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Scientists Used Protein Switches to Turn T Cells Into Cancer-Fighting Guided Missiles

CAR t-cells for cancer therapy

One of the main challenges in curing cancer is that unlike foreign invaders, tumor cells are part of the body and so able to hide in plain sight. Now researchers have found a way to turn white blood cells into precision guided missiles that can sniff out these wolves in sheep’s clothing. One of the […]

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CAR t-cells for cancer therapy

One of the main challenges in curing cancer is that unlike foreign invaders, tumor cells are part of the body and so able to hide in plain sight. Now researchers have found a way to turn white blood cells into precision guided missiles that can sniff out these wolves in sheep’s clothing.

One of the biggest breakthroughs in treating cancer in recent years has been the emergence of CAR-T cell therapies, which recruit the body’s immune system to fight tumors rather than relying on radiotherapy or powerful chemotherapy drugs that can have severe side effects.

The approach relies on T cells, the hunter-killer white blood cells that seek out and destroy pathogens. Therapies involve drawing blood from the patient, separating their T cells, and then genetically engineering them to produce “chimeric antigen receptors” (CARs) that target specific proteins called antigens on the surface of cancer cells. They are then re-administered to the patient to track down and destroy cancer cells.

The only problem is that very few cancers have unique antigens. Unlike the pathogens the T cells are used to hunting, tumor cells are not that dissimilar to the body’s other cells and often share many antigens. That means there’s a risk of T cells targeting the wrong cells and causing serious damage to healthy tissue. As a result the only therapies approved by the FDA so far are focused on blood cancers that affect cells with idiosyncratic antigens.

Now though, researchers at the University of Washington have found a way to help T cells target a far broader range of cancers. They’ve developed a system of proteins that can carry out logic operations just like a computer, which helps them target specific combinations of antigens that are unique to certain cancers.

“T cells are extremely efficient killers, so the fact that we can limit their activity on cells with the wrong combination of antigens yet still rapidly eliminate cells with the correct combination is game-changing,” said Alexander Salter, one of the lead authors of the study published in Science.

Their technique relies on a series of synthetic proteins that can be customized to create a variety of switches. These can be combined to carry out the AND, OR, and NOT operations at the heart of digital computing, which makes it possible to create instructions that focus on unique combinations of antigens such as “target antigen 1 AND antigen 2 but NOT antigen 3.”

When the correct collection of antigens is present, the proteins combine to create a kind of molecular beacon that can guide CAR-T cells to the tumor cell. To demonstrate the effectiveness of the approach, they showed how it helped CAR T cells pick out and destroy specific tumor cells in a mixture of several different cell types.

Most other approaches for helping target T cells are either only able to do basic AND operations to combine two antigens, or rely on engineering the targeting into the T cells themselves, which is far more complicated. There are still significant barriers to overcome, though.

For a start, the bespoke nature of CAR T-cell therapy means it can be extremely expensive—as high as $1.5mso access to this technology should it make it to the clinic will be limited. So far the researchers have only studied the proteins’ behavior in vitro, so it’s unclear how the body’s immune system would respond to them if they were injected into a human.

There are also other barriers to treating solid tumors using T cells beyond simply targeting the correct cells. T cells struggle to get inside large masses of cancer cells, and even if they do, these tumors often produce proteins that inhibit the effectiveness of the T cells.

This new protein logic system is still a major breakthrough in the fight against cancer, though. And the researchers point out the technique could be used to target all kinds of different biomedical processes, including gene therapies where you need to deliver DNA to a specific kind of cell. The potential applications of this new missile guidance system for cells are only just starting to be explored.

Image Credit: National Institutes of Health/Alex Ritter, Jennifer Lippincott Schwartz, Gillian Griffiths

Source: https://singularityhub.com/2020/08/24/scientists-used-protein-switches-to-turn-t-cells-into-cancer-fighting-guided-missiles/

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage. Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Hungaricana, a free service provided by Arcanum, […]

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Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage.

Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Hungaricana, a free service provided by Arcanum, which enables you to search and explore Hungarian cultural heritage, including 600,000 faces over 500,000 images. For example, you can find historical works by author Mór Jókai or photos on topics like weddings. The Arcanum team chose Amazon Rekognition to free valuable staff from time and cost-intensive manual labeling, and improved label accuracy to make 200,000 previously unsearchable images (approximately 40% of image inventory), available to users.

Amazon Rekognition makes it easy to add image and video analysis to your applications using highly scalable machine learning (ML) technology that requires no previous ML expertise to use. Amazon Rekognition also provides highly accurate facial recognition and facial search capabilities to detect, analyze, and compare faces.

