Designing real vegan cheese

Synthetic biology surely can get weirder — but this is a great start.

real_vegan_cheese_screenshot

I don’t think I will ever get tired of quoting Drew Endy’s “keep synthetic biology weird.” One of my favorite articles in the new issue of Biocoder is on the Real Vegan Cheese project.

If you’ve ever tried any of the various vegan cheese substitutes, they are (to put it kindly) awful. The missing ingredient in these products is the milk proteins, or caseins. And of course you can’t use real milk proteins in a vegan product.

But proteins are just organic compounds that are produced, in abundance, by any living cell. And synthetic biology is about engineering cell DNA to produce whatever proteins we want. That’s the central idea behind the Real Vegan Cheese project: can we design yeast to produce the caseins we need for cheese, without involving any animals? There’s no reason we can’t. Once we have the milk proteins, we can use traditional processes to make the cheese. No cows (or sheep, or goats) involved, just genetically modified yeast. And you never eat the yeast; they stay behind at the brewery.

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Need parking? There’s no app for that…yet

Our parking problems need nuanced civic tech solutions.

Parking meter, circa 1940. Via Wikimedia Commons

Parking meter, circa 1940. Photo: Wikimedia Commons.

“We will not abide businesses that hold hostage on-street public parking spots for their own private profit,” said the San Francisco city attorney last month, after declaring illegal Monkey ParkingParkModo, and Sweetch, new start-ups that provide mobile apps for buying and selling curb spaces.

Some might see this as a case of cities standing in the way of private-sector innovation, comparable to regulatory fights over Airbnb and Uber. However, those profiting by using Monkey Parking to “share” their on-street spots are effectively trying to sell something that doesn’t belong to them. Curb parking is a publicly owned “commons” — not the private possession of whomever occupies a space.

Even if their solutions are illegal, these start-ups are right: parking is broken in many American cities, and their solution is a business opportunity for both the public and private sectors. We need a thorough reassessment of parking management in American cities, and civically minded technologies are a big part of the solution. That’s the thinking of other departments of San Francisco’s government as well as one of urban planning’s most cited and least read theorists, Donald Shoup.  For the nascent field of civic technology, Shoup’s nuanced research demonstrates how new technologies and policy can together improve city life — and lead to true public good — at the same time as the city attorney’s enforcement actions highlight the potential pitfalls of “sharing” services designed only for private profit. Read more…

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Open source biology

Joe Schloendorn is creating and distributing plasmids that can freely be reproduced — a huge breakthrough for DIY bio.

Photo by mira66 on Flickr, used under a Creative Commons license.

At O’Reilly, we’ve long been supporters of the open source movement — perhaps not with the religious fervor of some, but with a deep appreciation for how open source has transformed the computing industry over the last three decades.

We also have a deep appreciation for the dangers that closed source, restrictive licenses, patent trolling, and other technocratic evils pose to areas that are just opening up — biology, in particular. So it is with great interest that I read Open Source Biotech Consumables in the latest issue of BioCoder.

I’m not going to rehash the article; you should read it yourself. The basic argument is that some proteins used in research cost thousands of dollars per milligram. They’re easily reproducible (we’re talking DNA, after all), but frequently tied up with restrictive licenses. In addition, many of the vendors will only sell to research institutions and large corporations, not home labs or small community labs. So, Joe Schloendorn is creating and distributing plasmids that can freely be reproduced. That in itself is a huge breakthrough.

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Five trends signaling a bright future for ebooks

It's time to place a moratorium on negativity and start working toward book publishing's bright future.

A desk with ereading equipment.

Editor’s note: this piece originally appeared on Medium; it is cross-posted here with permission. The writer is an O’Reilly employee, but he is expressing his personal views. We love his optimism about the future and wanted to share it with the Radar audience.

“THAT COMPANY is destroying my P&L, the entire book industry, and the fabric of civilized society.”

“I really like their free, two-day shipping, though.”

“Me, too.”

There’s a lot of tsoris in the publishing community right now over ebooks. Much of it has something to do with THAT COMPANY WITH THE WEBSITE THAT SELLS ALL THE THINGS, how THAT COMPANY has a stranglehold on the book market, how it’s devaluing our literary canon, how it has publishers right where it wants them.

But we’re not just cranky about THAT COMPANY. Other jeremiads include — but are not limited to — the painfully slow adoption curve of EPUB 3, the demise of beloved sites like Readmill, the failure of “enhanced” ebooks to gain tractionsundry ereader feculence, stagnating ebook sales, and sideloading.

I’m a cynic by nature, and count wallowing among my favorite hobbies, but after half a decade as a software engineer in the digital publishing space, even I’ve had enough and am issuing a moratorium on the negativity! Instead, I want to talk about some of the promising trends I’ve seen develop over the past year that foretell a bright future for the digital book. Forthwith: Five reasons for optimism about the future of ebooks.

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Governments can bridge costs and services gaps with sensor networks

Government sensor networks can streamline processes, cut labor costs, and improve services.

Contributing authors: Andre Bierzynski and Kevin Chrapaty.

Screenshot from the Waze app.

What if government agencies followed in the footsteps of Waze, a community-driven mobile phone app that collects location data through GPS and allows its users to report accidents and traffic jams, providing real-time, location-specific traffic alerts?

It’s not news to anyone who works in government that we live in a time of ever-tighter budgets and ever-increasing needs. The 2013 federal shutdown only highlighted this precarious situation: government finds it increasingly difficult to summon the resources and manpower needed to meet its current responsibilities, yet faces new ones after each Congressional session.

Sensor networks are an important emerging technology that some areas of government already are implementing to bridge the widening gap between the demand to reduce costs and the demand to improve services. The Department of Defense, for instance, uses RFID chips to monitor its supply chain more accurately, while the U.S. Geological Survey employs sensors to remotely monitor the bacterial levels of rivers and lakes in real time. Additionally, the General Services Administration has begun using sensors to measure and verify the energy efficiency of “green” buildings (PDF), and the Department of Transportation relies on sensors to monitor traffic and control traffic signals and roadways. All of which is productive, but more needs to be done. Read more…

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What is deep learning, and why should you care?

Announcing a new series delving into deep learning and the inner workings of neural networks.

OrganicNeuron

Editor’s note: this post is part of our Intelligence Matters investigation.

When I first ran across the results in the Kaggle image-recognition competitions, I didn’t believe them. I’ve spent years working with machine vision, and the reported accuracy on tricky tasks like distinguishing dogs from cats was beyond anything I’d seen, or imagined I’d see anytime soon. To understand more, I reached out to one of the competitors, Daniel Nouri, and he demonstrated how he used the Decaf open-source project to do so well. Even better, he showed me how he was quickly able to apply it to a whole bunch of other image-recognition problems we had at Jetpac, and produce much better results than my conventional methods.

I’ve never encountered such a big improvement from a technique that was largely unheard of just a couple of years before, so I became obsessed with understanding more. To be able to use it commercially across hundreds of millions of photos, I built my own specialized library to efficiently run prediction on clusters of low-end machines and embedded devices, and I also spent months learning the dark arts of training neural networks. Now I’m keen to share some of what I’ve found, so if you’re curious about what on earth deep learning is, and how it might help you, I’ll be covering the basics in a series of blog posts here on Radar, and in a short upcoming ebook. Read more…

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