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At least 1,747 records · Page 97

Evolution of neuronal cell classes and types in the vertebrate retina

The basic plan of the retina is conserved across vertebrates, yet species differ profoundly in their visual needs1. Retinal cell types may have evolved to accommodate these varied needs, but this has not been systematically studied. Here we generated and integrated single-cell transcriptomic atlases of the retina from 17 species: humans, two non-human primates, four rodents, three ungulates, opossum, ferret, tree shrew, a bird, a reptile, a teleost fish and a lamprey. We found high molecular conservation of the six retinal cell classes (photoreceptors, horizontal cells, bipolar cells, amacrine cells, retinal ganglion cells (RGCs) and Müller glia), with transcriptomic variation across species related to evolutionary distance. Major subclasses were also conserved, whereas variation among cell types within classes or subclasses was more pronounced. However, an integrative analysis revealed that numerous cell types are shared across species, based on conserved gene expression programmes that are likely to trace back to an early ancestral vertebrate. The degree of variation among cell types increased from the outer retina (photoreceptors) to the inner retina (RGCs), suggesting that evolution acts preferentially to shape the retinal output. Finally, we identified rodent orthologues of midget RGCs, which comprise more than 80% of RGCs in the human retina, subserve high-acuity vision, and were previously believed to be restricted to primates. By contrast, the mouse orthologues have large receptive fields and comprise around 2% of mouse RGCs. Projections of both primate and mouse orthologous types are overrepresented in the thalamus, which supplies the primary visual cortex. We suggest that midget RGCs are not primate innovations, but are descendants of evolutionarily ancient types that decreased in size and increased in number as primates evolved, thereby facilitating high visual acuity and increased cortical processing of visual information.

59 BASIC BIOLOGICAL SCIENCES↗

Topological prethermal strong zero modes on superconducting processors

Abstract Symmetry-protected topological phases 1–4 cannot be described by any local order parameter and are beyond the conventional symmetry-breaking model 5 . They are characterized by topological boundary modes that remain stable under symmetry respecting perturbations 1–4,6–8 . In clean, gapped systems without disorder, the stability of these edge modes is restricted to the zero-temperature manifold; at finite temperatures, interactions with mobile thermal excitations lead to their decay 9–11 . Here we report the observation of a distinct type of topological edge mode 12–14 , which is protected by emergent symmetries and persists across the entire spectrum, in an array of 100 programmable superconducting qubits. Through digital quantum simulation of a one-dimensional disorder-free stabilizer Hamiltonian, we observe robust long-lived topological edge modes over up to 30 cycles for a wide range of initial states. We show that the interaction between these edge modes and bulk excitations can be suppressed by dimerizing the stabilizer strength, leading to an emergent U(1) × U(1) symmetry in the prethermal regime of the system. Furthermore, we exploit these topological edge modes as logical qubits and prepare a logical Bell state, which exhibits persistent coherence, despite the system being disorder-free and at finite temperature. Our results establish a viable digital simulation approach 15–18 to experimentally study topological matter at finite temperature and demonstrate a potential route to construct long-lived, robust boundary qubits in disorder-free systems.

Science & Technology - Other Topics↗

Optical control of integer and fractional Chern insulators

Optical control of topology, particularly in the presence of electron correlations, is an interesting topic with broad scientific and technological impact. Twisted MoTe 2 bilayer (tMoTe 2 ) is a zero-field fractional Chern insulator (FCI), exhibiting the fractionally quantized anomalous Hall effect. As the chirality of the edge states and sign of the Chern number are determined by the underlying ferromagnetic polarization, manipulation of ferromagnetism would realize control of the Chern insulator (CI)/FCI states. Here, in this work, we demonstrate control of ferromagnetic polarization, and thus the CI and FCI states, by circularly polarized optical pumping in tMoTe 2 . At low excitation power, we achieve on-demand preparation of ferromagnetic polarization by optical training, that is, electrically tuning the system from non-ferromagnetic to desirable ferromagnetic states under helicity-selective optical pumping. With increased excitation power, we further realize direct optical switching of ferromagnetic polarization at a temperature far below the Curie temperature. Both optical training and direct switching are most effective near CI and FCI states, which we attribute to a gap-enhanced valley polarization of optically pumped holes. The magnetization can be dynamically switched by modulating the helicity of optical excitation. Spatially resolved measurements further demonstrate optical writing of ferromagnetic, and thus CI (or FCI) domains. Our work realizes precise optical control of a topological quantum many-body system with potential applications in topological spintronics, quantum memories and creation of exotic edge states by programmable patterning of integer and fractionally quantized anomalous Hall domains.