Arcanum uses this facial recognition feature in their image database services to help you find particular people in Arcanum’s articles. This post discusses their challenges and why they chose Amazon Rekognition as their solution.

Automated image labeling challenges

Arcanum dedicated a team of three people to start tagging and labeling content for Hungaricana. The team quickly learned that they would need to invest more than 3 months of time-consuming and repetitive human labor to provide accurate search capabilities to their customers. Considering the size of the team and scope of the existing project, Arcanum needed a better solution that would automate image and object labelling at scale.

Automated image labeling solutions

To speed up and automate image labeling, Arcanum turned to Amazon Rekognition to enable users to search photos by keywords (for example, type of historic event, place name, or a person relevant to Hungarian history).

For the Hungaricana project, preprocessing all the images was challenging. Arcanum ran a TensorFlow face search across all 28 million pages on a machine with 8 GPUs in their own offices to extract only faces from images.

The following screenshot shows what an extract looks like (image provided by Arcanum Database Ltd).

The images containing only faces are sent to Amazon Rekognition, invoking the IndexFaces operation to add a face to the collection. For each face that is detected in the specified face collection, Amazon Rekognition extracts facial features into a feature vector and stores it in an Amazon Aurora database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the SearchFaces and SearchFacesByImage operations.

The image preprocessing helped create a very efficient and cost-effective way to index faces. The following diagram summarizes the preprocessing workflow.

As for the web application, the workflow starts with a Hungaricana user making a face search request. The following diagram illustrates the application workflow.

The workflow includes the following steps:

  1. The user requests a facial match by uploading the image. The web request is automatically distributed by the Elastic Load Balancer to the webserver fleet.
  2. Amazon Elastic Compute Cloud (Amazon EC2) powers application servers that handle the user request.
  3. The uploaded image is stored in Amazon Simple Storage Service (Amazon S3).
  4. Amazon Rekognition indexes the face and runs SearchFaces to look for a face similar to the new face ID.
  5. The output of the search face by image operation is stored in Amazon ElastiCache, a fully managed in-memory data store.
  6. The metadata of the indexed faces are stored in an Aurora relational database built for the cloud.
  7. The resulting face thumbnails are served to the customer via the fast content-delivery network (CDN) service Amazon CloudFront.

Experimenting and live testing Hungaricana

During our test of Hungaricana, the application performed extremely well. The searches not only correctly identified people, but also provided links to all publications and sources in Arcanum’s privately owned database where found faces are present. For example, the following screenshot shows the result of the famous composer and pianist Franz Liszt.

The application provided 42 pages of 6×4 results. The results are capped to 1,000. The 100% scores are the confidence scores returned by Amazon Rekognition and are rounded up to whole numbers.

The application of Hungaricana has always promptly, and with a high degree of certainty, presented results and links to all corresponding publications.

Business results

By introducing Amazon Rekognition into their workflow, Arcanum enabled a better customer experience, including building family trees, searching for historical figures, and researching historical places and events.

The concept of face searching using artificial intelligence certainly isn’t new. But Hungaricana uses it in a very creative, unique way.

Amazon Rekognition allowed Arcanum to realize three distinct advantages:

  • Time savings – The time to market speed increased dramatically. Now, instead of spending several months of intense manual labor to label all the images, the company can do this job in a few days. Before, basic labeling on 150,000 images took months for three people to complete.
  • Cost savings – Arcanum saved around $15,000 on the Hungaricana project. Before using Amazon Rekognition, there was no automation, so a human workforce had to scan all the images. Now, employees can shift their focus to other high-value tasks.
  • Improved accuracy – Users now have a much better experience regarding hit rates. Since Arcanum started using Amazon Rekognition, the number of hits has doubled. Before, out of 500,000 images, about 200,000 weren’t searchable. But with Amazon Rekognition, search is now possible for all 500,000 images.

 “Amazon Rekognition made Hungarian culture, history, and heritage more accessible to the world,” says Előd Biszak, Arcanum CEO. “It has made research a lot easier for customers building family trees, searching for historical figures, and researching historical places and events. We cannot wait to see what the future of artificial intelligence has to offer to enrich our content further.”

Conclusion

In this post, you learned how to add highly scalable face and image analysis to an enterprise-level image gallery to improve label accuracy, reduce costs, and save time.

You can test Amazon Rekognition features such as facial analysis, face comparison, or celebrity recognition on images specific to your use case on the Amazon Rekognition console.

For video presentations and tutorials, see Getting Started with Amazon Rekognition. For more information about Amazon Rekognition, see Amazon Rekognition Documentation.


