Di Carlo et al. comment that our original results were insufficient to prove that the ghost cytometry technique is performing a morphologic analysis of cells in flow. We emphasize that the technique is primarily intended to acquire and classify morphological information of cells in a computationally efficient manner without reconstructing images. We provide additional supporting information, including images reconstructed from the compressive waveforms and a discussion of current and future throughput potentials. Ghost cytometry (GC) performs a direct analysis of compressive imaging waveforms and thereby substantially relieves the computational bottleneck hindering the realization of high-throughput cytometry based on morphological information (1). The comments by Di Carlo et al. argue against a number of our conclusions (2), but given the restricted length allowed for this response, we will address what we consider the most important points.

Ota et al. (Reports, 15 June 2018, p. 1246) report using pseudo-random optical masks and a spatial-temporal transformation to perform blur-free, high–frame rate imaging of cells in flow with a high signal-to-noise ratio. They also claim sorting at rates of 3000 cells per second, based on imaging data. The experiments conducted and results reported in their study are insufficient to support these conclusions. Ota et al. (1) proposed an approach to perform image-based flow cytometry and cell sorting that has attracted substantial attention because high throughput ( 3000 cells/s) and a high signal-to-noise ratio (SNR) were claimed. For example, on the basis of these assertions, the introductory commentary (2) referred to the system as an "ultrahigh-speed fluorescence imaging–activated cell sorter."

Service robots could assist people with severe disabilities to go beyond basic communication and movement enabled by current devices, but they would require an efficient and minimalist control system. Kuhner et al. developed a robotic service assistant that performs complex tasks in real-world environments and is controlled using thought. The robot can fetch and carry objects and also interact in close physical proximity to the user. This control is achieved by combining techniques from brain-signal decoding and natural language processing, where common terminology is used to maximize the overlap between the way the user sees the world and the way the task planner defines and controls each primitive action for the robot. This is an article distributed under the terms of the Science Journals Default License.

Like engineers who characterize the fidelity of signals flowing through a circuit, neuroscientists focus on quantifying the degree to which neuronal signals are "noisy" (1, 2). Engineers have the benefit of designing the system and knowing the form of the signal, making identification of corrupting noise relatively straightforward. For neuroscientists, the task is harder, as it entails figuring out first what the signal is, and only then, what the noise is. On pages 254, 253, and 255 of this issue, Gründemann et al. (3), Allen et al. (4), and Stringer et al. (5), respectively, report findings from large-scale neural recordings in the brains of mice and find brainwide activity that correlates with behavior that might usually be ignored as noise. These studies prompt reconsideration of the origin and impacts of "noise" in the nervous system.

The UK government's plan to prevent children and teenagers from viewing pornographic content online has a major flaw that means not all porn will be blocked. Critics have called the so-called porn ban "disastrous" for people's privacy, as it will require people to share their personal data online in order to visit porn sites. But the new rules, which come into effect on 15 July, can be skirted by visiting sites that are not subject to the age verification checks. We'll tell you what's true. You can form your own view.

Daisy, one of Apple's most valued resources, eats iPhones. She's very, very good at it, and getting better: trained with a precision that would have been unimaginable just a few years ago. She is a robot, with a variety of tools built to rip the phones apart. That includes, for instance, a tool that can chill phones down so that the battery holding the glue inside becomes brittle, and it can be knocked out with two aggressive bangs; precise pins that can pick the display off the housing that surrounds it; drills that can punch into the phone and drive out the things that might make it difficult to recycle. It won't surprise anyone to hear that Apple is pretty good at making iPhones.

Many Twitter users expressed dismay this week at the "loss" of Notre Dame following the blaze that engulfed the top of the Paris cathedral on 15 April. But, thankfully, the situation isn't as disastrous as that: most of the building is still intact thanks to its clever design, and hundreds of millions of euros have already been pledged for restoration. It took around 17 hours, and the efforts of 1000 firefighters and a 500-kilogram robot, to extinguish the fire.

Social and economic inequality has no easy fix, but now a system that automatically detects signs of inequality from street images could be used to help. Esra Suel and colleagues at Imperial College London trained artificial intelligence to detect inequalities in four UK cities, using a combination of government statistics and public images taken from Google Street View. The AI was trained on 525,860 images from 156,581 postcodes across London, along with income, health, crime, housing, and living environment statistics about the areas. A fifth of the data was withheld to test how closely the algorithm's estimation matched real distributions of inequality in London. The AI was most successful at spotting differences in quality of the living environment and mean income, scoring 0.86 for both on a statistical test of how closely its predictions matched with the real data, where a score of 1 is a complete overlap.

With Earth Day just around the corner, Apple announced it's quadrupling the number of locations US customers can send their iPhones for recycling. The company's recycling robot, Daisy, will now disassemble select iPhones returned to Best Buy stores in the US, KPN retailers in the Netherlands, as well as those recycled at any Apple Store or online through the Apple Trade In program. According to Apple, each of the Daisy robots can disassemble 1.2 million devices per year, or 15 different iPhone models at a rate of 200 per hour. Recovered materials are recycled back into the manufacturing process, and Apple sends the iPhone batteries Daisy removes to select manufacturing sites. There, for the first time, the cobalt is recovered, combined with scrap and used to make new batteries.

Starting today, YouTube Music is offering a free, ad-supported experience on Google Home speakers and other Google Assistant-powered speakers. Just navigate to account settings, tap services and select music, then set YouTube Music as the default music service. Then it's just a case of saying "Hey Google, play [whatever]" and you're away. However, the ad-supported YouTube Music experience won't let you request specific songs, albums or playlist. Instead, you can tell it a genre or style or mood of music you're looking for and your Google Home will play a station based on that request.