killer AI app. 

In the past five years, predictive maintenance has moved from a niche use case to a fast-growing, high return on investment (ROI) application that is delivering true value to users. These developments are an indication of the power of the Internet of Things (IoT) and AI together, a market considered in its infancy today. 

These observations are from research conducted by IoT Analytics, consultants who supply market intelligence, which recently estimated that the $6.9 billion predictive maintenance market will reach $28.2 billion by 2026.  

The company began its research coverage of the IoT-driven predictive maintenance market in 2016, at an industry maintenance conference in Dortmund, Germany. Not much was happening. “We were bitterly disappointed,” stated Knud Lasse Lueth, CEO at IoT Analytics, in an account in IoT Business News. “Not a single exhibitor was talking about predictive maintenance.”  

Things have changed. IoT Analytics analyst Fernando Alberto Brügge stated, Our research in 2021 shows that predictive maintenance has clearly evolved from the rather static condition-monitoring approach. It has become a viable IoT application that is delivering overwhelmingly positive ROI.” 

Technical developments that have contributed to the market expansion include: a simplified process for connecting IoT assets, major advances in cloud services, and improvements in the accessibility of machine learning/data science frameworks, the analysts state.  

Along with the technical developments, the predictive maintenance market has seen a steady increase in the number of software and service providers offering solutions. IoT Analytics identified about 100 companies in the space in 2016; today the company identifies 280 related solution providers worldwide. Many of them are startups who recently entered the field. Established providers including GE, PTC, Cisco, ABB, and Siemens, have entered the market in the past five years, many through acquisitions.  

The market still has room; the analysts predict 500 companies will be in the business in the next five years.  

In 2016, the ROI from predictive analytics was unclear. In 2021, a survey of about 100 senior IT executives from the industrial sector found that predictive maintenance projects have delivered a positive ROI in 83% of the cases. Some 45% of those reported amortizing their investments in less than a year. “This data demonstrated how attractive the investment has become in recent years,” the analysts stated.   

More IoT Sensors Means More Precision 

Implemented projects that the analysts studied in 2016 relied on a limited number of data sources, typically one sensor value, such as vibration or temperature. Projects described in the 2021 report described 11 classes of data sources, such as data from existing sensors or data from the controllers. As more sources are tapped, the precision of the predictions increase, the analysts state.  

Many projects today are using hybrid modeling approaches that rely on domain expertise, virtual sensors and augmented data. AspenTech and PARC are two suppliers identified in the report as embracing hybrid modeling approaches. AspenTech has worked with over 60 companies to develop and test hybrid models that combine physics with ML/data science knowledge, enhancing prediction accuracy. 

The move to edge computing is expected to further benefit predictive modeling projects, by enabling algorithms to run at the point where data is collected, reducing response latency. The supplier STMicroelectronics recently introduced some smart sensor nodes that can gather data and do some analytic processing. 

More predictive maintenance apps are being integrated with enterprise software systems, such as enterprise resource planning (ERP) or computerized maintenance  management systems (CMMS). Litmus Automation offers an integration service to link to any industrial asset, such as a programmable logic controller, a distributed control system, or a supervisory control and data acquisition system.   

Reduced Downtime Results in Savings 

Gains come from preventing downtime. Predictive maintenance is the result of monitoring operational equipment and taking action to prevent potential downtime or an unexpected or negative outcome,” stated Mike Leone, an analyst at IT strategy firm Enterprise Strategy Group, in an account from TechTarget.  

Felipe Parages, Senior Data Scientist, Valkyrie

Advances that have made predictive maintenance more practical today include sensor technology becoming more widespread, and the ability to monitor industrial machines in real time, stated Felipe Parages, senior data scientist at Valkyrie, data sense consultants. With more sensors, the volume of data has grown exponentially, and data analytics via cloud services has become available. 

It used to be that an expert had to perform an analysis to determine if a machine was not operating in an optimal way. “Nowadays, with the amount of data you can leverage and the new techniques based on machine learning and AI, it is possible to find patterns in all that data, things that are very subtle and would have escaped notice by a human being,” stated Parages. 

As a result, one person can now monitor hundreds of machines, and companies are accumulating historical data, which enables deeper trend analysis. “Predictive maintenance “is a very powerful weapon,” he stated.  