About the Authors

Siniša Mikašinović is a Senior Solutions Architect at AWS Luxembourg, covering Central and Eastern Europe—a region full of opportunities, talented and innovative developers, ISVs, and startups. He helps customers adopt AWS services as well as acquire new skills, learn best practices, and succeed globally with the power of AWS. His areas of expertise are Game Tech and Microsoft on AWS. Siniša is a PowerShell enthusiast, a gamer, and a father of a small and very loud boy. He flies under the flags of Croatia and Serbia.

Cameron Peron is Senior Marketing Manager for AWS Amazon Rekognition and the AWS AI/ML community. He evangelizes how AI/ML innovation solves complex challenges facing community, enterprise, and startups alike. Out of the office, he enjoys staying active with kettlebell-sport, spending time with his family and friends, and is an avid fan of Euro-league basketball.

Source: https://aws.amazon.com/blogs/machine-learning/arcanum-makes-hungarian-heritage-accessible-with-amazon-rekognition/

Continue Reading

AI

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage. Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Hungaricana, a free service provided by Arcanum, […]

Published

on

Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage.

Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Hungaricana, a free service provided by Arcanum, which enables you to search and explore Hungarian cultural heritage, including 600,000 faces over 500,000 images. For example, you can find historical works by author Mór Jókai or photos on topics like weddings. The Arcanum team chose Amazon Rekognition to free valuable staff from time and cost-intensive manual labeling, and improved label accuracy to make 200,000 previously unsearchable images (approximately 40% of image inventory), available to users.

Amazon Rekognition makes it easy to add image and video analysis to your applications using highly scalable machine learning (ML) technology that requires no previous ML expertise to use. Amazon Rekognition also provides highly accurate facial recognition and facial search capabilities to detect, analyze, and compare faces.

Arcanum uses this facial recognition feature in their image database services to help you find particular people in Arcanum’s articles. This post discusses their challenges and why they chose Amazon Rekognition as their solution.

Automated image labeling challenges

Arcanum dedicated a team of three people to start tagging and labeling content for Hungaricana. The team quickly learned that they would need to invest more than 3 months of time-consuming and repetitive human labor to provide accurate search capabilities to their customers. Considering the size of the team and scope of the existing project, Arcanum needed a better solution that would automate image and object labelling at scale.

Automated image labeling solutions

To speed up and automate image labeling, Arcanum turned to Amazon Rekognition to enable users to search photos by keywords (for example, type of historic event, place name, or a person relevant to Hungarian history).

For the Hungaricana project, preprocessing all the images was challenging. Arcanum ran a TensorFlow face search across all 28 million pages on a machine with 8 GPUs in their own offices to extract only faces from images.

The following screenshot shows what an extract looks like (image provided by Arcanum Database Ltd).

The images containing only faces are sent to Amazon Rekognition, invoking the IndexFaces operation to add a face to the collection. For each face that is detected in the specified face collection, Amazon Rekognition extracts facial features into a feature vector and stores it in an Amazon Aurora database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the SearchFaces and SearchFacesByImage operations.

The image preprocessing helped create a very efficient and cost-effective way to index faces. The following diagram summarizes the preprocessing workflow.

As for the web application, the workflow starts with a Hungaricana user making a face search request. The following diagram illustrates the application workflow.

The workflow includes the following steps:

  1. The user requests a facial match by uploading the image. The web request is automatically distributed by the Elastic Load Balancer to the webserver fleet.
  2. Amazon Elastic Compute Cloud (Amazon EC2) powers application servers that handle the user request.
  3. The uploaded image is stored in Amazon Simple Storage Service (Amazon S3).
  4. Amazon Rekognition indexes the face and runs SearchFaces to look for a face similar to the new face ID.
  5. The output of the search face by image operation is stored in Amazon ElastiCache, a fully managed in-memory data store.
  6. The metadata of the indexed faces are stored in an Aurora relational database built for the cloud.
  7. The resulting face thumbnails are served to the customer via the fast content-delivery network (CDN) service Amazon CloudFront.

Experimenting and live testing Hungaricana

During our test of Hungaricana, the application performed extremely well. The searches not only correctly identified people, but also provided links to all publications and sources in Arcanum’s privately owned database where found faces are present. For example, the following screenshot shows the result of the famous composer and pianist Franz Liszt.

The application provided 42 pages of 6×4 results. The results are capped to 1,000. The 100% scores are the confidence scores returned by Amazon Rekognition and are rounded up to whole numbers.

The application of Hungaricana has always promptly, and with a high degree of certainty, presented results and links to all corresponding publications.

