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Quantum objects, such as atoms, spins, and subatomic particles, haveunique physical properties that could be useful for many different applications, ranging from quantum information processing to magnetic resonance imaging. Molecular species also exhibit these quantum properties, and, importantly, these properties are fundamentally tunable by synthetic design, unlike ions isolated in a quadrupolar trap, for example. In this comment, we distill multiple, distinct, scientific efforts into an emergent field that is devoted to designing molecules that mimic the quantum properties of objects like trapped atoms or defects in solids. Mimicry is endemic in inorganic chemistry and featured heavily in the research interests of groups across the world. We describe this new field of using molecular inorganic chemistry to mimic the quantum properties (e.g. the lifetime of spin superpositions, or the resonant frequencies thereof) of other quantum objects as “quantum mimicry.” In this comment, we describe the philosophical design strategies and recent exciting results from the application of these strategies. 

The spatial organization of molecules in a cell is essential for their functions. While current methods focus on discerning tissue architecture, cell–cell interactions, and spatial expression patterns, they are limited to the multicellular scale. We present Bento, a Python toolkit that takes advantage of single-molecule information to enable spatial analysis at the subcellular scale. Bento ingests molecular coordinates and segmentation boundaries to perform three analyses: defining subcellular domains, annotating localization patterns, and quantifying gene–gene colocalization. We demonstrate MERFISH, seqFISH + , Molecular Cartography, and Xenium datasets. Bento is part of the open-source Scverse ecosystem, enabling integration with other single-cell analysis tools.

 

The seasonality of Earth’s climate is driven by two factors: the tilt of the Earth’s rotation axis relative to the plane of its orbit (hereafter thetilt effect), and the variation in the Earth–Sun distance due to the Earth’s elliptical orbit around the Sun (hereafter thedistance effect). The seasonal insolation change between aphelion and perihelion is only ~ 7% of the annual mean and it is thus assumed that the distance effect is not relevant for the seasons. A recent modeling study by the authors and collaborators demonstrated however that the distance effect is not small for the Pacific cold tongue: it drives an annual cycle there that is dynamically distinct and ~ 1/3 of the amplitude from the known annual cycle arising from the tilt effect. The simulations also suggest that the influence of distance effect is significant and pervasive across several other regional climates, in both the tropics and extratropics. Preliminary work suggests that the distance effect works its influence through the thermal contrast between the mostly ocean hemisphere centered on the Pacific Ocean (the ‘Marine hemisphere’) and the hemisphere opposite to it centered over Africa (the ‘Continental hemisphere’), analogous to how the tilt effect drives a contrast between the northern and southern hemispheres. We argue that the distance effect should be fully considered as an annual cycle forcing in its own right in studies of Earth’s modern seasonal cycle. Separately considering the tilt and distance effects on the Earth’s seasonal cycle provides new insights into the workings of our climate system, and of direct relevance to paleoclimate where there are outstanding questions for long-term climate changes that are related to eccentricity variations.

 

The interplay of the chemistry and physics that exists within astrochemically relevant sources can only be fully appreciated if we can gain a holistic understanding of their chemical inventories. Previous work by Lee et al. demonstrated the capabilities of simple regression models to reproduce the abundances of the chemical inventory of the Taurus Molecular Cloud 1 (TMC-1), as well as to provide abundance predictions for new candidate molecules. It remains to be seen, however, to what degree TMC-1 is a “unicorn” in astrochemistry, where the simplicity of its chemistry and physics readily facilitates characterization with simple machine learning models. Here we present an extension in chemical complexity to a heavily studied high-mass star-forming region: the Orion Kleinmann–Low (Orion KL) nebula. Unlike TMC-1, Orion KL is composed of several structurally distinct environments that differ chemically and kinematically, wherein the column densities of molecules between these components can have nonlinear correlations that cause the unexpected appearance or even lack of likely species in various environments. This proof-of-concept study used similar regression models sampled by Lee et al. to accurately reproduce the column densities from the XCLASS fitting program presented by Crockett et al.

 

ProMagBot introduces scalable electromagnetic control of magnetic beads. The device is a handheld, battery-powered, and field-deployable sample preparation device that can extract viral RNA from plasma samples in under 20 minutes.

 

Bosonic encoding of quantum information into harmonic oscillators is a hardware efficient approach to battle noise. In this regard, oscillator-to-oscillator codes not only provide an additional opportunity in bosonic encoding, but also extend the applicability of error correction to continuous-variable states ubiquitous in quantum sensing and communication. In this work, we derive the optimal oscillator-to-oscillator codes among the general family of Gottesman-Kitaev-Preskill (GKP)-stablizer codes for homogeneous noise. We prove that an arbitrary GKP-stabilizer code can be reduced to a generalized GKP two-mode-squeezing (TMS) code. The optimal encoding to minimize the geometric mean error can be constructed from GKP-TMS codes with an optimized GKP lattice and TMS gains. For single-mode data and ancilla, this optimal code design problem can be efficiently solved, and we further provide numerical evidence that a hexagonal GKP lattice is optimal and strictly better than the previously adopted square lattice. For the multimode case, general GKP lattice optimization is challenging. In the two-mode data and ancilla case, we identify the D4 lattice—a 4-dimensional dense-packing lattice—to be superior to a product of lower dimensional lattices. As a by-product, the code reduction allows us to prove a universal no-threshold-theorem for arbitrary oscillators-to-oscillators codes based on Gaussian encoding, even when the ancilla are not GKP states.