In an example project, Italy’s primary rail operator, Trenitalia, adopted predictive maintenance for its high-speed trains. The system is expected to save eight to 10% of an annual maintenance budget of 1.3 billion Euros, stated Paul Miller, an analyst with research firm Forrester, which recently issued a report on the project.  

They can eliminate unplanned failures which often provide direct savings in maintenance but just as importantly, by taking a train out of service before it breaks—that means better customer service and happier customers,” Miller stated. He recommended organizations start out with predictive maintenance by fielding a pilot project. 

In an example of the types of cooperation predictive maintenance projects are expected to engender, the CEOs of several European auto and electronics firms recently announced plans to join forces to form the “Software Republique,” a new ecosystem for innovation in intelligent mobility. Atos, Dassault Systèmes, Groupe Renault, and STMicroelectronics and Thales announced their decision to pool their expertise to accelerate the market.   

Luca de Meo, Chief Executive Officer, Groupe Renault

Luca de Meo, Chief Executive Officer of Groupe Renault, stated in a press release from STMicroelectronics, In the new mobility value chain, on-board intelligence systems are the new driving force, where all research and investment are now concentrated. Faced with this technological challenge, we are choosing to play collectively and openly. There will be no center of gravity, the value of each will be multiplied by others. The combined expertise in cybersecurity, microelectronics, energy and data management will enable us to develop unique, cutting-edge solutions for low-carbon, shared, and responsible mobility, made in Europe.”    

The Software République will be based in Guyancourt, a commune in north-central France at the Renault Technocentre in a building called Odyssée, a 12,000 square meter space which is eco-responsible. For example, its interior and exterior structure is 100 percent wood, and the building is covered with photovoltaic panels. 

Read the source articles in IoT Business News TechTarget, and in a press release from STMicroelectronics.

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Source: https://www.aitrends.com/predictive-analytics/predictive-maintenance-is-a-killer-ai-app/

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Post Office Looks to Gain an Edge With Edge Computing 

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By AI Trends Editor John P. Desmond  

NVIDIA on May 6 detailed a partnership with the US Postal Service underway for over a year to speed up mail service using AI, with a goal of reducing current processing time tasks that take days to hours.   

The project fields edge servers at 195 Post Services sites across the nation, which review 20 terabytes of images a day from 1,000 mail processing machines, according to a post on the NVIDIA blog.  

Anthony Robbins, Vice President of Federal, Nvidia

“The federal government has been for the last several years talking about the importance of artificial intelligence as a strategic imperative to our nation, and as an important funding priority. It’s been talked about in the White House, on Capitol Hill, in the Pentagon. It’s been funded by billions of dollars, and it’s full of proof of concepts and pilots,” stated Anthony Robbins, Vice President of Federal for NVIDIA, in an interview with Nextgov “And this is one of the few enterprisewide examples of an artificial intelligence deployment that I think can serve to inspire the whole of the federal government.”  

The project started with USPS AI architect at the time Ryan Simpson, who had the idea to try to expand an image analysis system a postal team was developing, into something much bigger, according to the blog post. (Simpson worked for USPS for over 12 years, and moved to NVIDIA as a senior data scientist eight months ago.) He believed that a system could analyze billions of images each center generated, and gain insights expressed in a few data points that could be shared quickly over the network.  

In a three-week sprint, Simpson worked with half a dozen architects at NVIDIA and others to design the needed deep-learning models. The work was done within the Edge Computing Infrastructure Program (ECIP), a distributed edge AI system up and running on Nvidia’s EGX platform at USPS. The EGX platform enables existing and modern, data-intensive applications to be accelerated and secure on the same infrastructure, from data center to edge. 

“It used to take eight or 10 people several days to track down items, now it takes one or two people a couple of hours,” stated Todd Schimmel, Manager, Letter Mail Technology, USPS. He oversees USPS systems including ECIP, which uses NVIDIA-Certified edge servers from Hewlett-Packard Enterprise.  

In another analysis, a computer vision task that would have required two weeks on a network of servers with 800 CPUs can now get done in 20 minutes on the four NVIDIA V100 Tensor Core GPUs in one of the HPE Apollo 6500 servers.  

Contract Awarded in 2019 for System Using OCR  

USPS had put out a request for proposals for a system using optical character recognition (OCR) to streamline its imaging workflow. “In the past, we would have bought new hardware, software—a whole infrastructure for OCR; or if we used a public cloud service, we’d have to get images to the cloud, which takes a lot of bandwidth and has significant costs when you’re talking about approximately a billion images,” stated Schimmel. 