Business results

By introducing Amazon Rekognition into their workflow, Arcanum enabled a better customer experience, including building family trees, searching for historical figures, and researching historical places and events.

The concept of face searching using artificial intelligence certainly isn’t new. But Hungaricana uses it in a very creative, unique way.

Amazon Rekognition allowed Arcanum to realize three distinct advantages:

  • Time savings – The time to market speed increased dramatically. Now, instead of spending several months of intense manual labor to label all the images, the company can do this job in a few days. Before, basic labeling on 150,000 images took months for three people to complete.
  • Cost savings – Arcanum saved around $15,000 on the Hungaricana project. Before using Amazon Rekognition, there was no automation, so a human workforce had to scan all the images. Now, employees can shift their focus to other high-value tasks.
  • Improved accuracy – Users now have a much better experience regarding hit rates. Since Arcanum started using Amazon Rekognition, the number of hits has doubled. Before, out of 500,000 images, about 200,000 weren’t searchable. But with Amazon Rekognition, search is now possible for all 500,000 images.

 “Amazon Rekognition made Hungarian culture, history, and heritage more accessible to the world,” says Előd Biszak, Arcanum CEO. “It has made research a lot easier for customers building family trees, searching for historical figures, and researching historical places and events. We cannot wait to see what the future of artificial intelligence has to offer to enrich our content further.”

Conclusion

In this post, you learned how to add highly scalable face and image analysis to an enterprise-level image gallery to improve label accuracy, reduce costs, and save time.

You can test Amazon Rekognition features such as facial analysis, face comparison, or celebrity recognition on images specific to your use case on the Amazon Rekognition console.

For video presentations and tutorials, see Getting Started with Amazon Rekognition. For more information about Amazon Rekognition, see Amazon Rekognition Documentation.


About the Authors

Siniša Mikašinović is a Senior Solutions Architect at AWS Luxembourg, covering Central and Eastern Europe—a region full of opportunities, talented and innovative developers, ISVs, and startups. He helps customers adopt AWS services as well as acquire new skills, learn best practices, and succeed globally with the power of AWS. His areas of expertise are Game Tech and Microsoft on AWS. Siniša is a PowerShell enthusiast, a gamer, and a father of a small and very loud boy. He flies under the flags of Croatia and Serbia.

Cameron Peron is Senior Marketing Manager for AWS Amazon Rekognition and the AWS AI/ML community. He evangelizes how AI/ML innovation solves complex challenges facing community, enterprise, and startups alike. Out of the office, he enjoys staying active with kettlebell-sport, spending time with his family and friends, and is an avid fan of Euro-league basketball.

Source: https://aws.amazon.com/blogs/machine-learning/arcanum-makes-hungarian-heritage-accessible-with-amazon-rekognition/

Continue Reading

AI

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage. Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Hungaricana, a free service provided by Arcanum, […]

Published

on

Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage.

Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Hungaricana, a free service provided by Arcanum, which enables you to search and explore Hungarian cultural heritage, including 600,000 faces over 500,000 images. For example, you can find historical works by author Mór Jókai or photos on topics like weddings. The Arcanum team chose Amazon Rekognition to free valuable staff from time and cost-intensive manual labeling, and improved label accuracy to make 200,000 previously unsearchable images (approximately 40% of image inventory), available to users.

Amazon Rekognition makes it easy to add image and video analysis to your applications using highly scalable machine learning (ML) technology that requires no previous ML expertise to use. Amazon Rekognition also provides highly accurate facial recognition and facial search capabilities to detect, analyze, and compare faces.

Arcanum uses this facial recognition feature in their image database services to help you find particular people in Arcanum’s articles. This post discusses their challenges and why they chose Amazon Rekognition as their solution.

Automated image labeling challenges

Arcanum dedicated a team of three people to start tagging and labeling content for Hungaricana. The team quickly learned that they would need to invest more than 3 months of time-consuming and repetitive human labor to provide accurate search capabilities to their customers. Considering the size of the team and scope of the existing project, Arcanum needed a better solution that would automate image and object labelling at scale.

Automated image labeling solutions

To speed up and automate image labeling, Arcanum turned to Amazon Rekognition to enable users to search photos by keywords (for example, type of historic event, place name, or a person relevant to Hungarian history).

For the Hungaricana project, preprocessing all the images was challenging. Arcanum ran a TensorFlow face search across all 28 million pages on a machine with 8 GPUs in their own offices to extract only faces from images.

The following screenshot shows what an extract looks like (image provided by Arcanum Database Ltd).

