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contains the only data about the generalizability of these destabilizing mutations into most other nanobodies. It will be useful for the readers to see not only "fold induction by antigen" as quantified, but also background TagBFP fluorescence, as the latter would affect the utility when the effector is a sensitive protein such as a recombinase.

3) While the method is powerful, the authors may want to add more caveats in their Discussion about the limitations. For instance, the subcellular compartment of the antigen distribution may affect its utility-a plasma membrane associated antigen is not going to be very useful for targeting a recombinase. Their in vivo results are mostly derived from a single, transgenically expressed GFP variant, etc.

Reviewer #2:

Recommendation: This is a nice, clever piece of work. All of the experiments are very carefully conducted and controlled. With some revisions (see below), and definitely a more even-handed Discussion of the limitations and true "killer apps" of the technique, rather than those that are more of a cute way to accomplish something that can be done already a different way (and sometimes more robustly), this paper should be very nice.

1) In the second paragraph of the Introduction: Nbs can indeed have "high" affinity and specificity, but by and large both of these are lower than for larger-format antibodies. This bears mentioning.

2) Results: “Strikingly, many variants showed fusion TagBFP aggregates within the cell when YFP is absent, but became soluble in the cytoplasm when YFP is present (data not shown)”, I think the authors should show this data in a supplementary figure This sort of information is critical for people trying to develop similar reagents to get a feel for how "soft" the engineering problem is.

3) I think the Discussion should be toned down a bit. It's great that they mention all the possible applications for dNbs, but they should also mention downsides versus other approaches. Many of the demonstrated applications are "toy" proof of principle, with many other more straightforward ways to accomplish the same end. Targeting endogenous proteins in non-genetic organisms is very intriguing, though, with few other immediate options. But the Discussion should be more even-handed to say that even though the data are great and it clearly works, these are not really experiments that one would find oneself doing.

4) Figure 1—figure supplement 1 : Is that a significant difference in unmodified GBP1 +/- YFP? Does YFP compete for transcription/translation? Does this complicate the analysis throughout?

5) Figure 2G : There are some increases in fluorescence by addition of antigen to unmodified Nb. Almost every one. This bears commenting.

6) Figure 2G : The color plots shown for dNb don't look consistent with the bar plots. For instance, 3K7U looks tiny in color plot, ~7x on bar plot; 4EIZ looks big on color plot, ~4x on bar plot.

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Closer relations. (AP Photo/Manish Swarup)
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By 2060, India may be the world’s largest economy. It will certainly be the world’s most populous country. At that point, Australians will ask, “What did we do in the 2020s to build a relationship with this superpower?” They may also ask: “How did economic cooperation with India benefit both countries in the early 21st century?”

The Australian federal government’s report, An India Economic Strategy to 2035 , was launched last week in Brisbane. Written by University of Queensland chancellor Peter Varghese, it is an excellent basis for reflecting on these questions and the wider issue of Australia-India cooperation.

The strategy identifies numerous sectors—health, education, and tourism, for example—that can help enhance economic cooperation, and in which Australia has some comparative advantage. It also specifies ten Indian states as targets for collaboration based on their economic heft, commitment to reform, and relevance to the sectors in which Australia has competitive advantages.

Importantly, the strategy is ambitious. It sets itself the goal by 2035 to lift India into Australia’s top three export markets. It intends for India to become “the third largest destination in Asia for Australian outward investment,” and for it to be brought “into the inner circle of Australia’s strategic partnerships.”

The strategy emphasises two areas that need attention in order to meet these objectives. First, Australia needs to leverage the strengths of the Indian diaspora, which now numbers about 455,000.

The rapid growth of the Indian diaspora population can be a spur to economic cooperation. The Indian population in places like Silicon Valley drive the IT and biotech booms in India and the US. Canada is highly adept in enrolling its Indian diaspora in projects of national and international development.

Second, Australia needs to build more knowledge of India and support organisations that work on the bilateral relationship. While Australia made a pivot towards China in the last quarter of the 20th century, businesses, governments, and the public developed comparatively little knowledge about India.

The emphasis on China led to a neglect of India in education, media, and the policy sphere. There is a need to rebuild public understanding of India and the institutions that can activate this understanding to achieve lasting impact. Culture and arts will be very important here, both as a sector and enabler—points implicit in the strategy.

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Stephen Hawking at an announcement of the Breakthrough Starshot initiative in New York. (Reuters/Lucas Jackson)
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For the last 10 years of his life, Stephen Hawking communicated only by tensing his cheek .

The famed physicist, known for his theories on black holes and for surviving ALS far longer than doctors’ estimates, wore a device on his glasses that monitored whether his cheek was flexed or not, and used the signal as a mouse-click to control his computer. When he first adopted this method, which one of his graduate students introduced in 2008, it was painfully slow and prone to error. It relied on a program called Word+ in which sentences had to be typed letter-by-letter, according to Wired .

Artificial intelligence helped make the system faster. Frustrated with the speed at which he could communicate, Hawking reached out to Intel, which had in the past provided him with the computers he used to speak. The company sent a team to Hawking, and after years of testing new systems, worked out a solution with smartphone keyboard company SwiftKey, called ACAT (Assistive Contextually Aware Toolkit).

The new software’s typing program relied on a machine learning algorithm that had been trained on Hawking’s work; Swiftkey and Intel showed the algorithm the physicist’s past lectures and books to help it predict what words he was about to type. For instance, when he typed “the,” the word “black” and then “hole” would appear as options, according to Wired. This improved Hawking’s typing speed from his previous one or two words per minute, giving him the ability to speak conversationally in pop culture jaunts like Last Week Tonight with John Oliver .

Despite the help machine learning gave Hawking in communicating, the physicist was somewhat pessimistic about the technology’s potential impact on society. He was known for his skepticism of developing “strong AI,” or general artificial intelligence, that would have similar reasoning capabilities as a human.

“We should shift the goal of AI from creating pure undirected artificial intelligence to creating beneficial intelligence. It might take decades to figure out how to do this, so let’s start researching this today rather than the night before the first strong AI is switched on,” Hawking wrote in his final Reddit Ask Me Anything thread .

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