AI algorithms were developed on these NVIDIA DGX servers at a US Postal Service Engineering facility. (Credit: Nvidia)

Today, the new OCR application will rely on a deep learning model in a container on ECIP managed by Kubernetes, the open source container orchestration system, and served by NVIDIA Triton, the company’s open-source inference-serving software. Triton allows teams to deploy trained AI models from any framework, such as TensorFlow or PyTorch. 

The deployment was very streamlined,” Schimmel stated. “We awarded the contract in September 2019, started deploying systems in February 2020 and finished most of the hardware by August—the USPS was very happy with that,” he added 

Multiple models need to communicate to the USPS OCR application to work. The app that checks for mail items alone requires coordinating the work of more than a half dozen deep-learning models, each checking for specific features. And operators expect to enhance the app with more models enabling more features in the future. 

“The models we have deployed so far help manage the mail and the Postal Service—they help us maintain our mission,” Schimmel stated.  

One model, for example, automatically checks to see if a package carries the right postage for its size, weight, and destination. Another one that will automatically decipher a damaged barcode could be online this summer.  

“We’re at the very beginning of our journey with edge AI. Every day, people in our organization are thinking of new ways to apply machine learning to new facets of robotics, data processing and image handling,” he stated. 

Accenture Federal Services, Dell Technologies, and Hewlett-Packard Enterprise contributed to the USPS OCR system incorporating AI, Robbins of NVIDIA stated. Specialized computing cabinets—or nodes—that contain hardware and software specifically tuned for creating and training ML models, were installed at two data centers.   

The AI work that has to happen across the federal government is a giant team sport,” Robbins stated to Nextgov. “And the Postal Service’s deployment of AI across their enterprise exhibited just that.” 

The new solutions could help the Postal Service improve delivery standards, which have fallen over the past year. In mid-December, during the last holiday season, the agency delivered as little as 62% of first-class mail on time—the lowest level in years, according to an account in VentureBeat . The rate rebounded to 84% by the week of March 6 but remained below the agency’s target of about 96%. 

The Postal Service has blamed the pandemic and record peak periods for much of the poor service performance. 

Read the source articles and information on the Nvidia blog, in Nextgov and in VentureBeat.

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Source: https://www.aitrends.com/edge-computing/post-office-looks-to-gain-an-edge-with-edge-computing/

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Here Come the AI Regulations  

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New proposed laws to govern AI are being entertained in the US and Europe, with China following a government-first approach. (Credit: Getty Images)  

By AI Trends Staff 

New laws will soon shape how companies use AI.   

The five largest federal financial regulators in the US recently released a request for information how banks use AI, signaling that new guidance is coming for the finance business. Soon after that, the US Federal Trade Commission released a set of guidelines on “truth, fairness and equity” in AI, defining the illegal use of AI as any act that “causes more harm than good,” according to a recent account in Harvard Business Review  

And on April 21, the European Commission issued its own proposal for the regulation of AI (See AI Trends, April 22, 2021)  

Andrew Burt, Managing Partner, bnh.ai

While we don’t know what these regulation will allow, “Three central trends unite nearly all current and proposed laws on AI, which means that there are concrete actions companies can undertake right now to ensure their systems don’t run afoul of any existing and future laws and regulations,” stated article author Andrew Burt, the managing partner of bnh.ai, a boutique law firm focused on AI and analytics.  

First, conduct assessments of AI risks. As part of the effort, document how the risks have been minimized or resolved. Regulatory frameworks that refer to these “algorithmic impact assessments,” or “IA for AI,” are available.  

For example, Virginia’s recently-passed Consumer Data Protection Act, requires assessments for certain types of high-risk algorithms. 

The EU’s new proposal requires an eight-part technical document to be completed for high-risk AI systems that outlines “the foreseeable unintended outcomes and sources of risks” of each AI system, Burt states. The EU proposal is similar to the Algorithmic Accountability Act filed in the US Congress in 2019. The bill did not go anywhere but is expected to be reintroduced.  

Second, accountability and independence. This suggestion is that the data scientists, lawyers and others evaluating the AI system have different incentives than those of the frontline data scientists. This could mean that the AI is tested and validated by different technical personnel than those who originally developed it, or organizations may choose to hire outside experts to assess the AI system.   

