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Inward Ca2+ currents conducted by voltage-gated Ca2+ (Cav) channels couple action potentials and other depolarizing stimuli to many Ca2+-dependent intracellular processes, including neurotransmission, hormone secretion, and muscle contraction (Zamponi et al., 2015). In presynaptic nerve terminals, Cav2.1, Cav2.2, and Cav2.3 channels conduct P/Q-type, N-type, and R-type Ca2+ currents that trigger rapid neurotransmission (for review, see Olivera et al., 1994; Zamponi et al., 2015; Nanou and Catterall, 2018). However, only P/Q-type Ca2+ currents conducted by Cav2.1 channels can mediate short-term synaptic facilitation at the calyx of Held in mice (Inchauspe et al., 2004), pointing to a unique role of these Ca2+ channels in short-term synaptic plasticity.

In transfected nonneuronal cells, Ca2+ entry mediated by Cav2.1 channels causes calcium-dependent facilitation (CDF) and inactivation (CDI) during single depolarizations and in trains of repetitive depolarizing pulses (Lee et al., 1999, 2000; DeMaria et al., 2001; Catterall and Few, 2008; Christel and Lee, 2012; Ben-Johny and Yue, 2014). Both CDF and CDI of Cav2.1 channels are dependent on calmodulin (CaM; Lee et al., 1999, 2000; DeMaria et al., 2001). CaM preassociates with the C-terminal domain of the pore-forming α1 subunit of Cav2.1 channels (Erickson et al., 2001). Following Ca2+ binding, CaM initially interacts with the nearby IQ-like motif (IM) and causes CDF, whereas further binding of Ca2+/CaM to the more distal CaM-binding domain (CBD) induces CDI of Cav2.1 channels (DeMaria et al., 2001; Lee et al., 2003). Introducing the IM-AA mutation into the IQ-like motif of Cav2.

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Recent developments like DALLE-2 and LaMDA are impressive and seem ready for impact. Is AI ready to change the world?

Whether you love, fear, or have mixed feelings about the future of artificial intelligence, the cultural fixation on the subject over the past decade has made it feel like the technology’s meteoric impact is just around the corner. The problem is that it is always just around the corner, yet never seems to arrive. Many hype-filled years have passed us by since the releases of Ex Machina (2014) and Westworld (2016), but it feels like we are still waiting on AI’s big splash. However, a handful of recent developments—specifically, OpenAI’s unveiling of GPT-3 and DALLE-2, and Google’s LaMDA controversy—have unleashed a new wave of excitement—and terror—around the possibility that AI’s game-changing moment is finally here.

There are several reasons why it feels it has taken a long time for AI projects to bear fruit. One is that pop culture seems almost exclusively focused on the possible endgames of the technology, rather than its broader journey. This isn’t much of a surprise. When we stream the latest sci-fi movie or binge Black Mirror episodes, we want to see killer robots and computer chip brain implants. No one is buying a ticket to see a movie about the slow, incremental rollout of existing technology—not unless it mutates and starts killing within the first 30 minutes. But while AI’s more futuristic forms are naturally the most entertaining, and provide an endless source of material for screenwriters, anyone who based their expectations for AI off of Bladerunner has got to be feeling disappointed by now.

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“If cells are dreaming, [these images] are what the cells are dreaming about,” neuroscientist Carlos Ponce told The Atlantic. “It exposes the visual vocabulary of the brain, in a way that’s unbiased by our anthropomorphic perspective.”

Some neurons responded to images that vaguely resembled objects that the scientists recognized, suggesting that the researchers identified the specific neurons that corresponded with particular real-world objects. A blur that resembled a monkey’s face accompanied by a red blotch may have corresponded to another monkey in the lab that wore a red collar. Another blur that resembled a human wearing a surgical mask may have represented the woman who took care of and fed the lab’s monkeys, who wore a similar mask.

Other images that the monkey neurons responded to the most were less realistic, instead taking the form of various streaks and splotches of color, according to The Atlantic.

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Circa 2020 This shape changing metal discovery can lead us closer to foglet machines.

Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, NJ, USA. E-mail: [email protected]

Received 25th August 2020, Accepted 16th November 2020.

Metal halide perovskites (MHPs) are frontrunners among solution-processable materials for lightweight, large-area and flexible optoelectronics. These materials, with the general chemical formula AMX3, are structurally complex, undergoing multiple polymorph transitions as a function of temperature and pressure. In this review, we provide a detailed overview of polymorphism in three-dimensional MHPs as a function of composition, with A = Cs+, MA+, or FA+, M = Pb2+ or Sn2+, and X = Cl−, Br−, or I−. In general, perovskites adopt a highly symmetric cubic structure at elevated temperatures. With decreasing temperatures, the corner-sharing MX6 octahedra tilt with respect to one another, resulting in multiple polymorph transitions to lower-symmetry tetragonal and orthorhombic structures. The temperatures at which these phase transitions occur can be tuned via different strategies, including crystal size reduction, confinement in scaffolds and (de-)pressurization.

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Eavesdropping on the earliest conversations between tissues in an emerging life could tell us a lot about organ growth, fertility, and disease in general. It could help prevent early miscarriages, or even tell us how to grow whole replacement organs from scratch.

In a monumental leap in stem cell research, an experiment led by researchers from the University of Cambridge in the UK has developed a living model of a mouse embryo complete with fluttering heart tissues and the beginnings of a brain.

The research advances the recent success of a team comprised of some of the same scientists who pushed the limits on mimicking the embryonic development of mice using stem cells that had never seen the inside of a mouse womb.

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Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.

Artificial intelligence (AI) systems must understand visual scenes in three dimensions to interpret the world around us. For that reason, images play an essential role in computer vision, significantly affecting quality and performance. Unlike the widely available 2D data, 3D data is rich in scale and geometry information, providing an opportunity for a better machine-environment understanding.

Data-driven 3D modeling, or 3D reconstruction, is a growing computer vision domain increasingly in demand from industries including augmented reality (AR) and virtual reality (VR). Rapid advances in implicit neural representation are also opening up exciting new possibilities for virtual reality experiences.

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