ferromagnetism↗

Precise transcript targeting by CRISPR-Csm complexes

Robust and precise transcript targeting in mammalian cells remains a difficult challenge using existing approaches due to inefficiency, imprecision and subcellular compartmentalization. Here we show that the clustered regularly interspaced short palindromic repeats (CRISPR)-Csm complex, a multiprotein effector from type III CRISPR immune systems in prokaryotes, provides surgical RNA ablation of both nuclear and cytoplasmic transcripts. As part of the most widely occurring CRISPR adaptive immune pathway, CRISPR-Csm uses a programmable RNA-guided mechanism to find and degrade target RNA molecules without inducing indiscriminate trans-cleavage of cellular RNAs, giving it an important advantage over the CRISPR-Cas13 family of enzymes. Using single-vector delivery of the Streptococcus thermophilus Csm complex, we observe high-efficiency RNA knockdown (90–99%) and minimal off-target effects in human cells, outperforming existing technologies including short hairpin RNA- and Cas13-mediated knockdown. We also find that catalytically inactivated Csm achieves specific and durable RNA binding, a property we harness for live-cell RNA imaging. These results establish the feasibility and efficacy of multiprotein CRISPR-Cas effector complexes as RNA-targeting tools in eukaryotes.

59 BASIC BIOLOGICAL SCIENCES↗

In vivo human T cell engineering with enveloped delivery vehicles

Viruses and virally derived particles have the intrinsic capacity to deliver molecules to cells, but the difficulty of readily altering cell-type selectivity has hindered their use for therapeutic delivery. Here, we show that cell surface marker recognition by antibody fragments displayed on membrane-derived particles encapsulating CRISPR–Cas9 protein and guide RNA can deliver genome editing tools to specific cells. Compared to conventional vectors like adeno-associated virus that rely on evolved capsid tropisms to deliver virally encoded cargo, these Cas9-packaging enveloped delivery vehicles (Cas9-EDVs) leverage predictable antibody–antigen interactions to transiently deliver genome editing machinery selectively to cells of interest. Antibody-targeted Cas9-EDVs preferentially confer genome editing in cognate target cells over bystander cells in mixed populations, both ex vivo and in vivo. By using multiplexed targeting molecules to direct delivery to human T cells, Cas9-EDVs enable the generation of genome-edited chimeric antigen receptor T cells in humanized mice, establishing a programmable delivery modality with the potential for widespread therapeutic utility.

42 ENGINEERING↗

Single-molecule live-cell RNA imaging with CRISPR–Csm

Abstract Understanding the diverse dynamic behaviors of individual RNA molecules in single cells requires visualizing them at high resolution in real time. However, single-molecule live-cell imaging of unmodified endogenous RNA has not yet been achieved in a generalizable manner. Here, we present single-molecule live-cell fluorescence in situ hybridization (smLiveFISH), a robust approach that combines the programmable RNA-guided, RNA-targeting CRISPR–Csm complex with multiplexed guide RNAs for direct and efficient visualization of single RNA molecules in a range of cell types, including primary cells. Using smLiveFISH, we track individual nativeNOTCH2andMAP1Btranscripts in living cells and identify two distinct localization mechanisms including the cotranslational translocation ofNOTCH2mRNA at the endoplasmic reticulum and directional transport ofMAP1BmRNA toward the cell periphery. This method has the potential to unlock principles governing the spatiotemporal organization of native transcripts in health and disease.