The images containing only faces are sent to Amazon Rekognition, invoking the IndexFaces operation to add a face to the collection. For each face that is detected in the specified face collection, Amazon Rekognition extracts facial features into a feature vector and stores it in an Amazon Aurora database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the SearchFaces and SearchFacesByImage operations.

The image preprocessing helped create a very efficient and cost-effective way to index faces. The following diagram summarizes the preprocessing workflow.

As for the web application, the workflow starts with a Hungaricana user making a face search request. The following diagram illustrates the application workflow.

The workflow includes the following steps:

  1. The user requests a facial match by uploading the image. The web request is automatically distributed by the Elastic Load Balancer to the webserver fleet.
  2. Amazon Elastic Compute Cloud (Amazon EC2) powers application servers that handle the user request.
  3. The uploaded image is stored in Amazon Simple Storage Service (Amazon S3).
  4. Amazon Rekognition indexes the face and runs SearchFaces to look for a face similar to the new face ID.
  5. The output of the search face by image operation is stored in Amazon ElastiCache, a fully managed in-memory data store.
  6. The metadata of the indexed faces are stored in an Aurora relational database built for the cloud.
  7. The resulting face thumbnails are served to the customer via the fast content-delivery network (CDN) service Amazon CloudFront.

Experimenting and live testing Hungaricana

During our test of Hungaricana, the application performed extremely well. The searches not only correctly identified people, but also provided links to all publications and sources in Arcanum’s privately owned database where found faces are present. For example, the following screenshot shows the result of the famous composer and pianist Franz Liszt.

The application provided 42 pages of 6×4 results. The results are capped to 1,000. The 100% scores are the confidence scores returned by Amazon Rekognition and are rounded up to whole numbers.

The application of Hungaricana has always promptly, and with a high degree of certainty, presented results and links to all corresponding publications.

Business results

By introducing Amazon Rekognition into their workflow, Arcanum enabled a better customer experience, including building family trees, searching for historical figures, and researching historical places and events.

The concept of face searching using artificial intelligence certainly isn’t new. But Hungaricana uses it in a very creative, unique way.

Amazon Rekognition allowed Arcanum to realize three distinct advantages:

  • Time savings – The time to market speed increased dramatically. Now, instead of spending several months of intense manual labor to label all the images, the company can do this job in a few days. Before, basic labeling on 150,000 images took months for three people to complete.
  • Cost savings – Arcanum saved around $15,000 on the Hungaricana project. Before using Amazon Rekognition, there was no automation, so a human workforce had to scan all the images. Now, employees can shift their focus to other high-value tasks.
  • Improved accuracy – Users now have a much better experience regarding hit rates. Since Arcanum started using Amazon Rekognition, the number of hits has doubled. Before, out of 500,000 images, about 200,000 weren’t searchable. But with Amazon Rekognition, search is now possible for all 500,000 images.

 “Amazon Rekognition made Hungarian culture, history, and heritage more accessible to the world,” says Előd Biszak, Arcanum CEO. “It has made research a lot easier for customers building family trees, searching for historical figures, and researching historical places and events. We cannot wait to see what the future of artificial intelligence has to offer to enrich our content further.”

Conclusion

In this post, you learned how to add highly scalable face and image analysis to an enterprise-level image gallery to improve label accuracy, reduce costs, and save time.

You can test Amazon Rekognition features such as facial analysis, face comparison, or celebrity recognition on images specific to your use case on the Amazon Rekognition console.

For video presentations and tutorials, see Getting Started with Amazon Rekognition. For more information about Amazon Rekognition, see Amazon Rekognition Documentation.


About the Authors

Siniša Mikašinović is a Senior Solutions Architect at AWS Luxembourg, covering Central and Eastern Europe—a region full of opportunities, talented and innovative developers, ISVs, and startups. He helps customers adopt AWS services as well as acquire new skills, learn best practices, and succeed globally with the power of AWS. His areas of expertise are Game Tech and Microsoft on AWS. Siniša is a PowerShell enthusiast, a gamer, and a father of a small and very loud boy. He flies under the flags of Croatia and Serbia.

Cameron Peron is Senior Marketing Manager for AWS Amazon Rekognition and the AWS AI/ML community. He evangelizes how AI/ML innovation solves complex challenges facing community, enterprise, and startups alike. Out of the office, he enjoys staying active with kettlebell-sport, spending time with his family and friends, and is an avid fan of Euro-league basketball.

Source: https://aws.amazon.com/blogs/machine-learning/arcanum-makes-hungarian-heritage-accessible-with-amazon-rekognition/

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

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