“Ensuring that clear processes create independence between the developers and those evaluating the systems for risk is a central component of nearly all new regulatory frameworks on AI,” Burt states.  

Third, continuous review. AI systems are “brittle and subject to high rates of failure,” with risks that grow and change over time, making it difficult to mitigate risk at a single point in time. “Lawmakers and regulators alike are sending the message that risk management is a continual process,” Burt stated.  

Approaches in US, Europe and China Differ  

The approaches between the US, Europe and China toward AI regulation differ in their approach, according to a recent account in The Verdict, based on analysis by Global Data, the data analytics and consulting company based in London. 

“Europe appears more optimistic about the benefits of regulation, while the US has warned of the dangers of over regulation,”’ the account states. Meanwhile, “China continues to follow a government-first approach” and has been widely criticized for the use of AI technology to monitor citizens. The account noted examples in the rollout by Tencent last year of an AI-based credit scoring system to determine the “trust value” of people, and the installation of surveillance cameras outside people’s homes to monitor the quarantine imposed after the breakout of COVID-19. 

Whether the US’ tech industry-led efforts, China’s government-first approach, or Europe’s privacy and regulation-driven approach is the best way forward remains to be seen,” the account stated. 

In the US, many companies are aware of the risk of new AI regulation that could stifle innovation and their ability to grow in the digital economy, suggested a recent report from pwc, the multinational professional services firm.   

It’s in a company’s interests to tackle risks related to data, governance, outputs, reporting, machine learning and AI models, ahead of regulation,” the pwc analysts state. They recommended business leaders assemble people from across the organization to oversee accountability and governance of technology, with oversight from a diverse team that includes members with business, IT and specialized AI skills.  

Critics of European AI Act Cite Too Much Gray Area 

While some argue that the European Commission’s proposed AI Act leaves too much gray area, the hope of the European Commission is that their proposed AI Act will provide guidance for businesses wanting to pursue AI, as well as a degree of legal certainty.   

Thierry Breton, European Commissioner for the Internal Market

“Trust… we think is vitally important to allow the development we want of artificial intelligence,” stated Thierry Breton, European Commissioner for the Internal Market, in an account in TechCrunch. AI applications “need to be trustworthy, safe, non-discriminatory — that is absolutely crucial — but of course we also need to be able to understand how exactly these applications will work.” 

“What we need is to have guidance. Especially in a new technology… We are, we will be, the first continent where we will give guidelines—we’ll say ‘hey, this is green, this is dark green, this is maybe a little bit orange and this is forbidden’. So now if you want to use artificial intelligence applications, go to Europe! You will know what to do, you will know how to do it, you will have partners who understand pretty well and, by the way, you will come also to the continent where you will have the largest amount of industrial data created on the planet for the next ten years.” 

“So come here—because artificial intelligence is about data—we’ll give you the guidelines. We will also have the tools to do it and the infrastructure,” Breton suggested. 

Another reaction was that the Commission’s proposal has overly broad exemptions, such as for law enforcement to use remote biometric surveillance including facial recognition technology, and it does not go far enough to address the risk of discrimination. 

Reactions to the Commission’s proposal included plenty of criticism of overly broad exemptions for law enforcement’s use of remote biometric surveillance (such as facial recognition tech) as well as concerns that measures in the regulation to address the risk of AI systems discriminating don’t go nearly far enough. 

“The legislation lacks any safeguards against discrimination, while the wide-ranging exemption for ‘safeguarding public security’ completely undercuts what little safeguards there are in relation to criminal justice,” stated Griff Ferris, legal and policy officer for Fair Trials, the global criminal justice watchdog based in London. “The framework must include rigorous safeguards and restrictions to prevent discrimination and protect the right to a fair trial. This should include restricting the use of systems that attempt to profile people and predict the risk of criminality.”  

To accomplish this, he suggested, “The EU’s proposals need radical changes to prevent the hard-wiring of discrimination in criminal justice outcomes, protect the presumption of innocence and ensure meaningful accountability for AI in criminal justice. 

Read the source articles and information in Harvard Business Review, in The Verdict and in TechCrunch. 

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Source: https://www.aitrends.com/data-privacy-and-security/here-come-the-ai-regulations/

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