Biotechnology & Applied Microbiology↗

Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method

A precise Arctic surface air temperature (SAT) dataset, that is regularly updated, has more complete spatial and temporal coverage, and is based on instrumental observations, is critically important for timely monitoring and improving understanding of the rapid change in the Arctic climate. In this study, a new monthly gridded Arctic SAT dataset dated back to 1979 was reconstructed with a deep learning method by combining surface air temperatures from multiple data sources. The source data include the observations from land station of GHCN (Global Historical Climatology Network), ICOADS (International Comprehensive Ocean-Atmosphere Data Set) over the oceans, drifting ice station of Russian NP (North Pole), and buoys of IABP (International Arctic Buoy Programme). The last two are crucial for improving the representation of the in-situ observed temperatures within the Arctic. The newly reconstructed dataset includes monthly Arctic SAT beginning in 1979 and daily Arctic SAT beginning in 2011. This dataset would represent a new improvement in developing observational temperature datasets and can be used for a variety of applications.

54 ENVIRONMENTAL SCIENCES↗

A high resolution, gridded product for vapor pressure deficit using Daymet

Vapor pressure deficit (VPD) is a critical variable in assessing drought conditions and evaluating plant water stress. Gridded products of global and regional VPD are not freely available from satellite remote sensing, model reanalysis, or ground observation datasets. We present two versions of the first gridded VPD product for the Continental US and parts of Northern Mexico and Southern Canada (CONUS+) at a 1 km spatial resolution and daily time step. We derived VPD from Daymet maximum daily temperature and average daily vapor pressure and scale the estimates based on (1) climate determined by the Köppen-Geiger classifications and (2) land cover determined by the International Geosphere-Biosphere Programme. Ground-based VPD data from 253 AmeriFlux sites representing different climate and land cover classifications were used to improve the Daymet-derived VPD estimates for every pixel in the CONUS+ grid to produce the final datasets. We evaluated the Daymet-derived VPD against independent observations and reanalysis data. The CONUS+ VPD datasets will aid in investigating disturbances including drought and wildfire, and informing land management strategies.

54 ENVIRONMENTAL SCIENCES↗

Integrated structured light architectures

Abstract The structural versatility of light underpins an outstanding collection of optical phenomena where both geometrical and topological states of light can dictate how matter will respond or display. Light possesses multiple degrees of freedom such as amplitude, and linear, spin angular, and orbital angular momenta, but the ability to adaptively engineer the spatio-temporal distribution of all these characteristics is primarily curtailed by technologies used to impose any desired structure to light. We demonstrate a laser architecture based on coherent beam combination offering integrated spatio-temporal field control and programmability, thereby presenting unique opportunities for generating light by design to exploit its topology.

42 ENGINEERING↗

Proton beam quality enhancement by spectral phase control of a PW-class laser system

We report on experimental investigations of proton acceleration from solid foils irradiated with PW-class laser-pulses, where highest proton cut-off energies were achieved for temporal pulse parameters that varied significantly from those of an ideally Fourier transform limited (FTL) pulse. Controlled spectral phase modulation of the driver laser by means of an acousto-optic programmable dispersive filter enabled us to manipulate the temporal shape of the last picoseconds around the main pulse and to study the effect on proton acceleration from thin foil targets. The results show that applying positive third order dispersion values to short pulses is favourable for proton acceleration and can lead to maximum energies of 70 MeV in target normal direction at 18 J laser energy for thin plastic foils, significantly enhancing the maximum energy compared to ideally compressed FTL pulses. The paper further proves the robustness and applicability of this enhancement effect for the use of different target materials and thicknesses as well as laser energy and temporal intensity contrast settings. We demonstrate that application relevant proton beam quality was reliably achieved over many months of operation with appropriate control of spectral phase and temporal contrast conditions using a state-of-the-art high-repetition rate PW laser system.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Searching for metastable particles using graph computing

The reconstruction of charged particle trajectories at the Large Hadron Collider and future colliders relies on energy depositions in sensors placed at distances ranging from a centimeter to a meter from the colliding beams. We propose a method of detecting charged particles that decay invisibly after traversing a short distance of about 25 cm inside the experimental apparatus. One of the decay products may constitute the dark matter known to be 84% of all matter at galactic and cosmological distance scales. Our method uses graph computing to cluster spacepoints recorded by two-dimensional silicon pixel sensors into mathematically-defined patterns. The algorithm may be implemented on silicon-based integrated circuits using field-programmable gate array technology to augment or replace traditional computing platforms.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Dephasingless laser wakefield acceleration in the bubble regime

Abstract Laser wakefield accelerators (LWFAs) have electric fields that are orders of magnitude larger than those of conventional accelerators, promising an attractive, small-scale alternative for next-generation light sources and lepton colliders. The maximum energy gain in a single-stage LWFA is limited by dephasing, which occurs when the trapped particles outrun the accelerating phase of the wakefield. Here, we demonstrate that a single space–time structured laser pulse can be used for ionization injection and electron acceleration over many dephasing lengths in the bubble regime. Simulations of a dephasingless laser wakefield accelerator driven by a 6.2-J laser pulse show 25 pC of injected charge accelerated over 20 dephasing lengths (1.3 cm) to a maximum energy of 2.1 GeV. The space–time structured laser pulse features an ultrashort, programmable-trajectory focus. Accelerating the focus, reducing the focused spot-size variation, and mitigating unwanted self-focusing stabilize the electron acceleration, which improves beam quality and leads to projected energy gains of 125 GeV in a single, sub-meter stage driven by a 500-J pulse.

43 PARTICLE ACCELERATORS↗

Redox active plant phenolic, acetosyringone, for electrogenetic signaling

Abstract Redox is a unique, programmable modality capable of bridging communication between biology and electronics. Previous studies have shown that the E. coli redox-responsive OxyRS regulon can be re-wired to accept electrochemically generated hydrogen peroxide (H 2 O 2 ) as an inducer of gene expression. Here we report that the redox-active phenolic plant signaling molecule acetosyringone (AS) can also induce gene expression from the OxyRS regulon. AS must be oxidized, however, as the reduced state present under normal conditions cannot induce gene expression. Thus, AS serves as a “pro-signaling molecule” that can be activated by its oxidation—in our case by application of oxidizing potential to an electrode. We show that the OxyRS regulon is not induced electrochemically if the imposed electrode potential is in the mid-physiological range. Electronically sliding the applied potential to either oxidative or reductive extremes induces this regulon but through different mechanisms: reduction of O 2 to form H 2 O 2 or oxidation of AS. Fundamentally, this work reinforces the emerging concept that redox signaling depends more on molecular activities than molecular structure. From an applications perspective, the creation of an electronically programmed “pro-signal” dramatically expands the toolbox for electronic control of biological responses in microbes, including in complex environments, cell-based materials, and biomanufacturing.

59 BASIC BIOLOGICAL SCIENCES↗

A low-latency graph computer to identify metastable particles at the Large Hadron Collider for real-time analysis of potential dark matter signatures

Abstract Image recognition is a pervasive task in many information-processing environments. We present a solution to a difficult pattern recognition problem that lies at the heart of experimental particle physics. Future experiments with very high-intensity beams will produce a spray of thousands of particles in each beam-target or beam-beam collision. Recognizing the trajectories of these particles as they traverse layers of electronic sensors is a massive image recognition task that has never been accomplished in real time. We present a real-time processing solution that is implemented in a commercial field-programmable gate array using high-level synthesis. It is an unsupervised learning algorithm that uses techniques of graph computing. A prime application is the low-latency analysis of dark-matter signatures involving metastable charged particles that manifest as disappearing tracks.

47 OTHER INSTRUMENTATION↗

Stochastic journeys of cell progenies through compartments and the role of self-renewal, symmetric and asymmetric division

Abstract Division and differentiation events by which cell populations with specific functions are generated often take place as part of a developmental programme, which can be represented by a sequence of compartments. A compartment is the set of cells with common characteristics; sharing, for instance, a spatial location or a phenotype. Differentiation events are transitions from one compartment to the next. Cells may also die or divide. We consider three different types of division events: (i) where both daughter cells inherit the mother’s phenotype (self-renewal), (ii) where only one of the daughters changes phenotype (asymmetric division), and (iii) where both daughters change phenotype (symmetric division). The self-renewal probability in each compartment determines whether the progeny of a single cell, moving through the sequence of compartments, is finite or grows without bound. We analyse the progeny stochastic dynamics with probability generating functions. In the case of self-renewal, by following one of the daughters after any division event, we may construct lifelines containing only one cell at any time. We analyse the number of divisions along such lines, and the compartment where lines terminate with a death event. Analysis and numerical simulations are applied to a five-compartment model of the gradual differentiation of hematopoietic stem cells and to a model of thymocyte development: from pre-double positive to single positive (SP) cells with a bifurcation to either SP4 or SP8 in the last compartment of the sequence.

Science & Technology - Other Topics↗

An open-source data storage and visualization platform for collaborative qubit control

Developing collaborative research platforms for quantum bit control is crucial for driving innovation in the field, as they enable the exchange of ideas, data, and implementation to achieve more impactful outcomes. Furthermore, considering the high costs associated with quantum experimental setups, collaborative environments are vital for maximizing resource utilization efficiently. However, the lack of dedicated data management platforms presents a significant obstacle to progress, highlighting the necessity for essential assistive tools tailored for this purpose. Current qubit control systems are unable to handle complicated management of extensive calibration data and do not support effectively visualizing intricate quantum experiment outcomes. In this paper, we introduce Qubit Control Storage and Visualization ( QubiCSV ), a platform specifically designed to meet the demands of quantum computing research, focusing on the storage and analysis of calibration and characterization data in qubit control systems. As an open-source tool, QubiCSV facilitates efficient data management of quantum computing, providing data versioning capabilities for data storage and allowing researchers and programmers to interact with qubits in real time. The insightful visualization are developed to interpret complex quantum experiments and optimize qubit performance. QubiCSV not only streamlines the handling of qubit control system data but also improves the user experience with intuitive visualization features, making it a valuable asset for researchers in the quantum computing domain.

97 MATHEMATICS AND COMPUTING↗

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗

A unified realization of electrical quantities from the quantum International System of Units

In the revised International System of Units (SI), the ohm and the volt are realized from the von Klitzing constant and the Josephson constant, and a practical realization of the ampere is possible by applying Ohm’s law directly to the quantum Hall and Josephson effects. As a result, it is possible to create an instrument capable of realizing all three primary electrical units, but the development of such a system remains challenging. Here, in this study, we report a unified realization of the volt, ohm and ampere by integrating a quantum anomalous Hall resistor (QAHR) and a programmable Josephson voltage standard (PJVS) in a single cryostat. Our system has a quantum voltage output that ranges from 0.24 mV to 6.5 mV with combined relative uncertainties down to 3 μV V −1 . The QAHR provides a realization of the ohm at zero magnetic field with uncertainties near 1 μΩ Ω −1 . We use the QAHR to convert a longitudinal current to a quantized Hall voltage and then directly compare that against the PJVS to realize the ampere. We determine currents in the range of 9.33–252 nA, and our lowest uncertainty is 4.3 μA A −1 at 83.9 nA. For other current values, a systematic error that ranges from −10 μA A −1 to −30 μA A −1 is present due to the imperfect isolation of the PJVS microwave bias.

Quantum metrology↗